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Thermoelectric technology has been widely used for key areas, including waste-heat recovery and solid-state cooling. We discovered tin selenide (SnSe) crystals with potential power generation and Peltier cooling performance. The extensive off-stoichiometric defects have a larger impact on the transport properties of SnSe, which motivated us to develop a lattice plainification strategy for defects engineering. We demonstrated that Cu can fill Sn vacancies to weaken defects scattering and boost carrier mobility, facilitating a power factor exceeding ~100 microwatts per centimeter per square kelvin and a dimensionless figure of merit ( ZT ) of ~1.5 at 300 kelvin, with an average ZT of ~2.2 at 300 to 773 kelvin. We further realized a single-leg efficiency of ~12.2% under a temperature difference (Δ T ) of ~300 kelvin and a seven-pair Peltier cooling Δ T max of ~61.2 kelvin at ambient temperature. Our observations are important for practical applications of SnSe crystals in power generation as well as electronic cooling.

Background: Although important progress has been made in understanding Lp(a) (lipoprotein[a])-mediated stroke risk, the contribution of Lp(a) to the progression of vulnerable plaque features associated with stroke risk remains unclear. This study aims to evaluate whether Lp(a) is associated with carotid plaque progression, new-onset plaque features, and plaque vulnerability in a prospective community-based cohort study. Methods: Baseline Lp(a) levels were measured using latex-enhanced turbidimetric immunoassay among 804 participants aged 45 to 74 years and free of cardiovascular disease in the Chinese Multi-provincial Cohort Study—Beijing project. Carotid atherosclerosis was measured twice by B-mode ultrasonography over a 10-year interval during the 2002 and 2012 surveys to assess the progression of total, vulnerable and stable plaques, and plaque vulnerability. The total plaque area and plaque vulnerability score were calculated. Results: The median baseline Lp(a) level was 10.20 mg/dL (interquartile range, 6.20 to 17.18 mg/dL). Modified Poisson regression analysis showed that Lp(a) ≥50 mg/dL was significantly associated with 10-year progression of total carotid plaque (relative risk [RR], 1.41 [95% CI, 1.21–1.64]; E -value=2.17), vulnerable plaque (RR, 1.93 [95% CI, 1.54–2.41]), and stable plaque (RR, 1.51 [95% CI, 1.11–2.07]) compared with Lp(a) P =0.018) and increased plaque vulnerability score (β=0.30 [95% CI, 0.01–0.60]; P =0.045) in multivariable linear regression. Conclusions: Elevated Lp(a) levels were associated with 10-year carotid plaque progression and plaque vulnerability, providing a basis for Lp(a) as a treatment target for stroke prevention.

Calcium ion (Ca

Robotic pancreaticoduodenectomy (RPD) has been reported to be safe and feasible for patients with pancreatic ductal adenocarcinoma (PDAC) of the pancreatic head. This study aimed to analyze the surgical outcomes and risk factors for poor long-term prognosis of these patients.Data from patients who underwent RPD for PDAC of pancreatic head were retrospectively analyzed. Multivariate Cox regression analysis was used to seek the independent prognostic factors for overall survival (OS), and an online nomogram calculator was developed based on the independent prognostic factors.Of the 273 patients who met the inclusion criteria, the median operative time was 280.0 minutes, the estimated blood loss was 100.0 mL, the median OS was 23.6 months, and the median recurrence-free survival (RFS) was 14.4 months. Multivariate analysis showed that preoperative carbohydrate antigen 19-9 (CA19-9) [hazard ratio (HR) = 2.607, 95% confidence interval (CI): 1.560-4.354, P 0.001], lymph node metastasis (HR = 1.429, 95% CI: 1.005-2.034, P = 0.047), tumor moderately (HR = 3.190, 95% CI: 1.813-5.614, P 0.001) or poorly differentiated (HR = 5.114, 95% CI: 2.839-9.212, P 0.001), and Clavien-Dindo grade ≥ III (HR = 1.657, 95% CI: 1.079-2.546, P == 0.021) were independent prognostic factors for OS. The concordance index (C-index) of the nomogram constructed based on the above four independent prognostic factors was 0.685 (95% CI: 0.640-0.729), which was significantly higher than that of the AJCC staging (8th edition): 0.541 (95% CI: 0.493-0.589) (P 0.001).This large-scale study indicated that RPD was feasible for PDAC of pancreatic head. Preoperative CA19-9, lymph node metastasis, tumor poorly differentiated, and Clavien-Dindo grade ≥ III were independent prognostic factors for OS. The online nomogram calculator could predict the OS of these patients in a simple and convenient manner.

