Miracle-dz/UniFAR-FTUs huggingface dataset:
The regulation of adipocyte growth in white adipose tissue

Adipocytes can increase in volume up to a thousand-fold, storing excess calories as triacylglycerol in large lipid droplets. The dramatic morphological changes required of adipocytes demands extensive cytoskeletal remodeling, including lipid droplet and plasma membrane expansion. Cell growth-related signalling pathways are activated, stimulating the production of sufficient amino acids, functional lipids and nucleotides to meet the increasing cellular needs of lipid storage, metabolic activity and adipokine secretion. Continued expansion gives rise to enlarged (hypertrophic) adipocytes. This can result in a failure to maintain growth-related homeostasis and an inability to cope with excess nutrition or respond to stimuli efficiently, ultimately leading to metabolic dysfunction. We summarize recent studies which investigate the functional and cellular structure remodeling of hypertrophic adipocytes. How adipocytes adapt to an enlarged cell size and how this relates to cellular dysfunction are discussed. Understanding the healthy and pathological processes involved in adipocyte hypertrophy may shed light on new strategies for promoting healthy adipose tissue expansion.
Novel therapeutic approach to slow down the inflammatory cascade in acute/subacute spinal cord injury: Early immune therapy with lipopolysaccharide enhanced neuroprotective effect of combinational therapy of granulocyte colony-stimulating factor and bone-marrow mesenchymal stem cell in spinal cord i

Bone-marrow mesenchymal stem cells (BM-MSCs) have not yet proven any significant therapeutic efficacy in spinal cord injury (SCI) clinical trials, due to the hostile microenvironment of the injured spinal cord at the acute phase. This study aims to modulate the inflammatory milieu by lipopolysaccharide (LPS) and granulocyte colony-stimulating factor (G-CSF) to improve the BM-MSCs therapy. For this purpose, we determined the optimum injection time and sub-toxic dosage of LPS following a T10 contusion injury. Medium-dose LPS administration may result in a local anti-inflammatory beneficial role. This regulatory role is associated with an increase in NF-200-positive cells, significant tissue sparing, and improvement in functional recovery compared to the SCI control group. The second aim was to examine the potential ability of LPS and LPS + G-CSF combination therapy to modulate the lesion site before BM-MSC (1 × 105 cells) intra-spinal injection. Our results demonstrated combination therapy increased potency to enhance the anti-inflammatory response (IL-10 and Arg-1) and decrease inflammatory markers (TNF-α and CD86) and caspase-3 compared to BM-MSC monotherapy. Histological analysis revealed that combination groups displayed better structural remodeling than BM-MSC monotherapy. In addition, Basso–Beattie–Bresnahan (BBB) scores show an increase in motor recovery in all treatment groups. Moreover, drug therapy shows faster recovery than BM-MSC monotherapy. Our results suggest that a sub-toxic dose of LPS provides neuroprotection to SCI and can promote the beneficial effect of BM-MSC in SCI. These findings suggest that a combination of LPS or LPS + G-CSF prior BM-MSC transplantation is a promising approach for optimizing BM-MSC-based strategies to treat SCI. However, because of the lack of some methodological limitations to examine the survival rate and ultimate fate of transplanted BM-MSCs followed by LPS administration in this study, further research needs to be done in this area. The presence of only one-time point for evaluating the inflammatory response (1 week) after SCI can be considered as one of the limitations of this study. We believed that the inclusion of additional time points would provide more information about the effect of our combination therapy on the microglia/macrophage polarization dynamic at the injured spinal cord. Graphical Abstract Schematic illustration of beneficial effect of combination therapy of LPS and G-CSF before BM-MSC transplantation in the spinal cord injury model.
Non-reflecting Inflow and Outflow in a Wind Tunnel for Transonic Time-Accurate Simulation

