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Author: Himanshu Sadulwad 'On 31 December 2019, the WHO China Country Office was informed of a pneumonia of unknown cause, detected in the city of Wuhan in Hubei province, China.' No human would have imagined that these cases would lead to a full blown global pandemic. As the pandemic now enters its third year let's take a look back at the steps which led to this disaster. Origin Following the initial report of these pneumonia cases in China, on 4th January 2020 WHO responded to the cluster of pneumonia cases to track the situation and provide information as it emerged. Following this, guidelines were released with reference to SARS and MERS and on 11th January 2020 the first case of this novel coronavirus outside China was confirmed in Thailand. Field visits were made to Wuhan to discuss the screening procedures at airports to limit the spread of the virus. The genome of the novel coronavirus was made publicly available on 11th January 2020. On 30th January 2020 WHO Director-General Dr. Tedros Ghebreyesus declared the novel coronavirus outbreak a Public Health Emergency of International Concern. On 11th February 2020 WHO announced that the disease caused by the novel coronavirus would be named COVID-19. The decision to not let the disease be named after a person or region was to prevent any stereotypes which may arise due to the name.The events that followed included release of guidelines by the WHO, multiple preventive measures and general information about the disease was released to educate the masses. This period also saw the spread of the disease outside China and a spike in the number of cases. "Pandemic is not a word to use lightly or carelessly. It is a word that, if misused, can cause unreasonable fear, or unjustified acceptance that the fight is over, leading to unnecessary suffering and death." On 11th March 2020 concerned by the alarming levels of spread and severity, and by the alarming levels of inaction, WHO officially declared that COVID-19 could be characterized as a pandemic. Following this massive announcement, countries stepped up their resolutions to combat the virus. Curfews and lockdowns were implemented to reduce its spread. Our major weapon to limit the spread of this disease was and remains to this day 'mask'. Let us evaluate how the use of a mask helped reduce cases and the stigma surrounding it. Masks Need for masks Among all plausible routes, airborne transmission of SARS-CoV-2 via respiratory droplets and aerosols is responsible for the rapid spread of COVID-19. Respiratory droplets, which have a relatively large size of 5–10 μm, are emitted when an infected individual coughs or sneezes. In comparison, aerosols are nuclei of respiratory droplets that form after evaporation, and are usually less than 5 μm in size. Viral particles have been found in respiratory droplets and exhaled aerosols of infected individuals, and are bound to inhalable aerosols in the atmosphere. Accumulating evidence suggests that respiratory droplets and aerosols expelled during a sneeze or cough can travel up to 12 to 26 feet, significantly farther than the 6-ft social distancing guideline recommended by the Centers for Disease Control and Prevention (CDC) of the United States. Due to their small size and hence negligible influence by gravity, aerosols generally remain afloat in the air for prolonged periods of time, causing an additional threat of airborne transmission, especially in indoor environments with poor ventilation. This is where the use of masks comes into the picture. This simple and low-cost method is highly effective at mitigating virus transmission. Reducing disease spread requires two things: limiting contacts of infected individuals via physical distancing and other measures and reducing the transmission probability per contact. The preponderance of evidence indicates that mask wearing reduces transmissibility per contact by reducing transmission of infected respiratory particles in both laboratory and clinical contexts. Public mask wearing is most effective at reducing spread of the virus when compliance is high. By the end of June 2020, nearly 90% of the global population lived in regions that had nearly universal mask use, or had laws requiring mask use in some public locations, and community mask use was recommended by nearly all major public health bodies. This was a radical change from the early days of the pandemic, when masks were infrequently recommended or used. Efficacy of the usage of masks If there is strong direct evidence, either a suitably powered randomized controlled trial (RCT),a suitably powered metaanalysis of RCTs, or a systematic review of unbiased observational studies that finds compelling evidence, then that would be sufficient for evaluating the efficacy of public mask wearing, at least in the contexts studied. Cochrane and the World Health Organization both point out that, for population health measures, we should not generally expect to be able to find controlled trials, due to logistical and ethical reasons, and should therefore instead seek a wider evidence base. This issue has been identified for studying community use of masks for COVID-19 in particular. Therefore, we should not be surprised to find that there is no RCT for the impact of masks on community transmission of any respiratory infection in a pandemic. The Australian influenza RCT and the Beijing households observational trial found around 80% efficacy among compliant subjects, and the one SARS household study of sufficient power found 70% efficacy for protecting the wearer. A Cochrane review on physical interventions to interrupt or reduce the spread of respiratory viruses included 67 RCTs and observational studies. It found that “overall masks were the best performing intervention across populations, settings and threats.” Multiple studies offer evidence in favor of widespread mask use as source control to reduce community transmission. Nonmedical masks use materials that obstruct particles of the necessary size; people are most infectious in the initial period postinfection, where it is common to have few or no symptoms; nonmedical masks have been effective in reducing transmission of respiratory viruses; and places and time periods where mask usage is required or widespread have shown substantially lower community transmission. Models suggest that public mask wearing is most effective at reducing spread of the virus when compliance is high. The Swiss Cheese Model The Swiss cheese model of accident causation is used in risk analysis and risk management. This model was originally proposed by James Reason. The metaphor itself is easy enough to grasp; multiple layers of protection, imagined as cheese slices, block the spread of SARS CoV-2. No one layer is perfect; each has holes, and when the holes align, the risk of infection increases. But several layers combined — social distancing, masks, hand-washing, testing and tracing, ventilation, government messaging, vaccination — significantly reduce the overall risk. Note that in the figure the 'misinformation mouse' is nibbling holes which weakens the barriers, leaving us vulnerable. The main feature of a Swiss cheese model is that a single layer doesn't guarantee protection as it has its shortcomings and errors, 'holes'. In order to intensify our protection we need multiple such layers. This way we can minimize our risk of contracting the virus. References Reason, J. “Human error: models and management.” BMJ (Clinical research ed.) vol. 320,7237 (2000): 768-70. doi:10.1136/bmj.320.7237.768 Ju, Jerry T J et al. “Face masks against COVID-19: Standards, efficacy, testing and decontamination methods.” Advances in colloid and interface science vol. 292 (2021): 102435. doi:10.1016/j.cis.2021.102435 Howard, Jeremy et al. “An evidence review of face masks against COVID-19.” Proceedings of the National Academy of Sciences of the United States of America vol. 118,4 (2021): e2014564118. doi:10.1073/pnas.2014564118 Cowling B. J., et al. , Impact assessment of non-pharmaceutical interventions against coronavirus disease 2019 and influenza in Hong Kong: An observational study. Lancet Public Health 5, E279-E288 (2020) Higgins J. P., et al. , Cochrane Handbook for Systematic Reviews of Interventions (John Wiley, 2019) Wang Y., et al. , Reduction of secondary transmission of SARS-CoV-2 in households by face mask use, disinfection and social distancing: A cohort study in Beijing, China. BMJ Global Health 5, e002794 (2020)

