Miracle-dz/UniFAR-FTUs huggingface dataset:
HERITABILITY AND GENETIC ADVANCE FOR YIELD CONTRIBUTING CHARACTERS IN PIGEONPEA ( Cajanus cajan ( L . ) Millsp . )

A field experiment was conducted during late kharif, 2016-17 at Agricultural College Farm, Mahanandi to study the effects of plant density, planting patterns and mulching techniques on, yield and economics of irrigated redgram. The results revealed that spacing of 120/60 cm × 20 cm as paired rows with plastic mulch in pairs recorded significantly higher seed yield (2,302 kg ha) of irrigated redgram. The net returns ( 69,014 ha) and B : C (3.30) ratio were realized with paired row spacing of 90/30 cm × 20 cm.
Dexamethasone-loaded thermo-sensitive hydrogel attenuates osteoarthritis by protecting cartilage and providing effective pain relief

Background We utilized the destabilization of medial meniscus (DMM)-induced mice to illustrate the osteoarthritis (OA) suppressing and pain-relieving effects of a novel prolonged-release intra-articular (IA)-dexamethasone-loaded thermo-sensitive hydrogel (DLTH). Methods The effects of temperature and pH on DLTH formation and in vitro DLTH release profile were assessed. C57BL/6J mice were randomly divided into three groups: Ctrl group, Model group and DLTH group. The DLTH group received joint injections of 10 µL DLTH (1 mg/kg) into the right knee once a week from week 2 to week 11. We performed micro-computed tomography (Micro-CT) and histological analyses of safranin O-fast green, hematoxylin and eosin, and tartrate-resistant acid phosphatase in knee joints. We also carried out immunohistochemical (IHC) staining for matrix metalloproteinase-9 (MMP-9), MMP-13, and a disintegrin and metalloproteinase with thrombospondin motifs-5 (ADAMTS-5) in cartilage and Ki-67 in synovia. Pain behavioral testing was carried out in all mice. The serum content of prostaglandin E2 (PGE2) and real-time polymerase chain reaction (PCR) of inflammatory cytokines and pain-related factors in dorsal root ganglia (DRGs) were evaluated. Results It took 20 minutes to form DLTH at pH 7.0 and 37 °C. The cumulative release profiles of dexamethasone (Dex) from DLTH at 37 °C revealed a rapid release in the first 24 h and a sustained slow release for 7 days. In vivo study illustrated that DLTH attenuated OA bone destruction and ameliorated synovitis and progression of OA in DMM-induced mice. The chondroprotective effects of DLTH were mediated by decreased expressions of MMP-9, MMP-13, and ADAMTS-5. The results showed that IA-DLTH exerted pain-relieving effects in OA mice. Upregulation of nociceptive response time (NRT) and downregulations of serum PGE2, inflammatory factors, and pain-related mediators in DRGs of mice in the DLTH group were recorded. Conclusions Data presented in this study elucidated that DLTH exhibited a long and lasting Dex release and it is a potential sustainable drug delivery system (DDS) to treat OA locally. IA-DLTH injection exerted chondroprotective and pain-relieving effects in DMM-induced arthritis. The involvement of MMP-9, MMP-13, ADAMTS-5, and inflammatory and pain-related factors, may account for the suppression of OA progression and pain.
Hedging Collateralized Debt Obligations

In an empirical study, we investigate the ability of the Gaussian OneFactor Model to describe adequate hedging schedule for the iTraxx CDO. The model is able to provide some insurance against changes in several input spreads, however, it is completely fails when hedging second order effects, and correlation risk. Closer investigation reveals that the model performs very well during regular market times, reducing mean absolute hedge errors and root mean squared errors by more than 50% compared to an unhedged portfolio. A very large proportion of the hedge error, however, is caused by the largest 2.5% of observations. In these cases, the model sometimes even underperforms unhedged positions. We break down the hedge errors into first and second order effects, and in changes in correlation and further study and analyze possible extensions.
First record of the subfamily Oxytorinae (Insecta, Hymenoptera, Ichneumonidae) from the Oriental Region, with descriptions of two new species from Vietnam

