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Abstract Cytokinin Response Factors (CRFs) play a crucial role in plant growth and development, hormone signaling, and responses to biotic and abiotic stresses. However, there have been no reports on CRF genes in rice until now. We analyzed the CRF families in four rice subspecies: cultivated rice Oryza sativa Japonica Group, Oryza sativa Indica Group, and Oryza sativa (circum-Aus1 var. N22), as well as wild rice Oryza rufipogon. We identified 7, 6, 6, and 7 CRF in their genomes, respectively, distributed across different chromosomes. The protein motifs and gene structures of CRF in these four types of rice show high conservation. Cis-regulatory element analysis revealed that the promoter regions of the CRF contain numerous hormone and stress-related elements. The number of CRF in these four types of rice is not influenced by gene duplication. The expression pattern showed that OsCRF exhibit significant tissue-specific expression. The qRT-PCR results showed that OsCRF strongly responded to low-temperature stress and can be induced by melatonin and cytokinin to increase expression levels. In addition, the nuclear localisation of OsCRF4/5 was confirmed to be as predicted. The results above will provide a foundation for further and deeper investigation of CRFs.

Abstract This paper employs two-way fixed effects models, mediation models and spatial panel models to examine the impact of digital economy development on carbon emission intensity in 72 countries from 2013 to 2020 using TIMG index as the proxy variable of digital economic development. The results of this paper show that (1) there is an inverted U-shaped relationship between the development of the digital economy and the carbon emissions in various countries. The development of digital economy first has a positive effect on carbon emissions, and then, its impact turns negative after it reaches a designated inflection point. (2) The results of the mechanism analysis show that the development of the digital economy can reduce carbon emissions by promoting industrial upgrading, while it could have an inverted U-shaped nonlinear effect on carbon emissions through energy efficiency. (3) The development of a country’s digital economy may have a significantly inverted U-shaped spatial spillover effect on neighboring countries. This paper provides reference for countries to formulate carbon emission reduction policies and promote the coordinated development of digital economy and environmental protection.

Abstract Moyamoya disease (MMD) suffers from impaired cerebrovascular hemodynamics and high perioperative complications occurrence. This study aims to propose and evaluate the relationship between intraoperative hemodynamics and perioperative complications, and propose a potential compensatory self-recirculation network in MMD. We prospectively enrolled 63 MMD patients undergone combined revascularization, and patients were divided into decreased and increased group according to decreased and increased microvascular transit time (MVTT), respectively. Mean age of all patients was 45.9 ± 9.4 years. The post-bypass hyperperfusion syndrome occurrence was significantly higher in the decreased MVTT group, and the cerebral infarction occurrence was significantly higher in the increased MVTT group. For the hemodynamics of the recipient artery around anastomosis, the parameters of distal site demonstrated a significant higher intensity and shorter time in the decreased MVTT group, while the parameters of the proximal site demonstrated a significant higher intensity and shorter time in the increased MVTT group. Pre-bypass and post-bypass collision of blood flow in artery and vein were firstly observed and illustrated. Intraoperative hemodynamics showed close relationship with perioperative complications. The blood flow of MMD seems to develop a unique compensatory self-recirculation system and contribute to the clinical complications, providing a new insight to the clinical management the pathology of the disease.

Abstract The post-processing of quantum key distribution mainly includes error correction and privacy amplification. The error correction algorithms and privacy amplification methods used in the existing quantum key distribution are completely unrelated. Based on the principle of correspondence between error-correcting codes and hash function families, we proposed the idea of time-division multiplexing for error correction and privacy amplification for the first time. That is to say, through the common error correction algorithms and their corresponding hash function families or the common hash function families and their corresponding error-correcting codes, error correction and privacy amplification can be realized by time-division multiplexing with the same set of devices. In addition, we tested the idea from the perspective of error correction and privacy amplification, respectively. The analysis results show that the existing error correction algorithms and their corresponding hash function families or the common privacy amplification methods and their corresponding error-correcting codes cannot realize time-division multiplexing for error correction and privacy amplification temporarily. However, according to the principle of correspondence between error-correcting codes and hash function families, the idea of time-division multiplexing is possible. Moreover, the research on time-division multiplexing for error correction and privacy amplification has some practical significance. Once the idea of time-division multiplexing is realized, it will further reduce the calculation and storage cost of the post-processing process, reduce the deployment cost of quantum key distribution, and help to remote the practical engineering of quantum key distribution.

Abstract This research breaks new ground in earthquake prediction for Los Angeles, California, by leveraging advanced machine learning and neural network models. We meticulously constructed a comprehensive feature matrix to maximize predictive accuracy. By synthesizing existing research and integrating novel predictive features, we developed a robust subset capable of estimating the maximum potential earthquake magnitude. Our standout achievement is the creation of a feature set that, when applied with the Random Forest machine learning model, achieves a high accuracy in predicting the maximum earthquake category within the next 30 days. Among sixteen evaluated machine learning algorithms, Random Forest proved to be the most effective. Our findings underscore the transformative potential of machine learning and neural networks in enhancing earthquake prediction accuracy, offering significant advancements in seismic risk management and preparedness for Los Angeles.

Abstract Patients with chronic kidney disease (CKD) often have impaired immune function, making them more prone to infections that can lead to sepsis. The coexistence of these conditions can result in decreased hemoglobin levels and is associated with a higher mortality rate. To investigate whether the transfusion of red blood cells (RBCs) improves the prognosis of septic patients with concomitant CKD and to explore the indications for red blood cell transfusion. This retrospective cohort study utilizes data from the MIMIC-IV (v2.0) database. The study enrolled 6,604 patients with sepsis and concomitant CKD admitted to the Intensive Care Unit (ICU). Propensity score matching (PSM) was applied to adjust for confounding factors. Multivariate Cox regression analysis revealed an association between RBC transfusion and a decreased risk of 28-day mortality (HR: 0.61, 95% CI: 0.54–0.70, P

Abstract The non-Hermitian skin effect and nonreciprocal behavior are sensitive to the boundary conditions, which are unique features of non-Hermitian systems. In such systems, eigenenergies can become complex, and all eigenstates tend to localize at the boundary, a phenomenon that contrasts with Hermitian topologies. In this work, we theoretically study the dynamic behavior of the propagation of Gaussian wavepackets inside a non-Hermitian lattice and analyze the self-acceleration process of bulk state or Gaussian wavepackets toward the system’s boundary. The initial wavepackets will not only propagate toward the side where the eigenstates are localized, but also their momentum will approach to a specific value. This value corresponds to the maximum imaginary components of the energy dispersion. In addition, if the wavepackets in the momentum space cover this specific momentum, they will eventually exhibit exponentially increasing amplitudes with time evolution, maintaining the dynamic protected condition for an extended period of time until they approach the boundary. We also take two widely used toy models as examples in one and two dimensions to verify the correspondence of the non-Hermitian skin effect and the dynamic protected state.

