Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards realistic HPC models of the neuromuscular system

Realistic simulations of detailed, biophysics-based, multi-scale models require very high resolution and, thus, large-scale compute facilities. Existing simulation environments, especially for biomedical applications, are designed to allow for a high flexibility and generality in model development. Flexibility and model development, however, are often a limiting factor for large-scale simulations. Therefore, new models are typically tested and run on small-scale compute facilities. By using a detailed biophysics-based, chemo-electromechanical skeletal muscle model and the international open-source software library OpenCMISS as an example, we present an approach to upgrade an existing muscle simulation framework from a moderately parallel version towards a massively parallel one that scales both in terms of problem size and in terms of the number of parallel processes. For this purpose, we investigate different modeling, algorithmic and implementational aspects. We present improvements addressing both numerical and parallel scalability. In addition, our approach includes a novel visualization environment, which is based on the MegaMol environment capable of handling large amounts of simulated data. It offers a platform for fast visualization prototyping, distributed rendering, and advanced visualization techniques. We present results of a variety of scaling studies at the Tier-1 supercomputer HazelHen at the High Performance Computing Center Stuttgart (HLRS). We improve the overall runtime by a factor of up to 2.6 and achieved good scalability on up to 768 cores, where the previous implementation used only 4 cores.
Sulfate Transporters in Dissimilatory Sulfate Reducing Microorganisms: A Comparative Genomics Analysis

The first step in the sulfate reduction pathway is the transport of sulfate across the cell membrane. This uptake has a major effect on sulfate reduction rates. Much of the information available on sulfate transport was obtained by studies on assimilatory sulfate reduction, where sulfate transporters were identified among several types of protein families. Despite our growing knowledge on the physiology of dissimilatory sulfate-reducing microorganisms (SRM) there are no studies identifying the proteins involved in sulfate uptake in members of this ecologically important group of anaerobes. We surveyed the complete genomes of 44 sulfate-reducing bacteria and archaea across six phyla and identified putative sulfate transporter encoding genes from four out of the five surveyed protein families based on homology. We did not find evidence that ABC-type transporters (SulT) are involved in the uptake of sulfate in SRM. We speculate that members of the CysP sulfate transporters could play a key role in the uptake of sulfate in thermophilic SRM. Putative CysZ-type sulfate transporters were present in all genomes examined suggesting that this overlooked group of sulfate transporters might play a role in sulfate transport in dissimilatory sulfate reducers alongside SulP. Our in silico analysis highlights several targets for further molecular studies in order to understand this key step in the metabolism of SRMs.
Imaging of Subsurface Corrosion Using Gradient-Field Pulsed Eddy Current Probes with Uniform Field Excitation

A corrosive environment leaves in-service conductive structures prone to subsurface corrosion which poses a severe threat to the structural integrity. It is indispensable to detect and quantitatively evaluate subsurface corrosion via non-destructive evaluation techniques. Although the gradient-field pulsed eddy current technique (GPEC) has been found to be superior in the evaluation of corrosion in conductors, it suffers from a technical drawback resulting from the non-uniform field excited by the conventional pancake coil. In light of this, a new GPEC probe with uniform field excitation for the imaging of subsurface corrosion is proposed in this paper. The excited uniform field makes the GPEC signal correspond only to the field perturbation due to the presence of subsurface corrosion, which benefits the corrosion profiling and sizing. A 3D analytical model of GPEC is established to analyze the characteristics of the uniform field induced within a conductor. Following this, experiments regarding the imaging of subsurface corrosion via GPEC have been carried out. It has been found from the results that the proposed GPEC probe with uniform field excitation not only applies to the imaging of subsurface corrosion in conductive structures, but provides high-sensitivity imaging results regarding the corrosion profile and opening size.
Low-power appliance monitoring using Factorial Hidden Markov Models

