Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating abundance of an elusive cetacean in a complex environment: Harbor porpoises (Phocoena phocoena) in inland waters of Southeast Alaska

The harbor porpoise (Phocoena phocoena) is common in temperate waters of the eastern North Pacific Ocean, including Southeast Alaska inland waters, a complex environment comprised of open waterways, narrow channels, and inlets. Two demographically independent populations are currently recognized in this region. Bycatch of porpoises in the salmon drift gillnet fisheries is suspected to occur regularly. In this study, we apply distance sampling to estimate abundance of harbor porpoise during ship surveys carried out in the summer of 2019. A stratified survey design was implemented to sample different harbor porpoise habitats. Survey tracklines were allocated following a randomized survey design with uniform coverage probability. Density and abundance for the northern and southern Southeast Alaska inland water populations were computed using a combination of design-based line- and strip-transect methods. A total of 2,893 km was surveyed in sea state conditions ranging from Beaufort 0 to 3 and 194 harbor porpoise groups (301 individuals) were detected. An independent sighting dataset from surveys conducted between 1991 and 2012 were used to calculate the probability of missing porpoise groups on the survey trackline (g[0]=0.53, CV=0.11). Abundance of the northern and southern populations were estimated at 1,619 (CV=0.26) and 890 (CV=0.37) porpoises, respectively. Bycatch estimates, which were only obtained for a portion of the drift gillnet fishery, suggest that mortality within the range of the southern population may be unsustainable. Harbor porpoises are highly vulnerable to mortality in gillnets, therefore monitoring abundance and bycatch is important for evaluating the potential impact of fisheries on this species in Southeast Alaska.
Counter-matching: A stratified nested case-control sampling method

SUMMARY The counter-matching design for sampling from risk sets is presented in which each sampled risk set includes the failure and a random sample of controls from 'sampling strata' defined by exposure information available for all cohort subjects. Asymptotic relative efficiency comparisons indicate that this type of sampling has superior efficiency to simple nested case-control sampling in situations of practical interest. A simple extension of the method is given which allows for nonrepresentative sampling of failures. Analysis of data from counter-matched studies may be performed using standard conditional logistic likelihood software which allows for an 'offset' in the model.
Editorial: Clinical application of artificial intelligence in emergency and critical care medicine, volume III

COPYRIGHT © 2022 Zhang, Kashyap, Su and Meng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Clinical application of artificial intelligence in emergency and critical care medicine, volume III
A lightweight convolutional neural network for recognition of severity stages of maydis leaf blight disease of maize

Maydis leaf blight (MLB) of maize (Zea Mays L.), a serious fungal disease, is capable of causing up to 70% damage to the crop under severe conditions. Severity of diseases is considered as one of the important factors for proper crop management and overall crop yield. Therefore, it is quite essential to identify the disease at the earliest possible stage to overcome the yield loss. In this study, we created an image database of maize crop, MDSD (Maydis leaf blight Disease Severity Dataset), containing 1,760 digital images of MLB disease, collected from different agricultural fields and categorized into four groups viz. healthy, low, medium and high severity stages. Next, we proposed a lightweight convolutional neural network (CNN) to identify the severity stages of MLB disease. The proposed network is a simple CNN framework augmented with two modified Inception modules, making it a lightweight and efficient multi-scale feature extractor. The proposed network reported approx. 99.13% classification accuracy with the f1-score of 98.97% on the test images of MDSD. Furthermore, the class-wise accuracy levels were 100% for healthy samples, 98% for low severity samples and 99% for the medium and high severity samples. In addition to that, our network significantly outperforms the popular pretrained models, viz. VGG16, VGG19, InceptionV3, ResNet50, Xception, MobileNetV2, DenseNet121 and NASNetMobile for the MDSD image database. The experimental findings revealed that our proposed lightweight network is excellent in identifying the images of severity stages of MLB disease despite complicated background conditions.
Attenuated Salmonella potentiate PD-L1 blockade immunotherapy in a preclinical model of colorectal cancer

