Air pollutants, genetic susceptibility and the risk of schizophrenia: large prospective study.

BACKGROUND: Evidence linking air pollutants and the risk of schizophrenia remains limited and inconsistent, and no studies have investigated the joint effect of air pollutant exposure and genetic factors on schizophrenia risk. AIMS: To investigate how exposure to air pollution affects schizophrenia risk and the potential effect modification of genetic susceptibility. METHOD: Our study was conducted using data on 485 288 participants from the UK Biobank. Cox proportional hazards models were used to estimate the schizophrenia risk as a function of long-term air pollution exposure presented as a time-varying variable. We also derived the schizophrenia polygenic risk score (PRS) utilising data provided by the UK Biobank, and investigated the modification effect of genetic susceptibility. RESULTS: During a median follow-up period of 11.9 years, 417 individuals developed schizophrenia (mean age 55.57 years, s.d. = 8.68; 45.6% female). Significant correlations were observed between long-term exposure to four air pollutants (PM2.5; PM10; nitrogen oxides, NOx; nitrogen dioxide, NO2) and the schizophrenia risk in each genetic risk group. Interactions between genetic factors and the pollutants NO2 and NOx had an effect on schizophrenia events. Compared with those with low PRS and low air pollution, participants with high PRS and high air pollution had the highest risk of incident schizophrenia (PM2.5: hazard ratio = 6.25 (95% CI 5.03-7.76); PM10: hazard ratio = 7.38 (95% CI 5.86-9.29); NO2: hazard ratio = 6.31 (95% CI 5.02-7.93); NOx: hazard ratio = 6.62 (95% CI 5.24-8.37)). CONCLUSIONS: Long-term exposure to air pollutants was positively related to the schizophrenia risk. Furthermore, high genetic susceptibility could increase the effect of NO2 and NOx on schizophrenia risk.

Single-Photon Interference over 8.4 km Urban Atmosphere: Toward Testing Quantum Effects in Curved Spacetime with Photons.

The emergence of quantum mechanics and general relativity has transformed our understanding of the natural world significantly. However, integrating these two theories presents immense challenges, and their interplay remains untested. Recent theoretical studies suggest that the single-photon interference covering huge space can effectively probe the interface between quantum mechanics and general relativity. We developed an alternative design using unbalanced Michelson interferometers to address this and validated its feasibility over an 8.4 km free-space channel. Using a high-brightness single-photon source based on quantum dots, we demonstrated single-photon interference along this long-distance baseline. We achieved a phase measurement precision of 16.2 mrad, which satisfied the measurement requirements for a gravitational redshift at the geosynchronous orbit by 5 times the standard deviation. Our results confirm the feasibility of the single-photon version of the Colella-Overhauser-Werner experiment for testing the quantum effects in curved spacetime.

[Meteorological Influences on Annual Variation and Trend of Autumn Surface Ozone in the Pearl River Delta].

Based on the ozone （O3） monitoring data of the Pearl River Delta （PRD） from 2015 to 2022 and the reanalysis of meteorological data, the impact of meteorological conditions on the annual variation and trends of the maximum daily 8-hour average O3 concentration （MDA8-O3） were quantified using multiple linear regression （MLR） and LMG methods. The results indicated that the MLR model constructed using meteorological parameters from individual months in autumn better simulated the variation in MDA8-O3 compared to that in the model built using meteorological parameters from the entire autumn season. The combined influence of total cloud cover, relative humidity, 2 m maximum temperature, and 850 hPa zonal wind led to a reduction of 34.1 µg·m-3 in MAD8-O3 in 2020 compared to that in 2019, with contributions of 31.3%, 45.2%, 15.8%, and 6.7%, respectively. The observed trends of MDA8-O3 in the PRD for September, October, November, and the autumn season during 2015-2022 were 7.3, 5.2, 4.8, and 5.8 µg·ï¼ˆm3·a）-1, respectively. Among these, the trends driven by meteorological factors were 3.6, 2.4, 2.4, and 3.1 µg·ï¼ˆm3·a）-1. Overall, meteorological conditions contributed 53.4% to the variations in autumn MDA8-O3 in the PRD from 2015 to 2022.

