Miracle-dz/UniFAR-FTUs huggingface dataset:
A Diary Study on When and With Whom Recovery Experiences Modulate Daily Stress and Worry During a COVID-19 Lockdown

In April 2020, almost six out of 10 people around the world were in lockdown due to the COVID-19 pandemic. Being locked down usually has a deleterious effect on the confined individual's mental health. In this exceptionally challenging context, finding ways to minimize negative mood about the pandemic is essential. Pandemic-related negative states (“negative mood”) and recovery experiences were investigated in a sample of 264 individuals who completed daily surveys four times per day over 7 consecutive days. MSEMs analyses revealed that negative mood persisted from moment-to-moment through the day, thus showing a response lag effect. Further analyses revealed that when someone experienced pandemic-related psychological detachment, relaxation, mastery, control, pleasure, or relatedness at specific periods of the day, mood had improved at the next measured time period, suggesting a protective effect. However, the pattern displayed by singles with dependents suggests that some recovery experiences at specific periods during the day seem to have a backfiring effect and worsen subsequent mood. These findings bring new insight into the role of recovery experiences during lockdowns and suggest that many could benefit from such experiences throughout the day when self-isolating. However, for individuals with multiple risk factors such as being single with dependents, some recovery experiences, at specific periods during the day, might not bring the desired outcome and future research is needed to examine if guilt or domestic burden may explain this finding. Results contribute to our understanding of how to take care of one's mental health during the current pandemic, and concrete recommendations adapted to individual contexts are provided.
Biomass-Derived Carbonaceous Materials to Achieve High-Energy-Density Supercapacitors

Biomass-derived carbonaceous materials are considered as one of the most perspective electrodes for symmetric supercapacitors working with alkaline-basic electrolytes. However, they still exhibit lower energy density. Herein, we demonstrate the capacitance performance of the commercial carbon product (YP-50F, “Kuraray Europe” GmbH), obtained from coconuts, in symmetric supercapacitors by using lithium and sodium organic electrolytes. It is found that YP-50F delivers higher energy density when lithium electrolyte containing LiBF4 salt is employed. The sodium electrolyte with NaPF6 salt is less aggressive toward YP-50F than that of LiPF6 salt, as a result of which a good capacitance performance is observed in the sodium electrolyte. The contributions of surface functional groups of YP-50F, as well as its compatibility with non-aqueous lithium and sodium electrolyte, are discussed on the basis of post-mortem scanning electron microscopy (SEM)/energy-dispersive X-ray spectroscopy (EDS) and X-ray photoelectron spectroscopy (XPS) data analyses. The obtained correlations could be of significance in order to design sustainable supercapacitors with high energy density.
Genomic and phenotypic characterization of finger millet indicates a complex diversification history

Advances in sequencing technologies mean that insights into crop diversification aiding future breeding can now be explored in crops beyond major staples. For the first time, we use a genome assembly of finger millet, an allotetraploid orphan crop, to analyze DArTseq single nucleotide polymorphisms (SNPs) at the sub-genome level. A set of 8,778 SNPs and 13 agronomic traits characterizing a broad panel of 423 landrace accessions from Africa and Asia suggested the crop has undergone complex, context-specific diversification consistent with a long domestication history. Both Principal Component Analysis and Discriminant Analysis of Principal Components of SNPs indicated four groups of accessions that coincided with the principal geographic areas of finger millet cultivation. East Africa, the considered origin of the crop, appeared the least genetically diverse. A Principal Component Analysis of phenotypic data also indicated clear geographic differentiation, but different relationships among geographic areas than genomic data. Neighbour-joining trees of sub-genomes A and B showed different features which further supported the crop’s complex evolutionary history. Our genome-wide association study indicated only a small number of significant marker-trait associations. We applied then clustering to marker effects from a ridge regression model for each trait which revealed two clusters of different trait complexity, with days to flowering and threshing percentage among simple traits, and finger length and grain yield among more complex traits. Our study provides comprehensive new knowledge on the distribution of genomic and phenotypic variation in finger millet, supporting future breeding intra- and inter-regionally across its major cultivation range. Core ideas 8,778 SNPs and 13 agronomic traits characterized a panel of 423 finger millet landraces. 4 clusters of accessions coincided with major geographic areas of finger millet cultivation. A comparison of phenotypic and genomic data indicated a complex diversification history. This was confirmed by the analysis of allotetraploid finger millet’s separate sub-genomes. Comprehensive new knowledge for intra- and inter-regional breeding is provided.
A Deep Learning Based Framework for Diagnosing Multiple Skin Diseases in a Clinical Environment

