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Brain metastasis is seen in 10% to 20% of all adult cancer patients. One of the main modalities of treatment is stereotactic radiosurgery (SRS). Here, we describe the step by step procedure for stereotactic planning of brain metastasis by using a clinical scenario. The management of brain metastasis starts with the clinical evaluation of the patient followed by imaging and SRS treatment in the present case. The paper highlights the sequential process of radiation planning for SRS—starting from simulation, planning, evaluation of plan, and treatment.

Primary testicular lymphoma (PTL) is a rare variant of non-Hodgkin’s lymphoma that is predominant in old age group. Painless testicular swelling is the most common presentation. The standard of care is surgery in the form of radical orchiectomy followed by adjuvant chemotherapy and central nervous system prophylaxis. Because of blood-testis barrier, contralateral testis acts as a sanctuary site for chemotherapy to act and hence scrototesticular radiation is advocated in order to reduce the chance of testicular relapse. Due to lack of any consensus simulation procedure, we propose here a step-by-step procedure for simulation of a case of PTL using a case scenario.

Background Glomus jugulare is a rare, slow-growing tumor that arise within the jugular foramen of the temporal bone. In the past, surgery was the primary modality of treatment for glomus Jugulare, but it leads to many complications and increased mortality. Radiotherapy was indicated in adjuvant setting in post-operative residual disease. But, with the advent of highly conformal radiation planning, stereotactic radiosurgery (SRS), is now one of the main modalities of radiation treatment in glomus jugulare. Objective To describe the procedural steps for radiation planning of SRS of glomus jugulare. Methods The step-by-step procedure for stereotactic planning of glomus jugulare has been described using a clinical scenario of glomus jugulare. Results The stereotactic radiation planning of glomus jugulare starts with the basic history and relevant clinical evaluation, that is, visual testing. Computed tomography (CT) scan and magnetic resonance imaging (MRI) of the brain is the imaging modality of choice. The radiation planning of glomus jugulare starts with CT simulation. MRI of brain should be done in the prescribed format to achieve uniformity in radiation planning. After CT and MRI image fusion, contouring of target, organs at risk (OAR) and radiation planning should be done. The plan evaluation includes target and OAR coverage index, conformity, homogeneity and gradient index, and beam arrangement. After radiation plan evaluation, treatment is delivered after quality assurance and dry run. Conclusion The paper highlights the sequential process of radiation planning for SRS in glomus jugulare—starting from simulation, planning, evaluation of plan, and treatment.

Women are active participants in almost every aspect of agricultural activity around the world. However, due to the deep-rooted gender division of labor in developing countries, their contribution is underestimated. Despite having the fastest growing economy in the world, Ethiopia is still one of the poorest nations. It is vulnerable to weather-related shocks and suffers from widespread food insecurity, particularly among rural populations and smallholder farmers. In Ethiopia, women farmers perform 75 percent of farm labor, which accounts for 70 percent of household food production, but they typically produce up to 35 percent less than male farmers because they have less access to extension services. Therefore, the study focused on reflecting on the role of women in Ethiopian agriculture. This study was conducted at Oda Dawt Kebele, Teyo District, Arsi Zone, and the Oromia Regional State in Ethiopia. The main objective of the study was to identify and examine the role of women in agriculture. Quantitative and qualitative techniques were used to collect data from primary and secondary sources. The researcher used a simple random sampling technique and chose 405 women as the sample size. The questionnaires were written in English for data collection and then translated into the local Afan Oromo language for easier comprehension by respondents. The data collected was also analyzed using a variety of statistical techniques. The main objectives of this evidence-based study were to identify and examine the role of women in agriculture and related sectors. The study results suggest that women play an important role in farming activities that are underestimated. In general, the study emphasizes the importance of the male and female workforce for the economic or agricultural sector. Recognition of the roles and contributions of women in social and economic development, particularly by planners and policymakers, to improve the status of women and increase food security at household and national levels. The purpose of this study is to highlight the important role of women in agricultural activities as the number of studies on the role of women in agriculture increases. In addition, the results of this research could be useful for planners, decision-makers, and practitioners as effective use of women's labor power are required. In general, the study discovers or emphasizes the importance of the female labor force, as opposed to the male labor force, in contributing to the economic activity of the agricultural sector.