Abstract Background The Oxytorinae is a small subfamily of the family Ichneumonidae (Insecta, Hymenoptera) with the single genus Oxytorus Förster and 23 described species. Species were previously known to occur in the Palaearctic, Nearctic and Neotropical Regions. New information The ichneumonid subfamily Oxytorinae is recorded for the first time from the Oriental Region. Two species, one from northern and one from central Vietnam, are described as new: Oxytoruscarinatus Riedel, sp. n. and O.rufopropodealis Riedel, sp. n.
A Comparison of the Heckman Selection Model, Ibrahim, and Lipsitz Methods for Dealing with Nonignorable Missing Data

The presence of missing data is a persistent issue that researchers, data analysts, and statisticians must address in their work. As a result, a number of methods have been developed for dealing with it, from simple single imputation schemes, to complex models based on multiply imputed data. A common assumption among most of these methods is that the information necessary to impute the data under a presumption of randomness is available. Given that this assumption holds, several approaches for imputing missing values, including Multiple Imputation by Chained Equations (MICE) have been shown to be very effective for dealing with missing values. However, when this assumption does not hold, MICE and other imputation methods are not so effective, and may produce imputed data that yields biased results. In order to address this issue, several methods have been developed for the case where data are Missing not at Random (MNAR). This simulation study compared two approaches for handling MNAR data for a dichotomous variable in the context of logistic regression. The results show that an approach based on the Heckman selection model, and adapted for the MICE context, as well as an adapted Ibrahim Lipsitz method performed well, producing data that results in relatively low estimation bias and high confidence interval coverage rates. Implications of these findings are discussed.
The impact of the COVID-19 pandemic on adult mental health in the UK: A rapid systematic review

Background. There is evidence that the COVID-19 pandemic has affected the mental health of the UK population, but this needs synthesising to guide effective policy recommendations and ensure support is targeted to populations most at risk. We conducted a rapid systematic review of the evidence of the impact of COVID-19 and associated restrictions on the mental health of UK adults, including risk and protective factors. Method. A range of databases were searched to identify eligible studies. Studies were eligible if they reported primary quantitative or qualitative research on the mental health of UK adults between March 2020 and March 2021. Journal publications and pre-prints were included. Reviews, position papers, protocol papers and studies published in languages other than English were excluded. The study authors screened papers for eligibility and included 102 papers in the analysis. Results. The evidence from this review indicates that the mental health of UK adults has declined since the start of the pandemic, with different populations being unequally affected. Populations particularly affected are women, young adults, ethnic minorities, people from lower socio-economic backgrounds, people with pre-existing conditions and people who have had COVID-19. Other risk factors include having to isolate and time spent watching pandemic related news. Protective factors include social contact and maintaining healthy behaviours, such as physical activity. Conclusions. Policy should aim to discourage risky behaviours while ensuring support is available for people to engage in protective behaviours. Interventions should be directed towards populations that have been most adversely affected. Addressing the decline in mental health across the UK population since the COVID-19 pandemic will require increasing mental health provision and ensuring equitable access to support.
A Review of Key Technologies and Properties for Graphene Cu-Based Composites

Cu-based composites are widely used in mechanical, electrical, communication, transportation and microelectronics industries due to their excellent properties. However, the difficult match between strength and conductivity is the main problem for Cu-based composites. And thermal-mechanical treatment technologies reached the limit to improve and control their comprehensive performance. Graphene is a two-dimensional layered material with carbon atoms hybridized by SP2 orbital, and has high strength, good electrical conductivity and thermal conductivity. It is expected to solve the contradiction between the strength and conductivity of the composites by introducing graphene into Cu-based composites. In this paper, the commonly used methods for effective dispersion of graphene and interface bonding were introduced, the researches on the strength, toughness, electrical conductivity and thermal conductivity were outlined, lastly the future development of graphene Cu-based composites was prospected.
Breaking the computation and communication abstraction barrier in distributed machine learning workloads