Abstract Porcine pasteurellosis is an infectious disease caused by Pasteurella multocida (P. multocida), which seriously endangers the healthy development of pig breeding industry. Early detection of disease transmission in animals is a crucial early warning for humans. Therefore, predicting risk areas for disease is essential for public health authorities to adopt preventive measures and control strategies against diseases. In this study, we developed a predictive model based on multi-criteria decision analysis (MCDA) and assessed risk areas for porcine pasteurellosis in the Chinese mainland. By using principal component analysis, the weights of seven spatial risk factors were determined. Fuzzy membership function was used to standardize all risk factors, and weight linear combination was used to create a risk map. The sensitivity of the risk map was analyzed by calculating the mean of absolute change rates of risk factors, as well as calculating an uncertainty map. The results showed that risk areas for porcine pasteurellosis were predicted to be locate in the south-central of the Chinese mainland, including Sichuan, Chongqing, Guangdong, and Guangxi. The maximum standard deviation of the uncertain map was less than 0.01and the ROC results showed that the prediction model has moderate predictive performance with the area under the curve (AUC) value of 0.80 (95% CI 0.75–0.84). Based on the above process, MCDA was combined with WebGIS technology to construct a system for predicting risk areas of porcine pasteurellosis. Risk factor data was directly linked to the developed model, providing decision support for disease prevention and control through monthly updates.

Abstract To compare the clinical efficacy of interlaminar endoscopic surgical system delta (iLESSYS-Delta) discectomy with that of classical fenestration discectomy for treating lumbar disc herniation. Patients who underwent iLESSYS-Delta or fenestration discectomy were enrolled in this study. Baseline information and clinical indicators were collected. The baseline data were matched using propensity score matching. Fifty-two patients were in each group. In the iLESSYS-Delta cohort, the volume of intraoperative bleeding was 18.17 ± 4.20 ml, the length of postoperative hospital stay was 4.16 ± 2.29 days, and the length of postoperative off-bed activity was 1.58 ± 0.88 days. In contrast, in the fenestration group, the volume of intraoperative bleeding was 32.50 ± 17.13 ml, the length of postoperative hospital stay was 6.66 ± 2.44 days, and the length of postoperative off-bed activity was 3.18 ± 1.28 days. The difference between the two groups was statistically significant (P 0.05). The two groups showed no significant differences in clinical effects, postoperative recurrence rates, or perioperative complications. iLESSYS-Delta can cause less intraoperative bleeding and faster recovery than fenestration discectomy.

Abstract The present study aimed to explore the association between serum cystatin C (Cys-C) levels and visceral fat area (VFA) in patients with type 2 diabetes mellitus (T2DM). A total of 208 previously diagnosed T2DM patients who visited our hospital from September 2019 to December 2021 were included and divided into three groups based on tertiles of Cys-C levels, namely, Groups C1, C2, and C3. The clinical data of the subjects were collected, biochemical parameters such as Cys-C levels were determined, and bioelectrical impedance analysis was applied to determine the VFA and subcutaneous fat area (SFA). The VFA in Group C1 was lower than that in Groups C2 and C3 (all P 0.05). Spearman’s correlation analysis revealed that the serum Cys-C level was positively correlated with age, VFA, SFA, insulin resistance index, waist circumference, body mass index, systolic blood pressure, serum creatinine level, and blood uric acid level (r = 0.543, 0.353, 0.168, 0.148, 0.365, 0.264, 0.25, 0.497, and 0.155, respectively; P

Abstract By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients.

Abstract Color-changing melon is an ornamental and edible fruit. Aiming at the problems of slow detection speed and high deployment cost for Color-changing melon in intelligent agriculture equipment, this study proposes a lightweight detection model YOLOv8-CML.Firstly, a lightweight Faster-Block is introduced to reduce the number of memory accesses while reducing redundant computation, and a lighter C2f structure is obtained. Then, the lightweight C2f module fusing EMA module is constructed in Backbone to collect multi-scale spatial information more efficiently and reduce the interference of complex background on the recognition effect. Next, the idea of shared parameters is utilized to redesign the detection head to simplify the model further. Finally, the α-IoU loss function is adopted better to measure the overlap between the predicted and real frames using the α hyperparameter, improving the recognition accuracy. The experimental results show that compared to the YOLOv8n model, the parametric and computational ratios of the improved YOLOv8-CML model decreased by 42.9% and 51.8%, respectively. In addition, the model size is only 3.7 MB, and the inference speed is improved by 6.9%, while mAP@0.5, accuracy, and FPS are also improved. Our proposed model provides a vital reference for deploying Color-changing melon picking robots.

Abstract Collaborative efforts in artificial intelligence (AI) are increasingly common between high-income countries (HICs) and low- to middle-income countries (LMICs). Given the resource limitations often encountered by LMICs, collaboration becomes crucial for pooling resources, expertise, and knowledge. Despite the apparent advantages, ensuring the fairness and equity of these collaborative models is essential, especially considering the distinct differences between LMIC and HIC hospitals. In this study, we show that collaborative AI approaches can lead to divergent performance outcomes across HIC and LMIC settings, particularly in the presence of data imbalances. Through a real-world COVID-19 screening case study, we demonstrate that implementing algorithmic-level bias mitigation methods significantly improves outcome fairness between HIC and LMIC sites while maintaining high diagnostic sensitivity. We compare our results against previous benchmarks, utilizing datasets from four independent United Kingdom Hospitals and one Vietnamese hospital, representing HIC and LMIC settings, respectively.

Abstract Chronic endometritis is associated with the imbalance of female reproductive tract microbiota and pathogenic microbial infection. This study aimed to identify the specific changes in the endometrial microbiome in patients with endometritis and to explore how Clostridium tyrobutyricum (C.t) influences the progression of endometritis in mice for further elucidating endometritis pathogenesis. For this purpose, endometrial tissues from 100 participants were collected and divided into positive, weakly positive, and negative groups based on CD138 levels, while endometrial microbiome differences were detected and analyzed using 16S rRNA gene sequencing. Staphylococcus aureus (S. aureus)-induced endometritis mouse model was established, followed by treatment with C.t, and inflammatory response, epithelial barrier, and TLR4/NF-κB pathway were evaluated. Results showed that α- and β-diversity was significantly lower in the positive group compared with the weakly positive or negative groups, where the negative group had more unique operational taxonomic units. The abundance of Proteobacteria was found to be increased, while that of Actinobacteria, Firmicutes, and Bacteroidetes was found to be reduced in the positive group, while the area under the curve value was found to be 0.664. Furthermore, C.t treatment resulted in the alleviation of S. aureus-induced inflammatory response, epithelial barrier damage, and activation of the TLR4/NF-κB pathway in mice. Clinical samples analysis revealed that the diversity and abundance of microbiota were altered in patients with endometritis having positive CD138 levels, while mechanistic investigations revealed C.t alleviated S. aureus-induced endometritis by inactivating TLR4/NF-κB pathway. The findings of this study are envisaged to provide a diagnostic and therapeutic potential of microbiota in endometritis.

