Miracle-dz/UniFAR-FTUs huggingface dataset:
GRADIENT DESCENT FOR TIKHONOV FUNCTIONALS WITH SPARSITY CONSTRAINTS: THEORY AND NUMERICAL COMPARISON OF STEP SIZE RULES∗

In this paper, we analyze gradient methods for minimization pro blems arising in the regularization of nonlinear inverse problems with sparsity constraints. In particular, we study a gradient method based on the subsequent minimization of quadratic approximations in Hilbe rt spaces, which is motivated by a recently proposed equivalent method in a finite-dimensional setting. We prove co nvergence of this method employing assumptions on the operator which are different compared to other approac hes. We also discuss accelerated gradient methods with step size control and present a numerical comparison of di fferent step size selection criteria for a parameter identification problem for an elliptic partial differentia l equation.
Retransmission Timeout in TCP Reproducing Stephen Edge’s "An adaptive timeout algorithm for retransmission across a packet switching network"

In this paper, we compare the retransmission performance of various congestion control algorithms. Congestion control algorithms should seek to avoid unnecessary retransmission in order to minimize load on a network, but when packets are lost in a network, waiting too long to retransmit will increase latency and decrease throughput. We measure unnecessary retransmission rates and retransmission latency for TCP Reno, Vegas, Cubic, BBR, and Westwood in a simulated environment. We find that network bandwidth and latency have little effect on retransmission performance when held constant, but when the network has extremely small buffers or unpredictable loss, TODO: finish this sentence: which algorithms perform the best in these scenarios?
Branch development of eight-year-old Mytilaria laosensis plantations in response to planting density Zweigentwicklung in achtjährigen Mytilaria laosensis Plantagen als Reaktion auf die Pflanzdichte

Branch development is related to the formation of wood knots and affects timber quality and future market value of plantations and native forests alike. This study provides a theoretical basis and reference for knot-free timber production in a Mytilaria laosensis plantation. The branch diameter, length, angle, status (alive or dead), azimuth, and branch height under 12 m were measured for 60 dominant or co-dominant trees in response to five planting density treatments (625, 833, 1111, 1667, and 2500 stems ha-1). With increasing planting density from 625 to 2500 stems ha-1, the mean branch diameter and branch length decreased by 7.09% and 8.70% respectively. Dead and live branch angle decreased by 10.54% and 9.66% respectively, while number of living branches increased by 31.67% from 625 to 2500 stems ha-1. Analyzing the spatial distribution using branch azimuth, we discovered that branches pointing north were more abundant and had larger diameter, larger branch length, Seite 248 Z. Wang, N. Yu, R. Li, W. Zou, J. Yang, G. Yin, T. Peng and higher branch angle than branches pointing south. Branch diameter, length, and number were positively related to the relative branch height. Our results underline that increasing the planting density from 625 to 2500 stems ha-1 considerably affect branch growth. Thus, optimizing planting density when establishing Mytilaria laosensis plantations according to the forest management targets and future pruning can improve the timber quality and future value.
In vitro anthelmintic activity of an aqueous extract of Glycyrrhiza glabra and of glycyrrhetinic acid against gastrointestinal nematodes of small ruminants

This study evaluated the in vitro anthelmintic activity of a liquorice (Glycyrrhiza glabra) root aqueous extract and of glycyrrhetinic acid at 30, 10, 5, 1, and 0.5 mg/mL against sheep gastrointestinal nematodes (GINs), using the egg hatch test (EHT), the larval development test (LDT), and the larval migration inhibition test (LMIT). The compounds were applied on a mixture of GIN eggs and larvae, mainly Trichostrongylus spp. and Teladorsagia/Ostertagia spp. Cytotoxicity assays were also performed. In the EHT, both candidates showed significant concentration-dependent efficacy and were significantly more effective (p < 0.001) at the highest concentrations (30 and 10 mg/mL) than the lowest ones. In the LDT, only G. glabra showed a concentration-dependent effect (R2 = 0.924), but glycyrrhetinic acid (R2 = 0.910) had significantly higher efficacy than G. glabra root extract. Moreover, the efficacy of glycyrrhetinic acid at 30, 10, and 5 mg/mL was significantly higher (p < 0.001) than at lower concentrations. In the LMIT, G. glabra showed concentration-dependent efficacy (R2 = 0.971), while considerably reduced efficacy was observed for glycyrrhetinic acid (R2 = 0.855) at the lowest concentrations. These data suggest that the two compounds may have different mechanisms of action. In the LMIT, the 50% lethal concentration (LC50) of glycyrrhetinic acid (~5.12 mg/mL) was > 2.0-fold lower when compared to G. glabra (12.25 mg/mL). Analysis and previous findings indicated low toxicity for both compounds. The results obtained encourage in vivo studies aimed at evaluating the potential use of the tested compounds as natural de-wormers in ruminants.
Serum concentrations of Thymidine kinase 1 measured using a novel antibody-based assay in patients with Hodgkin Lymphoma

