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Immersive virtual reality (iVR) employs head-mounted displays or cave-like environments to create a sensory-rich virtual experience that simulates the physical presence of a user in a digital space. The technology holds immense promise in neuroscience research and therapy. In particular, virtual reality (VR) technologies facilitate the development of diverse tasks and scenarios closely mirroring real-life situations to stimulate the brain within a controlled and secure setting. It also offers a cost-effective solution in providing a similar sense of interaction to users when conventional stimulation methods are limited or unfeasible. Although combining iVR with traditional brain imaging techniques may be difficult due to signal interference or instrumental issues, recent work has proposed the use of functional near infrared spectroscopy (fNIRS) in conjunction with iVR for versatile brain stimulation paradigms and flexible examination of brain responses. We present a comprehensive review of current research studies employing an iVR-fNIRS setup, covering device types, stimulation approaches, data analysis methods, and major scientific findings. The literature demonstrates a high potential for iVR-fNIRS to explore various types of cognitive, behavioral, and motor functions in a fully immersive VR (iVR) environment. Such studies should set a foundation for adaptive iVR programs for both training (e.g., in novel environments) and clinical therapeutics (e.g., pain, motor and sensory disorders and other psychiatric conditions).

How human genetic variation contributes to vaccine effectiveness in infants is unclear, and data are limited on these relationships in populations with African ancestries. We undertook genetic analyses of vaccine antibody responses in infants from Uganda (n = 1391), Burkina Faso (n = 353) and South Africa (n = 755), identifying associations between human leukocyte antigen (HLA) and antibody response for five of eight tested antigens spanning pertussis, diphtheria and hepatitis B vaccines. In addition, through HLA typing 1,702 individuals from 11 populations of African ancestry derived predominantly from the 1000 Genomes Project, we constructed an imputation resource, fine-mapping class II HLA-DR and DQ associations explaining up to 10% of antibody response variance in our infant cohorts. We observed differences in the genetic architecture of pertussis antibody response between the cohorts with African ancestries and an independent cohort with European ancestry, but found no in silico evidence of differences in HLA peptide binding affinity or breadth. Using immune cell expression quantitative trait loci datasets derived from African-ancestry samples from the 1000 Genomes Project, we found evidence of differential HLA-DRB1 expression correlating with inferred protection from pertussis following vaccination. This work suggests that HLA-DRB1 expression may play a role in vaccine response and should be considered alongside peptide selection to improve vaccine design.

BackgroundIn 2011, the American Board of Medical Specialties established clinical informatics (CI) as a subspecialty in medicine, jointly administered by the American Board of Pathology and the American Board of Preventive Medicine. Subsequently, many institutions created CI fellowship training programs to meet the growing need for informaticists. Although many programs share similar features, there is considerable variation in program funding and administrative structures.ObjectivesThe aim of our study was to characterize CI fellowship program features, including governance structures, funding sources, and expenses.MethodsWe created a cross-sectional online REDCap survey with 44 items requesting information on program administration, fellows, administrative support, funding sources, and expenses. We surveyed program directors of programs accredited by the Accreditation Council for Graduate Medical Education between 2014 and 2021.ResultsWe invited 54 program directors, of which 41 (76%) completed the survey. The average administrative support received was $27,732/year. Most programs (85.4%) were accredited to have two or more fellows per year. Programs were administratively housed under six departments: Internal Medicine (17; 41.5%), Pediatrics (7; 17.1%), Pathology (6; 14.6%), Family Medicine (6; 14.6%), Emergency Medicine (4; 9.8%), and Anesthesiology (1; 2.4%). Funding sources for CI fellowship program directors included: hospital or health systems (28.3%), clinical departments (28.3%), graduate medical education office (13.2%), biomedical informatics department (9.4%), hospital information technology (9.4%), research and grants (7.5%), and other sources (3.8%) that included philanthropy and external entities.ConclusionCI fellowships have been established in leading academic and community health care systems across the country. Due to their unique training requirements, these programs require significant resources for education, administration, and recruitment. T

Internal medicine physicians are increasingly interacting with systems that implement artificial intelligence (AI) and machine learning (ML) technologies. Some physicians and health care systems are even developing their own AI models, both within and outside of electronic health record (EHR) systems. These technologies have various applications throughout the provision of health care, such as clinical documentation, diagnostic image processing, and clinical decision support. With the growing availability of vast amounts of patient data and unprecedented levels of clinician burnout, the proliferation of these technologies is cautiously welcomed by some physicians. Others think it presents challenges to the patient-physician relationship and the professional integrity of physicians. These dispositions are understandable, given the "black box" nature of some AI models, for which specifications and development methods can be closely guarded or proprietary, along with the relative lagging or absence of appropriate regulatory scrutiny and validation. This American College of Physicians (ACP) position paper describes the College's foundational positions and recommendations regarding the use of AI- and ML-enabled tools and systems in the provision of health care. Many of the College's positions and recommendations, such as those related to patient-centeredness, privacy, and transparency, are founded on principles in the ACP Ethics Manual. They are also derived from considerations for the clinical safety and effectiveness of the tools as well as their potential consequences regarding health disparities. The College calls for more research on the clinical and ethical implications of these technologies and their effects on patient health and well-being.

ObjectivesThe number of surgeries performed in the United States has increased over the past two decades, with a shift to the ambulatory setting. Perioperative complications and mortality pose significant health care burdens. Inadequate preoperative assessment and documentation contribute to communication failure and poor patient outcomes. The aim of this quality improvement project was to design and implement a preoperative evaluation documentation template that not only improved communication during the perioperative pathway but also enhanced the overall user experience.MethodsWe implemented a revamped evidence-based documentation template in the electronic medical records of a health care organization across three internal medicine clinics on the downtown campus and seven satellite family medicine clinics. A pre- and postintervention design was used to assess the template utilization rate and clinician satisfaction.ResultsThe preoperative template utilization rate increased from 51.2% at baseline to 66.5% after the revamped template "went live" (p < 0.001). Clinician satisfaction with the preoperative documentation template also significantly increased (30.6 vs. 80.0%, p < 0.001).ConclusionAdopting a user-friendly, evidence-based documentation template can enhance the standardization of preoperative evaluation documentation and reduce the documentation burden.

