Miracle-dz/UniFAR-FTUs huggingface dataset:
Task-based Parallel Computation of the Density Matrix in Quantum-based Molecular Dynamics using Graph Partitioning

Quantum-based molecular dynamics (QMD) is a highly accurate and transferable method for material science simulations. However, the time scales and system sizes accessible to QMD are typically limited to picoseconds and a few hundred atoms. These constraints arise due to expensive self-consistent ground-state electronic structure calculations that can often scale cubically with the number of atoms. Linearly scaling methods depend on computing the density matrix $\mathbf{P}$ from the Hamiltonian matrix $\mathbf{H}$ by exploiting the sparsity in both matrices. The second-order spectral projection (SP2) algorithm is an $O(N)$ algorithm that computes $\mathbf{P}$ with a sequence of 40--50 matrix-matrix multiplications. In this paper, we present task-based implementations of a recently developed data-parallel graph-based approach to the SP2 algorithm, G-SP2. We represent the density matrix $\mathbf P$ as an undirected graph and use graph partitioning techniques to divide the computation into smaller independent.
A mutation in the viral sensor 2’-5’-oligoadenylate synthetase 2 causes failure of lactation

We identified a non-synonymous mutation in Oas2 (I405N), a sensor of viral double-stranded RNA, from an ENU-mutagenesis screen designed to discover new genes involved in mammary development. The mutation caused post-partum failure of lactation in healthy mice with otherwise normally developed mammary glands, characterized by greatly reduced milk protein synthesis coupled with epithelial cell death, inhibition of proliferation and a robust interferon response. Expression of mutant but not wild type Oas2 in cultured HC-11 or T47D mammary cells recapitulated the phenotypic and transcriptional effects observed in the mouse. The mutation activates the OAS2 pathway, demonstrated by a 34-fold increase in RNase L activity, and its effects were dependent on expression of RNase L and IRF7, proximal and distal pathway members. This is the first report of a viral recognition pathway regulating lactation.
Experimental and Simulation of Friction Effects in an Open-Die Microforging/Extrusion Process

Friction effects during a progressive microforming process for production of micropins of various diameters were experimentally investigated and were analytically modeled, using a hybrid friction model. The response surface method and ANOVA analysis were used to generalize the findings for various pin diameters. Besides, it was shown that to get an accurate result in simulation, the friction model must be considered locally instead of a global friction model for the whole process. The effect of friction factor on the final micropart dimensions (the effect on the instantaneous location of the neutral plane) and the forming pressure were investigated. The results showed a reduction in the friction factor as die diameter increased. Following that, the optimum frictional condition to obtain the highest micropart aspect ratio was defined as the maximum friction on the interface between the die upper surface and the punch surface, together with a minimum friction inside the die orifice.
The Relationship Between Index Hospitalizations, Sepsis, and Death or Transition to Hospice Care During 30-Day Hospital Readmissions

Background: Hospital readmissions are common, expensive, and increasingly used as a metric for assessing quality of care. The relationship between index hospitalizations and specific outcomes among those readmitted remains largely unknown. Objectives: Identify risk factors present during the index hospitalization associated with death or transition to hospice care during 30-day readmissions and examine the contribution of infection in readmissions resulting in death. Research Design: Retrospective cohort study. Subjects: A total of 17,716 30-day readmissions in an academic health system. Measures: We used mixed-effects multivariable logistic regression models to identify risk factors associated with the primary outcome, in-hospital death, or transition to hospice during 30-day readmissions. Results: Of 17,716 30-day readmissions, 1144 readmissions resulted in death or transition to hospice care (6.5%). Risk factors identified included: age, burden, and type of comorbid conditions, recent hospitalizations, nonelective index admission type, outside hospital transfer, low discharge hemoglobin, low discharge sodium, high discharge red blood cell distribution width, and disposition to a setting other than home. Sepsis (OR=1.33; 95% CI, 1.02–1.72; P=0.03) and shock (OR=1.78; 95% CI, 1.22–2.58; P=0.002) during the index admission were associated with the primary outcome, and in-hospital mortality specifically. In patients who died, infection was the primary cause for readmission in 51.6% of readmissions after sepsis and 28.6% of readmissions after a nonsepsis hospitalization (P=0.009). Conclusions: We identified factors, including sepsis and shock during the index hospitalization, associated with death or transition to hospice care during readmission. Infection was frequently implicated as the cause of a readmission that ended in death.
Network Traffic Driven Storage Repair

