Miracle-dz/UniFAR-FTUs huggingface dataset:
The Modified Early Warning Score as a Predictive Tool During Unplanned Surgical Intensive Care Unit Admission

Background: The Modified Early Warning Score (MEWS) has been proposed to warn healthcare providers of potentially serious adverse events. We evaluated this scoring system during unplanned escalation of care in hospitalized surgical patients during a 1-year period. Methods: Following institutional review board approval, all consecutive, unplanned surgical admissions into the surgical intensive care unit (SICU) during 2016 were entered into this study. MEWS and patient demographics during bedside evaluation for SICU admission were extracted from electronic medical records. Logistic regression was used to analyze the association of MEWS with the incidence of future mortality. P values were set at <0.01 for statistical significance. Results: In this series of 263 consecutive patients, the incidence of mortality following unplanned escalation of care was 29.3% (confidence interval [CI] 24.1% to 35.0%), ranging from 22% to 57%, with all positive MEWS values. The association of MEWS with future mortality was not statistically significant (P=0.0107). A misclassification rate of 0.29 (CI 0.24 to 0.35) was observed with this association. Conclusion: MEWS provided no clinical benefit as an early warning system, as mortality was elevated throughout the MEWS scale in this clinical setting. The high misclassification rate indicates MEWS does not provide discriminatory support for patients at risk for mortality.
Approximation Algorithms for Sparse Principal Component Analysis

We present three provably accurate, polynomial time, approximation algorithms for the Sparse Principal Component Analysis (SPCA) problem, without imposing any restrictive assumptions on the input covariance matrix. The first algorithm is based on randomized matrix multiplication; the second algorithm is based on a novel deterministic thresholding scheme; and the third algorithm is based on a semidefinite programming relaxation of SPCA. All algorithms come with provable guarantees and run in low-degree polynomial time. Our empirical evaluations confirm our theoretical findings.
The role of steroidogenic factor-1 (SF-1) in transcriptional regulation of the gonadotropin-releasing hormone (GnRH) receptor gene

April2001 DECLARATION I, the undersigned, hereby declare that the work contained in this thesis is my own original work and that I have not previously in its entirety or in part submitted it at any university for a degree. SUMMARY The GnRH receptor is a G-protein-coupled receptor in pituitary gonadotrope cells. Binding of its ligand, GnRH, results in synthesis and release of gonadotropin hormones luteinizing hormone (LH) and follicle stimulating hormone (FSH). Steroidogenic factor 1 (SF-1), a transcription factor, binds to specific sites in the promoter region of gonadotropin genes, and thus regulates transcription of these genes. The promoter region of the GnRH-receptor gene contains two SF-1-like binding sites, one at-14 to-8 (site 1) and another at-247 to-239 (site 2), relative to the methionine start codon. The role played by these two SF-1-like sites in basal transcription of the mouse GnRH receptor (mGnRH-R) gene in a pituitary precursor gonadotrope cell line, a T3 cells, was the first area of investigation during this study. Luciferase reporter constructs containing 580 bp of mGnRH-R gene promoter were prepared, where SF-1-like sites were either wildtype or mutated. Four such constructs were made, i.e. wildtype (LG), site 1 mutant (LGM1), site 2 mutant (LGM2) and mutated site 1 plus site 2 (LGM1/2). These constructs were transfected into aT3 cells to determine the effect of mutations of sites 1 and/or 2 on the basal expression of the mGnRH-R gene. Mutation of either site 1 or site 2 had no effect on basal expression of the mGnRH-R gene. It was found that only upon simultaneous mutation of both sites 1 and 2, a 50% reduction in basal transcription took place. The implications of this is that SF-1 protein seems to only require one intact DNA-binding site, to mediate basal transcription of the mGnRH-R gene, suggesting that these two sites lie in close proximity during basal transcription. The effect of the protein kinase A (PKA) pathway on the endogenou~ fTlGnRH-R gene was also investigated by incubating non-, \ transfected aT3 cells with the PKA activators, forskolin and 8-Br-cAMP. Similar incubations were also performed on the wild type and mutated site 1 constructs transfected into pituitary gonadotrope a T3 cells. It was found that forskolin and 8-Br-cAMP were able to increase endogenous mGnRH-R mRNA levels in a concentration-dependent fashion, showing that endogenous GnRH receptor gene expression is stimulated via a protein kinase A pathway. Similar results were obtained with …
IXmon: Detecting and Analyzing DRDoS Attacks at Internet Exchange Points