Mutant populations are crucial for functional genomics and discovering novel traits for crop breeding. Sorghum, a drought and heat-tolerant C4 species, requires a vast, large-scale, annotated, and sequenced mutant resource to enhance crop improvement through functional genomics research. Here, we report a sorghum large-scale sequenced mutant population with 9.5 million ethyl methane sulfonate (EMS)-induced mutations that covered 98% of sorghum's annotated genes using inbred line BTx623. Remarkably, a total of 610 320 mutations within the promoter and enhancer regions of 18 000 and 11 790 genes, respectively, can be leveraged for novel research of cis-regulatory elements. A comparison of the distribution of mutations in the large-scale mutant library and sorghum association panel (SAP) provides insights into the influence of selection. EMS-induced mutations appeared to be random across different regions of the genome without significant enrichment in different sections of a gene, including the 5' UTR, gene body, and 3'-UTR. In contrast, there were low variation density in the coding and UTR regions in the SAP. Based on the Ka /Ks value, the mutant library (~1) experienced little selection, unlike the SAP (0.40), which has been strongly selected through breeding. All mutation data are publicly searchable through SorbMutDB (https://www.depts.ttu.edu/igcast/sorbmutdb.php) and SorghumBase (https://sorghumbase.org/). This current large-scale sequence-indexed sorghum mutant population is a crucial resource that enriched the sorghum gene pool with novel diversity and a highly valuable tool for the Poaceae family, that will advance plant biology research and crop breeding.

Respiratory reductases enable microorganisms to use molecules present in anaerobic ecosystems as energy-generating respiratory electron acceptors. Here we identify three taxonomically distinct families of human gut bacteria (Burkholderiaceae, Eggerthellaceae and Erysipelotrichaceae) that encode large arsenals of tens to hundreds of respiratory-like reductases per genome. Screening species from each family (Sutterella wadsworthensis, Eggerthella lenta and Holdemania filiformis), we discover 22 metabolites used as respiratory electron acceptors in a species-specific manner. Identified reactions transform multiple classes of dietary- and host-derived metabolites, including bioactive molecules resveratrol and itaconate. Products of identified respiratory metabolisms highlight poorly characterized compounds, such as the itaconate-derived 2-methylsuccinate. Reductase substrate profiling defines enzyme–substrate pairs and reveals a complex picture of reductase evolution, providing evidence that reductases with specificities for related cinnamate substrates independently emerged at least four times. These studies thus establish an exceptionally versatile form of anaerobic respiration that directly links microbial energy metabolism to the gut metabolome.

Ewing sarcoma (ES) is an uncommon mesenchymal neoplasm that typically develops as a bone mass, although up to 30% arise in extraskeletal sites. ES of the gastrointestinal (GI) and hepatobiliary tract is rare and may be misdiagnosed as other, more common neoplasms that occur in these sites. However, the correct classification of extraskeletal ES is important for timely clinical management and prognostication. We reviewed our experience of ES in the GI and hepatobiliary tract in order to further highlight the clinicopathologic features of these neoplasms and document the potential for misdiagnosis in this setting. The archives and consultation files of 6 academic institutions were retrospectively queried for cases of ES occurring in the GI and hepatobiliary tract. The histologic slides and ancillary studies were reviewed and clinical data were retrieved for each case through the electronic medical records, when available. Twenty-three patients with ES in the GI and/or hepatobiliary tract were identified from 2000 to 2022. Of these, 11 were women and 12 were men with a median age of 38 years (range, 2 to 64). Tumor locations included the pancreas (n=5), liver (n=2), stomach (n=3), colorectum (n=3), and small intestine (n=5), as well as tumors involving multiple organs, pelvis and retroperitoneum (n=5). Tumor size varied between 2 cm and 18 cm. Twenty were primary and 3 were metastases. Of the 23 cases, only 17% were initially diagnosed as ES. The most common misdiagnoses involved various forms of neuroendocrine neoplasia due to expression of synaptophysin and other neuroendocrine markers (22%). A wide variety of diagnoses including GI stromal tumor was considered due to aberrant CD117 expression (4%). The diagnosis of ES was ultimately confirmed by detection of the EWSR1 rearrangement in 22 cases. The remaining case was diagnosed using traditional immunohistochemistry. Follow-up information was available in 20 cases, with follow-up time varying between 2 and

Bacopa monnieri is a plant with a rich history of use in traditional Ayurvedic medicine, spanning several centuries. It is also referred to as Brahmi in some regions. This product serves as a treatment for various skin conditions, such as inflammation and wound healing. Its properties also aid in the production of collagen and improve circulation. The inclusion of antioxidants enhances your skin's overall health, resulting in a rejuvenated and vibrant appearance. The present study aimed to prepare a nano-lipoidal system loaded with Bacopa monnieri (BM) extract and its characterization. Twelve formulations (F1-12) were developed using the ether injection method using different ratios of BM extract, L-alpha phosphatidylcholine (SPC), ethanol, and water. Bacoside A was used as a marker compound for estimation purposes. BM extract-loaded ethosomes were characterized in which formulation F-5 showed the highest entrapment efficiency of 89 %, with vesicle size 188 nm, while the zeta potential was -29.19 mV, and the polydispersity index (PDI) was 0.221±1.45. In vitro extract release using a dialysis membrane was performed for 12 hours, and it was found to be 44 % and 61 % at the end of 8 and 12 hours. The formulation followed zero-order non-Fickian diffusion kinetics, which is best for transdermal formulations. The goat skin was used for Confocal laser scanning microscopy (CLSM) study which showed the fluorescence intensity of Rhodamine B entrapped in those was 16.783, while it was 8.580 in the blank hydroethanolic solution, which confirmed the penetration of the ethosomal systems up to 30-40 µm deeper into the skin which gives a possibility that ethosomes can contribute in collagen synthesis and decrease the degradation of elastin in the deeper layers.

The nitrogen cycle is the biogeochemical cycle by which nitrogen is converted into multiple chemical forms as it circulates among atmospheric, terrestrial, and marine ecosystems, the conversion of nitrogen can be carried out through both biological and physical processes. Important processes in the nitrogen cycle include fixation, ammonification, nitrification, and denitrification. The majority of Earth's atmosphere (78%) is atmospheric nitrogen, making it the largest source of nitrogen. However, atmospheric nitrogen has limited availability for biological use, leading to a scarcity of usable nitrogen in many types of ecosystems. The nitrogen cycle is of particular interest to ecologists because nitrogen availability can affect the rate of key ecosystem processes, including primary production and decomposition. Human activities such as fossil fuel combustion, use of artificial nitrogen fertilizers, and release of nitrogen in wastewater have dramatically altered the global nitrogen cycle. Human modification of the global nitrogen cycle can negatively affect the natural environment system and also human health. Volatilization and its Relationship to N2 fascination in Nitrogen Cycle in agriculture field is discuss in this paper.