Psychotherapeutic and pharmacological agents for post-traumatic stress disorder with sleep disorder: network meta-analysis.

OBJECTIVE: The current guidelines and canonical norms of diagnosis or treatment for Post-traumatic stress disorder (PTSD) with sleep disorder are still conflicting and have not yet reached a consensus. This study aimed to unravel the most effective countermeasures between two categories (psychotherapy and pharmacotherapy) put forward by the National Institute for Health and Clinical Excellence (NICE) and World Federation of Societies of Biological Psychiatry (WFSBP) respectively to treat PTSD individuals co-exist with sleep disorders. METHODS: Four databases, including PubMed, EMBASE, Cochrane Library, and APA PsyNet, were searched from inception to February 02, 2023. RESULTS: Twenty articles with 24 Randomized controlled trials (RCTs) and a total number of 1,647 participants were included. As demonstrated in the network meta-analysis comparison results, CBT-I (standardized mean differences (SMD) = -1.51,95% confidence interval (CI):-2.55 to -0.47), CBT-I plus IRT (SMD = -1.71, 95%CI:-3.39, -0.03), prazosin (SMD = -0.87,95%CI:-1.59 to -0.16) and hydroxyzine (SMD = -1.06, 95%CI: -1.94 to -0.19) significantly reduced PTSD symptoms compared with placebo. In contrast to placebo, CBT-I (SMD = -5.61,95%CI:-8.82 to -2.40) significantly improved sleep quality. For nightmare severity, IRT (SMD =-0.65, 95%CI:-1.00 to -0.31), prazosin (SMD = -1.20,95%CI:-1.72 to -0.67) and hydroxyzine (SMD = -0.98,95%CI:-1.58 to -0.37) significantly reduced nightmare severity in comparison with placebo. CONCLUSIONS: This study suggested that under most circumstances, psychotherapy namely CBT-I had a favorable profile, but pharmacotherapy with prazosin was effective in managing nightmare severity. The sole avail of CBT-I was recommended to improving sleep quality while CBT-I and CBT-I plus IRT showed excellent management of PTSD symptom severity. Exposure to CBT-I isrecommended for depression. The relevant clinical guidelines for the management of individuals with PTSD and sleep disorders may regard this as a reference. PROSPERO: CRD42023415240.

Breaking down barriers: rationalisations and motivation to stop among Chinese male smokers under cigarette dependence.

BACKGROUND: Smoking rationalisation beliefs are a huge barrier to quitting smoking. What types of rationalisations should be emphasised in smoking cessation interventions? Although past literature has confirmed the negative relationship between those beliefs and motivation to stop smoking, little is known regarding the importance and performance of those beliefs on motivation with varying cigarette dependence. The study aimed to ascertain rationalisations that are highly important for motivation yet perform poorly in different cigarette dependence groups. METHODS: The cross-sectional study was conducted from November 19 to December 9, 2023 in Guiyang City, China. Adult male current smokers were enrolled. Partial least squares structural equation modelling was used to test the hypothesis. The multi-group analysis was used to determine the moderating effect of cigarette dependence, and the importance-performance map analysis was utilised to assess the importance and performance of rationalisations. RESULTS: A total of 616 adult male current smokers were analysed, and they were divided into the low cigarette dependence group (n = 297) and the high cigarette dependence group (n = 319). Except for risk generalisation beliefs, smoking functional beliefs (H1: -ß = 0.131, P < 0.01), social acceptability beliefs (H3: ß = -0.258, P < 0.001), safe smoking beliefs (H4: ß = -0.078, P < 0.05), self-exempting beliefs (H5: ß = -0.244, P < 0.001), and quitting is harmful beliefs (H6: ß = -0.148, P < 0.01) all had a significant positive influence on motivation. Cigarette dependence moderated the correlation between rationalisations and motivation. In the high-dependence group, the social acceptability beliefs and smoking functional beliefs were located in the "Concentrate Here" area. In the low-dependence group, the social acceptability beliefs were also situated in there. CONCLUSIONS: Social acceptability beliefs and smoking functional beliefs showed great potential and value for improvement among high-dependence smokers, while only social acceptability beliefs had great potential and value for improvement among low-dependence smokers. Addressing these beliefs will be helpful for smoking cessation. The multi-group analysis and the importance-performance map analysis technique have practical implications and can be expanded to other domains of health education and intervention practice.