Abstract High-altitude cerebral edema (HACE) is a severe neurological condition that can occur at high altitudes. It is characterized by the accumulation of fluid in the brain, leading to a range of symptoms, including severe headache, confusion, loss of coordination, and even coma and death. Exosomes play a crucial role in intercellular communication, and their contents have been found to change in various diseases. This study analyzed the metabolomic characteristics of blood exosomes from HACE patients compared to those from healthy controls (HCs) with the aim of identifying specific metabolites or metabolic pathways associated with the development of HACE conditions. A total of 21 HACE patients and 21 healthy controls were recruited for this study. Comprehensive metabolomic profiling of the serum exosome samples was conducted using ultraperformance liquid chromatography–tandem mass spectrometry (UPLC‒MS/MS). Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed to identify the metabolic pathways affected in HACE patients. Twenty-six metabolites, including ( +)-camphoric acid, choline, adenosine, adenosine 5′-monophosphate, deoxyguanosine 5′-monophosphate, guanosine, and hypoxanthine-9-β-D-arabinofuranoside, among others, exhibited significant changes in expression in HACE patients compared to HCs. Additionally, these differentially abundant metabolites were confirmed to be potential biomarkers for HACE. KEGG pathway enrichment analysis revealed several pathways that significantly affect energy metabolism regulation (such as purine metabolism, thermogenesis, and nucleotide metabolism), estrogen-related pathways (the estrogen signaling pathway, GnRH signaling pathway, and GnRH pathway), cyclic nucleotide signaling pathways (the cGMP-PKG signaling pathway and cAMP signaling pathway), and hormone synthesis and secretion pathways (renin secretion, parathyroid hormone synthesis, secretion and action, and aldosterone synthesis and secretion). In patients with HACE, adenosine, guanosine, and hypoxanthine-9-β-D-arabinofuranoside were negatively correlated with height. Deoxyguanosine 5′-monophosphate is negatively correlated with weight and BMI. Additionally, LPE (18:2/0:0) and pregnanetriol were positively correlated with age. This study identified potential biomarkers for HACE and provided valuable insights into the underlying metabolic mechanisms of this disease. These findings may lead to potential targets for early diagnosis and therapeutic intervention in HACE patients.

Abstract This study aims to investigate the relationship between weight-adjusted-waist index (WWI), a new body index, and sarcopenia, while also assessing the potential of WWI as a tool for screening sarcopenic patients. The cross-sectional study involved adults who possessed complete data on WWI and appendicular skeletal muscle mass from the 1999–2006 and 2011–2018 National Health and Nutrition Examination Surveys. Weighted multivariate regression and logistic regression analyses were employed to explore the independent relationship between WWI and sarcopenia. The study included 26,782 participants. The results showed that WWI demonstrated a positive correlation with sarcopenia risk. In the fully adjusted model, with each 1 unit increase in WWI, the risk of developing sarcopenia rose 14.55 times higher among males (OR: 14.55, 95% CI 12.33, 17.15) and 2.86 times higher among females (OR: 2.86, 95% CI 2.59, 3.15). The optimal cutoff values of WWI for sarcopenia were 11.26 cm/√kg for males and 11.39 cm/√kg for females. Individuals with a higher WWI have an increased risk of developing sarcopenia, and a high WWI functions as a risk factor for sarcopenia. Assessing WWI could assist in identifying individuals at risk of sarcopenia.

Abstract Viruses are crucial for regulating deep-sea microbial communities and biogeochemical cycles. However, their roles are still less characterized in deep-sea holobionts. Bathymodioline mussels are endemic species inhabiting cold seeps and harboring endosymbionts in gill epithelial cells for nutrition. This study unveiled a diverse array of viruses in the gill tissues of Gigantidas platifrons mussels and analyzed the viral metagenome and transcriptome from the gill tissues of Gigantidas platifrons mussels collected from a cold seep in the South Sea. The mussel gills contained various viruses including Baculoviridae, Rountreeviridae, Myoviridae and Siphovirdae, but the active viromes were Myoviridae, Siphoviridae, and Podoviridae belonging to the order Caudovirales. The overall viral community structure showed significant variation among environments with different methane concentrations. Transcriptome analysis indicated high expression of viral structural genes, integrase, and restriction endonuclease genes in a high methane concentration environment, suggesting frequent virus infection and replication. Furthermore, two viruses (GP-phage-contig14 and GP-phage-contig72) interacted with Gigantidas platifrons methanotrophic gill symbionts (bathymodiolin mussels host intracellular methanotrophic Gammaproteobacteria in their gills), showing high expression levels, and have huge different expression in different methane concentrations. Additionally, single-stranded DNA viruses may play a potential auxiliary role in the virus–host interaction using indirect bioinformatics methods. Moreover, the Cro and DNA methylase genes had phylogenetic similarity between the virus and Gigantidas platifrons methanotrophic gill symbionts. This study also explored a variety of viruses in the gill tissues of Gigantidas platifrons and revealed that bacteria interacted with the viruses during the symbiosis with Gigantidas platifrons. This study provides fundamental insights into the interplay of microorganisms within Gigantidas platifrons mussels in deep sea.

Abstract Compressive sensing is favored because it breaks through the constraints of Nyquist sampling law in signal reconstruction. However, the security defects of joint compression encryption and the problem of low quality of reconstructed image restoration need to be solved urgently. In view of this, this paper proposes a compressive sensing image encryption scheme based on optimized orthogonal measurement matrix. Utilizing a combination of DWT and OMP, along with chaos, the proposed scheme achieves high-security image encryption and superior quality in decryption reconstruction. Firstly, the orthogonal optimization method is used to improve the chaotic measurement matrix. Combined with Part Hadamard matrix, the measurement matrix with strong orthogonal characteristics is constructed by Kronecker product. Secondly, the original image is sparsely represented by DWT. Meanwhile, Arnold scrambling is used to disturb the correlation between its adjacent pixels. Following this, the image is compressed and measured in accordance with the principles of compressive sensing and obtain the intermediate image to be encrypted. Finally, the chaotic sequence generated based on 2D-LSCM is used to perform on odd-even interleaved diffusion and row-column permutation at bit-level to obtain the final ciphertext. The experimental results show that this scheme meets the cryptographic requirements of obfuscation, diffusion and avalanche effects, and also has a large key space, which is sufficient to resist brute-force cracking attacks. Based on the sparse and reconstruction algorithm of compressive sensing proposed in this paper, it has better image restoration quality than similar algorithms. Consequently, the compressive sensing image encryption scheme enhances both security and reconstruction quality, presenting promising applications in the evolving landscape of privacy protection for network big data.

Abstract This prospective, non-randomized, comparative study aimed to compare the visual outcomes and patient satisfaction after implantations of three presbyopia-correcting intraocular lenses (IOLs) after myopic refractive surgery. It was conducted from January 2020 to December 2021 in Shanghai Heping Eye Hospital. Patients were divided into three groups based on the type of IOL implanted. The visual acuity, refractive stability, high-order aberrations, objective visual quality, spectacle independence, and visual function index 14 questionnaire scores of the three groups were compared. This study included 78 eyes of 39 patients: 26 eyes with 839MP, 26 eyes with MF30, and 26 eyes with ZXR00. Uncorrected distance visual acuity improved significantly for all three groups. For a pupil diameter of 4.0 mm, the spherical aberrations of the three groups were 0.33 ± 0.16 μ, 0.50 ± 0.08 μ, and 0.39 ± 0.10 μ, respectively. The spectacle independence for distance vision was over 90% in each group; for near vision, it was only 25% for the ZXR00 group. All three types of presbyopia-correcting IOLs improved visual quality in post-LASIK or PRK patients. However, the high incidence of photic phenomena after presbyopia-correcting IOL implantation in patients who have undergone myopic LASIK should not be neglected.

Abstract Gestures are a common way people communicate. Gesture-based teleoperation control systems tend to be simple to operate and suitable for most people’s daily use. This paper employed a LeapMotion sensor to develop a mobile robot control system based on gesture recognition, which mainly established connections through a client/server structure. The principles of gesture recognition in the system were studied and the relevant self-investigated algorithms—GestureMoRo, for the association between gestures and mobile robots were designed. Moreover, in order to avoid the unstably fluctuated movement of the mobile robot caused by palm shaking, the Gaussian filter algorithm was used to smooth and denoise the collected gesture data, which effectively improved the robustness and stability of the mobile robot’s locomotion. Finally, the teleoperation control strategy of the gesture to the WATER2 mobile robot was realized, and the effectiveness and practicability of the designed system were verified through multiple experiments.

