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Abstract Background Lung cancer, a leading global cause of cancer-related mortality, necessitates enhanced prognostic markers for improved treatment outcomes. We have previously shown a tumor suppressive role of cytosolic arginine sensor for mTORC1 subunit 1 (CASTOR1), which is targeted for degradation upon phosphorylation at S14 (pCASTOR1) in multiple types of cancer. This study focuses on the predictive value of pCASTOR1 in lung adenocarcinoma (LUAD) patients with KRAS mutations. Results Employing a newly developed pCASTOR1 specific antibody, we found that tumor cells exhibited significantly elevated pCASTOR1 scores compared to non-tumor cells (P

Abstract Redox imbalance is reported to play a pivotal role in tumorigenesis, cancer development, and drug resistance. Severe oxidative damage is a general consequence of cancer cell responses to treatment and may cause cancer cell death or severe adverse effects. To maintain their longevity, cancer cells can rescue redox balance and enter a state of resistance to anticancer drugs. Therefore, targeting redox signalling pathways has emerged as an attractive and prospective strategy for enhancing the efficacy of anticancer drugs and decreasing their adverse effects. Over the past few decades, natural products (NPs) have become an invaluable source for developing new anticancer drugs due to their high efficacy and low toxicity. Increasing evidence has demonstrated that many NPs exhibit remarkable antitumour effects, whether used alone or as adjuvants, and are emerging as effective approaches to enhance sensitivity and decrease the adverse effects of conventional cancer therapies by regulating redox balance. Among them are several novel anticancer drugs based on NPs that have entered clinical trials. In this review, we summarize the synergistic anticancer effects and related redox mechanisms of the combination of NPs with conventional anticancer drugs. We believe that NPs targeting redox regulation will represent promising novel candidates and provide prospects for cancer treatment in the future. Graphical Abstract

Abstract Background The combination of virtual reality (VR) and palliative care potentially represents a new opportunity for palliative care. Many previous studies have evaluated the application of VR therapy to patients with advanced disease receiving palliative care. However, patient-perspective reviews to comprehensively understand the actual experiences and feelings of patients and provide practical guidance for designing future studies are currently lacking. This review of qualitative evidence aimed to explore the experiences and perceptions of patients receiving VR therapy in palliative care. Methods This study was conducted in accordance with the Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) statement guidelines. Ten databases, namely, PubMed, Web of Science, EBSCO, OVID MEDLINE, Scopus, John Wiley, ProQuest, CNKI, WANFANG DATA, and SinoMed, were searched, and qualitative and mixed studies from the establishment of each database to June 30, 2023 were included. The Joanna Briggs Institute Critical Appraisal Checklist for Qualitative Research was used to assess the quality of the included studies. The data included in the literature were analyzed and integrated by “thematic synthesis” to formalize the identification and development of themes. Results The nine selected studies altogether included 156 participants from seven hospice care facilities of different types and two oncology centers. Three key themes were identified: experiences of palliative care patients in VR therapy, the perceived value that palliative care patients gain in VR therapy, and perspectives of palliative care patients toward using VR therapy. Conclusions The patients’ feedback covered discomfort caused by VR devices, good sense of experiences, and situations that affected the interactive experience. Some patients were unable to tolerate VR therapy or reported newer forms of discomfort. The findings indicated that VR therapy may be an effective approach to relieve patients’ physical and psychological pain and help them gain self-awareness. Moreover, patients showed a preference for personalized VR therapy.

Abstract Background Bromo-adjacent homology-plant homeodomain domain containing protein 1 (BP1) is a reader of histone post-translational modifications in fungi. BP1 recognizes trimethylation of lysine 27 in histone H3 (H3K27me3), an epigenetic hallmark of gene silencing. However, whether and how BP1 participates in transcriptional repression remains poorly understood. Results We report that BP1 forms phase-separated liquid condensates to modulate its biological function in Fusarium graminearum. Deletion assays reveal that intrinsically disordered region 2 (IDR2) of BP1 mediates its liquid–liquid phase separation. The phase separation of BP1 is indispensable for its interaction with suppressor of Zeste 12, a component of polycomb repressive complex 2. Furthermore, IDR2 deletion abolishes BP1-H3K27me3 binding and alleviates the transcriptional repression of secondary metabolism-related genes, especially deoxynivalenol mycotoxin biosynthesis genes. Conclusions BP1 maintains transcriptional repression by forming liquid–liquid phase-separated condensates, expanding our understanding of the relationship between post-translational modifications and liquid–liquid phase separation.

Depression, a mood disorder characterized by a persistent low mood and lack of enjoyment, is considered the leading cause of non-fatal health losses worldwide. Neuroplasticity refers to the brain’s ability to adapt to external or internal stimuli, resulting in functional and structural changes. This process plays a crucial role in the development of depression. Traditional Chinese Medicine (TCM) shows significant potential as a complementary and alternative therapy for neurological diseases, including depression. However, there has been no systematic summary of the role of neuroplasticity in the pathological development of depression and TCM Interventions currently. This review systematically summarized recent literature on changes in neuroplasticity in depression and analyzed the regulatory mechanisms of active metabolites in TCM and TCM formulas on neuroplasticity in antidepressant treatment. Additionally, this review discussed the limitations of current research and the application prospects of TCM in regulating neuroplasticity in antidepressant research.

Due to a high degree of symptom overlap in the early stages, with movement disorders predominating, Parkinson's disease (PD) and multiple system atrophy (MSA) may exhibit a similar decline in motor areas, yet they differ in their spread throughout the brain, ultimately resulting in two distinct diseases. Drawing upon neuroimaging analyses and altered motor cortex excitability, potential diffusion mechanisms were delved into, and comparisons of correlations across distinct disease groups were conducted in a bid to uncover significant pathological disparities. We recruited thirty-five PD, thirty-seven MSA, and twenty-eight matched controls to conduct clinical assessments, electromyographic recording, and magnetic resonance imaging scanning during the ''on medication'' state. Patients with neurodegeneration displayed a widespread decrease in electrophysiology in bilateral M1. Brain function in early PD was still in the self-compensatory phase and there was no significant change. MSA patients demonstrated an increase in intra-hemispheric function coupled with a decrease in diffusivity, indicating a reduction in the spread of neural signals. The level of resting motor threshold in healthy aged showed broad correlations with both clinical manifestations and brain circuits related to left M1, which was absent in disease states. Besides, ICF exhibited distinct correlations with functional connections between right M1 and left middle temporal gyrus in all groups. The present study identified subtle differences in the functioning of PD and MSA related to bilateral M1. By combining clinical information, cortical excitability, and neuroimaging intuitively, we attempt to bring light on the potential mechanisms that may underlie the development of neurodegenerative disease.

Abstract Background Despite the critical progress of non-small cell lung cancer (NSCLC) therapeutic approaches, the clinical outcomes remain considerably poor. The requirement of developing novel therapeutic interventions is still urgent. In this study, we showed for the first time that diosbulbin C, a natural diterpene lactone component extracted from traditional Chinese medicine Dioscorea bulbifera L., possesses high anticancer activity in NSCLC. Methods A549 and NCI-H1299 cells were used. The inhibitory effects of the diosbulbin C on NSCLC cell proliferation were evaluated using cytotoxicity, clone formation, EdU assay, and flow cytometry. Network pharmacology methods were used to explore the targets through which the diosbulbin C inhibited NSCLC cell proliferation. Molecular docking, qRT-PCR, and western blotting were used to validate the molecular targets and regulated molecules of diosbulbin C in NSCLC. Results Diosbulbin C treatment in NSCLC cells results in a remarkable reduction in cell proliferation and induces significant G0/G1 phase cell cycle arrest. AKT1, DHFR, and TYMS were identified as the potential targets of diosbulbin C. Diosbulbin C may inhibit NSCLC cell proliferation by downregulating the expression/activation of AKT, DHFR, and TYMS. In addition, diosbulbin C was predicted to exhibit high drug-likeness properties with good water solubility and intestinal absorption, highlighting its potential value in the discovery and development of anti-lung cancer drugs. Conclusions Diosbulbin C induces cell cycle arrest and inhibits the proliferation of NSCLC cells, possibly by downregulating the expression/activation of AKT, DHFR, and TYMS.

