Miracle-dz/UniFAR-FTUs huggingface dataset:
Hairy black holes in disformal scalar-tensor gravity theories

We show that the no-hair theorem for scalar-tensor theories with bi-metric structure can be evaded. We find that hairy black hole solutions in the presence of an electric charge admit AdS, flat or dS asymptotics with spherical, flat, or hyperbolic base manifolds. Spherically symmetric, asymptotically flat black holes and asymptotically AdS configurations with any horizon topology are compatible with a regular scalar field on and outside the event horizon. The latter presents a rich thermodynamic behavior induced by the disformal factor that enters as a coupling parameter in the theory. In the grand canonical ensemble, there is an interplay of stability and first-order phase transitions between thermal AdS, the hairy black hole, and the Reissner-Nordstr\"om-AdS black hole, whose thermodynamic phase space resembles a solid-liquid-gas system, with an electric potential playing the role of pressure. In close analogy, there is a triple point where the three phases coexist, being equally probable.
SIGAN: A Novel Image Generation Method for Solar Cell Defect Segmentation and Augmentation

Solar cell electroluminescence (EL) defect segmentation is an interesting and challenging topic. Many methods have been proposed for EL defect detection, but these methods are still unsatisfactory due to the diversity of the defect and background. In this paper, we provide a new idea of using generative adversarial network (GAN) for defect segmentation. Firstly, the GAN-based method removes the defect region in the input defective image to get a defect-free image, while keeping the background almost unchanged. Then, the subtracted image is obtained by making difference between the defective input image with the generated defect-free image. Finally, the defect region can be segmented through thresholding the subtracted image. To keep the background unchanged before and after image generation, we propose a novel strong identity GAN (SIGAN), which adopts a novel strong identity loss to constraint the background consistency. The SIGAN can be used not only for defect segmentation, but also small-samples defective dataset augmentation. Moreover, we release a new solar cell EL image dataset named as EL-2019, which includes three types of images: crack, finger interruption and defect-free. Experiments on EL-2019 dataset show that the proposed method achieves 90.34% F-score, which outperforms many state-of-the-art methods in terms of solar cell defects segmentation results.
Stay Together: A System for Single and Split-antecedent Anaphora Resolution

The state-of-the-art on basic, single-antecedent anaphora has greatly improved in recent years. Researchers have therefore started to pay more attention to more complex cases of anaphora such as split-antecedent anaphora, as in “Time-Warner is considering a legal challenge to Telecommunications Inc’s plan to buy half of Showtime Networks Inc–a move that could lead to all-out war between the two powerful companies”. Split-antecedent anaphora is rarer and more complex to resolve than single-antecedent anaphora; as a result, it is not annotated in many datasets designed to test coreference, and previous work on resolving this type of anaphora was carried out in unrealistic conditions that assume gold mentions and/or gold split-antecedent anaphors are available. These systems also focus on split-antecedent anaphors only. In this work, we introduce a system that resolves both single and split-antecedent anaphors, and evaluate it in a more realistic setting that uses predicted mentions. We also start addressing the question of how to evaluate single and split-antecedent anaphors together using standard coreference evaluation metrics.
Chinese Association for the Study of Pain: Expert consensus on diagnosis and treatment for lumbar disc herniation

Lumbar disc herniation is a common disease in the clinical context and does great harm to either the physical or mental health of patients suffering from this disease. Many guidelines and consensus for the diagnosis and treatment of lumbar disc herniation have been published domestically and internationally. According to the expert consensus, clinicians could adopt tailored and personalized diagnosis and treatment management strategies for lumbar disc herniation patients.
The classification of EEG-based winking signals: a transfer learning and random forest pipeline

