Miracle-dz/UniFAR-FTUs huggingface dataset:
Trade-off between Multiple Constraints Enables Simultaneous Formation of Modules and Hubs in Neural Systems

The formation of the complex network architecture of neural systems is subject to multiple structural and functional constraints. Two obvious but apparently contradictory constraints are low wiring cost and high processing efficiency, characterized by short overall wiring length and a small average number of processing steps, respectively. Growing evidence shows that neural networks are results from a trade-off between physical cost and functional value of the topology. However, the relationship between these competing constraints and complex topology is not well understood quantitatively. We explored this relationship systematically by reconstructing two known neural networks, Macaque cortical connectivity and C. elegans neuronal connections, from combinatory optimization of wiring cost and processing efficiency constraints, using a control parameter , and comparing the reconstructed networks to the real networks. We found that in both neural systems, the reconstructed networks derived from the two constraints can reveal some important relations between the spatial layout of nodes and the topological connectivity, and match several properties of the real networks. The reconstructed and real networks had a similar modular organization in a broad range of , resulting from spatial clustering of network nodes. Hubs emerged due to the competition of the two constraints, and their positions were close to, and partly coincided, with the real hubs in a range of values. The degree of nodes was correlated with the density of nodes in their spatial neighborhood in both reconstructed and real networks. Generally, the rebuilt network matched a significant portion of real links, especially short-distant ones. These findings provide clear evidence to support the hypothesis of trade-off between multiple constraints on brain networks. The two constraints of wiring cost and processing efficiency, however, cannot explain all salient features in the real networks. The discrepancy suggests that there are further relevant factors that are not yet captured here.
Property evaluation of nanostructured fly ash-reinforced acrylonitrile butadiene styrene nanocomposites

Fly ash, a waste generated from thermal power plant, can be used as reinforcing agent for polymer composite applications due to its fine particle size and abundant availability. This article highlights the experimental findings of modification of fly ash to convert its size from micro to nano and its nanocomposites preparation through melt blending technique based on acrylonitrile butadiene styrene (ABS) as matrix material. The nanostructured fly ash (NFA) developed through mechanical milling is characterized to understand its viability toward the filler material in polymer composites. The study showed that the NFA produced after 40 h of milling has a broken rougher surface with improved amorphous nature. Further, a detailed study on the properties of ABS nanocomposites reinforced with NFA has been conducted. ABS/NFA composites at 7 wt% loading have showed better thermomechanical properties. These results are further supported with the fractured surface analysis of nanocomposite using various morphological techniques. The outcome of this study suggests the potential use of nano fly ash to develop sustainable polymer nanocomposites for high-end industrial applications.
An Evaluation Approach for Analyzing Workflow Management Systems from a Value-based Perspective

Workflow management systems (WfMS) have become a widely accepted software technology, which enables the effective management, execution, and monitoring of computerized processes. Though the introduction of WfMS results in high costs, corresponding investments are often justified solely by referring to assumed benefits such as improved business process performance. In fact, there exists no framework to systematically evaluate WfMS from a value-based perspective. One major reason for this drawback is that research about the economic impact of WfMS has to deal with many challenges like the potential identification of impact factors, the handling of dependencies among these impact factors, the collection of real-world data, and the quantification of costs and intangible benefits. This paper presents an evaluation methodology to tackle these problems, which is based upon modeling and simulation
The Causes of Boat Hull Blisters

Abstract : This report is a highly-readable and condensed summary of the work undertaken to investigate the causes of blistering in fiberglass reinforced polyester composites, the structural material of many small boats, which are subject to blistering. The report is divided into the following sections: Introduction; How Blisters Form; the Hull Material; Manufacturing Processes; Water Diffusion in Hull Materials; Stresses in Hull Materials; Testing for Blister Resistance; Types of Blisters; Leaching of Substances from Hull Material; Long Term Effects of Water Up-Take. Keywords: Blisters, Boat fiberglass.
Prediction of Compounds Activity in Nuclear Receptor Signaling and Stress Pathway Assays Using Machine Learning Algorithms and Low-Dimensional Molecular Descriptors

