Miracle-dz/UniFAR-FTUs huggingface dataset:
Activated Alleles of the Schizosaccharomyces pombegpa2+ Gα Gene Identify Residues Involved in GDP-GTP Exchange

ABSTRACT The Schizosaccharomyces pombe glucose/cyclic AMP (cAMP) signaling pathway includes the Gpa2-Git5-Git11 heterotrimeric G protein, whose Gpa2 Gα subunit directly binds to and activates adenylate cyclase in response to signaling from the Git3 G protein-coupled receptor. To study intrinsic and extrinsic regulation of Gpa2, we developed a plasmid-based screen to identify mutationally activated gpa2 alleles that bypass the loss of the Git5-Git11 Gβγ dimer to repress transcription of the glucose-regulated fbp1+ gene. Fifteen independently isolated mutations alter 11 different Gpa2 residues, with all but one conferring a receptor-independent activated phenotype upon integration into the gpa2+ chromosomal locus. Biochemical characterization of three activated Gpa2 proteins demonstrated an increased GDP-GTP exchange rate that would explain the mechanism of activation. Interestingly, the amino acid altered in the Gpa2(V90A) exchange rate mutant protein is in a region of Gpa2 with no obvious role in Gα function, thus extending our understanding of Gα protein structure-function relationships.
Visualizing music and audio using self-similarity

This paper presents a novel approach to visualizing the time structure of music and audio. The acoustic similarity between any two instants of an audio recording is displayed in a 2D representation, allowing identification of structural and rhythmic characteristics. Examples are presented for classical and popular music. Applications include content-based analysis and segmentation, as well as tempo and structure extraction.
Autophagy proteins in antigen processing for presentation on MHC molecules

Autophagy describes catabolic pathways that deliver cytoplasmic constituents for lysosomal degradation. Since major histocompatibility complex (MHC) molecules sample protein degradation products and present them to T cells for adaptive immunity, it is maybe not too surprising that autophagy contributes to this protein antigen processing for MHC presentation. However, the recently recognized breath of pathways, by which autophagy contributes to MHC antigen processing, is exciting. Macroautophagy does not only seem to deliver intracellular but facilitates also extracellular antigen processing by lysosomal hydrolysis for MHC class II presentation. Moreover, even MHC class I molecules that usually display proteasomal products are regulated by macroautophagy, probably using a pool of these molecules outside the endoplasmic reticulum, where MHC class I molecules are loaded with peptide during canonical MHC class I antigen processing. This review aims to summarize these recent developments and point out gaps of knowledge, which should be filled by further investigation, in order to harness the different antigen‐processing pathways via autophagy for vaccine improvement.
The Efficacy of Mirtazapine in the Treatment of Cocaine Dependence with Comorbid Depression

Background: Prior findings concerning the use of mirtazapine in the treatment of a variety of substance use disorders and its antagonistic actions at the serotonin 5-HT2A receptor suggest that this drug may have efficacy in the treatment of cocaine dependence in the presence of a depressive disorder. Methods: Depressed cocaine-dependent subjects received either mirtazapine (target dose 45 mg daily) or placebo for 12 weeks. Urine concentrations of benzoylecgonine and self-report were used to assess cocaine consumption. Depression and sleep quality were evaluated using the Hamilton Depression Rating Scale (HAM-D) and the Pittsburgh Sleep Quality Index, respectively. Results: Cocaine consumption during the treatment period did not differ significantly between the mirtazapine (n = 11) and placebo (n = 13) groups in this study. In week 4 sleep latency was significantly lower in the active medication than in the placebo group. Positive effects of mirtazapine treatment on early insomnia were suggested by an item analysis of the HAM-D. Conclusions and Scientific Significance: The results of this study suggest that mirtazapine is superior to placebo in improving sleep in patients with comorbid depression and cocaine dependence, but is not more effective than placebo in reducing cocaine use.
Long-term effects of ubiquitous surveillance in the home

