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Molybdenene, a full‐fledged metallene has been sensational among all Xenes. Apart from metallicity with excellent carrier concentration, it is anticorrosive, rendering it a superioor electrode material for electrochemical energy storage. As evident from atomic force microscopy, microwave‐synthesized molybdenene constitutes of monolayers (each layer ≈0.4 nm) and lateral dimensions extended to millimeters. Cris‐cross intertwinned crystals with close to square (0.20, 0.21 nm) lattice with fourfold symmetry were observed in electron imaging. Characteristic metallic signal (Mo–Mo vibration) in Raman peak at ≈405 cm−1 proves chemical phase purity. The electrochemical performance of synthesized molybdenene sheets is evaluated for supercapacitor applications in a 2 m KOH electrolyte. The as‐synthesized molybdenene demonstrates a specific capacitance of 327.78 F g−1 at a scan rate of 10 mV s−1 and 118.6 F g−1 at a current rate of 0.50 A g−1 in a three‐electrode configuration, with a capacitance retention of 81.0% over 5000 cycles. Furthermore, an asymmetric supercapacitor employing molybdenene as the positive electrode and activated carbon as the negative electrode exhibits an energy density of 15.94 Wh kg−1 at a power density of 399.72 W kg−1. These findings highlight molybdenene as a promising candidate for high‐performance electrochemical energy storage devices.

Abstract Background Adiposity profoundly impacts reproductive health in both humans and animals. However, the precise subpopulations contributing to infertility under obese conditions remain elusive. Results In this study, we established an obese mouse model through an eighteen-week high-fat diet regimen in adult female mice. Employing single-cell RNA sequencing (scRNA-seq), we constructed a comprehensive single-cell atlas of ovarian tissues from these mice to scrutinize the impact of obesity on the ovarian microenvironment. ScRNA-seq revealed notable alterations in the microenvironment of ovarian tissues in obese mice. Granulosa cells, stromal cells, T cells, and macrophages exhibited functional imbalances compared to the control group. We observed heightened interaction strength in the SPP1-CD44 pairing within lgfbp7+ granulosa cell subtypes and Il1bhigh monocyte subtypes in the ovarian tissues of obese mice. Moreover, the interaction strength between Il1bhigh monocyte subtypes and Pdgfrb+ stromal cell subtypes in the form of TNF − TNFrsf1α interaction was also enhanced subsequently to obesity, potentially contributing to ovarian fibrosis pathogenesis. Conclusions We propose a model wherein granulosa cells secrete SPP1 to activate monocytes, subsequently triggering TNF-α secretion by monocytes, thereby activating stromal cells and ultimately leading to the development of ovarian fibrosis. Intervening in this process may represent a promising avenue for improving clinical outcomes in fertility treatments for obese women.

Affordable and environmentally friendly electrochemically active raw energy storage materials are in high demand to switch to mass-scale renewable energy. One particularly promising avenue is the feasibility of utilizing food waste-derived nanoporous carbon. This material holds significance due to its widespread availability, affordability, ease of processing, and, notably, its cost-free nature. Over the years, various strategies have been developed to convert different food wastes into nanoporous carbon materials with enhanced electrochemical properties. The electrochemical performance of these materials is influenced by both intrinsic factors, such as the composition of elements derived from the original food sources and recipes, and extrinsic factors, including the conditions during pyrolysis and activation. While current efforts are dedicated to optimizing process parameters to achieve superior performance in electrochemical energy storage devices, it is timely to take stock of the current state of research in this emerging field. This review provides a comprehensive overview of recent developments in the fabrication and surface characterisation of porous carbons from different food wastes. A special focus is given on the applications of these food waste derived porous carbons for energy storage applications including batteries and supercapacitors.

Insomnia and depression are psychiatric disorders linked to substantial health burdens. The gut microbiome and metabolomic pathways are increasingly recognized as key contributors to these conditions’ pathophysiology. Suanzaoren Decoction (SZRD), a traditional Chinese herbal formulation, has demonstrated significant therapeutic benefits for both insomnia and depression. This study aims to elucidate the mechanistic effects of SZRD on insomnia and depression by integrating gut microbiome and metabolomic analyses and to assess the differential impacts of SZRD dosages. Using ultra-high-performance liquid chromatography-mass spectrometry (UHPLC–MS), we identified 66 chemical constituents within SZRD. Behavioral assays indicated that low-dose SZRD (LSZRD) significantly ameliorated insomnia symptoms in rat models, whereas high-dose SZRD (HSZRD) markedly improved depressive behaviors. 16S rRNA sequencing revealed that SZRD modulated gut microbiome dysbiosis induced by insomnia and depression, characterized by an increased abundance of short-chain fatty acid (SCFA)-producing genera. Metabolomic profiling demonstrated reduced plasma amino acid metabolites and disrupted γ-aminobutyric acid (GABA) and L-glutamic acid metabolism in the hippocampus of affected rats. SZRD administration restored fecal SCFA levels and ameliorated metabolic imbalances in both plasma and hippocampal tissues. These findings underscore the pivotal role of gut microbiome modulation and metabolic regulation in the therapeutic effects of SZRD, providing a scientific basis for its use in treating insomnia and depression.

