Miracle-dz/UniFAR-FTUs huggingface dataset:
PMP22 Regulates Self-Renewal and Chemoresistance of Gastric Cancer Cells

Cancer stem cells possess self-renewal and chemoresistance activities. However, the manner in which these features are maintained remains obscure. We sought to identify cell surface protein(s) that mark self-renewing and chemoresistant gastric cancer cells using the explorer antibody microarray. We identified PMP22, a target gene of the Wnt/β-catenin pathway, as the most upregulated cell surface protein in gastric cancer xenografts exposed to cisplatin (DDP). PMP22 expression was markedly upregulated in tumorspheric cells and declined with differentiation. Infecting gastric cancer cells with lentivirus expressing PMP22 shRNAs reduced proliferation, tumorsphere formation, and chemoresistance to cisplatin in vitro and in NOD/SCID mice. When combined with bortezomib, a PMP22 inhibitor, the chemotherapeutic sensitivity to cisplatin treatment was dramatically increased by inducing cell apoptosis in cultured cells and xenograft mouse models. Finally, mRNA expression levels of PMP22 were detected in 38 tumor specimens from patients who received six cycles of perioperative chemotherapy. A strong correlation between PMP22 level and tumor recurrence was revealed, thus showing a pivotal role of PMP22 in the clinical chemoresistance of gastric cancer. Our study is the first to show the role of PMP22 in gastric cancer stemness and chemoresistance and reveals a potential new target for the diagnosis and treatment of recurrent gastric cancer. Mol Cancer Ther; 16(6); 1187–98. ©2017 AACR.
Translation Task : Two-step Data Selection and RBMT-Style Synthetic Rules

This paper presents the machine translation systems submitted by the AbuMaTran project to the WMT 2014 translation task. The language pair concerned is English–French with a focus on French as the target language. The French to English translation direction is also considered, based on the word alignment computed in the other direction. Large language and translation models are built using all the datasets provided by the shared task organisers, as well as the monolingual data from LDC. To build the translation models, we apply a two-step data selection method based on bilingual crossentropy difference and vocabulary saturation, considering each parallel corpus individually. Synthetic translation rules are extracted from the development sets and used to train another translation model. We then interpolate the translation models, minimising the perplexity on the development sets, to obtain our final SMT system. Our submission for the English to French translation task was ranked second amongst nine teams and a total of twenty submissions.
Osmolar gap hyponatremia in critically ill patients: Evidence for the sick cell syndrome?

ObjectiveAccumulation of nondiffusible solutes in plasma leads to redistribution hyponatremia with an increased osmolar gap (i.e., the difference between measured and calculated osmolality). In critically ill patients, intracellular solutes may leak out of the cell because of an increased membrane permeability and may lead to redistribution hyponatremia with increased osmolar gap, a concept called the “sick cell syndrome. ” The aims of this prospective study were to determine whether an increased osmolar gap related to endogenous solutes accumulation was present in intensive care patients with true hyponatremia and to identify the solutes accounting for this increased osmolar gap. SettingA 14-bed medical intensive care unit in an 821-bed university hospital. DesignA 20-wk prospective observational study. PatientsFifty-five consecutive patients with a measured plasma sodium concentration ≤130 mmol/L (mean ± sd, 126 ± 6 mmol/L) were automatically identified by the Biochemistry Department. Patients were excluded in the case of reduced plasma water content resulting from hyperlipidemia or hyperproteinemia, in the case of hyperglycemia, or if exogenous compounds known to increase the osmolar gap were present. InterventionsPlasma osmolar gap was calculated. Measurements and Main ResultsPlasma osmolar gap was considered significant if >10 mosm/kg. Total plasma amino acid concentration also was measured. Organ dysfunctions were assessed with the Sequential Organ Failure Assessment. Thirty of the 55 patients (54%) had an osmolar gap >10 mosm/kg (17.2 ± 7.1 mosm/kg). Sequential Organ Failure Assessment score was significantly higher in the osmolar gap patients (6.4 ± 3.2 vs. 4.5 ± 2.0;p = .015). No difference of amino acids concentration was observed between osmolar gap and non-osmolar gap patients, and no correlation was observed between osmolar gap and amino acid concentration. Accumulation of ketone bodies and lactic acid was also unlikely. During correction of hyponatremia in osmolar gap patients, a significant decrease of plasma osmolar gap was observed and a statistically significant inverse relationship was demonstrated between osmolar gap decrease and plasma sodium concentration increase. ConclusionHyponatremia with increased osmolar gap related to endogenous solutes accumulation is observed frequently in hyponatremic intensive care patients, especially in patients with the most severe organ dysfunctions. The nature of the endogenous solutes accounting for the increased osmolar gap remains to be determined. Simultaneous correction of sodium and osmolar gap suggests a causal link between increased osmolar gap and hyponatremia and may support the concept of sick cell syndrome.
Extraction and comparison of gene expression patterns from 2D RNA in situ hybridization images