The prognosis of patients with pancreatic ductal adenocarcinoma (PDAC) remains poor even after radical pancreaticoduodenectomy (PD). The study aimed to develop and validate a novel preoperative prognostic model to accurately predict the long-term survival of patients with PDAC.Patients with PDAC of pancreatic head from Chinese PLA General Hospital were included. The preoperative PDAC model with contour plots was developed using a non-linear model in the training cohort and then tested in the validation cohort.Of 421 patients who met the inclusion criteria, 280 were in the training cohort and 141 in the validation cohort. Contour plots for preoperative PDAC model were established to visually predict the survival probabilities of these patients, based on preoperative carbohydrate antigen 19-9, preoperative fibrinogen to albumin ratio and pain symptoms. This model stratified patients into low- and high-risk groups with distinctly different long-term survival in the training cohort [median overall survival (OS) 32.1 vs. 17.5 months; median recurrence-free survival (RFS) 19.3 vs. 10.0 months, both P0.001] and the validation cohort (median OS 28.3 vs. 19.0 months; median RFS 17.5 vs. 11.2 months, both P0.001). Time-dependent receiver operating characteristic and decision curve analyses revealed that the model provided higher diagnostic accuracy and superior net benefit compared to other staging systems.This study constructed and validated a novel preoperative prognostic model that can accurately and conveniently predict the long-term survival of patients with resectable PDAC of pancreatic head. Besides, the model can screen high-risk patients with poor prognosis, which may provide references for personal treatment strategies in the future.

BACKGROUND: In recent years, malignant tumors have gradually become one of the main causes of death for Chinese residents, of which lung cancer ranks first in both the incidence and mortality in China. OBJECTIVE: To mine the text of traditional Chinese medicine (TCM) clinical medical cases after data cleaning, analyze it, and study the experience of TCM doctors in treating non-small cell lung cancer (NSCLC). METHODS: The applied approach was based on the data mining methods of decentralized and hierarchical system clustering of data from a drug and prescription database. This study involved 215 patients, 287 cases, and 147 types of clinical drugs. RESULTS: The data analysis of the clinical treatment of NSCLC in TCM showed that Erchen Decoction was the main method for the treatment of non-small cell lung cancer in clinical treatment of non-small cell lung cancer. Junjian recipes were close to each other, with Banzhilian, Lobelia, Shanci Mushroom, Hedyotis diffusa to anticancer and detoxify. CONCLUSION: This study analyzed the core TCM prescription for NSCLC by collecting the empirical essence and characteristics of specific medications. It has some guiding scientific significance for the clinical treatment of lung cancer.

Alternative RNA splicing is an essential mechanism linking genetic variation to human diseases. While the signals from genome-wide association studies (GWAS) have been linked to expression quantitative trait loci (eQTL) in previous studies, further work is needed to better elucidate the relationship to other genetic regulatory mechanisms, such as splicing QTLs (sQTL). Here, we performed a genome-wide sQTL analysis to identify variants that might affect RNA splicing in 1,010 non–small cell lung cancer (NSCLC) samples from The Cancer Genome Atlas. The identified sQTLs were largely independent of eQTLs and were predominantly enriched in exonic regions, genetic regulatory elements, RNA-binding protein (RBP) binding sites, and known NSCLC risk loci. In addition, target genes affected by sQTLs (sGenes) were involved in multiple processes in cancer, including cell growth, apoptosis, metabolism, immune infiltration, and drug responses, and sGenes were frequently altered genetically in NSCLC. Systematic screening of sQTLs associated with NSCLC risk using GWAS data from 15,474 cases and 12,375 controls identified an sQTL variant rs156697-G allele that was significantly associated with an increased risk of NSCLC. The association between the rs156697-G variant and NSCLC risk was further validated in two additional large population cohorts. The risk variant promoted inclusion of GSTO2 alternative exon 5 and led to higher expression of the GSTO2 full-length isoform (GSTO2-V1) and lower expression of the truncated GSTO2 isoform (GSTO2-V2), which was induced by RBP quaking (QKI). Mechanistically, compared with GSTO2-V1, GSTO2-V2 inhibited NSCLC cells proliferation by increasing S-glutathionylation of AKT1 and thereby functionally blocking AKT1 phosphorylation and activation. Overall, this study provides a comprehensive view of splicing variants linked to NSCLC risk and provides a set of genetic targets with therapeutic potential. Significance: Analysis of sQTL reveals the role of genetically driven mRNA splicing alterations in NSCLC risk and elucidates that rs156697 variant impacts risk by altering GSTO2 splicing.