Wavelength-tunable high-fidelity entangled photon sources enabled by dual Stark effects.

The construction of a large-scale quantum internet requires quantum repeaters containing multiple entangled photon sources with identical wavelengths. Semiconductor quantum dots can generate entangled photon pairs deterministically with high fidelity. However, realizing wavelength-matched quantum-dot entangled photon sources faces two difficulties: the non-uniformity of emission wavelength and exciton fine-structure splitting induced fidelity reduction. Typically, these two factors are not independently tunable, making it challenging to achieve simultaneous improvement. In this work, we demonstrate wavelength-tunable entangled photon sources based on droplet-etched GaAs quantum dots through the combined use of AC and quantum-confined Stark effects. The emission wavelength can be tuned by ~1 meV while preserving an entanglement fidelity f exceeding 0.955(1) in the entire tuning range. Based on this hybrid tuning scheme, we finally demonstrate multiple wavelength-matched entangled photon sources with f > 0.919(3), paving the way towards robust and scalable on-demand entangled photon sources for quantum internet and integrated quantum optical circuits.

microRNA-3037 targeting CYP6CY2 confers imidacloprid resistance to Sitobion miscanthi (Takahashi).

The wheat aphid Sitobion miscanthi is a dominant and destructive pest in agricultural production. Insecticides are the main substances used for effective control of wheat aphids. However, their extensive application has caused severe resistance of wheat aphids to some insecticides; therefore, exploring resistance mechanisms is essential for wheat aphid management. In the present study, CYP6CY2, a new P450 gene, was isolated and overexpressed in the imidacloprid-resistant strain (SM-R) compared to the imidacloprid-susceptible strain (SM-S). The increased sensitivity of S. miscanthi to imidacloprid after knockdown of CYP6CY2 indicates that it could be associated with imidacloprid resistance. Subsequently, the posttranscriptional regulation of CYP6CY2 in the 3' UTR by miR-3037 was confirmed, and CYP6CY2 participated in imidacloprid resistance. This finding is critical for determining the role of P450 in relation to the resistance of S. miscanthi to imidacloprid. It is of great significance to understand this regulatory mechanism of P450 expression in the resistance of S. miscanthi to neonicotinoids.

Generative learning facilitated discovery of high-entropy ceramic dielectrics for capacitive energy storage.

Dielectric capacitors offer great potential for advanced electronics due to their high power densities, but their energy density still needs to be further improved. High-entropy strategy has emerged as an effective method for improving energy storage performance, however, discovering new high-entropy systems within a high-dimensional composition space is a daunting challenge for traditional trial-and-error experiments. Here, based on phase-field simulations and limited experimental data, we propose a generative learning approach to accelerate the discovery of high-entropy dielectrics in a practically infinite exploration space of over 1011 combinations. By encoding-decoding latent space regularities to facilitate data sampling and forward inference, we employ inverse design to screen out the most promising combinations via a ranking strategy. Through only 5 sets of targeted experiments, we successfully obtain a Bi(Mg0.5Ti0.5)O3-based high-entropy dielectric film with a significantly improved energy density of 156 J cm-3 at an electric field of 5104 kV cm-1, surpassing the pristine film by more than eight-fold. This work introduces an effective and innovative avenue for designing high-entropy dielectrics with drastically reduced experimental cycles, which could be also extended to expedite the design of other multicomponent material systems with desired properties.

Capillary-Driven Separate Gas-Liquid Transport: Alleviating Mass Transport Losses for Efficient Hydrogen Evolution.

