Gravity Wave Momentum Fluxes from 1 km Global ECMWF Integrated Forecast System.

Progress in understanding the impact of mesoscale variability, including gravity waves (GWs), on atmospheric circulation is often limited by the availability of global fine-resolution observations and simulated data. This study presents momentum fluxes due to atmospheric GWs extracted from four months of an experimental "nature run", integrated at a 1 km resolution (XNR1K) using the Integrated Forecast System (IFS) model. Helmholtz decomposition is used to compute zonal and meridional flux of vertical momentum from ~1.5 petabytes of data; quantities often emulated by climate model parameterization of GWs. The fluxes are validated using ERA5 reanalysis, both during the first week after initialization and over the boreal winter period from November 2018 to February 2019. The agreement between reanalysis and IFS demonstrates its capability to generate reliable flux distributions and capture mesoscale dynamic variability in the atmosphere. The dataset could be valuable in advancing our understanding of GW-planetary wave interactions, GW evolution around atmospheric extremes, and as high-quality training data for machine learning (ML) simulation of GWs.

Enhancing the city-level thermal environment through the strategic utilization of urban green spaces employing geospatial techniques.

Smart urban planning needs to have a multicriteria-based approach to prevent the deteriorating local thermal climate. Maximizing the cooling potential using the available grey infrastructure would be the utmost priority of future smart cities. Remote sensing and GIS can be the appropriate tools to develop a climate-resilient urban planning framework. Studies are needed to include different features of vertical and horizontal landscaping to mitigate heat stress and enhance liveability at the city level. With this goal, the current work outlined a holistic approach to efficiently using green spaces with minimal reconstruction. The problem of regional climate threat was evaluated with urban heat island characterization. Moran's I clustering identified nearly 12% of the study area to be under considerable heat stress during summer days. Multiple techniques, such as mapping local climate zones, segment mean shift-based roof extraction, vegetation index computation, solar azimuth-based green wall site selection, etc., were applied to formulate solutions and provide an integrated method for city-level environment enhancement. A considerable area was identified as most suitable for green roof cover, and it was also computed that the transition towards green roof at only these locations may bring down the maximum heat island intensity by 0.74 °C. Additionally, solar zenith, illumination effect, and building height information were combined to create a distinct method where vertical plantation would flourish exceptionally. A rigorous assessment of more than 130 urban green spaces further quantified the relation between landscape geometry and cooling effect to provide optimum green space designs for future urban planning.

Revolutionizing Reproduction: The Impact of Robotics and Artificial Intelligence (AI) in Assisted Reproductive Technology: A Comprehensive Review.

Assisted reproductive technology (ART) has revolutionized the field of reproductive medicine, offering hope to millions of individuals and couples facing infertility challenges. In recent years, integrating robotics and artificial intelligence (AI) has emerged as a promising avenue for advancing ART. This comprehensive review explores the transformative impact of robotics and AI on ART, examining recent advancements, technological applications, clinical implications, and ethical considerations. Robotics enables precise and minimally invasive procedures, enhancing the efficiency and accuracy of various reproductive techniques such as sperm retrieval, embryo handling, and surgical interventions. Meanwhile, AI offers predictive analytics, personalized treatment protocols, and decision support systems tailored to individual patient needs, optimizing treatment outcomes and expanding access to reproductive care. Key findings highlight the significant advancements made possible by robotics and AI in ART, including improved success rates, reduced risks, and enhanced patient experience. However, challenges such as regulatory considerations, adoption barriers, and ethical dilemmas must be addressed to realize the full potential of these technologies. The transformative impact of robotics and AI on ART is profound, shaping the future of fertility treatment and family-building worldwide. Continued research, interdisciplinary collaboration, and investment are essential to further harness the potential of robotics and AI in advancing reproductive medicine and ensuring accessible, equitable, and effective care for all individuals and couples.

Text messages to improve young child diets: Results from a cluster-randomized controlled trial in Kanchanpur, Nepal.

