Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Drug Synergy and Antagonism from Genetic Interaction Neighborhoods

Although drug combinations have proven efficacious in a variety of diseases, the design of such regimens often involves extensive experimental screening due to the myriad choice of drugs and doses. To address these challenges, we utilize the budding yeast Saccharomyces cerevisiae as a model organism to evaluate whether drug synergy or antagonism is mediated through genetic interactions between their target genes. Specifically, we hypothesize that if the inhibition targets of one chemical compound are in close proximity to those of a second compound in a genetic interaction network, then the compound pair will exhibit synergy or antagonism. Graph metrics are employed to make precise the notion of proximity in a network. Knowledge of genetic interactions and small-molecule targets are compiled through literature sources and curated databases, with predictions validated according to experimentally determined gold standards. Finally, we test whether genetic interactions propagate through networks according to a “guilt-by-association” framework. Our results suggest that close proximity between the target genes of one drug and those of another drug does not strongly predict synergy or antagonism. In addition, we find that the extent to which the growth of a double gene mutant deviates from expectation is moderately anti-correlated with their distance in a genetic interaction network.
Prosody Modeling for Automatic Speech Understanding: An Overview of Recent Research at SRI

Prosody has long been studied as an important knowledge source for speech understanding. In recent years there has been a large amount of computational work aimed at prosodic modeling for automatic speech recognition and understanding. Whereas most current approaches to speech processing model only the words, prosody provides an additional knowledge source that is inherent in, and exclusive to, spoken language. It can therefore provide additional information that is not directly available from text alone, and also serves as a partially redundant knowledge source that may help overcome the errors resulting from faulty word recognition.
Pseudo-Codeword Analysis of Tanner Graphs From Projective and Euclidean Planes

We consider coded data transmission over a binary-input output-symmetric memoryless channel using a binary linear code. In order to understand the performance of maximum-likelihood (ML) decoding, one studies the codewords, in particular the minimal codewords, and their Hamming weights. In the context of linear programming (LP) decoding, one's attention needs to be shifted to the pseudo-codewords, in particular, to the minimal pseudo-codewords and their pseudo-weights. In this paper, we investigate some families of codes that have good properties under LP decoding, namely certain families of low-density parity-check (LDPC) codes that are derived from projective and Euclidean planes: we study the structure of their minimal pseudo-codewords and give lower bounds on their pseudo-weight. Besides this main focus, we also present some results that hold for pseudo-codewords and minimal pseudo-codewords of any Tanner graph, and we highlight how the importance of minimal pseudo-codewords under LP decoding varies depending on which binary-input output-symmetric memoryless channel is used.
CALCULATION OF STRENGTH OF CONCRETE FILLED STEEL BOX COLUMNS

The experimental - theoretical researches of concrete filled steel box columns (CFSBC) with pressed structure of concrete are executed. It is offered to make the CFSBC with preliminary pressed concrete core to improve their construction. The bearing capacity of centrally compressed samples of CFSBC has increased by 10 ÷ 15%. The received results are explained by the significant increase of the strength of concrete core in the preliminary compressed CFSBC samples due to the simultaneous display of three effects: long-term compression of concrete mix, preliminary lateral compression of concrete core and its work in the conditions of volume compression. The calculation of strength of CFSBC should be carried out on the basis of nonlinear deformation model of reinforced concrete. Three-axis tensely – deformed conditions are accepted for the analytical description of work of concrete core and external steel holder. Nonuniformity of stress state of concrete and bending of walls of external steel holder are taken into account. The basic dependences, which allow to carry out the approximate calculations of the strength of compressed CFSBC on limiting efforts, are also resulted.
Spark deployment and performance evaluation on the MareNostrum supercomputer

