Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of green human resource management on organizational reputation and attractiveness: The mediated-moderated model

There is increasing global concern for environmental sustainability, with firms being monitored by different stakeholders: customers, government, suppliers, and current and potential employees. This research aims to explore the impacts of green human resource management (GHRM) on organizational reputation (OR) and organizational attractiveness (OA). The conceptual model of the study is derived from social identity theory. A close-ended questionnaire was administered to collect data through a web-based survey link which was shared with 331 employees in a Democratic Republic of Congo hospitality organization. A partial least-square structural equation modeling approach was employed to run data analysis through SmartPLS 3.0 software. The findings of the study reflect that all of the hypotheses were supported. In particular, GHRM has a strong influence on both OR and OA. An OR of green culture and personal environmental orientation strongly affected OA, while it positively and significantly mediated the relationship between GHRM and OA. Personal environmental orientation significantly moderated the link between GHRM and OA. We could find no support for a positive moderating effect of green culture on the connection between GHRM and OA. This study enhanced the scope of social identity theory by proposing that OA can be improved by GHRM, OR, green culture, and personal environmental orientation. In addition, this research suggests that managers in the hospitality sector must incorporate green practices, promote green behavior and initiative, and increase environmental awareness so that employees feel pride in being part of the organization and are attracted to it.
Invasive ductal breast cancer molecular subtype prediction by MRI radiomic and clinical features based on machine learning

Background Most studies of molecular subtype prediction in breast cancer were mainly based on two-dimensional MRI images, the predictive value of three-dimensional volumetric features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting breast cancer molecular subtypes has not been thoroughly investigated. This study aimed to look into the role of features derived from DCE-MRI and how they could be combined with clinical data to predict invasive ductal breast cancer molecular subtypes. Methods From January 2019 to December 2021, 190 Chinese women with invasive ductal breast cancer were studied (32 triple-negative, 59 HER2-enriched, and 99 luminal lesions) in this institutional review board-approved retrospective cohort study. The image processing software extracted 1130 quantitative radiomic features from the segmented lesion area, including shape-based, first-order statistical, texture, and wavelet features. Three binary classifications of the subtypes were performed: triple-negative vs. non-triple-negative, HER2-overexpressed vs. non-HER2-overexpressed, and luminal (A + B) vs. non-luminal. For the classification, five machine learning methods (random forest, logistic regression, support vector machine, naïve Bayes, and eXtreme Gradient Boosting) were employed. The classifiers were chosen using the least absolute shrinkage and selection operator method. The area evaluated classification performance under the receiver operating characteristic curve, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean. Results EXtreme Gradient Boosting model showed the best performance in luminal and non-luminal groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.8282, 0.7524, 0.6542, 0.6964, 0.6086, 0.3458, 0.8524 and 0.7016, respectively. Meanwhile, the random forest model showed the best performance in HER2-overexpressed and non-HER2-overexpressed groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.8054, 0.2941, 0.9744, 0.7679, 0.4348, 0.0256, 0.8333 and 0.5353, respectively. Furthermore, eXtreme Gradient Boosting model showed the best performance in the triple-negative and non-triple-negative groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.9031, 0.9362, 0.4444, 0.8571, 0.9167, 0.5556, 0.8980 and 0.6450. Conclusion Clinical data and three-dimension imaging features from DCE-MRI were identified as potential biomarkers for distinguishing between three molecular subtypes of invasive ductal carcinomas breast cancer. In the future, more extensive studies will be required to evaluate the findings.
Dok3 restrains neutrophil production of calprotectin during TLR4 sensing of SARS-CoV-2 spike protein