The objective of this study was to test whether adding a text message campaign about the importance of eating eggs and other nutrition-related behaviours to an on-going package of large-scale, diverse social and behaviour change interventions would improve four types of nutrition-related knowledge and behaviour outcomes: child diets (egg consumption as the primary outcome), maternal diets, maternal nutrition knowledge, and maternal participation in additional interventions. The cluster-randomized controlled trial involved a repeat cross-sectional design, recruiting families with children 12-23 months of age at baseline and endline in one plains district of Nepal. Throughout the 1000-day period, 51 text messages were sent to each household at specific time points to reinforce ideal diets and other nutrition-related practices and promote engagement with community health workers and other intervention platforms. The primary outcome was egg consumption and dietary diversity among young children. We found no population-level effect. Some evidence supports that for those who received and opened the SMS intervention, it improved child egg consumption (odds ratio [OR]: 1.41, 95% confidence interval [CI]: 1.03-1.93), child minimum dietary diversity (OR: 1.36, 95% CI: 1.07-1.73), child dietary diversity scores (ß: 0.15, 95% CI: 0.01-0.24), as well as maternal IYCF knowledge (ß: 0.21, 95% CI: 0.08-0.35), participation in health mothers' group meetings (OR: 3.03, 95% CI: 1.91-4.84) and Bhanchhin Aama listenership (OR: 1.36, 95% CI: 1.07-1.73). This study highlights the importance of more research to understand the effectiveness of emerging digital interventions for behaviour change among specific populations, to facilitate nuanced targeting to those who can best benefit from these investments. Registered at clinicaltrials.gov with identifier NCT03926689.

A fusion approach using GIS, green area detection, weather API and GPT for satellite image based fertile land discovery and crop suitability.

Proper utilization of agricultural land is a big challenge as they often laid over as waste lands. Farming is a significant occupation in any country and improving it further by promoting more farming opportunities will take the country towards making a huge leap forward. The issue in achieving this would be the lack of knowledge of cultivable land for food crops. The objective of this work is to utilize modern computer vision technology to identify and map cultivable land for agricultural needs. With increasing population and demand for food, improving the farming sector is crucial. However, the challenge lies in the lack of suitable land for food crops cultivation. To tackle this issue, we propose to use sophisticated image processing techniques on satellite images of the land to determine the regions that are capable of growing food crops. The solution architecture includes enhancement of satellite imagery using sophisticated pan sharpening techniques, notably the Brovey transformation, aiming to transform dull satellite images into sharper versions, thereby improving the overall quality and interpretability of the visual data. Making use of the weather data on the location observed and taking into factors like the soil moisture, weather, humidity, wind, sunlight times and so on, this data is fed into a generative pre-trained transformer model which makes use of it and gives a set of crops that are suitable to be grown on this piece of land under the said conditions. The results obtained by the proposed fusion approach is compared with the dataset provided by the government for different states in India and the performance was measured. We achieved an accuracy of 80% considering the crop suggested by our model and the predominant crop of the region. Also, the classification report detailing the performance of the proposed model is presented.

Exploring clinical features and therapeutic outcomes in Indian children with mixed connective tissue disease: A multicenter study.

INTRODUCTION: Mixed connective tissue disease (MCTD) is a rare entity in children. There is a paucity of studies on juvenile-onset MCTD (jMCTD) worldwide especially from Southeast Asia. OBJECTIVES: To describe clinical and laboratory features of jMCTD diagnosed at pediatric rheumatology centers across India. METHODS: A predesigned detailed case proforma in an excel format was prepared and was sent to all the Pediatric Rheumatology centers in India. Eleven centers provided the clinical and laboratory data of their jMCTD patients, which was then compiled and analyzed in detail. RESULTS: Thirty-one jMCTD patients from 11 centers were included in the study. Our cohort had 27 females and four male patients over 12 months (August 2021 to July 2022). The median age at presentation was 12 years (range 5-18 years) and the median duration of symptoms was 24 months at diagnosis (range 2-96 months). The common features included arthritis (90%), malar rash (70.9%), and Raynaud's phenomenon (70.9%). At a mean follow-up of 43 months (range 1-168 months), 45% of them were in remission. There were two deaths reported, due to macrophage activation syndrome and sepsis respectively. CONCLUSION: We present the largest multicenter experience on jMCTD from the Indian subcontinent. The study's findings serve as a crucial stepping stone toward unraveling the complexities of jMCTD and improving patient care and management strategies.