Abstract Background This study aimed to establish a nomogram for predicting risk of recurrence and provide a model for decision‐making between lobectomy and sublobar resection in patients with stage IA lung adenocarcinoma. Methods Patients diagnosed with stage IA lung adenocarcinoma (LUAD) between December 2010 and October 2018 from Cancer Hospital Chinese Academy of Medical Sciences were included. Patients were randomly assigned to training and validation cohorts, accounting for 70% and 30% of the total cases, respectively. We collected laboratory variables before surgery. Univariate and multivariate analyses were performed in the training cohort to identify variables significantly associated with recurrence‐free survival (RFS) which were subsequently used to construct a nomogram. Validation was conducted in both cohorts. A receiver operating characteristic curve was used to determine the optional cutoff values of the scores calculated from the nomogram. Patients were then divided into low‐ and high‐risk groups. Survival was performed to determine if the nomogram could guide the operation method. Results A total of 543 patients were included in this study. Gender, albumin level, carcinoembryonic antigen level and cytokeratin‐19‐fragment level were included in the nomogram. In both cohorts, the nomogram stratified the patients into high‐ and low‐risk groups in terms of RFS. In particular, there was a significant difference in RFS between lobectomy and sublobar resection in the high‐risk group. Conclusions Gender, albumin level, carcinoembryonic antigen level and cytokeratin‐19‐fragment level are valuable markers in predicting recurrence and can guide surgical practice in patients with stage IA LUAD.

Abstract Oncogenic Kaposi’s sarcoma-associated herpesvirus (KSHV) consists of latent and lytic replication phases, both of which are important for the development of KSHV-related cancers. As one of the most abundant RNA modifications, N 6 -methyladenosine (m6A) and its related complexes regulate KSHV life cycle. However, the role of METTL16, a newly discovered RNA methyltransferase, in KSHV life cycle remains unknown. In this study, we have identified a suppressive role of METTL16 in KSHV lytic replication. METTL16 knockdown increased while METTL16 overexpression reduced KSHV lytic replication. METTL16 binding to and writing of m6A on MAT2A transcript are essential for its splicing, maturation and expression. As a rate-limiting enzyme in the methionine-S-adenosylmethionine (SAM) cycle, MAT2A catalyzes the conversion of L-methionine to SAM required for the transmethylation of protein, DNA and RNA, transamination of polyamines, and transsulfuration of cystathionine. Consequently, knockdown or chemical inhibition of MAT2A reduced intracellular SAM level and enhanced KSHV lytic replication. In contrast, SAM treatment was sufficient to inhibit KSHV lytic replication and reverse the effect of the enhanced KSHV lytic program caused by METTL16 or MAT2A knockdown. Mechanistically, METTL16 or MAT2A knockdown increased while SAM treatment decreased the intracellular reactive oxygen species level by altering glutathione level, which is essential for efficient KSHV lytic replication. These findings demonstrate that METTL16 suppresses KSHV lytic replication by modulating the SAM cycle to maintain intracellular SAM level and redox homeostasis, thus illustrating the linkage of KSHV life cycle with specific m6A modifications, and cellular metabolic and oxidative conditions.

Abstract Addictive substance use impairs cognitive flexibility, with unclear underlying mechanisms. The reinforcement of substance use is mediated by the striatal direct-pathway medium spiny neurons (dMSNs) that project to the substantia nigra pars reticulata (SNr). Cognitive flexibility is mediated by striatal cholinergic interneurons (CINs), which receive extensive striatal inhibition. Here, we hypothesized that increased dMSN activity induced by substance use inhibits CINs, reducing cognitive flexibility. We found that cocaine administration in rodents caused long-lasting potentiation of local inhibitory dMSN-to-CIN transmission and decreased CIN firing in the dorsomedial striatum (DMS), a brain region critical for cognitive flexibility. Moreover, chemogenetic and time-locked optogenetic inhibition of DMS CINs suppressed flexibility of goal-directed behavior in instrumental reversal learning tasks. Notably, rabies-mediated tracing and physiological studies showed that SNr-projecting dMSNs, which mediate reinforcement, sent axonal collaterals to inhibit DMS CINs, which mediate flexibility. Our findings demonstrate that the local inhibitory dMSN-to-CIN circuit mediates the reinforcement-induced deficits in cognitive flexibility.

The Sombor index of a graph $ G $, introduced by Ivan Gutman, is defined as the sum of the weights $ \sqrt{d_G(u)^2+d_G(v)^2} $ of all edges $ uv $ of $ G $, where $ d_G(u) $ denotes the degree of vertex $ u $ in $ G $. The Sombor coindex was recently defined as $ \overline{SO}(G) = \sum_{uv\notin E(G)}\sqrt{d_G(u)^2+d_G(v)^2} $. As a new vertex-degree-based topological index, the Sombor index is important because it has been proved to predict certain physicochemical properties. Two-trees are very important structures in complex networks. In this paper, the maximum and second maximum Sombor index, the minimum and second minimum Sombor coindex of two-trees and the extremal two-trees are determined, respectively. Besides, some problems are proposed for further research.

ABSTRACTHyperinflammation is the hallmark of Kaposi’s sarcoma (KS), the most common cancer in AIDS patients caused by Kaposi’s sarcoma-associated herpesvirus (KSHV) infection. However, the role and mechanism of induction of inflammation in KS remain unclear. In a screening for inhibitors of KSHV-induced oncogenesis, over half of the identified candidates were anti-inflammatory agents including dexamethasone, which functions by activating glucocorticoid receptor (GR) signaling. Here, we examined the mechanism mediating KSHV-induced inflammation. We found that numerous inflammatory pathways were activated in KSHV-transformed cells. Particularly, interleukin-1 alpha (IL-1α) and IL-1 receptor antagonist (IL-1Ra) from the IL-1 family were the most induced and suppressed cytokines, respectively. We found that KSHV miRNAs mediated IL-1α induction while both miRNAs and vFLIP mediated IL-1Ra suppression. Furthermore, GR signaling was inhibited in KSHV-transformed cells, which was mediated by vFLIP and vCyclin. Dexamethasone treatment activated GR signaling and inhibited cell proliferation and colony formation in soft agar of KSHV-transformed cells but had a minimal effect on matched primary cells. Consequently, dexamethasone suppressed the initiation and growth of KSHV-induced tumors in mice. Mechanistically, dexamethasone suppressed IL-1α but induced IL-1Ra expression. Treatment with recombinant IL-1α protein rescued the inhibitory effect of dexamethasone while overexpression of IL-1Ra caused a weak growth inhibition of KSHV-transformed cells. Furthermore, dexamethasone induced IκBα expression resulting in inhibition of the NF-κB pathway and IL-1α expression. These results reveal an important role of the IL-1 pathway in KSHV-induced inflammation and oncogenesis, which can be inhibited by dexamethasone-activated GR signaling, and identify IL-1-mediated inflammation as a potential therapeutic target for KSHV-induced malignancies.IMPORTANCEKaposi’s sarcoma (KS) is the most common cancer in HIV-infected patients caused by Kaposi’s sarcoma-associated herpesvirus (KSHV) infection. Hyperinflammation is the hallmark of KS. In this study, we have shown that KSHV mediates hyperinflammation by inducing IL-1α and suppressing IL-1Ra. Mechanistically, KSHV miRNAs and vFLIP induce hyperinflammation by activating the NF-κB pathway. A common anti-inflammatory agent dexamethasone blocks KSHV-induced hyperinflammation and tumorigenesis by activating glucocorticoid receptor signaling to suppress IL-1α and induce IL-1Ra. This work has identified IL-1-mediated inflammation as a potential therapeutic target and dexamethasone as a potential therapeutic agent for KSHV-induced malignancies.