Introduction Pediatric malignancy represents 5% of total cancer diagnosed in India. Due to delayed diagnosis and inaccessibility to healthcare system, the overall outcome is poor in our country. The clinicodemographic profile of childhood malignancy is well described in the Western world and in certain parts of India. The incidence of pediatric malignancy in Eastern India, especially Odisha, has not yet been reported that has motivated us to conduct such a study. Objective This study aims to evaluate the clinicodemographic profile and pattern of childhood malignancy among pediatric patients who received the treatment at a tertiary cancer institute of Odisha. Materials and Methods It was a retrospective observational study, carried out for a period of 8 years, from January 1, 2013 to December 31, 2020 at a tertiary cancer center in Eastern India. A total of 759 eligible childhood malignancy patients were recruited in the study. IBM SPSS v23 was used for descriptive statistical analysis, that is, number and percentage of various clinicodemographic parameters of the above patients. Result Childhood malignancy accounted for 1.6% of all cancers reported during the above study period. The male to female ratio was 1.8:1. Out of 759 eligible childhood cancer patients, majority of patients were suffering from leukemia (173; 22.8%) followed by malignant bone tumors (137; 18.0%), and lymphoma (122; 16%). Leukemia was predominant in the age group of 0 to 14 years; lymphoma, central nervous system neoplasms, germ cell tumors malignant bone tumors, and soft tissue sarcoma (STS) were common in the age group of 10 to 18 years; neuroblastoma, retinoblastoma, and renal and hepatic tumors were seen commonly in the age group of 0 to 9 years. The most common presentation in leukemia was fever, while lymphadenopathy was the chief complaint in lymphoma. Local swelling and pain were the presenting symptoms in malignant bone tumors, while STS patients had painless swelling. Conclusion This study provides an overview of the burden and pattern of childhood malignancy for the state of Odisha and acts as a roadmap for the clinicians to conduct further research in the field of pediatric oncology.

Background- Patients with Chronic Kidney Disease exhibit an elevated cardiovascular risk manifesting as Coronary Artery Disease , heart failure, arrhythmias and sudden cardiac death. CKD causes a systemic, chronic pro-inammatory state contributing to vascular and Myocardial remodeling processes resulting in atherosclerosis sclerotic lesions, vascular calcication as well as brosis and calcication of cardiac valves. In this respect, CKD mimics an accelerated aging of the cardiovascular system. Total Procedurenumber of 80 cases including both male and female who presented With CKD in Postgraduate Department of General Medicine JLNMCH was taken in study. The duration of study is two years. Study design is hospital based observational prospective study. In inclusion criteria of cases with GFR 30 - 59 ml/min, bilaterally contracted kidney with poor CMD on USG. Patients with established CKD irrespective of etiology was taken into account. In all patients detailed history of illness, all blood biochemical investigations ECG, ECHO and USG were performed, and analysis was done accordingly. CKD is increasingly recognized as a global publ Conclusion- ic health problem imposing huge medical and nancial burden on society and Healthcare systems with an estimated prevalence of 13.4% globally. Control of traditional risk factors as well as antiplatelet therapy are cornerstones to reduce cardiovascular risks. The effect of lipid-lowering strategies on CV risk reduction in CKD seems to be dependent on severity of CKD (SHARP STUDY).

Here, by employing a Barrow entropy and the standard holographic method at a cosmic framework, we formulate Barrow agegraphic dark energy (BADE), taking the Universe age as an IR cutoff scale in a flat FLRW Universe. For evaluation of statefinder parameters in [Formula: see text] and [Formula: see text] planes, trajectories have been plotted for BADE and discovered that for various values of [Formula: see text], the model exhibits both the behavior of Chaplygin gas and quintessence. Moreover, as a supplement to the statefinder study, we looked at the BADE model without interaction in the plane [Formula: see text], which might offer us a dynamic study using the energy density BADE parameter [Formula: see text] and [Formula: see text], as per VI-[Formula: see text]CDM observational data without interaction from Planck 2018 results.