For a new city that is committed to promoting Electric Vehicles (EVs), it is significant to plan the public charging infrastructure where charging demands are high. However, it is difficult to predict charging demands before the actual deployment of EV chargers for lack of operational data, resulting in a deadlock. A direct idea is to leverage the urban transfer learning paradigm to learn the knowledge from a source city, then exploit it to predict charging demands, and meanwhile determine locations and amounts of slow/fast chargers for charging stations in the target city. However, the demand prediction and charger planning depend on each other, and it is required to re-train the prediction model to eliminate the negative transfer between cities for each varied charger plan, leading to the unacceptable time complexity. To this end, we design an effective solution of S imultaneous Demand P rediction A nd P lanning ( SPAP ): discriminative features are extracted from multi-source data, and fed into an Attention-based Spatial-Temporal City Domain Adaptation Network ( AST-CDAN ) for cross-city demand prediction; a novel Transfer Iterative Optimization ( TIO ) algorithm is designed for charger planning by iteratively utilizing AST-CDAN and a charger plan fine-tuning algorithm. Extensive experiments on real-world datasets collected from three cities in China validate the effectiveness and efficiency of SPAP . Specially, SPAP improves at most 72.5% revenue compared with the real-world charger deployment.

Formation enthalpy (left), voltage and theoretical specific capacity (right) of Li1−xFeS2 based on different Li concentrations.

As one of continuous concern all over the world, the problem of water quality may cause diseases and poisoning and even endanger people's lives. Therefore, the prediction of water quality is of great significance to the efficient management of water resources. However, existing prediction algorithms not only require more operation time but also have low accuracy. In recent years, neural networks are widely used to predict water quality, and the computational power of individual neurons has attracted more and more attention. The main content of this research is to use a novel dendritic neuron model (DNM) to predict water quality. In DNM, dendrites combine synapses of different states instead of simple linear weighting, which has a better fitting ability compared with traditional neural networks. In addition, a recent optimization algorithm called AMSGrad (Adaptive Gradient Method) has been introduced to improve the performance of the Adam dendritic neuron model (ADNM). The performance of ADNM is compared with that of traditional neural networks, and the simulation results show that ADNM is better than traditional neural networks in mean square error, root mean square error and other indicators. Furthermore, the stability and accuracy of ADNM are better than those of other conventional models. Based on trained neural networks, policymakers and managers can use the model to predict the water quality. Real-time water quality level at the monitoring site can be presented so that measures can be taken to avoid diseases caused by water quality problems.

Illustration of the fabrication of NiCo-MOF/GO and its application in glucose determination.

International audience; In this paper, the state estimator design problem of interval type-2 Takagi–Sugeno fuzzy systems suffering from bounded disturbances is studied. To enhance the resilience of the estimator, a non-fragile design scheme is proposed to tackle the estimator gain variations. Meanwhile, an event-triggered communication mechanism is introduced for relieving the transmission burden over networks. To settle down the non-fragile estimator design issue subject to bounded disturbances and event-induced error, we propose a new definition of quadratic boundedness via the multiple Lyapunov functions. Based on this definition, a novel co-design method of estimator and event generator for fuzzy system models in the presence of both measurable and immeasurable premise variables is presented. In virtue of quadratic boundedness framework, less conservative conditions of the existence and quadratic stability of the fuzzy estimators are obtained, and the upper bound of estimation error is given explicitly. The desired estimator gains are determined by convex optimization technique using slack matrices. Two illustrative examples are exploited to validate the availability and superiority of the addressed design approach.

WLY-0, as an α-D-glucan with a molecular weight (Mw) of 11.12 kDa, was successfully isolated and purified from Huangshui (HS). The results of methylation and NMR indicated that the mainchain of WLY-0 was (1 → 4)-α-D-glucan, with side chains linking at O-6. Meanwhile, the surface morphology characterization showed that WLY-0 had an irregular flake-like morphology with a rough and uneven surface and varies in sizes from nanometers to microns. Furthermore, WLY-0 relieved the increased paracellular permeability of FD4 and decreased TEER challenged by LPS, meanwhile inhibited the production of pro-inflammatory cytokines (TNF-α, IL-6, and IL-1β) and up-regulated the expression of TJ protein (Occludin, Claudin-1, ZO-1, and JAM-A) in Caco-2 cells, so to improve the intestinal barrier function. Our findings about the structural characteristics and biological activities of WLY-0 provided a scientific foundation for the utilization of HS as a potent source of an effective adjuvant in intestinal barrier injury treatment.