Epstein-Barr virus (EBV) uses latency programs to colonize the memory B-cell reservoir, and each program is associated with human malignancies. However, knowledge remains incomplete of epigenetic mechanisms that maintain the highly restricted latency I program, present in memory and Burkitt lymphoma cells, in which EBNA1 is the only EBV-encoded protein expressed. Given increasing appreciation that higher order chromatin architecture is an important determinant of viral and host gene expression, we investigated roles of Wings Apart-Like Protein Homolog (WAPL), a host factor that unloads cohesin to control DNA loop size and that was discovered as an EBNA2-associated protein. WAPL knockout (KO) in Burkitt cells de-repressed LMP1 and LMP2A expression, but not other EBV oncogenes, to yield a viral program reminiscent of EBV latency II, which is rarely observed in B-cells. WAPL KO also increased LMP1/2A levels in latency III lymphoblastoid cells. WAPL KO altered EBV genome architecture, triggering formation of DNA loops between the LMP promoter region and the EBV origins of lytic replication (oriLyt). Hi-C analysis further demonstrated that WAPL KO reprogrammed EBV genomic DNA looping. LMP1 and LMP2A de-repression correlated with decreased histone repressive marks at their promoters. We propose that EBV coopts WAPL to negatively regulate latent membrane protein expression to maintain Burkitt latency I.

In order to realize the effects of blanching (95 ℃/2 min), separation (6000×g/10 min) and sterilization (600 MPa, 25 ℃/2 min) on the processing quality of freshly-squeezed lettuce juice, hydroponic green rosa lettuce were chosen as the test material. Changes of microorganisms, color, physical and chemical parameters, enzyme activities and antioxidant capacity were evaluated. The results suggested that the total aerobic bacteria of freshly-squeezed lettuce juice was less than 2 lgCFU/mL and the mold and yeast were less than 1.3 lgCFU/mL after sterilization process, all of which were within the national standard limit (GB 7101-2015). Separation process significantly affected the color and chlorophyll content of lettuce juice. The L*, a*, b* and C* values of fresh clear lettuce juice were significantly increased (P

Intervertebral disc degeneration (IVDD) is a significant cause of chronic pain and disability, necessitating innovative therapeutic strategies. This study investigates the combined effect of a novel siCOL1A2-encapsulated hydrogel and acupuncture on IVDD in a rat model. We developed a hydrogel system, siCOL1A2-encapsulated G5-PBA hydrogel (siCOL1A2@G5-PBA@Gel), designed for sustained siRNA delivery to the degenerated discs and assessed its therapeutic efficacy alongside acupuncture treatment. Key inflammatory genes were identified through RNA-seq analysis, with COL1A2 highlighted as a crucial regulator of inflammatory responses in IVDD. Our in vivo experiments involved treating rats with hydrogel alone, acupuncture alone, and combining both. The treatments were evaluated through behavioral pain assessments, imaging techniques (X-ray and MRI), and histological analyses. Results indicated that the combination therapy significantly alleviated pain, reduced inflammation, and promoted disc regeneration more effectively than individual treatments. The hydrogel proved biocompatible and facilitated targeted gene silencing, while acupuncture enhanced therapeutic outcomes by improving local blood circulation and modulating inflammatory responses. These findings suggest that integrating siCOL1A2 hydrogel with acupuncture offers a promising approach to treating IVDD.

In the context of global climate change and the continuous development of urban areas, rainfall-inundation modeling is a common approach that provides critical support for the protection and early warning of urban waterlogging protection. The present study conducts a data-driven model for hourly urban rainfall-inundation depth prediction, which is based on a gated recurrent unit (GRU) neural network and uses the simulated annealing (SA) algorithm for the hyperparameter optimization of GRU, namely the SA-GRU model. To verify the performance of the proposed model, backpropagation, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) neural networks are set as benchmarks. Results show that the SA-GRU has high accuracy in the case of short-term inundation prediction, with the Nash–Sutcliffe efficiency from 0.999 to 0.596 for the 1-h-ahead to 8-h-ahead predictions. And further research reveals that the SA-GRU integrates the significant optimization of SA, with an average 20% reduction of the root mean square error within the first eight prediction periods, and the efficient training speed of GRU, with 23.7% faster than LSTM and 44.2% faster than BiLSTM. In conclusion, the SA-GRU excels in urban inundation prediction, demonstrating its value in flood management and decision-making. HIGHLIGHTS A GRU-based neural network was used for urban waterlogging modeling to make inundation depth predictions for 1–12 h.; A simulated annealing (SA) algorithm was used as the hyperparameter optimization method to boost the performance of GRU.; The SA-GRU model has better performance rather than SA-BP, SA-LSTM, and SA-BiLSTM within its prediction period threshold (8 h).;

Structural engineering is crucial for tuning the properties of artificial materials. As one of the most important two-dimensional (2D) materials, black phosphorus (BP) has been rediscovered in 2014 and attracted tremendous attentions all over the world. It shows great potentials in optoelectronic and integrated photonic applications due to the direct and tunable bandgap, high carrier mobility, and intrinsic anisotropy. However, some deficiencies are yet to be settled before its large-scale applications, such as the massive synthesis methods and structure–property relationship. To investigate the influence of structural engineering on the performance of 2D BP-based devices thoroughly, recent studies on deformation, atomic defects, superlattice, alloying, thickness and phase transition are maximality summarized in this review covering from the intrinsic properties to optoelectronic applications. Benefiting from the broad research in this field, challenges and opportunities of 2D BP for advanced integrated photonic applications are also provided, which supplies a useful reference to realize large-scale 2D BP-based all-optical communications in future.