The use of immune checkpoint inhibitors to treat cancer resulted in unprecedented and durable clinical benefits. However, the response rate among patients remains rather modest. Previous work from our laboratory demonstrated the efficacy of using attenuated bacteria as immunomodulatory anti-cancer agents. The current study investigated the potential of utilizing a low dose of attenuated Salmonella typhimurium to enhance the efficacy of PD-L1 blockade in a relatively immunogenic model of colon cancer. The response of MC38 tumors to treatment with αPD-L1 monoclonal antibody (mAb) was variable, with only 30% of the mice being responsive. Combined treatment with αPD-L1 mAb and Salmonella resulted in 75% inhibition of tumor growth in 100% of animals. Mechanistically, the enhanced response correlated with a decrease in the percentage of tumor-associated granulocytic cells, upregulation in MHC class II expression by intratumoral monocytes and an increase in tumor infiltration by effector T cells. Collectively, these alterations resulted in improved anti-tumor effector responses and increased apoptosis within the tumor. Thus, our study demonstrates that a novel combination treatment utilizing attenuated Salmonella and αPD-L1 mAb could improve the outcome of immunotherapy in colorectal cancer.
Mitochondrial dysfunction in vascular endothelial cells and its role in atherosclerosis

The mitochondria are essential organelles that generate large amounts of ATP via the electron transport chain (ECT). Mitochondrial dysfunction causes reactive oxygen species accumulation, energy stress, and cell death. Endothelial mitochondrial dysfunction is an important factor causing abnormal function of the endothelium, which plays a central role during atherosclerosis development. Atherosclerosis-related risk factors, including high glucose levels, hypertension, ischemia, hypoxia, and diabetes, promote mitochondrial dysfunction in endothelial cells. This review summarizes the physiological and pathophysiological roles of endothelial mitochondria in endothelial function and atherosclerosis.
Facial emotion identification impairments in Chinese persons living with schizophrenia: A meta-analysis

Background Facial emotion identification (FEI) deficits are associated with impaired social functioning in persons living with schizophrenia (PLwS), but the research on emotion-specific FEI deficits remains inconclusive. Furthermore, existing studies on FEI deficits are limited by their small sample sizes. We performed a meta-analysis of studies comparing the FEI abilities between Chinese PLwS and healthy controls in terms of the six basic facial emotions (happiness, sadness, fear, disgust, anger, and surprise), as well as contempt, calmness, and neutral facial expressions. Methods Major Chinese- and English-language databases were searched to retrieve case-control studies that compared the FEI task performance between Chinese PLwS and healthy controls (HCs) and reported the emotion-specific correct identification scores for PLwS and HCs. The Joanna Briggs Institute Critical Appraisal Checklist for Case-control Studies (“JBI checklist,” hereafter) was used to assess the risk of bias (RoB) of the included studies. Statistical analysis was performed using the “meta” package of R 4.1. 2. Results Twenty-three studies with a total of 28 case-control cohorts and 1,894 PLwS and 1,267 HCs were included. The RoB scores of the included studies ranged from two to seven. PLwS had statistically significantly lower FEI scores than HCs and the corresponding emotion-specific pooled standard mean differences (95% confidence intervals) were −0.69 (−0.88, −0.50) for happiness, −0.88 (−1.12, −0.63) for sadness, −1.44 (−1.83, −1.06) for fear, −1.18 (−1.60, −0.76) for disgust, −0.91 (−1.24, −0.57) for anger, −1.09 (−1.39, −0.78) for surprise, −0.26 (−0.51, −0.01) for contempt, −0.31 (−0.52, −0.09) for calmness, and −0.42 (−0.65, −0.18) for neutral. In the analyses of sources of heterogeneity, drug-naïve status, clinical setting, positive and negative psychotic symptoms, and RoB were significant moderators of the magnitudes of FEI deficits. Conclusions Chinese PLwS have significant FEI impairments in terms of recognizing the six basic facial emotions, contempt, calmness, and neutral emotions, and the magnitude of impairment varies depending on the type of emotion, clinical characteristics, and the level of RoB of the study. It is necessary to consider the characteristics of FEI deficits and the clinical moderators in the FEI deficits to develop remediation strategies targeting FEI deficits in schizophrenia.
Assessing the broadscale effects of wildfire under extreme drought conditions to boreal peatlands