Distributed reflective denial of service (DRDoS) attacks are a popular choice among adversaries. In fact, one of the largest DDoS attacks ever recorded, reaching a peak of 1.3Tbps against GitHub, was a memcached-based DRDoS attack. More recently, a record-breaking 2.3Tbps attack against Amazon AWS was due to a CLDAP-based DRDoS attack. Although reflective attacks have been known for years, DRDoS attacks are unfortunately still popular and largely unmitigated. In this paper, we study in-the-wild DRDoS attacks observed from a large Internet exchange point (IXP) and provide a number of security-relevant measurements and insights. To enable this study, we first developed IXmon, an open-source DRDoS detection system specifically designed for deployment at large IXP-like network connectivity providers and peering hubs. We deployed IXmon at Southern Crossroads (SoX), an IXP-like hub that provides both peering and upstream Internet connectivity services to more than 20 research and education (R&E) networks in the South-East United States. In a period of about 21 months, IXmon detected more than 900 DRDoS attacks towards 31 different victim ASes. An analysis of the real-world DRDoS attacks detected by our system shows that most DRDoS attacks are short lived, lasting only a few minutes, but that large-volume, long-lasting, and highly-distributed attacks against R&E networks are not uncommon. We then use the results of our analysis to discuss possible attack mitigation approaches that can be deployed at the IXP level, before the attack traffic overwhelms the victim's network bandwidth.
Utilization of phosphate diester by the marine diatom Chaetoceros ceratosporus

Utilization of phosphate diester (PDE) and phosphodiesterase (PDEase) production by 5 marine phytoplankton s pecies were examined in the laboratory to evaluate the contribution of PDE to the growth of phytoplankton. Among the 5 marine phytoplankton species tested, only Chaetoceros ceratosporus was able to use the PDE compound (bis(p-nitrophenyl) phosphate) effectively as a sole phosphorus source. In addition, C. ceratosporus simultaneously produced both PDEase and alkaline phosphatase (APase) at almost equal activity-levels under the phosphate-deficient condition. These results suggest that PDE compounds presumably play an important role as a phosphorus source for PDEase-producing phytoplankton in coastal environments. Many studies have generally focused on dissolved inorganic phosphate (orthophosphate: PO4 3- ) as the major phosphorus source for the growth of phytoplankton to evaluate the factors controlling occurrences of algal blooms (Nalewajko and Lean, 1980). However, the
Controlling for Unknown Confounders in Neuroimaging

The aim of many studies in biomedicine is to infer cause-effect relationships rather then simple associations. For instance, researcher might gather data on dietary habits, and cognitive function in an elderly population to determine which factors affect cognitive decline and to predict the effects of changes in diet on cognition. Finding answers to questions where we want to know the outcome after some other variable has been manipulated, is the subject of causal inference. Inferring causal effects from observational data is challenging and requires making untestable assumptions about the data-generating process. One essential assumption is that of no unmeasured confounder. In complex neuroimaging studies, we neither know all potential confounders nor do we have data on them. Thus, estimating a substitute confounder would be desirable. While this is infeasible in general, we will illustrate that by considering multiple causes of interest simultaneously, we can leverage the dependencies among them to identify causal effects by means of a latent factor model. In our experiments, we quantitatively evaluate the effectiveness of our approach on semi-synthetic data, where we know the true causal effects, and illustrate its use on real data on Alzheimer's disease, where it reveals important causes that otherwise would have been missed.
Density-embedding layers: a general framework for adaptive receptive fields