Increased neutrophils and elevated level of circulating calprotectin are hallmarks of severe COVID-19 and they contribute to the dysregulated immune responses and cytokine storm in susceptible patients. However, the precise mechanism controlling calprotectin production during SARS-CoV-2 infection remains elusive. In this study, we showed that Dok3 adaptor restrains calprotectin production by neutrophils in response to SARS-CoV-2 spike (S) protein engagement of TLR4. Dok3 recruits SHP-2 to mediate the de-phosphorylation of MyD88 at Y257, thereby attenuating downstream JAK2-STAT3 signaling and calprotectin production. Blocking of TLR4, JAK2 and STAT3 signaling could prevent excessive production of calprotectin by Dok3-/- neutrophils, revealing new targets for potential COVID-19 therapy. As S protein from SARS-CoV-2 Delta and Omicron variants can activate TLR4-driven calprotectin production in Dok3-/- neutrophils, our study suggests that targeting calprotectin production may be an effective strategy to combat severe COVID-19 manifestations associated with these emerging variants.
Comprehensive analysis of the expression of N6-methyladenosine RNA methylation regulators in pulmonary artery hypertension

Background: Pulmonary arterial hypertension (PAH) is a progressive disease characterized by pulmonary vascular remodeling. The development of PAH involves N6-methyladenosine (m6A) modification. However, the functional role of m6A regulators in PAH and the underlying regulatory mechanisms remain unknown so far. Methods: Microarray data (GSE149713) for monocrotaline induced PAH (MCT-PAH) rat models were downloaded and screened for differentially expressed genes (DEGs) and m6A regulators. Next, we screened for differentially expressed m6A regulators in endothelial cells (ECs), smooth muscle cells (SMCs), fibroblasts, interstitial macrophages, NK cells, B cells, T cells, regulatory T cells (Tregs) using scRNA sequencing data. The target DEGs of m6A regulators in ECs, SMCs, fibroblasts, and Tregs were functionally annotated using the Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. In addition, the cellular interaction analysis was performed to reveal the receptor—ligand pairs regulated by m6A regulators. Pseudo-time trajectory analyses were performed and a ceRNA network of lncRNAs-miRNAs-mRNAs was constructed in SMCs. Furthermore, the RNA transcriptome sequencing data for the SMCs isolated from idiopathic PAH (IPAH) patients (GSE144274) were validated for differentially expressed m6A regulators. Moreover, the HNRNPA2B1 levels in the lung samples from PAH patients and MCT-PAH were determined using immunohistochemistry. Results: The m6A regulators were observed to be dysregulated in PAH. HNRNPA2B1expression level was increased in the PASMCs of scRNAs and IPAH patients. The target DEGs of HNRNPA2B1 were enriched in the regulation of muscle cell differentiation and vasculature development in PASMCs. The HNRNPA2B1 expression levels determined were consistent with the proliferation-related and collagen synthesis-related gene COL4A1. Moreover, the predicted transcription factors (TFs) foxd2/3 and NFκB could be involved in the regulation of HNRNPA2B1. HNRNPA2B1 might be regulating SMCs proliferation and phenotypic transition via rno-miR-330–3p/TGFβR3 and rno-miR-125a-3p/slc39a1. In addition, HNRNPA2B1 was observed to be highly expressed in the lung samples from MCT-PAH rat models and patients with PAH. Conclusion: In summary, the present study identified certain key functional m6A regulators that are involved in pulmonary vascular remodeling. The investigation of m6A patterns might be promising and provide biomarkers for diagnosis and treatment of PAH in the future.
Differences in brain activations between micro- and macro-expressions based on electroencephalography