Climate warming and changing fire regimes in the North American boreal zone have the capacity to alter the hydrology and ecology of the landscape with long term consequences to peatland ecosystems and their traditional role as carbon sinks. It is important to understand how peatlands are affected by wildfire in relation to both extent of burn and severity of burn to the organic soil (peat) layers where most of the C is stored. Peatlands cover more than 75% of the landscape in the southern Northwest Territories, Canada where extreme drought led to widespread wildfires in 2014–2015. To assess the wildfire effects across a 14.6 million ha study area including 136 wildfire events, we used an integration of field data collection, land cover mapping of peatland and upland ecotypes, Landsat-8-based mapping of burn severity to the soil organic layers, and MODIS-hotspot mapping of fire progression for season of burning. The intersection of these geospatial products allows for a broadscale assessment of wildfire effects across gradients of ecotype, ecoregions, seasons, and year of burn. Using a series of chi-squared goodness of fit tests, we found that peatlands are more susceptible to wildfire on the Taiga shield where they are smaller and hydrologically isolated by the rocky landscape. There burning affected proportionally larger peat areas with an evenness of burn severity to the organic soil layers which may lead to less spatial diversity in post-fire recovery, making the landscape less resilient to future fire. The most resilient peatlands are expected to be hydrologically well-connected to both ground water systems and larger peatland complexes such as those on the Taiga plains which exhibited large unburned and singed patches across the landscape, and greater variability in burn severity across seasons and ecotypes. Understanding the tipping point of drought conditions at which the landscape becomes connected, and peatlands are susceptible to wildfire with deeper burning of the organic soil layers is important for understanding the potential future effects of climate change and projected increases in wildfire on peatlands. This is critical for C accounting and climate mitigation strategies.
Multi-modal data combination strategy based on chest HRCT images and PFT parameters for intelligent dyspnea identification in COPD

Introduction Because of persistent airflow limitation in chronic obstructive pulmonary disease (COPD), patients with COPD often have complications of dyspnea. However, as a leading symptom of COPD, dyspnea in COPD deserves special consideration regarding treatment in this fragile population for pre-clinical health management in COPD. Methods: Based on the above, this paper proposes a multi-modal data combination strategy by combining the local and global features for dyspnea identification in COPD based on the multi-layer perceptron (MLP) classifier. Methods First, lung region images are automatically segmented from chest HRCT images for extracting the original 1,316 lung radiomics (OLR, 1,316) and 13,824 3D CNN features (O3C, 13,824). Second, the local features, including five selected pulmonary function test (PFT) parameters (SLF, 5), 28 selected lung radiomics (SLR, 28), and 22 selected 3D CNN features (S3C, 22), are respectively selected from the original 11 PFT parameters (OLF, 11), 1,316 OLR, and 13,824 O3C by the least absolute shrinkage and selection operator (Lasso) algorithm. Meantime, the global features, including two fused PFT parameters (FLF, 2), six fused lung radiomics (FLR, 6), and 34 fused 3D CNN features (F3C, 34), are respectively fused by 11 OLF, 1,316 OLR, and 13,824 O3C using the principal component analysis (PCA) algorithm. Finally, we combine all the local and global features (SLF + FLF + SLR + FLR + S3C + F3C, 5+ 2 + 28 + 6 + 22 + 34) for dyspnea identification in COPD based on the MLP classifier. Results Our proposed method comprehensively improves classification performance. The MLP classifier with all the local and global features achieves the best classification performance at 87.7% of accuracy, 87.7% of precision, 87.7% of recall, 87.7% of F1-scorel, and 89.3% of AUC, respectively. Discussion Compared with single-modal data, the proposed strategy effectively improves the classification performance for dyspnea identification in COPD, providing an objective and effective tool for COPD management.
Constraints on the Mass of Warm Dark Matter Particles and the Shape of the Linear Power Spectrum from the Lyα Forest