We consider a time-dependent flow problem in an infinitely long wind tunnel of circular cross-section. It is assumed that beginning with a distance up- and downstream from the stream-lined body, the flow is approximately described by the time-dependent Euler equations linearized about a uniform free-stream flow. By using this linear model, we transfer the boundary conditions from infinity to the front and back boundaries of a finite computational domain. The obtained boundary conditions called transparent ones (TBCs) are represented by explicit formulae in the physical space. The TBCs contain operators of Fourier expansion with respect to eigenfunctions of the cross-section and convolutions with respect to time, i.e., they are non-local in both space and time. One possibility of their numerical implementation is described; the main attention is paid to the evaluation of the convolution operator. Due to the representation of the convolution kernel by an exponential polynomial, the evaluation formulae become even local in time.
Deep learning magnetic resonance imaging predicts platinum sensitivity in patients with epithelial ovarian cancer

Objective The aim of the study is to develop and validate a deep learning model to predict the platinum sensitivity of patients with epithelial ovarian cancer (EOC) based on contrast-enhanced magnetic resonance imaging (MRI). Methods In this retrospective study, 93 patients with EOC who received platinum-based chemotherapy (≥4 cycles) and debulking surgery at the Sun Yat-sen Memorial Hospital from January 2011 to January 2020 were enrolled and randomly assigned to the training and validation cohorts (2:1). Two different models were built based on either the primary tumor or whole volume of the abdomen as the volume of interest (VOI) within the same cohorts, and then a pre-trained convolutional neural network Med3D (Resnet 10 version) was transferred to automatically extract 1,024 features from two MRI sequences (CE-T1WI and T2WI) of each patient to predict platinum sensitivity. The performance of the two models was compared. Results A total of 93 women (mean age, 50.5 years ± 10.5 [standard deviation]) were evaluated (62 in the training cohort and 31 in the validation cohort). The AUCs of the whole abdomen model were 0.97 and 0.98 for the training and validation cohorts, respectively, which was better than the primary tumor model (AUCs of 0.88 and 0.81 in the training and validation cohorts, respectively). In k-fold cross-validation and stratified analysis, the whole abdomen model maintained a stable performance, and the decision function value generated by the model was a prognostic indicator that successfully discriminates high- and low-risk recurrence patients. Conclusion The non-manually segmented whole-abdomen deep learning model based on MRI exhibited satisfactory predictive performance for platinum sensitivity and may assist gynecologists in making optimal treatment decisions.
Anti-PD-L1/PD-L2 therapeutic vaccination in untreated chronic lymphocytic leukemia patients with unmutated IgHV

Chronic lymphocytic leukemia (CLL) patients with unmutated immunoglobulin heavy chain (IgHV) are at risk of early disease progression compared to patients with mutated IgHV. As a preventive strategy, we treated 19 previously untreated CLL patients with unmutated IgHV in a phase 1/2 trial (clinicaltrials.gov, NCT03939234) exploring the efficacy and toxicity of a therapeutic cancer vaccine containing peptides derived from programmed death ligand 1 (PD-L1) and ligand 2 (PD-L2), hoping to restore immunological control of the disease. According to the International Workshop on Chronic lymphocytic Leukemia (iwCLL) response criteria, no patients obtained a response; however, during follow-up, one patient had complete normalization of the peripheral lymphocyte count and remained in biochemical remission after a follow-up time of 15 months. At the end of treatment, one patient had progressed, and 17 patients had stable disease. During follow-up with a median time of 23.5 months since inclusion, seven patients had progressed, and eight patients had stable disease. The median time to first treatment (TTFT) from diagnosis was 90.3 months with a median follow-up time of 50.1 months. This apparent favorable outcome in TTFT needs to be investigated in a randomized setting, as our population may have been biased. More than 80% of patients obtained vaccine-specific immune responses, confirming the immunogenicity of the vaccine. The vaccine was generally well tolerated with only grade I–II adverse events. Although there were some signs of clinical effects, the vaccine seems to be insufficient as monotherapy in CLL, possibly due to a high tumor burden. The efficacy of the vaccine should preferably be tested in combination with novel targeted therapies or as a consolidating treatment.
Phylogenomic resolution of order- and family-level monocot relationships using 602 single-copy nuclear genes and 1375 BUSCO genes

