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Enzymatic modification can effectively improve the gel property of soybean protein. In order to study the effect of transglutaminase (TGase) pre-crosslinking on the properties of salt induced soy protein isolate (SPI) gel, SPI dispersions with different crosslinking degree were prepared by controlling the enzyme concentration and the pre-crosslinking time, and their gelation properties under the action of CaSO4 were studied. The results showed that compared with SPI without TGase treatment, appropriate pre-crosslinking of protein could significantly improve the quality of SPI gels. After 3~5 U/g TGase crosslinking for 20 min or 3 U/g TGase crosslinking for 20~30 min, the properties of SPI gel were improved to different degrees, among which the elastic modulus, yield stress, yield strain and water holding capacity were increased by 124.6%, 269.0%, 135.0% and 53.0%, respectively. However, excessive crosslinking produced protein aggregates with too large size, resulting in coarse and porous gel structure and significantly decreased (P

DNA-mediated programming is emerging as an effective technology that enables controlled dynamic assembly/disassembly of inorganic nanocrystals (NC) with precise numbers and spatial locations for biomedical imaging applications. In this review, we will begin with a brief overview of the rules of NC dynamic assembly driven by DNA ligands, and the research progress on the relationship between NC assembly modes and their biomedical imaging performance. Then, we will give examples on how the driven program is designed by different interactions through the configuration switching of DNA-NC conjugates for biomedical applications. Finally, we will conclude with the current challenges and future perspectives of this emerging field. Hopefully, this review will deepen our knowledge on the DNA-guided precise assembly of NCs, which may further inspire the future development of smart chemical imaging devices and high-performance biomedical imaging probes.

Abstract Mothers can influence offspring phenotypes by transferring non-genetic information to the young, which provides them with a flexible tool to adjust the developmental trajectory of the young in fluctuating environments. Mothers can differentially deposit their resources in the same reproductive attempt in relation to the offspring position in the sibling hierarchy. However, whether embryos from different positions can be plastic in their response to the maternal signals, potentially leading to a mother–offspring conflict, is yet unclear. We used Rock pigeons (Columba livia), that lay two egg clutches where maternal androgen levels in second laid eggs at oviposition are higher than in first laid eggs, and investigated the plasticity of embryonic metabolism of maternal androgens. We experimentally elevated androstenedione and testosterone levels in first eggs to that present in second eggs and measured the change in androgen levels and its main metabolites (etiocholanolone and conjugated testosterone) after 3.5 days of incubation. We found that eggs with increased androgens show a different degree of androgen metabolism depending either on the egg laying sequence or initial androgen levels or both. Our findings indicate that embryos have certain plasticity in response to maternal androgen levels depending on maternal signals.

In this study， performances of NCAR Community Land Surface Model CLM （version 4.5） coupled with dynamic vegetation and carbon-nitrogen model （CN-DV） in representing the observed vegetation characteristics of satellite over the Qinghai-Xizang Plateau （QXP） during historical period 1981—2000 were evaluated by comparing with remote sensing datasets of leaf area index （LAI） and coverage fraction.It was shown CLM4.5-CNDV can generally reproduce the spatial distribution of LAI， coverage fractions and their linear trends shown in the satellite data， but LAI was seriously overestimated due to the significantly overestimated tree coverage， and underestimated grass coverage as well.The 1~2 month lag shown both in LAI and precipitation may suggest that the simulated LAI bias could also be caused by the bias of atmospheric forcing which come from 18 model of CMIP5.The spatial distribution of LAI linear trend had a great agreement with precipitation and 10-cm soil moisture， implying that the surface hydrological variations are the key factors to QXP vegetation change.

Abstract Insulin-like growth factor like family member 2 (IGFL2) is a gene in the IGFL family, located on chromosome 19, whose role in cancer is unclear, and the aim of this study was to investigate the relevance of IGFL2 expression, prognosis, immunity, and mutation in pan-cancer. Obtaining information from The Cancer Genome Atlas and The Genotype-Tissue Expression Project (GTEx) databases for expression analysis and combining with The Gene Expression Profile Interaction Analysis database for prognostic aspects. Analysis of immune cell infiltration by TIMER and CIBERSORT algorithms. Calculation of correlation of immune-related genes with IGFL2 expression and tumor mutational burden and microsatellite instability. Mutations and DNA methylation were analyzed using the cBioPortal database and the UALCAN database, and functional enrichment was performed using Gene set enrichment analysis (GSEA). IGFL2 expression is significantly elevated in tumor tissue and high expression has a worse prognosis in most cancers. In immune correlation analysis, it was associated with most immune cells and immune-related genes. In most cancers, IGFL2 methylation is lower and the group with mutations in IGFL2 has a worse prognosis than the normal group. The GSEA analysis showed that IGFL2 was significantly enriched in signaling and metabolism. IGFL2 may be involved in the development of many types of cancer, influencing the course of cancer with different biological functions. It may also be a biomarker for tumor immunotherapy.

Abstract Background Fusarium crown rot (FCR) is a chronic disease of cereals worldwide. Compared with tetraploid wheat, hexaploid wheat is more resistant to FCR infection. The underlying reasons for the differences are still not clear. In this study, we compared FCR responses of 10 synthetic hexaploid wheats (SHWs) and their tetraploid and diploid parents. We then performed transcriptome analysis to uncover the molecular mechanism of FCR on these SHWs and their parents. Results We observed higher levels of FCR resistance in the SHWs compared with their tetraploid parents. The transcriptome analysis suggested that multiple defense pathways responsive to FCR infection were upregulated in the SHWs. Notably, phenylalanine ammonia lyase (PAL) genes, involved in lignin and salicylic acid (SA) biosynthesis, exhibited a higher level of expression to FCR infection in the SHWs. Physiological and biochemical analysis validated that PAL activity and SA and lignin contents of the stem bases were higher in SHWs than in their tetraploid parents. Conclusion Overall, these findings imply that improved FCR resistance in SHWs compared with their tetraploid parents is probably related to higher levels of response on PAL-mediated lignin and SA biosynthesis pathways.

Knowledge of wind field characteristics and variation principles in complex topographical regions is of great importance for the development of numerical prediction models, aviation safety support, and wind energy utilization. However, there has been limited research focused on the lower-tropospheric wind fields in the Qinghai-Tibet Plateau. This paper aims to study the wind characteristics, vertical distributions, and temporal variations in the northeast of the plateau by analyzing a four-year continuous dataset collected from a Doppler wind LiDAR deployed in Xining, Qinghai Province of China. The results indicate that the prevailing horizontal wind direction in the low levels is primarily influenced by the mountain-valley wind circulation. However, as the altitude increases, the prevailing winds are predominantly affected by the westerlies. From a diurnal perspective, noticeable transition processes between up-valley and down-valley winds can be observed. The west-northwest wind (down-valley wind) dominates from late night to morning, while the east-southeast wind (up-valley wind) prevails from afternoon to early evening. The vertical winds in the low levels exhibit a downward motion during the daytime and an upward motion during the nighttime. In this plateau valley, the wind shear exponent is found to be highest in spring and lowest in winter, and it is generally lower during the daytime compared to the nighttime.