Author: Katie Bean Introduction January marks cervical cancer awareness month, and it has never been more important to shed light on seventh most common type of cancer (Manikandan et al. 2019, 314). Mainly affecting women or anyone with a cervix under the age of 45, cervical cancer is usually caused by HPV (human papilloma virus) infections. Though this form of cancer can indeed be fatal, it is extremely treatable if caught in early stages through screening, and largely preventable owing to the development of HPV vaccines. Raising awareness for cervical cancer encourages screening and vaccination and also helps to promote research and implement these essential screening and vaccination programmes in lower income countries. Aetiology and Pathogenesis Most cases of cervical cancer result from infection with the HPV virus. There are two particular strains of HPV that are associated with cervical carcinoma: HPV-16 and HPV-18. The earliest transformation of healthy cells into cancerous occurs in a region called the squamocolumnar junction (see figure 1). The premalignant transformation of cells in this region is termed cervical intraepithelial neoplasia (CIN) - the severity of which is systematically graded (see figure 2). For carcinogenesis to occur, HPV viral proteins must alter the host cells. The oncoproteins most responsible for this are called E6 and E7 - they prevent the function of the p53 tumour suppressor gene, allowing uncontrolled cell proliferation. Once the cells have become cancerous, angiogenesis (blood vessel formation) can occur, allowing tumours to grow and metastasize - thus exacerbating the condition. Risk factors There are a number of risk factors that might increase the chance that an individual develops cervical cancer. Essentially all people that are sexually active will ultimately become infected with HPV in their lives. These short-term, less aggressive infections are not the cause of cervical cancer, rather it is the higher risk, previously mentioned strains that hold responsibility. Therefore, one of the most significant risk factors for developing cervical cancer is having multiple sexual partners, as it increases the chance that an individual may become infected with a high-risk strain. Individuals that are also at a higher risk are the immunocompromised; if someone has a weakened immune system, it is harder for them to fight off a more persistent HPV infection. Smoking is also a pertinent risk factor; it has been hypothesised that the by-products of tobacco can damage DNA within cervical cells and that smoking reduces the capability of the immune system to respond to HPV. (National Cancer Institute 2023) However, the precise molecular mechanisms by which this occurs are yet to be proven - more research is needed to do so. (Castle 2008, 6084) Long-term use of oral contraceptives is also associated with increased risk - the risk increases by a factor of 1.9 for every 5 years of oral contraceptive use. (Johnson et al. 2019, 166-174) Again, the molecular mechanisms behind this association is not well understood. (National Cancer Institute 2023) Preventative Measures In 2018, the WHO initiated a global call for action focussing on the elimination of cervical cancer. This was followed by the Global Strategy to Accelerate the Elimination of Cervical Cancer put forward by the World Health Assembly. The Strategy proposes three central goals to achieve this - vaccination, screening and treatment. More specifically, the Strategy aims to have 90% of girls fully vaccinated with the HPV vaccine by the age of 15 and 70% of women screened using a high-performance test by the age of 35, and again by the age of 45 (The Lancet Regional Health – Americas 2017). In the UK, the vaccine offered by the NHS is called Gardasil 9, and is recommended around 12-13 (before an individual becomes sexually active) (GOV.UK 2023). The vaccine works by action of virus-like particles (VLPs), which induce antibody production. Upon reinfection with the actual virus, these antibodies prevent the HPV from entering and infecting the basal cells of the epithelium. (Harper, Vierthaler, and Santee 2010, 2) One of the reasons that the HPV vaccine has been found to be highly effective is due to the strong immunogenic nature of the virus-like particles; they stimulate high levels of antibody production (National Cancer Institute 2021). Condoms (an example of a physical barrier method) are also able to reduce the risk of HPV transmission between individuals, though infection can still occur in areas of skin that are exposed during intercourse. (National Cancer Institute 2023) Screening for Cervical Cancer Since cervical cancer is such a preventable disease, it is imperative to emphasise the importance of available screening procedures; these ensure the detection of abnormal cells, thus preventing the development of cervical cancer. The traditional method of cervical screening is a Pap smear (named after the Greek physician Georgios Papanikolaou). The procedure, that lasts no longer than 5 minutes, involves the insertion of a speculum and soft brush into the cervix to take a sample of cells. This service is available to women and those with a cervix aged 25-64 in England (NHS 2024). The Pap smear allows for detection of HPV - if present, this can be followed by a cytology test to check for abnormal cells. If abnormal cells are detected, the patient is referred for a colposcopy (GOV.UK 2024). During a colposcopy, a colposcope (lighted microscope) enables the magnification of cervical tissue. (Cleveland Clinic 2022). If a precancerous lesion is identified, a ‘large loop excision of the transformation zone’ is performed - this removes the precancerous cells, inhibiting development. (Burmeister et al. 2022) Disease management Treatment for cervical cancer varies depending on the progression of the tumour, as well as the health and preference of the patient (for example, if they desire to preserve fertility). In early stages of the disease, surgical procedures are able to remove the carcinoma. Microinvasive disease can be treated with conisation (the removal of a ‘cone-shaped portion’ of the cervix) (Cooper et al. 2023), as well as a simple trachelectomy (removal of the cervix). (Circle Health Group 2024). The aforementioned procedures are fertility-sparing, though a simple hysterectomy can also be performed if the patient does not desire to preserve fertility (Marth et al. 2017, 76-77). larger tumours require radical hysterectomy with bilateral lymph node dissection (the latter is used to assess whether cancer cells have spread to the lymph nodes of the pelvis - this would increase the risk of metastasis) (Marth et al. 2017, 76). Radiotherapy and chemotherapy are employed in more advanced stages of the disease (e.g., when the cancer has metastasised) (Marth et al. 2017, 77). Radiotherapy and chemotherapy can also be used as adjuvant therapy (this is administered after treatment to reduce the chance of the cancer returning) (Mayo Clinic 2024). Chemotherapy is often used palliatively, with the aim of improving the quality of life for terminal patients (Johnson et al. 2019, 170). Raising Awareness Although HPV infection, and ultimately cervical cancer, is largely preventable, there are various socioeconomic factors that reduce the efficiency of screening. A study on cervical cancer in Central and South America concluded that poverty is the most significant factor in the incidence of and deaths from the disease globally. (Murillo et al. 2016, 5). Economic determinants are significant particularly regarding access to medical resources for screening. For example, developed countries tend to have a higher standard of experimental conditions, enabling higher sensitivity for Pap smear analysis (usually 80-90%). In areas that have less sophisticated conditions, the sensitivity is limited to around 30-40% (Zhang et al. 2020, 724). There are other barriers that reduce access to screening, such as cultural beliefs and anxiety around gynaecological examinations; these factors tend to be more prevalent in low and middle-income countries (The Lancet Regional Health – Americas 2017). A general lack of awareness is also a crucial barrier; a 2018 study conducted in India investigated 100 female students between the ages of 18 and 24 found that only 5 individuals were aware about causes and risk factors regarding cervical cancer. 96 individuals were unaware about Pap testing or the HPV vaccine (Manikandan et al. 2019, 318). However, in order to align with the WHO’s global call, programmes are being implemented to overcome this. For example, the HOPE project in Peru recruits women from disadvantaged communities to spread information about HPV to women aged 30 and 49 years old (HOPE PERU 2024). They additionally provide self-sampling tests to women and follow the tests up with analysis, collaborating with a network of expert gynaecologists (HOPE PERU 2024). The HOPE ladies were successfully able to increase awareness in their community, the participation rate increased to 94% owing to their efforts. The success of this programme could potentially lead to similar schemes being implemented in other countries (The Lancet Regional Health – Americas 2017). Developments in Screening and Treatment As previously mentioned, there are a number of socioeconomic factors that act as barriers to cervical cancer screening. One particular manifestation of this is reduced sensitivity of Pap smear analysis, though an alternative method, liquid-based cytology is able to overcome this limitation. Its higher sensitivity enables cervical abnormalities to be detected despite less sophisticated experimental conditions (Zhang et al. 2020, 724). Regarding treatment, immunotherapy is fast becoming a useful method for managing cervical cancer. It focuses on targeting ‘checkpoints’ which can be turned on or off to stimulate an immune response; cancer cells often manipulate these to evade immune recognition. Drugs that inhibit these checkpoints are therefore able to treat cervical cancer (Johnson et al. 2019, 170). Pembrolizumab is an example of a drug that targets a checkpoint, the checkpoint in question being that between the two proteins PD-1 and PD-L1. Some tumours express protein PD-L1 at a high level, and when PD-L1 upon interaction with PD-1, it prevents T-cells from destroying the cancerous cells. Pembrolizumab works by inhibiting this interaction, enabling T-cells to efficiently destroy the tumour cells (Flynn and Gerriets 2023). In an article written in March 2023, the Royal Marsden announced pembrolizumab as the first approved immunotherapy treatment for cervical cancer provided by the NHS (under the name of Keytruda). It is administered in combination with chemotherapy to patients suffering with advanced-stage cervical cancer, where the cancer has not responded to previous treatments (The Royal Marsden 2023). Conclusion Overall, cervical cancer month functions as an essential reminder of the importance of raising awareness for one of the most common cancers globally. Measures such as screening and vaccination allow the detection and monitoring of HPV infections, though barriers to this prove to be hugely prevalent in countries with lower income. By implementing programmes such as the HOPE project in Peru and investing in research into more comprehensive analysis techniques and treatment, the risk of cervical cancer can be significantly reduced. There is still a long way to go in the journey towards cervical cancer elimination, but building on these advances could ensure a future free of cervical cancer. References Burmeister, Carly A., Saif F. Khan, Georgia Schaefer, Nomonde Mbatani, Tracey Adams, Jennifer Moodley, and Sharon Prince. 