Indirect surveys, in which respondents provide information about other people they know, have been proposed for scenarios where privacy is important or where the population to be surveyed is hard to reach. As an example, during various stages of the COVID-19 pandemic surveys, including indirect surveys, have been used to estimate the number of cases or the level of vaccination. The Network Scale-up Method (NSUM) is the classical approach to developing such estimates but was designed with discrete, time-limited indirect surveys in mind. Further, it requires asking for or estimating the number of individuals in each respondent's network. In recent years, surveys are being increasingly deployed online and collecting data continuously (e.g., COVID-19 surveys on Facebook during much of the pandemic). Conventional NSUM can be applied to these scenarios by analyzing the data independently during each time interval, but this misses the opportunity of leveraging the temporal dimension. Understanding the advantage of simply smoothing NSUM results to various degrees is not trivial. We propose to use the responses from indirect surveys collected over time and develop analytical tools (i) to prove that indirect surveys can be used to provide better estimates for the size of the hidden population compared to direct surveys, and (ii) to identify appropriate aggregations over time to further improve the estimates. We demonstrate through simulations that our approach outperforms traditional NSUM and direct surveying methods to estimate the size of a time-varying hidden population. We also demonstrate the superiority of our approach on an existing indirect survey dataset on COVID-19 confirmed cases., Comment: 10 pages, 12 figures, 2 tables, and 1 appendix

Background: Healthcare workers are at a higher risk of having Covid infection than other professionals. Thus, it is more important for them to wear face masks for themselves and for the sake of their co-workers and public health. The use of face masks is mainly limited by their perceived discomfort and concerns regarding inadequate gas exchange. Still, there are serious concerns about the use of masks over a long period. Aims: This study aimed to study the effects of different types of face masks on clinical parameters like oxygen saturation, Respiratory rate, and pulse rate. Methods: A cross-sectional observational study was conducted on 218 subjects from March 2021 to April 2021. They used different types of masks-like cloth masks, surgical masks, N95 masks, and double masks. A pulse oximeter applied to the index finger was used for the non-invasive determination of clinical parameters like oxygen saturation, heart rate, and respiratory rate. Results: There was a significant decrease in oxygen saturation seen in subjects using the surgical mask and N-95 mask, but there was no change in oxygen saturation in participants who wore cloth masks. In our study, heart rates increased significantly at the end of the study in all groups irrespective of the mask they wore. However, the respiratory rate increased significantly only in those participants who wore N-95 masks. Results: There was a significant decrease in oxygen saturation seen in subjects using the surgical mask and N-95 mask, but there was no change in oxygen saturation in participants who wore cloth masks. In our study, heart rates increased significantly at the end of the study in all groups irrespective of the mask they wore. However, the respiratory rate increased significantly only in those participants who wore N-95 masks. Conclusion: Our study concludes that wearing a face mask for a long period induces an increase in heart rate and shortness of breath along with a significant reduction in oxygen saturation.

eLife 12, e81916 (2023). doi:10.7554/eLife.81916, Background:Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022.Methods:We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance.Results:Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models.Conclusions:Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks., Published by eLife Sciences Publications, Cambridge

We present Samanantar, the largest publicly available parallel corpora collection for Indic languages. The collection contains a total of 49.7 million sentence pairs between English and 11 Indic languages (from two language families). Specifically, we compile 12.4 million sentence pairs from existing, publicly available parallel corpora, and additionally mine 37.4 million sentence pairs from the Web, resulting in a 4× increase. We mine the parallel sentences from the Web by combining many corpora, tools, and methods: (a) Web-crawled monolingual corpora, (b) document OCR for extracting sentences from scanned documents, (c) multilingual representation models for aligning sentences, and (d) approximate nearest neighbor search for searching in a large collection of sentences. Human evaluation of samples from the newly mined corpora validate the high quality of the parallel sentences across 11 languages. Further, we extract 83.4 million sentence pairs between all 55 Indic language pairs from the English-centric parallel corpus using English as the pivot language. We trained multilingual NMT models spanning all these languages on Samanantar which outperform existing models and baselines on publicly available benchmarks, such as FLORES, establishing the utility of Samanantar. Our data and models are available publicly at Samanantar and we hope they will help advance research in NMT and multilingual NLP for Indic languages.

