Miracle-dz/UniFAR-FTUs huggingface dataset:
FX11 limits Mycobacterium tuberculosis growth and potentiates bactericidal activity of isoniazid through host-directed activity

ABSTRACT Lactate dehydrogenase A (LDHA) mediates interconversion of pyruvate and lactate, and increased lactate turnover is exhibited by malignant and infected immune cells. Hypoxic lung granuloma in Mycobacterium tuberculosis-infected animals present elevated levels of Ldha and lactate. Such alterations in the metabolic milieu could influence the outcome of host-M. tuberculosis interactions. Given the central role of LDHA for tumorigenicity, targeting lactate metabolism is a promising approach for cancer therapy. Here, we sought to determine the importance of LDHA for tuberculosis (TB) disease progression and its potential as a target for host-directed therapy. To this end, we orally administered FX11, a known small-molecule NADH-competitive LDHA inhibitor, to M. tuberculosis-infected C57BL/6J mice and Nos2−/− mice with hypoxic necrotizing lung TB lesions. FX11 did not inhibit M. tuberculosis growth in aerobic/hypoxic liquid culture, but modestly reduced the pulmonary bacterial burden in C57BL/6J mice. Intriguingly, FX11 administration limited M. tuberculosis replication and onset of necrotic lung lesions in Nos2−/− mice. In this model, isoniazid (INH) monotherapy has been known to exhibit biphasic killing kinetics owing to the probable selection of an INH-tolerant bacterial subpopulation. However, adjunct FX11 treatment corrected this adverse effect and resulted in sustained bactericidal activity of INH against M. tuberculosis. As a limitation, LDHA inhibition as an underlying cause of FX11-mediated effect could not be established as the on-target effect of FX11 in vivo was unconfirmed. Nevertheless, this proof-of-concept study encourages further investigation on the underlying mechanisms of LDHA inhibition and its significance in TB pathogenesis. Summary: Targeting LDHA results in tumor regression in experimental models. Likewise, co-administration of a known small molecule LDHA inhibitor and an anti-tuberculosis drug has improved the tuberculosis therapeutic outcomes in murine models.
Tensor Networks for Latent Variable Analysis: Novel Algorithms for Tensor Train Approximation

Decompositions of tensors into factor matrices, which interact through a core tensor, have found numerous applications in signal processing and machine learning. A more general tensor model that represents data as an ordered network of subtensors of order-2 or order-3 has, so far, not been widely considered in these fields, although this so-called tensor network (TN) decomposition has been long studied in quantum physics and scientific computing. In this article, we present novel algorithms and applications of TN decompositions, with a particular focus on the tensor train (TT) decomposition and its variants. The novel algorithms developed for the TT decomposition update, in an alternating way, one or several core tensors at each iteration and exhibit enhanced mathematical tractability and scalability for large-scale data tensors. For rigor, the cases of the given ranks, given approximation error, and the given error bound are all considered. The proposed algorithms provide well-balanced TT-decompositions and are tested in the classic paradigms of blind source separation from a single mixture, denoising, and feature extraction, achieving superior performance over the widely used truncated algorithms for TT decomposition.
The Susceptibility of Human Melanoma Cells to Infection with the Leningrad-16 Vaccine Strain of Measles Virus

Oncolytic viruses, including live attenuated measles virus (MV) vaccine strains, have recently been shown as promising therapeutic agents against human malignancies. In this study, the oncolytic potential of the attenuated vaccine strain Leningrad-16 (L-16) of MV was evaluated in a panel of human metastatic melanoma cell lines. The L-16 measles virus was shown to replicate within melanoma cells mediating direct cell killing of tumor cells, although all melanoma cell lines varied in regard to their ability to respond to L-16 MV infection, as revealed by the different pattern of the Interferon Stimulated Gene expression, cytokine release and mechanisms of cell death. Furthermore, the statistically significant L-16 measles virus related tumor growth inhibition was demonstrated in a melanoma xenograft model. Therefore, L-16 MV represents an appealing oncolytic platform for target delivery of therapeutic genes along with other attenuated measles virus strains.
Proteasome Inhibitors: Harnessing Proteostasis to Combat Disease

