Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting the negative example sampling problem for predicting protein-protein interactions

MOTIVATION A number of computational methods have been proposed that predict protein-protein interactions (PPIs) based on protein sequence features. Since the number of potential non-interacting protein pairs (negative PPIs) is very high both in absolute terms and in comparison to that of interacting protein pairs (positive PPIs), computational prediction methods rely upon subsets of negative PPIs for training and validation. Hence, the need arises for subset sampling for negative PPIs. RESULTS We clarify that there are two fundamentally different types of subset sampling for negative PPIs. One is subset sampling for cross-validated testing, where one desires unbiased subsets so that predictive performance estimated with them can be safely assumed to generalize to the population level. The other is subset sampling for training, where one desires the subsets that best train predictive algorithms, even if these subsets are biased. We show that confusion between these two fundamentally different types of subset sampling led one study recently published in Bioinformatics to the erroneous conclusion that predictive algorithms based on protein sequence features are hardly better than random in predicting PPIs. Rather, both protein sequence features and the 'hubbiness' of interacting proteins contribute to effective prediction of PPIs. We provide guidance for appropriate use of random versus balanced sampling. AVAILABILITY The datasets used for this study are available at http://www.marcottelab. org/PPINegativeDataSampling. CONTACT yungki@mail.utexas. edu; marcotte@icmb.utexas. edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Newton-Raphson Consensus for Distributed Convex Optimization

We address the problem of distributed unconstrained convex optimization under separability assumptions, i.e., the framework where each agent of a network is endowed with a local private multidimensional convex cost, is subject to communication constraints, and wants to collaborate to compute the minimizer of the sum of the local costs. We propose a design methodology that combines average consensus algorithms and separation of time-scales ideas. This strategy is proved, under suitable hypotheses, to be globally convergent to the true minimizer. Intuitively, the procedure lets the agents distributedly compute and sequentially update an approximated Newton-Raphson direction by means of suitable average consensus ratios. We show with numerical simulations that the speed of convergence of this strategy is comparable with alternative optimization strategies such as the Alternating Direction Method of Multipliers. Finally, we propose some alternative strategies which trade-off communication and computational requirements with convergence speed.
Tumor-Associated Neutrophils as a New Prognostic Factor in Cancer: A Systematic Review and Meta-Analysis

Purpose Tumor-associated neutrophils (TAN) have been reported in a variety of malignancies. We conducted an up-to-date meta-analysis to evaluate the prognostic role of TAN in cancer. Method Pubmed, Embase and web of science databases were searched for studies published up to April 2013. Pooled hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) were calculated. The impact of neutrophils localization and primary antibody were also assessed. Results A total of 3946 patients with various solid tumors from 20 studies were included. High density of intratumoral neutrophils were independently associated with unfavorable survival; the pooled HRs were 1.68 (95%CI: 1.36–2.07, I2 = 55.8%, p<0.001) for recurrence-free survival (RFS)/disease-free survival (DFS), 3.36 (95%CI: 2.08–5.42, I2 = 0%, p<0.001) for cancer-specific survival (CSS) and 1.66 (95%CI: 1.37–2.01, I2 = 70.5%, p<0.001) for overall survival (OS). Peritumoral and stromal neutrophils were not statistically significantly associated with survival. When grouped by primary antibody, the pooled HRs were 1.80 (95%CI: 1.47–2.22, I2 = 67.7%, p<0.001) for CD66b, and 1.44 (95%CI: 0.90–2.30, I2 = 45.9%, p = 0.125) for CD15, suggesting that CD66b positive TAN might have a better prognostic value than CD15. Conclusion High levels of intratumoral neutrophils are associated with unfavorable recurrence-free, cancer-specific and overall survival.
Eye remember you: the effects of gaze direction on face recognition in children and adults

Children and adults were tested on a forced-choice face recognition task in which the direction of eye gaze was manipulated over the course of the initial presentation and subsequent test phase of the experiment. To establish the effects of gaze direction on the encoding process, participants were presented with to-be-studied faces displaying either direct or deviated gaze (i.e. encoding manipulation). At test, all the faces depicted persons with their eyes closed. To investigate the effects of gaze direction on the efficiency of the retrieval process, a second condition (i.e. retrieval manipulation) was run in which target faces were presented initially with eyes closed and tested with either direct or deviated gaze. The results revealed the encoding advantages enjoyed by faces with direct gaze was present for both children and adults. Faces with direct gaze were also recognized better than faces with deviated gaze at retrieval, although this effect was most pronounced for adults. Finally, the advantage for direct gaze over deviated gaze at encoding was greater than the advantage for direct gaze over deviated gaze at retrieval. We consider the theoretical implications of these findings.
Rapid Deployment Indoor Localization without Prior Human Participation