Depression is characterized by prominent indicators and manifestations, such as anhedonia, which refers to the inability to experience pleasure, and persistent feelings of hopelessness. In clinical practice, the primary treatment approach involves the utilization of selective serotonin reuptake inhibitors (SSRIs) and related pharmacological interventions. Nevertheless, it is crucial to recognize that these agents are associated with significant adverse effects. Traditional Chinese medicine (TCM) adopts a multifaceted approach, targeting diverse components, multiple targets, and various channels of action. TCM has potential antidepressant effects. Anomalies in adult hippocampal neurogenesis (AHN) constitute a pivotal factor in the pathology of depression, with the regulation of AHN emerging as a potential key measure to intervene in the pathogenesis and progression of this condition. This comprehensive review presented an overview of the pharmacological mechanisms underlying the antidepressant effects of active ingredients found in TCM. Through examination of recent studies, we explored how these ingredients modulated AHN. Furthermore, we critically assessed the current limitations of research in this domain and proposed novel strategies for preclinical investigation and clinical applications in the treatment of depression in future.

Ear rot caused by the Fusarium species has led to a decline in maize yield and kernel quality worldwide. The changes in the population structure of pathogens and the widespread planting of susceptible maize varieties have exacerbated the occurrence and harm of ear rot in China. Therefore, it is very important to establish the species composition of Fusarium and evaluate the resistance of the main cultivated hybrids. In this study, 366 single conidial isolates of Fusarium spp. were obtained from three provinces of Northeast China. F. verticillioides, F. subglutinans, F. proliferatum, F. oxysporum, and F. graminearum species complex (FGSC) were identified, with F. verticillioides being the most prevalent with a frequency of 44.0%. Based on the TEF-1α gene sequences analysis, the FGSC populations consisted of two independent species: F. boothii and F. graminearum, which account for 23.8% and 5.7% of the total isolates, respectively. Additionally, the resistance to ear rot by 97 maize hybrids commonly planted in Northeast China was evaluated by inoculation with F. verticillioides during 2021 and 2022. The results showed that the disease parameters of different hybrids varied significantly (p < 0.05). Approximately half of the hybrids had damage rates ranging from 0 to 15%, and 79.4% of the hybrids had a severity rating of less than 5.5. In total, 49 (50.5%) hybrids were rated as moderately resistant, which was the dominant resistance category, and 71 hybrids (73.2%) were identified as moderately to highly resistant to ear rot. Current research confirms that Fusarium ear rot in maize is mainly caused by F. verticillioides in Northeast China, and many hybrids are resistant to the disease. This study will guide growers to scientifically deploy resistant commercial hybrids to control ear rot.

The core symptoms of depression are anhedonia and persistent hopelessness. Selective serotonin reuptake inhibitors (SSRIs) and their related medications are commonly used for clinical treatment, despite their significant adverse effects. Traditional Chinese medicine with its multiple targets, channels, and compounds, exhibit immense potential in treating depression. Autophagy, a vital process in depression pathology, has emerged as a promising target for intervention. This review summarized the pharmacological mechanisms of antidepressants by regulating autophagy. We presented insights from recent studies, discussed current research limitations, and proposed new strategies for basic research and their clinical application in depression.

Abstract Background Deep learning (DL) models are highly vulnerable to adversarial attacks for medical image classification. An adversary could modify the input data in imperceptible ways such that a model could be tricked to predict, say, an image that actually exhibits malignant tumor to a prediction that it is benign. However, adversarial robustness of DL models for medical images is not adequately studied. DL in medicine is inundated with models of various complexity—particularly, very large models. In this work, we investigate the role of model complexity in adversarial settings. Results Consider a set of DL models that exhibit similar performances for a given task. These models are trained in the usual manner but are not trained to defend against adversarial attacks. We demonstrate that, among those models, simpler models of reduced complexity show a greater level of robustness against adversarial attacks than larger models that often tend to be used in medical applications. On the other hand, we also show that once those models undergo adversarial training, the adversarial trained medical image DL models exhibit a greater degree of robustness than the standard trained models for all model complexities. Conclusion The above result has a significant practical relevance. When medical practitioners lack the expertise or resources to defend against adversarial attacks, we recommend that they select the smallest of the models that exhibit adequate performance. Such a model would be naturally more robust to adversarial attacks than the larger models.

BackgroundThe immune response and safety of inactivated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines among patients with chronic hepatitis B (CHB), especially those with cirrhosis, are not clear. Therefore, this study was conducted to evaluate the efficacy and safety of inactivated SARS-CoV-2 vaccines among CHB patients with and without cirrhosis.Patients and methodsA total of 643 CHB patients who received two doses of inactivated SARS-CoV-2 vaccines (BBIBP-CorV and CoronaVac) were enrolled. Serum samples were collected and tested for SARS-CoV-2 S-receptor-binding domain (S-RBD) immunoglobulin G (IgG) at enrollment. Data on adverse events (AEs) within 7 days after the second dose were obtained using a questionnaire.ResultsA total of 416 non-cirrhotic and 227 cirrhotic patients were included in the analysis. Cirrhotic patients had lower antibody titers than non-cirrhotic patients after adjusting for age, sex, and time interval (2.45 vs. 2.60 ng/ml, p = 0.034). Furthermore, the study revealed that cirrhotic patients demonstrated a slower rate of seropositivity increase, with the highest rate being recorded at week 4 and reaching 94.7%. On the other hand, among non-cirrhotic patients, the seropositivity rate peak was observed at week 2 and reached 96.0%. In addition, cirrhotic patients displayed a more rapid decline in the seropositivity rate, dropping to 54.5% after ≥16 weeks, while non-cirrhotic patients exhibited a decrease to 67.2% after the same time period. The overall incidence of AEs was low (18.4%), and all AEs were mild and self-limiting. In addition, 16.0% of participants had mild liver function abnormalities, and half of them returned to normality within the next 6 months without additional therapy. The participants who experienced liver function abnormalities showed a higher seropositivity rate and antibody titer than those who did not (91.6% vs. 79.5%, p = 0.005; 2.73 vs. 2.41 ng/ml, p

Abstract N 6-methyladenosine (m6A) is a dynamic post-transcriptional RNA modification influencing all aspects of mRNA biology. While m6A modifications during numerous viral infections have been described, the role of m6A in innate immune response remains unclear. Here, we examined cellular m6A epitranscriptomes during infections of Pseudomonas aeruginosa and herpes simplex virus type 1 (HSV-1), and lipopolysaccharide (LPS) stimulation to identify m6A-regulated innate immune response genes. We showed that a significant portion of cellular genes including many innate immune response genes underwent m6A modifications in 5'UTR and 3'UTR. We identified common and distinct m6A-modified genes under different stimulating conditions. Significantly, the expression of a subset of innate immune response genes was positively correlated with m6A level. Importantly, we identified genes that had significant enrichments of m6A peaks during P. aeruginosa infection following knockdown of m6A “eraser” ALKBH5, confirming the regulation of these genes by m6A and ALKBH5. Among them, we confirmed the association of m6A modification with gene expression in immune response genes TNFAIP3, IFIT1, IFIT2 and IFIH1. Taken together, our results revealed the vital role of m6A in regulating innate immunity against bacterial and viral infections. These works also provided rich resources for the scientific community.