The Helsinki Privacy Experiment is a study of the long-term effects of ubiquitous surveillance in homes. Ten volunteering households were instrumented with video cameras with microphones, and computer, wireless network, smartphone, TV, DVD, and customer card use was logged. We report on stress, anxiety, concerns, and privacy-seeking behavior after six months. The data provide first insight into the privacy-invading character of ubiquitous surveillance in the home and explain how people can gradually become accustomed to surveillance even if they oppose it.
Statistical Link Label Modeling for Sign Prediction: Smoothing Sparsity by Joining Local and Global Information

One of the major issues in signed networks is to use network structure to predict the missing sign of an edge. In this paper, we introduce a novel probabilistic approach for the sign prediction problem. The main characteristic of the proposed models is their ability to adapt to the sparsity level of an input network. Building a model that has an ability to adapt to the sparsity of the data has not yet been considered in the previous related works. We suggest that there exists a dilemma between local and global structures and attempt to build sparsity adaptive models by resolving this dilemma. To this end, we propose probabilistic prediction models based on local and global structures and integrate them based on the concept of smoothing. The model relies more on the global structures when the sparsity increases, whereas it gives more weights to the information obtained from local structures for low levels of the sparsity. The proposed model is assessed on three real-world signed networks, and the experiments reveal its consistent superiority over the state of the art methods. As compared to the previous methods, the proposed model not only better handles the sparsity problem, but also has lower computational complexity and can be updated using real-time data streams.
Complete motif analysis of sequence requirements for translation initiation at non-AUG start codons

Abstract The initiation of mRNA translation from start codons other than AUG was previously believed to be rare and of relatively low impact. More recently, evidence has suggested that as much as half of all translation initiation utilizes non-AUG start codons, codons that deviate from AUG by a single base. Furthermore, non-AUG start codons have been shown to be involved in regulation of expression and disease etiology. Yet the ability to gauge expression based on the sequence of a translation initiation site (start codon and its flanking bases) has been limited. Here we have performed a comprehensive analysis of translation initiation sites that utilize non-AUG start codons. By combining genetic-reporter, cell-sorting, and high-throughput sequencing technologies, we have analyzed the expression associated with all possible variants of the -4 to +4 positions of non-AUG translation initiation site motifs. This complete motif analysis revealed that 1) with the right sequence context, certain non-AUG start codons can generate expression comparable to that of AUG start codons, 2) sequence context affects each non-AUG start codon differently, and 3) initiation at non-AUG start codons is highly sensitive to changes in the flanking sequences. Complete motif analysis has the potential to be a key tool for experimental and diagnostic genomics.
Game of Missuggestions: Semantic Analysis of Search-Autocomplete Manipulations

As a new type of blackhat Search Engine Optimization (SEO), autocomplete manipulations are increasingly utilized by miscreants and promotion companies alike to advertise desired suggestion terms when related trigger terms are entered by the user into a search engine. Like other illicit SEO, such activities game the search engine, mislead the querier, and in some cases, spread harmful content. However, little has been done to understand this new threat, in terms of its scope, impact and techniques, not to mention any serious effort to detect such manipulated terms on a large scale. Systematic analysis of autocomplete manipulation is challenging, due to the scale of the problem (tens or even hundreds of millions suggestion terms and their search results) and the heavy burdens it puts on the search engines. In this paper, we report the first technique that addresses these challenges, making a step toward better understanding and ultimately eliminating this new threat. Our technique, called Sacabuche, takes a semantics-based, two-step approach to minimize its performance impact: it utilizes Natural Language Processing (NLP) to analyze a large number of trigger and suggestion combinations, without querying search engines, to filter out the vast majority of legitimate suggestion terms; only a small set of suspicious suggestions are run against the search engines to get query results for identifying truly abused terms. This approach achieves a 96.23% precision and 95.63% recall, and its scalability enables us to perform a measurement study on 114 millions of suggestion terms, an unprecedented scale for this type of studies. The findings of the study bring to light the magnitude of the threat (0.48% Google suggestion terms we collected manipulated), and its significant security implications never reported before (e.g., exceedingly long lifetime of campaigns, sophisticated techniques and channels for spreading malware and phishing content).
Communication-Efficient Distributed Learning of Discrete Probability Distributions

