Miracle-dz/UniFAR-FTUs huggingface dataset:
Neonatal screening for congenital adrenal hyperplasia: 17-hydroxyprogesterone cut-off values based on birth weight

Introduction: Congenital adrenal hyperplasia (CAH) is an adrenal disorder, most commonly caused by 21hydroxylase enzyme deficiency. Newborn screening for CAH is done by the measurement of 17-hydroxyprogesterone (17-OHP). Aim: To establish the reference limits of 17-OHP in newborns. Materials and Methods: The study was conducted at Sri Ramachandra Medical College and Research Institute (SRMC& RI), Chennai. The clinical data of 360 neonates delivered over a period of two years from January 2015 to December 2016 were retrospectively analyzed. The newborns were grouped according to birth-weight: G1: < 1,250 g, G2: 1250-2249 g, G3: >2250 g. Based on birth weights 17-OHP cut-off values were determined for each group by rank number. Results: The reference limits for the three group, < 1,250 g, 1250-2249 g, >2250 g are 2.23 59.3, 1.9 29.7 and 1.9 15.1 nmol/L respectively. The upper reference limits of all the groups are less than the values showed in the kit literature. Conclusion: CAH can be diagnosed by measuring 17-OHP at birth as a part of neonatal screening. Population specific and birth weight specific cut off values of 17-OHP will reduce the false positives.
Encephalitis Associated to Metabotropic Glutamate Receptor 5 (mGluR5) Antibodies in Cerebrospinal Fluid

A 68-years-old Hispanic man, complained of night sweats, low grade fewer, unexplained weight loss, and memory problems over 3 months. Abdominal tomography showed multiple intra-abdominal adenopathy and biopsy confirmed classic Hodgkin's lymphoma. He commenced treatment with chemotherapy. Three months later, he had acute onset of inattention, auditory hallucinations and alterations of anterograde memory. The patient developed psychomotor agitation, unresponsive to a combination of neuroleptics and benzodiazepines. Brain MRI showed a small established cerebellar infarction. Electroencephalogram was normal. Tests for toxic metabolic encephalopathy were negative. One oligoclonal IgG bands was found in the Cerebrospinal fluid (CSF), which was not observed in corresponding serum, but cell count and protein were normal. Extensive testing for infectious encephalitis was unremarkable. CSF testing for commercially available neural and non-neural autoantibodies was negative. The patient fulfilled the Gultekin diagnostic criteria for paraneoplastic limbic encephalitis and methylprednisolone IV 1g/d for 5 days was given. He recovered rapidly, with progressive improvement in memory and psychomotor agitation. After treatment commenced, results for antibodies to mGluR5 in CSF taken prior to treatment were returned as positive. mGluR5 is found on post-synaptic terminals of neurons and microglia and is expressed primarily in the hippocampus and amygdala. This case highlights the difficulties in diagnosing this type of encephalitis: the CSF did not show pleocytosis, the MRI showed only chronic change and the electroencephalogram was normal. The dramatic recovery after methylprednisolone help to better characterized the clinical spectrum of auto-immune encephalitis. Diagnosing anti mGlutR5 encephalitis may lead to potentially highly effective treatment option and may anticipate the diagnostic of a cancer. A high index of suspicion is needed to avoid missed diagnosis. In patients with unexplained encephalitis, testing for antibodies to mGluR5 in CSF and serum should be considered. When there is a reasonable index of suspicion of auto-immune encephalitis, treatment should not be delayed for the antibody results.
Quasi-random number generators for multivariate distributions based on generative neural networks

Generative moment matching networks are introduced as quasi-random number generators for multivariate distributions. So far, quasi-random number generators for non-uniform multivariate distributions require a careful design, often need to exploit specific properties of the distribution or quasi-random number sequence under consideration, and are limited to few models. Utilizing generative neural networks, in particular, generative moment matching networks, allows one to construct quasi-random number generators for a much larger variety of multivariate distributions without such restrictions. Once trained, the presented generators only require independent quasi-random numbers as input and are thus fast in generating non-uniform multivariate quasi-random number sequences from the target distribution. Various numerical examples are considered to demonstrate the approach, including applications inspired by risk management practice.
Microgrids Technology: A Review Paper

Microgrids are currently rising centres, banks and pilot exhibition locales in business markets, driven by mechanical enhancements, diminishing costs, demonstrated involvement and developing acknowledgement of their advantages. They are utilised to enhance the dependability and strength of intensity frames, to deal with the expansion of conveyed clean vitality assets, for example, wind and sunlight based age to lessen petroleum derivative emanations and to give power in territories that are not don't have incorporated electrical framework. This survey provides a multidisciplinary representation of current microgrid controllers, specific applications, difficulties and prospects. KeywordsMicrogrids, Renewable integration Prosumers, Distribution energy
A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes

How can we efficiently gather information to optimize an unknown function, when presented with multiple, mutually dependent information sources with different costs? For example, when optimizing a robotic system, intelligently trading off computer simulations and real robot testings can lead to significant savings. Existing methods, such as multi-fidelity GP-UCB or Entropy Search-based approaches, either make simplistic assumptions on the interaction among different fidelities or use simple heuristics that lack theoretical guarantees. In this paper, we study multi-fidelity Bayesian optimization with complex structural dependencies among multiple outputs, and propose MF-MI-Greedy, a principled algorithmic framework for addressing this problem. In particular, we model different fidelities using additive Gaussian processes based on shared latent structures with the target function. Then we use cost-sensitive mutual information gain for efficient Bayesian global optimization. We propose a simple notion of regret which incorporates the cost of different fidelities, and prove that MF-MI-Greedy achieves low regret. We demonstrate the strong empirical performance of our algorithm on both synthetic and real-world datasets.
Is your function low dimensional?

We study the problem of testing if a function depends on a small number of linear directions of its input data. We call a function $f$ a linear $k$-junta if it is completely determined by some $k$-dimensional subspace of the input space. In this paper, we study the problem of testing whether a given $n$ variable function $f : \mathbb{R}^n \to \{0,1\}$, is a linear $k$-junta or $\epsilon$-far from all linear $k$-juntas, where the closeness is measured with respect to the Gaussian measure on $\mathbb{R}^n$. Linear $k$-juntas are a common generalization of two fundamental classes from Boolean function analysis (both of which have been studied in property testing) $\textbf{1. }$ $k$- juntas which are functions on the Boolean cube which depend on at most k of the variables and $\textbf{2. }$ intersection of $k$ halfspaces, a fundamental geometric concept class. We show that the class of linear $k$-juntas is not testable, but adding a surface area constraint makes it testable: we give a $\mathsf{poly}(k \cdot s/\epsilon)$-query non-adaptive tester for linear $k$-juntas with surface area at most $s$. We show that the polynomial dependence on $s$ is necessary. Moreover, we show that if the function is a linear $k$-junta with surface area at most $s$, we give a $(s \cdot k)^{O(k)}$-query non-adaptive algorithm to learn the function up to a rotation of the basis. In particular, this implies that we can test the class of intersections of $k$ halfspaces in $\mathbb{R}^n$ with query complexity independent of $n$.
miRNA miR-21 Is Largely Dispensable for Intrathymic T-Cell Development

Development of T cells in the thymus is tightly controlled to continually produce functional, but not autoreactive, T cells. miRNAs provide a layer of post-transcriptional gene regulation to this process, but the role of many individual miRNAs in T-cell development remains unclear. miR-21 is prominently expressed in immature thymocytes followed by a steep decline in more mature cells. We hypothesized that such a dynamic expression was indicative of a regulatory function in intrathymic T-cell development. To test this hypothesis, we analyzed T-cell development in miR-21-deficient mice at steady state and under competitive conditions in mixed bone-marrow chimeras. We complemented analysis of knock-out animals by employing over-expression in vivo. Finally, we assessed miR-21 function in negative selection in vivo as well as differentiation in co-cultures. Together, these experiments revealed that miR-21 is largely dispensable for physiologic T-cell development. Given that miR-21 has been implicated in regulation of cellular stress responses, we assessed a potential role of miR-21 in endogenous regeneration of the thymus after sublethal irradiation. Again, miR-21 was completely dispensable in this process. We concluded that, despite prominent and highly dynamic expression in thymocytes, miR-21 expression was not required for physiologic T-cell development or endogenous regeneration.
The Impact of Post-transcriptional Control: Better Living Through RNA Regulons

Traditionally, cancer is viewed as a disease driven by genetic mutations and/or epigenetic and transcriptional dysregulation. While these are undoubtedly important drivers, many recent studies highlight the disconnect between the proteome and the genome or transcriptome. At least in part, this disconnect arises as a result of dysregulated RNA metabolism which underpins the altered proteomic landscape observed. Thus, it is important to understand the basic mechanisms governing post-transcriptional control and how these processes can be co-opted to drive cancer cell phenotypes. In some cases, groups of mRNAs that encode protein involved in specific oncogenic processes can be co-regulated at multiple processing levels in order to turn on entire biochemical pathways. Indeed, the RNA regulon model was postulated as a means to understand how cells coordinately regulate transcripts encoding proteins in the same biochemical pathways. In this review, we describe some of the basic mRNA processes that are dysregulated in cancer and the biological impact this has on the cell. This dysregulation can affect networks of RNAs simultaneously thereby underpinning the oncogenic phenotypes observed.
Theory and Use of the EM Algorithm