The effectiveness and performance of artificial neural networks, particularly for visual tasks, depends in crucial ways on the receptive field of neurons. The receptive field itself depends on the interplay between several architectural aspects, including sparsity, pooling, and activation functions. In recent literature there are several ad hoc proposals trying to make receptive fields more flexible and adaptive to data. For instance, different parameterizations of convolutional and pooling layers have been proposed to increase their adaptivity. In this paper, we propose the novel theoretical framework of density-embedded layers, generalizing the transformation represented by a neuron. Specifically, the affine transformation applied on the input is replaced by a scalar product of the input, suitably represented as a piecewise constant function, with a density function associated with the neuron. This density is shown to describe directly the receptive field of the neuron. Crucially, by suitably representing such a density as a linear combination of a parametric family of functions, we can efficiently train the densities by means of any automatic differentiation system, making it adaptable to the problem at hand, and computationally efficient to evaluate. This framework captures and generalizes recent methods, allowing a fine tuning of the receptive field. In the paper, we define some novel layers and we experimentally validate them on the classic MNIST dataset.
Gaining insight into SARS-CoV-2 infection and COVID-19 severity using self-supervised edge features and Graph Neural Networks

A molecular and cellular understanding of how SARS-CoV-2 variably infects and causes severe COVID-19 remains a bottleneck in developing interventions to end the pandemic. We sought to use deep learning (DL) to study the biology of SARS-CoV-2 infection and COVID-19 severity by identifying transcriptomic patterns and cell types associated with SARS-CoV-2 infection and COVID-19 severity. To do this, we developed a new approach to generating self-supervised edge features. We propose a model that builds on Graph Attention Networks (GAT), creates edge features using self-supervised learning, and ingests these edge features via a Set Transformer. This model achieves significant improvements in predicting the disease state of individual cells, given their transcriptome. We apply our model to single-cell RNA sequencing datasets of SARS-CoV-2 infected lung organoids and bronchoalveolar lavage fluid samples of patients with COVID-19, achieving state-of-the-art performance on both datasets with our model. We then borrow from the field of explainable AI (XAI) to identify the features (genes) and cell types that discriminate bystander vs. infected cells across time and moderate vs. severe COVID-19 disease. To the best of our knowledge, this represents the first application of DL to identifying the molecular and cellular determinants of SARS-CoV-2 infection and COVID-19 severity using single-cell omics data.
Benchmarking learned indexes

Recent advancements in learned index structures propose replacing existing index structures, like B-Trees, with approximate learned models. In this work, we present a unified benchmark that compares well-tuned implementations of three learned index structures against several state-of-the-art "traditional" baselines. Using four real-world datasets, we demonstrate that learned index structures can indeed outperform non-learned indexes in read-only in-memory workloads over a dense array. We investigate the impact of caching, pipelining, dataset size, and key size. We study the performance profile of learned index structures, and build an explanation for why learned models achieve such good performance. Finally, we investigate other important properties of learned index structures, such as their performance in multi-threaded systems and their build times.
Fibroblast Growth Factor-21 Improves Pancreatic β-Cell Function and Survival by Activation of Extracellular Signal–Regulated Kinase 1/2 and Akt Signaling Pathways

