Miracle-dz/UniFAR-FTUs huggingface dataset:
Down-expressed GLT-1 in PSD astrocytes inhibits synaptic formation of NSC-derived neurons in vitro

ABSTRACT Little is known about the effect of astroglial GLT-1 of post-stroke depression (PSD) rat model on the function of neural stem cells (NSCs). This study aimed to investigate whether astroglial GLT-1 of PSD rats affect differentiation of NSCs from neonatal rat hippocampus and synaptic formation of NSC-derived neurons. Astrocytes were isolated from the left hippocampus of normal adult SD rats and PSD rats. A lentiviral vector was used to silence the expression of GLT-1 in astrocytes of PSD rats. NSCs were respectively co-cultured with normal (control), PSD, and GLT-1 silenced astrocytes for 7 days. GLT-1, GFAP, MAP2, Synaptophysin (SYN), glutamate (Glu) and glutamine (Gln) were respectively measured by qRT-PCR, western blot, immunofluorescence and efficient mass spectrometry (MS). PSD astrocytes increased the number of NSC-derived astrocytes, but inhibited the expression of GLT-1 of NSC-derived astrocytes and synapses of NSC-derived neurons. On the basis of the low expression of GLT-1 in PSD astrocytes, we further silenced GLT-1 in PSD astrocytes. Interestingly, GLT-1 silenced PSD astrocytes more obviously inhibited synapses of NSC-derived neurons, but increased the number of NSC-derived neurons and reversed the expression of GLT-1 in NSC-derived astrocytes. At the same time, concentration of glutamate in the medium elevated, and glutamine in the medium gradually reduced. In NSC-derived neurons and astrocytes, glutamate metabolism was also affected by changed GLT-1. Down-expressed GLT-1 in PSD astrocytes stimulated NSCs differentiating into astrocytes, but inhibiting the formation of functional synapses by influencing glutamate metabolism in vitro.
The Influence of Interdependence and Value Co-Creation on Relationship Quality

Based on the resource dependence theory and value co-creation theory, this paper puts forward a theoretical model of the relationship between interdependence, value co-creation and relationship quality and explores moderating role of market environment. The findings show as follows: Interdependence has positive influence on value co-creation and relationship quality; Value Co-creation has positive influence on relationship quality; Market environment plays regulatory roles. Finally, marketing suggestions for enterprises are provided after analysis and discussion. The disadvantages and trend of future research are also mentioned.
Factors associated with time from first-symptoms to diagnosis and treatment initiation of Multiple Sclerosis in Switzerland

Background Recent studies emphasise the importance of timely diagnosis and early initiation of disease-modifying treatment in the long-term prognosis of multiple sclerosis. Objectives The objective of this study was to investigate factors associated with extended time to diagnosis and time to disease-modifying treatment initiation in the Swiss Multiple Sclerosis Registry. Methods We used retrospective data (diagnoses 1996–2017) of the survey-based Swiss Multiple Sclerosis Registry and fitted logistic regression models (extended time to diagnosis ≥2 years from first symptoms, extended time to disease-modifying treatment initiation ≥1 year from diagnosis) with demographic and a priori defined variables. Results Our study, based on 996 persons with multiple sclerosis, suggests that 40% had an extended time to diagnosis, and extended time to disease-modifying treatment initiation was seen in 23%. Factors associated with extended time to diagnosis were primary progressive multiple sclerosis (odds ratio (OR) 5.09 (3.12–8.49)), diagnosis setting outside of hospital (neurologist (private practice) OR 1.54 (1.16–2.05)) and more uncommon first symptoms (per additional symptom OR 1.17 (1.06–1.30)). Older age at onset (per additional 5 years OR 0.84 (0.78–0.90)) and gait problems (OR 0.65 (0.47–0.89)) or paresthesia (OR 0.72 (0.54–0.95)) as first symptoms were associated with shorter time to diagnosis. Extended time to disease-modifying treatment initiation was associated with older age at diagnosis (per additional 5 years OR 1.18 (1.09–1.29)). In more recent years, time to diagnosis and time to disease-modifying treatment initiation tended to be shorter. Conclusions Even in recent periods, substantial and partially systematic variation regarding time to diagnosis and time to disease-modifying treatment initiation remains. With the emerging paradigm of early treatment, the residual variation should be monitored carefully.
An Investigation of Compensation and Adaptation to Auditory Perturbations in Individuals With Acquired Apraxia of Speech