We assess relationships among 192 species in all 12 monocot orders and 72 of 77 families, using 602 conserved single-copy (CSC) genes and 1375 benchmarking single-copy ortholog (BUSCO) genes extracted from genomic and transcriptomic datasets. Phylogenomic inferences based on these data, using both coalescent-based and supermatrix analyses, are largely congruent with the most comprehensive plastome-based analysis, and nuclear-gene phylogenomic analyses with less comprehensive taxon sampling. The strongest discordance between the plastome and nuclear gene analyses is the monophyly of a clade comprising Asparagales and Liliales in our nuclear gene analyses, versus the placement of Asparagales and Liliales as successive sister clades to the commelinids in the plastome tree. Within orders, around six of 72 families shifted positions relative to the recent plastome analysis, but four of these involve poorly supported inferred relationships in the plastome-based tree. In Poales, the nuclear data place a clade comprising Ecdeiocoleaceae+Joinvilleaceae as sister to the grasses (Poaceae); Typhaceae, (rather than Bromeliaceae) are resolved as sister to all other Poales. In Commelinales, nuclear data place Philydraceae sister to all other families rather than to a clade comprising Haemodoraceae+Pontederiaceae as seen in the plastome tree. In Liliales, nuclear data place Liliaceae sister to Smilacaceae, and Melanthiaceae are placed sister to all other Liliales except Campynemataceae. Finally, in Alismatales, nuclear data strongly place Tofieldiaceae, rather than Araceae, as sister to all the other families, providing an alternative resolution of what has been the most problematic node to resolve using plastid data, outside of those involving achlorophyllous mycoheterotrophs. As seen in numerous prior studies, the placement of orders Acorales and Alismatales as successive sister lineages to all other extant monocots. Only 21.2% of BUSCO genes were demonstrably single-copy, yet phylogenomic inferences based on BUSCO and CSC genes did not differ, and overall functional annotations of the two sets were very similar. Our analyses also reveal significant gene tree-species tree discordance despite high support values, as expected given incomplete lineage sorting (ILS) related to rapid diversification. Our study advances understanding of monocot relationships and the robustness of phylogenetic inferences based on large numbers of nuclear single-copy genes that can be obtained from transcriptomes and genomes.
Transcriptomic and metabolomic analyses reveal the antifungal mechanism of the compound phenazine-1-carboxamide on Rhizoctonia solani AG1IA

To explore the molecular mechanisms of the antifungal compound phenazine-1-carboxamide (PCN) inhibits Rhizoctonia solani and discover potential targets of action, we performed an integrated analysis of transcriptome and metabolome in R. solani mycelium by whether PCN treating or not. A total of 511 differentially expressed genes (DEGs) were identified between the PCN treatment and control groups. The fluorescence-based quantitative PCR (qPCR) got the accordant results of the gene expression trends for ten randomly selected DEGs. The Gene Ontology (GO) enrichment analysis revealed that fatty acid metabolic process, fatty acid oxidation, and lipid oxidation were among the most enriched in the biological process category, while integral component of membrane, plasma membrane, and extracellular region were among the most enriched in the cellular component category and oxidoreductase activity, cofactor binding, and coenzyme binding were among the most enriched in the molecular function category. KEGG enrichment analysis revealed the most prominently enriched metabolic pathways included ATP-binding cassette (ABC) transporters, nitrogen metabolism, aminobenzoate degradation. The DEGs related functions of cellular structures, cell membrane functions, cellular nutrition, vacuole-mitochondrion membrane contact site and ATPase activity, pH, anti-oxidation, were downregulated. A total of 466 differential metabolites were found between the PCN treatment and control groups after PCN treatment. KEGG enrichment found purine, arachidonic acid, and phenylpropanoid biosynthesis pathways were mainly affected. Further results proved PCN decreased the mycelial biomass and protein content of R. solani, and superoxide dismutase (SOD) activity reduced while peroxidase (POD) and cytochrome P450 activities increased. The molecule docking indicted that NADPH nitrite reductase, ATP-binding cassette transporter, alpha/beta hydrolase family domain-containing protein, and NADPH–cytochrome P450 reductase maybe the particular target of PCN. In conclusion, the mechanisms via which PCN inhibits R. solani AG1IA may be related to cell wall damage, cell membrane impairment, intracellular nutrient imbalance, disturbed antioxidant system, and altered intracellular pH, which laid foundation for the further new compound designing to improve antifungal efficacy.
Random planar graphs with given minimum degree