BACKGROUND: The CDC and ACIP recommend COVID-19 vaccination for patients with inborn errors of immunity (IEI). Not much is known about vaccine safety in IEI, and whether vaccination attenuates infection severity in IEI. OBJECTIVE: To estimate COVID-19 vaccination safety and examine effect on outcomes in patients with IEI. METHODS: We built a secure registry database in conjunction with the US Immunodeficiency Network to examine vaccination frequency and indicators of safety and effectiveness in IEI patients. The registry opened on January 1, 2022, and closed on August 19, 2022. RESULTS: Physicians entered data on 1245 patients from 24 countries. The most common diagnoses were antibody deficiencies (63.7%). At least one COVID-19 vaccine was administered to 806 patients (64.7%), and 216 patients received vaccination prior to the development of COVID-19. The most common vaccines administered were mRNA-based (84.0%). Seventeen patients were reported to seek outpatient clinic or emergency room care for a vaccine-related complication, and one patient was hospitalized for symptomatic anemia. Eight hundred twenty-three patients (66.1%) experienced COVID-19 infection. Of these, 156 patients required hospitalization (19.0%), 47 required ICU care (5.7%), and 28 died (3.4%). Rates of hospitalization (9.3% versus 24.4%, p < 0.001), ICU admission (2.8% versus 7.6%, p = 0.013), and death (2.3% versus 4.3%, p = 0.202) in patients who had COVID-19 were lower in patients who received vaccination prior to infection. In adjusted logistic regression analysis, not having at least one COVID-19 vaccine significantly increased the odds of hospitalization and ICU admission. CONCLUSION: Vaccination for COVID-19 in the IEI population appears safe and attenuates COVID-19 severity.

Coagulation factor VIII (FVIII) and its carrier protein von Willebrand factor (VWF) are critical to coagulation and platelet aggregation. We leveraged whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) program along with TOPMed-based imputation of genotypes in additional samples to identify genetic associations with circulating FVIII and VWF levels in a single-variant meta-analysis, including up to 45 289 participants. Gene-based aggregate tests were implemented in TOPMed. We identified 3 candidate causal genes and tested their functional effect on FVIII release from human liver endothelial cells (HLECs) and VWF release from human umbilical vein endothelial cells. Mendelian randomization was also performed to provide evidence for causal associations of FVIII and VWF with thrombotic outcomes. We identified associations (P < 5 × 10−9) at 7 new loci for FVIII (ST3GAL4, CLEC4M, B3GNT2, ASGR1, F12, KNG1, and TREM1/NCR2) and 1 for VWF (B3GNT2). VWF, ABO, and STAB2 were associated with FVIII and VWF in gene-based analyses. Multiphenotype analysis of FVIII and VWF identified another 3 new loci, including PDIA3. Silencing of B3GNT2 and the previously reported CD36 gene decreased release of FVIII by HLECs, whereas silencing of B3GNT2, CD36, and PDIA3 decreased release of VWF by HVECs. Mendelian randomization supports causal association of higher FVIII and VWF with increased risk of thrombotic outcomes. Seven new loci were identified for FVIII and 1 for VWF, with evidence supporting causal associations of FVIII and VWF with thrombotic outcomes. B3GNT2, CD36, and PDIA3 modulate the release of FVIII and/or VWF in vitro., Coagulation factor VIII (FVIII) and its carrier protein von Willebrand factor (VWF) are critical to coagulation and platelet aggregation. We leveraged whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) program along with TOPMed-based imputation of genotypes in additional samples to identify genetic associations with circulating FVIII and VWF levels in a single-variant meta-analysis, including up to 45 289 participants. Gene-based aggregate tests were implemented in TOPMed. We identified 3 candidate causal genes and tested their functional effect on FVIII release from human liver endothelial cells (HLECs) and VWF release from human umbilical vein endothelial cells. Mendelian randomization was also performed to provide evidence for causal associations of FVIII and VWF with thrombotic outcomes. We identified associations (P < 5 × 10−9) at 7 new loci for FVIII (ST3GAL4, CLEC4M, B3GNT2, ASGR1, F12, KNG1, and TREM1/NCR2) and 1 for VWF (B3GNT2). VWF, ABO, and STAB2 were associated with FVIII and VWF in gene-based analyses. Multiphenotype analysis of FVIII and VWF identified another 3 new loci, including PDIA3. Silencing of B3GNT2 and the previously reported CD36 gene decreased release of FVIII by HLECs, whereas silencing of B3GNT2, CD36, and PDIA3 decreased release of VWF by HVECs. Mendelian randomization supports causal association of higher FVIII and VWF with increased risk of thrombotic outcomes. Seven new loci were identified for FVIII and 1 for VWF, with evidence supporting causal associations of FVIII and VWF with thrombotic outcomes. B3GNT2, CD36, and PDIA3 modulate the release of FVIII and/or VWF in vitro.

Adipocyte size and fragility and commercial kit costs impose significant limitations on single-cell RNA sequencing of adipose tissue. Accordingly, we developed a workflow to isolate and sample-barcode nuclei from individual adipose tissue samples, integrating flow cytometry for quality control, counting, and precise nuclei pooling for direct loading onto the popular 103 Chromium controller. This approach can eliminate batch confounding, and significantly reduces poorquality nuclei, ambient RNA contamination, and droplet loading-associated reagent waste, resulting in pronounced improvements in information content and cost efficiency.

As the Indian economy grows, the epidemic of Medo Roga has spread across the  country. The food we eat on a daily basis contributes to our health. However, many  people's desire to eat more and exercise less is hastening the spread of the Medo  Roga. Obesity is spreading in the modern day due to lifestyle changes and increased  consumption of junk food combined with a stressful lifestyle. According to recent  studies, high cholesterol is present in 25-30% of urban and 15-20% of rural  subjects. This prevalence is lower in low-income countries than in high-income  countries. This negative health consequence could be the cause of a variety of  lifestyle disorders such as dyslipidemia, hyperlipidemia, malnutrition, and  osteoarthritis, among others. In today's fast-paced world, dyslipidemia is a major  lifestyle disorder. The ramifications of Medo Vikriti and its dangers have been well  documented in our classics. Medoroga is primarily an Agni imbalance caused by a  variety of etiological causes, including Aaharatmak, Viharatmak, Mansik, and  others such as Bijabhava (hereditary). There is no such phrase as dyslipidemia in  Ayurveda texts, however the qualities of Sneha Dravya are defined as Medodhatu  in Ayurveda.