Two auditory perturbation experiments were used to investigate the integrity of neural circuits responsible for speech sensorimotor adaptation in acquired apraxia of speech (AOS). This has implications for understanding the nature of AOS as well as normal speech motor control. Two experiments were conducted. In Experiment 1, compensatory responses to unpredictable fundamental frequency (F0) perturbations during vocalization were investigated in healthy older adults and adults with acquired AOS plus aphasia. F0 perturbation involved upward and downward 100-cent shifts versus no shift, in equal proportion, during 2 s vocalizations of the vowel /a/. In Experiment 2, adaptive responses to sustained first formant (F1) perturbations during speech were investigated in healthy older adults, adults with AOS and adults with aphasia only (APH). The F1 protocol involved production of the vowel /ε/ in four consonant-vowel words of Australian English (pear, bear, care, dare), and one control word with a different vowel (paw). An unperturbed Baseline phase was followed by a gradual Ramp to a 30% upward F1 shift stimulating a compensatory response, a Hold phase where the perturbation was repeatedly presented with alternating blocks of masking trials to probe adaptation, and an End phase with masking trials only to measure persistence of any adaptation. AOS participants showed normal compensation to unexpected F0 perturbations, indicating that auditory feedback control of low-level, non-segmental parameters is intact. Furthermore, individuals with AOS displayed an adaptive response to sustained F1 perturbations, but age-matched controls and APH participants did not. These findings suggest that older healthy adults may have less plastic motor programs that resist modification based on sensory feedback, whereas individuals with AOS have less well-established and more malleable motor programs due to damage from stroke.
Impact of virtual inertia on increasing the hosting capacity of island diesel-PV ac-grid

A key constraint to the penetration level or hosting capacity (HC) of PV power in ac-island power systems (IPS) is the resulting frequency volatility. Frequency volatility is directly tied to the inertia level in the grid. Since inertia-less PV power displaces synchronous generators (SG), and thereby their available rotational inertia, the inertia level is reduced and the frequency volatility increased. Frequency volatility can be quantified by the Rate of Change of Frequency (RoCoF). The RoCoF is used as a performance index to determine the HC. Virtual inertia compensation (VIR) by chemical battery storage (BESS) can increase the HC of the grid and allow for more PV power to be integrated. The impact of PI-based VIR on increasing the HC in a 17.5 MVA diesel powered IPS is studied. Results indicate that when added PV without VIR displaced SGs the RoCoF limit could not be upheld. When VIR is added the RoCoF is improved and an HC of up to 3 MW PV is obtained. For the suggested RoCoF limit of −1.45 Hz/s and the studied scenarios it is shown that, in percentage of peak load, diesel-based generation could be replaced by 25% PV, providing that 9% of BESS with VIR is added.
A Stock-Movement Aware Approach for Discovering Investors' Personalized Preferences in Stock Markets

It is very useful to endow machines with the ability to understand users' personalized preferences. In this paper, we propose a novel methodology for discovering investors' personalized preferences in stock markets. Our work is able to estimate investors' personalized preferences for each stock and thus helpful for realizing investment recommendation, for instance through recommending real-time news or others' opinions on stocks preferred by the target user. Compared to conventional approaches, our method effectively incorporates stock movements for estimating investors' preference. By capturing stock-movement patterns influencing users' preferences, our method can find users with a similar investment philosophy and then increase the effect of preference prediction. An experimental evaluation with two real-world datasets demonstrates the effectiveness of our approach.
A Task Manual Creation Support System Using Automatic Speech Recognition

