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Among the new way of exchanging money, using crypto currency has been very popular. Its also an investment to get good returns over the period of time. Cryptocurrency has grown to more than 120 million investors around the world as per a survey of 2021.Its growing at the 15 to 20% ratio around the world every year. This fact leads to a serious consideration of security and its vulnerabilities in block chain. Apart from market risks, high volatility, lack of rules and regulations, cyber risks are one of the most required types which needs proper attention and technical understanding. Because the crypto currencies are fully decentralized the risk of attacks is exposed and in most of the cases defenseless. Proof of stake and proof of work are two major algorithms followed by almost all crypto currencies to allot stocks to the holders. In this paper, different types of risks and attacks with POS and POW are explained with its mitigation. The problems and outcomes are examined, reviewed and conferred in case of Ethereum and Bitcoin crypto currencies. These currencies decentralized frameworks and anonymity attracts unlawful activities. Recognizing and preventing them needs understanding of the mechanism of attacks which are discussed in easiest possible ways for even a new-bee or an outsider person.

BACKGROUND: The first national severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serosurvey in India, done in May-June, 2020, among adults aged 18 years or older from 21 states, found a SARS-CoV-2 IgG antibody seroprevalence of 0·73% (95% CI 0·34-1·13). We aimed to assess the more recent nationwide seroprevalence in the general population in India. METHODS: We did a second household serosurvey among individuals aged 10 years or older in the same 700 villages or wards within 70 districts in India that were included in the first serosurvey. Individuals aged younger than 10 years and households that did not respond at the time of survey were excluded. Participants were interviewed to collect information on sociodemographics, symptoms suggestive of COVID-19, exposure history to laboratory-confirmed COVID-19 cases, and history of COVID-19 illness. 3-5 mL of venous blood was collected from each participant and blood samples were tested using the Abbott SARS-CoV-2 IgG assay. Seroprevalence was estimated after applying the sampling weights and adjusting for clustering and assay characteristics. We randomly selected one adult serum sample from each household to compare the seroprevalence among adults between the two serosurveys. FINDINGS: Between Aug 18 and Sept 20, 2020, we enrolled and collected serum samples from 29 082 individuals from 15 613 households. The weighted and adjusted seroprevalence of SARS-CoV-2 IgG antibodies in individuals aged 10 years or older was 6·6% (95% CI 5·8-7·4). Among 15 084 randomly selected adults (one per household), the weighted and adjusted seroprevalence was 7·1% (6·2-8·2). Seroprevalence was similar across age groups, sexes, and occupations. Seroprevalence was highest in urban slum areas followed by urban non-slum and rural areas. We estimated a cumulative 74·3 million infections in the country by Aug 18, 2020, with 26-32 infections for every reported COVID-19 case. INTERPRETATION: Approximately one in 15 individuals aged 10 years or older in India had SARS-CoV-2 infection by Aug 18, 2020. The adult seroprevalence increased approximately tenfold between May and August, 2020. Lower infection-to-case ratio in August than in May reflects a substantial increase in testing across the country. FUNDING: Indian Council of Medical Research.

Introduction The reported prevalence of gestational diabetes mellitus (GDM) varies widely across India. Given the short-term, long-term, and multigenerational health impacts of GDM, understanding its frequency and risk factors is important for population screening strategies. We estimated the prevalence of GDM and determined associated risk factors in rural, central India, where data is sparse. Methods We conducted a cross-sectional study of a convenience sample of 575 pregnant women attending antenatal care (ANC) clinics at Jan Swasthya Sahyog's (JSS) outreach clinics in rural Chhattisgarh, India. Study participants underwent a non-fasting 75g oral glucose tolerance test (OGTT) between 24-28 weeks gestation. Using Diabetes in Pregnancy Study Group of India (DIPSI) criteria, a 2-hour post-OGTT glucose ≥140 mg/dL was used to diagnose GDM. Results We found 11 patients (1.9%) who met diagnostic criteria for GDM. Median age, systolic blood pressure, and diastolic blood pressure were higher in those with GDM (26 vs 23 years, p = 0.02; 117 vs 106 mmHg, p = 0.04, 77 vs 68 mmHg, p < 0.01, respectively). Pre-hypertension was associated with increased odds of GDM on multivariate analysis (OR 4.0, 95% CI: 1.1, 14.8). BMI was not associated with GDM. With appropriate management there were no differences in fetal complications between GDM and normal glucose tolerance (NGT) groups. Conclusions In rural, central India the prevalence of GDM was 1.9% in the absence of traditional risk factors such as increased BMI. Further research is needed to define the applicability of optimal screening strategies in such settings., Gestational diabetes; India; Screening.