We determine the asymptotic growth of planar graphs with a condition on the minimum degree, and properties of random graphs from these classes. In particular we show that the size of the largest tree attached to the core of a random planar graph is of order c log(n) for an explicit constant c. These results provide new information on the structure of random planar graphs.
Cultural similarity predicts social inclusion of Muslims in Canada: A vignette-based experimental survey

Based on acculturation psychology and intergroup emotions theory, the current experimental study assessed the effects of Muslims’ perceived acculturation strategies by the majority group on social exclusion of Muslims in Canada, and to what extent religious resentment mediated the relationship between Muslims’ perceived acculturation strategies and social exclusion. The experimental study used a vignette-based approach. This model was examined among 190 non-Muslim Canadians. Results showed that when Muslims were viewed as assimilated in Canadian society, social exclusion of Muslims and religious resentment toward Muslims decreased. Furthermore, religious resentment mediated the association between Muslims’ perceived acculturation strategies and social exclusion only when Muslims were perceived as assimilated. Our findings suggest that Canadian majority-group members indicated positive attitude toward Muslims when they were identified as assimilated in Canadian society. Results are discussed in terms of implications for future studies and intergroup relations.
Perceived corporate social responsibility, ethical leadership, and moral reflectiveness impact on pro-environmental behavior among employees of small and medium enterprises: A double-mediation model

Corporate social responsibility (CSR) is ever-evolving concept and gaining significance in the business world. This research proposes a research model that elucidates the mechanism by which Corporate social responsibility (CSR), ethical leadership, and moral reflectiveness promote pro-environmental behavior among employees of Small and Medium Enterprises (SMEs). In today’s ever-changing environment, small and medium companies (SMEs) are critical for any economy to thrive and prosper. SMEs account for more than 30% of the GDP in Pakistan. However, little is known about the processes by which type of leadership influence the pro-environmental behavior, or the circumstances under which such impacts are strengthened or mitigated. The current study attempted to fill the gap by investigating a dual process model in which ethical leadership and a moral reflectiveness acted as two explanatory mechanisms in the impact of Corporate social responsibility (CSR) on pro-environmental behavior among employees working in small and medium enterprises (SMEs). The structural equation modeling technique was used to test the research model’s hypothesized relationships. A survey was used to collect data from 390 employees. Results showed that perceived CSR directly impacted moral reflectiveness and ethical leadership. Moreover, the moral reflectiveness and ethical leadership mediated the relationship between the perceived CSR and pro-environmental behavior. These findings contributed significantly to perceived CSR, ethical leadership, moral reflectiveness, and pro-environmental behavior among employees by exploring and integrating the holistic research work into one framework to add to the body of knowledge. Practical implications and future research directions are also highlighted.
CPUE retrieval from spaceborne lidar data: A case study in the Atlantic bigeye tuna fishing area and Antarctica fishing area

