Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in Selective Effects Over Time Facilitate Turnover of Enhancer Sequences

Correct gene expression is often critical and consequently stabilizing selection on expression is widespread. Yet few genes possess highly conserved regulatory DNA, and for the few enhancers that have been carefully characterized, substantial functional reorganization has often occurred. Given that natural selection removes mutations of even very small deleterious effect, how can transcription factor binding evolve so readily when it underlies a conserved phenotype? As a first step toward addressing this question, I combine a computational model for regulatory function that incorporates many aspects of our present biological knowledge with a model for the fitness effects of misexpression. I then use this model to study the evolution of enhancers. Several robust behaviors emerge: First, the selective effects of mutations at a site change dramatically over time due to substitutions elsewhere in the enhancer, and even the overall degree of constraint across the enhancer can change considerably. Second, many of the substitutions responsible for changes in binding occur at sites where previously the mutation would have been strongly deleterious, suggesting that fluctuations in selective effects at a site are important for functional turnover. Third, most substitutions contributing to the repatterning of binding and constraint are effectively neutral, highlighting the importance of genetic drift—even for enhancers underlying conserved phenotypes. These findings have important implications for phylogenetic inference of function and for interpretations of selection coefficients estimated for regulatory DNA.
Temperature interaction of Early voltage, current gain and breakdown characteristics of npn and pnp SiGe HBTs on SOI

We present a comprehensive investigation of temperature dependence of breakdown voltage, DC current gain (β), and Early voltage (V<inf>A</inf>) for complementary SiGe-npn and SiGe-pnp bipolar transistors fabricated on an advanced CBiCMOS technology on thick-film SOI. Both SiGe-npn and SiGe-pnp transistors show decreasing V<inf>A</inf> as ambient operating temperature increases from −60°C to +200°C for low collector current densities (J<inf>C</inf> ≤ 5.0 µA/µm<sup>2</sup>) with a near linear inverse temperature (1/T) relationship. We also demonstrate in the region historically defined by weak self-heating interactions that V<inf>A</inf> maintains minimal sensitivity to operating temperature.
Bootstrapping the Semantics of Tools: Affordance Analysis of Real World Objects on a Per-part Basis

This study shows how understanding of object functionality arises by analyzing objects at the level of their parts where we focus here on primary tools. First, we create a set of primary tool functionalities, which we speculate is related to the possible functions of the human hand. The function of a tool is found by comparing it to this set. For this, the unknown tool is segmented, using a data-driven method, into its parts and evaluated using the geometrical part constellations against the training set. We demonstrate that various tools and even uncommon tool-versions can be recognized. The system “understands” that objects can be used as makeshift replacements. For example, a helmet or a hollow skull can be used to transport water. Our system supersedes state-of-the-art recognition algorithms in recognition and generalization performance. To support the conjecture of a possible cognitive hand-to-tool transfer we analyze, at the end of this study, primary tools by also incorporating tool-dynamics. We create an ontology of tool functions where we find only 32 of them. Being such a small set this would indeed allow bootstrapping tool-understanding by exploration-based learning of hand function and hand-to-tool transfer.
Bibliometric Assessment of European and Sub-Saharan African Research Output on Poverty-Related and Neglected Infectious Diseases from 2003 to 2011