Background: NSAIDs are the most widely prescribed medications worldwide for their anti-inflammatory, antipyretic, and analgesic effects. However, their chronic use can lead to several adverse drug events including GI toxicity. The selective COX-2 inhibitors developed as gastrosparing NSAIDs also suffer from serious adverse effects which limit their efficacy. Objective: Local generation of reactive oxygen species is implicated in NSAID-mediated gastric ulceration and their combination with H2 antagonists like famotidine reduces the risk of ulcers. The objective of this work was to design and synthesize novel methanesulphonamido isoxazole derivatives by hybridizing the structural features of NSAIDs with those of antiulcer drugs (ranitidine, famotidine, etc.) to utilize a dual combination of anti-inflammatory activity and reducing (antioxidant) potential. Method: The designing process utilized three dimensional similarity studies and utilized an isoxazole core having a potential for anti-inflammatory as well as radical scavenging antioxidant activity. The compounds were assayed for their anti-inflammatory activity in established in vivo models. The in vitro antioxidant activity was assessed in potassium ferricyanide reducing power (PFRAP) assay employing ascorbic acid as the standard drug. Results: Compounds 5, 6, 9 and 10 showed antiinflammatory activity comparable to the standard drugs and were also found to be non-ulcerogenic at the test doses. Compounds 6-10 exhibited good antioxidant effect in the concentration range of 1.0- 50.0 μmol/ml. The test compounds were also found to comply with the Lipinski rule suggesting good oral absorption. Conclusion: A new series of isoxazole based compounds is being reported with good antiinflammatory activity coupled with antioxidant potential as gastro-sparing anti-inflammatory agents.

Energy consumption of embedded applications has rapidly increased with the advancement of technology and computing. There is a little improvement in energy consumption as compared to computing and storage capacity. Although computing performance has been continuously increasing, power/energy consumption is more critical in the design of real-time embedded systems. Real-time embedded applications need a power management technique to judicially balance the energy consumption and computing performance. It should be done in such a way that the system performance improves along with an increase in the lifespan of the system. The proposed methodology presented in this paper deals with the minimization of energy for time-critical embedded applications. Simulation studies, along with theoretical analysis, have been carried out to show the effectiveness of the proposed three-phase reliable energy-aware scheduling method. It is observed that the proposed approach provides better tolerance (approximately four times) and consumes less energy (35% to 45%) for a wide range of applications.

Hypoparathyroidism is a common encounter in endocrinology practice. A thorough search for the etiology is generally futile, and most cases are labeled as idiopathic. Familial idiopathic hypoparathyroidism is a large chunk of these idiopathic cases. Here we present 2 cases who presented with features of hypocalcemia and were eventually diagnosed with hypoparathyroidism. Our first case is that of a middle-age woman who presented with spontaneous tetany and perioral numbness. She had very low serum calcium values, low serum magnesium, hypokalemia, hypercalciuria, and undetectable parathormone levels. She was initially managed with parenteral calcium, magnesium, and oral potassium chloride, which was shifted to oral replacements once stabilized. Focused exome sequencing for causes of hypoparathyroidism and hypocalcemia revealed a frameshift mutation in glial cell missing homolog 2 (GCM2) (NM_004752.4) on chromosome 6, c737dupA variant (p. Asp246Glufs*25) located at exon 5. The second case presented is that of a 1-month-old infant presenting with hypocalcemic seizures, severe hypocalcemia, hyperphosphatemia, and low parathormone levels. The infant was stabilized with parenteral calcium and trial of subcutaneous teriparatide for further improvement. Oral calcium and calcitriol were instituted once stabilized, and teriparatide was tapered off. Focused exome sequencing revealed a homozygous mutation involving GCM2 (ENST0000379491.5) on chromosome 6, variant CM2 chr6:10876558_10877139insT located on exon1-2. Both of these mutations are novel and underscore the profound effect of GCM2 on parathyroid gland development in infants and maintenance in adults.