End users consider the data available through web as unmodified. Even when the web is secured by HTTPS, the data can be tampered in numerous tactical ways reducing trust on the integrity of data at the clients' end. One of the ways in which the web pages can be modified is via client side browser extensions. The extensions can transparently modify the web pages at client's end and can include new data to the web pages with minimal permissions. Clever modifications can be addition of a fake news or a fake advertisement or a link to a phishing website. We have identified through experimentation that such attacks are possible and have potential for serious damages. To prevent and detect such modifications we present a novel domain expressiveness based approach that uses DNS (Domain Name System) TXT records to express the Hash of important web pages that gets verified by the browsers to detect/thwart any modifications to the contents that are launched via client side malicious browser extensions or via cross site scripting. Initial experimentation suggest that the technique has potential to be used and deployed.

The objective of the present study is to develop the regression-based mathematical model in order to demonstrate the structural impact of the hydrocarbons on the octane number. To achieve this goal, a set of 66 (training set of 41 hydrocarbons and test set of 25 hydrocarbon) hydrocarbons has been considered and encoded into their structural descriptors, viz., Wiener Index (W), zero-order, first-order and second-order connectivity index (χ0, χ1, χ2), Schultz molecular topological index (SMTI), Balaban branching index (J) and indicator parameter (Ic). The multiple linear regression analysis (MLR) has been performed to obtain the structure–property relationship in the form of mathematical model. Moreover, the same model has also been used to predict the octane numbers of all the hydrocarbons. The model demonstrates the pivotal role of Schultz molecular topological index (SMTI), Balaban branching index (J), connectivity indices and indicator parameter in regulating the octane number of the hydrocarbons. In addition to the statistical parameters, the predictive ability and robustness of the model is further cross-validated by an external validation method by applying the model on the test set of 25 hydrocarbons. The proposed Quantitative Structure Property Relationship (QSPR) model provides a valuable insight to design the novel synthetic fuel that can be a sustainable energy solution for the future.

In order to solve complex, long-horizon tasks, intelligent robots need to carry out high-level, abstract planning and reasoning in conjunction with motion planning. However, abstract models are typically lossy and plans or policies computed using them can be unexecutable. These problems are exacerbated in stochastic situations where the robot needs to reason about, and plan for multiple contingencies. We present a new approach for integrated task and motion planning in stochastic settings. In contrast to prior work in this direction, we show that our approach can effectively compute integrated task and motion policies whose branching structures encoding agent behaviors handling multiple execution-time contingencies. We prove that our algorithm is probabilistically complete and can compute feasible solution policies in an anytime fashion so that the probability of encountering an unresolved contingency decreases over time. Empirical results on a set of challenging problems show the utility and scope of our methods.

Background: The first round of national serosurvey in India was conducted in May-June 2020 among adults from 21 States. The second serosurvey was conducted in August-September 2020 to estimate the nationwide seroprevalence for SARS-CoV-2 infection in the general population aged ten years and above. Methods: The household serosurvey was conducted among individuals aged ten years and above in the same 700 villages and wards from 70 districts selected during the first serosurvey. Blood samples were tested using the Abbott SARS-CoV-2 IgG assay. Seroprevalence was estimated after applying the sampling weights and adjusting for clustering and assay characteristics. In order to compare the adult seroprevalence between the two surveys, we randomly selected one adult serum sample from each household. Findings: The weighted and adjusted prevalence of infection among 29,082 individuals aged ten years and above was 6·6% (95% CI: 5·8% - 7·4%). The seroprevalence among adults was 7·1% (95% CI: 6·2% – 8·2%). Seroprevalence was similar across age groups, sex, and occupation. Seroprevalence was highest in urban slum areas followed by urban non-slum and rural areas. We estimated a cumulative 74·3 million infections in the country, with 26 – 32 infections for every reported COVID-19 case by August 2020. Interpretation: Nearly one in 15 individuals aged ten years and above had SARS-CoV-2 infection by August 2020. The adult seroprevalence increased ten times between May and August 2020. Lower infection to case ratio in August compared to May reflects a substantial increase in testing across the country. Funding: The study was funded by the Indian Council of Medical Research, New Delhi, India. The study sponsor was involved in reviewing the study design, writing of the manuscript and the decision to submit the paper for publication. Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: The authors obtained written informed consent from individuals aged 18 years and older. We obtained assent from children aged between 10–17 years, and written informed consent from their parents or guardians prior to the survey. The Central Ethics Committee of Health Research of Indian Council of Medical Research and the Institutional Human Ethics Committee of ICMR-National Institute of Epidemiology, Chennai approved the study protocol.