In this work, we propose RAD, a RApid Deployment localization framework without human sampling. The basic idea of RAD is to automatically generate a fingerprint database through space partition, of which each cell is fingerprinted by its maximum influence APs. Based on this robust location indicator, fine-grained localization can be achieved by a discretized particle filter utilizing sensor data fusion. We devise techniques for CIVD-based field division, graph-based particle filter, EM-based individual character learning, and build a prototype that runs on commodity devices. Extensive experiments show that RAD provides a comparable performance to the state-of-the-art RSS-based methods while relieving it of prior human participation.
Interactive Exploration of Semantic Clusters

Using visualization and exploration tools can be of great use for the understanding of a software system when only its source code is available. However, understanding a large software system by visualizing only its lower level artifacts (e.g., classes, methods) and the relations between them does not scale for industrial-size systems. To address the scalability issue, higher level hierarchical abstractions (e.g., package structure, clustered decompositions of the system) should be used together with relations between them that are usually aggregated from the lower level relations. In this paper, we present the concepts behind Softwarenaut, a tool aimed at exploring any kind of hierarchical decompositions of a system, and then we look at a specific exploration of a system. In the experiment, the hierarchical decomposition of the system is the result of applying a semantic clustering to group classes that use similar terms
CHAMELEON: A Self-Evolving, Fully-Adaptive Resource Arbitrator for Storage Systems

Enterprise applications typically depend on guaranteed performance from the storage subsystem, lest they fail. However, unregulated competition is unlikely to result in a fair, predictable apportioning of resources. Given that widespread access protocols and scheduling policies are largely best-effort, the problem of providing performance guarantees on a shared system is a very difficult one. Clients typically lack accurate information on the storage system's capabilities and on the access patterns of the workloads using it, thereby compounding the problem. CHAMELEON is an adaptive arbitrator for shared storage resources; it relies on a combination of self-refining models and constrained optimization to provide performance guarantees to clients. This process depends on minimal information from clients, and is fully adaptive; decisions are based on device and workload models automatically inferred, and continuously refined, at run-time. Corrective actions taken by CHAMELEON are only as radical as warranted by the current degree of knowledge about the system's behavior. In our experiments on a real storage system CHAMELEON identified, analyzed, and corrected performance violations in 3-14 minutes--which compares very favorably with the time a human administrator would have needed. Our learning-based paradigm is a most promising way of deploying large-scale storage systems that service variable workloads on an ever-changing mix of device types.
Inhibition Controls Asynchronous States of Neuronal Networks

Computations in cortical circuits require action potentials from excitatory and inhibitory neurons. In this mini-review, I first provide a quick overview of findings that indicate that GABAergic neurons play a fundamental role in coordinating spikes and generating synchronized network activity. Next, I argue that these observations helped popularize the notion that network oscillations require a high degree of spike correlations among interneurons which, in turn, produce synchronous inhibition of the local microcircuit. The aim of this text is to discuss some recent experimental and computational findings that support a complementary view: one in which interneurons participate actively in producing asynchronous states in cortical networks. This requires a proper mixture of shared excitation and inhibition leading to asynchronous activity between neighboring cells. Such contribution from interneurons would be extremely important because it would tend to reduce the spike correlation between neighboring pyramidal cells, a drop in redundancy that could enhance the information-processing capacity of neural networks.
Bayesian Group Factor Analysis

We introduce a factor analysis model that summarizes the dependencies between observed variable groups, instead of dependencies between individual variables as standard factor analysis does. A group may correspond to one view of the same set of objects, one of many data sets tied by co-occurrence, or a set of alternative variables collected from statistics tables to measure one property of interest. We show that by assuming groupwise sparse factors, active in a subset of the sets, the variation can be decomposed into factors explaining relationships between the sets and factors explaining away set-specific variation. We formulate the assumptions in a Bayesian model providing the factors, and apply the model to two data analysis tasks, in neuroimaging and chemical systems biology.
Supersymmetry and electroweak breaking from extra dimensions at the TeV scale

