Miracle-dz/UniFAR-FTUs huggingface dataset:
An optimal algorithm to compute maximal relaxations in parallel

The study of constraint satisfaction problems touches many aspects of artificial intelligence. It is the premise of dominant interactive constraint satisfaction algorithms that users' preference has complete order. To accord more with practical situation, Haijiao Shen [3] did some research on the situation in which users' preferences have partial order and put forward the related algorithm in 2011. By introducing a constraint set M to decrease the spread of redundancy, we optimize her algorithm. We also prove the validity of our new algorithm and test it on some benchmarks. It is indicated by test result that the optimized algorithm MulExp can save up to 18% of examine times and up to 12% of solving time, which greatly increases the efficiency of solving process.
Fuzzy social interaction genetic algorithm

This work has the purpose to present a new hybrid metaheuristic developed based on three fundamentals pillars extremely well known: Genetic Algorithms, Game Theory and Fuzzy Systems. This new approach tries to mimic a little bit more closer how a population of individuals evolves along time, like human social evolution emphasizing the social interaction between individuals and the non-binary behavior of human decision making against the classical cooperate-defect behavior present in the Prisoner's Dilemma. In this way it is also presented the SIGA Algorithm [9], the approach of an individual more complex with a genotype composed of two chromosomes, one for the solution of the problem and the other representing its strategy, a binary or fuzzy. Finally some results are presented to an instance of the Traveling Salesman Problem.
Deletion of the α-(1,3)-Glucan Synthase Genes Induces a Restructuring of the Conidial Cell Wall Responsible for the Avirulence of Aspergillus fumigatus

α-(1,3)-Glucan is a major component of the cell wall of Aspergillus fumigatus, an opportunistic human fungal pathogen. There are three genes (AGS1, AGS2 and AGS3) controlling the biosynthesis of α-(1,3)-glucan in this fungal species. Deletion of all the three AGS genes resulted in a triple mutant that was devoid of α-(1,3)-glucan in its cell wall; however, its growth and germination was identical to that of the parental strain in vitro. In the experimental murine aspergillosis model, this mutant was less pathogenic than the parental strain. The AGS deletion resulted in an extensive structural modification of the conidial cell wall, especially conidial surface where the rodlet layer was covered by an amorphous glycoprotein matrix. This surface modification was responsible for viability reduction of conidia in vivo, which explains decrease in the virulence of triple agsΔ mutant.
Individual transcriptional activity of estrogen receptors in primary breast cancer and its clinical significance

To predict the efficacy of hormonal therapy at the individual‐level, immunohistochemical methods are used to analyze expression of classical molecular biomarkers such as estrogen receptor (ER), progesterone receptor (PgR), and HER2. However, the current diagnostic standard is not perfect for the individualization of diverse cases. Therefore, establishment of more accurate diagnostics is required. Previously, we established a novel method that enables analysis of ER transcriptional activation potential in clinical specimens using an adenovirus estrogen response element–green fluorescence protein (ERE‐GFP) assay system. Using this assay, we assessed the ERE transcriptional activity of 62 primary breast cancer samples. In 40% of samples, we observed that ER protein expression was not consistent with ERE activity. Comparison of ERE activity with clinicopathological information revealed that ERE activity was significantly correlated with the ER target gene, PgR, rather than ER in terms of both protein and mRNA expression. Moreover, subgrouping of Luminal A‐type breast cancer samples according to ERE activity revealed that ERα mRNA expression correlated with ER target gene mRNA expression in the high‐, but not the low‐, ERE‐activity group. On the other hand, the low‐ERE‐activity group showed significantly higher mRNA expression of the malignancy biomarker Ki67 in association with disease recurrence in 5% of patients. Thus, these data suggest that ER expression does not always correlate with ER transcriptional activity. Therefore, in addition to ER protein expression, determination of ERE activity as an ER functional marker will be helpful for analysis of a variety of diverse breast cancer cases and the subsequent course of treatment.
Modeling Recursive RNA Interference

An important application of the RNA interference (RNAi) pathway is its use as a small RNA-based regulatory system commonly exploited to suppress expression of target genes to test their function in vivo. In several published experiments, RNAi has been used to inactivate components of the RNAi pathway itself, a procedure termed recursive RNAi in this report. The theoretical basis of recursive RNAi is unclear since the procedure could potentially be self-defeating, and in practice the effectiveness of recursive RNAi in published experiments is highly variable. A mathematical model for recursive RNAi was developed and used to investigate the range of conditions under which the procedure should be effective. The model predicts that the effectiveness of recursive RNAi is strongly dependent on the efficacy of RNAi at knocking down target gene expression. This efficacy is known to vary highly between different cell types, and comparison of the model predictions to published experimental data suggests that variation in RNAi efficacy may be the main cause of discrepancies between published recursive RNAi experiments in different organisms. The model suggests potential ways to optimize the effectiveness of recursive RNAi both for screening of RNAi components as well as for improved temporal control of gene expression in switch off–switch on experiments.
Vocal separation using extended robust principal component analysis with Schatten p/lp-norm and scale compression