An interconnected sponge structure and porous surface poly (acrylonitrile-co-methyl acrylate) (P(AN-MA)) microfiltration membranes (MF) were fabricated via thermally induced phase separation (TIPS) by using caprolactam (CPL), and acetamide (AC) as the mixed diluent. When the ternary system was composed of 15 wt.% P(AN-MA), 90 wt.% CPL, and 10 wt.% AC and formed in a 25 °C air bath, the membrane exhibited the highest water flux of 8107 L/m2·h. The P(AN-MA) membrane contained hydrophobic groups (-COOCH3) and hydrophilic groups (-CN), leading it to exhibit oleophobic properties underwater and hydrophobic properties in oil. The membrane demonstrates efficient separation of immiscible oil/water mixtures. The pure water flux of the petroleum ether/water mixture measured 870 L/m2·h, and the pure oil flux of the petroleum tetrachloride/water mixture measured 1230 L/m2·h under the influence of gravity. Additionally, the recovery efficiency of diluents through recrystallization was 85.3%, significantly reducing potential pollution and production costs.

With the application of computer vision in the field of agricultural disease recognition, the convolutional neural network is widely used in grape leaf disease recognition and has achieved remarkable results. However, most of the grape leaf disease recognition models have the problem of weak generalization ability. In order to overcome this challenge, this paper proposes an image identification method for grape leaf diseases in different domains based on Fourier domain adaptation. Firstly, Fourier domain adaptation is performed on the labeled source domain data and the unlabeled target domain data. To decrease the gap in distribution between the source domain data and the target domain data, the low-frequency spectrum of the source domain data and the target domain data is swapped. Then, three convolutional neural networks (AlexNet, VGG13, and ResNet101) were used to train the images after style changes and the unlabeled target domain images were classified. The highest accuracy of the three networks can reach 94.6%, 96.7%, and 91.8%, respectively, higher than that of the model without Fourier transform image training. In order to reduce the impact of randomness, when selecting the transformed image, we propose using farthest point sampling to select the image with low feature correlation for the Fourier transform. The final identification result is also higher than the accuracy of the network model trained without transformation. Experimental results showed that Fourier domain adaptation can improve the generalization ability of the model and obtain a more accurate grape leaf disease recognition model.

A novel DC-assisted fast hot-pressing (FHP) powder sintering technique was utilized to prepare Al/Diamond composites. Three series of orthogonal experiments were designed and conducted to explore the effects of sintering temperature, sintering pressure, and holding time on the thermal conductivity (TC) and sintering mechanism of an Al-50Diamond composite. Improper sintering temperatures dramatically degraded the TC, as relatively low temperatures (≤520 °C) led to the retention of a large number of pores, while higher temperatures (≥600 °C) caused unavoidable debonding cracks. Excessive pressure (≥100 MPa) induced lattice distortion and the accumulation of dislocations, whereas a prolonged holding time (≥20 min) would most likely cause the Al phase to aggregate into clusters due to surface tension. The optimal process parameters for the preparation of Al-50diamond composites by the FHP method were 560 °C-80 MPa-10 min, corresponding to a density and TC of 3.09 g cm−3 and 527.8 W m−1 K−1, respectively. Structural defects such as pores, dislocations, debonding cracks, and agglomerations within the composite strongly enhance the interfacial thermal resistance (ITR), thereby deteriorating TC performance. Considering the ITR of the binary solid-phase composite, the Hasselman–Johnson model can more accurately predict the TC of Al-50diamond composites for FHP technology under an optimal process with a 3.4% error rate (509.6 W m−1 K−1 to 527.8 W m−1 K−1). The theoretical thermal conductivity of the binary composites estimated by data modeling (Hasselman–Johnson Model, etc.) matches well with the actual thermal conductivity of the sintered samples using the FHP method.

Currently, the effects of dietary levels of n-3 highly unsaturated fatty acids (HUFAs) on the growth performance, antioxidant capacity, immunity, and serum oxylipin profiles of female F2-generation Yangtze sturgeon remain unknown. A total of 75 Yangtze sturgeons, an endangered freshwater fish species, with an average body weight of 3.60 ± 0.83 kg, were randomly allocated to 15 concrete pools, with each dietary group represented by 5 fish per pool. The fish were fed five different experimental diets containing various levels of n-3 HUFAs (0.5%, 1.0%, 1.5%, 2.0%, and 2.4%). After a feeding period of 5 months, no significant differences in the growth performances of the fish were observed among the five dietary groups (p > 0.05). However, we did note that the serum levels of low-density lipoprotein cholesterol (LDL-C), triglycerides (TGs), and total cholesterol (TCHO) exhibited a marked increase in the fish that consumed higher dietary n-3 HUFA levels (p < 0.05). Conversely, alkaline phosphatase (ALP) activities showed a notable decrease as dietary n-3 HUFA levels increased (p < 0.05). Serum antioxidant indices, such as the activity levels of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px), were significantly higher in the 2.4% HUFA group compared to the 0.5% HUFA group. Additionally, muscle antioxidant indices, including total antioxidant capacity (T-AOC), catalase (CAT), and SOD activity, exhibited notable increases as dietary n-3 HUFA levels increased (p < 0.05). Furthermore, there was a decrease in malondialdehyde (MDA) levels as dietary n-3 HUFA levels increased (p < 0.05). In relation to immune indices, only serum immunoglobulin M (IgM) and muscle complement 3 (C3) were found to be influenced by dietary n-3 HUFA levels (p < 0.05). A total of 80 oxylipins were quantified, and our subsequent K-means cluster analysis resulted in the classification of 62 oxylipins into 10 subclasses. Among the different n-3 HUFA diets, a total of 14 differential oxylipins were identified in the sera. These findings demonstrate that dietary supplementation with n-3 HUFAs exceeding a 1.0% level can enhance antioxidant capacity and regulate serum lipid metabolism, potentially through modulation of oxylipins derived from ARA, DHA, and EPA. These insights provide novel perspectives on the mechanisms underlying these observations.

Cardiovascular diseases pose a long-term risk to human health. This study focuses on the rich-spectrum mechanical vibrations generated during cardiac activity. By combining Fourier series theory, we propose a multi-frequency vibration model for the heart, decomposing cardiac vibration into frequency bands and establishing a systematic interpretation for detecting multi-frequency cardiac vibrations. Based on this, we develop a small multi-frequency vibration sensor module based on flexible polyvinylidene fluoride (PVDF) films, which is capable of synchronously collecting ultra-low-frequency seismocardiography (ULF-SCG), seismocardiography (SCG), and phonocardiography (PCG) signals with high sensitivity. Comparative experiments validate the sensor’s performance and we further develop an algorithm framework for feature extraction based on 1D-CNN models, achieving continuous recognition of multiple vibration features. Testing shows that the recognition coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) of the 8 features are 0.95, 2.18 ms, and 4.89 ms, respectively, with an average prediction speed of 60.18 us/point, meeting the re-quirements for online monitoring while ensuring accuracy in extracting multiple feature points. Finally, integrating the vibration model, sensor, and feature extraction algorithm, we propose a dynamic monitoring system for multi-frequency cardiac vibration, which can be applied to portable monitoring devices for daily dynamic cardiac monitoring, providing a new approach for the early diagnosis and prevention of cardiovascular diseases.