In this paper we present a framework to enable data-intensive Spark workloads on MareNostrum, a petascale supercomputer designed mainly for compute-intensive applications. As far as we know, this is the first attempt to investigate optimized deployment configurations of Spark on a petascale HPC setup. We detail the design of the framework and present some benchmark data to provide insights into the scalability of the system. We examine the impact of different configurations including parallelism, storage and networking alternatives, and we discuss several aspects in executing Big Data workloads on a computing system that is based on the compute-centric paradigm. Further, we derive conclusions aiming to pave the way towards systematic and optimized methodologies for fine-tuning data-intensive application on large clusters emphasizing on parallelism configurations.
Recombinant factor VIIa in the management of surgery and acute bleeding episodes in children with haemophilia and high responding inhibitors

Summary. The management of acute and surgical bleeding episodes in children with severe factor VIII or IX deficiency who develop high responding inhibitors presents a major therapeutic challenge to clinicians. Recombinant factor VIIa (rVIIa) is an effective, reliable and safe treatment that can be used to treat acute bleeding episodes prior to commencing an immune tolerance programme and to cover surgical procedures until the immune tolerance programme is successful. In a significant minority of patients, immune tolerance therapy is ineffective and an alternative haemostatic agent such as rVIIa is required for life‐long treatment. The present study evaluated the use of rVIIa in a paediatric setting. Twelve children, aged 1–16 years, were treated successfully with rVIIa to prevent surgical bleeding in 20 surgical procedures (19 central venous access device insertion or removal, 1 dental extraction). Minor postoperative haematomata developed in 2 out of 20 cases after regular rVIIa therapy had been discontinued and resolved with a short course of rVIIa in both cases. Three children had six life‐ or limb‐threatening bleeding episodes. All bleeding episodes resolved with regular rVIIa treatment although topical fibrin glue was needed in one child with a frenulum tear. One patient required two red cell transfusions for symptomatic anaemia resulting from two separate bleeding episodes. The rVIIa therapy was well tolerated and there was no evidence of treatment‐related complications. We conclude that rVIIa is the treatment of choice for the management of surgery and acute life‐ or limb‐threatening bleeding in children with haemophilia and high responding inhibitors.
Steerable Principal Components for Space-Frequency Localized Images

As modern scientific image datasets typically consist of a large number of images of high resolution, devising methods for their accurate and efficient processing is a central research task. In this paper, we consider the problem of obtaining the steerable principal components of a dataset, a procedure termed "steerable PCA" (steerable principal component analysis). The output of the procedure is the set of orthonormal basis functions which best approximate the images in the dataset and all of their planar rotations. To derive such basis functions, we first expand the images in an appropriate basis, for which the steerable PCA reduces to the eigen-decomposition of a block-diagonal matrix. If we assume that the images are well localized in space and frequency, then such an appropriate basis is the prolate spheroidal wave functions (PSWFs). We derive a fast method for computing the PSWFs expansion coefficients from the images' equally spaced samples, via a specialized quadrature integration scheme, and show that the number of required quadrature nodes is similar to the number of pixels in each image. We then establish that our PSWF-based steerable PCA is both faster and more accurate then existing methods, and more importantly, provides us with rigorous error bounds on the entire procedure.
Polymorphisms and a Haplotype in Heparanase Gene Associations with the Progression and Prognosis of Gastric Cancer in a Northern Chinese Population