This introduction to the expectation–maximization (EM) algorithm provides an intuitive and mathematically rigorous understanding of EM. Two of the most popular applications of EM are described in detail: estimating Gaussian mixture models (GMMs), and estimating hidden Markov models (HMMs). EM solutions are also derived for learning an optimal mixture of fixed models, for estimating the parameters of a compound Dirichlet distribution, and for dis-entangling superimposed signals. Practical issues that arise in the use of EM are discussed, as well as variants of the algorithm that help deal with these challenges.
Cognitive Assistants for Document-Related Tasks in Law and Government

The legal relationship between government and citizens is mediated by documents. This paper identiﬁes four classes of cognitive assistants having the potential to improve the experience of citizens and government ofﬁcials in using and understanding government documents: self-ﬁlling forms; error-detecting forms; proactive information search; and deductive document synthesis.
Privacy Preservation in Crowdsourced Health Research

Crowdsourced health research is a growing field toward achieve personalized healthcare. Health research often confronts with the small N problem; difficulty for statistical inference due to small sample size. Crowd- sourced science leverages the power of the mass scale and provides benefits. However, privacy concerns often prevent data sharing. Privacy preservation data mining (PPDM) deals with protecting the privacy of individual data or sensitive knowledge without sacrificing the utility of the data. This talk gives overview of recent PPDM techniques. We also discuss how to utilize PPDM for health research.
Faustovirus E12 Transcriptome Analysis Reveals Complex Splicing in Capsid Gene

Faustoviruses are the first giant viruses of amoebae isolated on Vermamoeba vermiformis. They are distantly related to African swine fever virus, the causative agent of lethal hemorrhagic fever in domestic pigs. Structural studies have shown the presence of a double protein layer encapsidating the double-stranded DNA genome of Faustovirus E12, the prototype strain. The major capsid protein (MCP) forming the external layer has been shown to be 645-amino acid-long. Unexpectedly, its encoding sequence has been found to be scattered along a 17 kbp-large genomic region. Using RNA-seq, we studied expression of Faustovirus E12 genes at nine time points over its entire replicative cycle. Paired-end 250 bp-long read sequencing on MiSeq instrument and double-round spliced alignment enabled the identification of 26 different splice-junctions. Reads corresponding to junctions represented 2% of mapped reads and mostly matched with the predicted MCP encoding sequences. Moreover, our study enabled describing a 1,939 bp-long transcript that corresponds to the MCP, delineating 13 exons. At least two types of introns coexist in the MCP gene: group I introns that can self-splice (n = 5) and spliceosome-like introns with non-canonical splice sites (n = 7). All splice-sites were non-canonical with five types of donor/acceptor splice-sites among which AA/TG was the most frequent association.
Dynamic Pose Estimation Using Multiple RGB-D Cameras

Human poses are difficult to estimate due to the complicated body structure and the self-occlusion problem. In this paper, we introduce a marker-less system for human pose estimation by detecting and tracking key body parts, namely the head, hands, and feet. Given color and depth images captured by multiple red, green, blue, and depth (RGB-D) cameras, our system constructs a graph model with segmented regions from each camera and detects the key body parts as a set of extreme points based on accumulative geodesic distances in the graph. During the search process, local detection using a supervised learning model is utilized to match local body features. A final set of extreme points is selected with a voting scheme and tracked with physical constraints from the unified data received from the multiple cameras. During the tracking process, a Kalman filter-based method is introduced to reduce positional noises and to recover from a failure of tracking extremes. Our system shows an average of 87% accuracy against the commercial system, which outperforms the previous multi-Kinects system, and can be applied to recognize a human action or to synthesize a motion sequence from a few key poses using a small set of extremes as input data.
The Effect of Dietary Mushroom Agaricus bisporus on Intestinal Microbiota Composition and Host Immunological Function

A study was designed to determine the potential prebiotic effect of dietary mushrooms on the host immune response, and intestinal microbiota composition and function. Thirty-one six-week-old pigs were fed a pig grower diet alone or supplemented with either three or six servings of freeze-dried white button (WB)-mushrooms for six weeks. Host immune response was evaluated in peripheral blood mononuclear cells (PBMC), and alveolar macrophages (AM) after stimulation with Salmonella typhymurium-Lipopolysaccharide (LPS). Isolated DNA from fecal and proximal colon contents were used for 16S rDNA taxonomic analysis and linear discriminant analysis effect size (LEfSe) to determine bacterial abundance and metabolic function. Pigs gained weight with no difference in body composition or intestinal permeability. Feeding mushrooms reduced LPS-induced IL-1β gene expression in AM (P < 0.05) with no change in LPS-stimulated PBMC or the intestinal mucosa transcriptome. LEfSe indicated increases in Lachnospiraceae, Ruminococcaceae within the order Clostridiales with a shift in bacterial carbohydrate metabolism and biosynthesis of secondary metabolites in the mushroom-fed pigs. These results suggested that feeding WB mushrooms significantly reduced the LPS-induced inflammatory response in AM and positively modulated the host microbiota metabolism by increasing the abundance of Clostridiales taxa that are associated with improved intestinal health.
Three Laws Learned from Web-Scale Reasoning

