Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Speech-in-Noise Test for Measuring Informational Masking in Speech Perception Among Elderly Listeners

Introduction Elderly listeners have reported concerns about speech perception in noisy environments. This partly occurs because of their increased informational masking (IM). This study aimed to develop a Persian coordinate response measure (CRM) corpus and a novel speech-in-noise test for measuring IM. Material and methods A cross-sectional validation study was conducted in two parts. Part one was the determination of the validity and reliability of the Persian CRM corpus. Part two consisted of measuring the IM at five signal-to-noise ratio (SNR; -6,-3, 0, +3, and +6) in two conditions: one with the target and masker speaker of the same sex and one with the target and masker speaker of different sexes. In each condition, the IM measurements were performed at a 45° separation angle of target and maskers and as a co-location of the speakers. A group of young listeners aged 20 to 40 years and a group of elderly listeners aged 60 to 75 years were recruited (50 study participants in part one and 47 in part two). The study was conducted from July 2018 to March 2019 at the Iran University Medical Sciences audiology clinic. Content validity ratio, content validity index, impact score, Spearman's test, and Mann-Whitney's test were used for statistical analysis. Results The Persian CRM corpus showed acceptable validity and reliability in each group (p < 0.001). The results suggested that in both azimuth locations and at SNRs of 0, -3, and -6, the IM amount in the elderly group was significantly higher (p < 0.003) than in the young group at conditions of target and masker speakers of opposite-sex. However, in cases where both target and masker speakers were of the same sex, a significant difference was observed at an SNR of 0 in angular separation and SNRs of +3 and 0 at co-located situations (p < 0.001). Conclusion A validated Persian CRM corpus has been collected for use in IM measurement studies. Overall, the IM of elderly listeners was higher than younger listeners in low-cue situations such as lower SNR. Therefore, a novel speech-in-noise test for measuring IM was validated to use in speech perception studies in the elderly population.
Acetylshikonin suppressed growth of colorectal tumour tissue and cells by inhibiting the intracellular kinase, T‐lymphokine‐activated killer cell‐originated protein kinase.

Overexpression or aberrant activation of the T‐lymphokine‐activated killer cell‐originated protein kinase (TOPK) promotes gene expression and growth of solid tumours, implying that TOPK would be a rational target in developing novel anticancer drugs. Acetylshikonin, a diterpenoid compound isolated from Lithospermum erythrorhizon root, exerts a range of biological activities. Here we have investigated whether acetylshikonin, by acting as an inhibitor of TOPK, can attenuate the proliferation of colorectal cancer cells and the growth of patient‐derived tumours, in vitro and in vivo.
RGB Pixel Brightness Characteristics of Linked Color Imaging in Early Gastric Cancer: A Pilot Study

Background and Aims Linked color imaging (LCI) helps screen and diagnose for early gastric cancer by color contrast in different mucosa. RGB (red, green, and blue) pixel brightness quantifies colors, which is relatively objective. Limited studies have combined LCI images with RGB to help screen for early gastric cancer (EGC). We aimed to evaluate the RGB pixel brightness characteristics of EGC and noncancer areas in LCI images. Methods We retrospectively reviewed early gastric cancer (EGC) patients and LCI images. All pictures were evaluated by at least two endoscopic physicians. RGB pixel brightness analysis of LCI images was performed in MATLAB software to compare the cancer with noncancer areas. Receiver operating characteristic (ROC) curve was analyzed for sensitivity, specificity, cut-off, and area under the curve (AUC). Results Overall, 38 early gastric cancer patients were enrolled with 38 LCI images. Pixel brightness of red, green, and blue in cancer was remarkably higher than those in noncancer areas (190.24 ± 37.10 vs. 160.00 ± 40.35, p < 0.001; 117.96 ± 33.91 vs. 105.33 ± 30.01, p = 0.039; 114.36 ± 34.88 vs. 90.93 ± 30.14, p < 0.001, respectively). Helicobacter plyori (Hp) infection was not relevant to RGB distribution of EGC. Whether the score of Kyoto Classification of Gastritis (KCG) is ≥4 or <4, the pixel brightness of red, green, and blue was not disturbed in both cancer and noncancer (p > 0.05). Receiver operating characteristic (ROC) curve for differentiating cancer from noncancer was calculated. The maximum area under the curve (AUC) was 0.767 in B/G, with a sensitivity of 0.605, a specificity of 0.921, and a cut-off of 0.97. Conclusions RGB pixel brightness was useful and more objective in distinguishing early gastric cancer for LCI images.
Structure-based design of antiviral drug candidates targeting the SARS-CoV-2 main protease