Background Human heparanase plays an important role in cancer development and single nucleotide polymorphisms (SNPs) in the heparanase gene (HPSE) have been shown to be correlated with gastric cancer. The present study examined the associations between individual SNPs or haplotypes in HPSE and susceptibility, clinicopathological parameters and prognosis of gastric cancer in a large sample of the Han population in northern China. Methodology/Principal Findings Genomic DNA was extracted from formalin-fixed, paraffin-embedded normal gastric tissue samples from 404 patients and from blood from 404 healthy controls. Six SNPs were genotyped by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. A chi-square (χ2) test and unconditional logistic regression were used to analyze the risk of gastric cancer; a Log-rank test and Cox proportional hazards model were used to produce survival analysis and a Kaplan-Meier method was used to map survival curves. The mean genotyping success rates were more than 99% in both groups. Haplotype CA in the block composed of rs11099592 and rs4693608 had a greater distribution in the group of Borrmann types 3 and 4 (P = 0.037), the group of a greater number of lymph node metastases (N3 vs N0 group, P = 0.046), and moreover was correlated to poor survival (CG vs CA: HR = 0.645, 95%CI: 0.421–0.989, P = 0.044). In addition, genotypes rs4693608 AA and rs4364254 TT were associated with poor survival (P = 0.030, HR = 1.527, 95%CI: 1.042–2.238 for rs4693608 AA; P = 0.013, HR = 1.546, 95%CI: 1.096–2.181 for rs4364254 TT). There were no correlations between individual SNPs or haplotypes and gastric cancer risk. Conclusions/Significance A functional haplotype in HPSE was found, which included the important SNP rs4693608. SNPs in HPSE play an important role in gastric cancer progression and survival, and perhaps may be a molecular marker for prognosis and treatment values.
Undermodeled equalization: Extrema of the Godard/Shalvi-Weinstein criterion

The more promising results to date in blind equalization are restricted to so-called sufficient-order settings, in which all configurations in the combined (channel-equalizer cascade) impulse response space are attainable for some setting of the equalizer coefficients. Here we address issues related to equalizer undermodelling, in which only a proper subset within the combined impulse response space is attainable by adjusting the equalizer coefficients. We derive an analytic characterization of stationary points for the Godard and Shalvi-Weinstein criteria in undermodeled cases, an establish relations to the convergence of super-exponential algorithms in undermodeled cases.
JOINT GRAPH COLORING AND ANT COLONY OPTIMIZATION BASED SUB-CHANNEL ALLOCATION ALGORITHMS FOR DOWNLINK LTE-A HETNETS

Graph Coloring and Ant Colony Optimization based Sub-channel Allocation Algorithms for LTE-A HetNets c ©Richa Siddavaatam, 2012 Master of Applied Science Electrical and Computer Engineering Ryerson University LTE-Advanced is a promising technology which supports much higher peak rates, higher throughput and coverage, and lower latencies, resulting in a better user experience. For extended indoor coverage LTE-A supports two-tier network composed of conventional macrocellular networks and femtocell networks known as HetNets. As the femtocell shares the same frequency band with underlying macrocell, the cross tier interference needs to be mitigated. The inter-femtocell and cross tier interference near femtocell boundary may result in unwarranted degradation of system performance. In LTE-A, as an OFDM based technology, we consider the use of prudent PRB (Physical Resource Block) allocation to mitigate downlink inter-femtocell interference as well as cross tier interference in the mentioned environment for this small HetNet. Allocation of PRBs to network users is formulated as a graph coloring problem. Based on this interpretation and initial sensitivity study, we propose dynamic resource allocation algorithms namely greedy allocation (GA), SINR based allocation (SINRA) and ant colony allocation (ACA). From the simulation results and its analysis we can conclude that, ACA algorithm outperforms rest of the considered algorithms while GA and SINRA provide performance with the significant improvement over random allocation (RA) algorithm as far as performance parameters such as averaged SINR experienced by a network user, outage probability and number of PRBs needed are concerned.
Body-Sensor-Network-Based Kinematic Characterization and Comparative Outlook of UPDRS Scoring in Leg Agility, Sit-to-Stand, and Gait Tasks in Parkinson's Disease