Micro-expressions can reflect an individual’s subjective emotions and true mental state and are widely used in the fields of mental health, justice, law enforcement, intelligence, and security. However, the current approach based on image and expert assessment-based micro-expression recognition technology has limitations such as limited application scenarios and time consumption. Therefore, to overcome these limitations, this study is the first to explore the brain mechanisms of micro-expressions and their differences from macro-expressions from a neuroscientific perspective. This can be a foundation for micro-expression recognition based on EEG signals. We designed a real-time supervision and emotional expression suppression (SEES) experimental paradigm to synchronously collect facial expressions and electroencephalograms. Electroencephalogram signals were analyzed at the scalp and source levels to determine the temporal and spatial neural patterns of micro- and macro-expressions. We found that micro-expressions were more strongly activated in the premotor cortex, supplementary motor cortex, and middle frontal gyrus in frontal regions under positive emotions than macro-expressions. Under negative emotions, micro-expressions were more weakly activated in the somatosensory cortex and corneal gyrus regions than macro-expressions. The activation of the right temporoparietal junction (rTPJ) was stronger in micro-expressions under positive than negative emotions. The reason for this difference is that the pathways of facial control are different; the production of micro-expressions under positive emotion is dependent on the control of the face, while micro-expressions under negative emotions are more dependent on the intensity of the emotion.
Antibody-mediated cell depletion therapies in multiple sclerosis

Development of disease-modifying therapies including monoclonal antibody (mAb)-based therapeutics for the treatment of multiple sclerosis (MS) has been extremely successful over the past decades. Most of the mAb-based therapies approved for MS deplete immune cell subsets and act through activation of cellular Fc-gamma receptors expressed by cytotoxic lymphocytes and phagocytes, resulting in antibody-dependent cellular cytotoxicity or by initiation of complement-mediated cytotoxicity. The therapeutic goal is to eliminate pathogenic immune cell components and to potentially foster the reconstitution of a new and healthy immune system. Ab-mediated immune cell depletion therapies include the CD52-targeting mAb alemtuzumab, CD20-specific therapeutics, and new Ab-based treatments which are currently being developed and tested in clinical trials. Here, we review recent developments in effector mechanisms and clinical applications of Ab-based cell depletion therapies, compare their immunological and clinical effects with the prototypic immune reconstitution treatment strategy, autologous hematopoietic stem cell transplantation, and discuss their potential to restore immunological tolerance and to achieve durable remission in people with MS.
Self-reported and genetically predicted effects of coffee intake on rheumatoid arthritis: Epidemiological studies and Mendelian randomization analysis

Background and aims Causal research concerning coffee intake and rheumatoid arthritis (RA) risk is controversial. The objective of this study was to further explore the causal relationship between coffee intake and RA risk. Methods The 4,310 participants from NHANES 2003–2006 were included in an epidemiological study to assess the association between coffee intake and RA by weighted multivariate logistic regression. The inverse variance weighted (IVW) method of two-sample Mendelian randomization (MR), employing genetic data from UK Biobank (428,860 cases) of coffee intake and MR-Base platform (14,361 cases and 43,923 controls) of RA, was performed to estimate the causal relationship between coffee intake and RA. Results Weighted multivariate logistic regression suggested no significant correlation between coffee intake and RA. Compared to the no-coffee group, the odds ratio for RA in the <1, 1–3, ≥4 cups/day group were 1.297, 1.378, and 1.125 (P = 0.204, 0.098, and 0.698, respectively). In the IVW of MR analysis, there was no causal relationship between coffee intake and RA (OR = 1.47, P = 0.218). Conclusion Our study did not support a causal association between coffee intake and RA risk. However, it is necessary to consider valid information on coffee intake, including brewing method, type of coffee, and quantity, in further analysis of coffee intake and RA.
Dual vs. mono antiplatelet therapy for acute ischemic stroke or transient ischemic attack with evidence of large artery atherosclerosis