Separating vocal and accompaniment signals from a monaural music signal is a challenging task. Recently, robust principal component analysis (RPCA) has been proposed for use in the magnitude spectrogram domain to separate the low-rank and sparse residual matrices, which are assumed to represent the accompaniment and vocal signals, respectively. In this paper, we propose two extended methods based on the RPCA algorithm for more effective vocal separation. First, we extend the conventional RPCA and propose to use in the spectrogram decomposition framework Schatten p- and lp-norms, which are generalized versions of the nuclear norm and l1-norm used in RPCA, respectively. Second, we apply proper scale compression to the magnitude spectrogram, making it a more appropriate representation for the decomposition. Experiments using the MIR-1K dataset show that the proposed methods yield significantly better separation performance than the conventional RPCA.
Stress affects the epigenetic marks added by Bari-Jheh : a natural insertion associated with two adaptive phenotypes in Drosophila

Transposable elements are emerging as an important source of cis-acting regulatory sequences and epigenetic marks that could influence gene expression. However, few studies have dissected the role of specific transposable element insertions on gene regulation. Bari-Jheh is a natural transposon that mediates resistance to oxidative stress by adding cis-regulatory sequences. In this work, we integrated publicly available data with chromatin immunoprecipitation and immune response assays to get a more comprehensive picture of Bari-Jheh molecular and functional effects. We showed that Bari-Jheh is associated with H3K27me3 enrichment, which is consistent with expression changes in adjacent genes. We further showed that stress affects the histone marks introduced by Bari-Jheh, which correlates with further expression changes. Finally, we found that flies with Bari-Jheh, which are resistant to oxidative stress, are also more tolerant to bacterial infection. We conclude that Bari-Jheh influences gene expression and enables stress response through two different mechanisms, by adding cis-regulatory sequences and by adding histone marks, leading to changes in two ecologically relevant phenotypes. 2 not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was . http://dx.doi. org/10.1101/037598 doi: bioRxiv preprint first posted online Jan. 22, 2016;
Immune disorders in depression: higher T helper/T suppressor‐cytotoxic cell ratio

This study investigated the leukocyte T helper and T suppressor‐cytotoxic cell (sub)set profile of minor, simple major and melancholic depressives versus normal controls. Using both monoclonal antibody staining and flow cytometry, we determined the absolute numbers and percentages of the following T cell immune subsets: T helper (CD4+), T virgin (CD4+ CD45+), T memory (CD4 + CD45‐), T suppressor/cytotoxic (CD8 +), CD8 + T suppressor (CD8 + CD57 ‐) and CD8 + T cytotoxic (CD8 + CD57 +) cells. After computing the CD4+/CD8 + ratio, we detected a significantly increased ratio in depressed patients as compared with healthy controls. Depression per se is characterized by a higher percentage of CD4+ and a lower percentage of CD8+CD57‐ cells. Melancholic depressed subjects exhibit a significantly increased number of CD4+ and CD4+ CD45‐ cells. The combined use of various percentages of CD4+ and CD8+ (sub)sets yields a high degree of marker positivity for melancholia: through cumulative evaluation of those percentages, the marker positivity increases to 68% (sensitivity) and the specificity is 95%. These results together with our previous reports may refer to a depression‐related state of T cell activation.
Probability of successful transmission in a random slotted-aloha wireless multihop network employing constant transmission power

In [3], it was shown that the optimal throughput scaling in wireless multihop networks is achieved using slotted Aloha, and quantitative performance results for this protocol were derived under the assumption of exponentially distributed transmission powers. In this paper, we extend the analysis of this MAC scheme: assuming that all nodes employ some common constant power, we evaluate the probability of successful transmission in a random time slot. When interfering nodes are assumed to be randomly located, this temporal probability is a random variable with its own distribution. We develop numerical approximations for evaluating both the mean and the tail probability of this distribution; as far as we are aware, the distribution itself has not been studied before. The accuracy of our approximations can be improved indefinitely, with the cost of added numerical computations. We validate the approximations against simulation results.
Simulations and analysis with FDTD method for aperture coupling properties in shielding cavity

The coupling properties of an enclosure with apertures illuminated by a high power microwave (HPM) are simulated and analyzed with the finite-difference time-domain (FDTD) method. The variations of coupling electronic field (EC) with positions are investigated, and comparisons among different arrangements and spacing of apertures are made. The resonance effect is also studied. Results from the modeling are shown to agree well with published measurements. Correspondingly, certain applications are provided for shielding enclosures in electromagnetic compatibility (EMC).
Differentiated end-to-end Internet services using a weighted proportional fair sharing TCP