MOTIVATION Recent advancements in high-throughput imaging have created new large datasets with tens of thousands of gene expression images. Methods for capturing these spatial and/or temporal expression patterns include in situ hybridization or fluorescent reporter constructs or tags, and results are still frequently assessed by subjective qualitative comparisons. In order to deal with available large datasets, fully automated analysis methods must be developed to properly normalize and model spatial expression patterns. RESULTS We have developed image segmentation and registration methods to identify and extract spatial gene expression patterns from RNA in situ hybridization experiments of Drosophila embryos. These methods allow us to normalize and extract expression information for 78,621 images from 3724 genes across six time stages. The similarity between gene expression patterns is computed using four scoring metrics: mean squared error, Haar wavelet distance, mutual information and spatial mutual information (SMI). We additionally propose a strategy to calculate the significance of the similarity between two expression images, by generating surrogate datasets with similar spatial expression patterns using a Monte Carlo swap sampler. On data from an early development time stage, we show that SMI provides the most biologically relevant metric of comparison, and that our significance testing generalizes metrics to achieve similar performance. We exemplify the application of spatial metrics on the well-known Drosophila segmentation network. AVAILABILITY A Java webstart application to register and compare patterns, as well as all source code, are available from: http://tools.genome. duke.edu/generegulation/image_analysis/insitu CONTACT uwe.ohler@duke.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Smart meter privacy with renewable energy and a finite capacity battery

We address the smart meter (SM) privacy problem by considering the availability of a renewable energy source (RES) and a battery which can be exploited by a consumer to partially hide the consumption pattern from the utility provider (UP). Privacy is measured by the mutual information rate between the consumer's energy consumption and the renewable energy generation process, and the energy received from the grid, where the latter is known by the UP through the SM readings, and the former two are to be kept private. By expressing the information leakage as an additive quantity, we cast the problem as a stochastic control problem, and formulate the corresponding Bellman equations.
Maximal Use of Central Differencing for Hamilton-Jacobi-Bellman PDEs in Finance

In order to ensure convergence to the viscosity solution, the standard method for discretizing Hamilton-Jacobi-Bellman partial differential equations uses forward/backward differencing for the drift term. In this paper, we devise a monotone method which uses central weighting as much as possible. In order to solve the discretized algebraic equations, we have to maximize a possibly discontinuous objective function at each node. Nevertheless, convergence of the overall iteration can be guaranteed. Numerical experiments on two examples from the finance literature show higher rates of convergence for this approach compared to the use of forward/backward differencing only.
Trace Processors: Moving to Fourth-Generation Microarchitectures

This article proposes a new architecture called "trace processors", which consist of multiple, distributed on-chip processor cores, each of which simultaneously executes a different trace. All but one core executes the traces speculatively, having used branch prediction to select traces that follow the one executing. (Although this architectural concept is similar to multiscalar processors, it does not require explicit compiler support). The authors argue that future processors will rely heavily on replication and hierarchy, and they show how their architecture exploits these concepts.
Rapid and Simple Determination of theEscherichia coli Phylogenetic Group