Fibroblast growth factor-21 (FGF-21) is a recently discovered metabolic regulator. Here, we investigated the effects of FGF-21 in the pancreatic β-cell. In rat islets and INS-1E cells, FGF-21 activated extracellular signal–regulated kinase 1/2 and Akt signaling pathways. In islets isolated from healthy rats, FGF-21 increased insulin mRNA and protein levels but did not potentiate glucose-induced insulin secretion. Islets and INS-1E cells treated with FGF-21 were partially protected from glucolipotoxicity and cytokine-induced apoptosis. In islets isolated from diabetic rodents, FGF-21 treatment increased islet insulin content and glucose-induced insulin secretion. Short-term treatment of normal or db/db mice with FGF-21 lowered plasma levels of insulin and improved glucose clearance compared with vehicle after oral glucose tolerance testing. Constant infusion of FGF-21 for 8 weeks in db/db mice nearly normalized fed blood glucose levels and increased plasma insulin levels. Immunohistochemistry of pancreata from db/db mice showed a substantial increase in the intensity of insulin staining in islets from FGF-21–treated animals as well as a higher number of islets per pancreas section and of insulin-positive cells per islet compared with control. No effect of FGF-21 was observed on islet cell proliferation. In conclusion, preservation of β-cell function and survival by FGF-21 may contribute to the beneficial effects of this protein on glucose homeostasis observed in diabetic animals.
Roles of Proximate Determinants of Fertility in Recent Fertility Decline in Ethiopia: Application of the Revised Bongaarts Model

Introduction In Ethiopia, the fertility rate declined from 5.4 in 2005 to 4.6 by 2016. Many factors have been contributing to this decline. Understanding the factors contributing to the fertility decline and their level of fertility inhibiting effect has a paramount policy implication in any country. This study aimed to assess the contribution of the four proximate determinants of fertility, ie, contraception use, postpartum infecundity, marriage and abortion rate, to fertility decline in Ethiopia since 2005. Methods This study used publicly available data from the Ethiopia Demographic and Health Surveys (EDHS) of 2005, 2011 and 2016. The EDHS data were the representative data collected from the reproductive-age women through a cross-sectional study. The revised and fine-tuned Bongaarts model of proximate fertility determinants was used for data analysis. The components needed for the analysis were extracted from the full EDHS data using the STAT compiler. Finally, the analysis was done using Microsoft Excel. Results Of the four proximate determinants of fertility, postpartum insusceptibility contributed the highest fertility inhibiting effect in all three EDHS, and its level was also more prominent among the poorest women. While post partum infecundity, marriage and abortion had a relatively constant effect on fertility over the last 15 years, the fertility inhibiting effect of contraceptive use significantly increased from 15% to 37%. Conclusion In conclusion, fertility variation in Ethiopia is largely due to the three intermediate determinants of fertility. Over the last one and half decades, contraceptive use was the single most important determinant responsible for fertility decline in Ethiopia. To achieve fertility at replacement level, the country needs a contraceptive prevalence rate of 69%, an increment of nearly 100% from its current contraceptive prevalence rate.
Need-based fertilizer nitrogen management using leaf colour chart in hybrid rice (Oryza sativa)

Field experiments were conducted during 2006–08 to study the response of hybrid rice cultivar PHB 71 to fertilizer N and to study the possibility of improving N-use efficiency by affecting fertilizer N as per need of the crop as guided by leaf colour chart (LCC). Treatments included six rates of N application (0, 60, 90, 120,150 and 180 kg/ha) applied in three equal splits at fixed growth stages. The two need-based N management treatments comprised of application of 20 kg N / ha as basal dose and application of 30 kg N /ha whenever greenness first fully opened leaf from the top was less than shade 4 or 5 on the LCC. Hybrid rice PHB 71 showed significant response to application of N up to 120 kg N /ha applied in three equal split doses. Beyond this level fertilizer N-use efficiency was significantly reduced. The LCC shade 4 proved better than LCC shade 5 for guiding real time N management and achieving higher N-use efficiency. Application of 80–110 kg N /ha using threshold LCC shade 4 inclusive of 20 kg N /ha as basal dose produced grain yield of rice which was at par with the application of 120 kg N /ha applied in three equal split doses.
Physiological effect of low digestible oligosaccharides in diets for animals and humans

