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Abstract The photonic Ising machine is a new paradigm of optical computing that takes advantage of the unique properties of light wave propagation, parallel processing, and low-loss transmission. Thus, the process of solving combinatorial optimization problems can be accelerated through photonic/optoelectronic devices, but implementing photonic Ising machines that can solve arbitrary large-scale Ising problems with fast speed remains challenging. In this work, we have proposed and demonstrated the Phase Encoding and Intensity Detection Ising Annealer (PEIDIA) capable of solving arbitrary Ising problems on demand. The PEIDIA employs the heuristic algorithm and requires only one step of optical linear transformation with simplified Hamiltonian calculation by encoding the Ising spins on the phase term of the optical field and performing intensity detection during the solving process. As a proof of principle, several 20 and 30-spin Ising problems have been solved with high ground state probability (≥0.97/0.85 for the 20/30-spin Ising model).

As a paradigm shift in tandem with the expansion of ICT, smart electronic health systems hold great promise for enhancing healthcare delivery and illness prevention efforts. These systems acquire an in-depth understanding of patient health states through the real-time collection and analysis of medical data enabled by the Internet of Things (IoT) and machine learning. With the widespread use of cutting-edge artificial intelligence and machine learning techniques, predictive analytics in medicine can assist in making the shift from a reactive to a proactive healthcare strategy. With the ability to rapidly and precisely evaluate massive amounts of data, draw intelligent conclusions, and solve difficult issues, artificial neural networks could revolutionize several industries. Two cardiac illnesses were assessed in this study using a multilayer perceptron artificial neural network that incorporated a genetic algorithm and an error-back propagation mechanism. The ability of artificial neural networks to handle consecutive time series data is crucial for optimizing resources in smart electronic health systems, especially with the increasing volume of patient information and the broad use of electronic clinical records. This requires the creation of more accurate predictive models. Through the use of Internet of Things (IoT) sensors, the proposed system gathers data, which is then used to do predictive analytics on patient history-related electronic clinical data saved in the cloud. A smart healthcare system that uses Mu-LTM (multidirectional long-term memory) to accurately monitor and predict the risk of heart disease has a coverage error of 97.94 %, an accuracy of 97.89 %, a sensitivity of 97.96 %, and a specificity of 97.99 %. In comparison to other smart heart disease prediction systems, the F1-score of 97.95 % and precision of 97.71 % is very good.

Uranium silicide (U3Si2) is regarded as a viable fuel option for improving the safety of nuclear power plants. In the present work, phase-field simulations were employed to investigate grain growth phenomena, encompassing both isotropic and anisotropic grain growth. In simulations of isotropic grain growth, it is commonly assumed that the energy and mobility of the grain boundaries (GBs) remain constant, represented by average values. The calculated grain growth kinetic rate constant, K, exhibits a close correspondence with the experimental measurements, indicating a strong agreement between the two. In simulations of anisotropic grain growth, the values of GB energy and mobility are correlated with the angular disparity between GBs. The simulation results demonstrated that the growth rate of U3Si2 can be influenced by both the energy anisotropy and mobility anisotropy of GBs. Furthermore, the anisotropy in mobility results in a greater prevalence of low-angle GB distribution in comparison to high-angle GBs. However, the energy anisotropy of GBs does not impact the frequency distribution of the angle difference between GBs.

Abstract The collision cross section (CCS) values derived from ion mobility spectrometry can be used to improve the accuracy of compound identification. Here, we have developed the Structure included graph merging with adduct method for CCS prediction (SigmaCCS) based on graph neural networks using 3D conformers as inputs. A model was trained, evaluated, and tested with >5,000 experimental CCS values. It achieved a coefficient of determination of 0.9945 and a median relative error of 1.1751% on the test set. The model-agnostic interpretation method and the visualization of the learned representations were used to investigate the chemical rationality of SigmaCCS. An in-silico database with 282 million CCS values was generated for three different adduct types of 94 million compounds. Its source code is publicly available at https://github.com/zmzhang/SigmaCCS . Altogether, SigmaCCS is an accurate, rational, and off-the-shelf method to directly predict CCS values from molecular structures.