Bifidobacterium longum is a common probiotic; both viable and heat-inactivated Bifidobacterium longum have many probiotic effects, such as anticancer effects. But some mechanisms of anticancer effects are still unclear, especially for heat-inactivated probiotics. In this study, we analyzed the effects of viable and heat-inactivated Bifidobacterium longum D42 on human colon cancer cells (HT-29). Cell proliferation, membrane permeability and apoptosis were detected by using the CCK-8 method, LDH method and Annexin V-FITC/PI kits. The ROS level and mitochondrial membrane potential were examined using the fluorescent probes DCFH-DA and JC-1. Real-time fluorescence quantitative PCR (RT-qPCR) and Western blot were used to detect the expression of mitochondrial apoptosis pathway genes and proteins. The results showed that viable and heat-inactivated Bifidobacterium longum D42 at concentrations of 1 × 106 CFU/mL significantly inhibited the proliferation of and increased the level of LDH release of HT-29 colon cancer cells. We found that they could increase the apoptosis rate of HT-29 cells. Moreover, they could also induce apoptosis by inducing cells to produce ROS and destroying the mitochondrial membrane potential of cells. Further studies found that they could increase the mRNA transcription and protein expression levels of the Caspase-3, Caspase-9 and Bax genes in cells, and reduce the mRNA transcription and protein expression levels of the Bcl-2 gene. In summary, our findings revealed that viable and heat-inactivated Bifidobacterium longum D42 have inhibitory effects on proliferation and promote the apoptosis of human colon cancer cells, and also have certain adjuvant drug therapeutic effects and have potential application value in the adjuvant treatment of colon cancer.

Abstract The presence of grand minima, characterized by significantly reduced solar and stellar activity, brings a challenge to the understanding of solar and stellar dynamo. The Maunder Minimum (1645–1715 AD) is a representative grand solar minimum. The cyclic variation of solar activity, especially the cycle length during this period, is critical to understand the solar dynamo but remains unknown. By analyzing the variations in solar activity‐related equatorial auroras recorded in Korean historical books in the vicinity of a low‐intensity paleo‐West Pacific geomagnetic anomaly, we find clear evidence of an 8‐year solar cycle rather than the normal 11‐year cycle during the Maunder Minimum. This result provides a key constraint on solar dynamo models and the generation mechanism of grand solar minima.

ObjectiveTo develop a Multi-Feature-Combined (MFC) model for proof-of-concept in predicting local failure (LR) in NSCLC patients after surgery or SBRT using pre-treatment CT images. This MFC model combines handcrafted radiomic features, deep radiomic features, and patient demographic information in an integrated machine learning workflow.MethodsThe MFC model comprised three key steps. (1) Extraction of 92 handcrafted radiomic features from the GTV segmented on pre-treatment CT images. (2) Extraction of 512 deep radiomic features from pre-trained U-Net encoder. (3) The extracted handcrafted radiomic features, deep radiomic features, along with 4 patient demographic information (i.e., gender, age, tumor volume, and Charlson comorbidity index), were concatenated as a multi-dimensional input to the classifiers for LR prediction. Two NSCLC patient cohorts from our institution were investigated: (1) the surgery cohort includes 83 patients with segmentectomy or wedge resection (7 LR), and (2) the SBRT cohort includes 84 patients with lung SBRT (9 LR). The MFC model was developed and evaluated independently for both cohorts, and was subsequently compared against the prediction models based on only handcrafted radiomic features (R models), patient demographic information (PI models), and deep learning modeling (DL models). ROC with AUC was adopted to evaluate model performance with leave-one-out cross-validation (LOOCV) and 100-fold Monte Carlo random validation (MCRV). The t-test was performed to identify the statistically significant differences.ResultsIn LOOCV, the AUC range (surgery/SBRT) of the MFC model was 0.858-0.895/0.868-0.913, which was higher than the three other models: 0.356-0.480/0.322-0.650 for PI models, 0.559-0.618/0.639-0.682 for R models, and 0.809/0.843 for DL models. In 100-fold MCRV, the MFC model again showed the highest AUC results (surgery/SBRT): 0.742-0.825/0.888-0.920, which were significantly higher than PI models: 0.464-0.564/0.538-0.628, R models: 0.557-0.652/0.551-0.732, and DL models: 0.702/0.791.ConclusionWe successfully developed an MFC model that combines feature information from multiple sources for proof-of-concept prediction of LR in patients with surgical and SBRT early-stage NSCLC. Initial results suggested that incorporating pre-treatment patient information from multiple sources improves the ability to predict the risk of local failure.

Well-Being analysis is an approach that integrates deterministic criteria with probabilistic methods, and it plays a crucial role in the operational planning of power systems. However, assessing the Well-Being of composite generation and transmission systems presents a formidable challenge, characterized by significant computational burdens and sluggish processing speeds. To tackle this issue, we embarked on an effort to enhance the computational efficiency of Well-Being assessment by employing the cross-entropy method (CEM). Nonetheless, our experimental pursuits revealed that the conventional employment of CEM for Well-Being assessment can lead to protracted convergence of the marginal index. To overcome this limitation, we introduce an enhanced multi-objective cross-entropy method (MCEM) that integrates weight factors, thereby ensuring an accelerated convergence rate for both the risk and marginal indices. To validate the effectiveness and advancement of our proposed MCEM approach, we conduct a comprehensive comparative analysis using the IEEE RTS79 and MRTS79 test systems as case studies. We contrast our method with the conventional MCS and CEM approaches, conducting a thorough examination of the computational performance of MCEM. This comprehensive comparative study unequivocally confirms the efficacy and progressive nature of the MCEM framework presented in this paper.

Background and Aims: There are limited data regarding the morphology, histopathology, and anatomical distribution of sporadic colorectal polyps in Chinese patients. We evaluated these characteristics of sporadic polyps to guide the endoscopic detection and excision of colorectal polyps. Methods: This was a retrospective single-center observational study involving 7381 patients with sporadic colorectal polyps. All polyps were removed endoscopically. The morphology and histopathology of polyps were evaluated according to the Paris classification and the World Health Organization classification, respectively. Results: A total of 22,174 polyps removed endoscopically from 7322 patients were included. In the sigmoid colon, 24.70% of colorectal polyps occurred, followed by the transverse colon (18.58%) in frequency. 0-Is type polyps accounted for 60.60% of all sporadic colorectal polyps. Polyps with 0-Ip, 0-Isp, and 0-IIa types were frequently found in the sigmoid colon, but laterally spreading lesions usually occurred in the ascending colon (24.61%) and rectum (20.51%). Irrespective of the Paris classification and anatomical location, as the polyps enlarge, the proportion of adenomatous polyps gradually increases while the proportion of serrated lesions decreases. Polyps with size ≥1 cm located in the left-sided colon were more likely to have villous/tubulovillous or high-grade dysplasia histology than those located in the right-sided colon, and about 1% of them were demonstrated with adenocarcinoma. Conclusion: Sigmoid colon should be detected adequately during colonoscopy, and polyps with size ≥1 cm should be treated carefully, especially in the left-sided colon.