We analyze some features of the role that extra dimensions, of radius R in the TeV 1 range, can play in the soft breaking of supersymmetry and the spontaneous breaking of electroweak symmetry. We use a minimal model where the gauge and Higgs sector of the MSSM are living in the bulk of five dimensions and the chiral multiplets in a four-dimensional boundary. Supersymmetry is broken in the bulk by the Scherk-Schwarz mechanism and transmitted to the boundary by radiative corrections. The particle spectrum is completely predicted as a function of a unique R-charge. The massless sector corresponds to the pure Standard Model and elec- troweak symmetry is radiatively broken with a light Higgs weighing < 110 GeV. The µ-problem is solved and Higgsinos, gauginos and heavy Higgses acquire masses ∼ 1/R. Chiral sfermions acquire radiative squared-masses ∼ �i/R 2 . The effective potential is explicitly computed in the bulk of extra dimensions and some cosmolog- ical consequences can be immediately drawn from it. Gauge coupling running and unification is studied in the presence of Scherk-Schwarz supersymmetry breaking. The unification is similar to that in the supersymmetric theory.
Hand gesture recognition based on online PCA with adaptive subspace

The learning method for hand gesture recognition that compute a space of eigenvectors by Principal Component Analysis(PCA) traditionally require a batch computation step, in which the only way to update the subspace is to rebuild the subspace by the scratch when it comes to new samples. In this paper, we introduce a new approach to gesture recognition based on online PCA algorithm with adaptive subspace, which allows for complete incremental learning. We propose to use different subspace updating strategy for new sample according to the degree of difference between new sample and learned sample, which can improve the adaptability in different situations, and also reduce the time of calculation and storage space. The experimental results show that the proposed method can recognize the unknown hand gesture, realizing online hand gesture accumulation and updating, and improving the recognition performance of system.
A Voltage Space Vector Diagram Formed by Six Concentric Dodecagons for Induction Motor Drives

A new voltage space vector diagram consisting of six concentric dodecagons is proposed in this paper. The space vector diagram is developed for an open-end induction motor drive fed from two three-level neutral point clamped (NPC) inverters with asymmetric isolated dc voltage sources. As already reported in literature, a dodecagonal space vector diagram is advantageous over a hexagonal one, since the 6n ± 1 (n = odd) harmonics are absent in the phase voltage and the linear modulation range is extended. The present work further improves the existing dodecagonal space vector diagrams by dividing the space vector plane into six concentric dodecagons, while retaining the aforementioned advantages. A high-resolution pulsewidth modulation strategy is developed in this paper, which compared to existing schemes, reduces the harmonic content of the motor phase voltage without resorting to higher switching frequency. A comparative study of output voltage quality produced by the proposed topology and a five-level inverter shows substantial improvement in the present work. At the same time, the present topology does not require any special design of the inverter and uses two popular three-level NPC inverters. Experimental verification on a laboratory prototype indicates the possible application of this concept for high power drives.
Inoculum Size Influences Bacterial Cross Contamination between Surfaces

ABSTRACT Many factors have been shown to influence bacterial transfer between surfaces, including surface type, bacterial species, moisture level, pressure, and friction, but the effect of inoculum size on bacterial transfer has not yet been established. Bacterial cross contamination rates during performance of common food service tasks were previously determined in our laboratory using nalidixic acid-resistant Enterobacter aerogenes. Eight different transfer rates were determined, each involving a minimum of 30 volunteers. The influence of source inoculum level on the percentage of bacteria transferred (percent transfer rates) and log10 CFU per recipient surface was determined using statistical analysis. The effect of inoculum size on transfer rate was highly statistically significant (P< 0.0001) for all transfer rate data combined (352 observations) and for each individual cross contamination rate, except for data on contamination via transfer from chicken to hand through a glove barrier (P = 0.1643). Where inoculum size on the source was greater, transfer rates were lower, and where inoculum size on the source was less, transfer rates were higher. The negative linear trend was more obvious for activities that had a larger range of inoculum sizes on the source surface. This phenomenon has serious implications for research seeking to determine bacterial cross contamination rates, since the different transfer efficiencies that were previously shown to be associated with certain activities may actually be the result of differing initial inoculum levels. The initial inoculum size on the source and the amount of bacteria transferred must both be considered to accurately determine bacterial transfer rates.
Joint Power Charging and Routing in Wireless Rechargeable Sensor Networks

The development of wireless power transfer (WPT) technology has inspired the transition from traditional battery-based wireless sensor networks (WSNs) towards wireless rechargeable sensor networks (WRSNs). While extensive efforts have been made to improve charging efficiency, little has been done for routing optimization. In this work, we present a joint optimization model to maximize both charging efficiency and routing structure. By analyzing the structure of the optimization model, we first decompose the problem and propose a heuristic algorithm to find the optimal charging efficiency for the predefined routing tree. Furthermore, by coding the many-to-one communication topology as an individual, we further propose to apply a genetic algorithm (GA) for the joint optimization of both routing and charging. The genetic operations, including tree-based recombination and mutation, are proposed to obtain a fast convergence. Our simulation results show that the heuristic algorithm reduces the number of resident locations and the total moving distance. We also show that our proposed algorithm achieves a higher charging efficiency compared with existing algorithms.
Directional neurite growth using carbon nanotube patterned substrates as a biomimetic cue