Emergency tents face challenges in harsh weather conditions and sometimes require the use of air conditioning for indoor thermal environment control. However, their lightweight structure makes their control methods different from conventional buildings. This study focuses on the indoor thermal environment and thermal comfort of air-conditioned tents during summer. Through experimental measurements, this study captures the distribution of air temperatures and inner surface temperatures within a tent, thus providing an understanding of the characteristics of indoor thermal environment in air-conditioned settings. Additionally, the numerical simulations conducted using the ANSYS FLUENT 2021 R1 calculate the Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD), thus contributing to a detailed analysis of the indoor thermal comfort states. The experiment revealed that the mean radiative temperature (MRT) inside the tent can exceed the air temperature by more than 10 °C. Even when the air temperature is around 26 °C, the excessively high MRT can cause occupants to feel uncomfortable, with the PMV exceeding 1.4 and the PPD surpassing 50%. Furthermore, the high MRT results in an increased demand for cooling airflow, with the cooling loss through gaps becoming a significant part in the cooling load. To ensure a comfortable thermal environment, the air-conditioning set temperature needs to be adjusted according to the weather conditions. For instance, even at the same air temperature of 35 °C, when solar radiation increases from 400 W/m2 to 1000 W/m2, the set temperature needs to be reduced from 24.7 °C to 20.7 °C. The findings of this study provide an important reference for establishing summer air-conditioning strategies for emergency tents.

IntroductionGenerally, Traditional Chinese Medicine (TCM) courses are now given to modern medicine students without proper course scheduling, resulting in poor teaching results.MethodsTo analyze the main factors affecting TCM learning, we surveyed the medical students and TCM teachers from Xiangya School of Medicine of Central South University via online questionnaires. The questionnaire comprised two parts, the students' part included the basic information, the subjective cognition in TCM, the attitude toward TCM course arrangements, and the attitude toward curriculum content and the design of TCM. The teachers' part included the basic information, the attitudes and opinions on TCM course arrangements, and suggestions and views on TCM teaching reform. The related data were collected from 187 medical students divided into two groups, namely, clinical medical students and non-clinical medical students.ResultsWe found a more positive attitude toward TCM [including “Scientific nature of TCM” (P = 0.03) and “Necessity for modern medicine students to learn TCM” (P = 0.037)] in clinical medical students compared with non-clinical medical students, clinical and non-clinical medical students tended to find TCM courses difficult, and the students prefer clinical training to be better than theoretical teaching, while the teachers believe that lecture-based education should have a more significant proportion.DiscussionHence, to optimize the current TCM teaching, we conducted education reform, including differentiated teaching, hybrid teaching, and selective teaching.