This paper proposes an application to support the creation of specific task manual by automatic speech recognition (ASR). In our daily life or work, while we performing some specific tasks like cooking in-house, assemble a computer or making a handcrafted product in a factory, people who are not expert in the specific task, they often need to reference a task manual. To create a specific task manual, the specific task expert has to list up all the procedures or take photos, edits or structure the contents by rearranging the images or texts to create the task manual. However, in such method, it is often that the important information such as tips, trick or specific procedures could lack information or be missing. To minimize this problem, we developed a prototype of a task manual creation support system. The specific task expert can capture the video of the specific task including the explanation speech uttered by the expert with a human eye perspective camera and a directional microphone. The subjective experimental result showed that the usage of ASR helped to create and edit the task descriptions and to improve the efficiency of task manual creation.
A Blockchain-Based Privacy Preserving Ticketing Service

This study proposes a privacy preserving Blockchain-Based Ticketing Service (BB Tickets), which stores information about events and related tickets in the blockchain network. As the blockchain technologies can ensure information integrity, ticket buyers can use the data stored in the blockchain network to ensure the authenticity of the purchased tickets and to resolve related disputes. Furthermore, the Non-Interactive Zero-Knowledge (NIZK) scheme is utilized in the proposed system to protect user privacy. Therefore, this study contributes to providing a privacy-preserving means for users to enjoy the reliable ticketing service provided by the blockchain technologies.
Anisotropic Thermal Conduction in Transition Metal Dichalcogenide Nanocomposites with Rough Interfaces

We present a theory of thermal conduction in a transition metal dichalcogenide nanocomposite structure with rough interfaces that accounts for the anisotropic conductivities of the host, the insert and the interface regions. The host and insert conductivities are calculated using a semi ab-initio method. The effects of specularity in phonon interface scattering and the thermal boundary resistance is incorporated through linking a phonon wavevector dependent specular scattering parameter to the average height of surface inhomogeneities, and the conductivity of the composite is calculated by employing an extension of a modified effective medium approach. Our work for spherical inserts of WS2 in MoS2 predicts that the effects of specular scattering due to surface roughness is more pronounced for inserts smaller than 100 nm, even at volume fractions of the order of 0.05.
Information Transfer Among the Components in Multi-Dimensional Complex Dynamical Systems

In this paper, a rigorous formalism of information transfer within a multi-dimensional deterministic dynamic system is established for both continuous flows and discrete mappings. The underlying mechanism is derived from entropy change and transfer during the evolutions of multiple components. While this work is mainly focused on three-dimensional systems, the analysis of information transfer among state variables can be generalized to high-dimensional systems. Explicit formulas are given and verified in the classical Lorenz and Chua’s systems. The uncertainty of information transfer is quantified for all variables, with which a dynamic sensitivity analysis could be performed statistically as an additional benefit. The generalized formalisms can be applied to study dynamical behaviors as well as asymptotic dynamics of the system. The simulation results can help to reveal some underlying information for understanding the system better, which can be used for prediction and control in many diverse fields.
Sequence Generative Adversarial Network for Long Text Summarization

In this paper, we propose a new adversarial training framework for text summarization task. Although sequence-to-sequence models have achieved state-of-the-art performance in abstractive summarization, the training strategy (MLE) suffers from exposure bias in the inference stage. This discrepancy between training and inference makes generated summaries less coherent and accuracy, which is more prominent in summarizing long articles. To address this issue, we model abstractive summarization using Generative Adversarial Network (GAN), aiming to minimize the gap between generated summaries and the ground-truth ones. This framework consists of two models: a generator that generates summaries, a discriminator that evaluates generated summaries. Reinforcement learning (RL) strategy is used to guarantee the co-training of generator and discriminator. Besides, motivated by the nature of summarization task, we design a novel Triple-RNNs discriminator, and extend the off-the-shelf generator by appending encoder and decoder with attention mechanism. Experimental results showed that our model significantly outperforms the state-of-the-art models, especially on long text corpus.
A Linked Data Browser with Recommendations