Background & objectives: India has been reporting the cases of coronavirus disease 2019 (COVID-19) since January 30, 2020. The Indian Council of Medical Research (ICMR) formulated and established laboratory surveillance for COVID-19. In this study, an analysis of the surveillance data was done to describe the testing performance and descriptive epidemiology of COVID-19 cases by time, place and person. Methods: The data were extracted from January 22 to April 30, 2020. The frequencies of testing performance were described over time and by place. We described cases by time (epidemic curve by date of specimen collection; seven-day moving average), place (area map) and person (attack rate by age, sex and contact status), and trends were represented along with public health measures and events. Results: Between January 22 and April 30, 2020, a total of 1,021,518 individuals were tested for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Testing increased from about 250 individuals per day in the beginning of March to 50,000 specimens per day by the end of April 2020. Overall, 40,184 (3.9%) tests were reported positive. The proportion of positive cases was highest among symptomatic and asymptomatic contacts, 2-3-fold higher than among those with severe acute respiratory infection, or those with an international travel history or healthcare workers. The attack rate (per million) by age was highest among those aged 50-69 yr (63.3) and was lowest among those under 10 yr (6.1). The attack rate was higher among males (41.6) than females (24.3). The secondary attack rate was 6.0 per cent. Overall, 99.0 per cent of 736 districts reported testing and 71.1 per cent reported COVID-19 cases. Interpretation & conclusions: The coverage and frequency of ICMR's laboratory surveillance for SARS-CoV-2 improved over time. COVID-19 was reported from most parts of India, and the attack rate was more among men and the elderly and common among close contacts. Analysis of the data indicates that for further insight, additional surveillance tools and strategies at the national and sub-national levels are needed.

Planning for large-scale epidemiological outbreaks in livestock populations often involves executing compute-intensive disease spread simulations. To capture the probabilities of various outcomes, these simulations are executed several times over a collection of representative input scenarios, producing voluminous data. The resulting datasets contain valuable insights, including sequences of events that lead to extreme outbreaks. However, discovering and leveraging such information is also computationally expensive. In this study, we propose a distributed approach for analyzing voluminous epidemiology data to locate and classify the most influential entities in a disease outbreak. Using our disease transmission network (DTN), planners or analysts can isolate entities that have a disproportionate effect on epidemiological outcomes, enabling effective allocation of limited resources such as vaccinations and field personnel. We use a representative dataset to verify our approach, including identification of influential entities and creation of machine learning models for accurate classifications that generalize to other datasets.

Telomerase serves a critical role in stem cell function and tissue homeostasis. This role depends on its ability to synthesize telomere repeats in a manner dependent on the reverse transcriptase (RT) function of its protein component telomerase RT (TERT), as well as on a novel pathway whose mechanism is poorly understood. Here, we use a TERT mutant lacking RT function (TERTci) to study the mechanism of TERT action in mammalian skin, an ideal tissue for studying progenitor cell biology. We show that TERTci retains the full activities of wild-type TERT in enhancing keratinocyte proliferation in skin and in activating resting hair follicle stem cells, which triggers initiation of a new hair follicle growth phase and promotes hair synthesis. To understand the nature of this RT-independent function for TERT, we studied the genome-wide transcriptional response to acute changes in TERT levels in mouse skin. We find that TERT facilitates activation of progenitor cells in the skin and hair follicle by triggering a rapid change in gene expression that significantly overlaps the program controlling natural hair follicle cycling in wild-type mice. Statistical comparisons to other microarray gene sets using pattern-matching algorithms revealed that the TERT transcriptional response strongly resembles those mediated by Myc and Wnt, two proteins intimately associated with stem cell function and cancer. These data show that TERT controls tissue progenitor cells via transcriptional regulation of a developmental program converging on the Myc and Wnt pathways., Author Summary Stem cells and progenitor cells within a tissue are required to maintain tissue homeostasis and to repair tissues after injury by giving rise to differentiated daughter cells. Many progenitor cells express telomerase, a reverse transcriptase enzyme that adds DNA repeats to telomeres, the protective structures that cap chromosome ends. Telomere addition by telomerase is important for normal progenitor cell function and is crucial for enabling cancer cells to divide an unlimited number of times. In addition to its telomere-lengthening function, telomerase reverse transcriptase (TERT) can directly activate quiescent epidermal stem cells. However, the mechanism underlying this novel function for TERT is still not understood. In this study, we demonstrate that the catalytic activity of TERT is dispensable for its ability to activate tissue progenitor cells in vivo. Furthermore, using gene microarrays, we show that TERT controls a developmental program that overlaps the natural transcriptional program of hair follicle cycling in mouse skin. Using pattern-matching algorithms, we find that the TERT-controlled genetic program significantly resembles programs regulated by Myc and Wnt, two pathways critical for stem cell function and tumorigenesis. This paper reveals critical new insights into novel mechanisms of non-telomerase functions of TERT, identifying TERT as a developmental regulator linked to control of transcriptional responses.