Navigating the Complexity: A Comprehensive Review of Hemophagocytic Lymphohistiocytosis Associated With Dengue Infection.

Hemophagocytic lymphohistiocytosis (HLH) associated with dengue infection presents a unique challenge in clinical practice due to its rarity, rapid progression, and overlapping clinical features. This comprehensive review navigates the complexity of HLH-dengue syndrome by examining its pathophysiology, clinical manifestations, diagnostic criteria, and therapeutic strategies. HLH, characterized by uncontrolled immune activation and cytokine dysregulation, can occur as a secondary complication of dengue infection, leading to severe multiorgan dysfunction and high mortality if not promptly recognized and treated. The review underscores the significance of early diagnosis through vigilant clinical monitoring and appropriate diagnostic tests, such as bone marrow examinations and genetic studies. Collaboration between infectious disease specialists, hematologists, and critical care teams is essential for optimal management. Despite advancements in understanding HLH-dengue syndrome, further research is needed to elucidate its underlying mechanisms and explore novel treatment approaches. This review provides insights into the clinical implications of HLH-dengue syndrome and emphasizes the importance of a multidisciplinary approach to improve patient outcomes in this challenging clinical scenario.

The Role of Neutrophil-to-Lymphocyte Ratio in Predicting Outcomes of Acute Organophosphorus Poisoning: A Comprehensive Review.

Organophosphorus poisoning (OPP) poses a significant threat to human health, necessitating accurate prognostic markers for timely intervention and improved outcomes. This review evaluates the potential of the neutrophil-to-lymphocyte ratio (NLR) as a prognostic indicator in acute organophosphorus poisoning (AOPP). A comprehensive analysis of existing literature reveals that elevated NLR values correlate with increased severity of poisoning and adverse clinical outcomes, including mortality and morbidity. NLR assessment offers valuable prognostic information beyond traditional markers, aiding risk stratification and guiding clinical decision-making. Integration of NLR into clinical practice holds promise for optimizing patient care through the early identification of high-risk individuals and tailored therapeutic interventions. Further research is needed to validate the utility of NLR in larger patient cohorts and standardize its incorporation into clinical guidelines. Leveraging NLR as a prognostic tool can enhance risk stratification, optimize treatment strategies, and ultimately improve outcomes in AOPP.

Adherence and acceptability of multiple micronutrient supplementation during pregnancy: Study protocol for a cluster-randomized non-inferiority trial in Cambodia.

BACKGROUND: Iron and folic acid (IFA) supplements are currently provided to Cambodian women during pregnancy. However, recent research has found benefits of a multiple micronutrient supplement (MMS) over just IFA alone on several outcomes of perinatal and infant health. The Ministry of Health in Cambodia has proposed a transition from IFA to MMS but to effectively guide this transition requires implementation research on the acceptability and adherence to MMS (over IFA). METHODS: This non-inferiority trial aims to assess the adherence and acceptability of IFA (60 mg elemental iron and 400 µg folic acid) compared to MMS (standard UNIMMAP formulation including 15 micronutrients) during antenatal care in Cambodia. A prospective cohort of 1545 pregnant women will be assigned to one of three trial arms: (1) IFA for 90 days [IFA-90]; (2) MMS for 180 days with two distributions of 90-count tablet bottles [MMS-90]; or (3) MMS for 180 days with one 180-count tablet bottle [MMS-180]. Each arm will enroll 515 women across 48 health centers (clusters) in Kampong Thom Province in Cambodia. The primary outcome is the non-inferiority of adherence rates of MMS-180 compared to IFA-90, as assessed by tablet counts. Mixed-effects logistic and linear regression models will be used to estimate the difference in the adherence rate between the two groups, with an 'a priori' determined non-inferiority margin of 15%. Acceptability of MMS and IFA will be measured using a quantitative survey conducted with enrolled pregnant women at 30-day, 90-day, and 180-day time-points. DISCUSSION: Findings from this study will guide an effective and feasible MMS scale-up strategy for Cambodia. Additionally, the findings will be shared globally with other stakeholders planning to scale up MMS in other countries. TRIAL REGISTRATION: NCT05867836 ( ClinicalTrials.gov , registered May 18, 2023).