Recently we constructed an explicit family of locally repairable and locally regenerating codes. Their existence was proven by Kamath et al. but no explicit construction was given. Our design is based on HashTag codes that can have different sub-packetization levels. In this work we emphasize the importance of having two ways to repair a node: repair only with local parity nodes or repair with both local and global parity nodes. We say that the repair strategy is network traffic driven since it is in connection with the concrete system and code parameters: the repair bandwidth of the code, the number of I/O operations, the access time for the contacted parts and the size of the stored file. We show the benefits of having repair duality in one practical example implemented in Hadoop. We also give algorithms for efficient repair of the global parity nodes.
Patchwork Kriging for Large-scale Gaussian Process Regression

This paper presents a new approach for Gaussian process (GP) regression for large datasets. The approach involves partitioning the regression input domain into multiple local regions with a different local GP model fitted in each region. Unlike existing local partitioned GP approaches, we introduce a technique for patching together the local GP models nearly seamlessly to ensure that the local GP models for two neighboring regions produce nearly the same response prediction and prediction error variance on the boundary between the two regions. This largely mitigates the well-known discontinuity problem that degrades the boundary accuracy of existing local partitioned GP methods. Our main innovation is to represent the continuity conditions as additional pseudo-observations that the differences between neighboring GP responses are identically zero at an appropriately chosen set of boundary input locations. To predict the response at any input location, we simply augment the actual response observations with the pseudo-observations and apply standard GP prediction methods to the augmented data. In contrast to heuristic continuity adjustments, this has an advantage of working within a formal GP framework, so that the GP-based predictive uncertainty quantification remains valid. Our approach also inherits a sparse block-like structure for the sample covariance matrix, which results in computationally efficient closed-form expressions for the predictive mean and variance. In addition, we provide a new spatial partitioning scheme based on a recursive space partitioning along local principal component directions, which makes the proposed approach applicable for regression domains having more than two dimensions. Using three spatial datasets and three higher dimensional datasets, we investigate the numerical performance of the approach and compare it to several state-of-the-art approaches.
Two Efficient Derivative-Free Iterative Methods for Solving Nonlinear Systems

In this work, two multi-step derivative-free iterative methods are presented for solving system of nonlinear equations. The new methods have high computational efficiency and low computational cost. The order of convergence of the new methods is proved by a development of an inverse first-order divided difference operator. The computational efficiency is compared with the existing methods. Numerical experiments support the theoretical results. Experimental results show that the new methods remarkably reduce the computing time in the process of high-precision computing.
Single-cell analyses of transcriptional heterogeneity in squamous cell carcinoma of urinary bladder

Cell-to-cell expression heterogeneity within a single tumor is a common phenotype among various cancer types including squamous cell carcinoma. To further study the fundamentals and importance of heterogeneity of cell functions and its potential mechanisms, we performed single-cell RNA-seq (scRNA-seq) on human squamous cell carcinoma of the bladder (SCCB) and its corresponding physiologically normal epithelia. Extensive differentially expressed genes were uncovered by comparing cancer and normal single cells, which were preferentially enriched in cancer-correlated pathways, such as p53 signaling and bladder cancer pathway. Furthermore, the most diversely expressed genes were particularly enriched in MAPK signaling pathway, such as CACNG4, CACNA1E and CACNA1H, which involve in cancer evolution and heterogeneity formation. Co-expression network and hub-gene analyses revealed several remarkable “hub genes” of each regulatory module. Some of them are cancer related, such as POU2F3, NKD1 and CYP2C8, while LINC00189, GCC2 and OR9Q1 genes are rarely reported in human diseases. The genes within an interesting module are highly correlated with others, which could be treated as potential targets for SCCB patients. Our findings have fundamental implications for SCCB biology and therapeutic strategies.
A bayesian hierarchical mixture of experts approach to estimate speech quality