It is becoming more common to publish data in a way that accords with the Linked Data principles. In an effort to improve the human exploitation of this data, we propose a Linked Data browser that is enhanced with recommendation functionality. Based on a user profile, also represented as Linked Data, we propose a technique that we call LDRec that chooses in a personalized way which of the resources that lie within a certain neighbourhood in a Linked Data graph to recommend to the user. The recommendation technique, which is novel, is inspired by a collective classifier known as the Iterative Classification Algorithm. We evaluate LDRec using both an off-line experiment and a user trial. In the off-line experiment, we obtain higher hit rates than we obtain using a simpler classifier. In the user trial, comparing against the same simpler classifier, participants report significantly higher levels of overall satisfaction for LDRec.
NIPS - Not Even Wrong? A Systematic Review of Empirically Complete Demonstrations of Algorithmic Effectiveness in the Machine Learning and Artificial Intelligence Literature

Objective: To determine the completeness of argumentative steps necessary to conclude effectiveness of an algorithm in a sample of current ML/AI supervised learning literature. Data Sources: Papers published in the Neural Information Processing Systems (NeurIPS, nee NIPS) journal where the official record showed a 2017 year of publication. Eligibility Criteria: Studies reporting a (semi-)supervised model, or pre-processing fused with (semi-)supervised models for tabular data. Study Appraisal: Three reviewers applied the assessment criteria to determine argumentative completeness. The criteria were split into three groups, including: experiments (e.g real and/or synthetic data), baselines (e.g uninformed and/or state-of-art) and quantitative comparison (e.g. performance quantifiers with confidence intervals and formal comparison of the algorithm against baselines). Results: Of the 121 eligible manuscripts (from the sample of 679 abstracts), 99\% used real-world data and 29\% used synthetic data. 91\% of manuscripts did not report an uninformed baseline and 55\% reported a state-of-art baseline. 32\% reported confidence intervals for performance but none provided references or exposition for how these were calculated. 3\% reported formal comparisons. Limitations: The use of one journal as the primary information source may not be representative of all ML/AI literature. However, the NeurIPS conference is recognised to be amongst the top tier concerning ML/AI studies, so it is reasonable to consider its corpus to be representative of high-quality research. Conclusion: Using the 2017 sample of the NeurIPS supervised learning corpus as an indicator for the quality and trustworthiness of current ML/AI research, it appears that complete argumentative chains in demonstrations of algorithmic effectiveness are rare.
Racial Disparity and Triple-Negative Breast Cancer in African-American Women: A Multifaceted Affair between Obesity, Biology, and Socioeconomic Determinants

Triple negative breast cancer (TNBC) is a molecularly heterogeneous disease whose incidence is disproportionately higher in African American (AA) women compared to European American (EA) women. Earlier onset, more advanced stage at diagnosis, and aggressive tumor phenotype are some of the characteristic features of TNBC in women with African ethnicity in comparison to EA women, denoting one of the most significant examples of racial disparity in oncology. It is still contentious whether health disparities result in aggressive behavior of TNBC in AA women or it is indeed a molecularly distinct disease. Given the “gaps-in-knowledge” surrounding racial disparity in TNBC, this review discusses various socioeconomic factors and the genetic predispositions contributing to poor prognosis of TNBC in AA women. While socioeconomic factors may contribute to poorer survival, multiple preclinical and clinical studies suggest inherent genetic risk factors and aberrant activation of oncogenic pathways in AA TNBC. Additionally, AA women are more likely to be obese and obesity is known to drive a molecular circuitry resulting in aggressive tumor progression indicating a potential obesity-TNBC axis at work in AA women. Given the multifactorial nature of AA TNBC, a transdisciplinary approach may help bridge the disparity that exists between AA and EA TNBC.
Raman Spectroscopy for Process Analytical Technologies of Pharmaceutical Secondary Manufacturing