BackgroundDuring the COVID-19 pandemic there has been a strong interest in forecasts of the short-term development of epidemiological indicators to inform decision makers. In this study we evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland for the period from January through April 2021.MethodsWe evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland. These were issued by 15 different forecasting models, run by independent research teams. Moreover, we study the performance of combined ensemble forecasts. Evaluation of probabilistic forecasts is based on proper scoring rules, along with interval coverage proportions to assess forecast calibration. The presented work is part of a pre-registered evaluation study and covers the period from January through April 2021.ResultsWe find that many, though not all, models outperform a simple baseline model up to four weeks ahead for the considered targets. Ensemble methods (i.e., combinations of different available forecasts) show very good relative performance. The addressed time period is characterized by rather stable non-pharmaceutical interventions in both countries, making short-term predictions more straightforward than in previous periods. However, major trend changes in reported cases, like the rebound in cases due to the rise of the B.1.1.7 (alpha) variant in March 2021, prove challenging to predict.ConclusionsMulti-model approaches can help to improve the performance of epidemiological forecasts. However, while death numbers can be predicted with some success based on current case and hospitalization data, predictability of case numbers remains low beyond quite short time horizons. Additional data sources including sequencing and mobility data, which were not extensively used in the present study, may help to improve performance.Plain language summaryThe goal of this study is to assess the quality of forecasts of weekly case and death numbers of COVID-19 in Germany and Poland during the period of January through April 2021. We focus on real-time forecasts at time horizons of one and two weeks ahead created by fourteen independent teams. Forecasts are systematically evaluated taking uncertainty ranges of predictions into account. We find that combining different forecasts into ensembles can improve the quality of predictions, but especially case numbers proved very challenging to predict beyond quite short time windows. Additional data sources, in particular genetic sequencing data, may help to improve forecasts in the future.

Background: Coronavirus disease 2019 (COVID-19) barrier box is being used by health-care workers for protection against aerosol-transmitted infection. Usually, a Macintosh laryngoscope (MC) or a video laryngoscope (VL) is used for endotracheal intubation (ETI). We aimed to determine the most suitable laryngoscope blade in terms of time to ETI, ease of ETI, and the first-pass success rate. Methods: American Society of Anesthesiologists Grade I and II patients undergoing surgery under general anesthesia were randomized into the MC and the King Vision VL groups in a 1:1 ratio. ETI was performed using either the MC (the MC group) or the King Vision VL (the VL group) with a COVID-19 barrier box. The first-pass intubation success rate, intubation time, and ease of ETI were analyzed. Results: The first-pass success rate was higher in the MC group (P = 0.43). The mean duration of ETI was 33 s and 47 s in the MC group and VL group, respectively. The difference was statistically significant between the groups (P = 0.002). The ease of ETI was comparable between the groups (P = 0.57), and the Cormack–Lehane grade was significantly different between the groups (P = 0.0025). Conclusion: ETI duration was shorter in the MC group than in the VL group. Hence, a MC can be used along with a COVID-19 barrier box by experienced operators for the prevention of aerosol spread.