The article reviews literature data and results of own investigations concerning biological properties of low digestible carbohydrates (LOOs): lactulose, inulin and different type of oligosaccharides. The following problems: (1) sources of oligosaccharides in animal and human diets, (2) behaviour of oligosaccharides in the gastrointestinal tract, (3) physiological effect of LOOs in diets for laboratory rodents, (4) physiological effect of LOOs in diets for non-ruminat livestock and (5) nutritional advances of LOOs in diets for humans were presented. The above-mentioned reports indicate that oligosaccharide preparations can be considered as feed additives in pigs and poultry feeding, e.g. being as effective as antibiotics in the control of pathogens and enhancement of growth performance. However much more research are needed to determine the appropriate role of these oligosaccharides in animal feeding. LDOs may influence animals and humans in various ways: improve intestinal health, modify lipid metabolism, modulate the immune response, and decrease the risk of intestinal and systemic diseases. The present state of knowledge on the mechanism of dietary action of prebiotic LDOs and physiological effects of different types and doses of low-digestible carbohydrates is not sufficient.
Fourier integrator for periodic NLS: low regularity estimates via discrete Bourgain spaces

In this paper, we propose a new scheme for the integration of the periodic nonlinear Schrodinger equation and rigorously prove convergence rates at low regularity. The new integrator has decisive advantages over standard schemes at low regularity. In particular, it is able to handle initial data in $H^s$ for $\frac1{18}\le s\le 1$. The key feature of the integrator is its ability to distinguish between low and medium frequencies in the solution and to treat them differently in the discretization. This new approach requires a well-balanced filtering procedure which is carried out in Fourier space. The convergence analysis of the proposed scheme is based on discrete (in time) Bourgain space estimates which we introduce in this paper. A numerical experiment illustrates the superiority of the new integrator over standard schemes for rough initial data.
Comprehensive Economic Benefit Evaluation Method of Coastal Enterprises Based on AHP

ABSTRACT Wang, J. and Deng, X. , 2020. Comprehensive economic benefit evaluation method of coastal enterprises based on AHP. In: Yang, Y. ; Mi, C. ; Zhao, L. , and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 24–28. Coconut Creek (Florida), ISSN 0749-0208. In the process of comprehensive economic benefit evaluation, coastal enterprises need to consider the factors of economy, technology and reliability. These factors affect each other and are difficult to distinguish accurately. In this context, the AHP and fuzzy comprehensive evaluation method are combined to evaluate the comprehensive economic benefits of coastal enterprises. First of all, the weight of each evaluation factor is determined by using the analytic hierarchy process (AHP) legally and quantitatively; then, the fuzzy comprehensive evaluation model based on AHP is constructed to evaluate the evaluation scheme containing uncertain factors; finally, the application of AHP in state-owned and private enterprises proves that the evaluation method is scientific and feasible.
ResiliNet: Failure-Resilient Inference in Distributed Neural Networks

Federated Learning aims to train distributed deep models without sharing the raw data with the centralized server. Similarly, in Split Learning, by partitioning a neural network and distributing it across several physical nodes, activations and gradients are exchanged between physical nodes, rather than raw data. Nevertheless, when a neural network is partitioned and distributed among physical nodes, failure of physical nodes causes the failure of the neural units that are placed on those nodes, which results in a significant performance drop. Current approaches focus on resiliency of training in distributed neural networks. However, resiliency of inference in distributed neural networks is less explored. We introduce ResiliNet, a scheme for making inference in distributed neural networks resilient to physical node failures. ResiliNet combines two concepts to provide resiliency: skip hyperconnection, a concept for skipping nodes in distributed neural networks similar to skip connection in resnets, and a novel technique called failout, which is introduced in this paper. Failout simulates physical node failure conditions during training using dropout, and is specifically designed to improve the resiliency of distributed neural networks. The results of the experiments and ablation studies using three datasets confirm the ability of ResiliNet to provide inference resiliency for distributed neural networks.