Erythema Multiforme: Unveiling the Unusual Effect of an Ayurvedic Medication 'Jambavasa' Used in the Treatment of Type 2 Diabetes Mellitus.

Erythema multiforme (EM) is a rare immune-mediated condition that can manifest as cutaneous, mucosal, or both types of lesions. The target lesion, with concentric zones of color change, is a cutaneous feature that is typical of this illness. Despite the fact that a number of factors can lead to EM, the most common being Herpes simplex virus (HSV) infection, drug-induced EM is a rare entity. As disease severity and mucosal involvement vary across individuals, treatment should be optimized for each patient, considering the risk versus benefit ratio. To distinguish EM from other clinical imitators and to confirm the diagnosis, histopathologic tests and other laboratory procedures may be utilized. Our patient presented with symptoms suggestive of a viral infection, such as fever and rash, but the RTPCR report for various viral infections came out to be negative, hence indicative of the diagnosis of drug-induced erythema multiforme.

A review on potential approach for in silico toxicity analysis of respirable fraction of ambient particulate matter.

Epidemiological and toxicological studies have shown the adverse effect of ambient particulate matter (PM) on respiratory and cardiovascular systems inside the human body. Various cellular and acellular assays in literature use indicators like ROS generation, cell inflammation, mutagenicity, etc., to assess PM toxicity and associated health effects. The presence of toxic compounds in respirable PM needs detailed studies for proper understanding of absorption, distribution, metabolism, and excretion mechanisms inside the body as it is difficult to accurately imitate or simulate these mechanisms in lab or animal models. The leaching kinetics of the lung fluid, PM composition, retention time, body temperature, etc., are hard to mimic in an artificial experimental setup. Moreover, the PM size fraction also plays an important role. For example, the ultrafine particles may directly enter systemic circulations while coarser PM10 may be trapped and deposited in the tracheo-bronchial region. Hence, interpretation of these results in toxicity models should be done judiciously. Computational models predicting PM toxicity are rare in the literature. The variable composition of PM and lack of proper understanding for their synergistic role inside the body are prime reasons behind it. This review explores different possibilities of in silico modeling and suggests possible approaches for the risk assessment of PM particles. The toxicity testing approach for engineered nanomaterials, drugs, food industries, etc., have also been investigated for application in computing PM toxicity.

Application of Convolutional Neural Networks for COVID-19 Detection in X-ray Images Using InceptionV3 and U-Net.

COVID-19 has expanded overall across the globe after its initial cases were discovered in December 2019 in Wuhan-China. Because the virus has impacted people's health worldwide, its fast identification is essential for preventing disease spread and reducing mortality rates. The reverse transcription polymerase chain reaction (RT-PCR) is the primary leading method for detecting COVID-19 disease; it has high costs and long turnaround times. Hence, quick and easy-to-use innovative diagnostic instruments are required. According to a new study, COVID-19 is linked to discoveries in chest X-ray pictures. The suggested approach includes a stage of pre-processing with lung segmentation, removing the surroundings that do not provide information pertinent to the task and may result in biased results. The InceptionV3 and U-Net deep learning models used in this work process the X-ray photo and classifies them as COVID-19 negative or positive. The CNN model that uses a transfer learning approach was trained. Finally, the findings are analyzed and interpreted through different examples. The obtained COVID-19 detection accuracy is around 99% for the best models.

Text messages to improve child diets: Formative research findings and protocol of a randomised controlled trial in Nepal.

Given the role of malnutrition in childhood morbidity and mortality, the prioritisation of maternal and child nutrition programmes has grown significantly in the 21st century. Policies and programmes aim to improve infant and young child feeding, but questions persist about the most effective combination of interventions to achieve desired behaviour change. There is increasing interest in mobile-based interventions globally, but scant evidence exists to guide donors, policymakers and programme implementers on their effectiveness. Formative research was conducted to assess the feasibility and acceptance of text message-based interventions and to guide the final design of the text message intervention. This protocol is for a cluster-randomised controlled trial to test the effectiveness of adding text messaging to other ongoing SBC interventions to promote egg consumption, dietary diversity and other ideal dietary practices, particularly among children 12-23 months of age in Kanchanpur, Nepal. The trial findings will contribute to the emerging body of evidence on the effectiveness of using text messages for behaviour change, specifically for young child dietary outcomes in South Asia. Recent studies have suggested that mobile-based interventions alone may be insufficient but valuable when added to other social and behavioural interventions; this trial will help to provide evidence for or against this emerging theory. This trial was registered at ClinicalTrials.gov on 11 March 2019 (ID: NCT03926689) and has been updated twice.

