Miracle-dz/UniFAR-FTUs huggingface dataset:
Kufs disease due to mutation of CLN6: clinical, pathological and molecular genetic features

Kufs disease is the major adult form of neuronal ceroid lipofuscinosis, but is rare and difficult to diagnose. Diagnosis was traditionally dependent on the demonstration of characteristic storage material, but distinction from normal age-related accumulation of lipofuscin can be challenging. Mutation of CLN6 has emerged as the most important cause of recessive Kufs disease but, remarkably, is also responsible for variant late infantile ceroid lipofuscinosis. Here we provide a detailed description of Kufs disease due to CLN6 pathogenic variants. We studied 20 cases of Kufs disease with CLN6 pathogenic variants from 13 unrelated families. Mean age of onset was 28 years (range 12-51) with bimodal peaks in teenage and early adult life. The typical presentation was of progressive myoclonus epilepsy with debilitating myoclonic seizures and relatively infrequent tonic-clonic seizures. Patients became wheelchair-bound with a mean 12 years post-onset. Ataxia was the most prominent motor feature. Dementia appeared to be an invariable accompaniment, although it could take a number of years to manifest and occasionally cognitive impairment preceded myoclonic seizures. Patients were usually highly photosensitive on EEG. MRI showed progressive cerebral and cerebellar atrophy. The median survival time was 26 years from disease onset. Ultrastructural examination of the pathology revealed fingerprint profiles as the characteristic inclusions, but they were not reliably seen in tissues other than brain. Curvilinear profiles, which are seen in the late infantile form, were not a feature. Of the 13 unrelated families we observed homozygous CLN6 pathogenic variants in four and compound heterozygous variants in nine. Compared to the variant late infantile form, there was a lower proportion of variants that predicted protein truncation. Certain heterozygous missense variants in the same amino acid position were found in both variant late infantile and Kufs disease. There was a predominance of cases from Italy and surrounding regions; this was partially explained by the discovery of three founder pathogenic variants. Clinical distinction of type A (progressive myoclonus epilepsy) and type B (dementia with motor disturbance) Kufs disease was supported by molecular diagnoses. Type A is usually caused by recessive pathogenic variants in CLN6 or dominant variants in DNAJC5. Type B Kufs is usually associated with recessive CTSF pathogenic variants. The diagnosis of Kufs remains challenging but, with the availability of genetic diagnosis, this will largely supersede the use of diagnostic biopsies, particularly as biopsies of peripheral tissues has unsatisfactory sensitivity and specificity.
Peripheral Insulin Extraction in Non-Diabetic Subjects and Type 2 Diabetes Mellitus Patients

Abstract Background Insulin has to be transported across the capillary endothelium to stimulate muscle glucose uptake. We investigated insulin uptake from the peripheral circulation in non-diabetic (ND) individuals and in type 2 diabetes (T2D) patients. Methods Single-center cross-sectional study involving 40 ND (age 65±11 years) and 30 T2D patients (age 67±8 years). Thirty-six participants were studied in the fasted state (22 ND subjects and 14 T2D patients termed NDF and T2DF) and 34 participants 1-h following a glucose challenge (18 ND subjects and 16 T2D patients indicated as NDG and T2DG). Main outcome measure was fractional extraction (FE) of insulin (FEins) and glucose using the forearm balance method. Results In NDF, FEins was 18 (10–26) % at lower insulin levels (63 51–80] pmol/l), while in NDG at higher insulin levels (776 [543–1176] pmol/l), FEins was 9 (4–16) % (p = 0.01 vs. NDF). In NDF only, a negative correlation was observed between FEins and arterial plasma insulin load (rho = − 0.575;p = 0.006) and fasting plasma glucose levels (rho = − 0.551;p = 0.01). In T2DF FEins was 6 (1–19) % and not different from FEins in T2DG (10 2–14) %), and was not associated to fasting glucose. FEins tended to be higher in NDF compared to T2DF (p = 0.07). Discussion We propose that in ND individuals, besides passive diffusion, an active high-affinity pathway with limited capacity around lower physiologic insulin levels exists for insulin transendothelial transport, contributing to glycemic control. In T2D patients, this mechanism of peripheral insulin uptake is diminished or even absent. Modulation of insulin extraction from the circulation may be a novel target to improve glucose metabolism in T2D.
Decentralized Collective Learning for Self-managed Sharing Economies