Purpose of the Study: Reversible proximal tubular dysfunction associated with distal renal tubular acidosis (dRTA) mimics type 3 RTA, a condition classically associated with features of both proximal RTA (pRTA) and dRTA. Proximal tubulopathy has been reported in children with primary dRTA, but the data in adults are lacking. Study Design: In this hospital record-based retrospective study, data from 66 consecutive cases of RTA, between January 2016 to December 2018, were retrieved and analyzed. Results: Mean age of the study population was 25.3 years (range: 3 months to 73 years). Six (9.1%) of them had pRTA, 58 (87.9%) had dRTA, 1 (1.5%) had type 3 RTA, and the remaining 1 (1.5%) had type 4 RTA. Ten patients (17.2%) with dRTA and 3 patients of pRTA (50%) had underlying secondary etiologies. Data on proximal tubular dysfunction were available for 30 patients with dRTA, of whom 1 had isolated dRTA, and the rest 29 patients had accompanying completely reversible proximal tubular dysfunction. Among the 10 cases of secondary dRTA, 6 were not evaluated for proximal tubular dysfunction. Of the remaining 4, 3 had reversible form of proximal tubular abnormality. Fifty-two patients with dRTA came from a population, indigenous to the “Rarh” region of India. Conclusions: Proximal tubular dysfunction often accompanies dRTA; 75% of the children with primary dRTA, at least 29% of adults with primary dRTA, and at least 30% of adults with secondary dRTA manifest such completely reversible form of proximal tubulopathy. “Rarh' region of India probably is a hotspot for endemic dRTA.

Persisters are a subpopulation of bacteria that resist killing by antibiotics, even though they are genetically similar to their drug-susceptible counterpart. Like in several other bacteria, persisters are also reported in the human pathogen Mycobacterium tuberculosis (Mtb). Stochastic formation of Mtb persisters with a high level of antimicrobial tolerance set the stage for subsequent multidrug-resistant mutations. Despite significant advancement in our understanding, much remains to be learnt about the biology of this drug-recalcitrant bacterial subpopulation. Most of the information pertaining to the metabolic evolution required for emergence of drug tolerance in tuberculosis (TB) pathogens has come from transcriptional, metabolomic, and mutagenesis studies. Since proteins are the key functional molecules regulating the majority of metabolic activities in the cell, investigation of the whole-cell protein expression profile will further provide valuable insights into the physiology of Mtb persisters. We performed a quantitative proteomic analysis of Mtb H37Rv cultured under an in vitro persistence model to identify the proteomic profile of the phenotypic drug-tolerant bacterial population. Our study reveals that proteins related to intermediary metabolism and respiration, cell-wall and cell processes, lipid metabolism, information pathways, and virulence, detoxification and adaptation functional categories are primarily modulated in the persister subpopulation. Further, we demonstrate that various surface-localized mycobacterial membrane protein large (MmpL) proteins, which exhibit a high level of expression in Mtb persisters, are crucial for the mycobacterial survival during persistent growth state. A drug-induced persister subpopulation of Mtb exhibit various differentially regulated proteins that might be critical in mitigating the antimicrobial effect of drugs and can be further explored to develop novel anti-TB agents. The peptide identifications and tandem mass spectra (MS/MS) have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD013621.

It has long been argued that federal countries, especially those with strong subnational taxing powers, might face difficulty in implementing a federal value-added tax (VAT) because of coordination issues involved, and therefore might be reluctant to adopt one. This article provides insights on how VAT structures are evolving in federal systems, where different tiers of government have separate (and sometimes overlapping) taxation powers. While the author focuses mainly on India's recently enacted goods and services tax (GST), he also offers a comparative perspective, with reference to the GST/VAT systems in Canada, Brazil, and the European Union, thus adding to the hitherto limited body of scholarly work on VAT coordination in federal jurisdictions. The GST is arguably India's biggest tax reform in several decades. Introduced primarily to create a unified national market and bring an end to tax wars and economic distortions, the tax reform's chief slogan was "GST—one-nation-one-tax-one-market." This article takes a closer look at a unique institutional design feature of the Indian GST—a centre-state body called the GST council. What makes this body unique is that it is envisaged as functioning on the principles of cooperative federalism. But can a concurrent tax system, whose very survival is based on cooperative federalism, guarantee a unified national market? If yes, for how long? The author highlights the role of the GST council in market integration and explains why the council has succeeded on several fronts while failing on others. He also addresses an unresolved constitutional issue that could affect the GST council's ability to function as the fulcrum for cooperative federalism—namely, the question of whether its decisions are binding. The uncertainty surrounding this issue could lead to a constitutional crisis if one or more states decide to opt out. The author discusses four possible ways to deal with this impending crisis.