Recent trends towards large machine learning models require both training and inference tasks to be distributed. Considering the huge cost of training these models, it is imperative to unlock optimizations in computation and communication to obtain best performance. However, the current logical separation between computation and communication kernels in machine learning frameworks misses optimization opportunities across this barrier. Breaking this abstraction can provide many optimizations to improve the performance of distributed workloads. However, manually applying these optimizations requires modifying the underlying computation and communication libraries for each scenario, which is both time consuming and error-prone. Therefore, we present CoCoNet, which contains (i) a domain specific language to express a distributed machine learning program in the form of computation and communication operations, (ii) a set of semantics preserving transformations to optimize the program, and (iii) a compiler to generate jointly optimized communication and computation GPU kernels. Providing both computation and communication as first class constructs allows users to work on a high-level abstraction and apply powerful optimizations, such as fusion or overlapping of communication and computation. CoCoNet enabled us to optimize data-, model- and pipeline-parallel workloads in large language models with only a few lines of code. Our experiments show that CoCoNet significantly outperforms state-of-the-art distributed machine learning implementations.
Spin-Orbit Coupling in Single-Layer Ferrimagnets: Direct Observation of Spin-Orbit Torques and Chiral Spin Textures

We demonstrate that effects of spin-orbit coupling and inversion asymmetry exist in a single GdFeCo ferrimagnetic layer, even without a heavy metal interface. We use electric transport measurements to quantify the spin-orbit torques. We measure the Dzyaloshinskii-Moriya interaction using Brillouin light scattering measurement technique, and we observe the resulting chiral magnetic textures using x-ray PEEM microscopy. We attribute these effects to a composition variation along the thickness, that we observed by scanning transmission electron microscopy. We show that these effects can be optimized by varying the GdFeCo thickness or by combining them with interfacial effects.
Mean-Maximum Drawdown Optimization of Buy-and-Hold Portfolios Using a Multi-objective Evolutionary Algorithm

Abstract We develop a novel Mean-Max Drawdown portfolio optimization approach using buy-and-hold portfolios. The optimization is performed utilizing a multi-objective evolutionary algorithm on a sample of S&P 100 constituents. Our optimization procedure provides portfolios with better Mean-Max Drawdown trade-offs compared to relevant benchmarks, regardless of the selected subsamples and market conditions. The superior performance of our approach is particularly pronounced in periods with reversing market trends (i.e. a market rally and a fall in the same subsample).
Does Market Orientation Lead to Firm Performance? Exploring the Mechanisms and Boundary Conditions

ABSTRACT This study explores the mechanisms and boundary conditions that will enhance the effective transformation of the firm’s market orientation (MO) into firm market performance (PERF). We examined how MO influences PERF directly and indirectly through the firm’s new product development capability (NPDC). We also examined the indirect conditional effect of social capital (SC) on the MO-PERF relationship through the NPDC of the firm. Data from 313 managers and owners of small and medium enterprises (SMEs) in Ghana was used for the study. Data were analyzed using the conditional process analysis software PROCESS in SPSS 23.0. Findings indicate that MO has a significant influence on PERF. Further, NPDC partially mediates the relationship between MO and PERF. We also found the indirect relationship between MO and PERF to be declining at higher levels of SC. The study clarifies the divergent views of the impact of MO on performance by introducing NPDC and SC in the relationship.
Clinical Characteristics and Risk Factors of Cerebral Hemorrhage in Patients with Occult Malignant Tumors