Brain Computer-Interface (BCI) technology plays a considerable role in the control of rehabilitation or peripheral devices for stroke patients. This is particularly due to their inability to control such devices from their inherent physical limitations after such an attack. More often than not, the control of such devices exploits electroencephalogram (EEG) signals. Nonetheless, it is worth noting that the extraction of the features and the classification of the signals is non-trivial for a successful BCI system. The use of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features. However, the employment of such a method towards BCI applications, particularly in regard to EEG signals, are somewhat limited. The present study aims to evaluate the effectiveness of different TL models in extracting features for the classification of wink-based EEG signals. The extracted features are classified by means of fine-tuned Random Forest (RF) classifier. The raw EEG signals are transformed into a scalogram image via Continuous Wavelet Transform (CWT) before it was fed into the TL models, namely InceptionV3, Inception ResNetV2, Xception and MobileNet. The dataset was divided into training, validation, and test datasets, respectively, via a stratified ratio of 60:20:20. The hyperparameters of the RF models were optimised through the grid search approach, in which the five-fold cross-validation technique was adopted. The optimised RF classifier performance was compared with the conventional TL-based CNN classifier performance. It was demonstrated from the study that the best TL model identified is the Inception ResNetV2 along with an optimised RF pipeline, as it was able to yield a classification accuracy of 100% on both the training and validation dataset. Therefore, it could be established from the study that a comparable classification efficacy is attainable via the Inception ResNetV2 with an optimised RF pipeline. It is envisaged that the implementation of the proposed architecture to a BCI system would potentially facilitate post-stroke patients to lead a better life quality.
Polysaccharides from sporoderm-removed spores of Ganoderma lucidum induce apoptosis in human gastric cancer cells via disruption of autophagic flux

The sporoderm-broken spores of Ganoderma lucidum (G. lucidum) polysaccharide (BSGLP) have been demonstrated to inhibit carcinogenesis in several types of cancer. However, to the best of our knowledge, the anticancer effects of polysaccharides extracted from the newly developed sporoderm-removed spores of G. lucidum (RSGLP) have not been assessed. The present study first compared the anticancer effects of RSGLP and BSGLP in three gastric cancer cell lines and it was found that RSGLP was more potent than BSGLP in decreasing gastric cancer cell viability. RSGLP significantly induced apoptosis in AGS cells, accompanied by downregulation of Bcl-2 and pro-caspase-3 expression levels, and upregulation of cleaved-PARP. Furthermore, RSGLP increased LC3-II and p62 expression, indicative of induction of autophagy and disruption of autophagic flux in AGS cells. These results were further verified by combined treatment of AGS cells with the late-stage autophagy inhibitor chloroquine, or early-stage autophagy inducer rapamycin. Adenoviral transfection with mRFP-GFP-LC3 further confirmed that autophagic flux was inhibited by RSGLP in AGS cells. Finally, the present study demonstrated that the RSGLP-induced autophagy and disruption of autophagic flux disruption was, at least in part, responsible for RSGLP-induced apoptosis in AGS cells. The results of the present study demonstrated for the first time that RSGLP is more effective than BSGLP in inhibiting gastric cancer cell viability, and RSGLP may serve as a promising autophagy inhibitor in the management of gastric cancer.
A Deep Learning Approach to Active Noise Control

We formulate active noise control (ANC) as a supervised learning problem and propose a deep learning approach, called deep ANC, to address the nonlinear ANC problem. A convolutional recurrent network (CRN) is trained to estimate the real and imaginary spectrograms of the canceling signal from the reference signal so that the corresponding anti-noise can eliminate or attenuate the primary noise in the ANC system. Large-scale multi-condition training is employed to achieve good generalization and robustness against a variety of noises. The deep ANC method can be trained to achieve active noise cancellation no matter whether the reference signal is noise or noisy speech. In addition, a delay-compensated strategy is introduced to address the potential latency problem of ANC systems. Experimental results show that the proposed method is effective for wide-band noise reduction and generalizes well to untrained noises. Moreover, the proposed method can be trained to achieve ANC within a quiet zone.
Proteomics applications in biomarker discovery and pathogenesis for abdominal aortic aneurysm