Employee Retention (ER) is an indispensable agenda for organizations operating in the extremely competitiveInformation Technology (IT) sector. This study investigates the multifaceted challenges associated with ERwithin the IT industry, emphasizing the need for customized strategies to motivate employees and cultivatetheir enduring commitment to organizations. The capability to retain highly skilled employees is critical formanaging talent pipeline through, nurturing innovation, and retaining them. This research examines thedeterminants influencing ER in the IT sector, with a specific focus on four independent variable as (jobsecurity (JS), work environment (WE), training and development (T&D), and employee compensation (EC)).Data were collected from a sample of 145 IT sector employees to examine these factors comprehensively.Moreover, this study explores the impact of demographic factors, such as age, gender, years of experience,and job designation, on ER within the IT sector. Through rigorous data analysis, we reveal correlationsbetween demographic variables and the key determinants, shedding light on their relative importance ininfluencing ER. The insights gained from this research significantly contribute to the prevailing knowledgeof ER strategies in the IT industry and organizational decision-makers. These findings have the potential toinform the development of targeted retention initiatives, specifically tailored to address the influence ofdemographic factors. Ultimately, this research seeks to empower software companies to cultivate a moreengaged and committed workforce, thereby enhancing their competitiveness in the IT sector.

We present ALTO, a network orchestrator for efficiently serving compound AI systems such as pipelines of language models. ALTO achieves high throughput and low latency by taking advantage of an optimization opportunity specific to generative language models: streaming intermediate outputs. As language models produce outputs token by token, ALTO exposes opportunities to stream intermediate outputs between stages when possible. We highlight two new challenges of correctness and load balancing which emerge when streaming intermediate data across distributed pipeline stage instances. We also motivate the need for an aggregation-aware routing interface and distributed prompt-aware scheduling to address these challenges. We demonstrate the impact of ALTO's partial output streaming on a complex chatbot verification pipeline, increasing throughput by up to 3x for a fixed latency target of 4 seconds / request while also reducing tail latency by 1.8x compared to a baseline serving approach.

It is challenging to balance the privacy and accuracy for federated query processing over multiple private data silos. In this work, we will demonstrate an end-to-end workflow for automating an emerging privacy-preserving technique that uses a deep learning model trained using the Differentially-Private Stochastic Gradient Descent (DP-SGD) algorithm to replace portions of actual data to answer a query. Our proposed novel declarative privacy-preserving workflow allows users to specify "what private information to protect" rather than "how to protect". Under the hood, the system automatically chooses query-model transformation plans as well as hyper-parameters. At the same time, the proposed workflow also allows human experts to review and tune the selected privacy-preserving mechanism for audit/compliance, and optimization purposes.

LiDAR datasets for autonomous driving exhibit biases in properties such as point cloud density, range, and object dimensions. As a result, object detection networks trained and evaluated in different environments often experience performance degradation. Domain adaptation approaches assume access to unannotated samples from the test distribution to address this problem. However, in the real world, the exact conditions of deployment and access to samples representative of the test dataset may be unavailable while training. We argue that the more realistic and challenging formulation is to require robustness in performance to unseen target domains. We propose to address this problem in a two-pronged manner. First, we leverage paired LiDAR-image data present in most autonomous driving datasets to perform multimodal object detection. We suggest that working with multimodal features by leveraging both images and LiDAR point clouds for scene understanding tasks results in object detectors more robust to unseen domain shifts. Second, we train a 3D object detector to learn multimodal object features across different distributions and promote feature invariance across these source domains to improve generalizability to unseen target domains. To this end, we propose CLIX$^\text{3D}$, a multimodal fusion and supervised contrastive learning framework for 3D object detection that performs alignment of object features from same-class samples of different domains while pushing the features from different classes apart. We show that CLIX$^\text{3D}$ yields state-of-the-art domain generalization performance under multiple dataset shifts., Comment: technical report

Data protection and privacy is becoming increasingly crucial in the digital era. Numerous companies depend on third-party vendors and service providers to carry out critical functions within their operations, encompassing tasks such as data handling and storage. However, this reliance introduces potential vulnerabilities, as these vendors' security measures and practices may not always align with the standards expected by regulatory bodies. Businesses are required, often under the penalty of law, to ensure compliance with the evolving regulatory rules. Interpreting and implementing these regulations pose challenges due to their complexity. Regulatory documents are extensive, demanding significant effort for interpretation, while vendor-drafted privacy policies often lack the detail required for full legal compliance, leading to ambiguity. To ensure a concise interpretation of the regulatory requirements and compliance of organizational privacy policy with said regulations, we propose a Large Language Model (LLM) and Semantic Web based approach for privacy compliance. In this paper, we develop the novel Privacy Policy Compliance Verification Knowledge Graph, PrivComp-KG. It is designed to efficiently store and retrieve comprehensive information concerning privacy policies, regulatory frameworks, and domain-specific knowledge pertaining to the legal landscape of privacy. Using Retrieval Augmented Generation, we identify the relevant sections in a privacy policy with corresponding regulatory rules. This information about individual privacy policies is populated into the PrivComp-KG. Combining this with the domain context and rules, the PrivComp-KG can be queried to check for compliance with privacy policies by each vendor against relevant policy regulations. We demonstrate the relevance of the PrivComp-KG, by verifying compliance of privacy policy documents for various organizations.

Popular representation learning methods encourage feature invariance under transformations applied at the input. However, in 3D perception tasks like object localization and segmentation, outputs are naturally equivariant to some transformations, such as rotation. Using pre-training loss functions that encourage equivariance of features under certain transformations provides a strong self-supervision signal while also retaining information of geometric relationships between transformed feature representations. This can enable improved performance in downstream tasks that are equivariant to such transformations. In this paper, we propose a spatio-temporal equivariant learning framework by considering both spatial and temporal augmentations jointly. Our experiments show that the best performance arises with a pre-training approach that encourages equivariance to translation, scaling, and flip, rotation and scene flow. For spatial augmentations, we find that depending on the transformation, either a contrastive objective or an equivariance-by-classification objective yields best results. To leverage real-world object deformations and motion, we consider sequential LiDAR scene pairs and develop a novel 3D scene flow-based equivariance objective that leads to improved performance overall. We show our pre-training method for 3D object detection which outperforms existing equivariant and invariant approaches in many settings., Comment: technical report

• Research produced during the pandemic showed pre-diagnosis physical activity was associated with substantially lower risk of severe coronavirus disease 2019 (COVID-19) outcomes. • Pandemic restrictions on common places for physical activity were associated with decreased physical activity and increased sedentary behavior. • There were few studies of interventions to increase physical activity during the pandemic, the role of physical activity in COVID-19 inequities, and built environment contributions to COVID-19 outcomes. • Emerging research priorities include physical activity and long COVID and physical activity as a vaccine adjuvant. • Except for recommendations from the World Health Organization, physical activity was widely ignored in public health guidance.

