Miracle-dz/UniFAR-FTUs huggingface dataset:
Na-K-Cl Cotransporter-1 as a Regulator of Manganese-induced Astrocyte Swelling

Astrocyte swelling leads to brain edema, intracranial pressure, brain herniation and acute liver failure (fulminant hepatic failure) which is the major cause of death in this condition. Manganese has been strongly implicated as an important factor in astrocyte swelling. Manganese in excess is neurotoxic and causes a CNS disorder that resembles Parkinsoni¦s disease (manganism). Manganese highly accumulates in astrocytes, which renders these cells more vulnerable to its toxicity. In addition to manganism, increased brain levels of manganese have been found in hepatic encephalopathy. Manganese is known to cause cell swelling in cultured astrocytes, although the means by which this occurs has not been fully elucidated. A disturbance in one or more of these systems may result in loss of ion homeostasis and cell swelling. In particular, activation of the Na-K-Cl cotransporter-1 (NKCC1) has been shown to be involved in cell swelling in several neurological disorders. We therefore examined the effect of manganese on NKCC activity and its potential role in the swelling of astrocytes. Cultured astrocytes were exposed to manganese (50 µM), and NKCC activity was measured. Manganese increased NKCC activity at 24 h. Inhibition of this activity by bumetanide diminished manganese-induced astrocyte swelling. Manganese (Mn) also increased total as well as phosphorylated NKCC1. These results suggest that activation of NKCC1 is an important factor in the mediation of astrocyte swelling by manganese and that such activation appears to be mediated by NKCC1 abundance.
The effect of doubled CO2 and model basic state biases on the monsoon‐ENSO system. II: Changing ENSO regimes

Integrations of a fully‐coupled climate model with and without flux adjustments in the equatorial oceans are performed under 2 × CO2 conditions to explore in more detail the impact of increased greenhouse gas forcing on the monsoon‐ENSO system. When flux adjustments are used to correct some systematic model biases, ENSO behaviour in the modelled future climate features distinct irregular and periodic (biennial) regimes. Comparison with the observed record yields some consistency with ENSO modes primarily based on air‐sea interaction and those dependent on basinwide ocean wave dynamics. Simple theory is also used to draw analogies between the regimes and irregular (stochastically forced) and self‐excited oscillations respectively. Periodic behaviour is also found in the Asian‐Australian monsoon system, part of an overall biennial tendency of the model under these conditions related to strong monsoon forcing and increased coupling between the Indian and Pacific Oceans. The tropospheric biennial oscillation (TBO) thus serves as a useful descriptor for the coupled monsoon‐ENSO system in this case. The presence of obvious regime changes in the monsoon‐ENSO system on interdecadal timescales, when using flux adjustments, suggests there may be greater uncertainty in projections of future climate, although further modelling studies are required to confirm the realism and cause of such changes. Copyright © 2007 Royal Meteorological Society
Stability of the Lanczos Method for Matrix Function Approximation

The ubiquitous Lanczos method can approximate $f(A)x$ for any symmetric $n \times n$ matrix $A$, vector $x$, and function $f$. In exact arithmetic, the method's error after $k$ iterations is bounded by the error of the best degree-$k$ polynomial uniformly approximating $f(x)$ on the range $[\lambda_{min}(A), \lambda_{max}(A)]$. However, despite decades of work, it has been unclear if this powerful guarantee holds in finite precision. We resolve this problem, proving that when $\max_{x \in [\lambda_{min}, \lambda_{max}]}|f(x)| \le C$, Lanczos essentially matches the exact arithmetic guarantee if computations use roughly $\log(nC\|A\|)$ bits of precision. Our proof extends work of Druskin and Knizhnerman [DK91], leveraging the stability of the classic Chebyshev recurrence to bound the stability of any polynomial approximating $f(x)$. We also study the special case of $f(A) = A^{-1}$, where stronger guarantees hold. In exact arithmetic Lanczos performs as well as the best polynomial approximating $1/x$ at each of $A$'s eigenvalues, rather than on the full eigenvalue range. In seminal work, Greenbaum gives an approach to extending this bound to finite precision: she proves that finite precision Lanczos and the related CG method match any polynomial approximating $1/x$ in a tiny range around each eigenvalue [Gre89]. For $A^{-1}$, this bound appears stronger than ours. However, we exhibit matrices with condition number $\kappa$ where exact arithmetic Lanczos converges in $polylog(\kappa)$ iterations, but Greenbaum's bound predicts $\Omega(\kappa^{1/5})$ iterations. It thus cannot offer significant improvement over the $O(\kappa^{1/2})$ bound achievable via our result. Our analysis raises the question of if convergence in less than $poly(\kappa)$ iterations can be expected in finite precision, even for matrices with clustered, skewed, or otherwise favorable eigenvalue distributions.
Effects of extrusion process on physicochemical properties, in situ rumen degradability, oxidative stability and α-linolenic acid retention in flaxseed during long term storage.