We initiate a systematic investigation of distribution learning (density estimation) when the data is distributed across multiple servers. The servers must communicate with a referee and the goal is to estimate the underlying distribution with as few bits of communication as possible. We focus on non-parametric density estimation of discrete distributions with respect to the `1 and `2 norms. We provide the first non-trivial upper and lower bounds on the communication complexity of this basic estimation task in various settings of interest. Specifically, our results include the following: 1. When the unknown discrete distribution is unstructured and each server has only one sample, we show that any blackboard protocol (i.e., any protocol in which servers interact arbitrarily using public messages) that learns the distribution must essentially communicate the entire sample. 2. For the case of structured distributions, such as k-histograms and monotone distributions, we design distributed learning algorithms that achieve significantly better communication guarantees than the naive ones, and obtain tight upper and lower bounds in several regimes. Our distributed learning algorithms run in near-linear time and are robust to model misspecification. Our results provide insights on the interplay between structure and communication efficiency for a range of fundamental distribution estimation tasks.
Identification of allosteric inhibitors of the ecto-5'-nucleotidase (CD73) targeting the dimer interface

The ecto-5’-nucleotidase CD73 plays an important role in the production of immune-suppressive adenosine in tumor micro-environment, and has become a validated drug target in oncology. Indeed, the anticancer immune response involves extracellular ATP to block cell proliferation through T-cell activation. However, in the tumor micro-environment, two extracellular membrane-bound enzymes (CD39 and CD73) are overexpressed and hydrolyze efficiently ATP into AMP then further into immune-suppressive adenosine. To circumvent the impact of CD73-generated adenosine, we applied an original bioinformatics approach to identify new allosteric inhibitors targeting the dimerization interface of CD73, which should impair the large dynamic motions required for its enzymatic function. Several hit compounds issued from virtual screening campaigns showed a potent inhibition of recombinant CD73 with inhibition constants in the low micromolar range and exhibited a non-competitive inhibition mode. The structure-activity relationships studies indicated that several amino acid residues (D366, H456, K471, Y484 and E543 for polar interactions and G453-454, I455, H456, L475, V542 and G544 for hydrophobic contacts) located at the dimerization interface are involved in the tight binding of hit compounds and likely contributed for their inhibitory activity. Overall, the gathered information will guide the upcoming lead optimization phase that may lead to potent and selective CD73 inhibitors, able to restore the anticancer immune response.
Almost linear-time algorithms for adaptive betweenness centrality using hypergraph sketches

Betweenness centrality measures the importance of a vertex by quantifying the number of times it acts as a midpoint of the shortest paths between other vertices. This measure is widely used in network analysis. In many applications, we wish to choose the k vertices with the maximum adaptive betweenness centrality, which is the betweenness centrality without considering the shortest paths that have been taken into account by already-chosen vertices. All previous methods are designed to compute the betweenness centrality in a fixed graph. Thus, to solve the above task, we have to run these methods $k$ times. In this paper, we present a method that directly solves the task, with an almost linear runtime no matter how large the value of k. Our method first constructs a hypergraph that encodes the betweenness centrality, and then computes the adaptive betweenness centrality by examining this graph. Our technique can be utilized to handle other centrality measures. We theoretically prove that our method is very accurate, and experimentally confirm that it is three orders of magnitude faster than previous methods. Relying on the scalability of our method, we experimentally demonstrate that strategies based on adaptive betweenness centrality are effective in important applications studied in the network science and database communities.
On the scaling of polynomial features for representation matching

In many neural models, new features as polynomial functions of existing ones are used to augment representations. Using the natural language inference task as an example, we investigate the use of scaled polynomials of degree 2 and above as matching features. We find that scaling degree 2 features has the highest impact on performance, reducing classification error by 5% in the best models.
Long-Term Cultivation and Metagenomics Reveal Ecophysiology of Previously Uncultivated Thermophiles Involved in Biogeochemical Nitrogen Cycle