Funding Agencies|Bioinformatics Core Facility; Faculty of Medicine and Health Sciences and Clinical Genomics Linkoping; Science for Life Laboratory; Department of Biomedical and Clinical Sciences; Linkoping University; Ingrid Asp Foundation; Welander Foundation; Swedish psoriasis association

Escalating wildfire activity in the western United States has accelerated adverse societal impacts. Observed increases in wildfire severity and impacts to communities have diverse anthropogenic causes-including the legacy of fire suppression policies, increased development in high-risk zones, and aridification by a warming climate. However, the intentional use of fire as a vegetation management tool, known as prescribed fire, can reduce the risk of destructive fires and restore ecosystem resilience. Prescribed fire implementation is subject to multiple constraints, including the number of days characterized by weather and vegetation conditions conducive to achieving desired outcomes. Here, we quantify observed and projected trends in the frequency and seasonality of western United States prescribed fire days. We find that while ~2 C of global warming by 2060 will reduce such days overall (-17%), particularly during spring (-25%) and summer (-31%), winter (+4%) may increasingly emerge as a comparatively favorable window for prescribed fire especially in northern states.

Coronary artery calcification (CAC) is a measure of atherosclerosis and a well-established predictor of coronary artery disease (CAD) events. Here we describe a genome-wide association study (GWAS) of CAC in 22,400 participants from multiple ancestral groups. We confirmed associations with four known loci and identified two additional loci associated with CAC (ARSE and MMP16), with evidence of significant associations in replication analyses for both novel loci. Functional assays of ARSE and MMP16 in human vascular smooth muscle cells (VSMCs) demonstrate that ARSE is a promoter of VSMC calcification and VSMC phenotype switching from a contractile to a calcifying or osteogenic phenotype. Furthermore, we show that the association of variants near ARSE with reduced CAC is likely explained by reduced ARSE expression with the G allele of enhancer variant rs5982944. Our study highlights ARSE as an important contributor to atherosclerotic vascular calcification, and a potential drug target for vascular calcific disease.

Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed toward solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology.

Funders seeking to invest in broad social and system change frequently partner with advocacy organizations to advance issues through policy change rather than solely resourcing direct service organizations. Many funders take this investment further and seek to support coalition-building instead of simply resourcing individual organizations. The Bill and Melinda Gates Foundation engaged TCC Group to research coalition-building and help funders better understand key considerations in convening and supporting coalitions.TCC Group interviewed foundations and coalition experts, conducted site visits, and reviewed academic and peer-reviewed literature on coalitions. The findings are organized in a four-part framework, each representing a domain of positioning in a coalition's environment. The four domains are coalition capacity and structure, context, support, and strategy. When all four areas are being effectively developed and considered by stakeholders, it should lead to effective positioning of the coalition as a whole.This report provides insights for funders into when, how, and why to support coalitions under different circumstances. Advocates may similarly find value in some of the concepts and findings to inform their conversations with current and potential funders.

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies., Funding Agencies|Netherlands Organization for Scientific Research (NWO) [628.011.023]; Philips Research; ZonMW (Vidi) [016.156.318]; Region Ostergoetland; Phyllis and Jerome Lyle Rappaport Foundation; BIAL Foundation; Brain and Behavior Research Foundation; Center for Depression, Anxiety, and Stress Research at McLean Hospital; Ad Astra Chandaria Foundation [DA1151/5-2, SFB-TRR58, Dan3/012/17]; German Research Foundation (DFG); Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Munster [1024570]; European Commission [KLI 597-827]; Australian National Health and Medical Research Council of Australia (NHMRC) [F3514-B1, GR 4510/2-1]; Austrian Science Fund (FWF) [WA1539/4-1]; Science Foundation Ireland (SFI); [FOR2107 DA1151/5-1]; [H2020-634541]; [1064643]; [KLI-148-B00]

Carbon dioxide (CO2) emissions from a variety of sources, such as transportation, fossil fuel burning, and cement manufacturing facilities, are widely regarded to be the root cause of global warming. The rising CO2 levels call for immediate improvements in CO2 capture, extraction, and utilization technology. Methods for capturing and converting CO2 into useful products have included the use of microbial enzymes, nonporous materials, metal -organic frameworks (MOFs), chemicals, and hybrid membranes. However, these methods possess limitations that make the scale up and commercialization challenging. Scientists are concentrating on maximizing CO2 utilization by incorporating CO2-philic components into enzyme-chemical-material combinations, due to the high solubility of CO2. Here, the focus is on the chemistry of CO2-phillic materials, enzymes and biomolecules engaged in CO2 conversion, and the hybrid micro-reactors that contain material and enzymes integrating together to convert the CO2 into value-added products (organic acids, bioelectricity, carbonates, carbamates, methane, methanol, etc.). The difficulties and obstacles inherent in creating and sustaining such systems have also been highlighted.

In 2002, the European Competence Network on Mastocytosis (ECNM) was launched as a multidisciplinary collaborative initiative to increase the awareness and to improve diagnosis and management of patients with mast cell (MC) disorders. The ECNM consists of a net of specialized centers, expert physicians, and scientists who dedicate their work to MC diseases. One essential aim of the ECNM is to timely distribute all available information about the disease to patients, doctors, and scientists. In the past 20 years, the ECNM has expanded substantially and contributed successfully to the development of new diagnostic concepts, and to the classification, prognostication, and treatments of patients with mastocytosis and MC activation disorders.

Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.