To optimize the energy utilization, intelligent energy management solutions require appliance-specific consumption statistics. One can obtain such information by deploying smart power outlets on every device of interest, however it incurs extra hardware cost and installation complexity. Alternatively, a single sensor can be used to measure total electricity consumption and thereafter disaggregation algorithms can be applied to obtain appliance specific usage information. In such a case, it is quite challenging to discern low-power appliances in the presence of high-power loads. To improve the recognition of low-power appliance states, we propose a solution that makes use of circuit-level power measurements. We examine the use of a specialized variant of Hidden Markov Model (HMM) known as Factorial HMM (FHMM) to recognize appliance specific load patterns from the aggregated power measurements. Further, we demonstrate that feature concatenation can improve the disaggregation performance of the model allowing it to identify device states with an accuracy of 90% for binary and 80% for multi-state appliances. Through experimental evaluations, we show that our solution performs better than the traditional event based approach. In addition, we develop a prototype system that allows real-time monitoring of appliance states.
An 11‐μ W, 9‐bit fully differential, cyclic/algorithmic ADC in 0.13 μm CMOS

This paper describes a fully differential, cyclic, analogue‐to‐digital converter (ADC). It utilizes a 4‐bit binary weighted capacitor array to obtain 9‐bit resolution. The ADC uses an operational amplifier to suppress supply voltage variations. The operational amplifier with the slew‐rate detection is used to increase the speed of the ADC. The ADC is fabricated in IBM 0.13 μm CMOS process and occupies 650 × 850μm2 active area. At 10 kS/s sampling rate, the ADC consumes 11 μW. In order to test immunity of the ADC on the supply voltage variations, static and dynamic performance of the ADC is measured with triangular supply voltage (VDC = 1.5 V, VAC = 200mVpp, f = 1 kHz). The measured peak of differential nonlinearity and integral nonlinearity is + 0.26/ − 0.67 and + 0.65/ − 0.59, respectively. At 250 Hz, effective number of bit is 8.4 bits, SFDR = 66.7 dB and SNDR = 52.6 dB. Copyright © 2016 John Wiley & Sons, Ltd.
Visibility Network Patterns and Methods for Studying Visual Relational Phenomena in Archeology

A review of the archaeological and non-archaeological use of visibility networks reveals the use of a limited range of formal techniques, in particular for representing visibility theories. This paper aims to contribute to the study of complex visual relational phenomena in landscape archaeology by proposing a range of visibility network patterns and methods. We propose first- and second-order visibility graph representations of total and cumulative viewsheds, and two-mode representations of cumulative viewsheds. We present network patterns that can be used to represent aspects of visibility theories, and that can be used in statistical simulation models to compare theorised networks with observed networks. We argue for the need to incorporate observed visibility network density in these simulation models, by illustrating strong differences in visibility network density in three example landscapes. The approach is illustrated through a brief case study of visibility networks of long barrows in Cranborne Chase.
An Overview of Two-Component Signal Transduction Systems Implicated in Extra-Intestinal Pathogenic E. coli Infections

Extra-intestinal pathogenic E. coli (ExPEC) infections are common in mammals and birds. The predominant ExPEC types are avian pathogenic E. coli (APEC), neonatal meningitis causing E. coli/meningitis associated E. coli (NMEC/MAEC), and uropathogenic E. coli (UPEC). Many reviews have described current knowledge on ExPEC infection strategies and virulence factors, especially for UPEC. However, surprisingly little has been reported on the regulatory modules that have been identified as critical in ExPEC pathogenesis. Two-component systems (TCSs) comprise the predominant method by which bacteria respond to changing environments and play significant roles in modulating bacterial fitness in diverse niches. Recent studies have highlighted the potential of manipulating signal transduction systems as a means to chemically re-wire bacterial pathogens, thereby reducing selective pressure and avoiding the emergence of antibiotic resistance. This review begins by providing a brief introduction to characterized infection strategies and common virulence factors among APEC, NMEC, and UPEC and continues with a comprehensive overview of two-component signal transduction networks that have been shown to influence ExPEC pathogenesis.
Targeting Complex Sentences in Older School Children With Specific Language Impairment: Results From an Early-Phase Treatment Study.