Toxicity evaluation of newly synthesized or used compounds is one of the main challenges during product development in many areas of industry. For example, toxicity is the second reason - after lack of efficacy - for failure in preclinical and clinical studies of drug candidates. To avoid attrition at the late stage of the drug development process, the toxicity analyses are employed at the early stages of a discovery pipeline, along with activity and selectivity enhancing. Although many assays for screening in vitro toxicity are available, their massive application is not always time and cost effective. Thus the need for fast and reliable in silico tools, which can be used not only for toxicity prediction of existing compounds, but also for prioritization of compounds planned for synthesis or acquisition. Here I present the benchmark results of the combination of various attribute selection methods and machine learning algorithms and their application to the data sets of the Tox21 Data Challenge. The best performing method: Best First for attribute selection with the Rotation Forest/ADTree classifier offers good accuracy for most tested cases. For 11 out of 12 targets, the AUROC value for the final evaluation set was ≥0.72, while for three targets the AUROC value was ≥ 0.80, with the average AUROC being 0.784±0.069. The use of two-dimensional descriptors sets enables fast screening and compound prioritization even for a very large database. Open source tools used in this project make the presented approach widely available and encourage the community to further improve the presented scheme.
Shannon Entropy based on the S-Transform Spectrogram applied on the classification of heart sounds

The aim of this study is to present a complexity measure (Normalized Shannon Entropy) based on the S-Transform Spectrogram (ST-Spectrogram) plane, which can be considered as a variant of the Cohen's class. The ST-Spectrogram verifies the non-negativity condition which makes the application of the famous Shannon Entropy measure possible. A concrete application presented in this paper consists to detect pathologic heart sounds with systolic murmurs. A systolic period which contains murmurs is usually more complex than normal sound. The complexity measure applied on the ST-Spectrogram is used as feature to classify normal and pathologic heart sounds. A comparison with the classical spectrogram (STFT-Spectrogram) is performed by calculating the different Receiver Operating Curves (ROC) and the robustness against additive Gaussian noise is discussed.
Retrospective analysis of the HIV-1 reverse transcriptase inhibitors’ resistance in Silesia, Poland

Summary Background This study was a retrospective analysis of drug resistance mutations among HIV-1 strains prevalent in Silesia, Poland, from the origin of the epidemic to 2004. The investigations included both type and frequency of the reverse transcriptase inhibitors’ resistance mutations and estimation of the drugs’ resistance levels. Material/Methods Proviral DNA, obtained from peripheral blood mononuclear cells of the 101 HIV-1–infected patients, was amplified and sequenced in the pol gene fragment covering the first 256 codons of the reverse transcriptase (RT). Reverse transcriptase inhibitors resistance mutations were determined and interpreted with the HIVdb: Genotypic Resistance Interpretation Algorithm available from the Stanford University HIV Drug Resistance Database. In the examined population, 35 subjects (34.7%) received no antiretroviral treatment by the time of specimen collection. Results The overall frequency of the RT inhibitors resistance mutations in the studied population was 15.8%. Substitutions related to the reverse transcriptase inhibitors resistance were identified in 10 pol gene sequences (9.9%), all of them were present in the HIV-1 sequences obtained from persons receiving antiretroviral therapy. Conclusions Lack of drug-resistant viruses among treatment-naïve Silesian patients HIV-1-infected before the year 2004 may indicate that there was no transmission of the drug-resistant viruses in the studied population to that time.
Two-photon and second harmonic generation microscopy of porcine eye: implications to conductive keratoplasty

Two-photon autofluorescence and second harmonic generation (SHG) microscopy are useful imaging techniques for studying tissue components. In this work, we apply this imaging modality to study porcine eye. In particular, we use SHG microscopy to investigate the structural changes to excised porcine corneas and found that this technique is useful for studying its structural changes under thermal treatment.
A powerful toolkit for synthetic biology: Over 3.8 billion years of evolution

The combination of evolutionary with engineering principles will enhance synthetic biology. Conversely, synthetic biology has the potential to enrich evolutionary biology by explaining why some adaptive space is empty, on Earth or elsewhere. Synthetic biology, the design and construction of artificial biological systems, substitutes bio‐engineering for evolution, which is seen as an obstacle. But because evolution has produced the complexity and diversity of life, it provides a proven toolkit of genetic materials and principles available to synthetic biology. Evolution operates on the population level, with the populations composed of unique individuals that are historical entities. The source of genetic novelty includes mutation, gene regulation, sex, symbiosis, and interspecies gene transfer. At a phenotypic level, variation derives from regulatory control, replication and diversification of components, compartmentalization, sexual selection and speciation, among others. Variation is limited by physical constraints such as diffusion, and chemical constraints such as reaction rates and membrane fluidity. While some of these tools of evolution are currently in use in synthetic biology, all ought to be examined for utility. A hybrid approach of synthetic biology coupled with fine‐tuning through evolution is suggested.
Probing Confined Phonon Modes by Transport through a Nanowire Double Quantum Dot

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Pragmatic optimizations for better scientific utilization of large supercomputers