2022. “Cervical cancer therapies: Current challenges and future perspectives.” Tumour Virus Research 13, no. 13 (June): 13. 10.1016/j.tvr.2022.200238. Castle, Philip E. 2008. “How Does Tobacco Smoke Contribute to Cervical Carcinogenesis?” Journal of Virology 82, no. 12 (June): 6084–6086. 10.1128/JVI.00103-08. Circle Health Group. 2024. “Fertility Sparing Treatment (Trachelectomy) for Cervical Cancer.” Circle Health Group. https://www.circlehealthgroup.co.uk/treatments/cervical-cancer-fertility-sparing-treatment. Cleveland Clinic. 2022. “Colposcopy: Biopsy, Purpose, Procedure, Risk & Results.” Cleveland Clinic. https://my.clevelandclinic.org/health/diagnostics/4044-colposcopy. Cooper, Danielle B., Jose Carungno, Charles J. Dunton, and Gary W. Menefee. 2023. “Cold Knife Conization of the Cervix - StatPearls.” NCBI. https://www.ncbi.nlm.nih.gov/books/NBK441845/. Flynn, James P., and Valerie Gerriets. 2023. “Pembrolizumab - StatPearls.” NCBI. https://www.ncbi.nlm.nih.gov/books/NBK546616/. GOV.UK. 2023. “Information on the HPV vaccination from September 2023.” GOV.UK. https://www.gov.uk/government/publications/hpv-vaccine-vaccination-guide-leaflet/information-on-the-hpv-vaccination-from-september-2023. GOV.UK. 2024. “Cervical screening: programme overview.” GOV.UK. https://www.gov.uk/guidance/cervical-screening-programme-overview. Harper, Diane M., Stephen L. Vierthaler, and Jennifer A. Santee. 2010. “Review of Gardasil.” J Vaccines Vaccin 1, no. 107 (November): 107. 10.4172/2157-7560.1000107. HOPE PERU. 2024. “THE PROJECT – HOPE PERU.” HOPE PERU. https://hopeperuproject.org/project/. Johnson, Cynae A., Deepthi James, Amelita Marzan, and Mona Armaos. 2019. “Cervical Cancer: An Overview of Pathophysiology and Management.” Seminars in Oncology Nursing 35, no. 2 (April): 166-174. https://doi.org/10.1016/j.soncn.2019.02.003. The Lancet Regional Health – Americas. 2017. YouTube: Home. https://www.sciencedirect.com/science/article/pii/S2667193X22000783. Manikandan, Saranya, Subasish Behera, Nageswarao M. Naidu, Vignesswary Angamuthu, Omar Mohammed, and Abhitosh Debata. 2019. “Knowledge and Awareness Toward Cervical Cancer Screening and Prevention Among the Professional College Female Students.” J Pharm Bioallied Sci 2019 May, no. 11 (May): 314-320. 10.4103/JPBS.JPBS_21_19. Marth, C., F. Landoni, S. Manher, A. Gonzalez-Martin, and N. Columbo. 2017. “Cervical cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up.” Annals of Oncology 28, no. 4 (July): 72-83. https://doi.org/10.1093/annonc/mdx220. Mayo Clinic. 2024. “Adjuvant therapy: Treatment to keep cancer from returning.” Mayo Clinic. https://www.mayoclinic.org/diseases-conditions/cancer/in-depth/adjuvant-therapy/art-20046687. Murillo, R., R. Herrero, M. S. Sierra, and D. Forman. 2016. “Etiology of cervical cancer (C53) in Central and South America.” International Agency for Research on Cancer. National Cancer Institute. 2021. “Human Papillomavirus (HPV) Vaccines - NCI.” National Cancer Institute. https://www.cancer.gov/about-cancer/causes-prevention/risk/infectious-agents/hpv-vaccine-fact-sheet. National Cancer Institute. 2023. “Cervical Cancer Causes, Risk Factors, and Prevention.” National Cancer Institute. https://www.cancer.gov/types/cervical/causes-risk-prevention. NHS. 2024. “Cervical screening.” NHS. https://www.nhs.uk/conditions/cervical-screening/. The Royal Marsden. 2023. “Immunotherapy available to NHS cervical cancer patients for the first time.” The Royal Marsden. https://www.royalmarsden.nhs.uk/news-and-events/news/immunotherapy-available-nhs-cervical-cancer-patients-first-time. Zhang, Shaokai, Huifang Xu, Luyao Zhang, and Youlin Qiao. 2020. “Cervical cancer: Epidemiology, risk factors and screening.” Chinese Journal of Cancer Research 32, no. 6 (December): 720-728. 10.21147/j.issn.1000-9604.2020.06.05.

Author : Himanshu Sadulwad Synopsis An estimated 37.7 million people are currently living with HIV. As of now, it has claimed 36.3 million lives. A disease with one of the highest death tolls in the world, we speak of none other than HIV-AIDS. AIDS remains a major public health issue that affects millions worldwide. On this World AIDS day, let us educate ourselves a bit more on this crucial topic. Origin In humans, AIDS is caused by two lentiviruses namely, Human Immunodeficiency Viruses types 1 and 2 (HIV-1 and HIV-2). In this section, we describe the origins of these viruses and the circumstances that lead to the AIDS pandemic. Both HIVs are the result of multiple cross-species transmissions of simian immunodeficiency viruses (SIVs) naturally infecting African primates. Most of these transfers resulted in viruses that spread in humans to only a limited extent. However, one transmission event, involving SIVcpz from chimpanzees in southeastern Cameroon gave rise to HIV-1 group M which is the principal cause of the AIDS pandemic ( Paul Sharp and Beatrice Hahn 2011). How humans acquired the ape precursors of HIV-1 groups M, N, O, and P is not known; however, based on the biology of these viruses, the transmission must have occurred through cutaneous or mucous membrane exposure to infected ape blood or body fluids. Such exposures occur most commonly in the context of bushmeat hunting (Peeters et. al. 2002). Whatever the circumstances it seems clear that human-ape encounters in west-central Africa have resulted in four independent cross-species transmission events. Since its first discovery, HIV-2 has remained largely restricted to West Africa, with the highest prevalence rates in Guinea-Bissau and Senegal ( de Silva et al. 2008). Most individuals infected with HIV-2 do not progress to AIDS, although those who do, show clinical symptoms indistinguishable from HIV-1 ( Rowland-Jones and Whittle 2007). A sooty mangabey origin of HIV-2 was first proposed in 1989 (Hirsch et al. 1989) and subsequently confirmed by demonstrating that humans in West Africa harbored HIV-2 strains that resembled locally circulating SIVsmm infections (Gao et al. 1992; Chen et al. 1996). The fact that sooty mangabeys are frequently hunted as agricultural pests in many areas of West Africa provided plausible routes of transmission. Virology The human immunodeficiency virus (HIV) is grouped to the genus Lentivirus within the family of Retroviridae. On the basis of genetic characteristics and differences in the viral antigens, HIV is classified into the types 1 and 2 (HIV-1, HIV-2). Epidemiologic and phylogenetic analyses currently available imply that HIV was introduced into the human population around 1920 to 1940. HIV Genome structure The HIV genome consists of two identical single-stranded RNA molecules that are enclosed within the core of the virus particle. The genome of the HIV provirus, also known as proviral DNA, is generated by the reverse transcription of the viral RNA genome into DNA, degradation of the RNA and integration of the double-stranded HIV DNA into the human genome. The DNA genome is flanked at both ends by LTR (long terminal repeat) sequences. In the direction 5′ to 3′ the reading frame of the gag gene follows, encoding the proteins of the outer core membrane, the capsid protein, the nucleocapsid and a smaller, nucleic acid-stabilising protein.The gag reading frame is followed by the pol reading frame coding for the enzymes protease, reverse transcriptase, RNase H and integrase . Adjacent to the pol gene, the env reading frame follows from which the two envelope glycoproteins gp120 (surface protein) and gp41 (transmembrane protein) are derived. Particle structure The mature HIV particle is round, measures approximately 100 nm in diameter, with an outer lipid membrane as its envelope.The viral envelope is composed of a lipid bi-layer and, in mature virus particles, the envelope proteins SU and TM. The conical capsid is assembled from the inner capsid protein p24.Two identical molecules of viral genomic RNA are located inside the capsid and several molecules of the viral enzymes RT/RNase H are bound to the nucleic acid. Infection of human cells The surface glycoprotein gp120 of the mature HIV particle binds to the CD4 receptor on the host cell. All CD4-positive cells such as T helper cells, macrophages, dendritic cells and astrocytes are susceptible to HIV. After attachment to the CD4 molecule via the C4-domain of gp120, a conformational change in CD4 and gp120 occurs, opening up an additional site for gp120 to enable binding to the co-receptor, i.e. chemokine receptor 5 (CCR5) or chemokine receptor 4 (CXCR4) on the cell surface. Fusion of the viral and cellular membranes leads to translocation of the viral capsid into the cytoplasm. The capsid is taken up by an endosome, and a change in the pH value in the phagosome induces the release of the capsid contents into the cytoplasm. Activation of reverse transcriptase takes place in the cytoplasm. HIV RT transcribes the single-strand HIV RNA genome into DNA (complementary DNA or cDNA). In parallel to DNA synthesis, the RNA strand is degraded enzymatically by RNase H, followed by conversion of single-stranded cDNA into double-stranded DNA (proviral DNA) by the DNA-dependent DNA polymerase activity of RT. This proviral DNA is transported via nucleopores into the cell nucleus in the form of a complex consisting of the integrase and linear or circular proviral DNA. The integrase then inserts at random the proviral genome into the human host cell genome. Integration of the proviral DNA finalizes the HIV infection of the cell and the establishment of a persistent infection. Epidemiology According to present knowledge, the spread of HIV started at the beginning of the 20th century. Zoonotic transmission of SIVcpz from chimpanzees (Pan troglodytes) occurred for HIV-1 group M and group O around 1920 and HIV-1 group N around 1960 in West Central Africa. HIV-2 was transmitted zoonotically from Sooty Mangabey (Cercocebus atys) to humans in West Africa around 1940. Molecular genetic analyses suggest that HIV-1 was exported to Haiti probably in 1966 and arrived in North America approximately 2 years later. Since the mid-1980s the different HIV-1 M subtypes have been spreading, leading to a global pandemic. In contrast to HIV-1, HIV-2 initially remained restricted to West Africa because of its significantly lower infectivity. After HIV-2 was exported to Portugal and France probably during the mid-1960s, the spreading of HIV-2 with a low prevalence especially in Europe, South America, and Asia is documented. Globally, an estimated 37.7 million people were living with HIV in 2020. Transmission HIV can enter the body via intact mucous membranes, eczematous or injured skin or mucosa, and parenteral inoculation. The major factors leading to the transmission of HIV are as follows: Having unprotected sexual intercourse with a person infected with HIV Sharing injections, drug equipment with someone who has HIV. Through vertical transmission i.e. from mother to child during pregnancy, birth, or breastfeeding. Receiving blood transfusions, organ transplants, or tissue transplants that are contaminated with HIV. Symptoms Acute HIV Infection A few weeks after getting HIV, many people have flu-like symptoms, which may last days or weeks. These symptoms can include fever, headache, tiredness, and enlarged lymph glands in the neck and groin area. Some people may have no symptoms. Chronic HIV Infection In the next stage of HIV infection, the virus still multiplies, but at very low levels. People may not feel sick or have any symptoms. If they are not getting treatment for HIV during this stage, they can still pass the virus to other people. Getting and staying on treatment prevents passing HIV to others. Without HIV treatment, people can stay in this stage for a decade or more, although some move through this stage faster. AIDS AIDS is the most advanced stage of HIV when a person's immune system is severely weakened and has difficulty fighting infections and certain cancers. At this stage, serious symptoms develop, such as: Rapid weight loss Serious infections Pneumonia Recurrent fevers Prolonged swelling of the lymph nodes Skin blotches Prolonged diarrhea Sores of the mouth, anus, or genitals Memory loss Depression Other neurologic disorders NOTE: The only way to know for sure if you have HIV is to get tested. One CANNOT rely on symptoms to tell whether you have HIV. Diagnosis The detection systems can be differentiated into two principles of detection namely Antibody and Virus detection. HIV RNA can be detected in the blood using a nucleic acid test (NAT) about 11 days after infection. Antibody screening tests HIV antibody screening tests are used for the primary diagnosis followed by a confirmation test in the case of a reactive result in the screening assay. In addition to the ELISA (enzyme-linked immunosorbent assay) or variants of this test system, particle agglutination tests are used. Approved ELISA tests contain antigens of HIV-1 group M, particularly HIV-1 M: B, group O, and HIV-2. Depending on the manufacturer, additional antigens derived from the reverse transcriptase and the p24 protein are included in the test systems. An infection can be detected serologically after 3 weeks. Confirmatory test (Western blot, Immunoblot) The Immunoblot/Western blot assay was introduced as serologic confirmatory tests, but these test systems have a lower sensitivity in the early phase of HIV infection than antibody screening tests or p24 antigen detection systems. Only if the criteria for a positive Immunoblot/Western blot are fulfilled can the HIV infection be considered as confirmed. p24 Antigen test The p24 protein forms the inner capsid. Each virus particle contains approximately 2,000 p24 molecules. Detection of p24 antigen is performed using a combination of polyclonal or monoclonal antibodies, following the principle of the sandwich ELISA technique. NAT - Nucleic Acid Amplification Technology Diagnosis of an HIV infection can be performed by determining proviral DNA in cells or of the viral RNA genome in plasma. For analysis of the viral load and the presence of HIV in blood donations, RNA is extracted from virus particles in plasma. Genome detection can be done either via direct amplification of defined target sequences or through the use of probes with subsequent signal amplification. Treatment HIV infection has a very complex pathogenesis and varies substantially in different patients. Therefore, it can easily be considered a very host-specific infection. The specificity of pathogenesis often complicates treatment options that are currently available for HIV infection. Effective management of HIV infection is possible using different combinations of available drugs. This method of treatment is collectively known as antiretroviral therapy (ART). Standard ART consists of a concoction of at least three medicines (termed as “highly active antiretroviral therapy” or HAART). Effective ART often helps control the multiplication of HIV in infected patients and increases the count of CD4 cells, thus, prolonging the asymptomatic phase of infection, slowing the progression of the disease, and also helping in reducing the risk of transmission. The following are the important HIV drug classes: Reverse transcriptase inhibitors Protease inhibitor Fusion inhibitor Chemokine Receptor 5 Antagonist Integrase Strand Transfer Inhibitors Prevention Anyone can get HIV but steps can be taken to protect self from HIV Protected sexual intercourse with the use of condoms, diaphragms, etc. Limit number of sexual partners Get tested and treated for STDs Get Pre-exposure Prophylaxis Use of sterile equipment while injecting drugs. Social impact and stigma "We live in a completely interdependent world, which means we cannot escape each other. How we respond to AIDS depends, in part, on whether we understand this interdependence. It is not someone else's problem. This is everybody's problem." - Bill Clinton HIV stigma refers to the negative attitudes and beliefs about people with HIV. It is the prejudice that comes with labeling an individual as part of a group that is believed to be socially unacceptable. The patients are being discriminated against based on outdated and prejudiced views of the people surrounding them. In many families and societies being diagnosed with HIV simply means being social outcasts. People fear they may contract the disease through mere casual contact or handling the articles being used by them. This leads to them being shunned by society. As with any disease, recovery from HIV needs the mental stability of the person. Unfortunately regarding this disease mental stability is a luxury that is not given to the person. It is considered that it is the individual's fault that they have contracted this disease. HIV is frequently transmitted through actions that are kept secret, hidden, or are illegal- a characteristic that makes this disease unique. Misconceptions have shaped beliefs; unfortunately, most people in our society do not realize that it is not the patient’s intention to be infected by HIV. Consequently, despair, exhaustion, and helplessness approaching panic are experienced by most patients, who are faced with society’s rejection, losing their hopes for a prosperous future. It is almost impossible to conceptualize the degree of suffering the human body undergoes and the despair of the human mind in a societal group devastated by AIDS. Like any other stigma it is rooted in the fear of HIV. Many of our ideas about this disease emerge from that which appeared first in the early 1980s. There still exist numerous misconceptions about how HIV is transmitted and what it means to live with HIV today. As with any other taboo, overcoming this social stigma requires the eradication of the fear associated with it. This fear can only be eradicated through education. Multiple efforts are being taken by WHO and local healthcare authorities to educate the public about this disease; mainly its transmission and modes of prevention. People need to realize that speaking with the patient or casual contact with the infected does NOT spread this disease. The people suffering from AIDS need to be accepted in society. They need support and strength from the ones surrounding them to combat it. The theme of World AIDS day 2021 is " End inequalities. End AIDS" , this year we take the fight not to the hospitals but we fight it within us. We fight to accept the harsh reality that these patients live in. This year we try to make this world more hospitable to them. Only with the acceptance of the true reality can mankind produce a united front to combat this disease. References Origin of HIV and the AIDS Pandemic by Paul M. Sharp and Beatrice H. Hahn Human Immunodeficiency virus (HIV) by German Advisory Committee Blood (Arbeitskreis Blut) Subgroup 'Assessment of Pathogens Transmissible by Blood' Current scenario of HIV/AIDS, Treatment Options, and Major Challenges with Compliance to Antiretroviral Therapy by Adnan Bashir Bhatti, Muhammad Usman and Venkataramana Kandi The social stigma of HIV-AIDS : society’s role by Emmanuel N Kontomanolis, Spurgeon Michalopoulos and Zacharias Fasoulakis