Seed coat colour is an important quality trait, domestication trait, and morphological marker, and it is closely associated with flavonoid and anthocyanin metabolism pathways. The seed coat colour of the adzuki bean, an important legume crop, influences the processing quality, the commodity itself, and its nutritional quality. In this review, a genetic analysis of different seed coat colours, gene mapping, metabolite content determination, and varietal improvement in adzuki bean are summarized. It provides further insight into gene mapping and cloning of seed coat colour genes and varietal improvements in adzuki beans.

ABSTRACT Mitogen-activated protein kinases (MAPKs) play critical roles in the induction of numerous cytokines, chemokines, and inflammatory mediators that mobilize the immune system to counter pathogenic infections. Dual-specificity phosphatase 1 (DUSP1) is a member of the dual-specificity phosphatases that inactivates MAPKs through a negative-feedback mechanism. Here, we report that in response to viral and bacterial infections, not only the DUSP1 transcript but also its N6-methyladenosine (m6A) levels rapidly increase together with that of the m6A reader protein YTHDF2, resulting in enhanced YTHDF2-mediated DUSP1 transcript degradation. The knockdown of DUSP1 promotes p38 and Jun N-terminal kinase (JNK) phosphorylation and activation, thus increasing the expression of innate immune response genes, including the interleukin-1β (IL-1β), colony-stimulating factor 3 (CSF3), transglutaminase 2 (TGM2), and proto-oncogene tyrosine-protein kinase Src (SRC) genes. Similarly, the knockdown of the m6A eraser ALKBH5 increases the DUSP1 transcript m6A level, resulting in accelerated transcript degradation, the activation of p38 and JNK, and the enhanced expression of IL-1β, CSF3, TGM2, and SRC. These results demonstrate that m6A and the reader protein YTHDF2 orchestrate optimal innate immune responses during viral and bacterial infections by downregulating the expression of a negative regulator, DUSP1, of the p38 and JNK pathways that are central to innate immune responses against pathogenic infections. IMPORTANCE Innate immunity is central to controlling pathogenic infections and maintaining the homeostasis of the host. In this study, we have revealed a novel mechanism regulating innate immune responses during viral and bacterial infections. We have found that N6-methyladenosine (m6A) and the reader protein YTHDF2 regulate dual-specificity phosphatase 1, a negative regulator of the mitogen-activated protein kinases p38 and JNK, to maximize innate immune responses during viral and bacterial infections. These results provide novel insights into the mechanism regulating innate immunity, which could help in the development of novel approaches for controlling pathogenic infections.

Changes in the function of peritoneal macrophages contribute to the homeostasis of the peritoneal immune microenvironment in endometriosis. The mechanism by which ectopic tissues escape phagocytic clearance by macrophages to achieve ectopic colonization and proliferation is unknown. The expression of CD163 in peritoneal macrophages in patients with endometriosis is increased, with the overexpression of MAPK, which can promote the M2-type polarization of macrophages and reduce their ability to phagocytose ectopic endometrial cells. As an upstream regulator of MAPK, MST1 expression is deficient in peritoneal macrophages of patients with endometriosis. This process is regulated by miR-887-5p, a noncoding RNA targeting MST1. Moreover, MST1-knockout macrophages secrete anti-inflammatory factor IL-10, which promotes autophagy of ectopic endometrial stromal cells. These results suggest that MST1 deficient macrophages may accelerate the autophagy of ectopic endometrium via IL-10 which was regulated by miR-887-5p.

Mangroves are high-productive ecosystems and globally protected. Establishing nature reserves aimed at counteracting the negative effects of anthropogenic activities is one of the most pivotal approaches to conserve mangrove ecosystems. Evaluation of the conservation effectiveness for mangrove nature reserves is thus indispensable for making knowledge-based conservation policies and funding-decisions by government and managers. In this study, using composited Landsat images by the Google Earth Engine cloud platform and object-oriented deep learning classification method, the land cover maps of national mangrove nature reserves (NMNRs) in China were obtained from 1987 to 2019. The systematic evaluation of conservation effectiveness for each NMNR was conducted by landscape metrics and an entropy weight model. Combined with the dynamics in mangrove distribution, human interference intensity, and natural environment change, the driving force factors affecting the conservation effectiveness for NMNRs were investigated. The results show that the total mangrove area in all NMNRs increased 968.6 ha during the study period, a 21.8 % rate of increase. Except for one NMNR with a slight decline, the conservation of remaining NMNRs was considered effective with increase varied from 14.8 % to 87.5 % in the level of protective efficacy. The conservation effectiveness of NMNRs was affected by both anthropogenic and natural factors, while the improvement to the conservation effectiveness was largely attributed to the implementation of protection policies, such as reforestation engineering. Further direct or indirect challenges in mangrove conservation effectiveness, e.g., pollution, natural disasters, and exotic species invasion, still require close attention. This study provides an effective and efficient approach to quantify the conservation effectiveness of mangrove nature reserves, which would facilitate mangrove conservation and management in the future.

The limitations and weaknesses of continuous melon cropping have worsened in recent years. A melon–broccoli rotation can possibly alleviate the problems associated with melon monoculture; however, the underlying mechanisms and their impact on the rhizosphere’s soil microbial community remain unclear. Thus, high-throughput sequencing was used to evaluate the rhizosphere soil’s microbial community’s relative abundance and diversity under melon–broccoli rotation and continuous melon monoculture cropping systems. We found that relative fungal and bacterial diversity and richness increased while fungi relative abundances, such as Fusarium spp. were significantly decreased under broccoli rotation. During continuous cropping, enriched Acidobacteria and Streptomyces spp., Sphingomonas spp., and Pseudomonas spp. were identified, which play important roles in alleviating melon continuous cropping obstacles. The soil under continuous cropping was rendered acidic, underwent secondary salinization, rapidly accumulated soil organic carbon and nitrogen, and lost abundant phosphorus and potassium. In contrast, broccoli rotation partially mitigated these negative physicochemical responses. Redundancy analysis revealed that the soil pH, soil soluble salt content, and soil organic carbon were linked to structures of the soil bacterial and fungal community. Melon–broccoli rotation could effectively equilibrate the soil microenvironment and overcome the challenges and deficiencies associated with continuous melon cropping.

Let $ G $ be a simple graph with edge set $ E(G) $. The modified Sombor index is defined as $ ^{m}SO(G) = \sum\limits_{uv\in E(G)}\frac{1}{\sqrt{d_{u}^{2}~~+~~d_{v}^{2}}} $, where $ d_{u} $ (resp. $ d_{v} $) denotes the degree of vertex $ u $ (resp. $ v $). In this paper, we determine some bounds for the modified Sombor indices of graphs with given some parameters (e.g., maximum degree $ \Delta $, minimum degree $ \delta $, diameter $ d $, girth $ g $) and the Nordhaus-Gaddum-type results. We also obtain the relationship between modified Sombor index and some other indices. At last, we obtain some bounds for the modified spectral radius and energy.