Article history Received: 18 May, 2015 Revised: 22 May, 2015 Accepted: 24 May, 2015 Abstract The current study was conducted to evaluate the effect of extrusion process on chemical composition, quality parameters, in situ rumen degradability, oxidative stability, and fatty acid profile of flaxseed during long term storage. In a completely randomized design, flaxseed ground (GF) or mixed with alfalfa hay and pistachio byproducts with 80:10:10 (by mass) proportions and extruded (EF). There was no significant difference between ground and extruded flaxseed for dry matter content (P>0.05). But, the content of crude protein (CP) and ether extract (EE) reduced and the ash content significantly increased after extrusion process (P<0.05). EF treatment significantly had higher oil lost (OL) and bulk density (BD) than GF (P<0.05) and the angle of repose (AR) in GF treatment was slightly higher than EF. Extrusion significantly reduced proportion of unsaturated fatty acids and increased peroxide value (PV) in the flaxseed (P<0.05). Also, PV linearly increased during storage period (P<0.05) and this increase was significantly higher in EF treatment than GF (P<0.05). In addition, slowly degradable fraction (b), potential DM degradability (a+b) and effective DM degradability of flaxseed reduced, but soluble DM fraction (a) and the rate of degradation of the slowly degradable DM (c) was increased by extrusion (P<0.05). In conclusion, it seems that extrusion reduced ALA retention, and increased peroxide value in the flaxseed.
Tink: A Temporal Graph Analytics Library for Apache Flink

We introduce the Tink library for distributed temporal graph analytics. Increasingly, reasoning about temporal aspects of graph-structured data collections is an important aspect of analytics. For example, in a communication network, time plays a fundamental role in the propagation of information within the network. Whereas existing tools for temporal graph analysis are built stand alone, Tink is a library in the Apache Flink ecosystem, thereby leveraging its advanced mature features such as distributed processing and query optimization. Furthermore, Flink requires little effort to process and clean the data without having to use different tools before analyzing the data. Tink focuses on interval graphs in which every edge is associated with a starting time and an ending time. The library provides facilities for temporal graph creation and maintenance, as well as standard temporal graph measures and algorithms. Furthermore, the library is designed for ease of use and extensibility.
Hierarchical Strategies for Efficient Fault Recovery on the Reconfigurable PAnDA Device

A novel hierarchical fault-tolerance methodology for reconfigurable devices is presented. A bespoke multi-reconfigurable FPGA architecture, the programmable analogue and digital array (PAnDA), is introduced allowing fine-grained reconfiguration beyond any other FPGA architecture currently in existence. Fault blind circuit repair strategies, which require no specific information of the nature or location of faults, are developed, exploiting architectural features of PAnDA. Two fault recovery techniques, stochastic and deterministic strategies, are proposed and results of each, as well as a comparison of the two, are presented. Both approaches are based on creating algorithms performing fine-grained hierarchical partial reconfiguration on faulty circuits in order to repair them. While the stochastic approach provides insights into feasibility of the method, the deterministic approach aims to generate optimal repair strategies for generic faults induced into a specific circuit. It is shown that both techniques successfully repair the benchmark circuits used after random faults are induced in random circuit locations, and the deterministic strategies are shown to operate efficiently and effectively after optimisation for a specific use case. The methods are shown to be generally applicable to any circuit on PAnDA, and to be straightforwardly customisable for any FPGA fabric providing some regularity and symmetry in its structure.
Talactoferrin in Severe Sepsis: Results From the Phase II/III Oral tAlactoferrin in Severe sepsIS Trial