Abstract Quantitative real-time polymerase chain reaction (qRT-PCR) has become a commonly used method for the quantification of gene expression. However, accurate qRT-PCR analysis requires a valid internal reference for data normalization. To determine the valid reference characterized with low expression variability among Spodoptera litura samples after microbial pesticide treatments, nine housekeeping genes, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), arginine kinase, ubiquitin C, actin-5C (ACT5C), actin, ribosomal protein S13 (RPS13), tubulin, acidic ribosomal protein P0 (RPLP0) and ubiquinol-cytochrome c reductase, were evaluated for their suitability using geNorm, Normfinder, BestKeeper, RefFinder and the comparative delta CT methods in this study. S. litura larvae after direct treatment (larvae were immersed in biopesticides), indirect treatment (larvae were fed with biopesticide immersed artificial diets) and comprehensive treatment (larvae were treated with the first two treatments in sequence), respectively with Metarhizium anisopliae, Empedobacter brevis and Bacillus thuringiensis, were investigated. The results indicated that the best sets of internal references were as follows: RPLP0 and ACT5C for direct treatment conditions; RPLP0 and RPS13 for indirect treatment conditions; RPS13 and GAPDH for comprehensive treatment conditions; RPS13 and RPLP0 for all the samples. These results provide valuable bases for further genetic researches in S. litura.

Abstract This paper constructs a two-layer road data asset revenue allocation model based on a modified Shapley value approach. The first layer allocates revenue to three roles in the data value realization process: the original data collectors, the data processors, and the data product producers. It fully considers and appropriately adjusts the revenue allocation to each role based on data risk factors. The second layer determines the correction factors for different roles to distribute revenue among the participants within those roles. Finally, the revenue values of the participants within each role are synthesized to obtain a consolidated revenue distribution for each participant. Compared to the traditional Shapley value method, this model establishes a revenue allocation evaluation index system, uses entropy weighting and rough set theory to determine the weights, and adopts a fuzzy comprehensive evaluation and numerical analysis to assess the degree of contribution of participants. It fully accounts for differences in both the qualitative and quantitative contributions of participants, enabling a fairer and more reasonable distribution of revenues. This study provides new perspectives and methodologies for the benefit distribution mechanism in road data assets, which aid in promoting the market-based use of road data assets, and it serves as an important reference for the application of data assetization in the road transportation industry.

Abstract Groundwater has a significant influence on the mechanical properties of surrounding rock. Aiming at the large deformation of surrounding rock of red layer soft rock tunnel affected by groundwater, the uniaxial graded loading tests were carried out on red beds soft rock with different water content. The failure process of the specimen was monitored by acoustic emission (AE) and the crack evolution law was analyzed, and the scanning electron microscopy (SEM) was used to compare the microstructure of the specimens before and after immersion. Combined with fractal theory, the monofractal and multifractal characteristics of AE ringing count during the loading process of red beds soft rock were analyzed. The results show that, with the gradual increase of water content, the AE ringing count before the yield stage gradually decreased, and the corresponding cumulative ringing count at the same time gradually decreased, and the decrease was large in the early stage of immersion, and decreased in the later stage. The cumulative ringing curve gradually slowed down, the internal crack appeared earlier, the cumulative ringing curve stepped significantly, the AE signal amplitude gradually weakened, and the bandwidth of each frequency band gradually decreased. The failure of red beds soft rock with different water content is dominated by shear crack, and with the gradual increase of water content, the proportion of shear crack increases gradually, and the AE b value decreases gradually. With the gradual increase of the relative peak strength, the correlation dimension D of red beds soft rock with different water content increases first and then decreases. At 80% of the relative peak strength, the correlation dimension D reaches its maximum value and then drops sharply until it is maintained at a relatively low level, and the correlation dimension D gradually decreases with the water content. The fitting correlation coefficients of different water content (lnC(r), lnr) are all above 0.9, indicating that the AE ringing count of water-bearing red beds soft rock has fractal characteristics, and the higher the correlation coefficient, the higher the self-similarity of AE ringing count sequence. As the weight q gradually increases, the generalized fractal dimension D(q) gradually decreases. When q ≠ 0, under the condition of the same q, D(q) presents a trend of first increasing and then decreasing. The multifractal characteristics of AE ringing count of red beds soft rock with different water content is inverted ‘U’ shape. From the natural state to immerse 1 d, the ∆α gradually increases, and from 1 to 7 d, the ∆α gradually decreases, where Δα = α max − α min represents the spectral width of the multifractal spectrum. When saturation is not reached, ∆f 0 indicates that a large number of cracks are generated inside the specimen and macro cracks are formed, where Δf = f (α max) − f (α min) represents the frequency relationship between different signals of different sizes. This research can provide a reliable theoretical basis for the construction and maintenance of large deformation of water-rich soft rock tunnel excavation, and have certain engineering significance.

Abstract Cellular automata (CA) are computational systems that exhibit complex global behavior arising from simple local rules, making them a fascinating candidate for various research areas. However, challenges such as limited flexibility and efficiency on conventional hardware platforms still exist. In this study, we propose a memristor-based circuit for implementing elementary cellular automata (ECA) by extending the stateful three-memristor logic operations derived from material implication (IMP) logic gates. By leveraging the inherent physical properties of memristors, this approach offers simplicity, minimal operational steps, and high flexibility in implementing ECA rules by adjusting the circuit parameters. The mathematical principles governing circuit parameters are analyzed, and the evolution of multiple ECA rules is successfully demonstrated, showcasing the robustness in handling the stochastic nature of memristors. This approach provides a hardware solution for ECA implementation and opens up new research opportunities in the hardware implementation of CA.

Abstract The purpose is to solve the safety production and management problems of the gold Industrial Chain and give early warning of the safety situation of the gold Industrial Chain. According to the theory of industrial chain security governance and the basic situation of the gold Industrial Chain, this work establishes a gold Industrial Chain model based on smart blockchain and system dynamics (SD), and discusses the application of the gold Industrial Chain in the Internet of Things (IoT) environment. The overall goal of the application of IoT technology to the safety management of intelligent gold mines is to take the gold mine production demand as the driving force. The digitalization of production, electromechanical, safety, dispatching, and other information realizes intelligent digital perception, assists decision-making, guides the safety management of gold mining operations, continuously improves the operation efficiency of the gold mining industry, and drives the development of the industry. Finally, it takes the resource reserve of China’s gold industry from 2011 to 2021 as the research data introduces the weighting method to assess the security situation of China’s gold Industrial Chain from 2011 to 2021. The safety performance of China's environmental industry chain is evaluated through the detailed introduction of the basic information of the gold Industrial Chain. The result shows that the security situation of China’s gold Industrial Chain from 2011 to 2021 shows an overall growth trend, 88.42% higher than in 2014. The security situation of China’s gold Industrial Chain from 2011 to 2021 positively impacted the opening of the domestic gold market and entering the international gold market, improving the security level of China’s gold Industrial Chain. In this work, a gold Industrial Chain security model based on smart blockchain and SD is established to solve the safety problem of the gold Industrial Chain, which can improve the safety level of the gold industry and promote its sustainable development.