BackgroundAdults living alone represent a growing population group in China. Understanding the prevalence of body mass index (BMI) categories and their associations with demographic and lifestyle factors among this group is essential for informing targeted interventions and public health policies.MethodsIn this population-based cross-sectional study, we used individual-level data from the 2011-2021 China General Social Survey. Main outcomes were prevalence of BMI categories adjusted for gender and age, using logistic regression and model-predicted marginal prevalence to estimate BMI categories prevalence.ResultsWe analyzed 9,077 single-living Chinese adult participants. The primary-adjusted prevalence of BMI categories varied across different genders and age groups. Underweight was more prevalent in females (12.73%; 95% CI: 12.31% - 13.14%) than in males (7.54%; 95% CI: 7.19% - 7.88%), while overweight and obesity were higher in males. Primary-adjusted underweight prevalence was highest among the 18-24 years age group (22.09%; 95% CI: 20.17% - 24.01%) and decreased with age. Primary-adjusted overweight prevalence increased with age, peaking in the 45-54 years age group (41.94%; 95% CI: 40.96% - 42.93%). Primary-adjusted obesity prevalence exhibited a fluctuating pattern across age groups, with the highest prevalence observed in the 45-54 years age group (9.81%; 95% CI: 9.19% - 10.44%).ConclusionOur findings reveal significant associations between BMI categories and demographic and lifestyle factors among adults living alone in China. These results can inform targeted interventions and public health policies aimed at promoting healthy weight management and addressing the unique health challenges faced by single-living individuals in China.

Abstract Alkylnitriles play important roles in many fields because of their unique electronic properties and structural characteristics. Incorporating cyanoalkyl with characteristic spectroscopy and reactivity properties into amino acids and peptides is of special interest for potential imaging and therapeutic purposes. Here, we report a copper-catalyzed asymmetric cyanoalkylation of C(sp3)-H. In the reactions, glycine derivatives can effectively couple with various cycloalkanone oxime esters with high enantioselectivities, and the reaction can be applied to the late-stage modification of peptides with good yields and excellent stereoselectivities, which is useful for modern peptide synthesis and drug discovery. The mechanistic studies show that the in situ formed copper complex by the coordination of glycine derivatives and chiral phosphine Cu catalyst can not only mediate the single electronic reduction of cycloalkanone oxime ester but also control the stereoselectivity of the cyanoalkylation reaction.

AbstractObjective To conduct a meta-analysis and systematic review on the association between anticholinergic medication uses and the risk of pneumonia in elderly adults.Materials and Methods Medical databases were searched included PubMed, Web of Science, EBSCO and Google Scholar (up to December 7, 2022). Studies evaluating association between anticholinergic medication uses and the risk of pneumonia in elderly adults were included. Studies without available data were excluded. We made meta-analysis by using adjusted odds ratio (aOR) with 95% confidence intervals (CIs) from random-effects model. The risk of bias was assessed using ROBINS-I tool and statistical heterogeneity using the I2 statistic. Registration: INPLASY202330070.Results A total of six studies with 107,012 participants were included. Meta-analysis results showed that anticholinergic medication uses was related with an increased risk of pneumonia (aOR = 1.59; 95%CI, 1.32–1.92) and stroke-associated pneumonia (aOR = 2.02; 95%CI, 1.76–2.33). Moreover, risk estimates of pneumonia for high-potency anticholinergics (aOR = 1.96; 95%CI, 1.22–3.14) were higher than those for low-potency anticholinergics (aOR = 1.58; 95%CI, 1.27–1.97). And increased risk of pneumonia was associated with the anticholinergic medication uses within 30 days (aOR = 2.13; 95%CI, 1.33–3.43), within 90 days (aOR = 2.03; 95%CI, 1.26–3.26) and chronic use (aOR = 1.65; 95%CI, 1.09–2.51).Conclusions The risk of pneumonia is increased in elderly adults with anticholinergic medication, especially with higher-potency anticholinergic drugs and in the initiation phase of anticholinergic medication. Clinicians should monitor their use in older patients carefully, especially when the pneumonia-related signs and symptoms are identified.

Light-matter interaction has been dominated by Born-Oppenheimer approximation for centuries. Here the authors proposed a light-matter interaction mechanism beyond this approximation, proposing stimulated phonon polariton-mediated light-matter interaction, showing further understanding and applications in the fields of optics/photonics and condensed-matter physics.

Abstract Immunotherapies have been explored in treating solid tumors, albeit with disparate clinical effects in distinct cancer types. Systematic interrogation of immune cells in the tumor microenvironment (TME) is vital to the prediction of immunotherapy response and the development of innovative immunotherapeutics. To comprehensively characterize the immune microenvironment in advanced biliary tract cancer (BTC), we utilized single-cell RNA sequencing in unselected viable cells from 16 matched samples, and identified nineteen cell subsets from a total of 45,851 cells, in which exhausted CD8+ T cells, macrophages, and dendritic cells (DCs) in BTC were shown to augment and communicate within the TME. Transcriptional profiles coupled with T cell receptor (TCR) sequences revealed that exhausted CD8+ T cells retained clonal expansion and high proliferation in the TME, and some of them highly expressed the endoplasmic reticulum stress (ER) response gene, XBP1, indicating the role of ER stress in remodeling TME. Functional assays demonstrated that XBP1 and common immune checkpoints (PD1, TIGIT) were significantly upregulated in CD8+ T cells cocultured within the TME of BTC cells (GBC-SD, HCCC-9810). When treating the coculture groups with the specific inhibitor of IRE1α-XBP1 (4μ8C), the downregulation of TIGIT was observed in the treatment group. Collectively, comprehensive transcriptome profiling provides deep insights into the immune atlas in advanced BTC, which might be instrumental in exploring innovative immunotherapy strategies.