Many thermophiles thriving in a natural high-temperature environment remain uncultivated, and their ecophysiological functions in the biogeochemical cycle remain unclear. In the present study, we performed long-term continuous cultivation at 65°C and 70°C using a microbial mat sample, collected from a subsurface geothermal stream, as the inoculum, and reconstructed the whole genome of the maintained populations using metagenomics. Some metagenome-assembled genomes (MAGs), affiliated into phylum-level bacterial and archaeal clades without cultivated representatives, contained genes involved in nitrogen metabolism including nitrification and denitrification. Our results show genetic components and their potential interactions for the biogeochemical nitrogen cycle in a subsurface geothermal environment.
Energy and Memory Efficient Clone Detection in Wireless Sensor Networks

In this paper, we propose an energy-efficient location-aware clone detection protocol in densely deployed WSNs, which can guarantee successful clone attack detection and maintain satisfactory network lifetime. Specifically, we exploit the location information of sensors and randomly select witnesses located in a ring area to verify the legitimacy of sensors and to report detected clone attacks. The ring structure facilitates energy-efficient data forwarding along the path towards the witnesses and the sink. We theoretically prove that the proposed protocol can achieve <inline-formula><tex-math notation="LaTeX">$100$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="liu-ieq1-2449847.gif"/></alternatives></inline-formula> percent clone detection probability with trustful witnesses. We further extend the work by studying the clone detection performance with untrustful witnesses and show that the clone detection probability still approaches <inline-formula><tex-math notation="LaTeX"> $98$</tex-math><alternatives><inline-graphic xlink:type="simple" xlink:href="liu-ieq2-2449847.gif"/></alternatives></inline-formula> percent when <inline-formula><tex-math notation="LaTeX">$10$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="liu-ieq3-2449847.gif"/></alternatives></inline-formula> percent of witnesses are compromised. Moreover, in most existing clone detection protocols with random witness selection scheme, the required buffer storage of sensors is usually dependent on the node density, i.e., <inline-formula><tex-math notation="LaTeX"> $O(\sqrt{n})$</tex-math><alternatives><inline-graphic xlink:type="simple" xlink:href="liu-ieq4-2449847.gif"/></alternatives> </inline-formula>, while in our proposed protocol, the required buffer storage of sensors is independent of <inline-formula><tex-math notation="LaTeX">$n$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="liu-ieq5-2449847.gif"/></alternatives></inline-formula> but a function of the hop length of the network radius <inline-formula><tex-math notation="LaTeX">$h$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="liu-ieq6-2449847.gif"/></alternatives></inline-formula>, i.e., <inline-formula> <tex-math notation="LaTeX">$O(h)$</tex-math><alternatives><inline-graphic xlink:type="simple" xlink:href="liu-ieq7-2449847.gif"/> </alternatives></inline-formula>. Extensive simulations demonstrate that our proposed protocol can achieve long network lifetime by effectively distributing the traffic load across the network.
First-Principles Study on the Thermodynamic Properties of Nb, Cr2Nb and Nb5Si3 Alloys

Nb-Si-Cr alloys have shown great potential as the aviation materials because of their good mechanical properties and high temperature oxidation resistance. The thermodynamic properties of Nb, Cr2Nb, α-Nb5Si3, β-Nb5Si3, as the main constituent phases of Nb-Si-Cr alloys, were calculated by first-principles methods combined with quasi-harmonic Debye model. The calculated results are in agreement with available experimental data. Compared the thermo-dynamic properties of different phases, it is found that the thermal expansions of these phases are similar and have the same trend with change of temperature. Thermal expansion coefficient of these phases are dependent with increases with temperature and decreases with pressure, respectively, while the bulk moduli decreases with increasing temperature. At last, the micro stress created by the differences in coefficients of thermal expansion between Nb and Cr2Nb upon cooling was evaluated. The result showed that the stress is too small to cause cracking at the interface between Nb and Cr2Nb.
SCORE ( NAS ) : Cross-cultural adaptation and validation to Portuguese language NURSING ACTIVITIES