Developing earth-abundant transition metal electrodes with high activity and durability is crucial for efficient and cost-effective hydrogen production. However, numerous studies in the hydrogen evolution reaction (HER) primarily focus on improving the inherent activity of catalysts, and the critical influence of gas-liquid countercurrent transport behavior is often overlooked. In this study, we introduce the concept of separate-path gas-liquid transport to alleviate mass transport losses for the HER by developing a novel hierarchical porous Ni-doped cobalt phosphide electrode (CoNix-P@Ni). The CoNix-P@Ni electrodes with abundant microvalleys and crack structures facilitate the gas-liquid cotransport by separating the bubble release and water supply paths. Visualization and numerical simulation results demonstrate that cracks primarily serve as water supply paths, with capillary pressure facilitating the transport of water from the cracks to the microvalleys. This process ensures the continuous wetting of electrolytes in the electrode, reduces hydrogen supersaturation near the active site, and increases hydrogen transport flux to the microvalleys for accelerating bubble growth. Additionally, the microvalleys act as preferential sites for bubble evolution, preventing bubble coverage on other active sites. By regulating the amount of nickel, the CoNi1-P@Ni electrode exhibited the smallest and densest microvalleys and cracks, achieving superior HER performance with an overpotential of 51 mV at 10 mA cm-2. The results offer a promising direction for constructing high-performance HER electrodes.

Potential mechanisms of hexaconazole resistance in Fusarium graminearum.

The Fusarium head blight (FHB) caused by Fusarium graminearum is a serious fungal disease that can dramatically impact wheat production. At present, control is mainly achieved by the use of chemical fungicides. Hexaconazole (IUPAC name: 2-(2,4-dichlorophenyl)-1-(1,2,4-triazol-1-yl)hexan-2-ol) is a widely used triazole fungicide, but the sensitivity of F. graminearum to this compound has yet to be established. The current study found that the EC50 values of 83 field isolates of F. graminearum ranged between 0.06 and 4.33 µg/mL, with an average EC50 of 0.78 µg/mL. Assessment of four hexaconazole-resistant laboratory mutants of F. graminearum revealed that their mycelial growth, and pathogenicity were reduced compared to their parental isolates, and that asexual reproduction was reduced by resistance to hexaconazole. Meanwhile, the mutants appeared to be more sensitive to abiotic stress associated with SDS, and H2O2, while their tolerance of high concentration of Congo red, and Na+ and K+ increased. Molecular analysis revealed numerous point mutations in the FgCYP51 target genes that resulted in amino acid substitutions, including L92P and N123S in FgCYP51A, as well as M331V, F62L, Q252R, A412V, and V488A in FgCYP51B, and S28L, S256A, V307A, D287G and R515I in FgCYP51C, three of which (S28L, S256A, and V307A) were conserved in all of the resistant mutants. Furthermore, the expression of the FgCYP51 genes in resistant strains was found to be significantly (p < 0.05) reduced compared to their sensitive parental isolates. Positive cross-resistance was found between hexaconazole and metconazole and flutriafol, as well as with the diarylamine fungicide fluazinam, but not with propiconazole, and the phenylpyrrole fungicide fludioxonil, or with tebuconazole, which actually exhibited negative cross-resistance. These results provide valuable insight into resistant mechanisms to triazole fungicides in F. graminearum, as well as the appropriate selection of fungicide combinations for the control of FHB to ensure optimal wheat production.

Biosynthetic pathway analysis combined with feature-based molecular networking to comprehensively characterize the chemical constituents in seeds of Sterculia lychnophora.

INTRODUCTION: The seeds of Sterculia lychnophora Hance, commonly known as Pangdahai (PDH) in Chinese, have found extensive use in both culinary and traditional medicinal practices. However, a comprehensive understanding of the chemical composition of PDH has been lacking. OBJECTIVES: This study proposes a strategy that integrates biosynthetic pathway analysis with feature-based molecular networking (FBMN), aiming for a thorough and global characterization of the chemical compositions of PDH. METHODOLOGY: The FBMN map reveals potential compounds with structural similarity, and the MS/MS fragments could be annotated based on library matches, which could predict the plausible biosynthetic pathways in PDH, accomplishing the annotation of compounds clustered in FBMN by integrating biosynthetic pathways. RESULTS: Consequently, 126 compounds were plausibly or unambiguously identified, including 37 phenolic acids and glycosides, 20 flavonoids and glycosides, 12 procyanidins, 21 alkaloids, 22 lipids, and 14 others. Leveraging the information, 40 compounds, including 1 unique isoquinoline alkaloid and 2 rare linear furocoumarins, were isolated and confirmed. CONCLUSIONS: This study not only demonstrates a highly effective approach for identifying compounds within complex herbal mixtures but also establishes a robust foundation for the further development of PDH.