As one of the paradigm examples to probe into pulsar magnetospheric dynamics, PSR B0943+10 (J0946+0951) manifests representatively, showing a mode switch, orthogonal polarization, and subpulse drifting, frequently studied below 600 MHz. Here, both integrated and single pulses are studied at a high frequency (1.25 GHz) with FAST. The mode switch is studied using a profile decomposition method. A phase space evolution for the pulsar’s mode switch shows a strange-attractor-like pattern. The radiative geometry is proposed by fitting polarization position angles with the rotating vector model. The pulsar pulse profile is then mapped to the sparking locations on the pulsar surface, and the differences between the main pulse’s and the precursor component’s radiative processes may explain the X-ray’s synchronization with radio mode switch. Detailed single pulse studies on B0943+10's orthogonally polarized radiation are presented, which may support certain models of radiative transfer of polarized emission. In particular, the difference in orthogonal polarization modes’ circular polarization might reflect the cyclotron absorption in pulsar magnetospheres. B0943+10's B and Q modes evolve differently with frequency and have different proportions of orthogonal modes, which indicates possible magnetospheric changes during mode switch. For Q mode pulse profile, the precursor and the main pulse components are orthogonally polarized, and are probably originated from different depths in the magnetosphere. The findings could impact significantly on the pulsar electrodynamics and the radiative mechanism related.

Aristolochic acids (AAs) have long been considered as a potent carcinogen due to its nephrotoxicity. Aristolochic acid I (AAI) reacts with DNA to form covalent aristolactam (AL)–DNA adducts, leading to subsequent A to T transversion mutation, commonly referred as AA mutational signature. Previous research inferred that AAs were widely implicated in liver cancer throughout Asia. In this study, we explored whether AAs exposure was the main cause of liver cancer in the context of HBV infection in mainland China. Totally 1256 liver cancer samples were randomly retrieved from 3 medical centers and a refined bioanalytical method was used to detect AAI–DNA adducts. 5.10% of these samples could be identified as AAI positive exposure. Whole genome sequencing suggested 8.41% of 107 liver cancer patients exhibited the dominant AA mutational signature, indicating a relatively low overall AAI exposure rate. In animal models, long-term administration of AAI barely increased liver tumorigenesis in adult mice, opposite from its tumor-inducing role when subjected to infant mice. Furthermore, AAI induced dose-dependent accumulation of AA–DNA adduct in target organs in adult mice, with the most detected in kidney instead of liver. Taken together, our data indicate that AA exposure was not the major threat of liver cancer in adulthood.

BackgroundOrally administered small-molecule drugs including tyrosine kinase 2 (TYK2) inhibitors and phosphodiesterase 4 (PDE4) inhibitors are new candidates for systemic therapy in plaque psoriasis. However, no previous articles evaluated the benefit and risk profile of TYK2 and PDE4 inhibitors in psoriasis.ObjectivesThe objective of this study was to compare the efficacy and safety of oral small-molecule drugs, including TYK2 and PDE4 inhibitors, in treating moderate-to-severe plaque psoriasis.MethodsPubMed, Embase, and Cochrane library were searched for eligible randomized clinical trials (RCTs). Response rates for a 75% reduction from baseline in Psoriasis Area and Severity Index (PASI-75) and Physician’s Global Assessment score of 0 or 1 (PGA 0/1) were used for efficacy assessment. Safety was evaluated with the incidence of adverse events (AEs). A Bayesian multiple treatment network meta-analysis (NMA) was performed.ResultsIn total, 13 RCTs (five for TYK2 inhibitors and eight for PDE4 inhibitors) involving 5274 patients were included. The study found that deucravacitinib at any dose (except for 3 mg QOD), ropsacitinib (200 and 400 mg QD), and apremilast (20 and 30 mg BID) had higher PASI and PGA response rates than placebo. In addition, deucravacitinib (3 mg BID, 6 mg QD, 6 mg BID, and 12 mg QD), and ropsacitinib (400 mg QD) showed superior efficacy than apremilast (30 mg BID). In terms of safety, deucravacitinib or ropsacitinib at any dose did not lead to a higher incidence of AEs than apremilast (30 mg BID). The ranking analysis of efficacy revealed that deucravacitinib 12 mg QD and deucravacitinib 3 mg BID had the highest chance of being the most effective oral treatment, followed by deucravacitinib 6 mg BID and ropsacitinib 400 mg QD.ConclusionsOral TYK2 inhibitors demonstrated satisfactory performance in treating psoriasis, surpassing apremilast at certain doses. More large-scale, long-term studies focusing on novel TYK2 inhibitors are needed.Systematic review registrationPROSPERO (ID: CRD42022384859), available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022384859, identifier CRD42022384859.