Propose To investigate the clinical characteristics and potential risk factors of the first onset of cerebral hemorrhage in patients with occult malignant tumors. Patients and methods In this retrospective study, 23 patients with occult malignant tumors with the first onset of cerebral hemorrhage were enrolled in the tumor group, and 92 patients without occult tumors in the same period were enrolled in the control group. There were no statistical differences in age and sex between both groups by propensity score matching. Collected clinical data included age, sex, smoking history, drinking history, hypertension history, diabetes history, past medical history, routine blood tests, neutrophil-to-lymphocyte ratio (NLR), liver and kidney function, fasting blood glucose level, coagulation function, tumor markers, imaging examinations, National Institute of Health stroke scale (NIHSS) score on admission, modified Rankin Scale (mRS) score 90 days after intracerebral hemorrhage and final mRS score. Results Compared with the control group, the tumor group had fewer patients with hypertension (52.2% vs 81.5%, P<0.05), and the NLR was significantly decreased in the tumor group (2.74 vs 5.46, P<0.05). The tumor group had a greater number of patients with the bleeding sites located in the lobar regions (43.5% vs.19.6%, P<0.05) and a higher coagulation dysfunction (52.2% vs 29.3%, P<0.05) than the control group. Multivariate logistic regression analysis revealed that no history of hypertension (OR: 3.141, 95% CI: 1.107–8.916), lobar cerebral hemorrhage (OR: 3.465 95% CI:1.172–10.243), and coagulation dysfunction (OR: 3.176, 95% CI: 1.131–8.913) were independent predictors of occult tumors, and the receiver operating characteristic (ROC) curve showed that the area under the curve of the three-index combined diagnosis was 0.748, C-statistic analysis also showed the same result. Conclusion No history of hypertension, lobar cerebral hemorrhage, and coagulation dysfunction may be predictors of the risk of occult malignancies in patients with cerebral hemorrhage.
Mitochondrial calcium uniporter promotes cell proliferation and migration in esophageal cancer

Increasing evidence has suggested that mitochondrial calcium uniporter (MCU) is involved in various types of cancer. However, its functions remain unclear in esophageal cancer. The aim of the present study was to explore its abnormal expression and clinical implications in esophageal cancer. A total of 110 patients with esophageal cancer were enrolled in the study. Western blotting was performed to examine the protein expression levels of MCU in 8 pairs of esophageal cancer and adjacent normal tissues. Using immunochemistry, a total of 110 esophageal cancer specimens were analyzed to identify the association between MCU expression and clinicopathological features of patients with esophageal cancer. Furthermore, immunofluorescence of MCU was performed. Pearson's correlation analysis was performed between MCU and hypoxia inducible factor (HIF)-1α/VEGF/E-cadherin/Vimentin expression based on western blotting. After KYSE-150 and TE-1 cells were treated with the MCU agonist Spermine and a small interfering RNA against MCU (si-MCU), a series of functional assays were performed, including Cell Counting Kit-8, colony formation and Transwell assays. The results revealed that, compared with in adjacent normal tissues, MCU was highly expressed in esophageal cancer tissues. MCU expression was significantly associated with depth of invasion, lymph node metastasis, TNM stage and distant metastasis. Moreover, MCU was significantly correlated with HIF-1α/VEGF/E-cadherin/Vimentin in esophageal cancer tissues. MCU overexpression promoted VEGF, MMP2, Vimentin and N-cadherin expression, while it inhibited E-cadherin expression in KYSE-150 and TE-1 cells, and opposite results were observed after transfection with si-MCU. Furthermore, MCU overexpression accelerated the proliferation and migration of KYSE-150 and TE-1 cells. Thus, the current findings suggested that high MCU expression may participate in cell proliferation, migration and epithelial-mesenchymal transition in esophageal cancer.
Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization

Adaptive gradient methods such as Adam have gained increasing popularity in deep learning optimization. However, it has been observed that compared with (stochastic) gradient descent, Adam can converge to a different solution with a significantly worse test error in many deep learning applications such as image classification, even with a fine-tuned regularization. In this paper, we provide a theoretical explanation for this phenomenon: we show that in the nonconvex setting of learning over-parameterized two-layer convolutional neural networks starting from the same random initialization, for a class of data distributions (inspired from image data), Adam and gradient descent (GD) can converge to different global solutions of the training objective with provably different generalization errors, even with weight decay regularization. In contrast, we show that if the training objective is convex, and the weight decay regularization is employed, any optimization algorithms including Adam and GD will converge to the same solution if the training is successful. This suggests that the inferior generalization performance of Adam is fundamentally tied to the nonconvex landscape of deep learning optimization.
Pattern and Outcomes of Fall Injuries and Associated Factors in Emergency Department at Addis Ababa Burn, Emergency and Trauma Hospital

