AgBiS\u3csub\u3e2\u3c/sub\u3e as a low-cost and eco-friendly all-inorganic photovoltaic material: nanoscale morphology– property relationship†

Solar cells made of low-cost solution-processed all-inorganic materials are a promising alternative to conventional solar cells made of high-temperature processed inorganic materials, especially because many high-temperature processed inorganic materials contain toxic element(s) such as lead or cadmium (e.g., CsPbI3 perovskite, PbS, CdTe and CdS(Se)). AgBiS2 nanocrystals, consisting of earth-abundant elements but without lead and cadmium, have already emerged as a promising candidate in highperformance solar cells. However, the nanoscale morphology–optoelectronic property relationship for AgBiS2 nanocrystals is still largely unknown. Herein, we investigate the electronic properties of various AgBiS2 nanocrystals by using first-principles computation. We show that the optoelectronic properties of bulk AgBiS2 are highly dependent on the M–S–M–S– (M: Ag or Bi) orderings. Moreover, because Ag–S– Ag–S– and Bi–S–Bi–S– in AgBiS2 bulk crystals contribute respectively to the valence band maximum and conduction band minimum, these unique chemical orderings actually benefit easy separation of mobile electrons and holes for photovoltaic application. More importantly, we find that AgBiS2 nanocrystals (NCs) can exhibit markedly different optoelectronic properties, depending on their stoichiometry. NCs with minor off-stoichiometry give rise to mid-gap states, whereas NCs with substantial off-stoichiometry give rise to many deep defect states in the band gap, and some NCs even show metallic-like electronic behavior. We also find that the deep-defect states can be removed through ligand passivation with optimal coverage. The new insights into the nanoscale morphology– optoelectronic property relationship offer a rational design strategy to engineer the band alignment of AgBiS2 NC layers while addressing some known challenging issues inherent in all-inorganic photovoltaic materials

Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts

Graph classification, aiming at learning the graph-level representations for effective class assignments, has received outstanding achievements, which heavily relies on high-quality datasets that have balanced class distribution. In fact, most real-world graph data naturally presents a long-tailed form, where the head classes occupy much more samples than the tail classes, it thus is essential to study the graph-level classification over long-tailed data while still remaining largely unexplored. However, most existing long-tailed learning methods in visions fail to jointly optimize the representation learning and classifier training, as well as neglect the mining of the hard-to-classify classes. Directly applying existing methods to graphs may lead to sub-optimal performance, since the model trained on graphs would be more sensitive to the long-tailed distribution due to the complex topological characteristics. Hence, in this paper, we propose a novel long-tailed graph-level classification framework via Collaborative Multi-expert Learning (CoMe) to tackle the problem. To equilibrate the contributions of head and tail classes, we first develop balanced contrastive learning from the view of representation learning, and then design an individual-expert classifier training based on hard class mining. In addition, we execute gated fusion and disentangled knowledge distillation among the multiple experts to promote the collaboration in a multi-expert framework. Comprehensive experiments are performed on seven widely-used benchmark datasets to demonstrate the superiority of our method CoMe over state-of-the-art baselines.Comment: Accepted by IEEE Transactions on Big Data (TBD 2024

Redundancy-Free Self-Supervised Relational Learning for Graph Clustering

Graph clustering, which learns the node representations for effective cluster assignments, is a fundamental yet challenging task in data analysis and has received considerable attention accompanied by graph neural networks in recent years. However, most existing methods overlook the inherent relational information among the non-independent and non-identically distributed nodes in a graph. Due to the lack of exploration of relational attributes, the semantic information of the graph-structured data fails to be fully exploited which leads to poor clustering performance. In this paper, we propose a novel self-supervised deep graph clustering method named Relational Redundancy-Free Graph Clustering (R$^2$FGC) to tackle the problem. It extracts the attribute- and structure-level relational information from both global and local views based on an autoencoder and a graph autoencoder. To obtain effective representations of the semantic information, we preserve the consistent relation among augmented nodes, whereas the redundant relation is further reduced for learning discriminative embeddings. In addition, a simple yet valid strategy is utilized to alleviate the over-smoothing issue. Extensive experiments are performed on widely used benchmark datasets to validate the superiority of our R$^2$FGC over state-of-the-art baselines. Our codes are available at https://github.com/yisiyu95/R2FGC.Comment: Accepted by IEEE Transactions on Neural Networks and Learning Systems (TNNLS 2024

CXCR4 Antagonist AMD3100 Modulates Claudin Expression and Intestinal Barrier Function in Experimental Colitis

Ulcerative colitis is a gastrointestinal disorder characterized by local inflammation and impaired epithelial barrier. Previous studies demonstrated that CXC chemokine receptor 4 (CXCR4) antagonists could reduce colonic inflammation and mucosal damage in dextran sulfate sodium (DSS)-induced colitis. Whether CXCR4 antagonist has action on intestinal barrier and the possible mechanism, is largely undefined. In the present study, the experimental colitis was induced by administration of 5% DSS for 7 days, and CXCR4 antagonist AMD3100 was administered intraperitoneally once daily during the study period. For in vitro study, HT-29/B6 colonic cells were treated with cytokines or AMD3100 for 24 h until assay. DSS-induced colitis was characterized by morphologic changes in mice. In AMD3100-treated mice, epithelial destruction, inflammatory infiltration, and submucosal edema were markedly reduced, and the disease activity index was also significantly decreased. Increased intestinal permeability in DSS-induced colitis was also significantly reduced by AMD3100. The expressions of colonic claudin-1, claudin-3, claudin-5, claudin-7 and claudin-8 were markedly decreased after DSS administration, whereas colonic claudin-2 expression was significantly decreased. Treatment with AMD3100 prevented all these changes. However, AMD3100 had no influence on claudin-3, claudin-5, claudin-7 and claudin-8 expression in HT-29/B6 cells. Cytokines as TNF-α, IL-6, and IFN-γ increased apoptosis and monolayer permeability, inhibited the wound-healing and the claudin-3, claudin-7 and claudin-8 expression in HT-29/B6 cells. We suggest that AMD3100 acted on colonic claudin expression and intestinal barrier function, at least partly, in a cytokine-dependent pathway