World is currently engulfed by the catastrophic pandemic of SARS CoV-2.The virus-driven protective response to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) remains cryptic with respect to the role of protective and specific antibodies in circulation. Such protective antibody response is vital, particularly in Health Care Workers (HCW), as they are the cardinal barrier and driver of the ever evolving pandemic. Hence, we performed Seroprevalence study to retrospectively identify the rate of asymptomatic infections in HCW not caring for COVID-19 patients. Peripheral blood serum was collected from five different tertiary care hospitals in Yavatmal hospital employees with no apparent COVID-19 symptoms and called the cohort as COMAPS-Y (Cohort of Medical And Paramedical Staff- Yavatmal). Spike (S) specific IgG were analyzed in the peripheral blood samples. We observed that 34% of the study candidates were found to harbor IgG antibodies against COVID-19 with no significant bias to a particular gender. We believe determination of asymptomatic but immune HCW (seropositive) can be vital for understanding the dynamics of nosocomial transmission and be pivotal in determining the necessary step for protection of HCW in the future.

Road hazards such as jaywalking pedestrians, stray animals, unmarked speed bumps, vehicles, and road damage can pose a significant threat in poor visibility conditions. Vehicles are fitted with safety technologies like advanced driver assistance systems (ADAS) and AW (automatic warning) systems to tackle these issues. However, these safety systems are complex and expensive, and these proprietary systems are exclusive to high-end models. The majority of the existing vehicles on the road lacks these systems. The YOLO model (You Only Look Once Architecture) was chosen owing to its lightweight architecture and low inference latency. Since YOLO is an open-source architecture, it can enhance interoperability and feasibility of aftermarket/retrofit ADAS devices, which helps in reducing road fatalities. An ADAS which implements a YOLO-based object detection algorithm to detect and mark obstacles (pedestrians, vehicles, animals, speed breakers, and road damage) using a visual bounding box was proposed. The performance of YOLOv3 and YOLOv5 has been evaluated on the Traffic in the Tamil Nadu Roads dataset. The YOLOv3 model has performed exceptionally well with an F1-Score of 76.3% and an mAP (mean average precision) of 0.755, whereas the YOLOv5 has achieved an F1-Score of 73.7% and an mAP of 0.7263.

Background: Nowadays, there is an immense increase in the demand for high power computation of real-time workloads and the trend towards multi-core and multiprocessor CPUs. The realtime system needs to be implemented upon multiprocessor platforms. Introduction: The nature of processors in an embedded real-time system is changing day by day. The two most significant challenges in a multiprocessor environment are scheduling and synchronization. The popularity of real-time multi-core systems has exploded in recent years, driving the rapid development of a variety of methods for multiprocessor scheduling of essential tasks; on the other hand; these systems have constraints when it comes to maintaining synchronization in order to access shared resources. Method: This research work presents a systematic review of different existing scheduling algorithms and synchronization protocols for shared resources in a real-time multiprocessor environment. The manuscript also presents a study based on various metrics of resource scheduling and comparison among different resource scheduling techniques. Result and Conclusion: The survey classifies open issues, key challenges, and likely useful research directions. Finally, we accept that there is still a lot of capacity in developing better resource management and further maintaining the overall quality. The paper considers such a future path of research in this field.