Background Thymidine kinase 1 (TK1) is an intracellular protein associated with DNA synthesis, expressed during the G1 phase and remained elevated through the M phase, with a potential as a biomarker for cell proliferation. In this study, we explore the possible use of TK1 in Hodgkin lymphoma (HL). Methods Serum concentrations of TK1 (S-TK1) were measured in 46 newly diagnosed HL patients using prospectively collected biobanked serum samples. The samples were analyzed using a novel antibody-based TK1 immunosorbent assay (ELISA). Results The concentrations of S-TK1 were elevated in HL patients compared with healthy controls (median 0.32 μg/L vs. 0.24 μg/L, P = 0.003). A further increase in S-TK1 was observed during the treatment. The S-TK1 concentrations were higher in patients with advanced stage disease, low B-Hb, elevated P-LD and in those with B-symptoms. A high ESR correlated with low S-TK1. Conclusions The study results suggest that S-TK1, measured using a novel antibody-based assay, has the potential to be a biomarker in HL. However, while S-TK1 levels are elevated at baseline compared with healthy controls, a limited number of patients and comparatively short follow-up time render reliable conclusions difficult.
Graph Fusion Network-Based Multimodal Learning for Freezing of Gait Detection

Freezing of gait (FoG) is identified as a sudden and brief episode of movement cessation despite the intention to continue walking. It is one of the most disabling symptoms of Parkinson’s disease (PD) and often leads to falls and injuries. Many computer-aided FoG detection methods have been proposed to use data collected from unimodal sources, such as motion sensors, pressure sensors, and video cameras. However, there are limited efforts of multimodal-based methods to maximize the value of all the information collected from different modalities in clinical assessments and improve the FoG detection performance. Therefore, in this study, a novel end-to-end deep architecture, namely graph fusion neural network (GFN), is proposed for multimodal learning-based FoG detection by combining footstep pressure maps and video recordings. GFN constructs multimodal graphs by treating the encoded features of each modality as vertex-level inputs and measures their adjacency patterns to construct complementary FoG representations, thus reducing the representation redundancy among different modalities. In addition, since GFN is devised to process multimodal graphs of arbitrary structures, it is expected to achieve superior performance with inputs containing missing modalities, compared to the alternative unimodal methods. A multimodal FoG dataset was collected, which included clinical assessment videos and footstep pressure sequences of 340 trials from 20 PD patients. Our proposed GFN demonstrates a great promise of multimodal FoG detection with an area under the curve (AUC) of 0.882. To the best of our knowledge, this is one of the first studies to utilize multimodal learning for automated FoG detection, which offers significant opportunities for better patient assessments and clinical trials in the future.
Clinical Significance of the Serum lncRNA NORAD Expression in Patients with Neonatal Sepsis and Its Association with miR-410-3p