Abstract Background TIMM8A is a protein-coding gene located on the X chromosome. There is evidence that TIMM8A plays an important role in mitochondrial morphology and fission. Studies have shown that mitophagy and fission could affect the function of immune cells. However, there is currently no research on this gene’s role in cancer occurrence and progression. Methods TIMM8A expression was analyzed via the Tumor Immune Estimation Resource (TIMER) site and UALCAN database. We evaluated the influence of TIMM8A on clinical prognosis using Kaplan-Meier plotter, the PrognoScan database, and Human Protein Atlas (HPA). The correlations between TIMM8A and cancer immune infiltrates were investigated via TIMER. Tumor Immune Dysfunction and Exclusion (TIDE) was used to evaluate the potential of tumor immune evasion. Functions of TIMM8A mutations and 50 genes significantly associated with TIMM8A mutations in breast cancer (BRCA) and uterine corpus endometrial cancer (UCEC) were analyzed by GO and KEGG in LinkedOmics database. Results We investigated the role of TIMM8A in multiple cancers and found that it was significantly associated with poor prognosis in BRCA and UCEC. After analyzing the effect of TIMM8A on immune infiltration, we found Th2 CD4+ T cells might be a common pathway by which TIMM8A contributed to poor prognosis in BRCA and UCEC. Our results suggested that myeloid-derived suppressor cells (MDSC) and tumor-associated M2 macrophages (TAM M2) might be important factors in immune evasion through T cell rejection in both cancers, and considered TIMM8A as a biomarker to predict the efficacy of this therapy in BRCA and UCEC. The results of TIMM8A enrichment analysis showed us that abnormally expressed TIMM8A might affect the mitochondrial protein in BRCA and UCEC. Conclusions Contributed to illustrating the value of TIMM8A as a prognostic biomarker, our findings suggested that TIMM8A was correlated with prognosis and immune infiltration, including CD8+ T cells, Th2 CD4+ T cells, and macrophages in BRCA and UCEC. In addition, TIMM8A might affect immune infiltration and prognosis in BRCA and UCEC by affecting mitophagy. We believed it could also be a biomarker to predict the efficacy of anti-PD-L1 therapy and proposed to improve the efficacy by eliminating MDSC and TAM M2.

Microchannels are the essential elements for the design of artificial devices but the fabrication of three dimensional (3D) microchannels with complex geometry and a high aspect ratio remains challenging. Here, the authors demonstrate a simple and solvent-free fabrication method capable of producing monolithic microchannels with complex 3D structures, long length, and small diameter.

Nuclear magnetic resonance (NMR) is a crucial technique for analyzing mixtures consisting of small molecules, providing non-destructive, fast, reproducible, and unbiased benefits. However, it is challenging to perform mixture identification because of the offset of chemical shifts and peak overlaps that often exist in mixtures such as plant flavors. Here, we propose a deep-learning-based mixture identification method (DeepMID) that can be used to identify plant flavors (mixtures) in a formulated flavor (mixture consisting of several plant flavors) without the need to know the specific components in the plant flavors. A pseudo-Siamese convolutional neural network (pSCNN) and a spatial pyramid pooling (SPP) layer were used to solve the problems due to their high accuracy and robustness. The DeepMID model is trained, validated, and tested on an augmented data set containing 50,000 pairs of formulated and plant flavors. We demonstrate that DeepMID can achieve excellent prediction results in the augmented test set: ACC = 99.58%, TPR = 99.48%, FPR = 0.32%; and two experimentally obtained data sets: one shows ACC = 97.60%, TPR = 92.81%, FPR = 0.78% and the other shows ACC = 92.31%, TPR = 80.00%, FPR = 0.00%. In conclusion, DeepMID is a reliable method for identifying plant flavors in formulated flavors based on NMR spectroscopy, which can assist researchers in accelerating the design of flavor formulations.

IntroductionGastric cancer (GC) remains the major constituent of cancer-related deaths and a global public health challenge with a high incidence rate. Helicobacter pylori (HP) plays an essential role in promoting the occurrence and progression of GC. Cancer-associated fibroblasts (CAFs) are regarded as a significant component in the tumor microenvironment (TME), which is related to the metastasis of GC. However, the regulation mechanisms of CAFs in HP-related GC are not elucidated thoroughly.MethodsHP-related genes (HRGs) were downloaded from the GSE84437 and TCGA-GC databases. The two databases were combined into one cohort for training. Furthermore, the consensus unsupervised clustering analysis was obtained to sort the training cohort into different groups for the identification of differential expression genes (DEGs). Weighted correlation network analysis (WGCNA) was performed to verify the correlation between the DEGs and cancer-associated fibroblasts which were key components in the tumor microenvironment. The least absolute shrinkage and selection operator (LASSO) was executed to find cancer-associated fibroblast-related differential expression genes (CDEGs) for the further establishment of a prognostic model.Results and discussionIn this study, 52 HP-related genes (HRGs) were screened out based on the GSE84437 and TCGA-GC databases. A total of 804 GC samples were analyzed, respectively, and clustered into two HP-related subtypes. The DEGs identified from the two subtypes were proved to have a relationship with TME. After WGCNA and LASSO, the CAFs-related module was identified, from which 21 gene signatures were confirmed. Then, a CDEGs-Score was constructed and its prediction efficiency in GC patients was conducted for validation. Overall, a highly precise nomogram was established for enhancing the adaptability of the CDEGs-Score. Furthermore, our findings revealed the applicability of CDEGs-Score in the sensitivity of chemotherapeutic drugs. In general, our research provided brand-new possibilities for comprehending HP-related GC, evaluating survival, and more efficient therapeutic strategies.