High-resolution N-body simulations of cold dark matter (CDM) models predict that galaxies and clusters have cuspy halos with considerable substructure. Observations reveal smooth halos with central density cores. One possible resolution of the substructure problem is that the dark matter is warm; this will suppress the power spectrum on small scales. The Lyα forest is a powerful probe of the linear power spectrum on these scales. We use collisionless N-body simulations to follow the evolution of structure in warm dark matter (WDM) models and analyze artificial Lyα forest spectra extracted from them. By requiring that there be enough small-scale power in the linear power spectrum to reproduce the observed properties of the Lyα forest in quasar spectra, we derive a lower limit to the mass of the WDM particle of 750 eV. This limit is robust to reasonable uncertainties in our assumption about the temperature of the gas at mean density (T0) at z = 3. We argue that any model that suppresses the CDM linear theory power spectrum more severely than a 750 eV WDM particle cannot produce the Lyα forest.
Mapping of surface-generated noise coherence

The performance of a hydrophone array can be evaluated by its coherent gain, which depends on the spatial correlation of both the signal of interest and the background noise between different array elements, where one hopes to maximize the former while minimizing the latter with array signal processing. In this paper, a computational vertical noise coherence map of the first zero-crossing is generated near Alvin Canyon, south of Martha’s Vineyard, Massachusetts, to study its dependence on the spatial variation in bathymetry, water column sound speed and sediment type. A two and three-dimensional Parabolic Equation propagation model based on reciprocity theory were used for the simulation. The results showed that the seabed parameters have the greatest impact on vertical noise coherence at the array location in the Alvin Canyon area, when compared to 3-D bathymetric and water column sound speed profile variability, especially in the shallower water. The analysis reveals the ideal spacing for a vertical hydrophone array for better signal detection in acoustic experiments. In the continental shelf and slope regions, the ideal spacing lies between 3λ⁄8 in deep water and λ⁄2 in shallow water, and for areas with strong bathymetric variations the ideal spacing can be determined by comprehensive numerical models.
The Inner-Disk and Stellar Properties of the Young Stellar Object WL 16

We present kinematic evidence for a rapidly rotating circumstellar disk around the young stellar object WL 16, based on new high-velocity-resolution data of the v = 2-0 CO bandhead emission. A Keplerian disk provides an excellent fit to the observed profile and requires a projected velocity for the CO-emitting region of roughly 250 km/s at the inner radius and 140 km/s at the outer radius, giving a ratio of the inner to the outer radius of about 0.3. We show that satisfying the constraints imposed by the gas kinematics, the observed CO flux, and the total source luminosity requires the mass of WL 16 to lie between 1.4 and 2.5 solar mass. The inner disk radius for the CO emission must be less than 8 solar radii.
Progression of metabolic syndrome and associated cardiometabolic risk factors from prepuberty to puberty in children: The PUBMEP study