ABSTRACT Phylogenetic analysis has shown that Escherichia coliis composed of four main phylogenetic groups (A, B1, B2, and D) and that virulent extra-intestinal strains mainly belong to groups B2 and D. Actually, phylogenetic groups can be determined by multilocus enzyme electrophoresis or ribotyping, both of which are complex, time-consuming techniques. We describe a simple and rapid phylogenetic grouping technique based on triplex PCR. The method, which uses a combination of two genes (chuA and yjaA) and an anonymous DNA fragment, was tested with 230 strains and showed excellent correlation with reference methods.
Dynamic control of sliding by robot hands for regrasping

The problem of dynamic control of a multifingered hand manipulating an object is considered, under the condition that some of the fingertips slide on the object surface. This work has many useful applications when considered in conjunction with work already done in the area of regrasping. In performing certain tasks with grasped objects, it is often necessary to change the contact locations of the fingers on the object. One method of achieving this is to break and remake the contacts; another method is to slide the fingertips on the object surface. This work provides a dynamic coordinated control scheme for a hand by which one can perform regrasping and reorientation of an object in the planar case. >
Understanding performance of I/O intensive containerized applications for NVMe SSDs

Our cloud-based IT world is founded on hyper-visors and containers. Containers are becoming an important cornerstone, which is increasingly used day-by-day. Among different available frameworks, docker has become one of the major adoptees to use containerized platform in data centers and enterprise servers, due to its ease of deploying and scaling. Further more, the performance benefits of a lightweight container platform can be leveraged even more with a fast back-end storage like high performance SSDs. However, increase in number of simultaneously operating docker containers may not guarantee an aggregated performance improvement due to saturation. Thus, understanding performance bottleneck in a multi-tenancy docker environment is critically important to maintain application level fairness and perform better resource management. In this paper, we characterize the performance of persistent storage option (through data volume) for I/O intensive, dockerized applications. Our work investigates the impact on performance with increasing number of simultaneous docker containers in different workload environments. We provide, first of its kind study of I/O intensive containerized applications operating with NVMe SSDs. We show that 1) a six times better application throughput can be obtained, just by wise selection of number of containerized instances compared to single instance; and 2) for multiple application containers running simultaneously, an application throughput may degrade upto 50% compared to a stand-alone applications throughput, if good choice of application and workload is not made. We then propose novel design guidelines for an optimal and fair operation of both homogeneous and heterogeneous environments mixed with different applications and workloads.
Non-Bayesian learning in social networks with time-varying weights

This paper investigates an social learning model with time-varying weights, in which the individual updates her belief through observing private signal caused by social event and communicating with those regarded as neighbors in the sense of network topology. The private signal is involved in the updating law through Bayes' rule. During the communication with neighbors, the individual obtains weighted average of others' beliefs. Using the convergence property of the transition matrix and coefficient of ergodicity, we show that, under mild assumptions, repeated observation and communications can lead beliefs of the entire group to the true state of the social event.
Feature Relevance for Kernel Logistic Regression and Application to Action Classification

An approach is proposed for incorporating feature relevance in mutinomial kernel logistic regression (MKLR) for classification. MKLR is a supervised classification method designed for separating classes with non-linear boundaries. However, it assumes all features are equally important, which may decrease classification performance when dealing with high-dimensional or noisy data. We propose a feature weighting algorithm for MKLR which automatically tunes features contribution according to their relevance for classification and reduces data over-fitting. The proposed algorithm produces more interpretable models and is more generalizable than MKLR, Kernel-SVM and LASSO methods. Application to simulated data and video action classification has provided very promising results compared to the aforementioned classification methods.
The Population Structure of Glossina palpalis gambiensis from Island and Continental Locations in Coastal Guinea