Abstract Background The important role of mental health in sustainable economic development is gradually being recognized. This study aimed to evaluate the prevalence and gender disparity of those who screen positive for depression in China by the employer and industrial classification. Methods We used data from a nationally representative survey, the China Family Panel Studies. Depression was judged by the Centre for Epidemiologic Studies Depression Scale. Employer classifications were categorized according to the local characteristics of Mainland China. Industrial classifications were defined using level-1 of the China version of the International Standard Industrial Classification of All Economic Activities. Weighted logistic regressions were fitted to estimate the gender disparities, controlling for confounders. Results Forty eight thousand six hundred twenty eight adults were included. 18.7% (95%CI 18.1–19.4) of sampled adults were screened positive for depression symptoms, with 16.6% (95%CI 15.8–17.5) in males vs 21.0% (95%CI 20.1–22.0) in females. By classification of the employer, the prevalence was lowest among those employed by Government/party organisations (11.8%, 95%CI 8.9–15.4), and highest in those self-employed (21.8%, 95%CI 20.8–22.9); the gender disparity was mainly found in those employed by Sole proprietorship (Adjusted odds ratio [AOR] = 1.95, 95%CI 1.19–3.19) and Private enterprise (AOR = 1.34, 95%CI 1.13–1.59), as well as those self-employed (AOR = 1.49, 95%CI 1.3–1.17). By industrial classification, the prevalence was lowest among those who worked in the industry of Real estate (7.2%, 95%CI 4.8–10.6), and highest among those who worked in the industry of Agriculture, forestry, animal husbandry and fishing (22.9%, 95%CI 15.5–32.4); the gender disparity was mainly found in those who worked in the industry of Agriculture, forestry, animal husbandry and fishing (AOR = 3.29, 95%CI 1.18–9.15), Manufacturing (AOR = 1.41, 95% CI 1.09–1.82), Wholesale and retail trade (AOR = 1.48, 95% CI 1.07–2.06), and Accommodation and food service (AOR = 1.91, 95% CI 1.15–3.18). Conclusion The prevalence of depression in China had a wide variation by classifications of the employer and industry. Gender disparities were identified among workers from Sole proprietorship, Private enterprise, and self-employed, or workers from the industry of Agriculture, forestry, animal husbandry and fishing, Manufacturing, Wholesale and retail trade, and Accommodation and food service.

Highly efficient and stable white LEDs were fabricated based on Eu3+ doped CsxK1-xPbCl3 PeNCs, in which the PeNC film experienced the band engineering and defect passivation treatment.

Abstract In recent years, convolutional neural networks (CNNs) have been applied successfully in many fields. However, these deep neural models are still considered as “black box” for most tasks. One of the fundamental issues underlying this problem is understanding which features are most influential in image recognition tasks and how CNNs process these features. It is widely believed that CNN models combine low‐level features to form complex shapes until the object can be readily classified, however, several recent studies have argued that texture features are more important than other features. In this paper, we assume that the importance of certain features varies depending on specific tasks, that is, specific tasks exhibit feature bias. We designed two classification tasks based on human intuition to train deep neural models to identify the anticipated biases. We designed experiments comprising many tasks to test these biases in the Res Net and Dense Net models. From the results, we conclude that (1) the combined effect of certain features is typically far more influential than any single feature; (2) in different tasks, neural models can perform different biases, that is, we can design a specific task to make a neural model biased towards a specific anticipated feature.

Abstract Background Given the limited effectiveness of the current Chinese colorectal cancer (CRC) screening procedure, adherence to colonoscopy remains low. We aim to develop and validate a scoring system based on individuals who were identified as having a high risk in initial CRC screening to achieve more efficient risk stratification and improve adherence to colonoscopy. Methods A total of 29,504 screening participants with positive High-Risk Factor Questionnaire (HRFQ) or faecal immunochemical test (FIT) who underwent colonoscopy in Tianjin from 2012–2020 were enrolled in this study. Binary regression analysis was used to evaluate the association between risk factors and advanced colorectal neoplasia. Internal validation was also used to assess the performance of the scoring system. Results Male sex, older age (age ≥ 50 years), high body mass index (BMI ≥ 28 kg/m2), current or past smoking and weekly alcohol intake were identified as risk factors for advanced colorectal neoplasm. The odds ratios (ORs) for significant variables were applied to construct the risk score ranging from 0–11: LR, low risk (score 0–3); MR, moderate risk (score 4–6); and HR, high risk (score 7–11). Compared with subjects with LR, those with MR and HR had ORs of 2.47 (95% confidence interval, 2.09–2.93) and 4.59 (95% confidence interval, 3.86–5.44), respectively. The scoring model showed an outstanding discriminatory capacity with a c-statistic of 0.64 (95% confidence interval, 0.63–0.65). Conclusions Our results showed that the established scoring system could identify very high-risk populations with colorectal neoplasia. Combining this risk score with current Chinese screening methods may improve the effectiveness of CRC screening and adherence to colonoscopy.

Abstract Background Screening recommendations for colorectal cancer (CRC) are mainly based on family history rather than lifestyle risk factors. We aimed to assess and compare risk factors for colorectal neoplasm (CRN) and evaluate trends in neoplasm detection rates during the three rounds of screening from 2012 to 2020 in Tianjin, China. Methods This study was based on 89,535 first-recorded colonoscopies in Tianjin CRC screening program, 2012–2020. Of these, 45,380 individuals with complete family history and lifestyle factors were included for population attributable fraction (PAF) estimation. Results The overall detection rate of nonadvanced adenomas, advanced adenomas and CRC was 39.3%, 5.9% and 1.5%, respectively. The PAFs of current smoking, alcohol consumption, physical activity, higher BMI and family history of CRC, respectively, were 8.9%, 2.6%, 1.9%, 5.8%, and 1.1% for males with nonadvanced CRN; 12.3%, 7.3%, 4.9%, 7.2%, and 0.8% for males with advanced CRN; 3.4%, 0.4%, 2.1%, 7.8%, and 0.7% for females with nonadvanced CRN; and 4.3%, 0.2%, 8.2%, 8.5%, and -0.6% for females with advanced CRN. The PAFs of selected lifestyle factors were 19.9% for males with nonadvanced CRN, 29.0% for males with advanced CRN, 9.7% for females with nonadvanced CRN and 13.8% for females with advanced CRN. Conclusions Modifiable lifestyle factors, including smoking, alcohol consumption, physical activity and BMI, have a larger contribution to CRN than family history of CRC. Our findings will provide references for developing guidelines of CRC prevention and control in China.