Author: Afreen Introduction: Time is like a big mystery that people have been curious about for a very long time. It's the fourth dimension, and it has puzzled us for centuries. As we learn more about the world, we also learn more about time and how it works. There's this cool thing called time dilation, which comes from Albert Einstein's ideas about space and time. Let's break it down: 1. Einstein's Idea : A really smart guy named Albert Einstein had some ideas about how time works. He said that time is not the same for everyone. It can be different depending on how fast you're moving. 2. Time is Relative : Imagine you and a friend have super fast toy cars. If your friend zooms by in their car, time might feel different for them compared to you. It's like time can stretch or squish depending on how fast you're going. 3. Two Parts : Einstein had two big ideas – special relativity and general relativity. Special relativity, from 1905, says that time can be different for different people depending on how they're moving. So, time dilation is basically the idea that time is not always the same. It can change depending on how fast you're moving. It sounds a bit tricky, but it helps us understand more about the universe and how things work together. The equation central to special relativity is the famous time dilation equation: Where This equation shows that as an object's velocity approaches the speed of light, time in its frame of reference appears to slow down from the perspective of a stationary observer. Let us take an example. Imagine you have two twins, just like two peas in a pod. One twin stays on Earth, and the other goes on a super-fast space trip. When the space-traveling twin comes back, they would find out that they got a bit younger compared to the twin who stayed on Earth. It's like time went slower for the twin in space. This is because of something called time dilation. Now, think about super tiny particles, like really, really small. Scientists have these cool machines, like the Large Hadron Collider (LHC), that make these tiny particles go super fast. And guess what? These speedy particles experience time dilation, just like the twin in space. This helps scientists understand more about how things work in our universe. Here's a real-world example: you know those GPS devices that help you find your way? The satellites in space, which make GPS work, move really fast. Because they're speedy, they experience time dilation. If scientists don't correct for this, your GPS might not give the right directions. So, understanding time dilation is like making sure your GPS takes you to the right place! Now, when we talk about really, really big things in space, like black holes, time dilation gets super extreme. It's like time slows down near these giant objects. Scientists have checked this out, and it turns out, time really does behave differently near black holes. So, time dilation isn't just a fancy idea – it helps us explore space and make things like GPS work better!

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Amartya Chakraborty Kendriya Vidyalaya Gangtok, Sikkim, India 737102 SUMMARY Nitrogen is an essential mineral nutrient that strongly affects crop growth and yield. However, Nitrogen deficiency is a common problem across all types of arable soils. This leads to heavy application of nitrogen fertilizer having detrimental effects on soil and human health. Therefore, accurate diagnosis of crop nitrogen status is crucial to mitigate the overuse of nitrogen fertilizer. The current methods of diagnosing nitrogen levels in plants, such as the soil-nitrate test and tissue analysis, are not capable of providing real-time status which is highly suited for reducing nitrogen overuse as per the plant’s demand. Hence, we have opted to utilize deep learning methods allowing the extraction of multiple intricate features from paddy leaf images. This method tests the hypothesis that paddy leaf features are positively associated with nitrogen deficiency and thereby it has been used for predicting nitrogen deficiency status. We have used the ResNet50 CNN model utilizing a 2D global average pooling layer that drastically reduces the dimensionality of feature maps by computing the mean of the height and width dimensions of the input images. This model has been trained with a publicly available dataset of 5258 paddy leaf images associated with nitrogen deficiency levels into 4 critical categories: slight, moderate, severe, and very severe. The model performance has shown the accuracy level of prediction > 85%. An Android app “PaddyVision” has also been developed which utilizes the same capable AI model. There is a large scope to improve the model further by including different datasets and other utilities to form a holistic real-time predictor of plant’s nutrient and health status. INTRODUCTION Rice is the primary staple food for more than half the world’s population—with Asia, Sub-Saharan Africa, and South America as the largest rice-consuming regions. Global production of rice has seen an increase reaching about 520.5 million tons in the 2023/2022 crop year [1]. Nitrogen (N) is one of the essential macronutrients for rice growth and one of the main factors to be considered for developing a high-yielding rice cultivar. Yet among the essential nutrients, nitrogen (N) is universally deficient in rice cropping systems worldwide and the main limiting factor in rice production. It, therefore, necessitates nitrogen monitoring as an effective method to combat its deficiency and its overuse which causes soil contamination and widespread health problems. Monitoring crop nitrogen content during the early vegetative growth stages is of major importance for the planning of fertilization measures, assessment of N during mature growth stages provides valuable indication of expected yield quality. Thus, continuous monitoring of crop nitrogen (N) levels throughout the growing season improves yield quality and quantity, as well as economic returns [2]. Current methods of nitrogen monitoring, such as soil monitoring, leaf tissue analysis, and portable rapid analysis systems, have major drawbacks. These methods are invasive, not real-time, and impractical in most cases. The accuracy and accessibility of these tools are also not up to par. Convolutional Neural Networks (CNNs) offer a robust method for image recognition, making them ideal for analyzing the visual characteristics of rice plants [3,4]. These networks can automatically extract features like leaf color, size, and shape from images, allowing for efficient monitoring of rice crops. The key advantage here is the ability to process and interpret large volumes of visual data quickly and accurately, providing valuable insights into the health and nutrient status of rice plants. In this study, we have efficiently utilized one of these key strengths of CNNs. Besides, in many regions where rice is a staple food, smartphones are widely available. This means that a mobile application like ‘PaddyVision’ based on this CNN approach can be easily downloaded and used by farmers, regardless of their location. Access to the technology becomes democratized, enabling even small-scale farmers in remote areas to benefit from crop monitoring. Traditional methods of nitrogen monitoring often come with substantial costs. They may require specialized equipment, chemicals, or expert labor, making them financially burdensome for many farmers. Our monitoring app “PaddyVision” can offer a free application toolkit addressing this issue. It eliminates the need for expensive equipment and reduces the financial barriers to entry, making advanced crop monitoring accessible to a broader range of farmers. However, to provide a more comprehensive understanding of crop health, it considers integrating additional data sources. Weather data, for example, can help correlate environmental conditions with crop performance. Information on soil quality can offer insights into nutrient availability [5]. Integrating these data sources with PaddyVision can give farmers a more holistic view of the factors affecting their crop growth. In this regard, collaborating with agricultural experts, research institutions, and local agricultural organizations can be highly beneficial. These partnerships can provide valuable insights into the specific needs of rice farmers and help tailor the app’s features to address those needs effectively and that can also support the development of accurate models and data collection methods. METHODOLOGY ResNet-50, an abbreviation for “Residual Network with 50 layers”, AI-CNN model has been used here which is a significant deep convolutional neural network (CNN) architecture designed for image classification and various computer vision tasks [6]. It is a variant of the ResNet architecture that is renowned for its remarkable depth, comprising 50 weight layers. At the core of ResNet-50 are residual building blocks that introduce skip connections, allowing for the learning of residual functions. These blocks come in two main types: identity blocks and convolutional blocks. Identity blocks contain two 3×3 convolutional layers with a skip connection that adds