ABSTRACT Introduction: Abdominal aortic aneurysm (AAA) is a common, complex, and life-threatening disease. Currently, the pathogenesis of AAA is not well understood. No biomarkers or specific drugs are available for AAA in clinical applications. Proteomics is a powerful tool in biomarker discovery, exploration of pathogenesis, and drug target identification. Areas covered: We review the application of mass spectrometry-based proteome analysis in AAA patients within the last ten years. Differentially expressed proteins associated with AAA were identified in multiple sample sources, including vascular tissue, intraluminal thrombus, tissue secretome, blood, and cells. Some potential disease biomarkers, pathogenic mechanisms, or therapeutic targets for AAA were discovered using proteome analysis. The challenges and prospects of proteomics applied to AAA are also discussed. Expert opinion: Since most of the previous proteomic studies used relatively small sample sizes, some promising biomarkers need to be validated in multicenter cohorts to accelerate their clinical application. With the rapid development of mass spectrometry technology, modification-specific proteomics and multi-omics research in the future will enhance our understanding of the pathogenesis of AAA and promote biomarker discovery and drug development for clinical translation.
Aladaip, beyond alara and towards personalized optimization for paediatric cone beam ct.

ALARA is a well-known principle that has been used to identify optimization of x-ray exposure in the field of radiology since the seventies. While ALARA implies the responsibility to minimize radiation exposures to a level As Low As Reasonably Achievable, some years ago, a variation of the acronym, ALADA, was proposed which took into account the evolution of imaging technology and any related dose increases 1 .
Machine learning based anti-cancer drug response prediction and search for predictor genes using cancer cell line gene expression

Although many models have been proposed to accurately predict the response of drugs in cell lines recent years, understanding the genome related to drug response is also the key for completing oncology precision medicine. In this paper, based on the cancer cell line gene expression and the drug response data, we established a reliable and accurate drug response prediction model and found predictor genes for some drugs of interest. To this end, we first performed pre-selection of genes based on the Pearson correlation coefficient and then used ElasticNet regression model for drug response prediction and fine gene selection. To find more reliable set of predictor genes, we performed regression twice for each drug, one with IC50 and the other with area under the curve (AUC) (or activity area). For the 12 drugs we tested, the predictive performance in terms of Pearson correlation coefficient exceeded 0.6 and the highest one was 17-AAG for which Pearson correlation coefficient was 0.811 for IC50 and 0.81 for AUC. We identify common predictor genes for IC50 and AUC, with which the performance was similar to those with genes separately found for IC50 and AUC, but with much smaller number of predictor genes. By using only common predictor genes, the highest performance was AZD6244 (0.8016 for IC50, 0.7945 for AUC) with 321 predictor genes.
Firefighting-Associated Cancers: Can Increased Physical Activity and Improved Cardiorespiratory Fitness Be Potential Countermeasures?

ht © 2021 American Colle Answered by Denise L. Sm Family Distinguished Profes the First Responder Health Skidmore College; Alexs Research Assistant, First Re and Safety Lab, Skidmore C D. Bode, BS, Project Coo Responder Health and Saf more College; Maria Korre Assistant Professor, First Re and Safety Lab, Skidmore C iting Scientist, Harvard T.H of Public Health; Irene Lido Research Fellow, First Dep gery, Laikon Hospital of A Mercedes Sotos-Prieto, P Cajal, University Autonom and Visiting Scientist, Harv School of Public Health; Ste MD, MPH, Professor, Ha School/Cambridge Health Professor, Department of Health, Harvard T.H. Chan lic Health.
β‐Arrestin1 and GPCR kinase2 play permissive roles in Src‐mediated endocytosis of α4β2 nicotinic ACh receptors

The α4β2 nicotinic ACh receptor (nAChR), a subtype of the ligand‐gated ion channel, is abundantly expressed in the brain and is implicated in several neurological disorders. The endocytosis of nAChRs plays important roles in the pathogenesis of neurological diseases, but the underlying molecular mechanisms remain poorly understood.
Temozolomide with irinotecan versus temozolomide, irinotecan plus bevacizumab for recurrent medulloblastoma of childhood: Report of a COG randomized Phase II screening trial

Background: Approximately 30% of children with medulloblastoma (MB) experience recurrence, which is usually incurable. This study compared the overall survival (OS) of patients receiving temozolomide (TMZ) and irinotecan with that of patients receiving TMZ, irinotecan, and bevacizumab for recurrent MB/central nervous system (CNS) primitive neuroectodermal tumor (PNET).
Symmetry analysis of complex magnetic structure in monoclinically distorted Er3Cu4Sn4.