Researchers have made extensive efforts to mimic or reverse-engineer in vivo neural circuits using micropatterning technology. Various surface chemical cues or topographical structures have been proposed to design neuronal networks in vitro. In this paper, we propose a carbon nanotube (CNT)-based network engineering method which naturally mimics the structure of extracellular matrix (ECM). On CNT patterned substrates, poly-L-lysine (PLL) was coated, and E18 rat hippocampal neurons were cultured. In the early developmental stage, soma adhesion and neurite extension occurred in disregard of the surface CNT patterns. However, later the majority of neurites selectively grew along CNT patterns and extended further than other neurites that originally did not follow the patterns. Long-term cultured neuronal networks had a strong resemblance to the in vivo neural circuit structures. The selective guidance is possibly attributed to higher PLL adsorption on CNT patterns and the nanomesh structure of the CNT patterns. The results showed that CNT patterned substrates can be used as novel neuronal patterning substrates for in vitro neural engineering.
Facile Synthesis of CuO Semiconductor Nanorods for Time Dependent Study of Dye Degradation and Bioremediation Applications

The manuscript reports facile one step synthesis of CuO semiconductor nanorods by sol-gel aaproach for photocatalytic and bioremediation applications. Spectroscopic characterization along with X-ray diffractometry and electron microscopy studies confirmed the formation of nanorods with 12 to 14 nm diameter and 50-100 nm length. As synthesized nanorods were subjected to photocatalytic degradation of dyes viz. Methylene Orange (MO), Methylene Blue (MB), Eriochrome Black T (ET) and Congo Red (CR) in a time bound study. Comparative analysis of the data depicted that time taken for degradation of equal amount of CR was more compared to the other three dyes owing to its high molecular weight and lower diffusion rate in aqueous medium. Subsequently, the antibacterial properties of the nanorods were investigated against the gram negative Escherichia coli and gram positive Bacillus bacteria. Zone of clearance was observed in disk diffusion assays, thereby confirming the antibacterial characteristics of the nanorods. These nanorods thus hold great promise as a simple, selective and a sensitive analytical platform for the effective bio-monitoring and photocatalyst for dye degradation.
Subjective ratings of masker disturbance during the perception of native and non-native speech

The aim of the present study was to address how 43 normal-hearing (NH) and hearing-impaired (HI) listeners subjectively experienced the disturbance generated by four masker conditions (i.e., stationary noise, fluctuating noise, Swedish two-talker babble and English two-talker babble) while listening to speech in two target languages, i.e., Swedish (native) or English (non-native). The participants were asked to evaluate their noise-disturbance experience on a continuous scale from 0 to 10 immediately after having performed each listening condition. The data demonstrated a three-way interaction effect between target language, masker condition, and group (HI versus NH). The HI listeners experienced the Swedish-babble masker as significantly more disturbing for the native target language (Swedish) than for the non-native language (English). Additionally, this masker was significantly more disturbing than each of the other masker types during the perception of Swedish target speech. The NH listeners, on the other hand, indicated that the Swedish speech-masker was more disturbing than the stationary and the fluctuating noise-maskers for the perception of English target speech. The NH listeners perceived more disturbance from the speech maskers than the noise maskers. The HI listeners did not perceive the speech maskers as generally more disturbing than the noise maskers. However, they had particular difficulty with the perception of native speech masked by native babble, a common condition in daily-life listening conditions. These results suggest that the characteristics of the different maskers applied in the current study seem to affect the perceived disturbance differently in HI and NH listeners. There was no general difference in the perceived disturbance across conditions between the HI listeners and the NH listeners.
Nephropathy in Townes-Brocks syndrome (SALL1 mutation): imaging and pathological findings in adulthood.

BACKGROUND Townes-Brocks syndrome (TBS) is a rare autosomal dominant disease, resulting from mutation in the developmental gene SALL1. The phenotype encompasses malformations of limbs (triphalangeal thumbs and pre-axial polydactyly), intestine (anal stenosis) and ears (dysplastic ear with perception hearing loss). Renal involvement (hypo-dysplasia, multicystic kidneys or unilateral absence) is observed in almost half of patients and may progress to end-stage renal failure in childhood. METHODS Herein, we report two adult patients diagnosed with TBS at age 28 and 35. RESULTS Both exhibited severe chronic renal failure and kidney hypodysplasia by imaging studies while focal and segmental glomerulosclerosis (FSGS) was demonstrated in one case. CONCLUSION Regular assessment of glomerular filtration rate is mandatory throughout life in all TBS patients.