The measurement of Catch Per Unit Effort (CPUE) supports the assessment of status and trends by managers. This proportion of total catch to the harvesting effort estimates the abundance of fishery resources. Marine environmental data obtained by satellite remote sensing are essential in fishing efficiency estimation or CPUE standardization. Currently, remote sensing chlorophyll data used for fisheries resource assessment are mainly from passive ocean color remote sensing. However, high-resolution data are not available at night or in high-latitude areas such as polar regions due to insufficient solar light, clouds, and other factors. In this paper, a CPUE inversion method based on spaceborne lidar data is proposed, which is still feasible for polar regions and at nighttime. First, Atlantic bigeye tuna CPUE was modeled using Cloud aerosol lidar and infrared pathfinder satellite observations (CALIPSO) lidar-retrieved chlorophyll data in combination with sea surface temperature data. The Generalized Linear Model (GLM), Artificial Neural Network (ANN) and Support Vector Machine Methods (SVM) were used for modeling, and the three methods were compared and validated. The results showed that the correlation between predicted CPUE and nominal CPUE was higher for the ANN method, with an R2 of 0.34, while the R2 was 0.08 and 0.22 for GLM and SVM, respectively. Then, chlorophyll data in the polar regions were derived using CALIPSO diurnal data, and an ANN was used for Antarctic krill. The inversion result performed well, and it showed that the R2 of the predicted CPUE to nominal CPUE was 0.92. Preliminary results suggest that (1) nighttime measurements can increase the understanding of the diurnal variability of the upper ocean; (2) CALIPSO measurements in polar regions fill the gap of passive measurements; and (3) comparison with field data shows that ANN-based lidar products perform well, and a neural network approach based on CALIPSO lidar data can be used to simulate CPUE inversions in polar regions.
Resilience and caregiving ability among caregivers of people with stroke: The mediating role of uncertainty in illness

Background In China, stroke survivors are usually cared for by their family members. However, the caregiving ability of these informal caregivers remain inadequate during the hospitalization of their family members following a sudden onset of a stroke, and this sudden need for care overwhelms caregivers even after the hospital discharge. Therefore, research is required to identify predictors of caregiving ability that could be targeted in future interventions aimed at improving caregiving skills and reducing the burden on caregivers who care for stroke survivors. Materials and methods From August 2019 to February 2020, stroke survivors were hospitalized for the first time, and their family caregivers were registered via convenience sampling. Caregiver demographic information, resilience status, uncertainty in illness, caregiving ability, and patients' severity of stroke were measured using standardized questionnaires. Structural equation modeling was used to test the proposed model, where caregiver resilience and stroke severity predicted caregiving ability directly, and uncertainty in illness mediated the association between caregiver resilience and caregiving ability. Results A total of 306 dyads were included in the study. The tested model fit the data well (χ2 = 118.2, df = 64, RMSEA = 0.053, CFI = 0.946, TLI = 0.923). Statistically significant pathways linked caregivers' resilience status to uncertainty in illness (β = −0.558, S.E. = 0.022, P < 0.01), caregivers' resilience to the status of caregiving ability (β = −0.269, S.E. = 0.013, P < 0.01) and caregivers' uncertainty about the illness to caregiving ability (β = 0.687, S.E. = 0.051, P < 0.01). We also found that caregivers' uncertainty in illness mediated the association between caregivers' resilience and caregiving ability (β = −0.384, S.E. = 0.061, P < 0.01). Conclusions Our structural equation modeling result identified resilience and uncertainty about the illness as predictors of the caregiving ability of informal family caregivers who suffered from care burdens. Supporting family caregivers to build their resilience and reduce illness uncertainty may improve caregiving for stroke survivors.
Interactions between arsenic exposure, high-fat diet and NRF2 shape the complex responses in the murine gut microbiome and hepatic metabolism

Inorganic arsenic (iAs) exposure has been associated to various detrimental effects such as development of metabolic syndrome and type 2 diabetes via oxidative stress and induced prolonged activation of the NRF2 transcription factor. Such effects can be aggravated by poor dietary habits. The role of gut microbiota in promoting metabolic changes in response to arsenic has yet to be precisely defined. To address the complexity of the interactions between diet, NFE2L2/NRF2, and gut microbiota, we studied the chronic effects of iAs exposure in wild-type (WT) and Nrf2-/- mice fed normal (ND) vs. high-fat diet (HFD), on the gut microbial community in the context of hepatic metabolism. We demonstrate that all treatments and interactions influenced bacteria and metabolic profiles, with dietary differences causing a strong overlap of responses between the datasets. By identifying five metabolites of known microbial origin and following their fate across treatments, we provide examples on how gut microbial products can participate in the development of iAs and HFD-induced metabolic disease. Overall, our results underline the importance of the microbial community in driving gut-liver-cross talk during iAs and HFD exposure.