Objective:Talactoferrin alfa is a recombinant form of the human glycoprotein, lactoferrin, which has been shown to have a wide range of effects on the immune system. This phase II/III clinical trial compared talactoferrin with placebo, in addition to standard of care, in patients with severe sepsis. Design:Multicenter, randomized, placebo-controlled, phase II/III clinical study. Setting:Seventy-seven centers in 10 countries. Patients:Adult (> 18 yr) patients admitted to one of the participating centers with severe sepsis who were receiving antimicrobial therapy and able to take liquid medication by mouth or feeding tube. Interventions:Patients were randomized to receive either talactoferrin (1.5 g, 15 mL) or placebo three times a day orally or by another enteral route for 28 days or until ICU discharge. Measurements and Main Results:The study was terminated after 305 patients had been enrolled (153 talactoferrin and 152 placebo) because of futility and safety concerns identified by the Data Safety Monitoring Board. There were no significant differences between groups in baseline characteristics including age, sex, site of infection, and severity scores. Twenty-eight-day mortality was higher in talactoferrin-treated patients although this difference was not statistically significant (24.8% vs 17.8% placebo; p = 0.117). The difference was largely the result of differences in patients with shock (talactoferrin, 33/105 [31.4%] vs placebo, 21/104 [20.2%]; p = 0.064); no mortality difference was seen in patients without shock (talactoferrin, 5/48 [10.4%] vs placebo, 6/48 [12.5%]; p = 0.806). In-hospital (43/153 [28.1%] vs 27/152 [17.8%]; p = 0.037) and 3-month (46/153 [30.1%] vs 31/152 [20.4%]; p = 0.036) mortality rates were significantly higher in talactoferrin-treated patients than in patients in the placebo group. The occurrence of treatment-related adverse or serious adverse events was similar between groups. Conclusions:Administration of oral talactoferrin was not associated with reduced 28-day mortality in patients with severe sepsis and may even be harmful.
Sensuous Ethnography: A Visual Dialogue with Iranian Transmigrants

Focusing on the growing trend of skilled emigration in Iran, which is often examined using a conventional textual discourse, I aim to develop a model that is multilingual and interdisciplinary to create a context that allows a deeper understanding of the sensorial implications of the notion of “transmigration. ” This visual ethnographic research is thus conducted through implementation of visual multi-narrative perspectives produced in collaboration with Iranian transmigrants.
Toward a cyber-physical topology language: applications to NERC CIP audit

Our Cyber-Physical Topology Language (CPTL) provides a language that utilities can use to programmatically analyze current and future cyber-physical architectures. The motivation for our research emerged from the importance and limitations of several audit scenarios: account management, vulnerability assessment, and configuration management. Those scenarios occur in the context of the North American Electric Reliability Corporation's Critical Infrastructure Protection (NERC CIP) audits. The NERC CIP standards define security controls by which utilities must be audited. Although the standards were designed to make power control networks less vulnerable to cyber attack and to decrease the chance of outages, the audit process is manual and costly. In order to save utilities and auditors time and money, we used the limitations of those audit scenarios in formally specifying and implementing CPTL, which consists of both a representation of cyber-physical assets and operations upon that representation. First, CPTL uses graph theory to represent a network of cyber-physical assets; we currently implement this representation in GraphML. Second, CPTL defines operations upon that representation. In this paper, we introduce operators to process attributes by expanding and contracting components of a network, and implement these operations using the Boost Graph Library (BGL). In order to demonstrate the potential for CPTL to save auditors and utilities time and money, we provide a detailed example of how CPTL could help with vulnerability assessment and discuss additional applications beyond the audit scenarios mentioned above. We describe current approaches to those scenarios and argue that CPTL improves upon both the state-of-the-art and current practice. In fact, we intend CPTL to enable a broad range of new research on realistic cyber-physical architectures by giving utilities, auditors, managers, and researchers a common language with which to communicate and analyze those architectures.
Types for Information Flow Control: Labeling Granularity and Semantic Models