During drinking water treatment, the uncertain changes of raw water quality bring great difficulties to the control of flocculant dosage, especially because the feedback information based on the effluent turbidimeter of the sedimentation tank can only be obtained after a long time when the influent water quality changes due to the large lag characteristics of the flocculation process. Prediction of effluent turbidity of the sedimentation tank can effectively solve the aforementioned problem. Given that it is difficult for the ordinary random forest (RF) model to accurately predict the effluent turbidity of a sedimentation tank for complicated changes of raw water quality, an improved random forest (IRF) model composed of long-term and short-term parts is proposed, which can capture the periodicity and time-varying characteristics of influent water quality data. The experimental results show that the root mean square error and mean absolute percentage error of IRF model in Baiyangwan waterworks are improved 67.52% and 67.91% respectively, compared with those of the ordinary RF model. The proposed effluent turbidity predictions are also successfully developed in Xujiang waterworks and Xiangcheng waterworks of Suzhou, China. This research provides an effective method for real-time prediction of the effluent turbidity of sedimentation tank according to the influent water quality data. HIGHLIGHTS A turbidity prediction method according to the influent water quality can provide real-time feedback information for flocculant dosing control.; The IRF model composed of long-term and short-term parts captures the periodicity and time-varying characteristics of influent water quality.; The IRF model has been experimented successfully in three water treatment plants with different raw water sources.;

Abstract Solar‐driven photothermal water evaporation is considered an elegant and sustainable technology for freshwater production. The existing systems, however, often suffer from poor stability and biofouling issues, which severely hamper their prospects in practical applications. Conventionally, photothermal materials are deposited on the membrane supports via vacuum‐assisted filtration or dip‐coating methods. Nevertheless, the weak inherent material‐membrane interactions frequently lead to poor durability, and the photothermal material layer can be easily peeled off from the hosting substrates or partially dissolved when immersed in water. In the present article, the discovery of the incorporation of borophene into cellulose nanofibers (CNF), enabling excellent environmental stability with a high light‐to‐heat conversion efficiency of 91.5% and water evaporation rate of 1.45 kg m−2 h−1 under simulated sunlight is reported. It is also demonstrated that borophene papers can be employed as an excellent active photothermal material for eliminating almost 100% of both gram‐positive and gram‐negative bacteria within 20 min under three sun irradiations. The result opens a new direction for the design of borophene‐based papers with unique photothermal properties which can be used for the effective treatment of a wide range of wastewaters.

Background: : The tumor microenvironment (TME) plays a critical role in shaping tumor progression and determining the outcome of the therapeutic response. In this study, we aimed to generate a comprehensive cellular landscape of the colorectal cancer (CRC) TME. Methods: : We generated a comprehensive single-cell atlas by collecting CRC cases that have been uploaded to the online database and conducting an in-depth secondary analysis. We then carried out spatial transcriptomic sequencing and multiple immunohistochemical analyses to verify the results of the single-cell analysis. Moreover, we applied our findings to the TCGA database and used tissue microarray (TMA) on CRC tissue specimens to validate clinical prognosis. Findings: We re-analyzed the transcriptomes of 23785 cells, revealing a pattern of cell heterogeneity in the tumor region, leading-edge region, and non-tumor region. A subtype of COL11A1+INHBA+ tumor-resident cancer-associated fibroblasts (CAFs) was identified, and marker genes, transcription factors, and tissue-specific expression differences were noted and suggested to have potential roles in promoting cancer. We further confirmed that COL11A1+INHBA+ tumor-resident CAFs are mainly located in the hypoxic TME and we propose that they interact with CD44+ CRC cells via INHBA. Elevation of INHBA in CRC is associated with a poor prognosis. Interpretation: : Our results demonstrated a single cell landscape of CRC in different regions and identified in hypoxic TME a special subtype of CAFs producing INHBA, which promotes CRC development and correlates with poor prognosis. This special subtype of CAFs is a candidate target for translational research.

Recently, a G-protein coupled receptor 44 (GPR44) was discovered to play a significant role in the process of inflammation-related diseases, including cancer and diabetes. However, the precise role of GPR44 has yet to be fully elucidated. Currently, there is a strong and urgent need for the development of GPR44 radiotracers as a non-invasive methodology to explore the exact mechanism of GPR44 on inflammation-related diseases and monitor the progress of therapy. TM-30089 is a potent GPR44 antagonist that exhibits a high specificity and selectivity for GPR44. Its structure contains a fluorine nuclide, which could potentially be replaced with 18F. In the present study, we successfully took a highly effective synthesis strategy that pretreated the unprotected carboxylic acid group of the precursor and developed a feasible one-step automatic radiosynthesis strategy for [18F]TM-30089 with a high radiochemical purity and a good radiochemical yield. We further evaluated this radiotracer using mice models implanted with 1.1 B4 cell lines (GPR44-enriched cell lines) and human islets (high GPR44 expression), respectively. The results revealed the persistent and specific uptake of [18F]TM-30089 in GPR44 region, indicating that [18F]TM-30089 is a promising candidate for targeting GPR44. Further evaluation is ongoing.

To address the shortcomings in the application of bioactive peptides as drugs, incorporation of unnatural amino acids (UAAs) has been used. Here, the authors report an ionic compound-promoted C-N cleavage of alkyl pyridinium to generate alkyl radicals upon excitation by visible light, and apply it for deaminative hydroalkylation of alkenes to synthesise diverse β-alkyl substituted UAAs.