Anthropometric traits, measuring body size and shape, are highly heritable and significant clinical risk factors for cardiometabolic disorders. These traits have been extensively studied in genome-wide association studies (GWASs), with hundreds of genome-wide significant loci identified. We performed a whole-exome sequence analysis of the genetics of height, body mass index (BMI) and waist/hip ratio (WHR). We meta-analyzed single-variant and gene-based associations of whole-exome sequence variation with height, BMI, and WHR in up to 22,004 individuals, and we assessed replication of our findings in up to 16,418 individuals from 10 independent cohorts from Trans-Omics for Precision Medicine (TOPMed). We identified four trait associations with single-nucleotide variants (SNVs; two for height and two for BMI) and replicated the LECT2 gene association with height. Our expression quantitative trait locus (eQTL) analysis within previously reported GWAS loci implicated CEP63 and RFT1 as potential functional genes for known height loci. We further assessed enrichment of SNVs, which were monogenic or syndromic variants within loci associated with our three traits. This led to the significant enrichment results for height, whereas we observed no Bonferroni-corrected significance for all SNVs. With a sample size of ∼20,000 whole-exome sequences in our discovery dataset, our findings demonstrate the importance of genomic sequencing in genetic association studies, yet they also illustrate the challenges in identifying effects of rare genetic variants.

Purpose of reviewThe study aims to provide a summary of recent developments for diagnosing and managing posterior cortical atrophy (PCA). We present current efforts to improve PCA characterisation and recommendations regarding use of clinical, neuropsychological and biomarker methods in PCA diagnosis and management and highlight current knowledge gaps.Recent findingsRecent multi-centre consensus recommendations provide PCA criteria with implications for different management strategies (e.g. targeting clinical features and/or disease). Studies emphasise the preponderance of primary or co-existing Alzheimer's disease (AD) pathology underpinning PCA. Evidence of approaches to manage PCA symptoms is largely derived from small studies.SummaryPCA diagnosis is frequently delayed, and people are likely to receive misdiagnoses of ocular or psychological conditions. Current treatment of PCA is symptomatic - pharmacological and non-pharmacological - and the use of most treatment options is based on small studies or expert opinion. Recommendations for non-pharmacological approaches include interdisciplinary management tailored to the PCA clinical profile - visual-spatial - rather than memory-led, predominantly young onset - and psychosocial implications. Whilst emerging disease-modifying treatments have not been tested in PCA, an accurate and timely diagnosis of PCA and determining underlying pathology is of increasing importance in the advent of disease-modifying therapies for AD and other albeit rare causes of PCA.

Adenylyl cyclase 6 (AC6) synthesizes second messenger cAMP in G protein-coupled receptor (GPCR) signaling. In cochlear hair cells, AC6 distribution relies on an adhesion GPCR, ADGRV1, which is associated with Usher syndrome (USH), a condition of combined hearing and vision loss. ADGRV1 is a component of the USH type 2 (USH2) protein complex in hair cells and photoreceptors. However, the role of AC6 in the inner ear and retina has not been explored. Here, we found that AC6 distribution in hair cells depends on the USH2 protein complex integrity. Several known AC6 regulators and effectors, which were previously reported to participate in ADGRV1 signaling in vitro, are localized to the stereociliary compartments that overlap with AC6 distribution in hair cells. In young AC6 knockout (Adcy6-/-) mice, the activity of cAMP-dependent protein kinase, but not Akt kinase, is altered in cochleas, while both kinases are normal in vestibular organs. Adult Adcy6-/- mice however exhibit normal hearing function. AC6 is expressed in mouse retinas but rarely in photoreceptors. Adcy6-/- mice have slightly enhanced photopic but normal scotopic vision. Therefore, AC6 may participate in the ADGRV1 signaling in hair cells but AC6 is not essential for cochlear and retinal development and maintenance.

While motor and cortical neurons are affected in C9orf72 amyotrophic lateral sclerosis and frontotemporal dementia (ALS/FTD), it remains largely unknown if and how non-neuronal cells induce or exacerbate neuronal damage. We differentiated C9orf72 ALS/FTD patient-derived induced pluripotent stem cells into microglia (iPSC-MG) and examined their intrinsic phenotypes. Similar to iPSC motor neurons, C9orf72 ALS/FTD iPSC-MG mono-cultures form G4C2 repeat RNA foci, exhibit reduced C9orf72 protein levels, and generate dipeptide repeat proteins. Healthy control and C9orf72 ALS/FTD iPSC-MG equally express microglial specific genes and perform microglial functions, including inflammatory cytokine release and phagocytosis of extracellular cargos, such as synthetic amyloid beta peptides and healthy human brain synaptoneurosomes. RNA sequencing analysis revealed select transcriptional changes of genes associated with neuroinflammation or neurodegeneration in diseased microglia yet no significant differentially expressed microglial-enriched genes. Moderate molecular and functional differences were observed in C9orf72 iPSC-MG mono-cultures despite the presence of C9orf72 pathological features suggesting that a diseased microenvironment may be required to induce phenotypic changes in microglial cells and the associated neuronal dysfunction seen in C9orf72 ALS/FTD neurodegeneration.

We have developed a robust method for the detection of heavy metal ions zinc, cadmium, and mercury in water. In this study we have synthesized a hydrogel based on biopolymer that is raw wool obtained from eszvMarino sheep and used this hydrogel as heavy metal ion sensor in the water. We have incorporated meta- benziporphodimethene ligand in-situ in the hydrogel synthesis, which shows selectively color-changing characteristics when immersed in Zn2+, Cd2+ and Hg2+ metal ions aqueous solution. This hydrogel shows selective response towards Zn2+, Cd2+ and Hg2+ metal ions in a competitive environment. Meta-BPDM was responsible for the selectivity of hydrogel towards Zn2+, Cd2+ and Hg2+ metal ions in water. This selective sensing behavior of raw wool based hydrogel has given a novel existence to our study. The synthesized hydrogel was studied for sensing applications and characterized by using FTIR, TGA, and SEM techniques.

BackgroundHeart failure is a prevailing diagnosis of hospitalization and readmission within 6 months, and nearly a quarter of these patients die within a year. Guideline-directed medication therapies reduce risk of mortality by 73% over 2 years; however, the implementation of these therapies to their target dose in clinical practice continues to be challenging. In 2020, the Veterans Affairs (VA) Health Care System developed a HF dashboard to monitor and improve outpatient HF management. The DASH-HF (Dashboard Activated Services and Telehealth for Heart Failure) study is a randomized, pragmatic clinical trial to evaluate proactive dashboard-directed telehealth clinics to improve the use and dosing of guideline-directed medication therapy for patients with heart failure with reduced ejection fraction not on optimal guideline-directed medication therapy within the VA.MethodsThree hundred veterans with heart failure with reduced ejection fraction met inclusion criteria with an optimization potential score (OPS) of 5 or less out of 10, representing nonoptimal guideline-directed medication therapy. The primary outcome was a composite score of guideline-directed medical therapy, the OPS, 6 months after the end of the intervention. Secondary outcomes included active prescriptions for each individual guideline-directed medical therapy class, HF-related hospitalizations, deaths, and clinician time per patient during the intervention clinics.ResultsThere was no significant difference between the intervention arm and usual care group in the primary outcome (OPS, 2.9; SD=2.1 versus OPS, 2.6, SD=2.1); adjusted mean difference 0.3 (95% CI, -0.1 to 0.7) or in the prespecified secondary outcomes for hospitalization and all-cause mortality for the intervention of proactive dashboard-based clinics.ConclusionsA dashboard-based clinic intervention did not improve the OPS or secondary outcomes of hospitalization and all-cause mortality. There remains a larger opportunity to better target