Introduction Fall injuries are an important public health concern and remain a growing health problem globally. Falls are predicted to rise in rank compared to other causes of death, to become the 17th leading cause of death by 2030 unless necessary intervention is given. Purpose This study was aimed to assess the pattern, outcomes of fall injuries, and associated factors. Methods A cross-sectional study design was employed to review a patient’s registration chart from January 1, 2019 to December 31, 2019. Data were collected using a structured checklist developed from previous similar studies. The checked data were entered into Epi data 4.1 versions. Then, it was exported to SPSS 25 versions for analysis. Data were analyzed using descriptive statistics and logistic regression. In the multivariate logistic regression predictors having a P-value < 0.05 were considered to be statistically associated. Results A total of 331 patients’ charts were reviewed. The most common outcome of falling injury was alive 284 (85.8%) while death accounts for 47 (14.2%). Among injured body parts, extremity accounts for around one-fourth, 88 (26.6%), followed by head injury 68 (20.5%). In multivariate logistic regression, those patients aged ≥65 years had 6.15 times more likely to die than those patients aged between 15 and 30 years (AOR 6.15; 95% CI 2.82–14.37, P=0.015), and participants with comorbidity were 1.6 times more likely to die than to be discharged (AOR 1.60; 95% CI, 1.01–5.09, P=0.005) due to falling injury, whereas patient’s occupation, marital status, and sex had no significant association with outcomes of a fall injury. Conclusion The most common outcomes of fall injury were discharged with improvement even though there was a high death rate. Extremity injuries were the most common pattern of injury. Fall is high-risk mortality in the older age group. Furthermore, patient with comorbidity, fall from height and injury to spinal cord and poly-trauma is more likely to die relative to other patients.
A multidimensional Mendelian randomization study on the impact of gut dysbiosis on chronic diseases and human longevity

Alterations of the gut microbiota, often referred to as gut dysbiosis, have been associated with several chronic diseases and longevity in pre-clinical models as well as in observational studies. Whether these relationships underlie causal associations in humans remains to be established. We aimed to determine whether gut dysbiosis influences the risk of chronic diseases and longevity using a comprehensive 2-Sample Mendelian randomization (2SMR) approach. We included as exposures inflammatory bowel disease (IBD) as a human model of gut dysbiosis, 11 gut-associated metabolites and pathways and 48 microbial taxa. Study outcomes included eight chronic diseases previously linked with gut dysbiosis using observational studies (Alzheimer's disease, depression, type 2 diabetes, non-alcoholic fatty liver disease, coronary artery disease (CAD), stroke, osteoporosis and chronic kidney disease) as well as parental longevity and life expectancy. Neither IBD, nor gut-associated metabolites were causally associated with chronic disease or lifespan. After multiple testing correction for 582 tests, no microbial taxa-chronic disease associations remained significant. After robustness analyses and multivariate MR to correct for body mass index and alcohol intake on all 42 nominally significant causal relationships, four associations remained. Altogether, results of this multidimensional Mendelian randomization study suggest that gut dysbiosis has little impact on chronic diseases and human longevity and that previous documented associations may not underly causal relationships. Studies with larger sample sizes and more optimal taxonomic discrimination may ultimately be required to determine whether the human gut microbiota plays a causal role in the etiology of chronic diseases and longevity.
The Word is Mightier than the Label: Learning without Pointillistic Labels using Data Programming

Most advanced supervised Machine Learning (ML) models rely on vast amounts of point-by-point labelled training examples. Hand-labelling vast amounts of data may be tedious, expensive, and error-prone. Recently, some studies have explored the use of diverse sources of weak supervision to produce competitive end model classifiers. In this paper, we survey recent work on weak supervision, and in particular, we investigate the Data Programming (DP) framework. Taking a set of potentially noisy heuristics as input, DP assigns denoised probabilistic labels to each data point in a dataset using a probabilistic graphical model of heuristics. We analyze the math fundamentals behind DP and demonstrate the power of it by applying it on two real-world text classification tasks. Furthermore, we compare DP with pointillistic active and semi-supervised learning techniques traditionally applied in data-sparse settings.