We proposed the SIkJalpha model in the beginning of the COVID-19 pandemic. Over the last two years, as the pandemic evolved, more complexities were added to capture crucial factors and variables that can assist with projecting desired future scenarios. Throughout the pandemic, multi-model collaborative efforts have been organized to predict short-term outcomes (cases, deaths, and hospitalizations) of COVID-19 and long-term scenario projections. We have been participating in five such efforts. This paper presents the evolution of the SIkJalpha model and its many versions that have been used to submit to these collaborative efforts since the beginning of the pandemic., 21 pages, 2 figures

With the rise of Big Data, there has been a significant effort in increasing compute power through GPUs, TPUs, and heterogeneous architectures. As a result, many applications are memory bound, i.e., they are bottlenecked by the movement of data from main memory to compute units. One way to address this issue is through data prefetching, which relies on accurate prediction of memory accesses. While recent deep learning models have performed well on sequence prediction problems, they are far too heavy in terms of model size and inference latency to be practical for data prefetching. Here, we propose clustering-driven compact LSTM models that can predict the next memory access with high accuracy. We introduce a novel clustering approach called Delegated model that can reliably cluster the applications. For each cluster, we train a compact meta-LSTM model that can quickly adapt to any application in the cluster. Prior LSTM-based work on access prediction has used orders of magnitude more parameters and developed one model for each application (trace). While one (specialized) model per application can result in more accuracy, it is not a scalable approach. In contrast, our models can predict for a class of applications by trading off specialization at the cost of few retraining steps at runtime, for a more generalizable compact meta-model. Our experiments on 13 benchmark applications demonstrate that clustering-driven ensemble compact meta-models can obtain accuracy close to specialized models using few batches of retraining for majority of the applications.

After a period of rapidly declining U.S. COVID-19 incidence during January-March 2021, increases occurred in several jurisdictions (1,2) despite the rapid rollout of a large-scale vaccination program. This increase coincided with the spread of more transmissible variants of SARS-CoV-2, the virus that causes COVID-19, including B.1.1.7 (1,3) and relaxation of COVID-19 prevention strategies such as those for businesses, large-scale gatherings, and educational activities. To provide long-term projections of potential trends in COVID-19 cases, hospitalizations, and deaths, COVID-19 Scenario Modeling Hub teams used a multiple-model approach comprising six models to assess the potential course of COVID-19 in the United States across four scenarios with different vaccination coverage rates and effectiveness estimates and strength and implementation of nonpharmaceutical interventions (NPIs) (public health policies, such as physical distancing and masking) over a 6-month period (April-September 2021) using data available through March 27, 2021 (4). Among the four scenarios, an accelerated decline in NPI adherence (which encapsulates NPI mandates and population behavior) was shown to undermine vaccination-related gains over the subsequent 2-3 months and, in combination with increased transmissibility of new variants, could lead to surges in cases, hospitalizations, and deaths. A sharp decline in cases was projected by July 2021, with a faster decline in the high-vaccination scenarios. High vaccination rates and compliance with public health prevention measures are essential to control the COVID-19 pandemic and to prevent surges in hospitalizations and deaths in the coming months.

Background: The COVID-19 Scenario Modeling Hub convened nine modeling teams to project the impact of expanding SARS-CoV-2 vaccination to children aged 5–11 years on COVID-19 burden and resilience against variant strains. Methods: Teams contributed state- and national-level weekly projections of cases, hospitalizations, and deaths in the United States from September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of 1) vaccination (or not) of children aged 5–11 years (starting November 1, 2021), and 2) emergence (or not) of a variant more transmissible than the Delta variant (emerging November 15, 2021). Individual team projections were linearly pooled. The effect of childhood vaccination on overall and age-specific outcomes was estimated using meta-analyses. Findings: Assuming that a new variant would not emerge, all-age COVID-19 outcomes were projected to decrease nationally through mid-March 2022. In this setting, vaccination of children 5–11 years old was associated with reductions in projections for all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880–0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834–0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797–1.020) compared with scenarios without childhood vaccination. Vaccine benefits increased for scenarios including a hypothesized more transmissible variant, assuming similar vaccine effectiveness. Projected relative reductions in cumulative outcomes were larger for children than for the entire population. State-level variation was observed. Interpretation: Given the scenario assumptions (defined before the emergence of Omicron), expanding vaccination to children 5–11 years old would provide measurable direct benefits, as well as indirect benefits to the all-age U.S. population, including resilience to more transmissible variants. Funding: Various (see acknowledgments).