The proteasome is the central component of the main cellular protein degradation pathway. During the past four decades, the critical function of the proteasome in numerous physiological processes has been revealed, and proteasome activity has been linked to various human diseases. The proteasome prevents the accumulation of misfolded proteins, controls the cell cycle, and regulates the immune response, to name a few important roles for this macromolecular “machine. ” As a therapeutic target, proteasome inhibitors have been approved for the treatment of multiple myeloma and mantle cell lymphoma. However, inability to sufficiently inhibit proteasome activity at tolerated doses has hampered efforts to expand the scope of proteasome inhibitor-based therapies. With emerging new modalities in myeloma, it might seem challenging to develop additional proteasome-based therapies. However, the constant development of new applications for proteasome inhibitors and deeper insights into the intricacies of protein homeostasis suggest that proteasome inhibitors might have novel therapeutic applications. Herein, we summarize the latest advances in proteasome inhibitor development and discuss the future of proteasome inhibitors and other proteasome-based therapies in combating human diseases.
Factors influencing weight management behavior among college students: An application of the Health Belief Model

Background Overweight and obesity have become a significant public health concern in both developing and developed countries. Due to the health implications of weight-reduction behaviors, it is important to explore the factors that predict their occurrence. Therefore, the present study was performed to examine factors affecting the behavioral intention of weight management as well as assess the predictive power of the Health Belief Model (HBM) for body mass index (BMI). Methods This cross-sectional study was conducted among 336 female students recruited from dormitories of Tabriz University of Medical Sciences, using quota sampling technique. Data were collected by a structured questionnaire in seven parts (including perceived severity, perceived susceptibility, perceived benefit, perceived barrier, cue to action, self-efficacy in dieting and physical activity, and behavioral intention of weight management), based on the HBM. Structural equation modeling (SEM) was conducted to identify the relationship between HBM constructs and behavioral intention of weight management. Linear regression model was performed to test the ability of the HBM to predict students’ BMIs. Results Higher level of perceived threats (sum of perceived susceptibility and severity) (β = 0.41, P<0.001), perceived benefits (β = 0.19, P = 0.009), self-efficacy in exercise (β = 0.17, P = 0.001), and self-efficacy in dieting (β = 0.16, P = 0.025) scales was significantly related to greater behavioral intention of weight management. Moreover, perceived threat mediated the relationships between perceived cue to action, perceived benefits, self-efficacy in exercise, and weight management practices. The fit indices of the SEM model seemed acceptable. The final regression model explained approximately 40% of variance in BMI (P<0.001). Additionally, perceived severity, barrier, and self-efficacy in dietary life were the significant variables to predict students’ BMIs. Conclusions These findings suggest that health education programs based on the HBM needs to be integrated in preventive health programs and health interventions strategies to ensure adherence and well-being of the participants.
Neonatal Outcomes Following Culture-negative Late-onset Sepsis Among Preterm Infants

Supplemental Digital Content is available in the text. Background: Culture-negative late-onset sepsis (LOS) is commonly diagnosed in neonatal intensive care units, while the outcomes of neonatal culture-negative LOS are not reported for large cohorts. This study aimed to examine the incidence and neonatal outcomes for culture-negative LOS in a contemporary multicenter cohort of preterm infants. Methods: We performed a retrospective analysis of data from a cluster-randomized controlled study. Infants <34 weeks of gestation and admitted to 25 neonatal intensive care units between May 1, 2015, and April 30, 2018, were included. Culture-negative LOS was diagnosed if infants had abnormal manifestations and laboratory tests but negative blood cultures. The primary outcome was a composite of mortality or morbidities including periventricular leukomalacia (PVL), retinopathy of prematurity (ROP) ≥ stage 3 or bronchopulmonary dysplasia (BPD). Results: Of 22,346 eligible infants, 1505 (6.7%) infants had culture-negative and 761 (3.4%) infants had culture-positive LOS. Compared with infants without LOS, infants with culture-negative LOS had higher rates of composite outcome (24.1% vs. 9.6%), death (3.8% vs. 1.8%), PVL (4.8% vs. 2.2%), severe ROP (3.3% vs. 1.1%) and BPD (18.1% vs. 7.0%). After adjustment, culture-negative LOS was independently associated with increased risk of composite outcome {adjusted odds ratio [aOR]: 1.8 [95% confidence interval (CI): 1.5–2.1]}, PVL [aOR: 2.0 (95% CI: 1.4–2.8)] and BPD [aOR: 1.8 (95% CI: 1.5–2.2)] relative to the absence of LOS. Conclusion: Culture-negative LOS was frequently diagnosed in preterm infants and was associated with increased risks of adverse outcomes. There is an emerging need for more precise diagnosis and treatment strategies for culture-negative LOS.
Localization of Fluorescent Targets in Deep Tissue With Expanded Beam Illumination for Studies of Cancer and the Brain