the input to the output, while convolutional blocks include an additional 1×1 convolutional layer at the start to adjust the number of channels. The ResNet-50 architecture is built by stacking multiple residual blocks, grouped in sets of varying block counts. Notably, ResNet-50 shuns fully connected layers in favor of Global Average Pooling (GAP), which condenses the feature maps to a fixed-size vector for classification. Batch normalization is applied to stabilize training, and the Rectified Linear Unit (ReLU) activation function introduces non-linearity in each residual block. The dataset used for the CNN-model training was a public dataset obtained from Kaggle (https://www.kaggle.com/datasets/myominhtet/nitrogen-deficiency-for-rice-crop ), which consisted of 5259 images. The images were categorized into 4 gradient levels of nitrogen deficiency, with swap 4 indicating one of the extremes of the gradient with ‘sufficient nitrogen’ and swap 1 indicating ‘severe nitrogen deficiency’. A hundred images were separated from each category of the dataset for evaluation of the model. The corresponding distribution of training and testing images in the dataset was set with 1407 training images considered for each category except swap 2 (i.e., 637) due to the lack of images of this category in the dataset. The images of the dataset were analyzed thoroughly by taking random samples from each category. Each leaf image’s mean color, surface area, perimeter, and GLCM energy were noted. GLCM energy index reveals the texture intensity of the leaves. Whereas mean color is used to check whether the leaf colors of the same nitrogen deficiency levels are similar or not. The mean color indexes were averaged to obtain the mean color index of each swap. The mean color index of each swap was used for highlighting color discrepancies between the swaps using the Euclidean distance measure in the RGB color space. Images of rice leaves are captured by matching the swap of the Leaf Colour Chart (LCC). The input images were captured using a smartphone camera of 13 Megapixels in daylight as per the user instructions of LCC. The LCC developed by ICAR-National Rice Research Institute (ICAR-NRRI), Cuttack, India has four swaps categorizing the highest to lowest/sufficient nitrogen deficiency levels [7]. Pre-trained models such as Inception V3, VGG16, VGG19, and ResNet50 were tested for accuracy in the categorical classification of rice diseases. ResNet 50 had the highest accuracy of 99.75% with a loss rate of 0.33. Furthermore, ResNet 50 achieved a validation accuracy of 99.69%, precision of 99.50%, F1-score of 99.70, and AUC of 99.83% [5]. We have, therefore, utilized the ResNet50 architecture for this study. ResNet50 had been initialized with the weights that have been trained on a large dataset from ImageNet. The top fully connected layer of the ResNet50 is trained to classify images into various categories based on the ImageNet dataset. Since there is no requirement for the fully connected layer for nitrogen deficiency classification, it was excluded to achieve a model that is more efficient and less prone to overfitting. Fig.1. The CNN based-model android app “PaddyVision”, underlying workflow chart: the left panel describes the workflow starting from the input of paddy leaf images into the ResNet50 CNN-model; the right panel describes the tasks that have been carried out by each model steps in the left. The middle panel defines specific algorithmic steps through which actual input tasks are processed. A Global Average Pooling layer is used to reduce the spatial dimensions of the output of the base model by averaging all values in the feature map. A dense layer with 256 neurons and a ReLU activation function is used to map the feature vector from the previous layer to a lower dimensional space. The final classification layer is a dense layer and SoftMax activation function which outputs the probability distribution over the different classes, which in this case are swap1, swap2, swap3, and swap4. SoftMax also ensures that the sum of the outputs is up to 1. The compiled model was trained for 10 epochs in batches of 32 images. The model workflow (Fig.1) has been implemented exclusively using TensorFlow, which is not supported by Android devices. However, TensorFlow Lite, a lightweight and efficient version is supported. Therefore, the AI model was converted into TensorFlow Lite. The Android application takes user inputs of Paddy Leaf Images. The images are pre-processed and loaded into the TensorFlow Lite AI model. The category with the maximum probability is given to the user as output. The application was uploaded to the Google Play Store to make it easily accessible to a global audience. RESULTS The mean color similarities of samples of swap1, swap2, swap3, and swap4 are 62.97, 21.47, 60.74, and 44.47 respectively. This indicates that samples of swap 2 and swap 4 have similar mean colors. Whereas, swap 1 and swap 3 samples show slight dissimilarities (Table 2). The color distance between the mean colors of swap 1 and swap 2 is 69.64, indicating that the colors are highly dissimilar. Likewise, the color distance between the mean colors of swap 2 and swap 3, and swap 2 and swap 4 are also high, above 56, indicating significant dissimilarity. The mean colors of swap 1 and swap 3, and swap 1 and swap 4 are distinguishable, with a color distance of about 20. However, the mean colors of swap 3 and swap 4 are very similar, with a color distance of only 5.2. In other words, swap 1 and swap 2 are the most different in terms of color, while swap 3 and swap 4 are the most similar. The remaining pairs are somewhere in between. The mean GLCM (Grey Level Co-occurrence Matrix) energy of swap 1 (0.3764) is the highest, followed by swap 4 (0.3154), swap 3 (0.3388), and swap 2 (0.0566). This indicates that swap 1 is the most homogeneous texture, while swap 2 is the most heterogeneous texture. Swaps 3 and 4 have similar GLCM energy indices, suggesting that they have similar texture homogeneity. Furthermore, the AI model can correctly classify 99% of the images in the benchmark, and its performance has remained consistently high across all metrics. This suggests that the model has the potential to be used in a variety of real-world applications, such as medical imaging, product inspection, and security surveillance. This AI model has been deployed in an Android mobile app “PaddyVision” (Fig.3). The AI runs strictly on the user’s mobile device, therefore removing the need for an internet connection and sharing data. A user can simply upload an image from the internal storage of the device and get quick feedback. Fig.2 Confusion matrix summarizes the results of model evaluation. Random selection of 100 actual images from each nitrogen deficiency category (swap1, swap2, swap3, swap4) evaluated against the model predicted category. While swap4 indicates one of the extremes of the N-deficiency gradient with ‘sufficient Nitrogen’, swap1 represents ‘severe nitrogen deficiency’. For evaluating the AI model, the confusion matrix as shown in Figure 2 has been utilized. The first evaluating parameter calculated is accuracy: (1) The net accuracy of the model is calculated using equation (1). The AI model has achieved an overall accuracy of 0.99 on a benchmark of 400 images. The second evaluating parameter is precision which is calculated using Equation 2: (2) In this equation, TP and FP denote ‘True Positive’ and ‘False Positive’ respectively. True cases are scenarios where the observed data matches the predicted data. False cases are scenarios where the observed data does not match the predicted data. Positive cases denote prediction is true, whereas negative denotes prediction is false. The AI model has scored highest precision in swap 1(1.00) and swap 3(1.00), followed closely by swap 4( 0.99), and swap 2( 0.97). Despite this metric accounting for ‘False Positives’ it fails to account for the ‘False Negative’ scenarios. The next evaluation metric considered is ‘recall’. This metric considers ‘False Negative’ scenarios. To calculate recall equation 3 is used: (3) The recall of the AI model is 1.00 in swap 1, swap 2, and swap 3. However, the AI model scored 0.4 lower in the swap 3 class. However, this metric has a major drawback. It fails to account for ‘False Positives’. To know if our model’s performance is good, we need both of these two measures: Recall and Precision. Therefore, the F1 score has been calculated: (4) An ideal situation is when the F1 score is 1. In the case of this AI model, the F1 score is ideal for swap 1 and swap 2, followed closely by swap 2(0.99) and swap 3 (0.98). Table 3 summarizes the model performance evaluated with the use of the metrics (2,3 and 4). Fig.3 Snapshots of the “PaddyVision” Android mobile app. This app accepts the input of paddy leaf image captured by the mobile camera and then it predicts its nitrogen deficiency/sufficiency category (i.e., swap1, swap2, swap3, and swap4). Moreover, by integrating additional data sources, such as weather and soil quality information, this technology can be improved further to holistically monitor crop health. (PaddyVision app download link:https://play.google.com/store/apps/details?id=com.paddyvision.paddyvisionnpkv3 PaddyVision AI code repository:https://github.com/AmartyaChakraborty/PaddyVisionAI ) CONCLUSIONS The use of Convolutional Neural Networks (CNNs) for rice crop monitoring represents a significant advancement in agriculture. It offers a practical solution to a critical issue – the deficiency of nitrogen in rice cropping systems. Traditional monitoring methods, such as soil analysis and leaf tissue examination, have several limitations, including