Abstract In this study, we present a novel surgical method that utilizes the ultrasonic bone scalpel (UBS) for the removal of large retrovertebral osteophytes in anterior cervical discectomy and fusion (ACDF) and evaluate its safety and efficacy in comparison to the traditional approach of using high-speed drill (HSD). A total of 56 patients who underwent ACDF for retrovertebral osteophytes were selected. We recorded patients' baseline information, operation time, intraoperative blood loss, complications, JOA and VAS scores, and other relevant data. The mean operation time and the mean intraoperative blood loss in the UBS group were less than those in the HSD group (P 0.05). Additionally, no significant disparities were found in bone graft fusion between the two groups at 6- and 12-months postsurgery. Notably, neither group exhibited complications such as dura tear or spinal cord injury. Our study found that the use of UBS reduced operative time, minimized surgical bleeding, and led to clinical outcomes comparable to HSD in ACDF. This technique offers an effective and safe method of removing large retrovertebral osteophytes.

Abstract Epidemiological studies have reported a positive association between chronic inflammation and cancer risk. However, the causal association between chronic inflammation and breast cancer (BC) risk remains unclear. Here, we performed a Mendelian randomization study to investigate the etiological role of chronic inflammation in BC risk. We acquired data regarding C-reactive protein (CRP), interleukin (IL)-1a, IL-1b, and IL-6 expression and BC related to single nucleotide polymorphisms (SNPs) from two larger consortia (the genome-wide association studies and the Breast Cancer Association Consortium). Next, we conducted the two-sample Mendelian randomization study to investigate the relationship of the abovementioned inflammatory factors with the incidence of BC. We found that genetically predicted CRP, IL-6, and IL-1a levels did not increase BC incidence (odds ratio (OR)CRP 1.06, 95% confidence interval (CI) 0.98–1.12, P = 0.2059, ORIL-6 1.05, 95% CI 0.95–1.16, P = 0.3297 and ORIL-1a 1.01, 95% CI 0.99–1.03, P = 0.2167). However, in subgroup analysis, genetically predicted IL-1b levels increased ER + BC incidence (OR 1.15, 95% CI 1.03–1.27, P = 0.0088). Our study suggested that genetically predicted IL-1b levels were found to increase ER + BC susceptibility. However, due to the support of only one SNP, heterogeneity and pleiotropy tests cannot be performed, which deserves further research.

Abstract Recently, the efficacy of two low-invasive treatments, ablation, and radiotherapy, has been fully compared for the patients with the early-stage hepatocellular carcinoma (HCC). However, the comparison between radiotherapy plus ablation and ablation alone has been less frequently reported. Data from the Surveillance, Epidemiology, and End Results (SEER) database were searched for early-stage HCC patients treated with ablation plus radiotherapy or ablation alone. The outcome measures were overall survival (OS) and cancer-specific survival (CSS). The propensity score matching (PSM) was used to reduce selection bias. We included 240 and 6619 patients in the radiotherapy plus ablation group and ablation group before the PSM. After PSM, 240 pairs of patients were included. The median OS (mOS) and median CSS (mCSS) of patients receiving ablation alone were longer than that of receiving radiotherapy plus ablation (mOS: 47 vs. 34 months, P = 0.019; mCSS: 77 vs. 40 months, P = 0.018, after PSM) before and after PSM. The multivariate analysis indicated that radiotherapy plus ablation independent risk factor for OS and CSS before PSM, but the significance disappeared after PSM. The detailed subgroup analyses indicated ablation alone brought more benefit in very early-stage HCC and older patients. In addition, we found different types of radiotherapy might lead to different outcomes when combined with ablation. In conclusion, ablation alone is noninferior to radiotherapy plus ablation in patients with early-stage HCC. The additional radiation prior to ablation may bring survival benefits in the patients with higher tumor stage. However, due to the risk of selection bias in that study, the results should be interpreted cautiously.

Abstract Subarachnoid hemorrhage (SAH) occurs most commonly after rupture of an aneurysm, resulting in high disability and mortality due to the absence of effective therapy. Its subsequent stage, early brain injury (EBI), promotes the sustainable development of injury in the brain and ultimately leads to poor prognosis. As a new antiepileptic drug, the effect of perampanel on EBI after SAH is unknown. Pyroptosis, a process of inflammatory programmed cell death, has been confirmed in most studies to play a substantial role in aggravating SAH-post EBI. Similarly, oxidative stress is closely involved in neuronal pyroptosis and the pathophysiological mechanism of SAH-post EBI, leading to a devastating outcome for SAH patients. Nonetheless, no studies have been conducted to determine whether perampanel reduces pyroptosis and oxidative stress in the context of SAH-induced EBI. Rat SAH model via endovascular perforation was constructed in this study, to assess the neuroprotective effect of perampanel on SAH-post EBI, and to clarify the possible molecular mechanism. By means of the neurological score, brain edema detection, FJB staining, immunofluorescence, WB, ELISA, and ROS assay, we found that perampanel can improve neuroscores and reduce brain edema and neuronal degeneration at 24 h after SAH; we also found that perampanel reduced oxidative stress, neuronal pyroptosis, and inhibition of the SIRT3-FOXO3α pathway at 24 h after SAH. When 3-TYP, an inhibitor of SIRT3, was administered, the effects of perampanel on the SIRT3-FOXO3a pathway, antioxidant stress, and neuronal pyroptosis were reversed. Taken together, our data indicate that perampanel attenuates oxidative stress and pyroptosis following subarachnoid hemorrhage via the SIRT3/FOXO3α pathway. This study highlights the application value of perampanel in subarachnoid hemorrhage and lays a foundation for clinical research and later transformation of perampanel in SAH.

Abstract This study was to develop a computer vision evaluation method to automatically measure the degree of scoliosis based on the machine learning algorithm. For the X-ray images of 204 patients with idiopathic scoliosis who underwent full-spine radiography, histogram equalization of original image was performed before a flipping method was used to magnify asymmetric elements, search for the global maximum pixel value in each line, and scan local maximal pixel value, with the intersection set of two point sets being regarded as candidate anchor points. All fine anchors were fitted with cubic spline algorithm to obtain the approximate curve of the spine, and the degree of scoliosis was measured by the standardized integral area. All measured data were analyzed. In manual measurement, the Cobb angle was 11.70–25.00 (20.15 ± 3.60), 25.20–44.70 (33.89 ± 5.41), and 45.10–49.40 (46.98 ± 1.25) in the mild, moderate and severe scoliosis group, respectively, whereas the value for the standardized integral area algorithm was 0.072–0.298 (0.185 ± 0.040), 0.100–0.399 (0.245 ± 0.050), and 0.246–0.901 (0.349 ± 0.181) in the mild, moderate and severe scoliosis group, respectively. Correlation analysis between the manual measurement of the Cobb angle and the evaluation of the standardized integral area algorithm demonstrated the Spearman correlation coefficient r = 0.643 (P

Abstract To evaluate the efficacy of remimazolam pretreatment in preventing propofol-induced injection pain (PIP) in patients undergoing gastroscopy. One hundred and forty patients (ASA I–II, aged 18–65 years, BMI 18–28 kg/m2) who were to undergo gastroscopy were randomized into either a saline group (group S) or a remimazolam group (group R) (n = 70 for each) on a computer-generated random number basis. The patients in group S received normal saline (0.1 ml/kg) and those in group R were administered remimazolam (0.1 mg/kg) via intravenous infusion for 60 s. 30 s after the injection of normal saline or remimazolam, patients received intravenously propofol (0.5 ml/s) until loss of consciousness. A different anesthesiologist who was unaware of the pretreatment was responsible for maintaining the outcome. The primary endpoint of our study was the incidence of PIP, which was measured using a 4-point scale. Secondary endpoints include the intensity of PIP, vital signs, characteristics of surgery and recovery, and adverse events. The incidence of PIP was significantly lower in group R than in group S (13 vs 51%, p