Introduction Metabolic syndrome (MetS) is a cluster of clinical and metabolic alterations related to the risk of cardiovascular diseases (CVD). Metabolic changes occurring during puberty, especially in children with overweight and obesity, can influence the risk of developing chronic diseases, especially CVD. Methods Longitudinal study based on the follow-up until puberty of a cohort of 191 prepubertal Spanish boys and girls without congenital, chronic, or inflammatory diseases: undernutrition: or intake of any drug that could alter blood glucose, blood pressure, or lipid metabolism. The following parameters were used to determine the presence of MetS: obesity, hypertension, hyperglycemia, hypertriglyceridemia, and low HDL-c. Results A total of 75·5% of participants stayed in the same BMI category from prepuberty to puberty, whereas 6·3% increased by at least one category. The prevalence of MetS was 9·1% (prepubertal stage) and 11·9% (pubertal stage). The risk of presenting alterations in puberty for systolic blood pressure (SBP), plasma triacylglycerols, HDL cholesterol (HDL-c), and HOMA-IR was significantly higher in those participants who had the same alterations in prepuberty. MetS prevalence in puberty was predicted by sex and levels of HOMA-IR, BMI-z, and waist circumference in the prepubertal stage, in the whole sample: in puberty, the predictors were levels of HOMA-IR, BMI-z, and diastolic blood pressure in participants with obesity. Two fast-and-frugal decision trees were built to predict the risk of MetS in puberty based on prepuberty HOMA-IR (cutoff 2·5), SBP (cutoff 106 mm of Hg), and TAG (cutoff 53 mg/dl). Discussion Controlling obesity and cardiometabolic risk factors, especially HOMA-IR and blood pressure, in children during the prepubertal stage appears critical to preventing pubertal MetS effectively.
Response strategies of fine root morphology of Cupressus funebris to the different soil environment

Understanding fine root morphology is crucial to uncover water and nutrient acquisition and transposition of fine roots. However, there is still a lack of knowledge regarding how the soil environment affects the fine root morphology of various root orders in the stable forest ecosystem. Therefore, this experiment assessed the response strategies of fine root morphology (first- to fifth -order fine roots) in four different soil environments. The results showed that fine root morphology was related to soil environment, and there were significant differences in specific root length (SRL), specific surface area (SRA), diameter (D), and root tissue density (RTD) of first- and second -order fine roots. Soil total nitrogen (TN), alkaline nitrogen (AN) and available phosphorus (AP) were positively correlated with SRL and SRA and negatively correlated with D and RTD. Soil moisture (SW) was positively correlated with the D and RTD of first- and second-order fine roots and negatively correlated with the SRL and SRA. Soil temperature (ST), organic carbon (OC), soil bulk density (SBD) and soil porosity (SP) were not significantly correlated with the D, SRL, SRA, and RTD of the first- and second -order fine roots. AN was positively correlated with SRL and SRA and negatively correlated with both D and RTD in the first- and second -order fine roots, and the correlation coefficient was very significant. Therefore, we finally concluded that soil AN was the most critical factor affecting root D, SRL, SRA and RTD of fine roots, and mainly affected the morphology of first- and second -order fine roots. In conclusion, our research provides support for understanding the relationship between fine root morphology and soil environment, and indicates that soil nutrient gradient forms good root morphology at intraspecific scale.
Effect of sleep loss on pain—New conceptual and mechanistic avenues

Introduction Sleep disturbances increase pain sensitivity in clinical and preclinical settings, but the precise mechanisms are unknown. This represents a major public health issue because of the growing sleep deficiency epidemic fueled by modern lifestyle. To understand the neural pathways at the intersection between sleep and pain processes, it is critical to determine the precise nature of the sleep disruptions that increase pain and the specific component of the pain response that is targeted. Methods We performed a review of the literature about sleep disturbances and pain sensitivity in humans and rodents by taking into consideration the targeted sleep stage (REMS, non–NREMS, or both), the amount of sleep lost, and the different types of sleep disruptions (partial or total sleep loss, duration, sleep fragmentation or interruptions), and how these differences might affect distinct components of the pain response. Results We find that the effects of sleep disturbances on pain are highly conserved among species. The major driver for pain hypersensitivity appears to be the total amount of sleep lost, while REMS loss by itself does not seem to have a direct effect on pain sensitivity. Sleep loss caused by extended wakefulness preferentially increases pain perception, whereas interrupted and limited sleep strongly dysregulates descending controls such as DNIC, especially in women. Discussion We discuss the possible mechanisms involved, including an increase in inflammatory processes, a loss of nociceptive inhibitory pathways, and a defect in the cognitive processing of noxious input.