Effects of 24-week treatment with acarbose on glucagon-like peptide 1 in newly diagnosed type 2 diabetic patients: a preliminary report

BACKGROUND: Treatment with the alpha-glucosidase inhibitor (AGI) acarbose is associated with a significant reduction the risk of cardiovascular events. However, the underlying mechanisms of this effect are unclear. AGIs were recently suggested to participate in stimulating glucagon-like peptide 1 (GLP-1) secretion. We therefore examined the effects of a 24-week treatment of acarbose on endogenous GLP-1, nitric oxide (NO) levels, nitric oxide synthase (NOS) activity, and carotid intima-media thickness (CIMT) in newly diagnosed patients with type 2 diabetes (T2D). METHODS: Blood was drawn from 24 subjects (14 male, 10 female, age: 50.7 ± 7.36 years, BMI: 26.64 ± 3.38 kg/m(2), GHbA1c: 7.00 ± 0.74%) with drug-naïve T2D at 0 and 120 min following a standard mixed meal for the measurements of active GLP-1, NO and NOS. The CIMT was measured prior to and following 24 weeks of acarbose monotherapy (mean dose: 268 mg daily). RESULTS: Following 24 weeks of acarbose treatment, both fasting and postprandial plasma GLP-1 levels were increased. In patients with increased postprandial GLP-1 levels, serum NO levels and NOS activities were also significantly increased and were positively related to GLP-1 levels. Although the CIMT was not significantly altered following treatment with acarbose, a decreased CIMT was negatively correlated with increased GLP-1 levels. CONCLUSIONS: Twenty-four weeks of acarbose monotherapy in newly diagnosed patients with T2D is associated with significantly increased levels of both fasting and postprandial GLP-1 as well as significantly increased NO levels and NOS activity for those patients in whom postprandial GLP-1 levels were increased. Therefore, the benefits of acarbose on cardiovascular risk may be related to its stimulation of GLP-1 secretion

Heparanase Promotes Tumor Growth and Liver Metastasis of Colorectal Cancer Cells by Activating the p38/MMP1 Axis

Heparanase (HPSE), the only known mammalian endoglycosidase responsible for heparan sulfate cleavage, is a multi-faceted protein affecting multiple malignant behaviors in cancer cells. In this study, we examined the expression of HPSE in different colorectal cancer (CRC) cell lines. Gene manipulation was applied to reveal the effect of HPSE on proliferation, invasion, and metastasis of CRC. Knockdown of HPSE resulted in decreased cell proliferation in vitro, whereas overexpression of HPSE resulted in the opposite phenomenon. Consistently, in vivo data showed that knockdown of HPSE suppressed tumor growth of CRC. Furthermore, knockdown of HPSE inhibited invasion and liver metastasis in vitro and in vivo. RNA-sequencing analysis was performed upon knockdown of HPSE, and several pathways were identified that are closely associated with invasion and metastasis. In addition, HPSE is positively correlated with MMP1 expression in CRC, and HPSE regulates MMP1 expression via p38 MAPK signaling pathway. In conclusion, our data demonstrate that HPSE knockdown attenuated tumor growth and liver metastasis in CRC, implying that HPSE might serve as a potential therapeutic target in the treatment of CRC

Highly stable and efficient all-inorganic lead-free perovskite solar cells with native-oxide passivation

There has been an urgent need to eliminate toxic lead from the prevailing halide perovskite solar cells (PSCs), but the current lead-free PSCs are still plagued with the critical issues of low efficiency and poor stability. This is primarily due to their inadequate photovoltaic properties and chemical stability. Herein we demonstrate the use of the lead-free, allinorganic cesium tin-germanium triiodide (CsSn0.5Ge0.5I3) solid-solution perovskite as the light absorber in PSCs, delivering promising efficiency of up to 7.11%. More importantly, these PSCs show very high stability, with less than 10% decay in efficiency after 500 h of continuous operation in N2 atmosphere under one-sun illumination. The key to this striking performance of these PSCs is the formation of a full-coverage, stable native-oxide layer, which fully encapsulates and passivates the perovskite surfaces. The native-oxide passivation approach reported here represents an alternate avenue for boosting the efficiency and stability of lead-free PSCs

Low-mass dark matter search results from full exposure of PandaX-I experiment

We report the results of a weakly-interacting massive particle (WIMP) dark matter search using the full 80.1\;live-day exposure of the first stage of the PandaX experiment (PandaX-I) located in the China Jin-Ping Underground Laboratory. The PandaX-I detector has been optimized for detecting low-mass WIMPs, achieving a photon detection efficiency of 9.6\%. With a fiducial liquid xenon target mass of 54.0\,kg, no significant excess event were found above the expected background. A profile likelihood analysis confirms our earlier finding that the PandaX-I data disfavor all positive low-mass WIMP signals reported in the literature under standard assumptions. A stringent bound on the low mass WIMP is set at WIMP mass below 10\,GeV/c$^2$, demonstrating that liquid xenon detectors can be competitive for low-mass WIMP searches.Comment: v3 as accepted by PRD. Minor update in the text in response to referee comments. Separating Fig. 11(a) and (b) into Fig. 11 and Fig. 12. Legend tweak in Fig. 9(b) and 9(c) as suggested by referee, as well as a missing legend for CRESST-II legend in Fig. 12 (now Fig. 13). Same version as submitted to PR