Promising antiviral protease inhibitors With no vaccine or proven effective drug against the virus that causes coronavirus disease 2019 (COVID-19), scientists are racing to find clinical antiviral treatments. A promising drug target is the viral main protease Mpro, which plays a key role in viral replication and transcription. Dai et al. designed two inhibitors, 11a and 11b, based on analyzing the structure of the Mpro active site. Both strongly inhibited the activity of Mpro and showed good antiviral activity in cell culture. Compound 11a had better pharmacokinetic properties and low toxicity when tested in mice and dogs, suggesting that this compound is a promising drug candidate. Science, this issue p. 1331 Two peptidomimetic aldehydes were designed, synthesized, and evaluated as antiviral drug candidates. SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is the etiological agent responsible for the global COVID-19 (coronavirus disease 2019) outbreak. The main protease of SARS-CoV-2, Mpro, is a key enzyme that plays a pivotal role in mediating viral replication and transcription. We designed and synthesized two lead compounds (11a and 11b) targeting Mpro. Both exhibited excellent inhibitory activity and potent anti–SARS-CoV-2 infection activity. The x-ray crystal structures of SARS-CoV-2 Mpro in complex with 11a or 11b, both determined at a resolution of 1.5 angstroms, showed that the aldehyde groups of 11a and 11b are covalently bound to cysteine 145 of Mpro. Both compounds showed good pharmacokinetic properties in vivo, and 11a also exhibited low toxicity, which suggests that these compounds are promising drug candidates.
Characterization of a high-resolution breath acetone meter for ketosis monitoring

The ketone bodies beta-hydroxybutyrate (BHB) and acetone are endogenous products of fatty acid metabolism. Although ketone levels can be monitored by measuring either blood BHB or breath acetone, determining the precise correlation between these two measurement methods has been challenging. The purpose of this study is to characterize the performance of a novel portable breath acetone meter (PBAM) developed by Readout, Inc., to compare single versus multiple daily ketone measurements, and to compare breath acetone (BrAce) and blood BHB measurements. Methods We conducted a 14-day prospective observational cohort study of 21 subjects attempting to follow either a low-carbohydrate/ketogenic or a standard diet. Subjects were asked to concurrently measure both blood BHB and BrAce five times per day and report the results using an online data entry system. We evaluated the utility of multiple daily measurements by calculating the coefficient of variation (CV) for each daily group of measurements. We calculated the correlation between coincident BrAce and blood BHB measurements using linear ordinary least squares regression analysis. We assessed the ability of the BrAce measurement to accurately predict blood BHB states using receiver operating characteristic (ROC) analysis. Finally, we calculated a daily ketone exposure (DKE) using the area under the curve (AUC) of a ketone concentration versus time graph and compared the DKE of BrAce and blood BHB using linear ordinary least squares regression. Results BrAce and blood BHB varied throughout the day by an average of 44% and 46%, respectively. The BrAce measurement accurately predicted whether blood BHB was greater than or less than the following thresholds: 0.3 mM (AUC = 0.898), 0.5 mM (AUC = 0.854), 1.0 mM (AUC = 0.887), and 1.5 mM (AUC = 0.935). Coincident BrAce and blood BHB measurements were moderately correlated with R2 = 0.57 (P < 0.0001), similar to literature reported values. However, daily ketone exposures, or areas under the curve, for BrAce and blood BHB were highly correlated with R2 = 0.83 (P < 0.0001). Conclusions The results validated the performance of the PBAM. The BrAce/BHB correlation was similar to literature values where BrAce was measured using highly accurate lab instruments. Additionally, BrAce measure- ments using the PBAM can be used to predict blood BHB states. The relatively high daily variability of ketone levels indicate that single blood or breath ketone measurements are often not sufficient to assess daily ketone exposure for most users. Finally, although single coincident blood and breath ketone measurements show only a moderate correlation, possibly due to the temporal lag between BrAce and blood BHB, daily ketone exposures for blood and breath are highly correlated.
Impact of anti-androgenic therapies on COVID-19: an observational study in male population from a COVID-19 regional centre of Lombardy (Italy)