Background and purpose Current pieces of evidence support the short-term use of dual antiplatelet (DAPT) in minor ischemic stroke or transient ischemic attack (TIA) based on the studies performed in patients with a broad range of non-cardioembolic stroke mechanisms. However, the efficacy and safety of DAPT use in ischemic stroke patients with large artery atherosclerosis (LAA) are still uncertain. We undertook a systemic search and formal meta-analysis to compare DAPT vs. mono-antiplatelet therapy (MAPT) in patients with etiology specifically presumed to be symptomatic LAA. Methods We conducted a systemic online search for completed randomized controlled trials that (1) compared DAPT vs. MAPT in patients with acute ischemic stroke or TIA, and (2) were confined to or had available subgroup data regarding population with symptomatic extra- or intracranial artery stenosis. Study-level meta-analysis was performed for outcomes, including ischemic stroke (IS) recurrence, intracranial hemorrhage (ICH), and major bleeding with the Mantel-Haenszel method and random effect models, and was described as risk difference (RD) and 95% CI. Results A total of 10 trials including 5,004 patients were pooled. Comparing to MAPT, DAPT significantly reduced IS recurrence (5.99 vs. 9.55%, RD: −3%, 95% CI: −5–0%). Across all agents, out of 100 treated patients, 3 fewer had a recurrent ischemic stroke with DAPT. The safety endpoints including ICH (0.28 vs. 0.32%, RD: 0%, 95% CI: −0–0%) and major bleeding (0.73 vs. 0.51%, RD: 0%, 95% CI: −0–0%) did not differ significantly. Conclusion In patients with symptomatic large artery extracranial or intracranial atherosclerosis, DAPT was superior to MAPT in preventing IS recurrence without increasing bleeding risks. The optimal DAPT regimens and duration of treatment in this population need to be clarified in further studies.
Machine learning identification of cuproptosis and necroptosis-associated molecular subtypes to aid in prognosis assessment and immunotherapy response prediction in low-grade glioma

Both cuproptosis and necroptosis are typical cell death processes that serve essential regulatory roles in the onset and progression of malignancies, including low-grade glioma (LGG). Nonetheless, there remains a paucity of research on cuproptosis and necroptosis-related gene (CNRG) prognostic signature in patients with LGG. We acquired patient data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) and captured CNRGs from the well-recognized literature. Firstly, we comprehensively summarized the pan-cancer landscape of CNRGs from the perspective of expression traits, prognostic values, mutation profiles, and pathway regulation. Then, we devised a technique for predicting the clinical efficacy of immunotherapy for LGG patients. Non-negative matrix factorization (NMF) defined by CNRGs with prognostic values was performed to generate molecular subtypes (i.e., C1 and C2). C1 subtype is characterized by poor prognosis in terms of disease-specific survival (DSS), progression-free survival (PFS), and overall survival (OS), more patients with G3 and tumour recurrence, high abundance of immunocyte infiltration, high expression of immune checkpoints, and poor response to immunotherapy. LASSO-SVM-random Forest analysis was performed to aid in developing a novel and robust CNRG-based prognostic signature. LGG patients in the TCGA and GEO databases were categorized into the training and test cohorts, respectively. A five-gene signature, including SQSTM1, ZBP1, PLK1, CFLAR, and FADD, for predicting OS of LGG patients was constructed and its predictive reliability was confirmed in both training and test cohorts. In both the training and the test datasets (cohorts), higher risk scores were linked to a lower OS rate. The time-dependent ROC curve proved that the risk score had outstanding prediction efficiency for LGG patients in the training and test cohorts. Univariate and multivariate Cox regression analyses showed the CNRG-based prognostic signature independently functioned as a risk factor for OS in LGG patients. Furthermore, we developed a highly reliable nomogram to facilitate the clinical practice of the CNRG-based prognostic signature (AUC > 0.9). Collectively, our results gave a promising understanding of cuproptosis and necroptosis in LGG, as well as a tailored prediction tool for prognosis and immunotherapeutic responses in patients.
Observation-based assessment of secondary water effects on seasonal vegetation decay across Africa