In this document we study the application of weighted proportional fairness to data flows in the Internet. We let the users set the weights of their connections in order to maximise the utility they get from the network. When combined with a pricing scheme where connections are billed by weight and time, such a system is known to maximise the total utility of the network. Our study case is a national Web cache server connected to long distance links. We propose two ways of weighting TCP connections by manipulating some parameters of the protocol and present results from simulations and prototypes. We finally discuss how proportional fairness could be used to implement an Internet with differentiated services.
Beyond the Matrix-Assisted Laser Desorption Ionization (MALDI) Biotyping Workflow: in Search of Microorganism-Specific Tryptic Peptides Enabling Discrimination of Subspecies

ABSTRACT A well-accepted method for identification of microorganisms uses matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) coupled to analysis software which identifies and classifies the organism according to its ribosomal protein spectral profile. The method, called MALDI biotyping, is widely used in clinical diagnostics and has partly replaced conventional microbiological techniques such as biochemical identification due to its shorter time to result (minutes for MALDI biotyping versus hours or days for classical phenotypic or genotypic identification). Besides its utility for identifying bacteria, MS-based identification has been shown to be applicable also to yeasts and molds. A limitation to this method, however, is that accurate identification is most reliably achieved on the species level on the basis of reference mass spectra, making further phylogenetic classification unreliable. Here, it is shown that combining tryptic digestion of the acid/organic solvent extracted (classical biotyping preparation) and resolubilized proteins, nano-liquid chromatography (nano-LC), and subsequent identification of the peptides by MALDI-tandem TOF (MALDI-TOF/TOF) mass spectrometry increases the discrimination power to the level of subspecies. As a proof of concept, using this targeted proteomics workflow, we have identified subspecies-specific biomarker peptides for three Salmonella subspecies, resulting in an extension of the mass range and type of proteins investigated compared to classical MALDI biotyping. This method therefore offers rapid and cost-effective identification and classification of microorganisms at a deeper taxonomic level.
MicroRNAs and Prostate Cancer: From Preclinical Research to Translational Oncology

AbstractThe management of prostate cancer patients is rapidly changing. The extended survival seen in randomized phase III trials with new molecules has significantly enriched the therapeutic armamentarium, and ongoing clinical trials are assessing whether the integration of these active drugs within established therapeutic regimens results in a further benefit for patients. This complex scenario is raising the need for the identification and validation of biomarkers able to drive the decision-making process during the course of the disease. Compelling evidence has documented the role of microRNAs in cancer biology, and their multifaceted biological activity makes them an attractive candidate as diagnostic, prognostic, and predictive biomarkers. This review summarizes the current knowledge about microRNA deregulation in prostate cancer, how these molecules have been investigated in the clinical setting, and strategies investigators should consider for sharpening their potential.
Effects of Levonantradol on Habituation of Startle in Rats

Abstract: Levonantradol, a nonopioid analgetic related to cannabinoids, produced a dose‐dependent impairment of the habituation of startle response in rats. Startle was elicited by air‐puff stimuli and monitored in stabilimeters by a computer. Habituation was assessed by presenting the same tactile stimulus repeatedly at 15‐second intervals. While a dose of 0.1 mg/kg reduced startle on all trials, doses ranging from 0.01 to 0.03 mg/kg had no effect on the initial level of reactivity to the first several stimuli but did impair the habituation of startle. This effect was not attributable to the time course of the drug's action and was also found with acoustic stimuli. The relatively inactive enantiomer of levonantradol was ineffective at 0.1 mg/kg. Thus, startle appears to be a sensitive measure of the effects of levonantradol in animals. Since comparable phenomena can be demonstrated in humans by measuring the blink reflex component of startle, direct comparisons can potentially be made across species.
Interleukin 1 Inhibits Insulin Secretion From Isolated Perifused Rat Islets

Preincubation of collagenase-isolated rat islets for 150 min with 100 U/ml purified human interleukin 1 (IL-1) altered their ability to secrete insulin. Whereas basal release rates with 4 mM glucose were comparable in control and IL-1-treated islets, both the first and second phases of release in response to 20 mM glucose were significantly reduced from IL-1-treated tissue. IL-1 pretreatment also impaired the secretory response to the combination of 100 nM cholecystokinin plus 7 mM glucose. However, the secretory response to 10 mM α-ketoisocaproate was comparable in control and IL-1-pretreated islets. Reducing the IL-1 exposure time to 60 min was accompanied by an augmented first phase of release to 20 mM glucose. Second phase secretion was diminished. The use of glucose measured after the perifusion was similar in control and IL-1-treated islets. Similar to other compounds that adversely impact on β-cell viability, the inhibitory effect of IL-1 on release may presage a cytotoxic action of monokine.
Fast Elliptic Curve Cryptography Using Minimal Weight Conversion of d Integers