Background: Numerous studies have attempted to apply artificial intelligence (AI) in the dermatological field, mainly on the classification and segmentation of various dermatoses. However, researches under real clinical settings are scarce. Objectives: This study was aimed to construct a novel framework based on deep learning trained by a dataset that represented the real clinical environment in a tertiary class hospital in China, for better adaptation of the AI application in clinical practice among Asian patients. Methods: Our dataset was composed of 13,603 dermatologist-labeled dermoscopic images, containing 14 categories of diseases, namely lichen planus (LP), rosacea (Rosa), viral warts (VW), acne vulgaris (AV), keloid and hypertrophic scar (KAHS), eczema and dermatitis (EAD), dermatofibroma (DF), seborrheic dermatitis (SD), seborrheic keratosis (SK), melanocytic nevus (MN), hemangioma (Hem), psoriasis (Pso), port wine stain (PWS), and basal cell carcinoma (BCC). In this study, we applied Google's EfficientNet-b4 with pre-trained weights on ImageNet as the backbone of our CNN architecture. The final fully-connected classification layer was replaced with 14 output neurons. We added seven auxiliary classifiers to each of the intermediate layer groups. The modified model was retrained with our dataset and implemented using Pytorch. We constructed saliency maps to visualize our network's attention area of input images for its prediction. To explore the visual characteristics of different clinical classes, we also examined the internal image features learned by the proposed framework using t-SNE (t-distributed Stochastic Neighbor Embedding). Results: Test results showed that the proposed framework achieved a high level of classification performance with an overall accuracy of 0.948, a sensitivity of 0.934 and a specificity of 0.950. We also compared the performance of our algorithm with three most widely used CNN models which showed our model outperformed existing models with the highest area under curve (AUC) of 0.985. We further compared this model with 280 board-certificated dermatologists, and results showed a comparable performance level in an 8-class diagnostic task. Conclusions: The proposed framework retrained by the dataset that represented the real clinical environment in our department could accurately classify most common dermatoses that we encountered during outpatient practice including infectious and inflammatory dermatoses, benign and malignant cutaneous tumors.
Expression of Lipid-Metabolism Genes Is Correlated With Immune Microenvironment and Predicts Prognosis in Osteosarcoma

Objectives Osteosarcoma was the most popular primary malignant tumor in children and adolescent, and the 5-year survival of osteosarcoma patients gained no substantial improvement over the past 35 years. This study aims to explore the role of lipid metabolism in the development and diagnosis of osteosarcoma. Methods Clinical information and corresponding RNA data of osteosarcoma patients were downloaded from TRGET and GEO databases. Consensus clustering was performed to identify new molecular subgroups. ESTIMATE, TIMER and ssGSEA analyses were applied to determinate the tumor immune microenvironment (TIME) and immune status of the identified subgroups. Functional analyses including GO, KEGG, GSVA and GSEA analyses were conducted to elucidate the underlying mechanisms. Prognostic risk model was constructed using LASSO algorithm and multivariate Cox regression analysis. Results Two molecular subgroups with significantly different survival were identified. Better prognosis was associated with high immune score, low tumor purity, high abundance of immune infiltrating cells and relatively high immune status. GO and KEGG analyses revealed that the DEGs between the two subgroups were mainly enriched in immune- and bone remodeling-associated pathways. GSVA and GSEA analyses indicated that, lipid catabolism downregulation and lipid hydroxylation upregulation may impede the bone remodeling and development of immune system. Risk model based on lipid metabolism related genes (LMRGs) showed potent potential for survival prediction in osteosarcoma. Nomogram integrating risk model and clinical characteristics could predict the prognosis of osteosarcoma patients accurately. Conclusion Expression of lipid-metabolism genes is correlated with immune microenvironment of osteosarcoma patients and could be applied to predict the prognosis of in osteosarcoma accurately.
Compositional Variation of Amphiboles During Magma Mixing: A Case Study of Huangyangshan A-Type Granite in Kalamaili Metallogenic Belt, East Junggar, China