Abstract Background Metabolism-related indicators have been suggested as possible prognostic indicators of liver disease in recent relevant studies, but their value in predicting autoimmune hepatitis (AIH) cirrhosis is unclear. This study evaluated the role of lipid levels in determining the prognosis of AIH-related cirrhosis. Methods We retrospectively included 345 patients with AIH who were initially diagnosed at Beijing Ditan Hospital from 2010-2019, and ultimately screened 196 patients who met the criteria. A logistic regression analysis was performed to screen factors associated with cirrhosis. Kaplan–Meier (KM) curves were constructed to analyze the effects of different triglyceride (TG) levels on the survival of patients with cirrhosis. A restricted cubic spline fitted Cox regression model was used to analyze the nonlinear relationship between serum TG levels and patient prognosis. Results Patients with AIH cirrhosis have lower TG levels than those without cirrhosis. Lower serum TG levels correlated with the severity of cirrhosis. The survival analysis showed that TG levels were associated with the overall survival of patients with AIH, as a lower 5-year survival rate (log-rank P

Abstract Park integrated energy system (PIES) can utilize multiple energy resources complementarily and promote comprehensive energy efficiency. However, the uncertainty of renewable energy generation poses significant challenges to the optimal operation of PIES. This paper proposes a data‐driven distributionally robust optimization (DDRO) model for the day‐ahead scheduling of PIESs with coordination of carbon capture and storage devices (CCS) and combined heat and power plants (CHP). First, a deterministic economic dispatch model of PIES was presented with the aim at minimizing the total operating costs of PIES and promoting the photovoltaic (PV) power accommodation. Then, a DDRO model was developed based on the historical data to yield the optimal solution in the worst PV output scenario, where the confidence set is established with comprehensive consideration of norm‐1 and norm‐inf constraints. Furthermore, an efficient solving framework was proposed for the DDRO based on the combination of the column‐and‐constraint generation (C&CG) algorithm and a duality‐free decomposition method. Finally, case studies are carried out to demonstrate the effectiveness of the proposed model.

Fast ion conductor materials screening based on high-throughput calculations involves enormous computing tasks. The process usually includes structural optimization, energy calculation, charge analysis and ionic migration performance estimation. The first one involves looking for the equilibrium atomic positions in huge amount of candidate compounds or derivative structures, and the computational cost is always high because of the task-intensive features. The last one relates to the kinetic problems, for which the time-consuming transition state theory and the molecular dynamics are the main simulation methods. In this work, two predictive models, ionic migration activation energy model and structural optimization model, are developed based on machine learning (ML) techniques to accelerate the process of estimating activation energy and relaxing the doped crystal structures, respectively. By training 3136 energy barrier data calculated by bond valence (BV) method, an ionic migration activation energy model (Ea model) with mean absolute error (MAE) of 0.26 eV on testing data set is obtained. We apply this model and filter LiBiOS as a promising fast Li+ conductor from 49 Li-containing hetero-anionic compounds. Although the model-predicted result shows relatively low energy barrier, further analysis indicates that the high carrier formation energy restricts the ionic transportability. Therefore, we substitute fractional Li+ with Mg2+ in LiBiOS to relieve the large difficulty of forming carriers in the structure. In order to fast explore the optimal doping scheme, we develop the structural optimization model (E-f model) containing the ML-based energy and force prediction to accelerate the structural optimization under various LiMg ratio and doping configurations. Decent doping scheme Li1-2xMgxBiOS (x = 0.1875) shows much better Li+ migration performance compared with LiBiOS without substitution. This method of screening fast ion conductor materials and finding optimal doping scheme will extremely accelerate materials explorations.

Encephalitis is a disease typically caused by viral infections or autoimmunity. The most common type of autoimmune encephalitis is anti-N-methyl-D-aspartate receptor (NMDAR) antibody-mediated, known as anti-NMDA receptor encephalitis, which is a rare disease. Specific EEG patterns, including “extreme delta brush” (EDB), have been reported in patients with anti-NMDA receptor encephalitis. The aim of this study was to develop an intelligent diagnostic model for encephalitis based on EEG signals. A total of 131 Participants were selected based on reasonable inclusion criteria and divided into three groups: health control (35 participants), viral encephalitis (58 participants), and anti NMDAR receptor encephalitis (55 participants). Due to the low prevalence of anti-NMDAR receptor encephalitis, it took several years to collect participants’ EEG signals while they were in an awake state. EEG signals were collected and analyzed following the international 10–20 system layout. We proposed a model called Temporal Transformer-Spatial Graph Convolutional Network (TT-SGCN), which consists of a Preprocess Module, a Temporal Transformer Module (TTM), and a Spatial Graph Convolutional Module (SGCM). The raw EEG signal was preprocessed according to traditional procedures, including filtering, averaging, and Independent Component Analysis (ICA) method. The EEG signal was then segmented and transformed using short-time Fourier transform (STFT) to produce concatenated power density (CPD) maps, which served as inputs for the proposed model. TTM extracted the time-frequency features of each channel, and SGCM fused these features using graph convolutional methods based on the location of electrodes. The model was evaluated in two experiments: classification of the three groups and pairwise classification among the three groups. The model was trained using two stages and achieved the performance, with an accuracy of 82.23%, recall of 80.75%, precision of 82.51%, and F1 score of 81.23% in the classification of the three groups. The proposed model has the potential to become an intelligent auxiliary diagnostic tool for encephalitis.

Exopolysaccharides (EPS) are biological macromolecules produced by Bifidobacterium to the outside of the cell wall during growth and metabolism. The EPS of Bifidobacterium have anti-inflammatory and antimicrobial activities and could regulate the immune system. In this study, the Bifidobacterium longum subsp. infantis E4 (B. infantis E4) has 21 specific genes for carbohydrate transport and metabolism compared with other 12 strains of B. infantis and could produce exopolysaccharides. The average molecular weights of EPS-1, EPS-2 and EPS-3 were 2.68 × 104, 3.79 × 104 and 4.72 × 104 Da, respectively. The EPS-1 mainly comprised of 7 kinds of monosaccharide, and EPS-2 and EPS-3 mainly comprised of 8 kinds of monosaccharide. The immunomodulatory and anti-inflammatory properties of EPS-1, EPS-2 and EPS-3 on RAW264.7 cells, spleen lymphocytes and NK cells of mouse were investigated in vitro of this study. EPS-1, EPS-2 and EPS-3 promoted the growth and increased phagocytic index of macrophage RAW264.7 at the concentration of 100 μg/mL. Subsequently, EPS-1, EPS-2 and EPS-3 groups were found to increase splenic lymphocyte proliferation and NK cell activity. Compared with the LPS group, EPS-1, EPS-2 and EPS-3 reduced the production of NO, IL-1β, IL-6, TNF-α and PGE-2 in RAW264.7 cells after in lipopolysaccharide (LPS) induced, with EPS-1 having the best effect. Meanwhile, EPS-1, EPS-2 and EPS-3 down-regulated the expression levels of LPS-induced NF-κβ signaling pathway-related genes p-p65, p-Iκβ, iNOS, COX-2 and reduced the nuclear translocation of p65 protein in RAW264.7 cells. In conclusion, our study suggested that EPS produced by B. infantis E4 has immunostimulatory and anti-inflammatory activities and may serve as a potential prebiotic to maintain future health.