In this paper, we reduce computation time of elliptic curve signature verification scheme by proposing the minimal joint Hamming weight conversion for any binary expansions of d integers. The computation time of multi-scalar multiplication, the bottleneck operation of the scheme, strongly depends on the joint Hamming weight. As we represent the scalars using redundant representations, we may represent a number by many expansions. The minimal joint Hamming weight conversion is the algorithm to select the expansion which has the least joint Hamming weight. Many existing works introduce the conversions for some specific representations, and it is not trivial to generalize their algorithms to other representations. On the other hand, our conversion, based on the dynamic programming scheme, is applicable to find the optimal expansions on any binary representations. We also propose the algorithm to generate the Markov chain used for exploring the minimal average Hamming density automatically from our conversion algorithm. In general, the sets of states in our Markov chains are infinite. Then, we introduce a technique to reduce the number of Markov chain states to a finite set. With the technique, we find the average joint Hamming weight of many representations that have never been found. One of the most significant results is that, for the expansion of integer pairs when the digit set is {0, ± 1, ± 3} often used in multi-scalar multiplication, we show that the minimal average joint Hamming density is 0.3575, which improves the upper bound value.
Efecto de las impurezas en las propiedades dieléctricas de la alúmina: el magnesio.

En este trabajo se estudia el efecto del magnesio sobre las propiedades dielectricas de la alumina. Para ello se han medido tanto la permitividad como la tangente de perdidas a temperatura ambiente, en funcion de la frecuencia en el rango entre 10^3 y 10^10 Hz, asi como la dependencia con la temperatura a alta frecuencia (15 GHz) en el intervalo -200 a 200 -C. Se ha observado que el magnesio en bajas concentraciones induce un proceso de relajacion a alta frecuencia (alrededor de 100 MHz), mientras que en alta concentracion induce procesos de relajacion de baja frecuencia.
Quantum lower bounds by polynomials

We examine the number T of queries that a quantum network requires to compute several Boolean functions on {0,1}/sup N/ in the black-box model. We show that, in the black-box model, the exponential quantum speed-up obtained for partial functions (i.e. problems involving a promise on the input) by Deutsch and Jozsa and by Simon cannot be obtained for any total function: if a quantum algorithm computes some total Boolean function f with bounded-error using T black-box queries then there is a classical deterministic algorithm that computes f exactly with O(T/sup 6/) queries. We also give asymptotically tight characterizations of T for all symmetric f in the exact, zero-error, and bounded-error settings. Finally, we give new precise bounds for AND, OR, and PARITY. Our results are a quantum extension of the so-called polynomial method, which has been successfully applied in classical complexity theory, and also a quantum extension of results by Nisan about a polynomial relationship between randomized and deterministic decision tree complexity.
Literature review on educational games for learning statistics

New methods and applications in learning statistics are of the main interest nowadays. Learning mathematics and statistics is essential at all stages of school education. Statistics learning in a broad sense, starts from early years and never ends. In our society, it is well recognized that developing students' statistics thinking at all levels of education, is of great importance and has a significant objective in later development [l]l-2[2]. Over the last decade several main reasons contributed to the effective use in professional training and learning contexts and they are based on: action rather than explanations; active involving students in problem solving; facilitating numerous learning styles and fostering decision making [3]. Within such a context, games and simulations are seen as a vital complement to traditional learning process and a primary driver in transformation of education. According to our literature review on this topic, the impact of educational games and interactive educational software on learning statistics has been augmented the last years. The main emphasis of this paper is given on certain issues of literature review of the educational games and especially on their relation to statistics curriculum and also on their support on teaching statistics to students. According to our findings, in formal educational settings, online statistical games are extensively used as supportive resources to statistics educational material.
Distributed downlink resource allocation in cellular networks through spatial adaptive play

In this work, we develop mathematical and algorithmic tools for distributed resource allocation in downlink of mobile cellular networks. Our algorithms perform power allocation, subcarrier selection and base station association simultaneously. We aim to maximize the aggregate utility of all the users where users' utilities can be arbitrary increasing functions of their throughputs; this allows us to capture both elastic and inelastic traffics. Our solution is via framing the problem as a potential game among users. We propose a highly scalable, asynchronous algorithm that provably converges to a Nash equilibrium of this game. This algorithm requires only local measurements, limited communication between neighboring nodes and limited computation. This algorithm may at times stuck at a local maximum. To alleviate this problem, we propose an enhanced randomized algorithm based on spatial adaptive play that provably converges to a system optimal resource allocation. We also present simulation results to illustrate convergence and performance of the proposed algorithms.