The Internet of Things equips citizens with a phenomenal new means for online participation in sharing economies. When agents self-determine options from which they choose, for instance, their resource consumption and production, while these choices have a collective systemwide impact, optimal decision-making turns into a combinatorial optimization problem known as NP-hard. In such challenging computational problems, centrally managed (deep) learning systems often require personal data with implications on privacy and citizens’ autonomy. This article envisions an alternative unsupervised and decentralized collective learning approach that preserves privacy, autonomy, and participation of multi-agent systems self-organized into a hierarchical tree structure. Remote interactions orchestrate a highly efficient process for decentralized collective learning. This disruptive concept is realized by I-EPOS, the Iterative Economic Planning and Optimized Selections, accompanied by a paradigmatic software artifact. Strikingly, I-EPOS outperforms related algorithms that involve non-local brute-force operations or exchange full information. This article contributes new experimental findings about the influence of network topology and planning on learning efficiency as well as findings on techno-socio-economic tradeoffs and global optimality. Experimental evaluation with real-world data from energy and bike sharing pilots demonstrates the grand potential of collective learning to design ethically and socially responsible participatory sharing economies.
Cortical activation during voluntary and passive movement of human index finger

Passive movement, motor imagery, and observation of movement have been able to activate the somatosensory and motor cortex of brain without voluntary movement. In this paper, we have investigated cortical activation in brain using a finger movement passive mode together with voluntary movement and analysis is based on within-subject design protocol. Five subjects have participated in movement of finger tasks meanwhile their brain activity is monitored using functional near-infrared spectroscopy (fNIRS). The two gamma functions are used to model the conventional hemodynamic response function (cHRF) to determine the active locations. The t-maps (brain maps) are generated for the comparison of oxy-hemoglobin (HbO) responses of both voluntary and passive movement of index finger tasks. The results have demonstrated that both movement modes activated sensorimotor areas. The neural activation pattern in voluntary execution of finger is somewhat similar to passive movement. The results of this study will help in checking whether passive movements can be able to induce suitable amount of somatosensory stimulation. Our results inveterate that the voluntary and passive movement task are strongly coupled, supporting the importance of passive tasks as a diagnostic tool in the clinical setting.
Virtualization for Scientific Workload

The computationally intensive applications (HPC) and the data analytics within the Big Data applications (BDA) constitute a combined workflow for the scientific discovery. Yet, the development and the execution environment for the HPC and the BDA have different origins and also within a single discipline there are large differences for the requirements for the provisioning of the libraries and tools within the Linux Operating System. Traditionally the HPC library versioning is addressed with the software Modules and other multi-root building frameworks by the system of administration. This does not necessary provide for the compatibility between different systems where the programs should be running. Such compatibility is required for the effective sharing of the code between scientific institutions and deployment on the centralized Supercomputing resources (Centers of Collective Usage). The flexibility of the software build model and the flexibility of subsequent deployment in the target computer center may be provided by the Virtualization and/or Containerization of the software. The Virtualization framework addresses also the problem of the unification between the HPC and the BDA workflow for a common scientific discovery model. Therefore virtualization is chosen as the software basis of the recent cluster deployment at Skoltech computer center. In the framework of this deployment model we have investigated the implications for the program run time when deployed in a virtualized environment. For the multitude of the scientific goals no compromise on the machine performance and program scalability can be accepted by the users of the system. Therefore a measurement of these parameters was undertaken for a representative range of scientific applications and for a multitude of running environments including the usage of CPU, GPU and High Speed networks. In this article we report a joined work between the Skolkovo Institute of Technology (Skoltech) and the National Research Center (Kurchatov Institute) comprising this evaluation. We have established the build and running environment using several container technologies. The setup retains more than 95% of the performance compared to the bare metal run times. The conditions for the usage of the Nvidia GPGPU within the containers are described and we have tested a model of the load balancing by migration of the containers to a different node. The presented work paves the way to a more flexible deploy everywhere model of execution that will speed up the scientific discovery and improve the opportunity for science.
Functional characterization of a novel arachidonic acid 12S‐lipoxygenase in the halotolerant bacterium Myxococcus fulvus exhibiting complex social living patterns

Lipoxygenases are lipid peroxidizing enzymes, which frequently occur in higher plants and mammals. These enzymes are also expressed in lower multicellular organisms but here they are not widely distributed. In bacteria, lipoxygenases rarely occur and evaluation of the currently available bacterial genomes suggested that <0.5% of all sequenced bacterial species carry putative lipoxygenase genes. We recently rescreened the public bacterial genome databases for lipoxygenase‐like sequences and identified two novel lipoxygenase isoforms (MF‐LOX1 and MF‐LOX2) in the halotolerant Myxococcus fulvus. Both enzymes share a low degree of amino acid conservation with well‐characterized eukaryotic lipoxygenase isoforms but they involve the catalytically essential iron cluster. Here, we cloned the MF‐LOX1 cDNA, expressed the corresponding enzyme as N‐terminal hexa‐his‐tag fusion protein, purified the recombinant enzyme to electrophoretic homogeneity, and characterized it with respect to its protein‐chemical and enzymatic properties. We found that M. fulvus expresses a catalytically active intracellular lipoxygenase that converts arachidonic acid and other polyunsaturated fatty acids enantioselectively to the corresponding n‐9 hydroperoxy derivatives. The enzyme prefers C20‐ and C22‐polyenoic fatty acids but does not exhibit significant membrane oxygenase activity. The possible biological relevance of MF‐LOX1 will be discussed in the context of the suggested concepts of other bacterial lipoxygenases.
A Syntax-Guided Neural Model for Natural Language Interfaces to Databases