Customizable Click Biochemistry Strategy for the Design and Preparation of Glucagon-like Peptide-1 Conjugates and Coagonists.

The development of oligomeric glucagon-like peptide-1 (GLP-1) and GLP-1-containing coagonists holds promise for enhancing the therapeutic potential of the GLP-1-based drugs for treating type 2 diabetes mellitus (T2DM). Here, we report a facile, efficient, and customizable strategy based on genetically encoded SpyCatcher-SpyTag chemistry and an inducible, cleavable self-aggregating tag (icSAT) scheme. icSAT-tagged SpyTag-fused GLP-1 and the dimeric or trimeric SpyCatcher scaffold were designed for dimeric or trimeric GLP-1, while icSAT-tagged SpyCatcher-fused GLP-1 and the icSAT-tagged SpyTag-fused GIP were designed for dual GLP-1/GIP (glucose-dependent insulinotropic polypeptide) receptor agonist. These SpyCatcher- and SpyTag-fused protein pairs were spontaneously ligated directly from the cell lysates. The subsequent icSAT scheme, coupled with a two-step standard column purification, resulted in target proteins with authentic N-termini, with yields ranging from 35 to 65 mg/L and purities exceeding 99%. In vitro assays revealed 3.0- to 4.1-fold increased activities for dimeric and trimeric GLP-1 compared to mono-GLP-1. The dual GLP-1/GIP receptor agonist exhibited balanced activity toward the GLP-1 receptor or the GIP receptor. All the proteins exhibited 1.8- to 3.0-fold prolonged half-lives in human serum compared to mono-GLP-1 or GIP. This study provides a generally applicable click biochemistry strategy for developing oligomeric or dual peptide/protein-based drug candidates.

Experimental Generation of Spin-Photon Entanglement in Silicon Carbide.

A solid-state approach for quantum networks is advantageous, as it allows the integration of nanophotonics to enhance the photon emission and the utilization of weakly coupled nuclear spins for long-lived storage. Silicon carbide, specifically point defects within it, shows great promise in this regard due to the easy of availability and well-established nanofabrication techniques. Despite of remarkable progresses made, achieving spin-photon entanglement remains a crucial aspect to be realized. In this Letter, we experimentally generate entanglement between a silicon vacancy defect in silicon carbide and a scattered single photon in the zero-phonon line. The spin state is measured by detecting photons scattered in the phonon sideband. The photonic qubit is encoded in the time-bin degree of freedom and measured using an unbalanced Mach-Zehnder interferometer. Photonic correlations not only reveal the quality of the entanglement but also verify the deterministic nature of the entanglement creation process. By harnessing two pairs of such spin-photon entanglement, it becomes straightforward to entangle remote quantum nodes at long distance.

Fucoidan Improves Early Stage Diabetic Nephropathy via the Gut Microbiota-Mitochondria Axis in High-Fat Diet-Induced Diabetic Mice.

Diabetic nephropathy (DN) is a common microvascular complication of diabetes. Fucoidan, a polysaccharide containing fucose and sulfate group, ameliorates DN. However, the underlying mechanism has not been fully understood. This study aimed to explore the effects and mechanism of fucoidan on DN in high-fat diet-induced diabetic mice. A total of 90 C57BL/6J mice were randomly assigned to six groups (n = 15) as follows: normal control (NC), diabetes mellitus (DM), metformin (MTF), low-dose fucoidan (LFC), medium-dose fucoidan (MFC), and high-dose fucoidan (HFC). A technique based on fluorescein isothiocyanate (FITC-sinistin) elimination kinetics measured percutaneously was applied to determine the glomerular filtration rate (GFR). After 24 weeks, the mice were sacrificed and an early stage DN model was confirmed by GFR hyperfiltration, elevated urinary creatinine, normal urinary albumin, tubulointerstitial fibrosis, and glomerular hypertrophy. Fucoidan significantly improved the GFR hyperfiltration and renal fibrosis. An enriched SCFAs-producing bacteria and increased acetic concentration in cecum contents were found in fucoidan groups, as well as increased renal ATP levels and improved mitochondrial dysfunction. The renal inflammation and fibrosis were ameliorated through inhibiting the MAPKs pathway. In conclusion, fucoidan improved early stage DN targeting the microbiota-mitochondria axis by ameliorating mitochondrial oxidative stress and inhibiting the MAPKs pathway.