Purpose This study investigated the effects of a complex sentence treatment at 2 dosage levels on language performance of 30 school-age children ages 10-14 years with specific language impairment. Method Three types of complex sentences (adverbial, object complement, relative) were taught in sequence in once or twice weekly dosage conditions. Outcome measures included sentence probes administered at baseline, treatment, and posttreatment phases and comparisons of pre-post performance on oral and written language tests and tasks. Relationships between pretest variables and treatment outcomes were also explored. Results Treatment was effective at improving performance on the sentence probes for the majority of participants; however, results differed by sentence type, with the largest effect sizes for adverbial and relative clauses. Significant and clinically meaningful pre-post treatment gains were found on a comprehensive oral language test, but not on reading and writing measures. There was no treatment advantage for the higher dosage group. Several significant correlations indicated a relationship between lower pretest scores and higher outcome measures. Conclusions Results suggest that a focused intervention can produce improvements in complex sentence productions of older school children with language impairment. Future research should explore ways to maximize gains and extend impact to natural language contexts. Supplemental Material https://doi.org/10.23641/asha.5923318.
Detection of specific DNA lesions by a combination of comet assay and FISH in plants

We have studied comet formation on Vicia faba nuclei embedded in agarose and treated with the endonucleases DNase I (to produce SSBs or DSBs at random sites), FokI (to produce DSBs preferentially within FokI repeats), or EcoRI (to produce DSBs at random sites but not within FokI elements). DNase I–induced SSBs were detected when enzyme treatment was followed by alkaline denaturation. DSBs efficiently mediated comet formation using neutral conditions. FISH with DNA probes, detecting specific chromosomal domains such as FokI element‐containing heterochromatin, NORs, or telomeres, was done on comets. The distribution of FISH signals between the head and tail of comets indicated to which degree these domains were damaged and reflected the distribution of cleavage sites for the applied restriction endonucleases within these domains. The data confirmed the expectation that the observed comet formation was based on enzyme‐specific DNA breakage. Environ. Mol. Mutagen. 35:132‐138, 2000 © 2000 Wiley‐Liss, Inc.
Comparison of Workflow, Maintenance, and Consumables in the GeneXpert Infinity 80 and Panther Instruments While Testing for Chlamydia trachomatis and Neisseria gonorrhoeae

Background The 2015 Sexually Transmitted Diseases Treatment Guidelines from the Centers for Disease Control and Prevention recommend testing for Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) using nucleic acid amplification tests, and prompt treatment of infected persons on site under direct observation. Faster time to results may enable treatment and management outcomes. Methods Workflow parameters for processing 1, 10, 48, 96, and 192 tests were determined in the GeneXpert Infinity 80 (Cepheid) and Panther (Hologic) instruments. Results In an Xpert CT/NG cartridge, the time to first results on the Infinity 80 was 1 hour 30 minutes for single or multiple tests and final results for 10, 48, 96, and 192 tests were available at 1 hour 37 minutes, 1 hour 54 minutes, 3 hour 17 minutes, and 5 hour 7 minutes, respectively. With the Aptima CT/GC assay on the Panther, the respective times were 3 hr 45 min for the first test result, and 3 hour 51 minutes, 4 hour 38 minutes, 5 hour 26 minutes, and 7 hour 4 minutes to final results. The Panther required more time for maintenance and consumed a greater variety of plastics and reagents but required less hands-on time when testing larger numbers of specimens. Conclusions The Infinity 80 is a versatile instrument for continuous random access testing of small or large numbers of clinical specimens and may provide diagnostic results, in some settings, in time for treatment of CT and NG infections.
Phase interrogated plasmonic optical fiber optrode with bimetallic layers