There are gender differences in susceptibility and vulnerability to the Coronavirus disease 2019 (COVID-19). The S protein of coronaviruses facilitates viral entry into target cells and employs the host cellular serine protease TMPRSS2 for S protein priming. The TMPRSS2 gene expression is responsive to androgen stimulation and it could partially explain gender differences. We tested the hypothesis that men who received 5-Alpha reductase inhibitors (5ARIs) or androgen deprivation therapy (ADT) for prostate cancer could have a different susceptibility to COVID-19. We carried out an observational study on patients who were referred to our COVID-19 regional centre in Lombardy from 1st to 31st March 2020. Data from 421 patients, 137 women (32.54%) and 284 men (67.44%) with laboratory-confirmed COVID-19, were included in this report. Overall 84 patients died: 28 women (33.33%) and 56 men (66.67%). Among men, 12 patients (4.22%) reported assuming 5ARI treatment, and 6 were under ADT. Over 12 patients under 5ARIs, 3 (25%) died; 2 deaths (33%) were reported in patients under ADT. Our findings showed that only 4.22% of the overall population received 5ARI anti-androgen therapy, a percentage, which revealed to be significantly lower (P<0.0001) than what observed in Italian men aged more than 40 years (14.97%).
Spatio-temporal associations between deforestation and malaria incidence in Lao PDR

As countries in the Greater Mekong Sub-region (GMS) increasingly focus their malaria control and elimination efforts on forest-going populations, greater understanding of the relationship between deforestation and malaria incidence will be essential for programs to assess and meet their 2030 elimination goals. Leveraging village-level health facility surveillance data and forest cover data in a spatio-temporal modeling framework, we found evidence that deforestation is associated with short-term increases, but long-term decreases in confirmed malaria case incidence in Lao People's Democratic Republic (Lao PDR). We identified strong associations with deforestation measured within 30 km of villages but not with deforestation in the near (10 km) and immediate (1 km) vicinity. Results appear driven by deforestation in densely forested areas and were more pronounced for infections with Plasmodium falciparum (P. falciparum) than for Plasmodium vivax (P. vivax). These findings highlight the influence of forest-going populations on malaria transmission in the GMS.
Metaphor Processing Dysfunctions in Schizophrenia Patients With and Without Substance Use Disorders

Background Patients with schizophrenia have difficulties comprehending metaphors, which significantly impedes communication. However, this topic has not been thoroughly studied in people with a dual diagnosis. On this basis, we formulated two research aims: a) to compare the ability to comprehend metaphors in schizophrenia patients without (SZ) and with substance use disorder (SZ-SUD) and b) to determine the relationship between the processing of metaphorical content and the severity of psychopathological symptoms in both clinical groups. Methods A total of 40 individuals with SZ and 40 individuals with SZ-SUD took part in the study. The control group was composed of 40 individuals without a psychiatric or neurological diagnosis. Four subtests from the Right Hemisphere Language Battery (Picture Metaphor Test, Written Metaphor Test, Picture Metaphor Explanation Test, Written Metaphor Explanation Test) were used to measure the ability to understand and explain metaphors. Results Both groups of individuals with schizophrenia (SZ and SZ-SUD) scored lower than individuals from the control group on all tests of metaphor processing. However, no differences were observed between the two clinical groups. SZ-SUD patients had better results for Picture Metaphor Explanation than for Written Metaphor Explanation. Negative symptoms were found to be significant predictors of difficulties with understanding and explaining metaphors. Conclusion Individuals with schizophrenia, regardless of their substance use disorder (SUD) status, exhibit impaired metaphorical content processing. SUD in schizophrenia is not associated with significant impairments in understanding and explaining metaphorical content. Moreover, impairments in processing metaphorical content are associated with more severe negative symptoms of schizophrenia.
Acid Experimental Evolution of the Haloarchaeon Halobacterium sp. NRC-1 Selects Mutations Affecting Arginine Transport and Catabolism