The study aimed to adapt to the Portuguese language and to evaluate the psychometric properties of the Nursing Activities Score (NAS), an instrument for measuring the nursing workload in ICUs. After the process of cross-cultural adaptation, the NAS was applied to a sample of 200 adult ICU patients. The analysis of internal consistency by Cronbach's alpha coefficient revealed that the NAS has 23 independent measures that do not allow for either consolidation or reduction. Assessment of inter-rater reliability showed high concordance level (99.8%) and a Kappa index average of 0.99. The concurrent validity was demonstrated by statistically significant correlation between the TISS-28 and NAS (r=0.67, p<0.0001), and by multivariate regression analysis (R = 94.4%, p<0.0001). The convergent validity was supported by the statistically significant association between the NAS and the SAPS II, when adjusted for age (R = 99.8%, p <0.0001). These results indicate that the NAS is a valid and reliable instrument to measure nursing workload of Brazilian ICUs.
Operation Loop-Based Optimization Model for Resource Allocation to Military Countermeasures versus Probabilistic Threat

Weapons development planning is an unstructured and complex multi-criteria decision-making problem, especially in antagonistic environments. In this paper, the defender’s decision was modelled as a high complexity non-linear optimization problem with limited resources. An operation loop with realistic link rules was first proposed to model the cooperation relationships among weapons in the defense system. The system dynamics principle was used to characterize the dynamic behavior of the nodes in a complex weapons network. Then, we used cumulative threat and development risk to measure different planning solutions by considering the opponent and uncertainties in the development process. Next, an improved Differential Evolution (DE) and Non-Dominated Sorting Differential Evolution (NSDE) were designed to determine the optimal planning solutions for a single objective and multi-objective. The compromise solution, based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), was used to evaluate the Pareto solution set of the multi-objective. Finally, an illustrative case was studied to verify the feasibility and validity of the proposed model.
Effects of an Endothelin Receptor Antagonist on a Model of Hypertensive Retinopathy

Hypertensive retinopathy manifests itself as progressive retinal microvascular pathology in response to aberrant blood flow. The current study sought to evaluate whether dysfunction of the vasoactive endothelin-1 (ET-1) system is involved in the pathogenesis of hypertension-induced retinopathy in an animal model of systemic hypertension. The endothelin receptor antagonist, bosentan, was administered to spontaneously hypertensive rats (SHRs) and comparisons were made with untreated SHRs and normotensive Wistar Kyoto (WKY) rats. The retinal mRNA expression of ET-1, ET-converting enzyme-1, ETA and ETB receptors and the basement membrane proteins, laminin β1, collagen IV and fibronectin was quantified using real-time RT-PCR. In addition, retinal arteriole and/or capillary bed damage was assessed by qualitative and quantitative microscopy. mRNA for the ETA receptor was increased in SHRs, when compared to WKY control animals (p < 0.001). Treatment with bosentan in SHRs significantly reduced the expression of ET-1 (p < 0.05), and both the ETA (p < 0.0001) and ETB (p < 0.05) receptor subtypes. The laminin β1, collagen IV and fibronectin mRNA expression was significantly higher in SHRs when compared to WKY control animals (p < 0.001). Treatment with bosentan abolished these responses and also the appearance of various microvascular lesions. ET-mediated vasoregulation abnormalities in the retinal microvasculature could play an associative role in lesion formation during hypertensive retinopathy.
Ovarian histopathological and ubiquitin‐immunophenotypic features in fragile X‐associated primary ovarian insufficiency: a study of five cases and selected controls

Sir: The fragile X premutation, an expansion of a CGG triplet repeat in the 5′-untranslated region of the FMR1 gene (55–200 repeats), is one of the most common aetiologies of primary ovarian insufficiency: fragile X-associated primary ovarian insufficiency (FXPOI). 1–3 Men who carry the premutation are at increased risk for a late-onset neurodegenerative disorder termed fragile X tremor/ataxia syndrome (FXTAS). 4 In premutation carriers, FMR1 is transcribed at increased levels, and the large rCGG tracks may cause disease by acting in a toxic gain of function manner.5–8