Local studies and modeling experiments suggest that shallow groundwater and lateral redistribution of soil moisture, together with soil properties, can be highly important secondary water sources for vegetation in water-limited ecosystems. However, there is a lack of observation-based studies of these terrain-associated secondary water effects on vegetation over large spatial domains. Here, we quantify the role of terrain properties on the spatial variations of dry season vegetation decay rate across Africa obtained from geostationary satellite acquisitions to assess the large-scale relevance of secondary water effects. We use machine learning based attribution to identify where and under which conditions terrain properties related to topography, water table depth, and soil hydraulic properties influence the rate of vegetation decay. Over the study domain, the machine learning model attributes about one-third of the spatial variations of vegetation decay rates to terrain properties, which is roughly equally split between direct terrain effects and interaction effects with climate and vegetation variables. The importance of secondary water effects increases with increasing topographic variability, shallower groundwater levels, and the propensity to capillary rise given by soil properties. In regions with favorable terrain properties, more than 60% of the variations in the decay rate of vegetation are attributed to terrain properties, highlighting the importance of secondary water effects on vegetation in Africa. Our findings provide an empirical assessment of the importance of local-scale secondary water effects on vegetation over Africa and help to improve hydrological and vegetation models for the challenge of bridging processes across spatial scales.
Integrated volatile metabolomic and transcriptomic analysis provides insights into the regulation of floral scents between two contrasting varieties of Lonicera japonica

Lonicera japonica Thunb. , belonging to the Caprifoliaceae family, is an important traditional Chinese medicinal plant. The L. japonica flower (LJF) is widely used in medicine, cosmetics, drinks, and food due to its medicinal and sweet-smelling properties. Considerable efforts have been devoted to investigating the pharmacological activities of LJF; however, the regulatory mechanism of the floral scents remains unknown. We previously selected and bred an elite variety of L. japonica var. chinensis Thunb. called ‘Yujin2’, which has a strong aroma and is used in functional drinks and cosmetics. In order to reveal the regulatory mechanism of the floral scents of LJF, volatile metabolomic and transcriptomic analyses of the LJF at the silver flowering stage of ‘Yujin2’ (strong aroma) and ‘Fengjin1’ (bland odor) were performed. Our results revealed that a total of 153 metabolites and 9,523 genes were differentially regulated in LJF between ‘Yujin2’ and ‘Fengjin1’. The integrated analysis of omics data indicated that the biosynthetic pathways of terpenoids (i.e., monoterpenoids, including geraniol and alpha-terpineol; sesquiterpenoids, including farnesol, farnesal, and alpha-farnesene; triterpenoid squalene), tryptophan and its derivatives (methyl anthranilate), and fatty acid derivatives, were major contributors to the stronger aroma of ‘Yujin2’ compared to ‘Fengjin1’. Moreover, several genes involved in the terpenoid biosynthetic pathway were characterized using quantitative real-time PCR. These results provide insights into the metabolic mechanisms and molecular basis of floral scents in LJF, enabling future screening of genes related to the floral scent regulation, such as alpha-terpineol synthase, geranylgeranyl diphosphate synthase, farnesyl pyrophosphate synthase, anthranilate synthase, as well as transcription factors such as MYB, WRKY, and LFY. The knowledge from this study will facilitate the breeding of quality-improved and more fragrant variety of L. japonica for ornamental purpose and functional beverages and cosmetics.
Environmental performance and financial performance during COVID-19 outbreak: Insight from Chinese firms

The global pressure to reduce carbon emissions on high-carbon-emitting economies has intensified significantly in recent years. However, these efforts’ effect on the firm’s financial performance (FP) has been a major concern. This research investigates the relationship between environmental performance (EP) and FP of Chinese firms considering the effect of the COVID-19 outbreak. Data was collected from Refinitiv DataStream and span the period of 2017–2020. In addition to the fixed-effects regression, the novel dynamic panel bootstrap corrected fixed effects and panel corrected standard errors methods were utilized to test the hypotheses. Obtained results revealed two key findings. First, there is weak evidence that higher EP increases firms’ FP. Second, the relationship between EP and FP is positive in times of economic distress, meaning that firms must continue investing in environmentally ethical and sustainable projects during the crisis. Our empirical findings extend the existing literature by showing that even in times of crisis, such as COVID-19, an environmentally friendly business model positively affects the firm’s financial structure. We discuss the policy recommendations implied by our findings for investors, business owners, managers, and officials in the conclusion section.