Abstract We develop a novel computational method, NucHMM, to identify functional nucleosome states associated with cell type-specific combinatorial histone marks and nucleosome organization features such as phasing, spacing and positioning. We test it on publicly available MNase-seq and ChIP-seq data in MCF7, H1, and IMR90 cells and identify 11 distinct functional nucleosome states. We demonstrate these nucleosome states are distinctly associated with the splicing potentiality of skipping exons. This advances our understanding of the chromatin function at the nucleosome level and offers insights into the interplay between nucleosome organization and splicing processes.

An accurate personality model is crucial to many research fields. Most personality models have been constructed using linear factor analysis (LFA). In this paper, we investigate if an effective deep learning tool for factor extraction, the Variational Autoencoder (VAE), can be applied to explore the factor structure of a set of personality variables. To compare VAE with LFA, we applied VAE to an International Personality Item Pool (IPIP) Big 5 dataset and an IPIP HEXACO (Humility-Honesty, Emotionality, Extroversion, Agreeableness, Conscientiousness, Openness) dataset. We found that LFA tends to break factors into ever smaller, yet still significant fractions, when the number of assumed latent factors increases, leading to the need to organize personality variables at the factor level and then the facet level. On the other hand, the factor structure returned by VAE is very stable and VAE only adds noise-like factors after significant factors are found as the number of assumed latent factors increases. VAE reported more stable factors by elevating some facets in the HEXACO scale to the factor level. Since this is a data-driven process that exhausts all stable and significant factors that can be found, it is not necessary to further conduct facet level analysis and it is anticipated that VAE will have broad applications in exploratory factor analysis in personality research.

A key goal of cognitive neuroscience is to better understand how dynamic brain activity relates to behavior. Such dynamics, in terms of spatial and temporal patterns of brain activity, are directly measured with neurophysiological methods such as EEG, but can also be indirectly expressed by the body. Autonomic nervous system activity is the best-known example, but, muscles in the eyes and face can also index brain activity. Mostly parallel lines of artificial intelligence research show that EEG and facial muscles both encode information about emotion, pain, attention, and social interactions, among other topics. In this study, we examined adults who stutter (AWS) to understand the relations between dynamic brain and facial muscle activity and predictions about future behavior (fluent or stuttered speech). AWS can provide insight into brain-behavior dynamics because they naturally fluctuate between episodes of fluent and stuttered speech behavior. We focused on the period when speech preparation occurs, and used EEG and facial muscle activity measured from video to predict whether the upcoming speech would be fluent or stuttered. An explainable self-supervised multimodal architecture learned the temporal dynamics of both EEG and facial muscle movements during speech preparation in AWS, and predicted fluent or stuttered speech at 80.8% accuracy (chance=50%). Specific EEG and facial muscle signals distinguished fluent and stuttered trials, and systematically varied from early to late speech preparation time periods. The self-supervised architecture successfully identified multimodal activity that predicted upcoming behavior on a trial-by-trial basis. This approach could be applied to understanding the neural mechanisms driving variable behavior and symptoms in a wide range of neurological and psychiatric disorders. The combination of direct measures of neural activity and simple video data may be applied to developing technologies that estimate brain state from subtle bodily signals.

Setosphaeria turcica f. sp. zeae and S. turcica f. sp. sorghi, the two formae speciales of S. turcica, cause northern leaf blight disease of corn and sorghum, respectively, and often cause serious economic losses. They have obvious physiological differentiation and show complete host specificity. Host specificity is often closely related to pathogen virulence factors, including secreted protein effectors and secondary metabolites. Genomic sequencing can provide more information for understanding the virulence mechanisms of pathogens. However, the complete genomic sequence of S. turcica f. sp. sorghi has not yet been reported, and no comparative genomic information is available for the two formae speciales. In this study, S. turcica f. sp. zeae was predicted to have fewer secreted proteins, pathogen-host interaction (PHI) genes and carbohydrate-active enzymes (CAZys) than S. turcica f. sp. sorghi. Fifteen and 20 polyketide synthase (PKS) genes were identified in S. turcica f. sp. zeae and S. turcica f. sp. sorghi, respectively, which maintained high homology. There were eight functionally annotated effector protein-encoding genes specifically in S. turcica f. sp. zeae, among which the encoding gene StCEL2 of endo-1, 4-β-D-glucanase, an important component of cellulase, was significantly up-regulated during the interaction process. Finally, gluconolactone inhibited cellulase activity and decreased infection rate and pathogenicity, which indicates that cellulase is essential for maintaining virulence. These findings demonstrate that cellulase plays an important role in the pathogenicity of S. turcica f. sp. zeae. Our results also provide a theoretical basis for future research on the molecular mechanisms underlying the pathogenicity of the two formae speciales and for identifying any associated genes.

During the plant infection process, pathogens can secrete several effectors. Some of the effectors are well-known for their roles in regulating plant immunity and promoting successful pathogen colonization. However, there are few studies on the ribonuclease (RNase) effectors secreted by fungi. In the present study, we discovered a secretable RNase (SRE1) in the secretome of Setosphaeria turcica that was significantly upregulated during the early stages of S. turcica infection in maize. Knockdown of SRE1 significantly reduced the virulence of S. turcica. SRE1 can induce cell death in maize and Nicotiana benthamiana. However, unlike the conventional hypersensitive response (HR) caused by other effectors, SRE1 is not dependent on its signal peptide (SP) or plant receptor kinases (such as BAK1 and SOBIR1). SRE1-induced cell death depends upon its enzymatic activity and the N-terminal β-hairpin structure. SRE1 relies on its N-terminal β-hairpin structure to enter cells, and then degrades plant's RNA through its catalytic activity causing cytotoxic effects. Additionally, SRE1 enhances N. benthamiana's resistance to pathogenic fungi and oomycetes. In summary, SRE1 promotes the virulence of S. turcica, inducing plant cell death and activating plant immune responses.

Let G be a graph of order n and μ be an adjacency eigenvalue of G with multiplicity k≥1. A star complement for μ in G is an induced subgraph of G of order n−k with no eigenvalue μ. In this paper, we characterize the maximal graphs with the bipartite graph K2,s as a star complement for eigenvalues μ=−2,1 and study the cases of other eigenvalues for further research.

Abstract Background The state-of-the-art deep learning based cancer type prediction can only predict cancer types whose samples are available during the training where the sample size is commonly large. In this paper, we consider how to utilize the existing training samples to predict cancer types unseen during the training. We hypothesize the existence of a set of type-agnostic expression representations that define the similarity/dissimilarity between samples of the same/different types and propose a novel one-shot learning model called CancerSiamese to learn this common representation. CancerSiamese accepts a pair of query and support samples (gene expression profiles) and learns the representation of similar or dissimilar cancer types through two parallel convolutional neural networks joined by a similarity function. Results We trained CancerSiamese for cancer type prediction for primary and metastatic tumors using samples from the Cancer Genome Atlas (TCGA) and MET500. Network transfer learning was utilized to facilitate the training of the CancerSiamese models. CancerSiamese was tested for different N-way predictions and yielded an average accuracy improvement of 8% and 4% over the benchmark 1-Nearest Neighbor (1-NN) classifier for primary and metastatic tumors, respectively. Moreover, we applied the guided gradient saliency map and feature selection to CancerSiamese to examine 100 and 200 top marker-gene candidates for the prediction of primary and metastatic cancers, respectively. Functional analysis of these marker genes revealed several cancer related functions between primary and metastatic tumors. Conclusion This work demonstrated, for the first time, the feasibility of predicting unseen cancer types whose samples are limited. Thus, it could inspire new and ingenious applications of one-shot and few-shot learning solutions for improving cancer diagnosis, prognostic, and our understanding of cancer.