Unfolded protein response (UPR) signaling is activated under endoplasmic reticulum (ER) stress, an emerging cancer hallmark, leading to either adaptive survival or cell death, while the mechanisms underlying adaptation-death switch remain poorly understood. Here, we examined whether oncogene iASPP regulates the switch and how the mechanisms can be used in colon cancer treatment. iASPP is downregulated when cells undergo transition from adaptation to death during therapy-induced ER stress. Blocking iASPP's downregulation attenuates stress-induced cell death. Mechanistically, Hu-antigen R (HuR)-mediated stabilization of iASPP mRNA and subsequent iASPP protein production is significantly impaired with prolonged ER stress, which facilitates the degradation of GRP78, a key regulator of the UPR, in the cytosol. Because iASPP competes with GRP78 in binding the ER-resident E3 ligase RNF185, and tips the balance in favor of cell death. Positive correlation between the levels of HuR, iASPP, and GRP78 are detectable in colon cancer tissues in vivo. Genetic inhibition of iASPP/GRP78 or chemical inhibition of HuR not only inhibits tumor growth, but also sensitizes colon cancer cells' responses to BRAF inhibitor-induced ER stress and cell death. This study provides mechanistic insights into the switch between adaptation and death during ER stress, and also identifies a potential strategy to improve BRAF-inhibitor efficiency in colon cancers.

The slime mould algorithm (SMA) has become a classical algorithm applied in many fields since it was presented. Nevertheless, when faced with complex tasks, the algorithm converges slowly and tends to fall into the local optimum. So, there is still room for improvement in the performance of SMA. This work proposes a novel SMA variant (SDSMA), combining the adaptive Lévy diversity mechanism and directional crossover mechanism. Firstly, the adaptive Lévy diversity mechanism can improve population diversity. Then, the directional crossover mechanism can enhance the balance of exploration and exploitation, thus helping SDSMA to increase the convergence speed and accuracy. SDSMA is compared with SMA variants, original algorithms, improved algorithms, improved-SMAs, and others on the benchmark function set to verify its performance. Meanwhile, the Wilcoxon signed-rank test, the Friedman test, and other analytical methods are considered to analyze the experimental results. The analysis results show that SDSMA with two strategies significantly improves the performance of SMA. Meanwhile, the computational cost of SDSMA is smaller than that of SMA on benchmark function. Finally, the proposed algorithm is applied to three real-world engineering design problems. The experiments prove that SDSMA is an effective aid tool for computationally complex practical tasks.

As science and technology advance, more engineering-type problems emerge. Technology development has likewise led to an increase in the complexity of optimization problems, and the need for new optimization techniques has increased. The swarm intelligence optimization algorithm is popular among researchers as a flexible, gradient-independent optimization method. The cuckoo search (CS) algorithm in the population intelligence algorithm has been widely used in various fields as a classical optimization algorithm. However, the current CS algorithm can no longer satisfy the performance requirements of the algorithm for current optimization problems. Therefore, in this paper, an improved CS algorithm based on a crossover optimizer (CC) and decentralized foraging (F) strategy is proposed to improve the search ability and the ability to jump out of the local optimum of the CS algorithm (CCFCS). Then, in order to verify the performance of the algorithm, this paper demonstrates the performance of CCFCS from six perspectives: core parameter setting, balance analysis of search and exploitation, the impact of introduced strategies, the impact of population dimension, and comparison with classical algorithms and similar improved algorithms. Finally, the optimization effect of CCFCS on real engineering problems is tested by five classic cases of engineering optimization. According to the experimental results, CCFCS has faster convergence and higher solution quality in the algorithm performance test and maintains the same excellent performance in engineering applications.

Aging results in progressive decline of renal function as well as histological alterations including glomerulosclerosis and interstitial fibrosis. The objective of current study was to test the benefits of moderate swimming exercise in aged rats on renal function and structure and investigate its molecular mechanisms. Aged rats of 21-months old were given moderate swimming exercise for 12 weeks. Swimming exercise in aged rats led to reduced plasma levels of creatinine and blood urea nitrogen. Periodic acid-Schiff staining results revealed reduced renal injury scores in aged rats after swimming exercise. Swimming exercise in aged rats mitigated renal fibrosis and downregulated the mRNA expression of Acta2, Fn, Col1a, Col4a, and Tgfb1 in kidneys. Swimming exercise in aged rats attenuated lipid accumulation and reduced levels of triglyceride in kidneys. Swimming exercise in aged rats abated oxidative stress, evidenced by reduced MDA levels and increased MnSOD activities in kidneys. Swimming exercise in aged rats inhibited NF-κB activities and reduced renal expression of pro-inflammatory cytokines including MCP-1, IL-1β and IL-6. Mechanistically, swimming exercise restored mRNA and protein expression of PPAR-α in kidney of aged rats. Furthermore, swimming exercise in aged rats increased expression of PPAR-α-targeting microRNAs including miR-21 and miR-34a. Collectively, swimming exercise activated PPAR-α, which partly explained the benefits of moderate swimming exercise in aging kidneys.