Abstract Karst caves are distinctive ecosystems that have limited nutrients, darkness, low to moderate temperatures, and high moisture levels, which allow for a diverse range of fungal communities to thrive. Despite their significance, little is understood about the fungi found in karst caves in Thailand. In 2019, we studied the cultured mycobiota from five substrate types (air, water, rock, soil/sediment, and organic debris) in two karst caves (Le Stegodon and Phu Pha Phet Caves) of the Satun UNESCO Global Geopark, southern Thailand. A cumulative count of 829 distinct fungal morphological types was identified, encompassing 319 fungal culturable were observed. Based on preliminary analyses of the internal transcribed spacer (ITS) sequence using BLAST searches, the most common phylum among the fungal morphotypes was Ascomycota, harboring 282 species in 91 genera, 93.4% of which were distributed in the classes Eurotiomycetes, Sordariomycetes, and Dothideomycetes. The most common fungal genera identified in the two karst caves were Aspergillus, Penicillium, Cladosporium, Talaromyces, Xylaria, and Trichoderma, with 45, 41, 24, 14, 14, and 6 species identified, respectively. Discovering fungi in Thai karst caves highlights the extensive fungal diversity in the Satun UNESCO Global Geopark, implying undiscovered species, and emphasizing the need for comprehensive investigations in other unexplored Thai karst caves.

Abstract Botulinum neurotoxin (BoNT) shows high lethality and toxicity, marking it as an important biological threat. The only effective post-exposure therapy is botulinum antitoxin; however, such products have great potential for improvement. To prevent or treat BoNT, monoclonal antibodies (mAbs) are promising agents. Herein, we aimed to construct a bispecific antibody (termed LUZ-A1-A3) based on the anti-BoNT/A human monoclonal antibodies (HMAb) A1 and A3. LUZ-A1-A3 binds to the Hc and L-HN domains of BoNT/A, displaying potent neutralization activity against BoNT/A (124 × higher than that of HMAb A1 or HMAb A3 alone and 15 × higher than that of the A1 + A3 combination). LUZ-A1-A3 provided effective protection against BoNT/A in an in vivo mouse model. Mice were protected from infection with 500 × LD50 of BoNT/A by LUZ-A1-A3 from up to 7 days before intraperitoneal administration of BoNT/A. We also demonstrated the effective therapeutic capacity of LUZ-A1-A3 against BoNT/A in a mouse model. LUZ-A1-A3 (5 μg/mouse) neutralized 20 × LD50 of BoNT/A at 3 h after intraperitoneal BoNT/A administration and complete neutralized 20 × LD50 of BoNT/A at 0.5 h after intraperitoneal BoNT/A administration. Thus, LUZ-A1-A3 is a promising agent for the pre-exposure prophylaxis and post-exposure treatment of BoNT/A.

Abstract Sediment deposition in cascade reservoirs is not only related to the utilization efficiency of the reservoir itself but also to the boundary conditions for the operation of other reservoirs in the same group. The Baihetan Reservoir is the largest hydropower project with the highest unit capacity in the world, and it is necessary to consider sediment deposition within it, as this affects the comprehensive operation of cascade reservoirs in the lower Jinshajiang River. In this study, the input water, sediment, and deposition characteristics were analyzed based on both field hydrological and topographic data of the Baihetan Reservoir during its initial impoundment period. The results showed that water entering Baihetan Reservoir was mainly derived from the upper main stream, and approximately 41% was concentrated in the third quarter. Ten times the amount of sediment derived from the main stream was received from tributaries and uncontrolled areas of the reservoir, and these are the main sediment input sources. The fluctuating backwater area influenced by the upstream Wudongde Reservoir was slightly eroded, and siltation mainly occurred in the dead storage capacity (below 765 m) of the main stream and tributary estuaries in the perennial backwater area; approximately 15.8 times that in the regulating storage capacity (between 765 and 785 m). The differences between the results of this study and those from the reservoir demonstration stage indicate that was a lack of understanding about how climate change, human activities, and uncontrolled areas would affect siltation patterns. In future projects, research focusing on climate trend analyses and the comprehensive consideration of human activities should be combined with extensive sediment production monitoring and model parameter calibration.

Abstract A novel tissue-specific functional tissue engineering scaffold for cartilage repair should have a three-dimensional structure, good biosafety and biological activity, and should be able to promote cartilage tissue regeneration. This study aimed to determine the effect of ultrasound-treated collagen/silk fibroin (Col/SF) composite scaffolds with good mechanical properties and high biological activity on cartilage repair. The characteristics of the scaffolds with different Col/SF ratios (7:3, 8:2, and 9:1) were determined by scanning electron microscopy, Fourier-transform infrared spectroscopy, and porosity, water absorption, and compression tests. In vitro evaluations revealed the biocompatibility of the Col/SF scaffolds. Results suggested that the optimal ratio of Col/SF composite scaffolds was 7:3. The Col/SF scaffolds induced adipose-derived stem cells to undergo chondrogenic differentiation under chondrogenic culture conditions. The efficiency of Col/SF scaffolds for cartilage regeneration applications was further evaluated using an in vivo model of full-thickness articular cartilage defects in New Zealand rabbits. The Col/SF scaffolds effectively promoted osteochondral regeneration as evidenced by macroscopic, histological, and immunohistochemical evaluation. The study demonstrates that ultrasound-treated Col/SF scaffolds show great potential for repairing cartilage defects.

Abstract Epidemic spatial–temporal risk analysis, e.g., infectious number forecasting, is a mainstream task in the multivariate time series research field, which plays a crucial role in the public health management process. With the rise of deep learning methods, many studies have focused on the epidemic prediction problem. However, recent primary prediction techniques face two challenges: the overcomplicated model and unsatisfactory interpretability. Therefore, this paper proposes an Interpretable Spatial IDentity (ISID) neural network to predict infectious numbers at the regional weekly level, which employs a light model structure and provides post-hoc explanations. First, this paper streamlines the classical spatio-temporal identity model (STID) and retains the optional spatial identity matrix for learning the contagion relationship between regions. Second, the well-known SHapley Additive explanations (SHAP) method was adopted to interpret how the ISID model predicts with multivariate sliding-window time series input data. The prediction accuracy of ISID is compared with several models in the experimental study, and the results show that the proposed ISID model achieves satisfactory epidemic prediction performance. Furthermore, the SHAP result demonstrates that the ISID pays particular attention to the most proximate and remote data in the input sequence (typically 20 steps long) while paying little attention to the intermediate steps. This study contributes to reliable and interpretable epidemic prediction through a more coherent approach for public health experts.