The Huangyangshan A-type granitic pluton, distributed along the thrust fault in the Kalamaili region of East Junggar, Xinjiang, China, consists of alkaline granite containing abundant dioritic enclaves that formed via magma mixing. Both the host granite and the enclaves contain sodic amphiboles. The textural evidence indicates that amphiboles crystallized as a magmatic phase in both units. We determined major and trace element contents of amphiboles from both units to investigate the compositional variation of the amphiboles during the magma mixing process. The results show that cations of W- and C-site are influenced by chemical compositions of the magma whereas cations of A-, B- and T-site and Al3+ are controlled by crystal structure. Therefore, the variations of W- and C-site cations can reflect magma evolution. The core and rim of the amphiboles show similar trace element patterns, which also suggests that the amphiboles are late-stage phases. Furthermore, the amphibole-only thermometers yield reasonable estimates that are consistent with petrographic evidence. However, thermometers based on partition coefficients and all the currently available amphibole-based barometers that rely on Al contents or DAl cannot be applied to Fe-rich and Al-poor amphiboles.
A rarefaction-without-resampling extension of PERMANOVA for testing presence-absence associations in the microbiome

Background PERMANOVA [1] is currently the most commonly used method for testing community-level hypotheses about microbiome associations with covariates of interest. PERMANOVA can test for associations that result from changes in which taxa are present or absent by using the Jaccard or unweighted UniFrac distance. However, such presence-absence analyses face a unique challenge: confounding by library size (total sample read count), which occurs when library size is associated with covariates in the analysis. It is known that rarefaction (subsampling to a common library size) controls this bias, but at the potential costs of information loss and the introduction of a stochastic component into the analysis. Methods Here we develop a non-stochastic approach to PERMANOVA presence-absence analyses that aggregates information over all potential rarefaction replicates without actual resampling, when the Jaccard or unweighted UniFrac distance is used. We compare this new approach to three possible ways of aggregating PERMANOVA over multiple rarefactions obtained from resampling: averaging the distance matrix, averaging the (element-wise) squared distance matrix, and averaging the F-statistic. Results Our simulations indicate that our non-stochastic approach is robust to confounding by library size and outperforms each of the stochastic resampling approaches. We also show that, when overdispersion is low, averaging the (element-wise) squared distance outperforms averaging the unsquared distance, currently implemented in the R package vegan. We illustrate our methods using an analysis of data on inflammatory bowel disease (IBD) in which samples from case participants have systematically smaller library sizes than samples from control participants.
What About the Environment? How the Physical Activity–Related Health Competence Model Can Benefit From Health Literacy Research

Research cultivates a multitude of frameworks, models, and theories with different determinants internal and/or external to the individual contributing to the understanding and explaining of physical activity levels. The physical activity–related health competence (PAHCO) model can be located at the interface between research of health literacy and physical activity. Because of its primary person orientation, however, the model has not yet undergone discussions on the relevance of the environment. Encouraged by the developments in the area of health literacy, the goal of the present perspective article was to stimulate some initial reflections on potential solutions for the competence–environment relationship within the PAHCO model. We extracted three potential solutions for this issue. Dubbed the solution of integration, we first discussed that the PAHCO model could be placed into overarching, more holistic, and abstract models of health-enhancing physical activity, such as the capability approach or the socioecological model. Applying a solution of elaboration, researchers could second substantiate existing components of the PAHCO model, such as control competence or self-regulation competence, by further explanations. Characterizing the solution of extension, it would third be possible to introduce (a) separate competence component(s) that highlight(s) the manageability of the environment, for instance, by establishing a (socio)ecological competence. The article concludes with a short overview of potential empirical approaches, given their potential to assist researchers in identifying preferences for the theoretical advancement and to put the development on a stronger evidence base.
Pembrolizumab Plus Chemotherapy or Anlotinib vs. Pembrolizumab Alone in Patients With Previously Treated EGFR-Mutant NSCLC

Objectives More and more encouraging evidence revealed that immunotherapy could improve clinical outcomes in patients with previously treated non-small cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) variations. However, immunotherapy is still a controversy for NSCLC patients with EGFR mutation. Method In this retrospective analysis, we compared the clinical efficacy of pembrolizumab monotherapy (PM), pembrolizumab combined with chemotherapy (P+C) and pembrolizumab combined with anlotinib (P+A) in NSCLC patients with EGFR mutation who had failed on EGFR-TKI and platinum-based chemotherapy. Result Eighty-six patients were included in this study. The overall median progression free survival (PFS) was 3.24 months. Multivariate analysis suggested that EGFR L858R and combined therapy were positive prognostic factors of PFS. The overall median OS was 12.28 months. Multivariate analysis found that high PD-L1 expression (≥50%) and combined therapy seemed to be positive prognostic factors of OS. Among the population, 32 patients received PM, 26 patients received P+C and 28 patients received P+A. Up to Jan 30, 2021, the median progression-free survival was 1.5 months in the PM group, 4.30 months in the P+C group and 3.24 months in the P+A group. The median OS were 7.41, 14.92 and 15.97 months, respectively. The ORR were 3.1%, 23.1% and 21.4%. Conclusion The addition of chemotherapy or antiangiogenic therapy to pembrolizumab resulted in significantly longer PFS, OS and ORR than pembrolizumab alone in our study. EGFR L858R might be a positive prognostic factor of PFS and high PD-L1 expression might be a positive prognostic factor of OS.
Role of mTOR-Regulated Autophagy in Synaptic Plasticity Related Proteins Downregulation and the Reference Memory Deficits Induced by Anesthesia/Surgery in Aged Mice