Reasoning on a web-scale is one of the most important problems of the emerging semantic web. In this short paper, we look into our previous experience in this ﬁeld to identify whether there are important lessons that we have learned so far. From this process, we extracted three important principles that we identiﬁed as three “laws” that arguably hold on the current Semantic Web. We believe that these “laws” can be useful to solve the remaining issues that still prevent the applicability of reasoning on a web-scale. To this end, we brieﬂy analyze two of such issues, performance prediction and reasoning approximation, and explain how these three laws can drive the research towards a truly reason(able) Web.
Natural Language Access to Data: It Takes Common Sense!

Commonsense reasoning proves to be an essential tool for natural-language access to data. In a deductive approach to this problem, language processing technology translates English queries into a ﬁrst-order logical form, which is regarded as a conjecture to be established by a theorem prover. Subject domain knowledge is encoded in an axiomatic theory equipped with links to appropriate databases. Commonsense reasoning is necessary to disambiguate the query, to connect the query with relevant tables in the databases, to deal with logical relationships in the query, and to achieve interoperability between disparate databases. This is illustrated with examples from a proof-of-concept system called Quest, which deals with queries over business enterprise data for an industrial QA system.
Food-Derived Bioactive Peptides in Human Health: Challenges and Opportunities

Recent scientific evidence suggests that food proteins not only serve as nutrients, but can also modulate the body’s physiological functions. These physiological functions are primarily regulated by some peptides that are encrypted in the native protein sequences. These bioactive peptides can exert health beneficial properties and thus are considered as a lead compound for the development of nutraceuticals or functional foods. In the past few decades, a wide range of food-derived bioactive peptide sequences have been identified, with multiple health beneficial activities. However, the commercial application of these bioactive peptides has been delayed because of the absence of appropriate and scalable production methods, proper exploration of the mechanisms of action, high gastro-intestinal digestibility, variable absorption rate, and the lack of well-designed clinical trials to provide the substantial evidence for potential health claims. This review article discusses the current techniques, challenges of the current bioactive peptide production techniques, the oral use and gastrointestinal bioavailability of these food-derived bioactive peptides, and the overall regulatory environment.
Diabetes in Pregnancy and MicroRNAs: Promises and Limitations in Their Clinical Application

Maternal diabetes is associated with an increased risk of complications for the mother and her offspring. The latter have an increased risk of foetal macrosomia, hypoglycaemia, respiratory distress syndrome, preterm delivery, malformations and mortality but also of life-long development of obesity and diabetes. Epigenetics have been proposed as an explanation for this long-term risk, and microRNAs (miRNAs) may play a role, both in short- and long-term outcomes. Gestation is associated with increasing maternal insulin resistance, as well as β-cell expansion, to account for the increased insulin needs and studies performed in pregnant rats support a role of miRNAs in this expansion. Furthermore, several miRNAs are involved in pancreatic embryonic development. On the other hand, maternal diabetes is associated with changes in miRNA both in maternal and in foetal tissues. This review aims to summarise the existing knowledge on miRNAs in gestational and pre-gestational diabetes, both as diagnostic biomarkers and as mechanistic players, in the development of gestational diabetes itself and also of short- and long-term complications for the mother and her offspring.
The Curse of Competitive Crowd Intelligence

In this paper, we aim to understand the impact of introducing competitiveness in crowd intelligence by studying a real-life crowd-powered environment. In a competitive environment, tasks are solved with competing interests of the crowd. Such an environment has rarely been studied under real-life observations, albeit much is known about the behavior of collaborative crowd-powered systems. The limited studies to gain insights about competitive crowd intelligence mainly involve specialized crowd (like coders or professionals). Here we study a competitive crowdsourcing environment, which is an older version of Flightfox, involving non-specialized people as crowd workers. It is a platform to launch contests for finding the best itineraries through crowdsourcing. We analyze this crowdsourced data, comprising about thirteen thousand contests completed within a duration close to two years, to investigate the guiding force of competitive crowd intelligence. We analyze the global competitive behavior of the system through network analyses. Overall, we gain some important insights about the deplorable effect of competitiveness in controlling the sustainability of such systems.