Advances in modeling and algorithms, combined with growth in computing resources, have enabled simulations of multiphysics–multiscale phenomena that can greatly enhance our scientific understanding. However, on currently available high-performance computing (HPC) resources, maximizing the scientific outcome of simulations requires many trade-offs. In this paper we describe our experiences in running simulations of the explosion phase of Type Ia supernovae on the largest available platforms. The simulations use FLASH, a modular, adaptive mesh, parallel simulation code with a wide user base. The simulations use multiple physics components: hydrodynamics, gravity, a sub-grid flame model, a three-stage burning model, and a degenerate equation of state. They also use Lagrangian tracer particles, which are then post-processed to determine the nucleosynthetic yields. We describe the simulation planning process, and the algorithmic optimizations and trade-offs that were found to be necessary. Several of the optimizations and trade-offs were made during the course of the simulations as our understanding of the challenges evolved, or when simulations went into previously unexplored physical regimes. We also briefly outline the anticipated challenges of, and our preparations for, the next-generation computing platforms.
Interrelationships between Yeast Ribosomal Protein Assembly Events and Transient Ribosome Biogenesis Factors Interactions in Early Pre-Ribosomes

Early steps of eukaryotic ribosome biogenesis require a large set of ribosome biogenesis factors which transiently interact with nascent rRNA precursors (pre-rRNA). Most likely, concomitant with that initial contacts between ribosomal proteins (r-proteins) and ribosome precursors (pre-ribosomes) are established which are converted into robust interactions between pre-rRNA and r-proteins during the course of ribosome maturation. Here we analysed the interrelationship between r-protein assembly events and the transient interactions of ribosome biogenesis factors with early pre-ribosomal intermediates termed 90S pre-ribosomes or small ribosomal subunit (SSU) processome in yeast cells. We observed that components of the SSU processome UTP-A and UTP-B sub-modules were recruited to early pre-ribosomes independently of all tested r-proteins. On the other hand, groups of SSU processome components were identified whose association with early pre-ribosomes was affected by specific r-protein assembly events in the head-platform interface of the SSU. One of these components, Noc4p, appeared to be itself required for robust incorporation of r-proteins into the SSU head domain. Altogether, the data reveal an emerging network of specific interrelationships between local r-protein assembly events and the functional interactions of SSU processome components with early pre-ribosomes. They point towards some of these components being transient primary pre-rRNA in vivo binders and towards a role for others in coordinating the assembly of major SSU domains.
An Improved MPPT Method of Wind Turbine Based on HCS Method by Using Fuzzy Logic System

In this paper presents a Maximum Power Point Tracking (MPPT) technique based on the Hill Climbing Search (HCS) method and fuzzy logic system for Wind Turbines (WTs) including of Permanent Magnet Synchronous Generator (PMSG) as generator. In the conventional HCS method the step size is constant, therefor both steady-state response and dynamic response of method cannot provide at the same time and in the fixed step size of HCS method. The propose method of this paper is improvement the performance HCS method, in order to reach this goal; the fuzzy logic system has been used. The fuzzy logic system based on operation condition determined the step size instantaneously, such as both steady-state response and dynamic response of method be proper at the same time, therefore, efficiency of the new method that used variable step size strategy, will be guaranteed, the results of simulation in environment MATLAB/Simulink software have been shown to be effectiveness of the proposed method.
Refactoring the Genia Event Extraction Shared Task Toward a General Framework for IE-Driven KB Development

For its fourth organization, the Genia event extraction (GE) shared task is refactored toward a general platform for shared information extraction (IE) tasks, and for an IEdriven knowledge base (KB) system. On the newly implemented shared task platform, the GE task is run as an experimental task. The task and the platform has been tested by two teams who cooperated with the organizers. The paper presents the new shared task system and discusses on the experimental submissions.
The chromatin landscape of the moss Physcomitrella patens and its dynamics during development and drought stress.

The moss Physcomitrella patens is an important model organism for evo-devo studies. Here, we determined the genome-wide chromatin landscape of five important histone three (H3) modifications (H3K4me3, H3K27me3, H3K27Ac, H3K9Ac and H3K9me2) and describe the changes to these histone marks in two contrasted situations, developmental transition and abiotic (drought) stress. Integrative analysis of these histone H3 modifications revealed their preferential association into 15 chromatin states (CS) in genic regions of the P. patens genome. Synergistic relationships that influence expression levels were revealed for the three activating marks H3K4me3, H3K27Ac and H3K9Ac, while an antagonistic relationship was found between CS containing the H3K27me3 and H3K27Ac marks, suggesting that H3K27 is a key indexing residue regarding transcriptional output. Concerning the alteration of histone marks in response to developmental transition (juvenile to adult) and drought stress, the three activating marks H3K4me3, H3K27Ac and H3K9Ac show significant changes in both situations. However, changes to H3K27me3 are central only for genes differentially expressed during development. Interestingly, genes induced during drought stress show significant histone mark toggling during developmental transition. This situation suggests that drought induced adult (gametophore expressed) genes are primed to respond to this stress during the juvenile to adult transition.
Integrating Dataflow Abstractions into the Shared Memory Model