Purpose: The study aims to examine the role of Sustainable Entrepreneurship through the lens of Triple Bottom Line (TBL) with intervention of environment dimensions in Indian MSMEs context. Theoretical framework: Sustainability has become the most important concern in today’s world due to the changes in the climate and environment deterioration. The TBL approach includes the people, planet and profit perspective; environment dimensions include the green and non-green initiatives that would help entrepreneurs to achieve their sustainable goals. Design/methodology/approach: The quantitative research design is adopted to collect 107 entrepreneurs’ data from Delhi NCR using purposive sampling. The study examines the relationship among Sustainable Entrepreneurs (profit, planet and people) and environmental dimensions (green and non-green initiative) using SPSS and Hayes PROCESS. Findings: The study concludes that there exists a correlation between people and planet; people and non-green initiatives; planet and profit; planet and non-green initiatives; and lastly green initiatives and non-green initiatives. In addition, non-green initiative mediates the relationship between people and planet. However, planet is correlated with only profit. Research, Practical & Social implications: The research discusses various approaches for MSME to endeavour sustainability with the help of environment dimensions. The appropriate practice of adopting such sustainable approaches will eventually drive enterprises towards profit, thus becoming sustainable entrepreneurs. Originality/value: TBL approach is viewed along with the environmental dimensions for the purpose of protecting environment and enabling an enterprise move towards higher profit. The study also discusses strategies for policymakers, education institutes for promoting sustainable entrepreneurship.

Introduction: Cerebral venous sinus thrombosis associated with vaccine-induced immune thrombotic thrombocytopenia (CVST-VITT) is a severe disease with high mortality. There are few data on sex differences in CVST-VITT. The aim of our study was to investigate the differences in presentation, treatment, clinical course, complications, and outcome of CVST-VITT between women and men. Patients and methods: We used data from an ongoing international registry on CVST-VITT. VITT was diagnosed according to the Pavord criteria. We compared the characteristics of CVST-VITT in women and men. Results: Of 133 patients with possible, probable, or definite CVST-VITT, 102 (77%) were women. Women were slightly younger [median age 42 (IQR 28–54) vs 45 (28–56)], presented more often with coma (26% vs 10%) and had a lower platelet count at presentation [median (IQR) 50x109/L (28–79) vs 68 (30–125)] than men. The nadir platelet count was lower in women [median (IQR) 34 (19–62) vs 53 (20–92)]. More women received endovascular treatment than men (15% vs 6%). Rates of treatment with intravenous immunoglobulins were similar (63% vs 66%), as were new venous thromboembolic events (14% vs 14%) and major bleeding complications (30% vs 20%). Rates of good functional outcome (modified Rankin Scale 0-2, 42% vs 45%) and in-hospital death (39% vs 41%) did not differ. Discussion and conclusions: Three quarters of CVST-VITT patients in this study were women. Women were more severely affected at presentation, but clinical course and outcome did not differ between women and men. VITT-specific treatments were overall similar, but more women received endovascular treatment.

Irrigation accounts for ~70% of global freshwater withdrawals and ~90% of consumptive water use, driving myriad Earth system impacts. In this Review, we summarize how irrigation currently impacts key components of the Earth system. Estimates suggest that more than 3.6 million km2 of currently irrigated land, with hot spots in the intensively cultivated US High Plains, California Central Valley, Indo-Gangetic Basin and northern China. Process-based models estimate that ~2,700 ± 540 km3 irrigation water is withdrawn globally each year, broadly consistent with country-reported values despite these estimates embedding substantial uncertainties. Expansive irrigation has modified surface energy balance and biogeochemical cycling. A shift from sensible to latent heat fluxes, and resulting land–atmosphere feedbacks, generally reduce regional growing season surface temperatures by ~1–3 °C. Irrigation can ameliorate temperature extremes in some regions, but conversely exacerbates moist heat stress. Modelled precipitation responses are more varied, with some intensive cropping regions exhibiting suppressed local precipitation but enhanced precipitation downstream owing to atmospheric circulation interactions. Additionally, irrigation could enhance cropland carbon uptake; however, it can also contribute to elevated methane fluxes in rice systems and mobilize nitrogen loading to groundwater. Cross-disciplinary, integrative research efforts can help advance understanding of these irrigation–Earth system interactions, and identify and reduce uncertainties, biases and limitations. © 2023, Springer Nature Limited.

Amyotrophic lateral sclerosis (ALS) is a fatal and incurable neurodegenerative disease affecting motor neurons and characterized by microglia-mediated neurotoxic inflammation whose underlying mechanisms remain incompletely understood. In this work, we reveal that MAPK/MAK/MRK overlapping kinase (MOK), with an unknown physiological substrate, displays an immune function by controlling inflammatory and type-I interferon (IFN) responses in microglia which are detrimental to primary motor neurons. Moreover, we uncover the epigenetic reader bromodomain-containing protein 4 (Brd4) as an effector protein regulated by MOK, by promoting Ser-phospho-Brd4 levels. We further demonstrate that MOK regulates Brd4 functions by supporting its binding to cytokine gene promoters, therefore enabling innate immune responses. Remarkably, we show that MOK levels are increased in the ALS spinal cord, particularly in microglial cells, and that administration of a chemical MOK inhibitor to ALS model mice can modulate Ser-phospho-Brd4 levels, suppress microglial activation, and modify the disease course, indicating a pathophysiological role of MOK kinase in ALS and neuroinflammation.