Background The European & Developing Countries Clinical Trials Partnership (EDCTP) is a partnership of European and sub-Saharan African countries that aims to accelerate the development of medical interventions against poverty-related diseases (PRDs). A bibliometric analysis was conducted to 1) measure research output from European and African researchers on PRDs, 2) describe collaboration patterns, and 3) assess the citation impact of clinical research funded by EDCTP. Methodology/Principal Findings Disease-specific research publications were identified in Thomson Reuters Web of Science using search terms in titles, abstracts and keywords. Publication data, including citation counts, were extracted for 2003–2011. Analyses including output, share of global papers, normalised citation impact (NCI), and geographical distribution are presented. Data are presented as five-year moving averages. European EDCTP member countries accounted for ~33% of global research output in PRDs and sub-Saharan African countries for ~10% (2007–2011). Both regions contributed more to the global research output in malaria (43.4% and 22.2%, respectively). The overall number of PRD papers from sub-Saharan Africa increased markedly (>47%) since 2003, particularly for HIV/AIDS (102%) and tuberculosis (TB) (81%), and principally involving Southern and East Africa. For 2007–2011, European and sub-Saharan African research collaboration on PRDs was highly cited compared with the world average (NCI in brackets): HIV/AIDS 1.62 (NCI: 1.16), TB 2.11 (NCI: 1.06), malaria 1.81 (NCI: 1.22), and neglected infectious diseases 1.34 (NCI: 0.97). The NCI of EDCTP-funded papers for 2003–2011 was exceptionally high for HIV/AIDS (3.24), TB (4.08) and HIV/TB co-infection (5.10) compared with global research benchmarks (1.14, 1.05 and 1.35, respectively). Conclusions The volume and citation impact of papers from sub-Saharan Africa has increased since 2003, as has collaborative research between Europe and sub-Saharan Africa. >90% of publications from EDCTP-funded research were published in high-impact journals and are highly cited. These findings corroborate the benefit of collaborative research on PRDs.
Pattern Extraction , Classification and Comparison Between Attribute Selection Measures

In this research, we have compared three different attribute selection measures algorithms. We have used ID3 algorithm, C4.5 algorithm and CART algorithm. All these algorithms are decision tree based algorithm. We have got the accuracy of three different algorithms and we observed that the accuracy of ID3 algorithm is greater than C4.5 algorithm. But the accuracy of CART algorithm is greater than other two algorithms. We have also calculated the time complexity of three different algorithms. To compare these algorithms, we have used heart disease dataset which is collected from UCI machine learning repository. Keywords— Pattern extraction, Classification, Decision Tree, ID3 algorithm, C4.5 algorithm, CART algorithm.
Bandwidth-Aware Scheduling With SDN in Hadoop: A New Trend for Big Data

Software-defined networking (SDN) is a revolutionary network architecture that separates out network control functions from the underlying equipment and is an increasing trend to help enterprises build more manageable data centers where big data processing emerges as an important part of applications. To concurrently process large-scale data, MapReduce with an open-source implementation named Hadoop is proposed. In practical Hadoop systems, one kind of issue that vitally impacts the overall performance is known as the NP-complete minimum make span11Make span means the time between job’s start time and job’s finish time. problem. One main solution is to assign tasks on data local nodes to avoid link occupation since network bandwidth is a scarce resource. Many methodologies for enhancing data locality are proposed such as the Hadoop default scheduler (HDS) and state-of-the-art scheduler balance-reduce scheduler (BAR). However, all of them either ignore allocating tasks in a global view or disregard available bandwidth as the basis for scheduling. In this paper, we propose a heuristic bandwidth-aware task scheduler bandwidth-aware scheduling with SDN in Hadoop (BASS) to combine Hadoop with SDN. It is not only able to guarantee data locality in a global view but also can efficiently assign tasks in an optimized way. Both examples and experiments demonstrate that BASS has the best performance in terms of job completion time. To our knowledge, BASS is the first to exploit talent of SDN for job scheduling of big data processing and we believe that it points out a new trend for large-scale data processing.
Cascades of Regression Tree Fields for Image Restoration