Optical fiber optrodes are attractive sensing devices due to their ability to perform point measurement in remote locations. Mostly, they are oriented to biochemical sensing, quite often relying on fluorescent and spectroscopic techniques, but with the refractometric approach being also considered when the objective is high measurement performance, particularly when focusing on measurand resolution. In this work, we address this subject proposing and theoretically analyzing the characteristics of a fiber optic optrode relying on plasmonic interaction. The optrode structure is a fiber optic tapered tip layout incorporating a lateral bimetallic layer (silver + gold) and operating in reflection.
Applied Soft Computing Approximation of Functions with Spatial Inhomogeneity Based on " True " Ortho-ridgelet Neural Network

To approximate the multivariate functions with spatial inhomogeneity, in this paper we proposed an ortho-ridgelet neural network (ORNN) model. By taking orthonormal ridgelet, which is a " true " ridgelet function different with the " classic " ridgelet, as the activation function of the hidden neurons, the network is characterized of more efficient representation of a set of functions with linear and curvilinear singularities. Although the ortho-ridgelet is initially more complicated to be understood than ridgelet, it can be easily applied to function approximation theory in a more effective manner. Moreover, we extended the ORNN model to the time-varied situation, and the time-varied ortho-ridgelet neural network (TV-ORRN) is established for time-varied function approximation, with an incremental learning of the ridgelets output layer. The theoretical analysis is presented and some experiments are taken, and the experimental results prove its efficiency in approximation of multivariate functions with linear and curvilinear singularities, in both the stationary and time-varied environment.
An Intelligent Intersection

Intersections are hazardous places. Threats arise from interactions among pedestrians, bicycles and vehicles, more complicated vehicle trajectories in the absence of lane markings, phases that prevent knowing who has the right of way, invisible vehicle approaches, vehicle obstructions, and illegal movements. These challenges are not fully addressed by the "road diet" and road redesign prescribed in Vision Zero plans. Nor will they be completely overcome by autonomous vehicles with their many on-board sensors and tireless attention to sensor readings. Accidents can also occur because drivers, bicyclists and pedestrians do not have the information they need to avoid wrong decisions. In these cases, the missing information can be calculated and communicated by an intelligent intersection. The information gives the current full signal phase, an estimate of the time when the phase will change, the occupancy of the blind spots of the driver or autonomous vehicle, and detection of red-light violators. The paper develops a design of the intelligent intersection, motivated by the analysis of an accident at an intersection in Tempe, AZ, between an automated Uber Volvo and a manual Honda CRV. The intelligent intersection functions as a `protected intersection' using I2V communication.
A unified framework for predicting attributes and links in social networks

Node attributes and their associated links are two crucial components of user profiling in social networks. Recently, several research works show that both attributes and links are partially predictable by various classification methods. However, most of these works suffer from an isolation of attributes and links. In this paper, we propose a novel unified framework to predict attributes and links simultaneously, by a two-layer artificial neural network that encodes them in the same network. We obtain a better predictive accuracy on both attributes and links than the previous state-of-the-art methods, tested on different real-world datasets. Our results show that we can discover much more useful information in the whole social networks than in node attributes or their associated links alone.
Semantic Indexing of Wearable Camera Images: Kids'Cam Concepts

In order to provide content-based search on image media, including images and video, they are typically accessed based on manual or automatically assigned concepts or tags, or sometimes based on image-image similarity depending on the use case. While great progress has been made in very recent years in automatic concept detection using machine learning, we are still left with a mis-match between the semantics of the concepts we can automatically detect, and the semantics of the words used in a user's query, for example. In this paper we report on a large collection of images from wearable cameras gathered as part of the Kids'Cam project, which have been both manually annotated from a vocabulary of 83 concepts, and automatically annotated from a vocabulary of 1,000 concepts. This collection allows us to explore issues around how language, in the form of two distinct concept vocabularies or spaces, one manually assigned and thus forming a ground-truth, is used to represent images, in our case taken using wearable cameras. It also allows us to discuss, in general terms, issues around mis-match of concepts in visual media, which derive from language mis-matches. We report the data processing we have completed on this collection and some of our initial experimentation in mapping across the two language vocabularies.
Training Neural Networks by Using Power Linear Units (PoLUs)