Importance COVID-19 was the underlying cause of death for more than 940 000 individuals in the US, including at least 1289 children and young people (CYP) aged 0 to 19 years, with at least 821 CYP deaths occurring in the 1-year period from August 1, 2021, to July 31, 2022. Because deaths among US CYP are rare, the mortality burden of COVID-19 in CYP is best understood in the context of all other causes of CYP death. Objective To determine whether COVID-19 is a leading (top 10) cause of death in CYP in the US. Design, Setting, and Participants This national population-level cross-sectional epidemiological analysis for the years 2019 to 2022 used data from the US Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (WONDER) database on underlying cause of death in the US to identify the ranking of COVID-19 relative to other causes of death among individuals aged 0 to 19 years. COVID-19 deaths were considered in 12-month periods between April 1, 2020, and August 31, 2022, compared with deaths from leading non–COVID-19 causes in 2019, 2020, and 2021. Main Outcomes and Measures Cause of death rankings by total number of deaths, crude rates per 100 000 population, and percentage of all causes of death, using the National Center for Health Statistics 113 Selected Causes of Death, for ages 0 to 19 and by age groupings (<1 year, 1-4 years, 5-9 years, 10-14 years, 15-19 years). Results There were 821 COVID-19 deaths among individuals aged 0 to 19 years during the study period, resulting in a crude death rate of 1.0 per 100 000 population overall; 4.3 per 100 000 for those younger than 1 year; 0.6 per 100 000 for those aged 1 to 4 years; 0.4 per 100 000 for those aged 5 to 9 years; 0.5 per 100 000 for those aged 10 to 14 years; and 1.8 per 100 000 for those aged 15 to 19 years. COVID-19 mortality in the time period of August 1, 2021, to July 31, 2022, was among the 10 leading causes of death in CY, Importance: COVID-19 was the underlying cause of death for more than 940 000 individuals in the US, including at least 1289 children and young people (CYP) aged 0 to 19 years, with at least 821 CYP deaths occurring in the 1-year period from August 1, 2021, to July 31, 2022. Because deaths among US CYP are rare, the mortality burden of COVID-19 in CYP is best understood in the context of all other causes of CYP death. Objective: To determine whether COVID-19 is a leading (top 10) cause of death in CYP in the US. Design, Setting, and Participants: This national population-level cross-sectional epidemiological analysis for the years 2019 to 2022 used data from the US Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (WONDER) database on underlying cause of death in the US to identify the ranking of COVID-19 relative to other causes of death among individuals aged 0 to 19 years. COVID-19 deaths were considered in 12-month periods between April 1, 2020, and August 31, 2022, compared with deaths from leading non-COVID-19 causes in 2019, 2020, and 2021. Main Outcomes and Measures: Cause of death rankings by total number of deaths, crude rates per 100 000 population, and percentage of all causes of death, using the National Center for Health Statistics 113 Selected Causes of Death, for ages 0 to 19 and by age groupings (<1 year, 1-4 years, 5-9 years, 10-14 years, 15-19 years). Results: There were 821 COVID-19 deaths among individuals aged 0 to 19 years during the study period, resulting in a crude death rate of 1.0 per 100 000 population overall; 4.3 per 100 000 for those younger than 1 year; 0.6 per 100 000 for those aged 1 to 4 years; 0.4 per 100 000 for those aged 5 to 9 years; 0.5 per 100 000 for those aged 10 to 14 years; and 1.8 per 100 000 for those aged 15 to 19 years. COVID-19 mortality in the time period of August 1, 2021, to July 31, 2022, was among the 10 leading causes of death in CYP aged 0 to 19 years in the

Background: Adenovirus-based COVID-19 vaccines are extensively used in low- and middle-income countries (LMICs). Remarkably, cases of cerebral venous sinus thrombosis due to vaccine-induced immune thrombotic thrombocytopenia (CVST-VITT) have rarely been reported from LMICs. Aims: We studied the frequency, manifestations, treatment, and outcomes of CVST-VITT in LMICs. Methods: We report data from an international registry on CVST after COVID-19 vaccination. VITT was classified according to the Pavord criteria. We compared CVST-VITT cases from LMICs to cases from high-income countries (HICs). Results: Until August 2022, 228 CVST cases were reported, of which 63 were from LMICs (all middle-income countries [MICs]: Brazil, China, India, Iran, Mexico, Pakistan, Turkey). Of these 63, 32 (51%) met the VITT criteria, compared to 103 of 165 (62%) from HICs. Only 5 of the 32 (16%) CVST-VITT cases from MICs had definite VITT, mostly because anti-platelet factor 4 antibodies were often not tested. The median age was 26 (interquartile range [IQR] 20–37) versus 47 (IQR 32–58) years, and the proportion of women was 25 of 32 (78%) versus 77 of 103 (75%) in MICs versus HICs, respectively. Patients from MICs were diagnosed later than patients from HICs (1/32 [3%] vs. 65/103 [63%] diagnosed before May 2021). Clinical manifestations, including intracranial hemorrhage, were largely similar as was intravenous immunoglobulin use. In-hospital mortality was lower in MICs (7/31 [23%, 95% confidence interval (CI) 11–40]) than in HICs (44/102 [43%, 95% CI 34–53], p = 0.039). Conclusions: The number of CVST-VITT cases reported from LMICs was small despite the widespread use of adenoviral vaccines. Clinical manifestations and treatment of CVST-VITT cases were largely similar in MICs and HICs, while mortality was lower in patients from MICs.

Climate-smart agriculture (CSA) is an approach to developing the technical, policy and investment conditions to achieve sustainable agricultural development for food security under climate change. multiple cropping systems using crop rotations or intercropping, growing of nitrate catch crops, growing of deep rooting crops following harvest of short duration and shallow rooting crops like vegetables,. winter wheat with deep rooting nature, high n demand and high n harvest index, crop livestock integration system, climate hardy crops such as pigeon pea, chickpea,inetnsive integrated farming systems, relay mixed cropping and lots of more systems have been time tested to be climate smart practices. Adoption of such cropping system strategy could substantially reduce offset the ill effects of climate vagaries. Pulse crops are also popular for their suitability in different cropping systems, owing to their short–life cycle. Development of large number of high yielding cultivars of crops with varying maturity duration and resistance to biotic and abiotic stresses, have made it possible to include them in irrigated crop sequence as well. Legume–based cropping systems can transform the rural livelihoods by overcoming the production constraints largely experienced over the decades due to cereal–based production systems and bring farmers I the league of defending boldily any vagaries of the nature.. Several conferences deliberated on such issues and number of lead papers were submitted in some of the conferences such as PRAGATI,ICFA,DISHA,ARISE,RAINFED etc. conducted during 2015 to 2018. The idea to bring out the entire edited chapters in the form a book was long felt and stressed upon by the eminent speakers and contributors.