As the process analytical technology (PAT) mindset is progressively introduced and adopted by the pharmaceutical companies, there is an increasing demand for effective and versatile real-time analyzers to address the quality assurance challenges of drug manufacturing. In the last decades, Raman spectroscopy has emerged as one of the most promising tools for non-destructive and fast characterization of the pharmaceutical processes. This review summarizes the achieved results of the real-time application of Raman spectroscopy in the field of the secondary manufacturing of pharmaceutical solid dosage forms, covering the most common secondary process steps of a tablet production line. In addition, the feasibility of Raman spectroscopy for real-time control is critically reviewed, and challenges and possible approaches to moving from real-time monitoring to process analytically controlled technologies (PACT) are discussed.
Enhancing Multi-Camera People Detection by Online Automatic Parametrization Using Detection Transfer and Self-Correlation Maximization †

Finding optimal parametrizations for people detectors is a complicated task due to the large number of parameters and the high variability of application scenarios. In this paper, we propose a framework to adapt and improve any detector automatically in multi-camera scenarios where people are observed from various viewpoints. By accurately transferring detector results between camera viewpoints and by self-correlating these transferred results, the best configuration (in this paper, the detection threshold) for each detector-viewpoint pair is identified online without requiring any additional manually-labeled ground truth apart from the offline training of the detection model. Such a configuration consists of establishing the confidence detection threshold present in every people detector, which is a critical parameter affecting detection performance. The experimental results demonstrate that the proposed framework improves the performance of four different state-of-the-art detectors (DPM , ACF, faster R-CNN, and YOLO9000) whose Optimal Fixed Thresholds (OFTs) have been determined and fixed during training time using standard datasets.
Genome-wide association mapping of spot blotch resistance in wheat association mapping initiative (WAMI) panel of spring wheat (Triticum aestivum L.)

Spot blotch (SB) caused by Bipolaris sorokiniana, is one of the most important diseases of wheat in the eastern part of south Asia causing considerable yield loss to the wheat crop. There is an urgent need to identify genetic loci closely associated with resistance to this pathogen for developing resistant cultivars. Hence, genomic regions responsible for SB resistance were searched using a wheat association mapping initiative (WAMI) panel involving 287 spring wheat genotypes of different origin. Genome-wide association mapping (GWAM) was performed using single nucleotide polymorphism (SNP) markers from a custom 90 K wheat SNP array. A mixed linear model (MLM) was used for assessing the association of SNP markers with spot blotch resistance in three consecutive years. Three traits were measured: incubation period, lesion number and area under the disease progress curve (AUDPC). Significant SNP markers were found linked to five, six and four quantitative trait loci (QTLs) for incubation period, lesion number and AUDPC respectively. They were detected on 11 different chromosomes: 1A, 1B, 1D, 4A, 5A, 5B, 6A, 6B, 6D, 7A, 7B with marker R2 range of 0.083 to 0.11. The greatest number of significant SNP-markers was found for lesion number and AUDPC on chromosome 6B and 5B, respectively, representing a better coverage of B-genome by SNPs. On the other hand, the most significant and largest SNP markers for incubation period were detected on 6A and 4A chromosomes indicating that this trait is associated with the A-genome of wheat. Although, QTLs for spot blotch resistance have been reported in wheat on these same chromosomes, the association of incubation period and lesion number with SB resistance has not been reported in previous studies. The panel exhibits considerable variation for SB resistance and also provides a good scope of marker-assisted selection using the identified SNP markers linked to resistant QTLs.
High-Resolution ADCs for Biomedical Imaging Systems

With the rapid development of biomedical imaging systems, the requirement of the accuracy of an Analog-to-Digital Converter (ADC) becomes more and more critical. Traditional Nyquist-rate analog to digital data converters cannot meet the requirement any more. Sigma-Delta analog to digital data converter, which makes a trade-off between speed and accuracy, can achieve an ideal accuracy by employing the oversampling and noise-shaping technology. In this work, a Verilog-AMS model for sigma-delta modulator is built, including the topologic, the order of stage, oversampling rate, system parameters, etc. On the basic of theoretical analysis and scientific flow path, finally, a third-order 1-bit sigma-delta modulator is designed and optimized, whose performance agrees well with the theoretical module.