Distal coronary artery abnormalities in Kawasaki disease: experience on CT coronary angiography in 176 children.

OBJECTIVE: Precise evaluation of coronary artery abnormalities (CAAs) in Kawasaki disease (KD) is essential. The aim of this study is to determine role of CT coronary angiography (CTCA) for detection of CAAs in distal segments of coronary arteries in patients with KD. METHODS: CTCA findings of KD patients with distal coronary artery involvement were compared with those on transthoracic echocardiography (TTE) during the period 2013-21. RESULTS: Among 176 patients with KD who underwent CTCA (128-Slice Dual Source scanner), 23 (13.06%) had distal CAAs (right coronary-15/23; left anterior descending-14/23; left circumflex-4/23 patients). CTCA identified 60 aneurysms-37 proximal (36 fusiform; 1 saccular) and 23 distal (17 fusiform; 6 saccular); 11 patients with proximal aneurysms had distal contiguous extension; 9 patients showed non-contiguous aneurysms in both proximal and distal segments; 4 patients showed distal segment aneurysms in absence of proximal involvement of same coronary artery; 4 patients had isolated distal CAAs. On TTE, only 40 aneurysms could be identified. Further, distal CAAs could not be identified on TTE. CTCA also identified complications (thrombosis, mural calcification and stenosis) that were missed on TTE. CONCLUSIONS: CAAs can, at times, occur in distal segments in isolation and also in association with, or extension of, proximal CAAs. CTCA demonstrates CAAs in distal segments of coronary arteries, including branches, in a significant number of children with KD-these cannot be detected on TTE. CTCA may therefore be considered as a complimentary imaging modality in children with KD who have CAAs on TTE.

Echocardiographic indices of left ventricular function and filling pressure are not related to blood pool activity on pyrophosphate scintigraphy.

BACKGROUND: Pyrophosphate (PYP) imaging has a high diagnostic accuracy for transthyretin cardiac amyloidosis (ATTR-CA). Indeterminate findings are often reported due to persistent blood pool activity, presumed to be from low cardiac output. We evaluated the relationship between blood pool activity on PYP imaging and echocardiographic indices of cardiac function. METHODS: Clinical and imaging data of 189 patients referred for PYP scintigraphy were evaluated. All patients underwent planar imaging and SPECT (diagnostic standard). Among those with a negative PYP SPECT, persistent left ventricular blood pool activity on planar images was inferred by a visual score ≥2 or a heart-to-contralateral (HCL) ratio ≥ 1.5. Absence of blood pool activity was inferred when both visual score was < 2 and HCL was < 1.5. Left ventricular ejection fraction (LVEF), global longitudinal strain (GLS), stroke volume index (SVi), and left atrial pressure (LAP) were calculated from standard transthoracic echocardiograms. RESULTS: ATTR-CA was present in 43 (23%) patients. Among those with a negative PYP SPECT, 11 patients had significant blood pool activity. Patients with ATTR-CA had a lower LVEF, SVi, and GLS, with a higher LAP, compared to those without ATTR-CA. Among those without ATTR-CA, there were no significant differences in these parameters. CONCLUSION: Approximately 8% of patients with a negative PYP SPECT have significant blood pool activity. Measures of cardiac function are not different among those with and without blood pool activity. PYP SPECT should be routinely performed in all patients to avoid false image interpretation.

A Study of the International Stock Market Behavior During COVID-19 Pandemic Using a Driven Iterated Function System.

We propose a novel approach to visualize and compare financial markets across the globe using chaos game representation (CGR) of iterated function systems (IFS). We modified a fractal method, widely used in life sciences, and applied it to study the effect of COVID-19 on global financial markets. This modified driven IFS approach is used to generate compact fractal portraits of the financial markets in form of percentage CGR (PC) plots and subtraction percentage (SP) plots. The markets over different periods are compared and the difference is quantified through a parameter called the proximity (Pr) index. The reaction of the financial market across the globe and volatility to the current pandemic of COVID-19 is studied and modeled successfully. The imminent bearish and a surprise bullish pattern of the financial markets across the world is revealed by this fractal method and provides a new tool to study financial markets.