Recent advances in neural code generation have incorporated syntax to improve the generation of the target code based on the user's request in natural language. We adapt the model of [1] to the Natural Language Interface to Databases (NLIDB) problem by taking into account the database schema. We evaluate our model on the recently introduced WIKISQL and SENLIDB datasets. Our results show that the syntax-guided model outperforms a simple sequence-to-sequence (SEQ2SEQ) baseline on WIKISQL, but has trouble with the SENLIDB dataset due to its complexity.
The Discourse of the Sublime: Readings in History, Aesthetics and the Subject

The Phantasmic Body Introductory: Towards the Subject The Discourse on the Sublime The Discourse of the Sublime The Discourse of Debt The Voice of Liberty Of the Gesture of the Orator The Speaking Subject The Body in Place Of the Distance of the Picture The Viewing Subject A Licentious Portrait Of the Transport of the Reader The Reading Subject Of the Sublime.
An Efficient Ant Colony Optimization Algorithm for Protein Structure Prediction

Protein structure prediction is considered as one of the most long-standing and challenging problem in bioinformatics. In this paper, we present an efficient ant colony optimization algorithm to predict the protein structure on three-dimensional face-centered cubic lattice coordinates, using the hydrophobic-polar model and the Miyazawa-Jernigan model to calculate the free energy. The reinforcement learning information is expressed in the k-order Markov model, and the heuristic information is determined based on the increase of the total energy. On a set of benchmark proteins, the results show a remarkable efficiency of our algorithm in comparison with several state-of-the-art algorithms.
Study on the Optimization of CNN Based on Image Identification

The feature extraction method of traditional image classification is difficult to deal with complex image problems. Although feature detector based on convolutional neural network can easily extract image features, many current network models have poor recognition accuracy and too many parameters. This paper proposes a multi-scale dual-channel dimension reduction module (DR module) to extract image features. Based on the AlexNet model, a deep global optimization model (GONET model) is proposed by exploiting the DR module, dropout and global pooling strategy. Compared with the AlexNet model, this model has better recognition performance. The accuracy on the Caltech256 dataset reaches 58.8% with GONET model, exceeding that of the AlexNet model by about 4.0%. The accuracy on the 101_food dataset reaches 69.0% with GONET model, exceeding that of the AlexNet model by about 8.9%. The experimental results show that the GONET model has superior image recognition effect, and it significantly reduces the network parameters while improving the recognition accuracy.
5G Centralized Multi-Cell Scheduling for URLLC: Algorithms and System-Level Performance

We study centralized radio access network (C-RAN) with multi-cell scheduling algorithms to overcome the challenges for supporting ultra-reliable low-latency communications (URLLC) in the fifth-generation new radio (5G NR) networks. Low-complexity multi-cell scheduling algorithms are proposed for enhancing the URLLC performance. In comparison with the conventional distributed scheduling, we show that the C-RAN architecture can significantly reduce undesirable queuing delay of URLLC traffic. The gain of user scheduling with different metrics and the benefit of packet segmentation are analyzed. The performance of the proposed solutions is evaluated with an advanced 5G NR compliant system-level simulator with high degree of realism. The results show that the centralized multi-cell scheduling achieves up to 60% latency improvement over the traditional distributed scheduling while fulfilling the challenging reliability of URLLC. It is shown that segmentation brings additional performance gain for both centralized and distributed scheduling. The results also highlight the significant impact of channel- and delay-aware scheduling of URLLC payloads.
Inference and control of the nosocomial transmission of methicillin-resistant Staphylococcus aureus

Methicillin-resistant Staphylococcus aureus (MRSA) is a continued threat to human health in both community and healthcare settings. In hospitals, control efforts would benefit from accurate estimation of asymptomatic colonization and infection importation rates from the community. However, developing such estimates remains challenging due to limited observation of colonization and complicated transmission dynamics within hospitals and the community. Here, we develop an inference framework that can estimate these key quantities by combining statistical filtering techniques, an agent-based model, and real-world patient-to-patient contact networks, and use this framework to infer nosocomial transmission and infection importation over an outbreak spanning 6 years in 66 Swedish hospitals. In particular, we identify a small number of patients with disproportionately high risk of colonization. In retrospective control experiments, interventions targeted to these individuals yield a substantial improvement over heuristic strategies informed by number of contacts, length of stay and contact tracing.
Text Mining Analysis of Teaching Evaluation Questionnaires for the Selection of Outstanding Teaching Faculty Members