Background We undertook a population genetics analysis of the tsetse fly Glossina palpalis gambiensis, a major vector of sleeping sickness in West Africa, using microsatellite and mitochondrial DNA markers. Our aims were to estimate effective population size and the degree of isolation between coastal sites on the mainland of Guinea and Loos Islands. The sampling locations encompassed Dubréka, the area with the highest Human African Trypanosomosis (HAT) prevalence in West Africa, mangrove and savannah sites on the mainland, and two islands, Fotoba and Kassa, within the Loos archipelago. These data are discussed with respect to the feasibility and sustainability of control strategies in those sites currently experiencing, or at risk of, sleeping sickness. Principal Findings We found very low migration rates between sites except between those sampled around the Dubréka area that seems to contain a widely dispersed and panmictic population. In the Kassa island samples, various effective population size estimates all converged on surprisingly small values (10<Ne<30) that suggest either a recent bottleneck, and/or other biological or ecological factors such as strong variance in the reproductive success of individuals. Conclusion/Significance Whatever their origin, the small effective population sizes suggest high levels of inbreeding in tsetse flies within the island samples in marked contrast to the large diffuse deme in Dubréka zones. We discuss how these genetic results suggest that different tsetse control strategies should be applied on the mainland and islands.
Use of Chemical Tests for Pregnancy Diagnosis in a Reproductive Management Program

Early identification of nonpregnant dairy cows post breeding can improve reproductive efficiency and pregnancy rate by decreasing the interval between AI services and increasing AI service rate. Thus, new technologies to identify nonpregnant dairy cows early after artificial insemination (AI) may play a key role in systematic management strategies to improve reproductive efficiency and profitability on commercial dairy farms. Transrectal palpation is the oldest and most widely used method for early nonpregnancy diagnosis in dairy cattle (Cowie, 1948); however, a newer technology may emerge to replace transrectal palpation as the method of choice for nonpregnancy diagnosis in the dairy industry. One such technology is the development of commercially available tests to detect pregnancy-associated glycoproteins (PAGs) in maternal serum; however, this new technology must be practically integrated into a systematic on-farm reproductive management strategy for it to succeed. Research reviewed in this paper support use of PAGs as a method for early nonpregnacy diagnosis in dairy cattle; however, there are some caveats and limitations with regard to their incorporation into a systematic reproductive management program that must be considered to determine the appropriate timing of nonpregnancy diagnosis.
Rough Relation Algebras Revisited

Rough relation algebras arise from Pawlak's information systems by considering as object ordered pairs on a fixed set X. Thus, the subsets to be approximated are binary relations over X, and hence, we have at our disposal not only the set theoretic operations, but also the relational operators; b, and the identity relation 1'. In the present paper, which is a continuation of [6], we further investigate the structure of abstract rough relation algebras.
Maturation of Dendritic Cells Is Accompanied by Rapid Transcriptional Silencing of Class II Transactivator (Ciita) Expression

Cell surface expression of major histocompatibility complex class II (MHCII) molecules is increased during the maturation of dendritic cells (DCs). This enhances their ability to present antigen and activate naive CD4+ T cells. In contrast to increased cell surface MHCII expression, de novo biosynthesis of MHCII mRNA is turned off during DC maturation. We show here that this is due to a remarkably rapid reduction in the synthesis of class II transactivator (CIITA) mRNA and protein. This reduction in CIITA expression occurs in human monocyte-derived DCs and mouse bone marrow–derived DCs, and is triggered by a variety of different maturation stimuli, including lipopolysaccharide, tumor necrosis factor α, CD40 ligand, interferon α, and infection with Salmonella typhimurium or Sendai virus. It is also observed in vivo in splenic DCs in acute myelin oligodendrocyte glycoprotein induced experimental autoimmune encephalitis. The arrest in CIITA expression is the result of a transcriptional inactivation of the MHC2TA gene. This is mediated by a global repression mechanism implicating histone deacetylation over a large domain spanning the entire MHC2TA regulatory region.
Posterior-based confidence measures for spoken term detection