This paper demonstrates the potential of theoretically motivated learning methods in solving the problem of non-intrusive quality estimation for which the state-of-the-art is represented by ITU-T P.563 standard. To construct our estimator, we adopt the speech features from P.563, while we use a different mapping of features to form quality estimates. In contrast to P.563 which assumes distortion-classes to divide the feature space, our approach divides the feature space based on a clustering which is learned from the data using Bayesian inference. Despite using weaker modeling assumptions, we are still able to achieve comparable accuracy on predicting mean-opinion-scores with P.563. Our work suggests Bayesian model-evidence as an alternative metric to correlation-coefficient for determining the necessary number of experts for modeling the data.
A Lipidomic Approach to Understanding Free Fatty Acid Lipogenesis Derived from Dissolved Inorganic Carbon within Cnidarian-Dinoflagellate Symbiosis

The cnidarian-dinoflagellate symbiosis is arguably one of the most important within the marine environment in that it is integral to the formation of coral reefs. However, the regulatory processes that perpetuate this symbiosis remain unresolved. It is essential to understand these processes, if we are to elucidate the mechanisms that support growth and resource accumulation by coral host, and conversely, recently observed reduction and/or mortality of corals in response to rapid environmental change. This study specifically focused on one area of metabolic activity within the symbiosis, that of free fatty acid synthesis within both the dinoflagellate symbionts and cnidarian host. The main model system used was Aiptasia pulchella and Symbiodinium sp. in combination with aposymbiotic A. pulchella, the symbiotic coral Acropora millepora system and dinoflagellate culture. Fatty acids (FAs) were selected because of their multiple essential roles inclusive of energy storage (resource accumulation), membrane structure fluidity and cell signaling. The study addressed free FA lipogenesis by using a new method of enriched stable isotopic (13C) incorporation from dissolved inorganic carbon (DI13C) combined with HPLC-MS. FAs derived from DI13C aligned with a mixture of known lipogenesis pathways with the addition of some unusual FAs. After 120 hr, 13C-enriched FA synthesis rates were attributed to only a complex integration of both n–3 and n–6 lipogenesis pathways within the dinoflagellate symbionts. Furthermore, there was no detectible evidence of symbiont derived enriched isotope fatty acids, catabolized 13C derivatives or DI13C being directly utilized, in host late n–6 pathway long-chain FA lipogenesis. These findings do not align with a popular mutualistic translocation model with respect to the use of translocated symbiont photoassimilates in host long-chain FA lipogenesis, which has important connotations for linking nutrient sources with metabolite production and the dynamic regulation of this symbiosis.
Globally optimal hand-eye calibration under free choice of cost-function

This paper introduces globally optimal hand-eye self-calibration of camera orientation under a wide range of cost-functions, which includes L2 and Blake-Zisserman. The main contributions are new feasibility tests to integrate this hand-eye calibration problem into a branch-and-bound parameter space search. The presented method constitutes the first guaranteed globally optimal estimator, which is not limited to L∞-type cost-functions. The algorithm directly minimizes the desired cost-function based on reprojection errors of interest point correspondences and does not depend on any structure and motion preprocessing to estimate camera poses. The system is evaluated in both synthetic and real world scenarios. Typical cost-functions are exemplarily integrated to validate the algorithm's flexibility.
Imaging the emergence and natural progression of spontaneous autoimmune diabetes