The magnetic structure in Er3Cu4Sn4 has been determined using high-resolution powder neutron diffraction, supported by symmetry analysis. At low temperatures, Er3Cu4Sn4 assumes a crystal structure of the Tm3Cu4Sn4 type (in the monoclinic space group C2/m). The Er atoms occupy two distinct Wyckoff sites: 2c and 4i. It has been found that the Er magnetic moments on the 2c site form a commensurate antiferromagnetic structure (k1 = [0, 0, ½]) below 6 K. The magnetic moments reach 8.91 (8) μB at 1.4 K and are parallel to the b axis. The Er magnetic moments on the 4i site order below 2 K and form an incommensurate antiferromagnetic sine-modulated structure (k2 = [1, 0.4667 (1), ½]), with magnetic moments lying in the ac plane and perpendicular to the a axis. The amplitude of modulation equals 8.7 (1) μB at 1.4 K.
In-plane resolved in-situ measurements of the membrane resistance in PEFCs

The in-situ resistance of Nafion® membranes with different thickness was measured in H 2 /O 2 fuel cells as function of current density and gas humidity. The resistance distribution across the thickness of membranes was determined by using membranes composed from several thin sheets with interlaying thin gold wires as potential probes. The same method was used to measure the resistance at different in-plane locations (i.e. at gas inlets and outlets). It was found that with increasing current density the resistance at the anode side of the membrane is increasing. When oxygen was fed dry, then complex in-plane patterns of membrane hydration distribution were observed.
Late-Life Vascular Risk Score in Association With Postmortem Cerebrovascular Disease Brain Pathologies

Background and Purpose: The general cardiovascular Framingham risk score (FRS) identifies adults at increased risk for stroke. We tested the hypothesis that baseline FRS is associated with the presence of postmortem cerebrovascular disease (CVD) pathologies. Methods: We studied the brains of 1672 older decedents with baseline FRS and measured CVD pathologies including macroinfarcts, microinfarcts, atherosclerosis, arteriolosclerosis, and cerebral amyloid angiopathy. We employed a series of logistic regressions to examine the association of baseline FRS with each of the 5 CVD pathologies. Results: Average age at baseline was 80.5±7.0 years and average age at death was 89.2±6.7 years. A higher baseline FRS was associated with higher odds of macroinfarcts (odds ratio, 1.10 [95% CI, 1.07–1.13], P<0.001), microinfarcts (odds ratio, 1.04 [95% CI, 1.01–1.07], P=0.009), atherosclerosis (odds ratio, 1.07 [95% CI, 1.04–1.11], P<0.001), and arteriolosclerosis (odds ratio, 1.04 [95% CI, 1.01–1.07], P=0.005). C statistics for these models ranged from 0.537 to 0.595 indicating low accuracy for predicting CVD pathologies. FRS was not associated with the presence of cerebral amyloid angiopathy. Conclusions: A higher FRS score in older adults is associated with higher odds of some, but not all, CVD pathologies, with low discrimination at the individual level. Further work is needed to develop a more robust risk score to identify adults at risk for accumulating CVD pathologies.
A novel mesocosm setup for benthic‐pelagic coupling experiments

A new mesocosm setup containing both water column and sediment suitable for benthic‐pelagic coupling experiments is described. The presence of a large volume of water (>1.5 m3) with sufficient depth (4 m) on the top of the sediment, combined with a large volume of sediment on the bottom (30 cm height, 80 L volume) offers new opportunities for benthic‐pelagic experiments on the mesocosm scale. The experimental setup includes a mesocosm bag, a securing ring with a cap on the top, a sediment container on the bottom, sediment traps, an autonomous water sampling system at specific depths, and a newly developed sediment sampler for collecting samples without disturbing the system. The mesocosm setup was successfully deployed in the context of a benthic‐pelagic coupling eutrophication experiment in the CRETACOSMOS mesocosm facilities of the Hellenic Centre for Marine Research in Crete. A number of variables related to the technical aspects and proper ecosystem functions of the mesocosm setup were monitored throughout the duration of the experiment (58 d), which proved that the proposed setup is suitable for benthic‐pelagic coupling experiments, providing an intermediate experimental tool between small‐scale micro/benthocosm experiments and large scale in situ sea experiments.
A multimodal approach to detect and monitor early lung disease in cystic fibrosis