Halobacterium sp. NRC-1 (NRC-1) is an extremely halophilic archaeon that is adapted to multiple stressors such as UV, ionizing radiation and arsenic exposure; it is considered a model organism for the feasibility of microbial life in iron-rich brine on Mars. We conducted experimental evolution of NRC-1 under acid and iron stress. NRC-1 was serially cultured in CM+ medium modified by four conditions: optimal pH (pH 7.5), acid stress (pH 6.3), iron amendment (600 μM ferrous sulfate, pH 7.5), and acid plus iron (pH 6.3, with 600 μM ferrous sulfate). For each condition, four independent lineages of evolving populations were propagated. After 500 generations, 16 clones were isolated for phenotypic characterization and genomic sequencing. Genome sequences of all 16 clones revealed 378 mutations, of which 90% were haloarchaeal insertion sequences (ISH) and ISH-mediated large deletions. This proportion of ISH events in NRC-1 was five-fold greater than that reported for comparable evolution of Escherichia coli. One acid-evolved clone had increased fitness compared to the ancestral strain when cultured at low pH. Seven of eight acid-evolved clones had a mutation within or upstream of arcD, which encodes an arginine-ornithine antiporter; no non-acid adapted strains had arcD mutations. Mutations also affected the arcR regulator of arginine catabolism, which protects bacteria from acid stress by release of ammonia. Two acid-adapted strains shared a common mutation in bop, which encodes bacterio-opsin, apoprotein for the bacteriorhodopsin light-driven proton pump. Thus, in the haloarchaeon NRC-1, as in bacteria, pH adaptation was associated with genes involved in arginine catabolism and proton transport. Our study is among the first to report experimental evolution with multiple resequenced genomes of an archaeon. Haloarchaea are polyextremophiles capable of growth under environmental conditions such as concentrated NaCl and desiccation, but little is known about pH stress. Interesting parallels appear between the molecular basis of pH adaptation in NRC-1 and in bacteria, particularly the acid-responsive arginine-ornithine system found in oral streptococci.
Methanogens and Methanotrophs Show Nutrient-Dependent Community Assemblage Patterns Across Tropical Peatlands of the Pastaza-Marañón Basin, Peruvian Amazonia

Tropical peatlands are globally important carbon reservoirs that play a crucial role in fluxes of atmospheric greenhouse gases. Amazon peatlands are expected to be large source of atmospheric methane (CH4) emissions, however little is understood about the rates of CH4 flux or the microorganisms that mediate it in these environments. Here we studied a mineral nutrient gradient across peatlands in the Pastaza-Marañón Basin, the largest tropical peatland in South America, to describe CH4 fluxes and environmental factors that regulate species assemblages of methanogenic and methanotrophic microorganisms. Peatlands were grouped as minerotrophic, mixed and ombrotrophic categories by their general water source leading to different mineral nutrient content (rich, mixed and poor) quantified by trace elements abundance. Microbial communities clustered dependent on nutrient content (ANOSIM p < 0.001). Higher CH4 flux was associated with minerotrophic communities compared to the other categories. The most dominant methanogens and methanotrophs were represented by Methanobacteriaceae, and Methylocystaceae, respectively. Weighted network analysis demonstrated tight clustering of most methanogen families with minerotrophic-associated microbial families. Populations of Methylocystaceae were present across all peatlands. Null model testing for species assemblage patterns and species rank distributions confirmed non-random aggregations of Methylococcacae methanotroph and methanogen families (p < 0.05). We conclude that in studied amazon peatlands increasing mineral nutrient content provides favorable habitats for Methanobacteriaceae, while Methylocystaceae populations seem to broadly distribute independent of nutrient content.
New Screening Test Improves Detection of Prostate Cancer Using Circulating Tumor Cells and Prostate-Specific Markers