In this paper we present Atomic Dataflow model (ADF), a new task-based parallel programming model for C/C++ which integrates dataflow abstractions into the shared memory programming model. The ADF model provides pragma directives that allow a programmer to organize a program into a set of tasks and to explicitly define input data for each task. The task dependency information is conveyed to the ADF runtime system which constructs the dataflow task graph and builds the necessary infrastructure for dataflow execution. Additionally, the ADF model allows tasks to share data. The key idea is that computation is triggered by dataflow between tasks but that, within a task, execution occurs by making atomic updates to common mutable state. To that end, the ADF model employs transactional memory which guarantees atomicity of shared memory updates. We show examples that illustrate how the programmability of shared memory can be improved using the ADF model. Moreover, our evaluation shows that the ADF model performs well in comparison with programs parallelized using OpenMP and transactional memory.
Injectable systems and implantable conduits for peripheral nerve repair

Acute sensory problems following peripheral nerve injury include pain and loss of sensation. Approximately 360 000 people in the United States suffer from upper extremity paralytic syndromes every year. Restoration of sufficient functional recovery after long-gap peripheral nerve damage remains a clinical challenge. Potential nerve repair therapies have increased in the past decade as the field of tissue engineering expands. The following review describes the use of biomaterials in nerve tissue engineering. Namely, the use of both synthetic and natural biomaterials, including non-degradable and degradable nerve grafts, is addressed. The enhancement of axonal regeneration can be achieved by further modification of the nerve guides. These approaches include injectable hydrogel fillers, controlled drug delivery systems, and cell incorporation. Hydrogels are a class of liquid–gel biomaterials with high water content. Injectable and gelling hydrogels can serve as growth factor delivery vehicles and cell carriers for tissue engineering applications. While natural hydrogels and polymers are suitable for short gap nerve repair, the use of polymers for relatively long gaps remains a clinical challenge.
Significance of Aphasia after First-Ever Acute Stroke: Impact on Early and Late Outcomes

Background: We assessed the incidence and determinants of aphasia attributable to first-ever acute stroke. We also investigated early and long-term mortality and 1-year dependence in post-stroke patients. Methods: A 10-year prospective hospital-based study was conducted in the prefecture of Athens, Greece. Results: In total, 2,297 patients were included in the study, of whom 806 (35.1%) had aphasia. The presence of aphasia was independently associated with increasing age (OR: 1.19 per 10-year increase, 95% CI: 1.12–1.21) and atrial fibrillation (OR: 1.35, 95% CI: 1.08–1.67), and inversely associated with Scandinavian Stroke Scale (SSS) score (OR: 0.55 per 10-point increase, 95% CI: 0.52–0.59) and hypertension (OR: 0.77, 95% CI: 0.63–0.96). One-year dependence score (calculated with the modified Rankin score) was higher in aphasic patients compared to non-aphasics (p < 0.001). Moreover, severity of aphasia (estimated with a subscale of SSS) was found as an independent predictor of 1-year dependence. Most of the deaths in the aphasic patients were attributed to infections and neurological damage. Using the Kaplan-Meier limit method, the unadjusted probability of 10-year mortality was demonstrated to increase with the severity of aphasia (log-rank test: 233.9, p < 0.001) and, even after adjustment for several other factors, severity of aphasia remained an independent predictor of 10-year mortality. Conclusions: Increasing age, atrial fibrillation and severity of stroke were associated with the risk of aphasia after stroke. Severity of aphasia is a strong predictor of long-term mortality and dependence of post-stroke patients.
Cognitive WMNs: A distributed mechanism for leasing cellular bandwidth

We study flow optimization in cognitive Wireless Mesh Networks (WMN) that lease bandwidth from a collocated cellular network (CN). We develop a distributed mechanism that such WMNs can use to optimize their traffic given their own utilities and costs and the costs that the CN is charging for the use of its channels. We evaluate the mechanism in a synthetic scenario using the topology of a deployed WMN and time-varying traffic requirements induced from real-life vehicle mobility traces. Simulations show that the mechanism can track well the changes in the problem parameters, continuously keeping close to the optimum operation point.