ABSTRACT Introduction: In the early stages, lung involvement in cystic fibrosis (CF) can be silent, with disease progression occurring in the absence of clinical symptoms. Irreversible airway damage is present in the early stages of disease; however, reliable biomarkers of early damage due to inflammation and infection that are universally applicable in day-to-day patient management have yet to be identified. Areas covered: At present, the main methods of detecting and monitoring early lung disease in CF are the lung clearance index (LCI), computed tomography (CT), and magnetic resonance imaging (MRI). LCI can be used to detect patients who may require more intense monitoring, identify exacerbations, and monitor responses to new interventions. High-resolution CT detects structural alterations in the lungs of CF patients with the best resolution of current imaging techniques. MRI is a radiation-free imaging alternative that provides both morphological and functional information. The role of MRI for short-term follow-up and pulmonary exacerbations is currently being investigated. Expert opinion: The roles of LCI and MRI are expected to expand considerably over the next few years. Meanwhile, closer collaboration between pulmonology and radiology specialties is an important goal toward improving care and optimizing outcomes in young patients with CF.
Evaluation of predictive model performance of an existing model in the presence of missing data

In medical research, the Brier score (BS) and the area under the receiver operating characteristic (ROC) curves (AUC) are two common metrics used to evaluate prediction models of a binary outcome, such as using biomarkers to predict the risk of developing a disease in the future. The assessment of an existing prediction models using data with missing covariate values is challenging. In this article, we propose inverse probability weighted (IPW) and augmented inverse probability weighted (AIPW) estimates of AUC and BS to handle the missing data. An alternative approach uses multiple imputation (MI), which requires a model for the distribution of the missing variable. We evaluated the performance of IPW and AIPW in comparison with MI in simulation studies under missing completely at random, missing at random, and missing not at random scenarios. When there are missing observations in the data, MI and IPW can be used to obtain unbiased estimates of BS and AUC if the imputation model for the missing variable or the model for the missingness is correctly specified. MI is more efficient than IPW. Our simulation results suggest that AIPW can be more efficient than IPW, and also achieves double robustness from miss‐specification of either the missingness model or the imputation model. The outcome variable should be included in the model for the missing variable under all scenarios, while it only needs to be included in missingness model if the missingness depends on the outcome. We illustrate these methods using an example from prostate cancer.
Feasibility of assessing progression of transthyretin amyloid polyneuropathy using nerve conduction studies: Findings from the Transthyretin Amyloidosis Outcomes Survey (THAOS)

Patients with transthyretin amyloid polyneuropathy (ATTR‐PN) show decreased motor and sensory nerve amplitudes and conduction. Electrophysiological changes over time may be sensitive indicators of progression. This analysis from the Transthyretin Amyloidosis Outcomes Survey (THAOS) assessed longitudinal changes in nerve conduction as signals of neurologic disease progression in patients with hereditary ATTR (ATTRv) amyloidosis. Patients with ATTRv in THAOS with recorded nerve conduction values were included (data cut‐off: January 6, 2020); changes in nerve amplitude and velocity over time were assessed. Patients (n = 1389) were 45.0% male; 80.4% were the Val30Met (p.Val50Met) genotype. Mean (SD) age at enrollment was 43.6 (14.5) years; duration of symptoms was 9.3 (6.4) years. Median (10th, 90th percentile) sural nerve amplitude and velocity was 18.0 (4.9, 35.0) μV and 50.7 (41.0, 57.9) m/s; peroneal conduction was 13.0 (4.4, 27.0) μV and 51.0 (41.7, 59.7) m/s, respectively. Median (10th, 90th percentile) percentage change from baseline in sural nerve amplitude was variable, but generally decreased over time from −7.4 (−43.2, 52.4) at year 1 to −14.4 (−76.9, 46.7) at year 8. Percent change from baseline in sural nerve velocity declined similarly: −0.1 (−14.5, 15.3) at year 1 and − 6.4 (−21.3, 10.5) at year 8. The decline was more pronounced in patients with greater disability at baseline. Similar patterns were observed for the peroneal nerve. These data show an association between nerve amplitudes and velocities and disease severity, suggesting progressive deterioration in nerve conduction may be an indicator of ATTRv amyloidosis disease progression.