Butyrate is a short-chain fatty acid with various pharmacological effects, which was produced by intestinal flora metabolism, we investigated the role and potential mechanism of sodium butyrate (NaBu) in angiotensin II (Ang II)-induced hypertensive kidney injury. A microosmotic pump containing Ang II was implanted into SD rats, and NaBu was injected intramuscularly from the third day of implantation for 28 days. We found that NaBu reduced the expression of Ang II-induced NLRP3 inflammasome-related proteins expression to mitigate NF-κB/IL-1β-mediated inflammation and pyroptosis mediated by gasdermin D (GSDMD) upregulation. Furthermore, NaBu repaired autophagy dysfunction due to lysosomal damage in macrophages. More importantly, we found that NaBu positively regulates Ser295 phosphorylation to modify NLRP3. Based on these results, we inferred that NaBu targets NLRP3 inflammasome degradation by regulating the p-NLRP3 (Ser295) to inhibit excessive pyroptosis, promoting the recovery of normal autophagy levels, thus indirectly affecting pyroptosis and ultimately improving hypertensive nephropathy.

The systemic benefits of anti-inflammatory pharmacotherapy vary across cardiovascular diseases in clinical practice. We aimed to evaluate the application of artificial intelligence to acute type A aortic dissection (ATAAD) patients to determine the optimal target population who would benefit from urinary trypsin inhibitor use (ulinastatin). Patient characteristics at admission in the Chinese multicenter 5A study database (2016–2022) were used to develop an inflammatory risk model to predict multiple organ dysfunction syndrome (MODS). The population (5,126 patients from 15 hospitals) was divided into a 60% sample for model derivation, with the remaining 40% used for model validation. Next, we trained an extreme gradient-boosting algorithm (XGBoost) to develop a parsimonious patient-level inflammatory risk model for predicting MODS. Finally, a top-six-feature tool consisting of estimated glomerular filtration rate, leukocyte count, platelet count, De Ritis ratio, hemoglobin, and albumin was built and showed adequate predictive performance regarding its discrimination, calibration, and clinical utility in derivation and validation cohorts. By individual risk probability and treatment effect, our analysis identified individuals with differential benefit from ulinastatin use (risk ratio [RR] for MODS of RR 0.802 [95% confidence interval (CI) 0.656, 0.981] for the predicted risk of 23.5%–41.6%; RR 1.196 [0.698–2.049] for the predicted risk of 41.6%). By using artificial intelligence to define an individual’s benefit based on the risk probability and treatment effect prediction, we found that individual differences in risk probability likely have important effects on ulinastatin treatment and outcome, which highlights the need for individualizing the selection of optimal anti-inflammatory treatment goals for ATAAD patients.

Necrotizing pneumonia (NP) is a rare but serious complication that occurs after foreign body retention. We report a case of severe NP in an infant caused by foreign body retention in the airway with no choking history. After a timely tracheoscopy and effective antibiotic treatment, her initial clinical symptoms were alleviated. However, she subsequently exhibited pulmonary manifestations of necrotizing pneumonia. To reduce the risk of NP from foreign body aspiration, for patients with airway obstruction and asymmetrical opacity of both lungs, timely diagnostic bronchoscopic evaluation is essential.

Foods containing tea could be widely utilized due to the addition of good tea ingredients, especially large-leaf yellow tea, which is rich with a good flavor. Applying this change to bread containing tea would improve its product quality. In this research, large-leaf yellow tea bread (LYB), possessing a special flavor, was developed using ultrafine large-leaf yellow tea powder and flour as the main raw materials. The amount of ultrafine large-leaf yellow tea powder added to bread was optimized using texture, sensation, and specific volume as comprehensive evaluation indicators. At the optimal dosage, the free amino acids, volatile flavor compounds, antioxidant activity, and in vitro starch digestibility of LYB were measured. Response surface optimization experimental results showed that the comprehensive score of bread was highest when the added amount of ultrafine large-leaf yellow tea powder was 3%. In particular, compared to blank bread (BB), adding ultrafine large-leaf yellow tea powder into bread could effectively increase its amino acid composition, enhance its volatile flavor compounds, improve the antioxidant capacity, and reduce the digestibility of starch.

Clubroot, caused by Plasmodiophora brassicae, is a destructive soil-borne disease significantly harming global Brassica crop production. This study employed the Williams and European Clubroot Differential (ECD) and Williams systems to identify the pathotypes of P. brassicae collected from Hangzhou City, Yuhang District and Quzhou City, Kaihua County in Zhejiang Province. Greenhouse and field trials were conducted to evaluate the effects of plastic film covering and four chemical agents on the growth parameters and clubroot severity of the Chinese cabbage cultivar ‘Granaat’. Potential treatment mechanisms on clubroot were explored through a qPCR analysis of the resting spore density and pH measurement of the soil. Furthermore, treatment with 1-napthaleneacetic acid (NAA), a synthetic auxin, was also evaluated for its potential role in suppressing clubroot. The results indicate that the pathotypes of P. brassicae in the two districts were P1, ECD20/31/12, and P3, ECD20/15/4. While an individual application of plastic film covering could not effectively control clubroot, calcium cyanamid, dazomet and ammonium bicarbonate demonstrated significant efficacy in its management. These three agents significantly reduced the resting spore density in the soil, with calcium cyanamid and ammonium bicarbonate also increasing soil alkalinity. Additionally, ammonium bicarbonate promotes lateral root development in ‘Granaat,’ helping infected plants access adequate water and nutrients. However, NAA exhibited no efficacy in clubroot control. Therefore, sustained lateral root development is crucial for effectively resisting P. brassicae invasion. Considering application costs and environmental friendliness, we propose the field application of ammonium bicarbonate as the optimal method for clubroot disease management in Zhejiang Province.

In this study, a deep-learning-based intelligent detection model was designed and implemented to rapidly detect cotton pests and diseases. The model integrates cutting-edge Transformer technology and knowledge graphs, effectively enhancing pest and disease feature recognition precision. With the application of edge computing technology, efficient data processing and inference analysis on mobile platforms are facilitated. Experimental results indicate that the proposed method achieved an accuracy rate of 0.94, a mean average precision (mAP) of 0.95, and frames per second (FPS) of 49.7. Compared with existing advanced models such as YOLOv8 and RetinaNet, improvements in accuracy range from 3% to 13% and in mAP from 4% to 14%, and a significant increase in processing speed was noted, ensuring rapid response capability in practical applications. Future research directions are committed to expanding the diversity and scale of datasets, optimizing the efficiency of computing resource utilization and enhancing the inference speed of the model across various devices. Furthermore, integrating environmental sensor data, such as temperature and humidity, is being considered to construct a more comprehensive and precise intelligent pest and disease detection system.

The utilisation of numerical process simulation has greatly facilitated the challenging task of liquid composite moulding (LCM) process optimisation, providing ease of solution evaluation at a significantly reduced cost compared to complete reliance on physical prototyping. However, due to the process complexity, such process simulation is still considerably expensive at present. In this paper, cost-saving approaches to minimising the computational cost of simulation-based optimisation for LCM processes are compiled and discussed. Their specific applicability, efficacy, and suitability for various optimisation/moulding scenarios are extensively explored in detail. The comprehensive analysation and assimilation of their operation alongside applicability for the problem domain of interest are accomplished in this paper to further complement and contribute to future simulation-based optimisation capabilities for composite moulding processes. The importance of balancing the cost-accuracy trade-off is also repeatedly emphasised, allowing for substantial cost reductions while ensuring a desirable level of optimization reliability.