invasiveness, lack of real-time data, and inaccessibility. The proposed CNN-based approach, exemplified by the PaddyVision mobile app, addresses these concerns by allowing farmers to non-invasively and efficiently monitor their rice crops’ health and nutrient status. By analyzing visual characteristics such as leaf color, size, and shape, the CNN system can provide valuable insights. This democratizes advanced monitoring technology, making it accessible even to small-scale farmers in remote areas. Moreover, by integrating additional data sources, such as weather and soil quality information, this technology can offer a holistic view of crop health. Collaboration with agricultural experts and local organizations is essential for tailoring the app to specific needs and ensuring its effectiveness. In conclusion, the CNN-based rice crop monitoring approach is poised to improve agriculture practices by enhancing yield quality, reducing costs, and promoting sustainable practices. ACKNOWLEDGEMENT I thank all my teachers and classmates at the KV School Gangtok for their support while completing computational work in the computer lab. I highly appreciate Mr. Digbijay Mahto for assisting me with the manuscript preparation and pointing out some important corrections. I also thank our school principal for extending all kinds of support for executing such an exciting school project. REFERENCES “Rice Outlook: May 2023” United States Department of Agriculture (USDA), Foreign Agricultural Service.. Accessed October 13, 2023. https://www.ers.usda.gov/webdocs/outlooks/106554/rcs-23d.pdf?v=2560.5 Good, A. G., and P. R. Sheard. “Nitrogen Loss from Agricultural Systems: Effects on the Environment.” Agricultural and Environmental Science 5, no. 2 (2000): 231–56. Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep Learning. Cambridge, MA: MIT Press, 2016. Wang, Z., Y. Wang, W. Li, L. Gao, and X. Yang. “Leaf nutrient deficiency detection of rice plant based on convolutional neural network.” Sensors 19, no. 10 (2019): 2389. Lobell, David B., Wolfram Schlenker, and Justin Costa-Roberts. “Climate change and crop yields: Are agricultural impacts emerging earlier in the 21st century?” Proceedings of the National Academy of Sciences 104, no. 32 (2007): 12369-12374. He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. “Deep Residual Learning for Image Recognition.” arXiv preprint arXiv:1512.03385 (2015). Indian Council of Agricultural Research – National Rice Research Institute. “Leaf Colour Chart for Nitrogen Management in Rice.” Cuttack, Odisha, India: ICAR-NRRI, 2019

Author: Zebedee Bell Abstract This article aims to demystify the Holographic Principle, an emerging theory that encompasses as disparate fields as black hole physics, string theory, cosmology, and quantum mechanics. As such, it is no mean feat to attempt to provide a working knowledge of this theory in a single article. However, it is the author’s goal to illuminate and make accessible this extremely interesting and exciting theory for as many students as possible. This article will examine the origins of the theory and the cascade of discoveries which led to its conception; the current standing of the theory; and its startling implications. Introduction The holographic principle is an emerging notion within physics that the universe we know could be viewed as holographic. A hologram is essentially a means of storing the information for a system in a form that exists a dimension lower, like a 3-d image projected from a 2-d film. So, in applying this idea to the entire universe, for however many dimensions there are (string theory being unhelpful in narrowing this down), all the information and mechanics and laws of the universe must be able to be encoded onto a semi-separate system one dimension lower. To make the model visually simpler, it is helpful to imagine the universe as 3-d, in which case there must be a 2-d membrane surrounding this 3-d universe to allow for holography to take place. This 3-d interior is often referred to as the ‘bulk’, and the 2-d external membrane as the ‘boundary’. Now, this theory may sound as if it is edging into sci-fi territory, so how did this seemingly ludicrous theory come to the forefront of science? Precedent for the Theory The first notion of holographic space-time emerged from every physicist’s (least) favorite phenomenon: the black hole. Black holes, specifically their nature of being unobservable past the event horizon, keep physicists up at night due to something called the Black Hole Information Paradox [1]. As might be suggested by the name, this paradox is concerned with violating the Conservation of Information, a conservation demanded by quantum mechanics, which means that information cannot be destroyed. Black holes, by the famous no-hair conjecture [2], exhibit only three properties: mass, charge, and angular momentum (pictured right). All the other properties of the particles which fall into the black hole are hidden behind the event horizon. This is not a problem on its own, since the information still exists, it is just not available. However, Hawking [3] demonstrated that black holes evaporate due to Hawking Radiation, which showed that black holes radiate like a black-body emitter, where this radiation is proportional only to the mass (which, and remember this, is proportional to the surface area of a black hole), which means that all the other information locked inside the black hole does not affect Hawking Radiation, meaning that this information is lost when the black hole disappears. An obvious solution to the paradox is that the information which needs to be conserved escapes via the Hawking Radiation itself. Physicist Jacob Bekenstein [4] realized that if black holes are thermal emitters (hot), then they must have entropy. Entropy is, in essence, the measure of how spread-out energy within a system is. He in fact calculated the entropy of a black hole as: Where S represents entropy; A is the surface area of the event horizon; c is the speed of light; G is the gravitational constant; and h-bar is the reduced Planck constant. You may notice that here, as for Hawking Radiation, the entropy of a black hole is proportional to its surface area. Another definition for entropy is that it is the number of micro-states contributing to a macro-state [5], essentially meaning that the event horizon cannot be smooth, but must have a level of disorder. It is possible, therefore, that the event horizon of a black hole is coded like a vinyl record to consist of the entropy, and therefore the information of a black hole. It was Gerard ‘t Hooft [6] who described the mechanism by which in-falling particles can imprint on the event horizon, showing that the in-falling particles cause gravitational deformation, essentially making bumps on the horizon, which totally describe the particle, and so can transfer this information to out-going radiation. This formalized an explanation for how the event horizon can totally describe the contents of the black hole. In other words, the information for the black hole, a 3-d system, is encoded onto the 2-d event horizon (right). From our earlier definition, we can then say that a black hole is a hologram. Problems were raised with this explanation, however. If information is imprinted in this way on the event horizon, this fits with an outside observer’s perspective since time ceases to exist when approaching the immense gravitational effects of a black hole and so any in-falling objects appear trapped (invisible) on the event horizon. However, from the perspective of the in-falling object, they, and their accompanying information, pass straight through. Assuming the information on the event horizon stays there for all frames of reference, this then raises a problem since it violates the no-cloning theorem, whereby information cannot be duplicated in this way. Leonard Susskind [7] replied to this criticism by inventing the idea of black hole complementarity. You have likely heard of the Heisenberg Uncertainty Principle in the context that we cannot know both the momentum and position of a system with 100% certainty at the same time. We say that momentum and position have complementarity. There is another form of complementarity in energy and time, in which the conservation of energy can actually be broken if it is only for a very short time. For example, an electron-positron pair can appear out of a vacuum and then almost immediately annihilate (within about 10^-40 seconds), without breaking the laws of nature. Similarly, in black hole complementarity, the no-cloning theorem can be “broken” by saying that since it is impossible to observe both the exterior and interior of a black hole at the same time, information can be duplicated on the event horizon [8]. Extrapolation Okay, sure, but black holes are really weird things, so how can physicists possibly say from this that we can extend the notion of black hole holography to the entire universe? Foremostly, in calculating his equation for black hole entropy, Bekenstein [9] worked out the maximum information that can be stored within the black hole, finding it proportional to the black hole surface area. Well, Bekenstein also extrapolated this principle into Bekenstein Bounds which measured the maximum information storage of any space, finding it again proportional to the surface area of the enclosed space. Moreover, similar (in that it is exactly opposite) to the black hole’s event horizon is a boundary in our observable universe beyond which the universe is accelerating faster than the speed of light (the universe’s expansion accelerating away from each point in space), making it unobservable relative to our