Imaging fluorescence through millimeters or centimeters of tissue has important in vivo applications, such as guiding surgery and studying the brain. Often, the important information is the location of one of more optical reporters, rather than the specifics of the local geometry, motivating the need for a localization method that provides this information. We present an optimization approach based on a diffusion model for the fast localization of fluorescent inhomogeneities in deep tissue with expanded beam illumination that simplifies the experiment and the reconstruction. We show that the position of a fluorescent inhomogeneity can be estimated while assuming homogeneous tissue parameters and without having to model the excitation profile, reducing the computational burden and improving the utility of the method. We perform two experiments as a demonstration. First, a tumor in a mouse is localized using a near infrared folate-targeted fluorescent agent (OTL38). This result shows that localization can quickly provide tumor depth information, which could reduce damage to healthy tissue during fluorescence-guided surgery. Second, another near infrared fluorescent agent (ATTO647N) is injected into the brain of a rat, and localized through the intact skull and surface tissue. This result will enable studies of protein aggregation and neuron signaling.
Enhancing fNIRS Analysis Using EEG Rhythmic Signatures: An EEG-Informed fNIRS Analysis Study

Neurovascular coupling represents the relationship between changes in neuronal activity and cerebral hemodynamics. Concurrent Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) recording and integration analysis has emerged as a promising multi-modal neuroimaging approach to study the neurovascular coupling as it provides complementary properties with regard to high temporal and moderate spatial resolution of brain activity. In this study we developed an EEG-informed-fNIRS analysis framework to investigate the neuro-correlate between neuronal activity and cerebral hemodynamics by identifying specific EEG rhythmic modulations which contribute to the improvement of the fNIRS-based general linear model (GLM) analysis. Specifically, frequency-specific regressors derived from EEG were used to construct design matrices to guide the GLM analysis of the fNIRS signals collected during a hand grasp task. Our results showed that the EEG-informed fNIRS GLM analysis, especially the alpha and beta band, revealed significantly higher sensitivity and specificity in localizing the task-evoked regions compared to the canonical boxcar model, demonstrating the strong correlations between hemodynamic response and EEG rhythmic modulations. Results also indicated that analysis based on the deoxygenated hemoglobin (HbR) signal slightly outperformed the oxygenated hemoglobin (HbO)-based analysis. The findings in our study not only validate the feasibility of enhancing fNIRS GLM analysis using simultaneously recorded EEG signals, but also provide a new perspective to study the neurovascular coupling of brain activity.
Recalibrating 3D ConvNets With Project & Excite

Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for segmentation tasks in computer vision and medical imaging. Recently, computational blocks termed squeeze and excitation (SE) have been introduced to recalibrate F-CNN feature maps both channel- and spatial-wise, boosting segmentation performance while only minimally increasing the model complexity. So far, the development of SE blocks has focused on 2D architectures. For volumetric medical images, however, 3D F-CNNs are a natural choice. In this article, we extend existing 2D recalibration methods to 3D and propose a generic compress-process-recalibrate pipeline for easy comparison of such blocks. We further introduce Project & Excite (PE) modules, customized for 3D networks. In contrast to existing modules, Project & Excite does not perform global average pooling but compresses feature maps along different spatial dimensions of the tensor separately to retain more spatial information that is subsequently used in the excitation step. We evaluate the modules on two challenging tasks, whole-brain segmentation of MRI scans and whole-body segmentation of CT scans. We demonstrate that PE modules can be easily integrated into 3D F-CNNs, boosting performance up to 0.3 in Dice Score and outperforming 3D extensions of other recalibration blocks, while only marginally increasing the model complexity. Our code is publicly available on https://github.com/ai-med/squeeze_and_excitation.
A 2-Biomarker Model Augments Clinical Prediction of Mortality in Melioidosis

Abstract Background Melioidosis, infection caused by Burkholderia pseudomallei, is a common cause of sepsis with high associated mortality in Southeast Asia. Identification of patients at high likelihood of clinical deterioration is important for guiding decisions about resource allocation and management. We sought to develop a biomarker-based model for 28-day mortality prediction in melioidosis. Methods In a derivation set (N = 113) of prospectively enrolled, hospitalized Thai patients with melioidosis, we measured concentrations of interferon-γ, interleukin-1β, interleukin-6, interleukin-8, interleukin-10, tumor necrosis factor-ɑ, granulocyte-colony stimulating factor, and interleukin-17A. We used least absolute shrinkage and selection operator (LASSO) regression to identify a subset of predictive biomarkers and performed logistic regression and receiver operating characteristic curve analysis to evaluate biomarker-based prediction of 28-day mortality compared with clinical variables. We repeated select analyses in an internal validation set (N = 78) and in a prospectively enrolled external validation set (N = 161) of hospitalized adults with melioidosis. Results All 8 cytokines were positively associated with 28-day mortality. Of these, interleukin-6 and interleukin-8 were selected by LASSO regression. A model consisting of interleukin-6, interleukin-8, and clinical variables significantly improved 28-day mortality prediction over a model of only clinical variables [AUC (95% confidence interval [CI]): 0.86 (.79–.92) vs 0.78 (.69–.87); P = . 01]. In both the internal validation set (0.91 [0.84–0.97]) and the external validation set (0.81 [0.74–0.88]), the combined model including biomarkers significantly improved 28-day mortality prediction over a model limited to clinical variables. Conclusions A 2-biomarker model augments clinical prediction of 28-day mortality in melioidosis.
DEAD-Box Helicases: Sensors, Regulators, and Effectors for Antiviral Defense

DEAD-box helicases are a large family of conserved RNA-binding proteins that belong to the broader group of cellular DExD/H helicases. Members of the DEAD-box helicase family have roles throughout cellular RNA metabolism from biogenesis to decay. Moreover, there is emerging evidence that cellular RNA helicases, including DEAD-box helicases, play roles in the recognition of foreign nucleic acids and the modulation of viral infection. As intracellular parasites, viruses must evade detection by innate immune sensing mechanisms and degradation by cellular machinery while also manipulating host cell processes to facilitate replication. The ability of DEAD-box helicases to recognize RNA in a sequence-independent manner, as well as the breadth of cellular functions carried out by members of this family, lead them to influence innate recognition and viral infections in multiple ways. Indeed, DEAD-box helicases have been shown to contribute to intracellular immune sensing, act as antiviral effectors, and even to be coopted by viruses to promote their replication. However, our understanding of the mechanisms underlying these interactions, as well as the cellular roles of DEAD-box helicases themselves, is limited in many cases. We will discuss the diverse roles that members of the DEAD-box helicase family play during viral infections.
Transcriptome-Wide Identification, Evolutionary Analysis, and GA Stress Response of the GRAS Gene Family in Panax ginseng C. A. Meyer