Recently, we have proposed a body-sensor-network-based approach, composed of a few body-worn wireless inertial nodes, for automatic assignment of Unified Parkinson's Disease Rating Scale (UPDRS) scores in the following tasks: Leg agility (LA), Sit-to-Stand (S2S), and Gait (G). Unlike our previous works and the majority of the published studies, where UPDRS tasks were the sole focus, in this paper, we carry out a comparative investigation of the LA, S2S, and G tasks. In particular, after providing an accurate description of the features identified for the kinematic characterization of the three tasks, we comment on the correlation between the most relevant kinematic parameters and the UPDRS scoring. We analyzed the performance achieved by the automatic UPDRS scoring system and compared the estimated UPDRS evaluation with the one performed by neurologists, showing that the proposed system compares favorably with typical interrater variability. We then investigated the correlations between the UPDRS scores assigned to the various tasks by both the neurologists and the automatic system. The results, based on a limited number of subjects with Parkinson's disease (PD) (34 patients, 47 clinical trials), show poor-to-moderate correlations between the UPDRS scores of different tasks, highlighting that the patients' motor performance may vary significantly from one task to another, since different tasks relate to different aspects of the disease. An aggregate UPDRS score is also considered as a concise parameter, which can provide additional information on the overall level of the motor impairments of a Parkinson's patient. Finally, we discuss a possible implementation of a practical e-health application for the remote monitoring of PD patients.
Performance analysis and assessment of a tf-idf based archetype-SNOMED-CT binding algorithm

Term bindings in archetypes are at a boundary between health information models and health terminology for dual model-based electronic health-care record (EHR) systems. The development of archetypes and the population of archetypes with bound terms is in its infancy. Terminological binding is currently performed “manually” by the teams who create archetypes. This process could be made more efficient, if it was supported by automatic tools. This paper presents a method for evaluating the performance of automatic code search approaches. In order to assess the quality of the automatic search, the authors extracted all the unique bound codes from 1133 archetypes from an archetype repository. These “manually bound ”SNOMED-CT codes were compared against the codes suggested by the authors' automatic search and used for assessing the algorithm's performance in terms of accuracy and category matching. The result of this study shows a sensitivity analysis of a set of parameters relevant to the matching process.
Asymmetry Factors Shaping Regular and Irregular Bursting Rhythms in Central Pattern Generators

Central Pattern Generator (CPG) circuits are neural networks that generate rhythmic motor patterns. These circuits are typically built of half-center oscillator subcircuits with reciprocally inhibitory connections. Another common property in many CPGs is the remarkable rich spiking-bursting dynamics of their constituent cells, which balance robustness and flexibility to generate their joint coordinated rhythms. In this paper, we use conductance-based models and realistic connection topologies inspired by the crustacean pyloric CPG to address the study of asymmetry factors shaping CPG bursting rhythms. In particular, we assess the role of asymmetric maximal synaptic conductances, time constants and gap-junction connectivity to establish the regularity of half-center oscillator based CPGs. We map and characterize the synaptic parameter space that lead to regular and irregular bursting activity in these networks. The analysis indicates that asymmetric configurations display robust regular rhythms and that large regions of both regular and irregular but coordinated rhythms exist as a function of the asymmetry in the circuit. Our results show that asymmetry both in the maximal conductances and in the temporal dynamics of mutually inhibitory neurons can synergistically contribute to shape wide regimes of regular spiking-bursting activity in CPGs. Finally, we discuss how a closed-loop protocol driven by a regularity goal can be used to find and characterize regular regimes when there is not time to perform an exhaustive search, as in most experimental studies.
3 D-Stacked Memory-Side Acceleration : Accelerator and System Design

Specialized hardware acceleration is an effective technique to mitigate the dark silicon problems. A challenge in designing on-chip hardware accelerators for data-intensive applications is how to efficiently transfer data between the memory hierarchy and the accelerators. Although the Processingin-Memory (PIM) technique has the potential to reduce the overhead of data transfers, it is limited by the traditional process technology. Recent process technology advancements such as 3Ddie stacking enable efficient PIM architectures by integrating accelerators to the logic layer of 3D DRAM, thus leading to the concept of the 3D-stacked Memory-Side Accelerator (MSA). In this paper, we initially present the overall architecture of the 3D-stacked MSA, which relies on a configurable array of domain-specific accelerators. Thereafter, we describe a full-system prototype that is built upon a novel software stack and a hybrid evaluation methodology. Experimental results demonstrate that the 3D-stacked MSA achieves up to 179x and 96x better energyefficiency than the Intel Haswell processor for the FFT and matrix transposition algorithms, respectively.
Tracking join and self-join sizes in limited storage