Recently, growing evidence of the relationship between G-protein coupled receptor 44 (GPR44) and the inflammation-cancer system has garnered tremendous interest, while the exact role of GPR44 has not been fully elucidated. Currently, there is a strong and urgent need for the development of non-invasive in vivo GPR44 positron emission tomography (PET) radiotracers that can be used to aid the exploration of the relationship between inflammation and tumor biologic behavior. Accordingly, the choosing and radiolabeling of existing GPR44 antagonists containing a fluorine group could serve as a viable method to accelerate PET tracers development for in vivo imaging to this purpose. The present study aims to evaluate published (2000-present) indole-based and cyclopentenyl-indole-based analogues of the GPR44 antagonist to guide the development of fluorine-18 labeled PET tracers that can accurately detect inflammatory processes. The selected analogues contained a crucial fluorine nuclide and were characterized for various properties including binding affinity, selectivity, and pharmacokinetic and metabolic profile. Overall, 26 compounds with favorable to strong binding properties were identified. This review highlights the potential of GPR44 analogues for the development of PET tracers to study inflammation and cancer development and ultimately guide the development of targeted clinical therapies.

Flood risk assessment and mapping are considered essential tools for the improvement of flood management. This research aims to construct a more comprehensive flood assessment framework by emphasizing factors related to human resilience and integrating them with meteorological and geographical factors. Moreover, two ensemble learning models, namely voting and stacking, which utilize heterogeneous learners, were employed in this study, and their prediction performance was compared with that of traditional machine learning models, including support vector machine, random forest, multilayer perceptron, and gradient boosting decision tree. The six models were trained and tested using a sample database constructed from historical flood events in Hefei, China. The results demonstrated the following findings: (1) the RF model exhibited the highest accuracy, while the SVR model underestimated the extent of extremely high-risk areas. The stacking model underestimated the extent of very-high-risk areas. It should be noted that the prediction results of ensemble learning methods may not be superior to those of the base models upon which they are built. (2) The predicted high-risk and very-high-risk areas within the study area are predominantly clustered in low-lying regions along the rivers, aligning with the distribution of hazardous areas observed in historical inundation events. (3) It is worth noting that the factor of distance to pumping stations has the second most significant driving influence after the DEM (Digital Elevation Model). This underscores the importance of considering human resilience factors. This study expands the empirical evidence for the ability of machine learning methods to be employed in flood risk assessment and deepens our understanding of the potential mechanisms of human resilience in influencing urban flood risk.

BackgroundThe recent uptrend in colorectal cancer (CRC) incidence in China is causing an increasingly overwhelming social burden. And its occurrence can be effectively reduced by sensitizing CRC screening for early diagnosis and treatment. However, a large number of people in China do not undergo screening due to multiple factors. To address this issue, since 2012, a CRC screening program has been initiated in Tianjin.MethodsResidents aged 40-74 years were eligible for CRC screening. The first was to complete the high-risk factor questionnaire (HRFQ) and undergo fecal immunochemical test (FIT). Then those with a positive result in any of the two screening methods were recommended for a free colonoscopy.ResultsThe detection rate of intestinal diseases increased with age, had a male predominance, and was higher in residents from central urban areas and those with primary school above education level. The sensitivity of predicting CRC after colonoscopy in the high-risk group was 76.02%; the specificity was 25.33%.A significant decrease in the detection rate of intestinal disease, CRC and advanced adenoma was observed from positive FIT, the high-risk group and positive HRFQ, 47.13%, 44.79%, 42.30%; 3.15%, 2.44%, 1.76%; 7.72%, 6.42%, 5.08%, in that order, while no inter-group difference was found for the detection of polyps. In addition, the different combinations of HRFQ and FIT can enroll more high-risk population than FIT or (and) HRFQ only, and thus detect more intestinal diseases (include CRC/AA/Polyp).ConclusionThe superimposition of different screening method for HRFQ and FIT is an effective strategy for the detection of CRC, AA, and Polyp, compared to HRFQ or FIT alone. However, further improvements in screening and interventions are needed to promote colonoscopy compliance.

Connectomics is a developing field aiming at reconstructing the connection of the neural system at the nanometer scale. Computer vision technology, especially deep learning methods used in image processing, has promoted connectomic data analysis to a new era. However, the performance of the state-of-the-art (SOTA) methods still falls behind the demand of scientific research. Inspired by the success of ImageNet, we present an annotated ultra-high resolution image segmentation dataset for cell membrane (U-RISC), which is the largest cell membrane-annotated electron microscopy (EM) dataset with a resolution of 2.18 nm/pixel. Multiple iterative annotations ensured the quality of the dataset. Through an open competition, we reveal that the performance of current deep learning methods still has a considerable gap from the human level, different from ISBI 2012, on which the performance of deep learning is closer to the human level. To explore the causes of this discrepancy, we analyze the neural networks with a visualization method, which is an attribution analysis. We find that the U-RISC requires a larger area around a pixel to predict whether the pixel belongs to the cell membrane or not. Finally, we integrate the currently available methods to provide a new benchmark (0.67, 10% higher than the leader of the competition, 0.61) for cell membrane segmentation on the U-RISC and propose some suggestions in developing deep learning algorithms. The U-RISC dataset and the deep learning codes used in this study are publicly available.