Conditional random fields (CRFs) are popular discriminative models for computer vision and have been successfully applied in the domain of image restoration, especially to image denoising. For image deblurring, however, discriminative approaches have been mostly lacking. We posit two reasons for this: First, the blur kernel is often only known at test time, requiring any discriminative approach to cope with considerable variability. Second, given this variability it is quite difficult to construct suitable features for discriminative prediction. To address these challenges we first show a connection between common half-quadratic inference for generative image priors and Gaussian CRFs. Based on this analysis, we then propose a cascade model for image restoration that consists of a Gaussian CRF at each stage. Each stage of our cascade is semi-parametric, i.e., it depends on the instance-specific parameters of the restoration problem, such as the blur kernel. We train our model by loss minimization with synthetically generated training data. Our experiments show that when applied to non-blind image deblurring, the proposed approach is efficient and yields state-of-the-art restoration quality on images corrupted with synthetic and real blur. Moreover, we demonstrate its suitability for image denoising, where we achieve competitive results for grayscale and color images.
Whole plant foods intake is associated with fewer menopausal symptoms in Chinese postmenopausal women with prehypertension or untreated hypertension

ObjectiveNutritional factors have been suggested to be associated with menopausal symptoms (MS). However, the role of overall diet in MS has seldom been examined in Asian populations. This study aims to examine the association of dietary patterns with MS in Chinese postmenopausal women. MethodsThis was a cross-sectional study of 726 women with prehypertension or untreated hypertension who attended the screening visit for a soy trial. Dietary data were collected using a validated food frequency questionnaire containing 85 food items. Principal components factor analysis was used to derive dietary patterns based on 11 food groups. Factors were rotated by orthogonal transformation. Menopause-related symptoms were assessed by a 20-item validated and structured checklist. ResultsThree dietary patterns were identified: processed foods, whole plant foods, and animal foods. Higher tertile of whole plant foods (P for trend <0.05) or lower tertile of processed foods (P for trend <0.05) was associated with fewer MS in a dose-dependent manner. Multivariate linear regression analyses showed that whole plant foods scores were negatively associated with MS scores, even after adjustments for a range of potential confounders (P < 0.01). Logistic regression analyses showed that whole plant foods intake was associated with a significant reduction in risk for nonvasomotor symptoms only. ConclusionsOur study demonstrates that high intake of whole plant foods is independently associated with fewer nonspecific MS. Further evidence from well-designed prospective studies is required to confirm this finding.
Stochastic Local Search for Bayesian Networks

The paper evaluates empirically the suitability of Stochastic Local Search algorithms (SLS) for nding most probable explanations in Bayesian networks. SLS algorithms (e.g., GSAT, WSAT [16]) have recently proven to be highly e ective in solving complex constraint-satisfaction and satis ability problems which cannot be solved by traditional search schemes. Our experiments investigate the applicability of this scheme to probabilistic optimization problems. Speci cally, we show that algorithms combining hill-climbing steps with stochastic steps (guided by the network's probability distribution) called G+StS, outperform pure hill-climbing search, pure stochastic simulation search, as well as simulated annealing. In addition, variants of G+StS that are augmented on top of alternative approximation methods are shown to be particularly e ective.
Tag propagation based recommendation across diverse social media

Many real applications demand accurate cross-domain recommendation, e.g., recommending a Weibo (the largest Chinese Twitter) user with the products in an e-commerce Web site. Since many social media have rich tags on both items or users, tag-based profiling became popular for recommendation. However, most previous recommendation approaches have low effectiveness in handling sparse data or matching tags from different social media. Addressing these problems, we first propose an optimized local tag propagation algorithm to generate tags for profiling Weibo users and then use a Chinese knowledge graph accompanied by an improved ESA (explicit semantic analysis) for semantic matching of cross-domain tags. Empirical comparisons to the state-of-the-art approaches justify the efficiency and effectiveness of our approaches.
Mimicking an Atomically Thin “Vacuum Spacer” to Measure the Hamaker Constant between Graphene Oxide and Silica

The Hamaker constant between graphene oxide and silica, which quantifies the strength of van der Waals forces is determined, by mimicking a “vacuum spacer” in an atomic force microscopyforce study. It is demonstrated that, a 2D spacer is expected to yield an accurately defined separation, owing to the high atom density and strength in planar direction compared with other dimensional spacers.
Production and Extraction of Quercetin and (+)-Catechin from Phyllanthus niruri Callus Culture