Postoperative cognitive dysfunction increases mortality and morbidity in perioperative patients and has become a major concern for patients and caregivers. Previous studies demonstrated that synaptic plasticity is closely related to cognitive function, anesthesia and surgery inhibit synaptic function. In central nervous system, autophagy is vital to synaptic plasticity, homeostasis of synapticproteins, synapse elimination, spine pruning, proper axon guidance, and when dysregulated, is associated with behavioral and memory functions disorders. The mammalian target of rapamycin (mTOR) negatively regulates the process of autophagy. This study aimed to explore whether rapamycin can ameliorate anesthesia/surgery-induced cognitive deficits by inhibiting mTOR, activating autophagy and rising synaptic plasticity-related proteins in the hippocampus. Aged C57BL/6J mice were used to establish POCD models with exploratory laparotomy under isoflurane anesthesia. The Morris Water Maze (MWM) was used to measure reference memory after anesthesia and surgery. The levels of mTOR phosphorylation (p-mTOR), Beclin-1 and LC3-II were examined on postoperative days 1, 3 and 7 by western blotting. The levels of synaptophysin (SYN) and postsynaptic density protein 95 (PSD-95) in the hippocampus were also examined by western blotting. Here we showed that anesthesia/surgery impaired reference memory and induced the activation of mTOR, decreased the expression of autophagy-related proteins such as Beclin-1 and LC3-II. A corresponding decline in the expression of neuronal/synaptic, plasticity-related proteins such as SYN and PSD-95 was also observed. Pretreating mice with rapamycin inhibited the activation of mTOR and restored autophagy function, also increased the expression of SYN and PSD-95. Furthermore, anesthesia/surgery-induced learning and memory deficits were also reversed by rapamycin pretreatment. In conclusion, anesthesia/surgery induced mTOR hyperactivation and autophagy impairments, and then reduced the levels of SYN and PSD-95 in the hippocampus. An mTOR inhibitor, rapamycin, ameliorated anesthesia/surgery-related cognitive impairments by inhibiting the mTOR activity, inducing activation of autophagy, enhancing SYN and PSD-95 expression.
Captive-breeding and catch-and-release’s effects on the reproductive success of Atlantic salmon (Salmo salar L.)

Captive-breeding programs as well as and catch-and-release are among the most commonly adopted conservation practices in recreational fisheries. However, risks and benefits associated with their implementation are rarely evaluated. In the case of Atlantic Salmon, while previous studies revealed that captive-bred fish show reduced fitness compared to their wild counterparts in nature. Yet, few examined the extent and causes of their reduced reproductive success or directly compared their contribution to enhance genetic diversity to that of wild fish, including mature male parr. Furthermore, only one study specifically measured the reproductive success of caught and released Atlantic salmon in natural settings, and no study to date evaluated if released salmon are able to reproduce when released at temperature above 20°C which is known to increase post-release mortality. Here, we use high-throughput microsatellite sequencing of 38 loci to accurately assign 2500 offspring to a comprehensive set of possible parents from a supplemented Atlantic salmon population in Québec, Canada. The resolved molecular pedigree provided informative insight on the reproductive pattern of both captive-bred salmon and caught-and-released salmon. Captive-bred salmon had fewer partners than their wild conspecifics which lead to a significant reduction of reproductive success relative to that of their wild counterparts. Supplementation of captive-bred salmon significantly contributed to increase genetic diversity but mature male parr did so to an even greater extent and significantly inflated the number of alleles found among offspring. Moreover, our results showed that that at least 83% of caught-and-released salmon did successfully reproduced although caught-and-released female salmon have a significantly reduced reproductive success, averaging 73% of the reproductive output of non-caught salmon. Reproductive success of released salmon was not influenced by water temperature over 20°C which suggests either that the studied population is locally adapted to warm waters or that they behaviorally regulated body temperature by accessing nearby thermal refugia. Our results should help refining managers’ ability to analyze the risks and benefits associated with captive-breeding and catch-and-release, and thus, optimize conservation practices used for the preservation of Atlantic salmon populations.