Ultra-high resolution, multi-scale, context-aware approach for detection of small cancers on mammography.

While detection of malignancies on mammography has received a boost with the use of Convolutional Neural Networks (CNN), detection of cancers of very small size remains challenging. This is however clinically significant as the purpose of mammography is early detection of cancer, making it imperative to pick them up when they are still very small. Mammography has the highest spatial resolution (image sizes as high as 3328 × 4096 pixels) out of all imaging modalities, a requirement that stems from the need to detect fine features of the smallest cancers on screening. However due to computational constraints, most state of the art CNNs work on reduced resolution images. Those that work on higher resolutions, compromise on global context and work at single scale. In this work, we show that resolution, scale and image-context are all important independent factors in detection of small masses. We thereby use a fully convolutional network, with the ability to take any input size. In addition, we incorporate a systematic multi-scale, multi-resolution approach, and encode image context, which we show are critical factors to detection of small masses. We show that this approach improves the detection of cancer, particularly for small masses in comparison to the baseline model. We perform a single institution multicentre study, and show the performance of the model on a diagnostic mammography dataset, a screening mammography dataset, as well as a curated dataset of small cancers < 1 cm in size. We show that our approach improves the sensitivity from 61.53 to 87.18% at 0.3 False Positives per Image (FPI) on this small cancer dataset. Model and code are available from  https://github.com/amangupt01/Small_Cancer_Detection.

Automated detection of mild cognitive impairment and dementia from voice recordings: A natural language processing approach.

INTRODUCTION: Automated computational assessment of neuropsychological tests would enable widespread, cost-effective screening for dementia. METHODS: A novel natural language processing approach is developed and validated to identify different stages of dementia based on automated transcription of digital voice recordings of subjects' neuropsychological tests conducted by the Framingham Heart Study (n = 1084). Transcribed sentences from the test were encoded into quantitative data and several models were trained and tested using these data and the participants' demographic characteristics. RESULTS: Average area under the curve (AUC) on the held-out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal cognition from Dementia, Normal or Mild Cognitive Impairment (MCI) from Dementia, and Normal from MCI, respectively. DISCUSSION: The proposed approach offers a fully automated identification of MCI and dementia based on a recorded neuropsychological test, providing an opportunity to develop a remote screening tool that could be adapted easily to any language.

Sustaining Agriculture and Nutrition Interventions: Continued Engagement of Village Model Farmers in Nepal.

BACKGROUND: In homestead food production (HFP) programs, village model farmers (VMFs), after training, implement agriculture and nutrition activities to improve household knowledge and practices. Little evidence exists on what enables VMFs to remain actively engaged and for impacts to be sustained. OBJECTIVE: To examine variables explaining active engagement of VMFs, at least 4 years post-training, in an HFP program in Nepal. METHODS: We used cross-sectional data, collected from 2018 to 2019, among 4750 VMFs of Suaahara, a multisectoral nutrition program. We assessed whether respondents registered their HFP group with the local government, conducted regular group meetings, discussed vegetable growing and chicken rearing practices with group members, or engaged in saving and credit activities in their HFP group. Outcome variable was a count of these 4 activities in which the VMF engaged. Socioeconomic, demographic, and programmatic explanatory variables were identified a priori and by bivariate analysis and were adjusted in ordinal regression models accounting for clusters. RESULTS: On average, VMFs engaged in 1.4 activities. Having attended primary or secondary school (adjusted odds ratios [AOR] = 1.39), being a female community health volunteer (AOR = 1.27), being from an advantaged caste/ethnic group (AOR = 1.34), receiving additional trainings (AOR = 1.56) and inputs (AOR = 1.31) were associated with more active engagement of VMFs. CONCLUSION: Village model farmers receiving more training and inputs were more likely to remain actively engaged. Female community health workers, people from higher caste/ethnic groups, and those with primary or secondary education were more likely to remain active VMFs and could be targeted for this role in HFP programs leading to sustained impact.