Capturing the distinctive spectral fingerprints of molecules in the infrared (IR) region is of vital importance in gas detection. However, it is still limited by the resolution, sensitivity and signal to noise ratio of IR detectors. Here, a broadband frequency conversion scheme from the middle IR band (MIR) to the telecom band based on four-wave mixing process is proposed and theoretically investigated, combining the advantages of well-established detectors in the telecom band and unique molecular vibrations in the MIR band. A flat and low dispersion profile is generated in an asymmetric Ge-As-Se/SiO2 hybrid waveguide, which exhibits four zero-dispersion wavelengths and a dispersion variation of sub-20 ps/nm/km. Furthermore, taking advantage of the high order phase-matching, an ultra-broad 3-dB continuous wavelength conversion bandwidth covering 1454–4521 nm is achieved, which to the best of our knowledge is the widest frequency conversion bandwidth in the chip-scale devices. In addition, a fabrication scheme is proposed for the precise manipulation of dispersion. It holds great potential for practical applications in photonic integrated gas sensing, biomedical diagnostics.

The negative impact of COVID-19 pandemic has seen SME's struggling around the world. With many quickly adopting digital technologies, such as AI, in their manufacturing or services operations to achieve sustainable development. This study aims to develop a framework that informs AI-enabled sustainable development for SMEs by integrating the relevant research in the field. In this framework, we identify the opportunities that the deployment of AI technology can do to alleviate the plights of SMEs in the post-pandemic era, including the impacts on work, organizations, and performance. We further explore the challenges that SMEs face in AI transformation and recommend strategies to take on those challenges. Finally we propose an agenda for future research based on technological challenges and environmental threats.

ABSTRACT: The Duck Tembusu virus (DTMUV) is a novel flavivirus that occurs mainly in poultry. DTMUV infection can cause common neurological symptoms in ducklings, but the pathogenesis of DTMUV has not been elucidated yet. In this study, a DTMUV-infected duckling model was constructed to investigate the apoptosis in the duckling brains. After DTMUV infection, apoptotic cells were observed by transmission electron microscopy. It was found that the abundances of apoptosis-related genes and proteins were not obviously changed in the early stage of infection but significantly changed in the middle and late stages of the disease. Terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling assay staining results were also consistent with the above phenomena. Interestingly, although apoptosis occurred in the duckling brains infected by DTMUV, some antiapoptotic genes in the brain increased in varying degrees. In conclusion, DTMUV infection could induce apoptosis in ducklings’ brains, and the occurrence of apoptosis was accompanied by the virus infection process with certain regularity. This study provides a scientific basis for elucidating the apoptotic mechanism of brain lesions induced by DTMUV infection.

Abstract Background Precise prediction of cancer types is vital for cancer diagnosis and therapy. Through a predictive model, important cancer marker genes can be inferred. Several studies have attempted to build machine learning models for this task however none has taken into consideration the effects of tissue of origin that can potentially bias the identification of cancer markers. Results In this paper, we introduced several Convolutional Neural Network (CNN) models that take unstructured gene expression inputs to classify tumor and non-tumor samples into their designated cancer types or as normal. Based on different designs of gene embeddings and convolution schemes, we implemented three CNN models: 1D-CNN, 2D-Vanilla-CNN, and 2D-Hybrid-CNN. The models were trained and tested on gene expression profiles from combined 10,340 samples of 33 cancer types and 713 matched normal tissues of The Cancer Genome Atlas (TCGA). Our models achieved excellent prediction accuracies (93.9–95.0%) among 34 classes (33 cancers and normal). Furthermore, we interpreted one of the models, 1D-CNN model, with a guided saliency technique and identified a total of 2090 cancer markers (108 per class on average). The concordance of differential expression of these markers between the cancer type they represent and others is confirmed. In breast cancer, for instance, our model identified well-known markers, such as GATA3 and ESR1. Finally, we extended the 1D-CNN model for the prediction of breast cancer subtypes and achieved an average accuracy of 88.42% among 5 subtypes. The codes can be found at https://github.com/chenlabgccri/CancerTypePrediction. Conclusions Here we present novel CNN designs for accurate and simultaneous cancer/normal and cancer types prediction based on gene expression profiles, and unique model interpretation scheme to elucidate biologically relevance of cancer marker genes after eliminating the effects of tissue-of-origin. The proposed model has light hyperparameters to be trained and thus can be easily adapted to facilitate cancer diagnosis in the future.

In this paper, we define $k$-uniform tensors for $k\geq 2$, which are more closely related to the $k$-uniform hypergraphs than the general tensors, and introduce the parameter $r^{(q)}_{i}(\mathbb{A})$ for a tensor $\mathbb{A}$, which is the generalization of the $i$-th slice sum $r_ {i}(\mathbb{A})$ (also the $i$-th average 2-slice sum $m_{i}(\mathbb{A})$). By using $r^{(q)}_{i}(\mathbb{A})$ for $q\geq1$, we obtain a general result on the sharp upper bound for the spectral radius of a nonnegative $k$-uniform tensor. When $k=2, q=1, 2, 3$, this result deduces the main results for nonnegative matrices in [1,8,27]; when $k\geq 3, q=1$, this result deduces the main results in [5,20]. We also find that the upper bounds obtained from different $q$ can not be compared. Furthermore, we can obtain some known or new upper bounds by applying the general result to $k$-uniform hypergraphs and $k$-uniform directed hypergraphs, respectively.

Post-transcriptional RNA modification occurs on all types of RNA and plays a vital role in regulating every aspect of RNA function. Thanks to the development of high-throughput sequencing technologies, transcriptome-wide profiling of RNA modifications has been made possible. With the accumulation of a large number of high-throughput datasets, bioinformatics approaches have become increasing critical for unraveling the epitranscriptome. We review here the recent progress in bioinformatics approaches for deciphering the epitranscriptomes, including epitranscriptome data analysis techniques, RNA modification databases, disease-association inference, general functional annotation, and studies on RNA modification site prediction. We also discuss the limitations of existing approaches and offer some future perspectives.

ABSTRACT Infection by Kaposi’s sarcoma-associated herpesvirus (KSHV) is causally associated with numerous cancers. The mechanism of KSHV-induced oncogenesis remains unclear. By performing a CRISPR-Cas9 screening in a model of KSHV-induced cellular transformation of primary cells, we identified epigenetic regulators that were essential for KSHV-induced cellular transformation. Examination of TCGA data sets of the top 9 genes, including glutamate-rich WD repeat containing 1 (GRWD1), a WD40 family protein upregulated by KSHV, that had positive effects on cell proliferation and survival of KSHV-transformed cells (KMM) but not the matched primary cells (MM), uncovered the predictive values of their expressions for patient survival in numerous types of cancer. We revealed global epigenetic remodeling including H3K4me3 epigenetic active mark in KMM cells compared to MM cells. Knockdown of GRWD1 inhibited cell proliferation, cellular transformation, and tumor formation and caused downregulation of global H3K4me3 mark in KMM cells. GRWD1 interacted with WD repeat domain 5 (WDR5), the core protein of H3K4 methyltransferase complex, and several H3K4me3 methyltransferases, including myeloid leukemia 2 (MLL2). Knockdown of WDR5 and MLL2 phenocopied GRWD1 knockdown, caused global reduction of H3K4me3 mark, and altered the expression of similar sets of genes. Transcriptome sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) analyses further identified common and distinct cellular genes and pathways that were regulated by GRWD1, WDR5, and MLL2. These results indicate that KSHV hijacks the GRWD1-WDR5-MLL2 epigenetic complex to regulate H3K4me3 methylation of specific genes, which is essential for KSHV-induced cellular transformation. Our work has identified an epigenetic complex as a novel therapeutic target for KSHV-induced cancers. IMPORTANCE By performing a genome-wide CRISPR-Cas9 screening, we have identified cellular epigenetic regulators that are essential for KSHV-induced cellular transformation. Among them, GRWD1 regulates epigenetic active mark H3K4me3 by interacting with WDR5 and MLL2 and recruiting them to chromatin loci of specific genes in KSHV-transformed cells. Hence, KSHV hijacks the GRWD1-WDR5-MLL2 complex to remodel cellular epigenome and induce cellular transformation. Since the dysregulation of GRWD1 is associated with poor prognosis in several types of cancer, GRWD1 might also be a critical driver in other viral or nonviral cancers.