Purpose Neonatal sepsis (NS) is one of the most crucial causes of death in newborns. This investigation aimed to validate the expression level of NORAD and the probable mechanism underlying the function of NORAD in NS. Patients and Methods The expression of NORAD and miR-410-3p was identified by qRT-PCR. The diagnostic sensitivity and specificity of NORAD were examined by the ROC curve. The NS cell models were established by the treatment of lipopolysaccharide (LPS) in the macrophage RAW264.7 cells. The luciferase report assay was performed to detect the target relationship between NORAD and miR-410-3p and the association between them was revealed by Pearson correlation. Results The expression of NORAD was at a higher level in the NS group than in the pneumonia controls. The levels of NORAD could sever as a diagnostic marker on discriminating NS patients from pneumonia neonates. The expression of IL-6, IL-8, and TNF-α was enhanced in the macrophage cells under LPS circumstances, while NORAD knockdown reversed the overexpression of these pro-inflammatory cytokines. Besides, miR-410-3p was a possible ceRNA of NORAD by the finding that the luciferase activity fell in the co-transfection of miR-410-3p mimics and WT-NORAD group. In vitro, LPS management could inhibit the expression of miR-410-3p, while silenced NORAD ameliorated the suppressed miR-410-3p levels. Decreased expression of miR-410-3p was discovered in NS patients and the changes of miR-410-3p expression were correlated with the levels of NORAD in the NS patients. Conclusion We found a raised level of NORAD in the NS patients and it might be a diagnostic indicator for NS patients. NORAD elimination meliorated the inflammation actions steered by LPS. MiR-410-3p was a target of NORAD and lowly expressed in the NS patients.
Fine-Grained Versus Coarse-Grained Data for Estimating Time-on-Task in Learning Programming

The time that students spend on assignments, i.e. time-on-task , has been used frequently in prior research to understand student aﬀect, study habits, and course performance, among others. The choice for how time-on-task is calculated, however, is typically based on available data. This data can be very coarse-grained, such as the timestamps from students’ assignment submissions. Using coarse-grained data to calculate time-on-task has limitations, such as not being able to determine whether students take breaks when working on an assignment. In this work, we analyze the diﬀerences be-tween two time-on-task metrics, one based on coarse-grained data—in this case, student submissions—and one based on ﬁne-grained data—in this case, students’ keystrokes during an assignment. We compare these two metrics and examine how well they correlate to ﬁnd out whether time-on-task based on coarse-grained data can be an accurate metric for understanding the time spent by students on an assignment. Our results show that the correlation between the two metrics that are supposed to measure the same underlying phenomena—time-on-task—is only weak to moderate. This suggests that ﬁne-grained data might be needed to accurately estimate time-on-task.
Embedding and Beamforming: All-Neural Causal Beamformer for Multichannel Speech Enhancement

Standing upon the intersection of traditional beamformers and deep neural networks, we propose a causal neural beamformer paradigm called Embedding and Beamforming, and two core modules are devised accordingly, namely EM and BM. For EM, instead of estimating spatial covariance matrix explicitly, the 3-D embedding tensor is learned with the network, where the spatial-spectral discriminative information can be implicitly represented. For BM, a network is directly leveraged to derive the beamforming weights so as to implement filter-and-sum operation. To further improve the speech quality, a post-processing module is introduced to further suppress the residual noise. Based on the DNS-Challenge dataset, we conduct the experiments for multichannel speech enhancement and the results show that the proposed system outperforms previous advanced baselines by a large margin in terms of multiple evaluation metrics.
Ebselen Not Only Inhibits Clostridioides difficile Toxins but Displays Redox-Associated Cellular Killing