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most important diseases threatening the yield and stability of wheat production in China and many other countries. Identification and utilization of new genes for durable stripe rust resistance are important for ongoing control of this disease. The objectives of this study were to identify quantitative trait loci (QTL) associated with adult-plant stripe rust resistance in the Chinese wheat landrace Yibinzhuermai (YBZR) and to provide wheat breeders with new sources of potentially durable resistance. A total of 117 recombinant inbred lines (RILs) (F5:8) derived from a cross between YBZR and highly susceptible cultivar Taichung 29 (TC29) were assessed for stripe rust severity in field experiments at Wenjiang in 2016 and 2017 and Chongzhou in 2016, 2017, 2018, and 2019 in Sichuan following inoculation with a mixture of current Pst races. The RILs were genotyped using the Wheat55K single nucleotide polymorphism (SNP) array. Three QTL were identified on chromosome arms 6AL, 5BL and 7DS. QYr.YBZR-6AL and QYr.YBZR-7DS conferred major effects in all field environments, explaining 10.6% to 14.7% and 11.5% to 21.2% of phenotypic variation, respectively. The QTL on 5BL and 7DS likely correspond to previously known QTL, whereas QYr.YBZR-6AL is probably novel. Haplotype analysis revealed that the resistance allele at QYr.YBZR-6AL was present in 2.8% of 324 Chinese wheat landraces. SNP markers closely linked with QYr.YBZR-6AL were converted to kompetitive allele-specific PCR markers and validated in the RIL population and a subset of 92 wheat cultivars. QYr.YBZR-6AL and its markers should be useful in breeding programs to improve the level and durability of stripe rust resistance.

The strength and configuration of the geomagnetic field control the average shape of the magnetosphere. The pure dipole assumption and the virtual dipole moment (VDM), determined by individual records, have been widely adopted to evaluate the strength of the geomagnetic field in geological time. However, such an assumption might not be valid during geomagnetic transitions, such as reversals and excursions. The traditional spherical harmonic modeling of the geomagnetic field could be difficult to implement because accurate global records are lacking. Here, we report that an empirical relationship exists between the ratio of the VDM to the true axial dipole moment (VDM/ADM) and the ratio of the power of the axial dipole to that of the non-axial dipole (AD/NAD) based on a new method utilizing globally distributed inclination records. The root mean square global deviation of inclination (RMSΔI) to the standard inclination distribution of the AD was fitted to the AD/NAD with a cubic polynomial by utilizing a large number of geodynamo simulations. Tests with geomagnetic field models showed that the AD/NAD derived from the RMSΔI agreed well with that calculated by using the Gauss coefficients, and the estimated ADM was consistent with the true value. Finally, the application of volcanic records during the Laschamp excursion showed the VDM might overestimate the ADM by a factor of 3. Our new method will be useful in future studies that characterize the configuration of the geomagnetic field and the strength of the axial dipole.

Abstract As essential high-end equipment for nuclear emergency monitoring, the portable HPGe γ spectrometer currently lacks supporting in-situ measurement methods, limiting its role and value in emergency missions. For this practical problem, this paper studies the measurement of ground surface radioactivity by portable HPGe γ spectrometer in nuclear emergency monitoring in view of the particularity of nuclear emergency source items. Firstly, the detection efficiency of point sources at different horizontal distances when the spectrometer is installed at the height of 1 m from the center of the detector to the ground is calculated. Secondly, the concept of effective contribution distance is defined and analyzed. Thirdly, the point source detection efficiency is obtained using the numerical integration method of calculation. Integrate to calculate the detection efficiency of the surface source, and then calculate the radioactive surface activity of the surface. Finally, the effectiveness of the method is verified through experiments.

ObjectiveThis study aimed to investigate the predictive value of a clinical nomogram model based on serum YKL-40 for major adverse cardiovascular events (MACE) during hospitalization in patients with acute ST-segment elevation myocardial infarction (STEMI).MethodsIn this study, 295 STEMI patients from October 2020 to March 2023 in the Second People’s Hospital of Hefei were randomly divided into a training group (n = 206) and a validation group (n = 89). Machine learning random forest model was used to select important variables and multivariate logistic regression was included to analyze the influencing factors of in-hospital MACE in STEMI patients; a nomogram model was constructed and the discrimination, calibration, and clinical effectiveness of the model were verified.ResultsAccording to the results of random forest and multivariate analysis, we identified serum YKL-40, albumin, blood glucose, hemoglobin, LVEF, and uric acid as independent predictors of in-hospital MACE in STEMI patients. Using the above parameters to establish a nomogram, the model C-index was 0.843 (95% CI: 0.79–0.897) in the training group; the model C-index was 0.863 (95% CI: 0.789–0.936) in the validation group, with good predictive power; the AUC (0.843) in the training group was greater than the TIMI risk score (0.648), p

To study the internal flow characteristics and energy characteristics of a large bulb perfusion pump. Based on the CFX software of the ANSYS platform, the steady calculation of the three-dimensional model of the pump device is carried out. The numerical simulation results obtained by SST k-ω and RNG k-ε turbulence models are compared with the experimental results. Finally, SST k-ω is selected for subsequent calculation. With the help of the flow line diagram and turbulent kinetic energy table of the whole flow channel of the pump device, the flow components of the pump device under different working conditions are analyzed, and the pressure and velocity distribution at the impeller and guide vane are analyzed by pressure cloud diagram and velocity cloud diagram. It is found that there are three high-pressure areas in the impeller and guide vane section, and the high-pressure regions are mainly distributed in the middle of the impeller channel. As the head decreases, the pressure at the impeller and guide vane positions decreases gradually, and the flow rate increases. Based on the entropy production principle, the wall entropy production and the distribution of mainstream entropy production at the impeller and guide vane parts are analyzed.

Objectives: The aim of the present study was to develop a machine learning model to predict the risk of molar incisor hypomineralization (MIH) and to identify factors associated with MIH in an endemic fluorosis region in central China.Methods: A cross-sectional study was conducted with 1,568 schoolchildren from selected regions. The clinical examination included an investigation of MIH based on the European Academy of Paediatric Dentistry (EAPD) criteria. In this study, supervised machine learning (e.g., logistic regression) and correlation analysis (e.g., Spearman correlation analysis) were used for classification and prediction.Results: The overall prevalence of MIH was 13.7%. The nomograph showed that non-dental fluorosis (DF) had a considerable influence on the early occurrence of MIH and that this influence became weaker as DF severity increased. We examined the association between MIH and DF and found that DF had a protective correlation with MIH; the protective effect became stronger as DF severity increased. Furthermore, children with defective enamel were more likely to experience caries, and dental caries were positively correlated with MIH (OR = 1.843; 95% CI: 1.260–2.694). However, gender, oral hygiene, and exposure to poor-quality shallow underground water did not increase the likelihood of developing MIH.Conclusions: DF should be considered a protective factor within the multifactorial etiology of MIH.