Language-based information flow control (IFC) tracks dependencies within a program using sensitivity labels and prohibits public outputs from depending on secret inputs. In particular, literature has proposed several type systems for tracking these dependencies. On one extreme, there are fine-grained type systems (like Flow Caml) that label all values individually and track dependence at the level of individual values. On the other extreme are coarse-grained type systems (like HLIO) that track dependence coarsely, by associating a single label with an entire computation context and not labeling all values individually. In this paper, we show that, despite their glaring differences, both these styles are, in fact, equally expressive. To do this, we show a semantics- and type-preserving translation from a coarse-grained type system to a fine-grained one and vice-versa. The forward translation isn't surprising, but the backward translation is: It requires a construct to arbitrarily limit the scope of a context label in the coarse-grained type system (e.g., HLIO's ``toLabeled'' construct). As a separate contribution, we show how to extend work on logical relation models of IFC types to higher-order state. We build such logical relations for both the fine-grained type system and the coarse-grained type system. We use these relations to prove the two type systems and our translations between them sound.
Semantic Scene Models for Visual Localization under Large Viewpoint Changes

We propose an approach for camera pose estimation under large viewpoint changes using only 2D RGB images. This enables a mobile robot to relocalize itself with respect to a previously-visited scene when seeing it again from a completely new vantage point. In order to overcome large appearance changes, we integrate a variety of cues, including object detections, vanishing points, structure from motion, and object-to-object context in order to constrain the camera geometry, while simultaneously estimating the 3D pose of covisible objects represented as bounding cuboids. We propose an efficient sampling-based approach that quickly cuts down the high-dimensional search space, and a robust correspondence algorithm that matches covisible objects via inter-object spatial relationships. We validate our approach using the publicly available Sun3D dataset, in which we demonstrate the ability to handle camera translations of up to 5.9 meters and camera rotations of up to 110 degrees.
Discovery of Siglec‐14, a novel sialic acid receptor undergoing concerted evolution with Siglec‐5 in primates

Immune receptors that show high mutual sequence similarity and have antagonizing signaling properties are called paired receptors, and are believed to fine‐tune immune responses. Siglecs are sialic acid‐recognizing receptors of the immunoglobulin (Ig) superfamily expressed on immune cells. Human Siglec‐5, encoded by SIGLEC5 gene, has four extracellular Ig‐like domains and a cytosolic inhibitory motif. We discovered human Siglec‐14 with three Ig‐like domains, encoded by the SIGLEC14 gene, adjacent to SIGLEC5. Human Siglec‐14 has almost complete sequence identity with human Siglec‐5 at the first two Ig‐like domains, shows a glycan binding preference similar to that of human Siglec‐5, and associates with the activating adapter protein DAP12. Thus, Siglec‐14 and Siglec‐5 appear to be the first glycan binding paired receptors. Near‐complete sequence identity of the amino‐terminal part of human Siglec‐14 and Siglec‐5 indicates partial gene conversion between SIGLEC14 and SIGLEC5. Remarkably, SIGLEC14 and SIGLEC5 in other primates also show evidence of gene conversions within each lineage. Evidently, balancing the interactions between Siglec‐14, Siglec‐5 and their common ligand(s) had selective advantage during the course of evolution. The “essential arginine” critical for sialic acid recognition in both Siglec‐14 and Siglec‐5 is present in humans but mutated in almost all great ape alleles. —Angata, T. , Hayakawa, T. , Yamanaka, M. , Varki, A. , Nakamura, M. Discovery of Siglec‐14, a novel sialic acid receptor undergoing concerted evolution with Siglec‐5 in primates. FASEB J. 20, 1964–1973 (2006)
Individualized Prediction of Reading Comprehension Ability Using Gray Matter Volume