Query optimizers rely on fast, high-quality estimates of result sizes in order to select between various join plans. Selfjoin sizes of relations provide bounds on the join size of any pairs of such relations. It also indicates the degree of skew in the data, and has been advocated for several estimation procedures. Exact computation of the self-join size requires storage proportional to, the number of distinct attribute values, which may be prohibitively large. In this paper, we study algorithms for tracking (approximate) self-join sizes in limited storage in the presence of insertions and deletions to the relations. Such algorithms detect changes in the degree of skew without an expensive recomputation from the base data. We show that an algorithm based on a tug-ofwar approach provides a more accurate estimation than one based on a sample-and-count approach which is in turn more accurate than a sampling-only approach. Next, we study algorithms for tracking (approximate) join sizes in limited storage; the goal is to maintain a small signature of each relation such that join sizes can be accurately estimated between any pairs of relations. We show that taking random samples for join signatures can lead to inaccurate estimation unless the sample size is quite large; moreover, by a lower bound we show, no other signature scheme can significantly improve upon sampling without further assumptions. These negative results are shown to hold even in the presence of sanity bounds. On the other hand, we present a join signature scheme based on tug-ofwar signatures that probvides guarantees on join size estimation as a function of t:he self-join sizes of the joining relations; this scheme can significantly improve upon the sampling scheme.
Reachability Queries in Very Large Graphs: A Fast Refined Online Search Approach

A key problem in many graph-based applications is the need to know, given a directed graph G and two vertices u,v ∈ G, whether there is a path between u and v, i.e., if u reaches v. This problem is particularly challenging in the case of very large real-world graphs. A common approach is the preprocessing of the graphs, in order to produce an efficient index structure, which allows fast access to the reachability information of the vertices. However, the majority of existing methods can not handle very large graphs. We propose, in this paper, a novel indexing method called FELINE (Fast rEfined onLINE search), which is inspired by Dominance Graph Drawing. FELINE creates an index from the graph representation in a two-dimensional plane, which provides reachability information in constant time for a significant portion of queries. Experiments demonstrate the efficiency of FELINE compared to state-of-the-art approaches.
Downregulated Ku70 and ATM associated to poor prognosis in colorectal cancer among Chinese patients

Background Double-strand DNA breaks (DSBs) are a key factor in carcinogenesis. The necessary repair of DSBs is pivotal in maintaining normal cell division. To address the relationship between altered expression of DSB repair of proteins Ku70 and ataxia-telangiectasia mutated (ATM) in colorectal cancer (CRC), we examined the expression levels and patterns of Ku70 and ATM in CRC samples. Methods Expression and coexpression of Ku70 and ATM were investigated by using real-time quantitative polymerase chain reaction assays and confirmed further with fluorescent immunohistochemistry in CRC and pericancerous samples from 112 Chinese patients. Results Downexpression patterns for both Ku70 and ATM were found in the CRC samples and were significantly associated with advanced tumor node metastasis stage and decreased 5-year overall survival rate. Conclusion Downregulated Ku70 and ATM were associated with poor disease-free survival. Loss of Ku70 and ATM expression might act as a biomarker to predict poor prognosis in patients with CRC.
Design and Implementation of an SFQ-Based Single-Chip FFT Processor

We have been working on the development of a high-speed fast Fourier transform (FFT) processor using single-flux-quantum (SFQ) logic circuits. In our previous studies, we designed and demonstrated a 4-bit butterfly processor, a data-shuffling circuit, and a twiddle factor ROM for 4-bit 8-point FFT using the AIST 10 kA/cm2 Nb advanced process 2 at maximum frequencies of 51.6, 59.5, and 51.5, respectively. In this study, to complete the FFT processor design, we designed and demonstrated residual component circuits called a rounding circuit and a data buffer at a target frequency of 50 GHz. We also designed and experimentally confirmed the operation of an SFQ-based single-chip FFT processor integrating all the component circuits.