Significance Dynamics and interactions of immunocytes infiltrating the pancreas during the natural progression of autoimmune diabetes are largely unknown. The construction of diabetes-prone nonobese diabetic mice with a panel of fluorescent reporters that illume infiltrating cells of the innate and adaptive immune systems, combined with intravital imaging of the pancreas, provide novel perspectives on the autoimmune process and on the ballet between aggressive and regulatory cells. Type 1 diabetes in the nonobese diabetic mouse stems from an infiltration of the pancreatic islets by a mixed population of immunocytes, which results in the impairment and eventual destruction of insulin-producing β-cells. Little is known about the dynamics of lymphocyte movement in the pancreas during disease progression. Using advanced intravital imaging approaches and newly created reporter mice (Flt3-BFP2, Mertk-GFP-DTR, Cd4-tdTomato, Cd8a-tdTomato), we show that the autoimmune process initiates first with a T cell infiltration into the islets, where they have restricted mobility but reside and are activated in apposition to CX3CR1+ macrophages. The main expansion then occurs in the connective tissue outside the islet, which remains more or less intact. CD4+ and CD8+ T cells, Tregs, and dendritic cells (DCs) are highly mobile, going along microvascular tracks, while static macrophages (MF) form a more rigid structure, often encasing the islet cell mass. Transient cell–cell interactions are formed between T cells and both MFs and DCs, but also surprisingly between MFs and DCs themselves, possibly denoting antigen transfer. In later stages, extensive islet destruction coincides with preferential antigen presentation to, and activation of, CD8+ T cells. Throughout the process, Tregs patrol the active compartments, consistent with the notion that they control the activation of many cell types.
High Resolution Schemes for Conservation Laws with Locally Varying Time Steps

We develop upwind methods which use limited high resolution corrections in the spatial discretization and local time stepping for forward Euler and second order time discretizations. $L^\infty$ stability is proven for both time stepping schemes for problems in one space dimension. These methods are restricted by a local CFL condition rather than the traditional global CFL condition, allowing local time refinement to be coupled with local spatial refinement. Numerical evidence demonstrates the stability and accuracy of the methods for problems in both one and two space dimensions.
Sentiment classification of movie reviews using dual training and dual predition

BOW (Bag-of-Words) is the most common machine learning method used for sentiment classification. However, this model does not address the problem of polarity shift due to fundamental limitations. This is one of the reasons which affect its overall accuracy. In this paper, a method is discussed which uses dual training and dual prediction for sentiment classification while addressing polarity shift. The terms dual training and dual prediction refer to usage of both original review sample and opposite review sample for training and prediction. The opposite review samples are polarity opposite to original sample. These samples are artificially generated by polarity reversion. The overall method is evaluated on a movie review dataset and has shown noticeable improvements in comparison to machine learning methods based on bag of words model.
Sex-Biased Gene Expression on the Avian Z Chromosome: Highly Expressed Genes Show Higher Male-Biased Expression

Dosage compensation, the process whereby expression of sex-linked genes remains similar between sexes (despite heterogamety) and balanced with autosomal expression, was long believed to be essential. However, recent research has shown that several lineages, including birds, butterflies, monotremes and sticklebacks, lack chromosome-wide dosage compensation mechanisms and do not completely balance the expression of sex-linked and autosomal genes. To obtain further understanding of avian sex-biased gene expression, we studied Z-linked gene expression in the brain of two songbirds of different genera (zebra finch, Taeniopygia guttata, and common whitethroat, Sylvia communis) using microarray technology. In both species, the male-bias in gene expression was significantly higher for Z than for autosomes, although the ratio of Z-linked to autosomal expression (Z:A) was relatively close to one in both sexes (range: 0.89–1.01). Interestingly, the Z-linked male-bias in gene expression increased with expression level, and genes with low expression showed the lowest degree of sex-bias. These results support the view that the heterogametic females have up-regulated their single Z-linked homologues to a high extent when the W-chromosome degraded and thereby managed to largely balance their Z:A expression with the exception of highly expressed genes. The male-bias in highly expressed genes points towards male-driven selection on Z-linked loci, and this and other possible hypotheses are discussed.
Identification of a Novel Viral Protein Expressed from the PB2 Segment of Influenza A Virus