Reading comprehension is a crucial reading skill for learning and putatively contains 2 key components: reading decoding and linguistic comprehension. Current understanding of the neural mechanism underlying these reading comprehension components is lacking, and whether and how neuroanatomical features can be used to predict these 2 skills remain largely unexplored. In the present study, we analyzed a large sample from the Human Connectome Project (HCP) dataset and successfully built multivariate predictive models for these 2 skills using whole-brain gray matter volume features. The results showed that these models effectively captured individual differences in these 2 skills and were able to significantly predict these components of reading comprehension for unseen individuals. The strict cross-validation using the HCP cohort and another independent cohort of children demonstrated the model generalizability. The identified gray matter regions contributing to the skill prediction consisted of a wide range of regions covering the putative reading, cerebellum, and subcortical systems. Interestingly, there were gender differences in the predictive models, with the female-specific model overestimating the males' abilities. Moreover, the identified contributing gray matter regions for the female-specific and male-specific models exhibited considerable differences, supporting a gender-dependent neuroanatomical substrate for reading comprehension.
DEEP LEARNING AND INTELLIGENT AUDIO MIXING

Mixing multitrack audio is a crucial part of music production. With recent advances in machine learning techniques such as deep learning, it is of great importance to conduct research on the applications of these methods in the ﬁeld of automatic mixing. In this paper, we present a survey of intelligent audio mixing systems and their recent incorporation of deep neural networks. We propose to the community a research trajectory in the ﬁeld of deep learning applied to intelligent music production systems. We conclude with a proof of concept based on stem audio mixing as a content-based transformation using a deep autoencoder
Synteny Approach of Drug Target Prediction among Unique Hypothetical Proteins of Streptococcus Gordonii Causing Infective Endocarditis

At the time of infection, many of the gene products of completely sequenced organisms yet remain ‘hypothetical’ meaning they remain unsimilar to any previously characterized and may disguise some true virulent factors. Domain scanning provides a means of understanding functional information in these cases, extending facilitated identification of their virulence factors, proceeding for antimicrobial drug and vaccine design. In developing countries, mortality rate due to Infective endocarditis is accelerating along with retardation in efficiency of pathogen specific drugs. We have re-annotated at domain level and predicted cellular localization of 200 unique and hypothetical proteins obtained by syntenic comparison of Streptococcus gordonii among other strains of similar species for similar infection. The study resulted into 200 unique and hypothetical proteins, of which, domains of 85 proteins are predictable, representing 15 with no similarity with human proteome. Later, 9 proteins with 8 domains predicted to be antimicrobial targets. Further, these can be experimentally validated for drug and vaccine target ability.
The Role of Insulin and Incretins in Neuroinflammation and Neurodegeneration

Glucagon-like peptide 1 (GLP-1) and glucose-dependent insulinotrophic polypeptide (GIP) are incretin hormones secreted from intestinal cells in response to incoming nutrients. The release of these incretins triggers the production and secretion of insulin from pancreatic β cells, which upregulates the transport of glucose from the blood stream into the muscles, liver and adipose tissue for storage; therefore, insulin and incretins are vital to maintaining energy homeostasis within the body. Recently, it has been recognized that the activity of these hormones is not limited to the periphery, but extends to the central nervous system (CNS) as well. Within the CNS, insulin and incretins function as components of the anti-apoptotic and growth-regulating signaling cascades. Moreover, anti-inflammatory and neuroprotective roles for these hormones in the CNS have also been demonstrated. Specific discoveries have been made suggesting that insulin, GLP-1 and GIP may inhibit pathological processes in several CNS diseases, such as Alzheimer’s disease, Parkinson’s disease, and schizophrenia.
Validated Explicit and Implicit Runge-Kutta Methods

A set of validated numerical integration methods based on explicit and implicit Runge-Kutta schemes is presented to solve, in a guaranteed way, initial value problems of ordinary differential equations. Runge-Kutta methods are well-known to have strong stability properties which make them appealing to be the basis of validated numerical integration methods. A new approach to bound the local truncation error of any Runge-Kutta methods is the main contribution of this article which pushes back the current state of the art. More precisely, an efficient solution to the challenge of making validated Runge-Kutta methods is presented based on the theory of John Butcher. We also present a new interval contractor approach to solve implicit Runge-Kutta methods. A complete experimentation based on Vericomp benchmark is described.