position, very akin to the event horizon, only pushing outwards rather than pulling inwards [10]. A black hole to the universe is still, however, a gigantic leap, so now, for the missing connections. It is easy to imagine a black hole since it is necessarily a spherical object with finite bounds. The universe, however, is a little bit harder to imagine. There are essentially three contenders, which revolve around what the value is for the curvature of space-time. If the value is positive, then space-time curves in on itself, forming a finite sphere in which two parallel lines will eventually converge; this is known as de-Sitter space [11]. If this value for curvature is zero, then space-time is a perfectly flat plane in which two parallel lines will stay parallel. The final, and hardest, to imagine is where the curvature is negative, in which case the universe is a sort of saddle shape, or a hyperbolic space-time, where initially parallel lines will diverge; this is known as anti- de Sitter space (AdS) [12]. Now, it is difficult to imagine any sort of coordinate system for mapping AdS space-time; various ‘compactifications’ have been formulated which, like how the spherical world can be compressed onto a 2-d plane, try to map the infinite hyperbolic universe onto a 2-d plane. One of the best models was created (not for researching the holography principle) by mathematician Poincare [13]. As shown left, the Poincare board conformally (meaning the angles remain the same) compactifies hyperbolic space-time onto a circle in which the tessellation pattern becomes increasingly smaller towards the infinite boundaries. Though difficult to visualise, if the centre of the board were to be shifted to the right, with the right side therefore enlarging and the left side growing, the pattern remains exactly the same; in other words, the shape has the same observed boundaries regardless of the point of reference. This fits with our notion of the cosmic light horizon. Even more exciting is the fractal nature of the Poincare board since any hologram must have a fractal nature in that the entire image is contained within any section of the hologram. The connection was made at last by Juan Maldecena [14] who formulated the idea of Ads/CFT correspondence. CFT stands for conformal field theory, where the rules which govern interactions within a field are scale invariant, so remain the same at whatever scale. Another common application of scale invariance is in string theory, where the vibrating strings theorized to constitute all matter behave the same at whatever length/energy they are at. Maldecena imagined a series of stacked, almost overlapping, d-branes (objects in string theory of variable dimensionality which close open strings), connected by scale-invariant strings, and he found that this produced a conformal field theory in line with quantum mechanics, behaving like Minkowski space-time [15]. The next step was for him [16] to imagine these brane structures in AdS space, where a cylinder is formed from the Poincare board being given height by time (right). He found that the surface of this cylinder behaved exactly like Minkowski space-time as before, with the CFT for quantum theory still present, while the interior also demonstrated a field theory which incorporated gravity. He had, in essence, formulated a holographic view of space-time in which interactions from the CFT on the lower dimensional ‘boundary’ can project a set of laws onto the AdS ‘bulk’. As a note, it is helpful conceptually to restrict models to three spatial dimensions, but Maldacena’s calculations necessitate that the boundary be four-dimensional, and the bulk be five-dimensional [17]. Implications Perhaps the most startling prospect of the holographic principle is finally formulating a theory for quantum gravity. After all, the AdS/CFT correspondence does demonstrate a system in which quantum mechanics and gravity can be described in a unified model [18]. Considering the universe as a hologram allows physicists to consider gravity as a projection of quantum mechanics in a higher dimension. This is especially useful due to the inverse correspondence between the boundary and the bulk, in that extremely large gravitational phenomena in the bulk (like black holes) become far easier to calculate from when looked at in the boundary, where the event becomes very weak. Similarly, extremely complex quantum systems become far easier to deal with when viewed as their projected form in the bulk, becoming far simpler. Another intriguing possibility which arises from the holographic nature of the universe comes from the fractal properties of holograms mentioned earlier. The bulk, as would be expected, is not a fractal since it has an integer dimensionality of 2 (calculated from a number of red-shift surveys) [19], but the holographic ‘film’ itself, the boundary may be. If the boundary, and therefore the universe’s quantum mechanical interactions are fractal in nature, this provides an intriguing explanation for quantum entanglement that particles become entangled because they are simply the self-similar repeating patterns of one another. This could be extrapolated further to support the one-electron theory [20] which states that each electron in the universe has the same mass and charge because it is actually just the same electron. The traditional explanation is that electrons could be deflected backwards and forwards through time to result in this, but perhaps a more elegant theory is that each electron is essentially the same because they are all segments of a fractal nature to the universe on a quantum scale. Conclusion The holographic principle is a theory which arose from decades of mathematical interpretation and theoretical exploration, a collaboration between some of the strangest and most apparently disparate fields. It is a theory with cosmic-scale implications to the way we understand physics and the way in which we consider our own place within the universe. The theory is not without its problems and its contenders, but it is the culmination of some of the greatest minds of our generation which has led to what may be one of the most drastic cosmological revolutions in our history. It is my hope that this article has served to elucidate this intriguing field and to have provided a sufficient level of knowledge to allow for a profound understanding of this theory. Bibliography 1. Polchinski, J. “The Black Hole Information Problem”. New Frontiers in Fields and Strings (TASI 2015) https://doi.org/10.1142/9789813149441_0006 2. “Leonard Susskind on The World As Hologram”. YouTube video, 55:26. Posted by “TVO Docs”, 4 Nov 2011 https://www.youtube.com/watch?v=2DIl3Hfh9tY 3. Hawking, S.W. “Particle creation by black holes”. Commun.Math. Phys. 43, 199–220 (1975). https://doi.org/10.1007/BF02345020 4. Bekenstein, J. “Black Holes and Entropy”. American Physical Society, 1973 https://doi.org/10.1103/PhysRevD.7.2333 5. Thibault, D. “The Entropy of Black Holes: A Primer” Progress in Mathematical Physics, Volume 38. (2004) https://doi.org/10.1007/978-3-0348-7932-3_10 6. ‘t Hooft, G “The Good, the Bad, and the Ugly of Gravity and Information”. arXiv: High Energy Physics – Theory (2016): https://arxiv.org/abs/1609.01725 7. “Leonard Susskind on The World As Hologram” 8. Carlip, S. “Black Hole Thermodynamics”. International Journal of Modern Physics D, Volume 23, Issue 11 (2014) https://doi.org/10.1142/S0218271814300237 9. Bekenstein “Black Holes and Entropy” 10. “Leonard Susskind on The World As Hologram 11. “Understanding the Holographic Universe”. YouTube playlist, 2:02:26. Posted by PBS Space Time, Last Updated 25 Oct 2019 https://www.youtube.com/playlist?list=PLsPUh22kYmNCHVpiXDJyAcRJ8gluQtOJR 12. Witten, E “Anti de Sitter Space and Holography”. Advances in Theoretical and Mathematical Physics vol. 2 (1998): 253-291 https://dx.doi.org/10.4310/ATMP.1998.v2.n2.a2 13. “Understanding the Holographic Universe 14. Maldacena, J. “The Large-N Limit of Superconformal Field Theories and Supergravity”. International Journal of Theoretical Physics 38, 1113–1133 (1999). https://doi.org/10.1023/A:1026654312961 15. Witten, “Anti de Sitter Space and Holography” 16. Maldacena “The Large-N Limit of Superconformal Field Theories and Supergravity” 17. Maldacena “The Large-N Limit of Superconformal Field Theories and Supergravity” 18. Suvrat. R. “Lessons from the Information Paradox” eprint arXiv:2012.05770 (2020) 19. Mureika J. “Fractal holography: a geometric re-interpretation of cosmological large-scale structure”. Journal of Cosmology and Astroparticle Physics Vol. 2007 https://doi.org/10.1088/1475-7516/2007/05/021 20. “Understanding the Holographic Universe” Figure References Figure 1 – Popular Perception of Holography, credit: Amy Richau “6 Ways Holograms Play an Important Role in Star Wars Storytelling” Figure 2 – Visualisation of No-Hair Conjecture, credit: Norman Gürlebeck “No-Hair Theorem for Black Holes in Astrophysical Environments” Figure 3 – Black Body Emission Spectrum, credit: Encyclopaedia Britannica, Inc. Figure 4 – Bekenstein’s Black Hole Entropy Equation, credit: Brian Greene, @bgreene on Twitter 15 March 2018 Figure 5 – Visualisation of ‘t Hooft’s Entropy Solution, credit: vystavil luboš motl v “Hawking, Perry, Strombinger on soft hair” Figure 6 – Euclidian and Non-Euclidian Geometry, credit: naidseyes “The use of non-Euclidian geometry in art” Figure 7 – Poincare Board, credit: Weisstein, Eric W. \”Poincaré Hyperbolic Disk.\” From MathWorld–A Wolfram Web Resource. https://mathworld.wolfram.com/PoincareHyperbolicDisk.html Figure 8 – AdS/CFT correspondence, credit: Alex Dunkel (based off an image in Maldacena, J “The Illusion of Gravity” (2005))