GRAS transcription factors are a kind of plant-specific transcription factor that have been found in a variety of plants. According to previous studies, GRAS proteins are widely involved in the physiological processes of plant signal transduction, stress, growth and development. The Jilin ginseng (Panax ginseng C.A. Meyer) is a heterogeneous tetraploid perennial herb of the Araliaceae family, ginseng genus. Important information regarding the GRAS transcription factors has not been reported in ginseng. In this study, 59 Panax ginseng GRAS (PgGRAS) genes were obtained from the Jilin ginseng transcriptome data and divided into 13 sub-families according to the classification of Arabidopsis thaliana. Through systematic evolution, structural variation, function and gene expression analysis, we further reveal GRAS’s potential function in plant growth processes and its stress response. The expression of PgGRAS genes responding to gibberellin acids (GAs) suggests that these genes could be activated after application concentration of GA. The qPCR analysis result shows that four PgGRAS genes belonging to the DELLA sub-family potentially have important roles in the GA stress response of ginseng hairy roots. This study provides not only a preliminary exploration of the potential functions of the GRAS genes in ginseng, but also valuable data for further exploration of the candidate PgGRAS genes of GA signaling in Jilin ginseng, especially their roles in ginseng hairy root development and GA stress response.
Synthetic Aperture Radar Imaging for Burn Wounds Diagnostics

The need for technologies to monitor the wound healing under dressing materials has led us to investigate the feasibility of using microwave and millimetre wave radiations due to their sensitivity to water, non- ionising nature, and transparency to dressing materials and clothing. This paper presents synthetic aperture radar (SAR) images obtained from an active microwave and millimetre wave scanner operating over the band 15–40 GHz. Experimental images obtained from porcine skin samples with the presence of dressing materials and after the application of localised heat treatments reveal that SAR images can be used for diagnosing burns and for potentially monitoring the healing under dressing materials. The experimental images were extracted separately from the amplitude and phase measurements of the input reflection coefficient (S11). The acquired images indicate that skin and burns can be detected and observed through dressing materials as well as features of the skin such as edges, irregularities, bends, burns, and variation in the reflectance of the skin. These unique findings enable a microwave and millimetre-wave scanner to be used for evaluating the wound healing progress under dressing materials without their often-painful removal: a capability that will reduce the cost of healthcare, distress caused by long waiting hours, and the healthcare interventional time.
Cluster Consensus of Multiagent Systems With Weighted Antagonistic Interactions

This article addresses the problem of cluster consensus for multiagent systems (MASs) associated with weighted antagonistic interactions. Compared with some existing results, a general communication topology among agents is introduced in this article, where there is no need to confine directed cycles to the root of a spanning tree. By taking into consideration the structures of directed cycles and multiple paths, the judgment of structural balance is simplified significantly. A novel cluster consensus protocol is also proposed for structurally unbalanced digraphs. Moreover, two necessary and sufficient conditions are derived, by which cluster consensus can be achieved in an MAS if and only if its communication topology contains a directed spanning tree. Then, by employing an algebra theorem, a sufficient criterion for the unstable system under a directed cycle is obtained, whether the number of agents on this cycle is odd or even. Some illustrative examples are given to demonstrate the effectiveness of theoretical results.
Phosphorylation Time‐Course Study of the Response during Adenovirus Type 2 Infection

PTMs such as phosphorylations are usually involved in signal transduction pathways. To investigate the temporal dynamics of phosphoproteome changes upon viral infection, a model system of IMR‐90 cells infected with human adenovirus type 2 (Ad2) is used in a time‐course quantitative analysis combining titanium dioxide (TiO2) particle enrichment and SILAC‐MS. Quantitative data from 1552 phosphorylated sites clustered the highly altered phosphorylated sites to the signaling by rho family GTPases, the actin cytoskeleton signaling, and the cAMP‐dependent protein kinase A signaling pathways. Their activation is especially pronounced at early time post‐infection. Changes of several phosphorylated sites involved in the glycolysis pathway, related to the activation of the Warburg effect, point at virus‐induced energy production. For Ad2 proteins, 32 novel phosphorylation sites are identified and as many as 52 phosphorylated sites on 17 different Ad2 proteins are quantified, most of them at late time post‐infection. Kinase predictions highlighted activation of PKA, CDK1/2, MAPK, and CKII. Overlaps of kinase motif sequences for viral and human proteins are observed, stressing the importance of phosphorylation during Ad2 infection.