We show that ebselen kills pathogenic C. difficile by disrupting its redox homeostasis, changing the normal concentrations of NAD+ and NADH, which are critical for various metabolic functions in cells. However, this antimicrobial action is hampered by host components, namely, blood. Future discovery of ebselen analogues, or mechanistically similar compounds, that remain active in blood could be drug leads for CDI or probes to study C. difficile redox biology in vivo. ABSTRACT Ebselen, a reactive organoselenium compound, was shown to inhibit toxins TcdA and TcdB by covalently binding to their cysteine protease domains. It was suggested that ebselen lacked antimicrobial activity against Clostridioides difficile. However, this perception conflicts with C. difficile having essential cysteine-containing enzymes that could be potential targets and the reported antimicrobial activity of ebselen against other species. Hence, we reevaluated the anti-C. difficile properties of ebselen. Susceptibility testing revealed that its activity was either slightly reduced by pyruvate found in Wilkins-Chalgren agar or obliterated by blood in brucella agar. In brain heart infusion (BHI) agar, ebselen inhibited most C. difficile strains (MICs of 2 to 8 μg/ml), except for ribotype 078 that was intrinsically resistant (MIC = 32 to 128 μg/ml). Against C. difficile R20291, at concentrations below its minimal bactericidal concentration (MBC), 16 μg/ml, ebselen inhibited production of toxins and spores. Transcriptome analysis revealed that ebselen altered redox-associated processes and cysteine metabolism and enhanced expression of Stickland proline metabolism, likely to regenerate NAD+ from NADH. In cellular assays, ebselen induced uptake of cysteine, depleted nonprotein thiols, and disrupted the NAD+/NADH ratio. Taken together, killing of C. difficile cells by ebselen occurs by a multitarget action that includes disrupting intracellular redox, which is consistent with ebselen being a reactive molecule. However, the physiological relevance of these antimicrobial actions in treating acute C. difficile infection (CDI) is likely to be undermined by host factors, such as blood, which protect C. difficile from killing by ebselen. IMPORTANCE We show that ebselen kills pathogenic C. difficile by disrupting its redox homeostasis, changing the normal concentrations of NAD+ and NADH, which are critical for various metabolic functions in cells. However, this antimicrobial action is hampered by host components, namely, blood. Future discovery of ebselen analogues, or mechanistically similar compounds, that remain active in blood could be drug leads for CDI or probes to study C. difficile redox biology in vivo.
Etiology and Incidence of Moderate-to-Severe Diarrhea in Young Children in Niger

Using quantitative PCR diagnostics in a phase 3 clinical trial of Rotasiil in Niger, we identified Shigella, Cryptosporidium, rotavirus, and heat-stabile toxin-producing Escherichia coli to be the top 4 leading etiologies of moderate-to-severe diarrhea in children 0-2 years of age.
Does Corporate Social Responsibility moderate the relationship between the Machiavellian trait and earnings management?

Objective: to analyze the relationship between the Machiavellian personality trait and Corporate Social Responsibility (CSR) and earnings management, in the light of the Upper Echelons theory. Method: theoretical empirical study, using a survey as the data collection procedure, applied to 208 business professionals. Descriptive statistics, hypothesis testing and regression were applied. Results: The professionals who care about compliance with policies that refer to legal responsibility do not agree with the earnings management practice, while those who focus on economic responsibility displayed willingness towards this management. As for the Machiavellian influence, those more endowed with these traits tend less towards legal and ethical corporate responsibility and display greater acceptance towards earnings management. In addition, against expectations, the moderation of CSR did not reduce the positive relationship between Machiavellianism and earnings management. Contributions: The results permit a broader discussion on interdisciplinary practice in behavioral accounting and provide insights on how Machiavellianism can influence corporate decisions and social conduct. Economic and social implications of the findings are of interest to accounting professionals, investors and auditors in order to understand the impact or damage earnings management causes to accounting information.
A Separable Temporal Convolution Neural Network with Attention for Small-Footprint Keyword Spotting

Keyword spotting (KWS) on mobile devices generally requires a small memory footprint. However, most current models still maintain a large number of parameters in order to ensure good performance. To solve this problem, this paper proposes a separable temporal convolution neural network with attention, it has a small number of parameters. Through the time convolution combined with attention mechanism, a small number of parameters model (32.2K) is implemented while maintaining high performance. The proposed model achieves 95.7% accuracy on the Google Speech Commands dataset, which is close to the performance of Res15(239K), the state-of-the-art model in KWS at present.
An Integrated Framework for the Heterogeneous Spatio-Spectral-Temporal Fusion of Remote Sensing Images