Confidence measures play a key role in spoken term detection (STD) tasks. The confidence measure expresses the posterior probability of the search term appearing in the detection period, given the speech. Traditional approaches are based on the acoustic and language model scores for candidate detections found using automatic speech recognition, with Bayes' rule being used to compute the desired posterior probability. In this paper, we present a novel direct posterior-based confidence measure which, instead of resorting to the Bayesian formula, calculates posterior probabilities from a multi-layer perceptron (MLP) directly. Compared with traditional Bayesian-based methods, the direct-posterior approach is conceptually and mathematically simpler. Moreover, the MLP-based model does not require assumptions to be made about the acoustic features such as their statistical distribution and the independence of static and dynamic co-efficients. Our experimental results in both English and Spanish demonstrate that the proposed direct posterior-based confidence improves STD performance.
Spatio-Temporal Data Correlation with Adaptive Strategies in Wireless Sensor Networks

One of the major task of sensor nodes in wireless sensor networks is to transmit a subset of sensor readings to the sink node estimating a desired data accuracy. Therefore in this paper, we propose an accuracy model using Steepest Decent method called Adaptive Data Accuracy (ADA) model which doesn't require any a priori information of input signal statistics to select an optimal set of sensor nodes in the network. Moreover we develop another model using LMS filter called Spatio-Temporal Data Prediction (STDP) model which captures the spatial and temporal correlation of sensing data to reduce the communication overhead under data reduction strategies. Finally using STDP model, we illustrate a mechanism to trace the malicious nodes in the network under extreme physical environment. Computer simulations illustrate the performance of ADA and STDP models respectively.
Pressure-correction algorithm to solve Poisson system with constant coefficients for fast two-phase simulations

It is a challenging problem to numerically simulate incompressible two-phase flow owing to its complexity and high computational cost. The complexity of two-phase flow are caused by the moving interface between the phases, where density and viscosity abruptly change. Various numerical methods such as the marker method, the volume-of-fluid (VoF) method and the level set method, have been developed to handle arbitrarily shaped interfaces in a fixed grid. Each of these phase tracking methods has its merits and demerits. In the present work, the level set method is used to track the phase interface. The advantages of the level set method is that the topology and movement of the interface are automatically captured. Thus, it is easy to implement numerically. The main drawback of the level set method is that the conservation of liquid volume is not guaranteed. Hybrid methods combining the level set method with VoF Sussman (2000) have been proposed to enhance the mass conservation property. However, the delocalization method has to be used to avoid local curvature error and it makes numerical scheme more complex. High computational cost is another big issue in turbulent simulations for two-phase flow because it is difficult to apply fast solvers to the Poisson equations. For incompressible two-phase flow, the continuity equation of divergence-free velocity is used to form the Poisson equation for the pressure-correction in the fractional step method. Since the phase interface is moving in time, the coefficients of the Poisson equation is also function of time, while the coefficients is constant in case of single phase flow where density is constant. Because the coefficients of the Poisson equation change in time, the gradientbased method such as the Biconjugate Gradient Stabilized Method is often used instead of the much faster Multigrid method. This results in large extra computational cost since the Poisson solver is the main bottleneck in numerical simulations. In the present work, a new pressure-correction algorithm is developed to improve the computational speed of the Poisson solver in two-phase flow simulations. In the pressurecorrection step, the mass conservation equation or VoF equation is used to obtain the Poisson equation with constant coefficients. Using this method, the mass conservation equation is fulfilled at every time step instead of the divergence-free continuity equation. Since the Poisson equation with constant coefficients is much easier to solve than variable coefficients, the computational speed is much improved. The canonical test problems are solved to demonstrate the accuracy and performance of the method.