In this paper, we introduce "Power Linear Unit" (PoLU) which increases the nonlinearity capacity of a neural network and thus helps improving its performance. PoLU adopts several advantages of previously proposed activation functions. First, the output of PoLU for positive inputs is designed to be identity to avoid the gradient vanishing problem. Second, PoLU has a non-zero output for negative inputs such that the output mean of the units is close to zero, hence reducing the bias shift effect. Thirdly, there is a saturation on the negative part of PoLU, which makes it more noise-robust for negative inputs. Furthermore, we prove that PoLU is able to map more portions of every layer's input to the same space by using the power function and thus increases the number of response regions of the neural network. We use image classification for comparing our proposed activation function with others. In the experiments, MNIST, CIFAR-10, CIFAR-100, Street View House Numbers (SVHN) and ImageNet are used as benchmark datasets. The neural networks we implemented include widely-used ELU-Network, ResNet-50, and VGG16, plus a couple of shallow networks. Experimental results show that our proposed activation function outperforms other state-of-the-art models with most networks.
A Comparative Survey of LPWA Networking

Motivated by the increasing variance of suggested Internet of Things (IoT) applications and the lack of suitability of current wireless technologies in scalable, long range deployments, a number of diverging Low Power Wide Area (LPWA) technologies have been developed. These technologies promise to enable a scalable high range network on cheap low power devices, facilitating the development of a ubiquitous IoT. This paper provides a definition of this new LPWA paradigm, presents a systematic approach to defined suitable use cases, and undertakes a detailed comparison of current LPWA standards, including the primary technologies, upcoming cellular options, and remaining proprietary solutions.
Neuronal populations in the occipital cortex of the blind synchronize to the temporal dynamics of speech

The occipital cortex of early blind individuals (EB) activates during speech processing, challenging the notion of a hard-wired neurobiology of language. But, at what stage of speech processing do occipital regions participate in EB? Here we demonstrate that parieto-occipital regions in EB enhance their synchronization to acoustic fluctuations in human speech in the theta-range (corresponding to syllabic rate), irrespective of speech intelligibility. Crucially, enhanced synchronization to the intelligibility of speech was selectively observed in primary visual cortex in EB, suggesting that this region is at the interface between speech perception and comprehension. Moreover, EB showed overall enhanced functional connectivity between temporal and occipital cortices sensitive to speech intelligibility and altered directionality when compared to the sighted group. These findings suggest that the occipital cortex of the blind adopts an architecture allowing the tracking of speech material, and therefore does not fully abstract from the reorganized sensory inputs it receives.
A comparison of the immune performance of a 1991 commercial broiler with a 1957 randombred strain when fed "typical" 1957 and 1991 broiler diets.

The general objective of the present study was to assess the contribution that changes in genetic selection and dietary regimen have made on the immune performance of broilers. Chicks were hatched from 1991 and 1957 strains and placed on diets thought to be typical of those fed during 1957 and 1991. Immune responses were measured as total, IgM, and IgG antibody production, macrophage, and natural killer (NK) cell functions. Significant differences were observed between strains in antibody production. For example, 1957 males fed 1957 diets had the highest total (P < .0001), IgM (P < .0016), and IgG (P < .015) anti-sheep red blood cell antibodies as compared with all other strain-diet-sex groups. Both strains behaved similarly in terms of inflammatory macrophage recruitment, substrate adherence potential, and in the phagocytosis of sheep red blood cells. A greater percentage of the 1991 strain birds exhibited NK cell activity than all other groups. These studies suggest that genetic selection towards enhanced performance traits has negatively influenced the adaptive arm of the immune system (antibody production) with little or no effect on the nonadaptive components (macrophage and NK functions).