Abuse of heavy metals and antibiotics results in the dissemination of metal resistance genes (MRGs) and antibiotic resistance genes (ARGs). Ditch wetlands are important sinks for heavy metals and antibiotics. The relationships between bacterial communities and MRG/ARG dissemination under dual stresses of heavy metals and antibiotics remain unclear. The responses of MRGs and ARGs to the co-selective pressure of cadmium (Cd) and doxycycline (DC) in ditch wetlands were investigated after 7-day and 84-day exposures. In ecological ditches, residual rates of Cd and DC varied from 0.4 to –5.73% and 0 to –0.61%, respectively. The greatest total relative abundance of ARGs was observed in the Cd 5 mg L–1 + DC 50 mg L–1 group. A significant level of DC (50 mg L–1) significantly reduced the total relative abundances of MRGs at a concentration of 5 mg L–1 Cd stress. Redundancy analysis indicated that Cd and DC had strong positive effects on most ARGs and MRGs after a 7-day exposure. Meanwhile, the class 1 integron gene (intI1) exhibited strong positive correlations with most ARGs and cadmium resistance genes (czcA) after an 84-day exposure. Network analysis showed that Acinetobacter and Pseudomonas were the potential dominant host genera for ARGs and MRGs, and tetracycline resistance genes (tetA), czcA, and intI1 shared the same potential host bacteria Trichococcus after an 84-day exposure.

Since the results of basic and specific classification in male androgenetic alopecia are subjective, and trichoscopic data, such as hair density and diameter distribution, are potential quantitative indicators, the aim of this study was to develop a deep learning framework for automatic trichoscopic image analysis and a quantitative model for predicting basic and specific classification in male androgenetic alopecia. A total of 2,910 trichoscopic images were collected and a deep learning framework was created on convolutional neural networks. Based on the trichoscopic data provided by the framework, correlations with basic and specific classification were analysed and a quantitative model was developed for predicting basic and specific classification using multiple ordinal logistic regression. A deep learning framework that can accurately analyse hair density and diameter distribution on trichoscopic images and a quantitative model for predicting basic and specific classification in male androgenetic alopecia were established.

Pulmonary hypertension (PH) induced by hypoxia is common in clinical practice and often suggests a poor prognosis. The oxidative stress and proliferation of pulmonary vascular endothelial cells caused by hypoxia are the major mechanisms involved in the pathophysiology of PH. It has been reported in recent years that the carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1) promotes angiogenesis. In this study, normal human pulmonary microvascular endothelial cells (HPMVECs) and HPMVECs with stable knockout of CEACAM1 by CRISPR-Cas9 were subjected to oxygen-glucose deprivation/reperfusion (OGD/R) to induce hypoxic conditions. JC-1, ROS, and cell cycle profile were analyzed for each cell line and controls, using flow cytometry. A tube formation assay was used to detect angiogenesis, along with expression levels of CEACAM1, TNF-α, VEGF, VEGFR-2, p-P38/P38, and CyclinD1 proteins (to distinguish profiles of angiogenic growth and cell proliferation). We observed increased expression of CEACAM1 in HPMVECs after OGD/R, while ROS production was reduced and mitochondrial membrane potential was increased after OGD/R in CEACAM1-/- HPMVECs. Furthermore, we observed increased cell division in CEACAM-/- HPMVECs, accompanied by enhanced angiogenesis and reduced TNF-α protein expression and increased VEGF, VEGFR-2, and CyclinD1 expression. Together, these data suggest that upregulation of CEACAM1 in HPMVECs under hypoxic conditions may damage cells by increasing oxidative stress and inhibiting cell proliferation.

Abstract Circular dichroism (CD) is an interesting phenomenon originating from the interaction of light with chiral molecules or other nanostructures lacking mirror symmetries in three-dimensional (3D) or two-dimensional (2D) space. While the observable effects of optical chirality are very weak in most of the natural materials, they can be designed and significantly enhanced in synthetic chiral structures, where the spatial symmetry of their component are broken on a nanoscale. Therefore, fabrication of composites capable of cheap, time-saving, and giant CD is desirable for the advanced optical technologies. Here, the giant CD of large-area metal nanocrescent array structures was investigated theoretically and experimentally. The largest value of the CD spectrum measured was larger than 0.5, and the CD spectrum was tuned effectively and extensively while maintaining a large peak intensity, which can be attributed to the selective excitation of the lattice surface modes (LSMs) by circularly polarized light. The analysis of the extrinsic chiral structure shows potential applications in chiral molecule sensing and polarizing imaging.

With the change in global climate and environment, the prevalence of extreme rainstorms and flood disasters has increased, causing serious economic and property losses. Therefore, accurate and rapid prediction of waterlogging has become an urgent problem to be solved. In this study, Jianye District in Nanjing City of China is taken as the study area. The time series data recorded by rainfall stations and ponding monitoring stations from January 2015 to August 2018 are used to build a ponding prediction model based on the long short-term memory (LSTM) neural network. MSE (mean square error), MAE (mean absolute error) and MSLE (mean squared logarithmic error) were used as loss functions to conduct and train the LSTM model, then three ponding prediction models were built, namely LSTM (mse), LSTM (mae) and LSTM (msle), and a multi-step model was used to predict the depth of ponding in the next 1 h. Using the measured ponding data to evaluate the model prediction results, we selected rmse (root mean squared error), mae, mape (mean absolute percentage error) and NSE (Nash–Sutcliffe efficiency coefficient) as the evaluation indicators. The results showed that LSTM (msle) was the best model among the three models, with evaluation indicators as follows: rmse 5.34, mae 3.45, mape 53.93% and NSE 0.35. At the same time, we found that LSTM (mae) has a better prediction effect than the LSTM (mse) and LSTM (msle) models when the ponding depth exceeds 30 mm.