Summary: The bioaccumulation of polychlorinated dibenzo-p-dioxins/furans (PCDD/Fs), known as dioxins, in fatty meat is one of primary pathways of entry into the human body, but levels of human exposure to dioxins in fatty meat subject to global trade are unknown. We show high dioxin estimated dietary intake (EDI) via pork consumption in Europe, the United States, and China, owing to stronger dioxin environmental contamination and high pork consumption in these countries. The dioxin risk transfer embodied in pork trade is mostly significant in high-latitude countries and regions of Canada, Russia, and Greenland because these regions with low dioxin environmental levels import large amounts of pork meat from more severely dioxin-contaminated Europe and the United States. We demonstrate that global pig feed trading decreases the exposure of pork consumers to dioxins via the import of feed from countries with low dioxin environmental contamination by pig breeding countries.

BackgroundBillions of doses of medicines are donated for mass drug administrations in support of the World Health Organization's "Roadmap to Implementation," which aims to control, eliminate, and eradicate Neglected Tropical Diseases (NTDs). The supply chain to deliver these medicines is complex, with fragmented data systems and limited visibility on performance. This study empirically evaluates the impact of an online supply chain performance measurement system, "NTDeliver," providing understanding of the value of information sharing towards the success of global health programs.MethodsRetrospective secondary data were extracted from NTDeliver, which included 1,484 shipments for four critical medicines ordered by over 100 countries between February 28, 2006 and December 31, 2018. We applied statistical regression models to analyze the impact on key performance metrics, comparing data before and after the system was implemented.FindingsThe results suggest information sharing has a positive association with improvement for two key performance indicators: purchase order timeliness (β = 0.941, p = 0.003) and-most importantly-delivery timeliness (β = 0.828, p = 0.027). There is a positive association with improvement for three variables when the data are publicly shared: shipment timeliness (β = 2.57, p = 0.001), arrival timeliness (β = 2.88, p = 0.003), and delivery timeliness (β = 2.82, p = 0.011).ConclusionsOur findings suggest that information sharing between the NTD program partners via the NTDeliver system has a positive association with supply chain performance improvements, especially when data are shared publicly. Given the large volume of medicine and the significant number of people requiring these medicines, information sharing has the potential to provide improvements to global health programs affecting the health of tens to hundreds of millions of people.

Alzheimer’s disease (AD) seriously endangers the health and life of elderly individuals worldwide. However, despite all scientific efforts, at the moment there are no effective clinical treatment options for AD. In this work, the effect of the class I histone deacetylase inhibitor (HDACI) BG45 on synapse-related proteins was investigated in primary neurons from APP/PS1 transgenic mice. The results showed that BG45 can upregulate the expression of synaptotagmin-1 (SYT-1) and neurofilament light chain (NF-L) in primary neurons. In vivo, the APPswe/PS1dE9 (APP/PS1) transgenic mice were treated with BG45 (30 mg/kg) daily for 12 days. Behavioral testing of BG45-treated APP/PS1 mice showed improvements in learning and memory. BG45 can alleviate damage to the dendritic spine and reduce the deposition of Aβ. Similar to the in vitro results, synapse-related proteins in the prefrontal cortex were increased after BG45 treatment. Proteomic analysis results highlighted the differences in the biological processes of energy metabolism and calmodulin regulation in APP/PS1 mice with or without BG45 treatment. Further verification demonstrated that the effect of BG45 on synapses and learning and memory may involve the CaMKII/ITPKA/Ca2+ pathway. These results suggest that class I HDACI BG45 might be a promising drug for the early clinical treatment of AD.

Water and energy are critical resources for human well-being. The quantification and explanation of the water–energy nexus is important to enrich our understanding of sustainable utilization of water and energy resources in a unified framework. Herein, from the perspective of production efficiency, this study investigates the water–energy nexus in China’s industrial sector by developing a network data envelopment analysis (DEA) approach. Under two practical principles, we evaluate the water and energy usage efficiencies as well as give projection points in terms of industrial production and wastewater treatment processes for 30 provinces or urban areas in China during 2011–2015. The results show that the overall efficiency values of most provinces in the industrial water–energy nexus system are high although the efficiency value of each province fluctuates up and down in the sample period. When comparing two subsystems, for most provinces, the sub-efficiencies of the industrial production subsystem are relatively higher than those of the wastewater treatment subsystem under the economic priority principle or environment priority principle. Considering the projection points, the potential improvement space of the environment priority strategy is higher than that of the economic priority strategy. Based on the empirical results, relevant policy suggestions are provided to improve the provincial or urban industrial water–energy nexus system in reality.

BackgroundEndometriosis is a common nonmalignant gynecological disorder that affects 10–15% women of reproductive age and causes several symptoms that result in decreased quality of life and a huge social burden. In recent decades, extracellular vesicles (EVs) have gained attention as a potential therapeutic tool; however, the therapeutic effects of EVs against endometriosis have not been reported. Accordingly, in this study, we investigated the feasibility of nanovesicles (NVs) derived from M1 macrophages (M1NVs) in treating endometriosis.MethodsM1NVs were prepared by serial extrusion. Co-culture assays were performed to investigate changes in tube formation and migration/invasion of eutopic endometrial stroma cells (ESCs) obtained from patients with endometriosis (EM-ESCs). A mouse model of endometriosis was established, and mice were treated with phosphate-buffered saline, M0NVs, or M1NVs to evaluate the efficacy and safety of M1NV for treating endometriosis.ResultsM1NVs directly or indirectly inhibited the migration and invasion of EM-ESCs and reduced tube formation. In the mouse model, M1NVs suppressed the development of endometriosis through reprogramming of M2 macrophages, without causing damage to the organs.ConclusionsM1NVs inhibit the development of endometriosis directly, or through repolarizing macrophages from M2 to M1 phenotype. Hence, administration of M1NVs may represent a novel method for the treatment of endometriosis.

Abstract Biomass burning contributes considerably to black carbon (BC) emissions in South Asia, but such emissions have not been linked with the Green Revolution (GR) which has enabled substantial crop production growth in South Asian countries, India in particular. Here, we use an Earth system model to quantify climate change through the direct radiative forcing (DRF) by agriculture‐emitted BC associated with the GR in India. We show that the BC DRF in India has increased significantly since the GR, especially during the post‐GR period. The estimated BC DRF in India rose from +0.197 W/m2 in 1961 to +0.805 W/m2 in 2011; this represents a fourfold increase in DRF since the onset of the GR. The contribution of BC DRF by India's intensive agriculture to the global BC forcing also increased from 2.6% to 4.4% during the GR. Our results reveal that increasing BC emissions associated with the GR raises the importance of emission mitigation from agriculture source.