Abstract Penetration of immune cells into tumor cells was believed to be immune-suppressive via cell-in-cell (CIC) mediated death of the internalized immune cells. We unexpectedly found that CIC formation largely led to the death of the host tumor cells, but not the internalized immune cells, manifesting typical features of death executed by NK cells; we named this “in-cell killing” which displays the efficacy superior to the canonical way of “kiss-killing” from outside. By profiling isogenic cells, CD44 on tumor cells was identified as a negative regulator of “in-cell killing” via inhibiting CIC formation. CD44 functions to antagonize NK cell internalization by reducing N-cadherin-mediated intercellular adhesion and by enhancing Rho GTPase-regulated cellular stiffness as well. Remarkably, antibody-mediated blockade of CD44 signaling potentiated the suppressive effects of NK cells on tumor growth associated with increased heterotypic CIC formation. Together, we identified CIC-mediated “in-cell killing” as a promising strategy for cancer immunotherapy.

Abstract Background Cryptococcal meningitis (CM) associated immune reconstitution inflammatory syndrome (CM-IRIS) is the second most common complication in HIV-infected individuals with cryptococcal meningitis, with a reported mortality rate ranging from 8 to 30%. Given the devastating consequences of CM-IRIS related intracranial neuroinflammation and its challenging in diagnosis, we conducted a study to explore the risk factors and the occurrence of paradoxical CM-IRIS in HIV-infected patients, which is of great value for prevention and clinical management. Methods We conducted a retrospective cohort study to identify the indicators associated with paradoxical CM-IRIS among 86 HIV-infected patients with CM using univariate and multivariate cox analysis. A nomogram was constructed using selected variables to evaluate the occurrence of paradoxical CM-IRIS at 6 months and 12 months after ART initiation. The discrimination and calibration of the nomogram were assessed by concordance index (C-index) and calibration plots. Decision curves analysis (DCA) were used to evaluate clinical effectiveness of the nomogram. Subsequently, to help clinicians recognize patients at high risk faster, patients were divided into high-risk and low-risk groups according to the best cutoff point identified by X-tile. Results Of 86 AIDS patients with CM, 22.1% experienced paradoxical CM-IRIS at a median of 32 days after antiretroviral therapy (ART) initiation. The occurrence of paradoxical CM-IRIS was associated with age, ART initiation within 4 weeks of antifungal treatment, a four-fold increase in CD4 T cell counts, C-reactive protein levels, and hemoglobin levels independently. These five variables were further used to construct a predictive nomogram. The C-index (0.876) showed the favorable discriminative ability of the nomogram. The calibration plot revealed a high consistency between the predicted and actual observations. DCA showed that the nomogram was clinically useful. Risk stratification based on the total score of the nomogram showed well-differentiated in the high-risk and low-risk groups. Clinicians should pay attention to patients with total points high than 273. Conclusions We identified the predictive factors of paradoxical CM-IRIS and constructed a nomogram to evaluate the occurrence of paradoxical CM-IRIS in 6 months and 12 months. The nomogram represents satisfactory performance and might be applied clinically to the screening and management of high-risk patients.

The shale reservoirs of the Lower Cambrian Qiongzhusi Formation are widely distributed in the Sichuan Basin and have abundant gas resources. However, the shale lithofacies of the Qiongzhusi Formation are complex due to frequent sea level changes. The reservoir pore structure characteristics and gas content of different shale lithofacies vary significantly, which makes identifying the ‘sweet spot’ a challenging task. In this study, core observation and X-ray diffraction (XRD) were used to analyze the lithofacies types and characteristics of the study area. The pore types of different shale lithofacies were observed using field emission-scanning electron microscopy. Pore structures were studied using low-temperature gas (including N2 and CO2) physisorption, and the pore volume (PV), specific surface area (SSA) and pore structure were systematically characterized. The primary factors influencing pore formation in different types of shale lithofacies were analyzed by combining geochemical experiments and mineral contents. The results indicate that the lithofacies of the Qiongzhusi Formation shale in the study area can be classified into five categories according to mineral compositions: Siliceous argillaceous shale (CM-1), Argillaceous siliceous mixed shale (M-2), Argillaceous siliceous shale (S-3), Siliceous rock (S) and Calcareous siliceous shale (S-2). Pores are abundant in S-3 shale, M-2 shale and CM-1 shale. The S-3 shale is more enriched in organic pores and clay mineral pores compared to other lithofacies shales, and the pore morphology is mainly wedge-shaped and plate-like. M-2 shale and CM-1 shale are rich in clay minerals and mainly develop clay mineral pores and are mainly wedge-shaped and plate-like. The S shale and S-2 shale mainly develop interparticle pores and clay mineral pores, which are mainly slit-like. The results show that TOC, pyrite content, quartz and feldspar mineral content, clay mineral type and content affect the pore structure in the study area. Quartz and feldspar content have a negative effect on micropore and mesopore volumes. TOCs have a weak positive correlation with micropore volume and micropore SSA. Clay mineral content has significant positive effects on the PV and SSA of micropores and mesopores, indicating that clay mineral content is the main factor affecting the pore structure of shale.

In this paper, a novel power sintering technique, named fast hot-pressing sintering (FHP), which is able to achieve an ultrahigh heating rate similar to the spark plasma sintering (SPS) technique, but at a much lower cost, was applied to prepare a series of Al/Si composites with different Si volume ratios (12 vol.% to 70 vol.%) to meet the requirements of advanced packaging materials for electronic devices. In contrast to SPS, the FHP oven possesses a safe and budget-friendly current power supply, rather than a complex and expensive pulse power supply, for its heating power. The optimized sintering parameters (temperature, pressure and holding time) of FHP for preparing Al/Si composites were investigated and determined as 470 °C, 300 MPa and 5 min, respectively. In order to characterize the potential of Al/Si composites as packaging materials, thermal conductivities and coefficients of thermal expansion were studied. The thermal conductivity of the Al-40Si composite sintered by the FHP method is higher than that of the conventional SPS method (139 to 107 W m−1 K−1). With the increase in Si, the thermal conductivities and coefficients of thermal expansion on both decreases. Furthermore, the thermal conductivities obey the Agari model, whereas the coefficient of thermal expansion and Si volume ratios obey additivity. The numeric modeling would help develop required packaging materials based on the thermal performances of the substrate materials, like Si or GaAs semiconductor devices.

High-performance pressure sensors provide the necessary conditions for smart shoe applications. In this paper, the elastic Macroporous Graphene Aerogel (MGA) was synthesized via the modified Hummers’ method, and it was further combined with Expanded-Thermoplastic polyurethane (ETPU) particles to assemble MGA-ETPU flexible sensors. The MGA-ETPU has a low apparent density (3.02 mg/cm3), high conductivity (0.024 S/cm) and fast response time (50 ms). The MGA-ETPU has a large linear sensing range (0–10 kPa) and consists of two linear regions: the low-pressure region (0 to 8 kPa) and the high-pressure region (8 to 10 kPa), with sensitivities of 0.08 kPa−1, and 0.246 kPa−1, respectively. Mechanical test results show that the MGA-ETPU sensor showed 19% reduction in maximum stress after 400 loading–unloading compression cycles at 40% strain. Electrical performance tests showed that the resistance of MGA-ETPU sensor decreased by 12.5% when subjected to sudden compression at 82% strain and returned to its original state within 0.05 s. Compared to existing flexible sensors, the MGA-ETPU sensors offer excellent performance and several distinct advantages, including ease of fabrication, high sensitivity, fast response time, and good flexibility. These remarkable features make them ideally suited as flexible pressure sensors for smart shoes.