Abstract To explore the effect of IL-6 on the activity and secretory function of B cells and analyze its effect on clinical indicators and efficacy in wAIHA patients. This study included 25 hemolytic wAIHA patients, 13 remission patients, and 10 HCs. Plasma levels of various cytokines were detected using CBA. PBMCs were extracted from 12 hemolytic wAIHA patients and divided into three wells, stimulation with IL-6 and IL-6 + tocilizumab, the blank control wells were also set. After 48 h of in vitro cell culture, percentage of CD5+CD80+, CD5–CD80+,CD5+CD86+,CD5–CD86+,CD5+IL-10+,CD5–IL-10+B cells were determined by flow-cytometry. Plasma levels of IL-6 and IL-10 in hemolytic episode group were significantly higher than that in HCs group (p = 0.0243; p = 0.0214). RBC and Hb levels were negatively correlated with IL-6 levels in wAIHA patients, while LDH levels were positively correlated.Therapeutic effects of glucocorticoid and duration of efficacy were also significantly correlated with IL-6 levels in wAIHA patients. After 48 h in vitro cell culture, percentages of CD80+/CD5+CD19+and CD80+/CD5–CD19+ cells in the IL-6 stimulation group were higher than those in blank control group (p = 0.0019; p = 0.0004), while CD86+/CD5+ CD19+ and CD86+/CD5–CD19+ cells were not statistically different before and after IL-6 stimulation. Percentage of IL-10+/CD5+ CD19+ cells in IL-6 stimulation group was lower than that in blank control (p = 0.0017) and IL-6 + toc (p = 0.0117) group. Percentage of IL-10+/CD5– CD19+cells in the IL-6 stimulation group was lower than that in the blank control group (p = 0.0223). Plasma levels of IL-6 were significantly elevated in hemolytic wAIHA patients and correlated with clinical indicators and efficacy. IL-6 promotes the activation of B cells. Although the results were not statistically significant, IL-6R antagonist tocilizumab may hopefully become a targeted therapy for wAIHA patients.

Abstract The association between the embryo quality, clinical characteristics, miRNAs (secreted by blastocysts in the culture medium) and pregnancy outcomes has been well-established. Studies on prediction models for pregnancy outcome, using clinical characteristics and miRNA expression, are limited. We aimed to establish the prediction model for prediction of pregnancy outcome of woman after a fresh cycle of Day 5 single blastocyst transfer (Day 5 SBT) based on clinical data and miRNA expression. A total of 86 women, 50 with successful pregnancy and 36 with pregnancy failure after fresh cycle of Day 5 SBT, were enrolled in this study. All samples were divided into training set and test set (3:1). Based on clinical index statistics of enrolled population and miRNA expression, the prediction model was constructed, followed by validation of the prediction model. Four clinical indicators, female age, sperm DNA fragmentation index, anti-mullerian hormone, estradiol, can be used as independent predictors of pregnancy failure after fresh cycle of Day 5 SBT. Three miRNAs (hsa-miR-199a-3p, hsa-miR-199a-5p and hsa-miR-99a-5p) had a potential diagnostic value for pregnancy failure after Day 5 SBT. The predictive effect of model combining 4 clinical indicators and 3 miRNAs (area under the receiver operating characteristic curve, AUC = 0.853) was better than models combining single 4 clinical indicators (AUC = 0.755) or 3 miRNAs (AUC = 0.713). Based on 4 clinical indicators and 3 miRNAs, a novel model to predict pregnancy outcome in woman after fresh cycle of Day 5 SBT has been developed and validated. The predictive model may be valuable for clinicians to make the optimal clinical decision and patient selection.

Abstract Ensuring the rational and orderly circulation of medical supplies during a public health emergency is crucial to quickly containing the further spread of the epidemic and restoring the order of rescue and treatment. However, due to the shortage of medical supplies, there are challenges to rationalizing the allocation of critical medical supplies among multiple parties with conflicting interests. In this paper, a tripartite evolutionary game model is constructed to study the allocation of medical supplies in the rescue environment of public health emergencies under conditions of incomplete information. The game’s players include Government-owned Nonprofit Organizations (GNPOs), hospitals, and the government. By analyzing the equilibrium of the tripartite evolutionary game, this paper makes an in-depth study on the optimal allocation strategy of medical supplies. The findings indicate that: (1) the hospital should reasonably increase its willingness to accept the allocation plan of medical supplies, which can help medical supplies allocate more scientifically. (2) The government should design a reasonable reward and punishment mechanism to ensure the rational and orderly circulation of medical supplies, which can reduce the interference of GNPOs and hospitals in the allocation process of medical supplies. (3) Higher authorities should strengthen the supervision of the government and the accountability for loose supervision. The findings of this research can guide the government in promoting better circulation of medical supplies during public health emergencies by formulating more reasonable allocation schemes of emergency medical supplies, as well as incentives and penalties. At the same time, for GNPOs with limited emergency medical supplies, the equal allocation of emergency supplies is not the optimal solution to improve the efficiency of emergency relief, and it is simpler to achieve the goal of maximizing social benefits by allocating limited emergency resources to the demand points that match the degree of urgency. For example, in Corona Virus Disease 2019, emergency medical supplies should be prioritized for allocation to government-designated fever hospitals that are have a greater need for medical supplies and greater treatment capacity.

Abstract We aimed to identify potential novel predictors for breast cancer among post-menopausal women, with pre-specified interest in the role of polygenic risk scores (PRS) for risk prediction. We utilised an analysis pipeline where machine learning was used for feature selection, prior to risk prediction by classical statistical models. An “extreme gradient boosting” (XGBoost) machine with Shapley feature-importance measures were used for feature selection among $$\approx$$ ≈ 1.7 k features in 104,313 post-menopausal women from the UK Biobank. We constructed and compared the “augmented” Cox model (incorporating the two PRS, known and novel predictors) with a “baseline” Cox model (incorporating the two PRS and known predictors) for risk prediction. Both of the two PRS were significant in the augmented Cox model ( $$p

Abstract The prevalence of high birth weight or large for gestational age (LGA) infants is increasing, with increasing evidence of pregnancy-related factors that may have long-term impacts on the health of the mother and baby. We aimed to determine the association between excessive fetal growth, specifically LGA and macrosomia, and subsequent maternal cancer by performing a prospective population-based cohort study. The data set was based on the Shanghai Birth Registry and Shanghai Cancer Registry, with medical records from the Shanghai Health Information Network as a supplement. Macrosomia and LGA prevalence was higher in women who developed cancer than in women who did not. Having an LGA child in the first delivery was associated with a subsequently increased risk of maternal cancer (hazard ratio [HR] = 1.08, 95% confidence interval [CI] 1.04–1.11). Additionally, in the last and heaviest deliveries, there were similar associations between LGA births and maternal cancer rates (HR = 1.08, 95% CI 1.04–1.12; HR = 1.08, 95% CI 1.05–1.12, respectively). Furthermore, a substantially increased trend in the risk of maternal cancer was associated with birth weights exceeding 2500 g. Our study supports the association between LGA births and increased risks of maternal cancer, but this risk requires further investigation.

Abstract We investigated the growth and evolution of Si-based Ge quantum dots (Ge/Si QDs) under low Ge deposition (1.2–4.4 nm thick) using magnetron sputtering. The morphology and structure of QDs were analyzed with the help of an atomic force microscope (AFM), scanning electron microscope, transmission electron microscope, Raman, surface energy theory and dynamics theory, the photoelectric properties of QDs were characterized by photoluminescence (PL) spectra. The results showed that the growth mechanism of QDs conformed to Stranski–Krastanow mode, but the typical thickness of the wetting layer was nearly three times higher than those derived from conventional technologies such as molecular beam epitaxy, chemical vapor deposition, solid phase epitaxy and so on. Meanwhile, the shape evolution of QDs was very different from existing reports. The specific internal causes of these novel phenomena were analyzed and confirmed and reported in this paper. In addition, the AFM, Raman, and PL tests all indicated that the QDs grown when 3.4 nm Ge was deposited have the most excellent morphology, structure, and optoelectronic performance. Our work lays a foundation for further exploration of the controllable growth of QDs at high deposition rates, which is a new way to realize the industrialization of QDs used for future devices.