ABSTRACT Over the past 2 decades, several novel influenza virus proteins have been identified that modulate viral infections in vitro and/or in vivo. The PB2 segment, which is one of the longest influenza A virus segments, is known to encode only one viral protein, PB2. In the present study, we used reverse transcription-PCR (RT-PCR) targeting viral mRNAs transcribed from the PB2 segment to look for novel viral proteins encoded by spliced mRNAs. We identified a new viral protein, PB2-S1, encoded by a novel spliced mRNA in which the region corresponding to nucleotides 1513 to 1894 of the PB2 mRNA is deleted. PB2-S1 was detected in virus-infected cells and in cells transfected with a protein expression plasmid encoding PB2. PB2-S1 localized to mitochondria, inhibited the RIG-I-dependent interferon signaling pathway, and interfered with viral polymerase activity (dependent on its PB1-binding capability). The nucleotide sequences around the splicing donor and acceptor sites for PB2-S1 were highly conserved among pre-2009 human H1N1 viruses but not among human H1N1pdm and H3N2 viruses. PB2-S1-deficient viruses, however, showed growth kinetics in MDCK cells and virulence in mice similar to those of wild-type virus. The biological significance of PB2-S1 to the replication and pathogenicity of seasonal H1N1 influenza A viruses warrants further investigation. IMPORTANCE Transcriptome analysis of cells infected with influenza A virus has improved our understanding of the host response to viral infection, because such analysis yields considerable information about both in vitro and in vivo viral infections. However, little attention has been paid to transcriptomes derived from the viral genome. Here we focused on the splicing of mRNA expressed from the PB2 segment and identified a spliced viral mRNA encoding a novel viral protein. This result suggests that other, as yet unidentified viral proteins encoded by spliced mRNAs could be expressed in virus-infected cells. A viral transcriptome including the viral spliceosome should be evaluated to gain new insights into influenza virus infection.
Bayesian optimization for refining object proposals

We develop a general-purpose algorithm using a Bayesian optimization framework for the efficient refinement of object proposals. While recent research has achieved substantial progress for object localization and related objectives in computer vision, current state-of-the-art object localization procedures are nevertheless encumbered by inefficiency and inaccuracy. We present a novel, computationally efficient method for refining inaccurate bounding-box proposals for a target object using Bayesian optimization. Offline, image features from a convolutional neural network are used to train a model to predict an object proposal's offset distance from a target object. Online, this model is used in a Bayesian active search to improve inaccurate object proposals. In experiments, we compare our approach to a state-of-the-art bounding-box regression method for localization refinement of pedestrian object proposals.
The complete mitochondrial genome of Colias erate (Lepidoptera: pieridae)

Abstract The complete mitochondrial genome of Colias erate was investigated and analyzed. The mitochondrial genome is a circular molecule of 15,184 bp, containing 13 protein-coding genes (PCGs), two rRNA genes, 22 tRNA genes, and an A + T-rich region. The nucleotide composition of the C. erate mitogenome is strongly biased toward A + T nucleotides (81.34%). Nine protein-coding genes and 14 tRNA genes are encoded on the H strand, and the other four protein-coding genes and eight tRNA genes are encoded on the L strand. The arrangement of genes is identical to all know the Pieridae species. Finally, the phylogenetic relationships of 12 Pieridae species were reconstructed based on the nucleotide sequences of 13 mitochondrial PCGs using the Bayesian inference method. These molecular-based phylogenies support the traditional morphologically based view of relationships within the Pieridae.