Catastrophic cascade of failures in interdependent hypergraphs.

The failures of individual agents can significantly impact the functionality of associated groups in interconnected systems. To reveal these impacts, we develop a threshold model to investigate cascading failures in double-layer hypergraphs with interlayer interdependence. We hypothesize that a hyperedge disintegrates when the proportion of failed nodes within it exceeds a threshold. Due to the interdependence between a node and its replica in the other layer, the disintegrations of these hyperedges could trigger a cascade of events, leading to an iterative collapse across these two layers. We find that double-layer hypergraphs undergo abrupt, discontinuous first-order phase transitions during systemic collapse regardless of the specific threshold value. Additionally, the connectivity measured by average cardinality and hyperdegree plays a crucial role in shaping system robustness. A higher average hyperdegree always strengthens system robustness. However, the relationship between system robustness and average cardinality exhibits non-monotonic behaviors. Specifically, both excessively small and large average cardinalities undermine system robustness. Furthermore, a higher threshold value can boost the system's robustness. In summary, our study provides valuable insights into cascading failure dynamics in double-layer hypergraphs and has practical implications for enhancing the robustness of complex interdependent systems across domains.

Construction of S100 family members prognosis prediction model and analysis of immune microenvironment landscape at single-cell level in pancreatic adenocarcinoma: a tumor marker prognostic study.

Pancreatic adenocarcinoma characterized by a mere 10% 5-year survival rate, poses a formidable challenge due to its specific anatomical location, making tumor tissue acquisition difficult. This limitation underscores the critical need for novel biomarkers to stratify this patient population. Accordingly, this study aimed to construct a prognosis prediction model centered on S100 family members. Leveraging six S100 genes and their corresponding coefficients, an S100 score was calculated to predict survival outcomes. The present study provided comprehensive internal and external validation along with power evaluation results, substantiating the efficacy of the proposed model. Additionally, the study explored the S100-driven potential mechanisms underlying malignant progression. By comparing immune cell infiltration proportions in distinct patient groups with varying prognoses, the research identified differences driven by S100 expression. Furthermore, the analysis explored significant ligand-receptor pairs between malignant cells and immune cells influenced by S100 genes, uncovering crucial insights. Notably, the study identified a novel biomarker capable of predicting the sensitivity of neoadjuvant chemotherapy, offering promising avenues for further research and clinical application.

Targeting ATP-binding site of WRN Helicase: Identification of novel inhibitors through pocket analysis and Molecular Dynamics-Enhanced virtual screening.

WRN helicase is a critical protein involved in maintaining genomic stability, utilizing ATP hydrolysis to dissolve DNA secondary structures. It has been identified as a promising synthetic lethal target for microsatellite instable (MSI) cancers. However, few WRN helicase inhibitors have been discovered, and their potential binding sites remain unexplored. In this study, we analyzed potential binding sites for WRN inhibitors and focused on the ATP-binding site for screening new inhibitors. Through molecular dynamics-enhanced virtual screening, we identified two compounds, h6 and h15, which effectively inhibited WRN's helicase and ATPase activity in vitro. Importantly, these compounds selectively targeted WRN's ATPase activity, setting them apart from other non-homologous proteins with ATPase activity. In comparison to the homologous protein BLM, h6 exhibits some degree of selectivity towards WRN. We also investigated the binding mode of these compounds to WRN's ATP-binding sites. These findings offer a promising strategy for discovering new WRN inhibitors and present two novel scaffolds, which might be potential for the development of MSI cancer treatment.

Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables.