—Quercetin and (+)-catechin are metabolites present in Phyllanthus niruri plant, have potential in medicinal uses as anticancer and antioxidant agents. Studies on production of quercetin and (+)-catechin from P. niruri callus culture via in vitro technique were carried out and the results were compared to the intact plant. P. niruri explants were cultured on Murashige and Skoog (MS) solidified media supplemented with several phytohormone combinations for one month. The metabolites were extracted from P. niruri callus and intact plant by using carbon dioxide supercritical fluid extraction (SFE) with ethanol as modifier and solvent extraction techniques. The extracts were analyzed by means of HPLC method. Results showed that P. niruri callus culture was successfully established. The highest content of quercetin (1.72%) was found from P. niruri callus grown in media supplemented with 0.8mg/L kinetin and 0.2mg/L 2,4-dicholophenoxyacetic acid (2,4-D), which was 1.2 fold higher than intact plant. Meanwhile, the highest amounts of (+)-catechin (0.63%) was found from P. niruri callus grown in media with addition of 0.2mg/L 1-naphthalene acetic acid (NAA) and 0.8mg/L 2,4-D. The SFE condition in this study showed better extraction efficiency when higher contents of selected metabolites were found in all SFE extracts compared to the common solvent extracts.
Object Detection Based on Combination of Conditional Random Field and Markov Random Field

Many approaches for object detection are based on Markov random field (MRF) and conditional random field (CRF) respectively. MRF and CRF have very different characteristics. This work discusses in detail their strength and weaknesses. From the discussion, a new object detection algorithm using combination of CRF and MRF was derived. We utilize the algorithm to detect urban areas, and corresponding to the urban area object, we introduce a generic feature vector for each image site. The proposed algorithm was tested extensively on a large number of remote sensing images, and very promising results can be presented
Developing a Testing Maturity Model : Part I

Testing is a critical component of a mature software development process. It is one of the most challenging and costly process activities, and in its fullest definition provides strong support for the production of quality software. In spite of its vital role in software development, existing maturity models have not adequately addressed testing issues nor has the nature of a mature testing process been well defined. We are developing a Testing Maturity Model to address deficiencies these areas. The TMM will complement the Software Engineering Institute's Capability Maturity Model (CMM) by specifically addressing issues important to test managers, test specialists, and software quality assurance staff. The TMM will contain a set of maturity levels through which an organization can progress toward testing process maturity, a set of recommended practices at each level of maturity, and an assessment model that will allow organizations to evaluate and improve their testing process.
Effect of Friction Stir Welding on the Tensile Properties of AA6063 under Different Conditions

The basic aim of present study is to examine the effect of main friction stir welding (FSW) parameters on the quality of AA 6063 plate welds. Welding of AA6063 was carried out by a vertical milling machine, using different tools with a stationary shoulder and during the welding no external heating system was used. Different welding parameters studied were the tool rotational speed which varied from 2700 to 5400 (rpm), the traverse speed of table which varied from 12 to 17 (mm/min), and the tool shoulder diameter ranging from 17 to 22 mm. In the present research three levels, three factorial designs were used for optimization by taguchi approach. The results indicated that tensile strength of welded joint was increasing with rotational speed and decreasing with increasing transverse feed because frictional heat was directly proportional to rotational speed of tool.
A nonlinear oscillator describing storm track variability

We construct a two‐variable model that describes the interaction between local baroclinicity and eddy heat flux in order to understand aspects of the variance in storm tracks. It is a heuristic model for diabatically forced baroclinic instability close to baroclinic neutrality. The two‐variable model has the structure of a nonlinear oscillator. It exhibits some realistic properties of observed storm track variability, most notably the intermittent nature of eddy activity. This suggests that apparent threshold behaviour can be more accurately and succinctly described by a simple nonlinearity. An analogy is drawn with the triggering of convective events.
A Low-Complexity Delay-Tunable Coding Scheme for Visible Light Communication Systems