Abstract Meningioma was the most primary intracranial tumor, but the molecular characteristics and the treatment of malignant meningioma were still unclear. Nine malignant progression-related genes based prognostic signatures were identified by transcriptome analysis between benign meningioma and malignant meningioma. The external dataset GEO136661 and quantitative Real-time Polymerase Chain Reaction were used to verify the prognostic factors. has-miR-3605-5p, hsa-miR-664b-5p, PNRC2, BTBD8, EXTL2, SLFN13, DGKD, NSD2, and BVES were closed with malignant progression. Moreover, Doxorubicin was identified by Connectivity Map website with the differential malignant progression-related genes. CCK-8 assay, Edu assay, wound healing assay, and trans-well experiment were used to reveal that Doxorubicin could inhibit proliferation, migration and invasion of IOMM-Lee Cells.

Abstract Porous biomaterials design for bone repair is still largely limited to regular structures (e.g. rod-based lattices), due to their easy parameterization and high controllability. The capability of designing stochastic structure can redefine the boundary of our explorable structure–property space for synthesizing next-generation biomaterials. We hereby propose a convolutional neural network (CNN) approach for efficient generation and design of spinodal structure—an intriguing structure with stochastic yet interconnected, smooth, and constant pore channel conducive to bio-transport. Our CNN-based approach simultaneously possesses the tremendous flexibility of physics-based model in generating various spinodal structures (e.g. periodic, anisotropic, gradient, and arbitrarily large ones) and comparable computational efficiency to mathematical approximation model. We thus successfully design spinodal bone structures with target anisotropic elasticity via high-throughput screening, and directly generate large spinodal orthopedic implants with desired gradient porosity. This work significantly advances stochastic biomaterials development by offering an optimal solution to spinodal structure generation and design.

Abstract Existing salient object detection networks are large, have many parameters, are bulky and take up a lot of computational resources. Seriously hinder its application and promotion in boning robot. To solve this problem, this paper proposes a lightweight saliency detection algorithm for real-time localization of livestock meat bones. First, a lightweight feature extraction network based on multi-scale attention is constructed in the encoding stage. To ensure that more adequate salient object features are extracted with fewer parameters. Second, the fusion of jump connections is introduced in the decoding phase. Used to capture fine-grained semantics and coarse-grained semantics at full scale. Finally, we added a residual refinement module at the end of the backbone network. For optimizing salient target regions and boundaries. Experimental results on both publicly available datasets and self-made Pig leg X-ray (PLX) datasets show that. The proposed method is capable of ensuring first-class detection accuracy with 40 times less parameters than the conventional model. In the most challenging SOD dataset. The proposed algorithm in this paper achieves a value of Fωβ of 0.699. And the segmentation of livestock bones can be effectively performed on the homemade PLX dataset. Our model has a detection speed of 5fps on industrial control equipment.

Abstract Moyamoya disease (MMD) is a chronic occlusive cerebrovascular disease that can be treated with revascularization. Surgery increases the risk of poor wound healing (PWH) due to the impact on the blood supply to the flap. We aimed to analyze risk factors for PWH in MMD with a complete Y-shaped incision. A total of 125 patients with MMD were enrolled in this prospective observational study. The wounds were assessed and measured on the third and seventh days after surgery. The mean age of these patients was 43.3 ± 10.0 years. The ratio of male to female was 1:1.3. 15 (12.0%) patients had incision complications. 5 patients (4.0%) had redness; 2 patients (1.6%) had swelling; 2 patients (1.6%) had fat necrosis; 3 patients (2.4%) had incision infection; and 3 patients (2.4%) had flap necrosis. Student’s t test showed significant differences in BMI (P = 0.040) and fever time (P = 0.050). The standard chi-squared test showed significant differences in incision infection (P = 0.010), suture mode (P = 0.047), and cutting off large branch vessels in the flap (P

Abstract Quantifying relationships between animal behavior and habitat use is essential to understanding animal decision-making. High-resolution location and acceleration data allows unprecedented insights into animal movement and behavior. These data types allow researchers to study the complex linkages between behavioral plasticity and habitat distribution. We used a novel Markov model in a Bayesian framework to quantify the influence of behavioral state frequencies and environmental variables on transitions among landcover types through joint use of location and tri-axial accelerometer data. Data were collected from 56 greater white-fronted geese (Anser albifrons frontalis) across seven ecologically distinct winter regions over two years in midcontinent North America. We showed that goose decision-making varied across landcover types, ecoregions, and abiotic conditions, and was influenced by behavior. We found that time spent in specific behaviors explained variation in the probability of transitioning among habitats, revealing unique behavioral responses from geese among different habitats. Combining GPS and acceleration data allowed unique study of potential influences of an ongoing large-scale range shift in the wintering distribution of a migratory bird across midcontinent North America. We anticipate that behavioral adaptations among variable landscapes is a likely mechanism explaining goose use of highly variable ecosystems during winter in ways which optimize their persistence.

Abstract The efficacy and safety of self-expanding metallic stent (SEMS) placement as a bridge to elective surgery versus emergency surgery to treat malignant colonic obstruction is debated. This study aimed to evaluate the outcomes of patients with malignant colonic obstruction treated using different procedure. Subjects admitted to the authors’ department with colonic obstruction (n = 87) were studied. They underwent colonic stenting as a bridge to elective surgery (SEMS group: n = 14) or emergency surgery (ES group: n = 22).Their demographic characteristics, stoma rate, laparoscopy rate and postoperative complications were analyzed, and the potential risk factors of postoperative complications and the optimal time interval from SEMS implantation to elective surgery were explored. The stoma rate was 15.4% in the SEMS group versus 60.0% in the ES group (P = 0.015), and the postoperative complication rate was 7.7% in the SEMS group versus 40.0% in the ES group (P = 0.042). The proportion of patients undergoing laparoscopy in SEMS group was significantly higher than that in ES group (69.2% vs. 15.0%; P = 0.003).The effect of ASA grade on postoperative complications was statistically significant (OR = 24.565; P = 0.008). The Receiving operating characteristic (ROC) curve could not determine the optimal time interval between SEMS implantation and elective surgery (AUC = 0.466). SEMS implantation has the advantages of lower temporary stoma rate, less postoperative complications and higher laparoscopy rate compared with ES in the treatment of left malignant intestinal obstruction. ASA grade is a risk factor for postoperative complications. However, larger sample size prospective randomized controlled trials (RCT) are still needed to confirm long-term oncological outcomes.

Abstract Corrosion can initiate cracking that leads to structural integrity reduction. Quantitative corrosion assessment is challenging, and the modeling of corrosion-induced crack initiation is essential for model-based corrosion reliability analysis of various structures. This paper proposes a probabilistic computational analysis framework for corrosion-to-crack transitions by integrating a phase-field model with machine learning and uncertainty quantification. An electro-chemo-mechanical phase-field model is modified to predict pitting corrosion evolution, in which stress is properly coupled into the electrode chemical potential. A crack initiation criterion based on morphology is proposed to quantify the pit-to-cracking transition. A spatiotemporal surrogate modeling method is developed to facilitate this, consisting of a Convolution Neural Network (CNN) to map corrosion morphology to latent spaces, and a Gaussian Process regression model with a nonlinear autoregressive exogenous model (NARX) architecture for prediction of corrosion dynamics in the latent space over time. It enables the real-time prediction of corrosion morphology and crack initiation behaviors (whether, when, and where the corrosion damage triggers the crack initiation), and thus makes it possible for probabilistic analysis, with uncertainty quantified. Examples at various stress and corrosion conditions are presented to demonstrate the proposed computational framework.