The East Asian VLBI Network (EAVN) is an international VLBI facility in East Asia and is operated under mutual collaboration between East Asian countries, as well as part of Southeast Asian and European countries. EAVN currently consists of 16 radio telescopes and three correlators located in China, Japan, and Korea, and is operated mainly at three frequency bands, 6.7, 22, and 43 GHz with the longest baseline length of 5078 km, resulting in the highest angular resolution of 0.28 milliarcseconds at 43 GHz. One of distinct capabilities of EAVN is multi-frequency simultaneous data reception at nine telescopes, which enable us to employ the frequency phase transfer technique to obtain better sensitivity at higher observing frequencies. EAVN started its open-use program in the second half of 2018, providing a total observing time of more than 1100 h in a year. EAVN fills geographical gap in global VLBI array, resulting in enabling us to conduct contiguous high-resolution VLBI observations. EAVN has produced various scientific accomplishments especially in observations toward active galactic nuclei, evolved stars, and star-forming regions. These activities motivate us to initiate launch of the ’Global VLBI Alliance’ to provide an opportunity of VLBI observation with the longest baselines on the earth.

Abstract The spatial heterogeneity of immune microenvironment in hepatocellular carcinoma (HCC) remains elusive. Here, a single‐cell study involving 17 432 600 immune cells of 39 matched HCC (T), nontumor (N), and leading‐edge (L) specimens by mass cytometry is conducted. The tumor‐associated CD4/CD8 double‐positive T (DPT) cells are found enriched in L regions with synergetic expression of PD‐1/HLA‐DR/ICOS/CD45RO and exhibit a higher level of IFN‐γ, TNF‐α, and PD‐1 upon stimulation. The enrichment of DPT and PD‐1+DPT in L regions indicates favorable prognosis. These tumor‐associated DPT cells with similar phenotype are also verified in other tumors and HCC animal models. Single‐cell RNA‐seq further characterizes the molecular features of DPT cells and uncovers 11 clusters with different cytotoxicity, exhaustion, and activation scores. TCR‐based trajectory analysis reveals that tumor‐associated DPT clusters share separated ancestries with local CD4+ or CD8+SPT cells rather than CD3+PBMC cells. TCR clones with frequency above 10 are mainly found coexisting in DPT and CD8+SPT cells. Specifically, PD‐1highDPT cluster (TDPT_10) shares the same ancestry with exhausted CD8+SPT cluster (TCD8T_2) and shows higher expression similarity and closer pathological location to PD‐1+CD8+ than PD‐1+CD4+T cells. Together, this study systematically characterizes the unique distribution of PD‐1+DPTs in HCC and puts forward new insights for the function and origin of tumor‐associated DPT cells.

Interval prediction of wind power, which features the upper and lower limits of wind power at a given confidence level, plays a significant role in accurate prediction and stability of the power grid integrated with wind power. However, the conventional methods of interval prediction are commonly based on a hypothetic probability distribution function, which neglects the correlations among various variables, leading to decreased prediction accuracy. Therefore, in this paper, we improve the multi-objective interval prediction based on the conditional copula function, through which we can fully utilize the correlations among variables to improve prediction accuracy without an assumed probability distribution function. We use the multi-objective optimization method of non-dominated sorting genetic algorithm-II (NSGA-II) to obtain the optimal solution set. The particular best solution is weighted by the prediction interval average width (PIAW) and prediction interval coverage probability (PICP) to pick the optimized solution in practical examples. Finally, we apply the proposed method to three wind power plants in different Chinese cities as examples for validation and obtain higher prediction accuracy compared with other methods, i.e., relevance vector machine (RVM), artificial neural network (ANN), and particle swarm optimization kernel extreme learning machine (PSO-KELM). These results demonstrate the superiority and practicability of this method in interval prediction of wind power.

Compared with the point prediction, the interval prediction of the load could more effectively guarantee the safe operation of the power system. In view of the problem that the correlation between adjacent load data is not fully utilized so that the prediction accuracy is reduced, this paper proposes the conditional copula function interval prediction method, which could make full use of the correlation relationship between adjacent load data so as to obtain the interval prediction result. At the same time, there are the different prediction results of the method under different parameters, and the evaluation results of the two accuracy evaluation indicators containing PICP (prediction interval coverage probability) and the PIAW (prediction interval average width) are inconsistent, the above result that the optimal parameters and prediction results cannot be obtained, therefore, the NSGA-II (Non-dominated Sorting Genetic Algorithm-II) multi-objective optimization algorithm is proposed to seek out the optimal solution set, and by evaluating the solution set, obtain the optimal prediction model parameters and the corresponding prediction results. Finally, the proposed method is applied to the three regions of Shaanxi Province, China to conduct ultra-short-term load prediction, and compare it with the commonly used load interval prediction method such as Gaussian process regression (GPR) algorithm, artificial neural network (ANN), extreme learning machine (ELM) and others, and the results show that the proposed method always has better prediction accuracy when applying it to different regions.