The current screening-test for prostate cancer, affecting 10% of men worldwide, has a high false negative rate and a low true positive rate. A more reliable screening test is needed. Circulating-Tumor-Cells (CTC) provide a biomarker for early carcinogenesis, cancer progression and treatment effectiveness. The cytology-based ISET®-CTC Test is a clinically validated blood test with high sensitivity and specificity. This study aimed to evaluate the ISET®-CTC test combined with prostate-specific-marker staining as a screening test for the detection of prostate cancer. We selected a group of 47 men from our ongoing CTC screening study involving 2,000 patient-tests from Sep-2014 to July-2019, who also underwent standard diagnostic cancer testing before or after CTC testing. While 20 of the 47 men were diagnosed with prostate cancer before the ISET®-CTC test, 27 men underwent screening. We studied the CTC identified in 45 CTC-positive men by Immuno-Cyto-Chemistry (ICC) assays with the prostate-specific-marker PSA. CTC were ICC-PSA-marker positive in all men diagnosed with primary prostate cancer (n = 20). Secondary cancers were detected in 63% (n = 7/11) of men with mixed CTC-population (ICC-PSA-positive/ICC-PSA-negative). Of the 27 men screened, 25 had CTC, and 84% of those (n = 20) were positive for the prostate-specific-PSA-marker. Follow-up testing suggested suspected prostate cancer in 20/20 men by a positive PSMA-PET scan, and biopsies performed in 45% (n = 9/20) men confirmed the diagnosis of early prostate cancer. Kidney cancer or B-cell lymphoma were detected in two men with ICC-PSA-marker negative CTC. Our study suggests that the combination of ISET®-CTC and ICC-PSA-marker-testing has an estimated positive-predictive-value (PPV) of 99% and a negative-predictive-value (NPV) of 97%, providing a more reliable screening test for prostate cancer than the standard PSA-blood-test (PPV = 25%; NPV = 15.5%). Our findings warrant further studies to evaluate the new test's potential for prostate cancer screening on a population level.
Contextual Integration in Cortical and Convolutional Neural Networks

It has been suggested that neurons can represent sensory input using probability distributions and neural circuits can perform probabilistic inference. Lateral connections between neurons have been shown to have non-random connectivity and modulate responses to stimuli within the classical receptive field. Large-scale efforts mapping local cortical connectivity describe cell type specific connections from inhibitory neurons and like-to-like connectivity between excitatory neurons. To relate the observed connectivity to computations, we propose a neuronal network model that approximates Bayesian inference of the probability of different features being present at different image locations. We show that the lateral connections between excitatory neurons in a circuit implementing contextual integration in this should depend on correlations between unit activities, minus a global inhibitory drive. The model naturally suggests the need for two types of inhibitory gates (normalization, surround inhibition). First, using natural scene statistics and classical receptive fields corresponding to simple cells parameterized with data from mouse primary visual cortex, we show that the predicted connectivity qualitatively matches with that measured in mouse cortex: neurons with similar orientation tuning have stronger connectivity, and both excitatory and inhibitory connectivity have a modest spatial extent, comparable to that observed in mouse visual cortex. We incorporate lateral connections learned using this model into convolutional neural networks. Features are defined by supervised learning on the task, and the lateral connections provide an unsupervised learning of feature context in multiple layers. Since the lateral connections provide contextual information when the feedforward input is locally corrupted, we show that incorporating such lateral connections into convolutional neural networks makes them more robust to noise and leads to better performance on noisy versions of the MNIST dataset. Decomposing the predicted lateral connectivity matrices into low-rank and sparse components introduces additional cell types into these networks. We explore effects of cell-type specific perturbations on network computation. Our framework can potentially be applied to networks trained on other tasks, with the learned lateral connections aiding computations implemented by feedforward connections when the input is unreliable and demonstrate the potential usefulness of combining supervised and unsupervised learning techniques in real-world vision tasks.