Visible light communication (VLC) systems are expected to support a variety of applications, such as common-information broadcasting, real-time multimedia streaming, and large file downloading. Typically, these applications have different delay requirements. Hence, it is desirable to design high-performance coding scheme capable of supporting a wide range of delays but with acceptable hardware complexity. To achieve this, we propose a delay-tunable coding scheme for VLC systems based on block Markov superposition transmission of short non-binary low-density parity-check (NBLDPC) codes. The proposed coding scheme includes the following advantages: 1) it is easily configurable to fulfill different delay requirements while keeping the code rate constant; 2) it requires essentially the same hardware modules to implement the encoder/decoder as the involved short NBLDPC code; and 3) it can have a larger coding gain if a longer delay is tolerated. Numerical results are presented to show the expected tradeoff between delay and energy in a VLC system.
Greedy Adaptive Critics for LQR Problems : Convergence ProofsTomas Landelius

A number of success stories have been told where reinforcement learning has been applied to problems in continuous state spaces using neural nets or other sorts of function approximators in the adaptive critics. However, the theoretical understanding of why and when these algorithms work is inadequate. This is clearly exempliied by the lack of convergence results for a number of important situations. To our knowledge only two such results been presented for systems in the continuous state space domain. The rst is due to Werbos and is concerned with linear function approximation and heuristic dynamic programming. Here no optimal strategy can be found why the result is of limited importance. The second result is due to Bradtke and deals with linear quadratic systems and quadratic function approximators. Bradtke's proof is limited to ADHDP and policy iteration techniques where the optimal solution is found by a number of successive approximations. This paper deals with greedy techniques, where the optimal solution is directly aimed for. Convergence proofs for a number of adaptive critics, HDP, DHP, ADHDP and ADDHP, are presented. Optimal controllers for linear quadratic regulation (LQR) systems can be found by standard techniques from control theory but the assumptions made in control theory can be weakened if adaptive critic techniques are employed. The main point of this paper is, however, not to emphasize the diierences but to highlight the similarities and by so doing contribute to a theoretical understanding of adaptive critics.
Sex-Dependent Effects of Cadmium Exposure in Early Life on Gut Microbiota and Fat Accumulation in Mice

Background: Environmental cadmium, with a high average dietary intake, is a severe public health risk. However, the long-term health implications of environmental exposure to cadmium in different life stages remain unclear. Objectives: We investigated the effects of early exposure to cadmium, at an environmentally relevant dosage, on adult metabolism and the mechanism of action. Methods: We established mouse models with low-dose cadmium (LDC) exposure in early life to examine the long-term metabolic consequences. Intestinal flora measurement by 16S rDNA sequencing, microbial ecological analyses, and fecal microbiota transplant was conducted to explore the potential underlying mechanisms. Results: Early LDC exposure (100 nM) led to fat accumulation in adult male mice. Hepatic genes profiling revealed that fatty acid and lipid metabolic processes were elevated. Gut microbiota were perturbed by LDC to cause diversity reduction and compositional alteration. Time-series studies indicated that the gut flora at early-life stages, especially at 8 weeks, were vulnerable to LDC and that an alteration during this period could contribute to the adult adiposity, even if the microbiota recovered later. The importance of intestinal bacteria in LDC-induced fat accumulation was further confirmed through microbiota transplantation and removal experiments. Moreover, the metabolic effects of LDC were observed only in male, but not female, mice. Conclusions: An environmental dose of cadmium at early stages of life causes gut microbiota alterations, accelerates hepatic lipid metabolism, and leads to life-long metabolic consequences in a sex-dependent manner. These findings provide a better understanding of the health risk of cadmium in the environment. Citation: Ba Q, Li M, Chen P, Huang C, Duan X, Lu L, Li J, Chu R, Xie D, Song H, Wu Y, Ying H, Jia X, Wang H. 2017. Sex-dependent effects of cadmium exposure in early life on gut microbiota and fat accumulation in mice. Environ Health Perspect 125:437–446; http://dx.doi. org/10.1289/EHP360