This paper was conducted in collaboration with the Office of Institutional Research at National Ilan University in Taiwan to analyze textual opinions found in teaching evaluation questionnaires and applied the analysis results to assisting the selection of outstanding teaching faculty members. The selection of outstanding teachers requires that selection committee members spend a large amount of time reviewing written data. Therefore, this paper develops a set of systems for the analysis of textual opinions in teaching evaluation questionnaires, providing reference materials for the selection committee. The teaching evaluation questionnaire is a form of educational data. This paper analyzes these data using educational data mining. In text mining, text sentiment analysis is a common textual data quantification method that can analyze the sentiment tendency of a text author. This paper uses a text sentiment analysis to quantify the students’ textual opinions and to provide the selection committee with the sentiment tendency of students’ comments on teaching faculty members. We analyze text sentiment separately for different classifiers by using the Chinese text sentiment analysis kit SnowNLP. We compare the efficacy of classifiers that do not take time series factors into consideration (naïve Bayes and fully connected neural network) to those that do [recurrent neural network (RNN), long short-term memory (LSTM) RNN, and attention RNN]. We found that classifiers that consider time series factors are more effective at analyzing text sentiment. Furthermore, adding LSTM cells and an attention mechanism to a traditional RNN classifier effectively improved its efficacy on long-sequence tasks. As a result, we chose the attention LSTM classifier—with a positive sentiment recognition rate of 97% and a negative sentiments recognition rate of 87%—as our preferred text sentiment classifier. Finally, we set up an analytics server that will be modularized to facilitate its integration into the systems of different schools.
Proportional Resonant Based Current Control Of Three-Phase Single Stage Grid Connected PV System

This paper presents an analysis of Proportional Integral (PI) controller based current control strategy and a Proportional Resonant (PR) controller based current control strategy for a three phase single stage grid connected PV system. PR controller has an advantage of tracking sinusoidal reference current without steady state error. It eliminates the number of coordinate transformation and reduces the complexity of control. It also reduces the current harmonics effectively and injects high quality power to the grid. The system is simulated using MATLAB and the results demonstrate that the PR based control strategy exhibits an excellent performance in tracking the reference current and in reducing the Total Harmonic Distortion (THD) in the current spectrum compared to the PI based control strategy.
Substrate inhibition imposes fitness penalty at high protein stability

Significance Why highly stable proteins are not commonly observed in mesophilic organisms is an important evolutionary question. It has been suggested that high stability has a fitness cost in the form of loss of activity. However, this hypothesis has not yet been experimentally demonstrated. Here, we use an essential bacterial enzyme adenylate kinase (Adk) to explore this hypothesis and show that, as Adk’s stability increases, one of its own substrates inhibits its activity. Bacterial strains carrying such stable Adks show substantial fitness defects, which can be mapped to the loss of Adk activity due to substrate inhibition. Overall, our study adds “substrate inhibition” to the “toolbox” that is used to rationalize the stability distribution of proteins. Proteins are only moderately stable. It has long been debated whether this narrow range of stabilities is solely a result of neutral drift toward lower stability or purifying selection against excess stability—for which no experimental evidence was found so far—is also at work. Here, we show that mutations outside the active site in the essential Escherichia coli enzyme adenylate kinase (Adk) result in a stability-dependent increase in substrate inhibition by AMP, thereby impairing overall enzyme activity at high stability. Such inhibition caused substantial fitness defects not only in the presence of excess substrate but also under physiological conditions. In the latter case, substrate inhibition caused differential accumulation of AMP in the stationary phase for the inhibition-prone mutants. Furthermore, we show that changes in flux through Adk could accurately describe the variation in fitness effects. Taken together, these data suggest that selection against substrate inhibition and hence excess stability may be an important factor determining stability observed for modern-day Adk.
Deep Holistic Representation Learning from EHR

In recent years there has been a surge of interest in applying deep neural networks to electronic health records (EHRs) for predictive clinical tasks. EHR data cannot be mined like traditional image or text data because it has unique characteristics including temporality, irregularity, heterogeneity (both structured and unstructured) and incompleteness. We begin by identifying weaknesses in the way deep learning is currently being applied to health data. Then, leveraging these insights, we propose an end-to-end strategy for extracting complimentary deep feature representations from EHRs. This strategy is based on a “bringing model to data” machine learning approach instead of “transforming data to model”. It uses multiple neural networks, that have each been optimised for the characteristics of their input data, to extract features. Then, the output of these neural networks is combined. We show that prediction accuracy improves as the output of each neural network is contributed. This work demonstrates the value of extracting relevant insights from different aspects of a patients record, which is analogous to how a clinician makes decisions.