Abstract Background Steroidogenesis is an indispensable process that is indirectly associated with spermatogenesis in the Leydig cell (LC) to utilize the lipid droplets (LDs) that are critical to maintaining normal testosterone synthesis. The regulation of LD mobilization, known as lipophagy, in the LC is still largely unknown. Method In the present study, the LC of the Chinese soft-shelled turtle was investigated to identify the steroidogenic activity and lipophagy during the annual reproductive cycle by light microscopy, immunohistochemistry (IHC), immunofluorescence (IF), and transmission electron microscopy (TEM). Results The LC showed a dynamic steroidogenic function with strong activity of 3β-HSD, vimentin and tubular ER during hibernation by IHC and TEM. The tubulo-vesicular ER had a weak immunopositive reaction for 3β-HSD in the LC during reproductive phase, suggesting persistent steroidogenic activity. ORO staining and TEM demonstrated that a larger number of LDs had accumulated in the LC during hibernation than in the reproductive phase. These LDs existed in close association with mitochondria and lysosomes by being dynamically surrounded by intermediate filaments to facilitate LD utilization. Lysosomes were found directly attached to large LDs, forming an autophagic tube and engulfing LDs, suggesting that micro-lipophagy occurs during hibernation. Furthermore, the IHC of ATG7 (Autophagy Related Gene 7) and the IF of the LC3 (Microtubule-associated protein light chain 3), p62 (Sequestosome-1 (SQSTM1) and LAMP1(Lysosomal-associated membrane protein 1) results demonstrated strong expression, and further confirmation by TEM showed the existence of an autophagosome and an autolysosome and their fusion during the hibernation season. Conclusion In conclusion, the present study provides clear evidence of LD consumption in the LC by lipophagy, lysosome and mitochondria during the hibernation period, which is a key aspect of steroidogenesis in the Chinese soft-shelled turtle.

Abstract Background The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent study screened for the response of a thousand human cancer cell lines to a wide collection of anti-cancer drugs and illuminated the link between cellular genotypes and vulnerability. However, due to essential differences between cell lines and tumors, to date the translation into predicting drug response in tumors remains challenging. Recently, advances in deep learning have revolutionized bioinformatics and introduced new techniques to the integration of genomic data. Its application on pharmacogenomics may fill the gap between genomics and drug response and improve the prediction of drug response in tumors. Results We proposed a deep learning model to predict drug response (DeepDR) based on mutation and expression profiles of a cancer cell or a tumor. The model contains three deep neural networks (DNNs), i) a mutation encoder pre-trained using a large pan-cancer dataset (The Cancer Genome Atlas; TCGA) to abstract core representations of high-dimension mutation data, ii) a pre-trained expression encoder, and iii) a drug response predictor network integrating the first two subnetworks. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods (linear regression and support vector machine) and four analog DNN models of DeepDR, including DNNs built without TCGA pre-training, partly replaced by principal components, and built on individual types of input data. We then applied the model to predict drug response of 9059 tumors of 33 cancer types. Using per-cancer and pan-cancer settings, the model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets, such as vinorelbine for TTN-mutated tumors. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies. Conclusions Here we present, as far as we know, the first DNN model to translate pharmacogenomics features identified from in vitro drug screening to predict the response of tumors. The results covered both well-studied and novel mechanisms of drug resistance and drug targets. Our model and findings improve the prediction of drug response and the identification of novel therapeutic options.

Varying degrees of central nervous system neuropathy induced by diabetes mellitus (DM) contribute to a cognitive disorder known as diabetic encephalopathy (DE), which is also one of the independent risk factors for Alzheimer’s disease (AD). Endoplasmic reticulum stress (ERS) plays a critical role in the occurrence and development of DE and AD. However, its molecular mechanism remains largely unknown. This study aims to investigate whether thioredoxin-1 (Trx-1) could alleviate DE and AD through ERS, oxidative stress (OS) and apoptosis signaling pathways. Mice were randomly divided into a wild-type group (WT-NC), a streptozotocin (STZ)-treated DM group (WT-DM), a Trx-1-TG group (TG-NC) and a Trx-1-TG DM group (TG-DM). Diabetic animals showed an increase in the time spent in the target quadrant and the number of platform crossings as well as AD-like behavior in the water maze experiment. The immunocontent of the AD-related protein Tau and the levels of cell apoptosis, β-amyloid (Aβ) plaque formation and neuronal degeneration in the hippocampus of the diabetic group were increased. Some key factors associated with ERS, such as protein disulfide isomerase (PDI), glucose-regulated protein 78 (GRP78), inositol-requiring enzyme 1α (IRE1α), tumor necrosis factor receptor-associated factor 2 (TRAF2), apoptosis signal-regulating kinase-1 (ASK1), c-Jun N-terminal kinase (JNK), protein kinase RNA (PKR)-like ER kinase (PERK), and C/EBP homologous protein (CHOP), were upregulated, and other factors related to anti-oxidant stress, such as nuclear factor erythroid 2-related factor (Nrf2), were downregulated in the DM group. Moreover, DM caused an increase in the immunocontents of caspase-3 and caspase-12. However, these changes were reversed in the Trx-1-tg DM group. Therefore, we conclude that Trx-1 might be a key factor in alleviating DE and AD by regulating ERS and oxidative stress response, thus preventing apoptosis.

The Chinese soft-shelled turtle (Pelodiscus sinensis) is among the most primitive amphibians and reptiles in nature. On account of its environmental suitability and unique hibernation habit, the peculiar physiological phenomenon in P. sinensis attracted the attention of researchers in the field of marine science. The present study aimed to investigate the molecular mechanisms underlying the periodic variation of lipid droplet (LD) in the liver of P. sinensis. Histological results indicated that accumulated LD in the liver of P. sinensis during non-hibernation was gradually consumed during hibernation. RNA-Seq results revealed that genes responsible for carbohydrate catabolism were down-regulated during hibernation, while genes involved in lipid oxidation were up-regulated. These results suggest that energy metabolism in the liver of P. sinensis changes during hibernation, i.e., the energy generation mode shifted from carbohydrate catabolism to lipid oxidation. Further analysis of RNA-Seq results indicated that both lipolysis and autophagy could promote the degradation of hepatic LD during hibernation. To further determine the relationship between lipolysis and autophagy in the process of LD breakdown, we applied the inhibitors of lipolysis and autophagy (diethylumbelliferyl phosphate and 3-Methyladenine) in cultured primary hepatocytes of P. sinensis. The results indicated that lipolysis is the main way for LD degradation in the hepatocyte of P. sinensis. These data provide clear evidence about the seasonal changes in hepatocytes, corresponding with the different energy generation mode in the liver of P. sinensis.

On-chip mid-infrared (MIR) supercontinuum generation (SCG) covering the molecular functional spectral region (3–12 μm) offers the advantages of robustness, simplicity, and compactness. Yet, the spectral range still cannot be expanded beyond 10 μm. In this study, on-chip ultrabroadband MIR SCG in a high numerical aperture chalcogenide (ChG) waveguide is numerically investigated. The ChG waveguide with a Ge-As-Se-Te core and Ge-Se upper and lower cladding is designed to optimize the nonlinear coefficients and dispersion profile. Assisted by dispersive wave generation in both short- and long-wavelength range, broadband SCG ranging from 2 to 13 µm is achieved. Besides, a fabrication scheme is proposed to realize precise manipulation of dispersion design. Such results demonstrate that such sources are suitable for compact, chip-integrated molecular spectroscopy applications.