Image fusion technology is widely used to fuse the complementary information between multi-source remote sensing images. Inspired by the frontier of deep learning, this paper first proposes a heterogeneous-integrated framework based on a novel deep residual cycle GAN. The proposed network consists of a forward fusion part and a backward degeneration feedback part. The forward part generates the desired fusion result from the various observations; the backward degeneration feedback part considers the imaging degradation process and regenerates the observations inversely from the fusion result. The proposed network can effectively fuse not only the homogeneous but also the heterogeneous information. In addition, for the first time, a heterogeneous-integrated fusion framework is proposed to simultaneously merge the complementary heterogeneous spatial, spectral and temporal information of multi-source heterogeneous observations. The proposed heterogeneous-integrated framework also provides a uniform mode that can complete various fusion tasks, including heterogeneous spatio-spectral fusion, spatio-temporal fusion, and heterogeneous spatio-spectral-temporal fusion. Experiments are conducted for two challenging scenarios of land cover changes and thick cloud coverage. Images from many remote sensing satellites, including MODIS, Landsat-8, Sentinel-1, and Sentinel-2, are utilized in the experiments. Both qualitative and quantitative evaluations confirm the effectiveness of the proposed method.
Fourier series representation of Ferrers function P

Regarding the convergence of the series in (2) we have the following result. Theorem 2. Let θ ∈ (0, π), ν, μ ∈ C such that ν + μ ∈ C \ −N. (a) If Rμ < 0 then the series in (2) converges absolutely. (b) If 0 ≤ Rμ < 1 2 then the series in (2) converges, but, if θ 6= 12π, it does not converge absolutely. (c) If Rμ ≥ 1 2 and θ 6= 12π, then the series in (2) diverges. Proof. (a) It is known [2, §15.2(i)] that the Gauss hypergeometric series 2F1 (
ROCK2 Promotes Osteosarcoma Growth and Glycolysis by Up-Regulating HKII via Phospho-PI3K/AKT Signalling

Background: Osteosarcoma (OS) is a malignant bone tumour that exhibits a high mortality. While tumours thrive in a state of malnutrition, the mechanism by which OS cells adapt to metabolic stress through metabolic reprogramming remains unclear. Methods: We analysed the expression of ROCK2 in osteosarcoma tissues by RT-qPCR and Western blot. Cell proliferation were analysed using CCK8, EdU and colony formation assays. The level of cell glycolysis was detected by glucose-6 phosphate, glucose consumption, lactate production and ATP levels. Results: Herein, our study showed that ROCK2 expression in OS tissues was higher than in adjacent tissues. Functional assays have demonstrated that ROCK2 contributes to the growth of OS cells by inducing aerobic glycolysis. The current study revealed that ROCK2 knock-down decreased the levels of mitochondrial hexokinase II (HKII). And also indicated that ROCK2 served as a key enzyme in glycolysis and that it served an important role in tumour growth. A significant positive correlation was identified between the mRNA and protein expressions of ROCK2 and HKII, further demonstrating that ROCK2-induced glycolysis and proliferation was dependent on HKII expression in OS cells. Mechanistically, ROCK2 promotes HKII expression by activating the phospho-PI3K/AKT signalling pathway. Conclusion: Taken together, the results of the current study linked the two drivers of OS growth and aerobic glycolysis and identified a new mechanism of ROCK2 control in OS.
A Machine Learning Approach for Identifying Disease-Treatment Relations in Short Texts

Medical information retrieval plays an increasingly important role to help physicians and domain experts to better access medical related knowledge and information, and support decision making. Integrating the medical knowledge bases has the potential to improve the information retrieval performance through incorporating medical domain knowledge for relevance assessment. However, this is not a trivial task because of the challenges to effectively utilize the domain knowledge in the medical knowledge bases. In this paper, we proposed a novel medical information retrieval system with a two-stage query expansion strategy, which is able to effectively model and incorporate the latent semantic associations to improve the performance. This system consists of two parts. First, we applied a heuristic approach to enhance the widely used pseudo relevance feedback method for more effective query expansion, through iteratively expanding the queries to boost the similarity score between queries and documents. Second, to improve the retrieval performance with structured knowledge bases, we presented a latent semantic relevance model based on tensor factorization to identify semantic association patterns under sparse settings. These identified patterns are then used as inference paths to trigger knowledge-based query expansion in medical information retrieval. Experimentsshowed that the performance of the proposed system is significantly better than the baseline system, and is comparable with state-of-the-art systems; (b) demonstrated the capability of tensor-based semantic enrichment methods for medical information retrieval tasks.