OBJECTIVE: This retrospective observational study aims to develop and validate artificial intelligence (AI) pathomics models based on pathological Hematoxylin-Eosin (HE) slides and pathological immunohistochemistry (Ki67) slides for predicting the pathological staging of colorectal cancer. The goal is to enable AI-assisted accurate pathological staging, supporting healthcare professionals in making efficient and precise staging assessments. METHODS: This study included a total of 267 colorectal cancer patients (training cohort: n = 213; testing cohort: n = 54). Logistic regression algorithms were used to construct the models. The HE image features were used to build the HE model, the Ki67 image features were used for the Ki67 model, and the combined model included features from both the HE and Ki67 images, as well as tumor markers (CEA, CA724, CA125, and CA242). The predictive results of the HE model, Ki67 model, and tumor markers were visualized through a nomogram. The models were evaluated using ROC curve analysis, and their clinical value was estimated using decision curve analysis (DCA). RESULTS: A total of 260 deep learning features were extracted from HE or Ki67 images. The AUC for the HE model and Ki67 model in the training cohort was 0.885 and 0.890, and in the testing cohort, it was 0.703 and 0.767, respectively. The combined model and nomogram in the training cohort had AUC values of 0.907 and 0.926, and in the testing cohort, they had AUC values of 0.814 and 0.817. In clinical DCA, the net benefit of the Ki67 model was superior to the HE model. The combined model and nomogram showed significantly higher net benefits compared to the individual HE model or Ki67 model. CONCLUSION: The combined model and nomogram, which integrate pathomics multi-modal data and clinical-pathological variables, demonstrated superior performance in distinguishing between Stage I-II and Stage III colorectal cancer. This provides valuable support for clinical decision-making and may improve treatment strategies and patient prognosis. Furthermore, the use of immunohistochemistry (Ki67) slides for pathomics modeling outperformed HE slide, offering new insights for future pathomics research.

Experimental Quantum Non-Gaussian Coincidences of Entangled Photons.

Quantum non-Gaussianity, a more potent and highly useful form of nonclassicality, excludes all convex mixtures of Gaussian states and Gaussian parametric processes generating them. Here, for the first time, we conclusively test quantum non-Gaussian coincidences of entangled photon pairs with the Clauser-Horne-Shimony-Holt-Bell factor S=2.328±0.004 from a single quantum dot with a depth up to 0.94±0.02 dB. Such deterministically generated photon pairs fundamentally overcome parametric processes by reducing crucial multiphoton errors. For the quantum non-Gaussian depth of the unheralded (heralded) single-photon state, we achieve the value of 8.08±0.05 dB (19.06±0.29 dB). Our Letter experimentally certifies the exclusive quantum non-Gaussianity properties highly relevant for optical sensing, communication, and computation.

[Research on the Evaluation Method and Application of Provincial Differentiated Carbon Peaking in China].

Promoting regions with favorable conditions to take the lead in reaching a carbon peak is an inevitable step towards achieving the dual carbon goals under the "nationwide coordinated action" plan. Considering the differences among Chinese provinces, this study measured the peaking pressure of each province based on the spatial distribution of carbon emissions. We then constructed a provincial peaking capacity evaluation system based on five dimensions, namely, peaking pressure, emission reduction status, economic development, policy support, and resource endowment, to comprehensively evaluate the carbon peaking capacity of 30 provincial administrative regions in China, excluding Hong Kong, Macau, Taiwan, and Tibet, using the entropy value method to determine the index weights. The 30 provinces were divided into five peaking tiers according to the evaluation results. The results showed that:① 18 regions, such as Hainan and Beijing, displayed a surplus in carbon emission space; eight regions, including Hebei and Shandong, showed a deficit in carbon emission space; and the carbon emission spaces allocated to Zhejiang, Anhui, Henan, and Hubei were comparable to their respective actual emissions. ② Developed regions generally had a higher carbon peaking capacity than that of less developed regions, with Beijing and Shanghai showing outstanding carbon peaking capacity, whereas Jiangxi and Guizhou had more room to improve their capacity. Finally, differentiated peaking targets and priority actions were proposed according to the provinces' different peaking tiers and local conditions.