Inconsistent training programs for public health emergency (PHE) have been criticized as a contributing factor in PHE's managerial weak points. In response, to analyze the relevant discrepancies among the medical students in the class of 2021 from Xiangya School of Medicine of Central South University, the present study conducted an online questionnaire survey using convenience sampling. The questionnaire comprised four sections, including the basic information, the subjective cognition in PHE, the rescue knowledge and capabilities of PHE, and the mastery of PHE regulations and psychological intervention abilities. To compare the abovementioned aspects, related data were collected from 235 medical students divided into two groups, namely, clinical medical students (Group A) and preventive medical students (Group B). We found a more positive attitude in PHE (P = 0.014) and a better grasp of the PHE classification (P = 0.027) and the reporting system in group B compared with group A. In addition, even if group B showed the same response capability in communicable diseases as group A, the former had less access to clinical practice, resulting in poorer performance in the noncommunicable diseases during a fire, flood, and traffic accidents (P = 0.002, P = 0.018, P = 0.002). The different emphasis of each training program contributed to the uneven distribution of abilities and cognition. Meanwhile, the lack of an integrated PHE curriculum led to unsystematic expertise. Hence, to optimize the PHE management system, equal attention should be paid to medical students with diverse majors along with a complete integrated PHE curriculum.

IntroductionCutaneous squamous cell carcinoma (cSCC) is the second most common skin cancer, and photodynamic therapy (PDT) is a promising modality against cSCC. This study investigated the impact of PDT on the MAPK pathway and cell cycle alternation of cSCC as well as the related molecular mechanisms.MethodExpressing mRNA profile data sets GSE98767, GSE45216, and GSE84758 were acquired from the GEO database. The functions of differently expressed genes (DEGs) were enriched by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Least absolute shrinkage and selection operator (Lasso) analysis were used to establish a diagnosis model based on GSE98767. A correlation analysis and a protein–protein interaction (PPI) network were used to evaluate the relationship between cSCC-PDT-related genes and the MAPK pathway. Single-sample gene set enrichment analysis (ssGSEA) was performed on GSE98767 to estimate MAPK activation and cell cycle activity. Finally, the effect of MAPK activation on the cell cycle was explored in vitro.ResultFour cSCC-PDT-related genes, DUSP6, EFNB2, DNAJB1, and CCNL1, were identified as diagnostic markers of cSCC, which were upregulated in cSCC or LC50 PDT-protocol treatment and negatively correlated with the MAPK promoter. Despite having a smaller MAPK activation score, cSCC showed higher cell cycle activity. The PDT treatment suppressed the G1 to G2/M phase in JNK overexpressed A431 cells.ConclusionCCNL1, DNAJB1, DUSP6, and EFNB2 were identified as potential PDT target genes in cSCC treatment, whose potential therapeutic mechanism was inhibiting the MAPK pathway and inducing cell cycle alternation.

BackgroundMetastatic disease remains the primary cause of death in patients with oral squamous cell carcinoma (OSCC), especially those who use betel nut. The different steps of the metastatic cascade rely on reciprocal interactions between cancer cells and the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs) are regarded as a significant component in the TME of OSCC. However, the precise mechanisms regulating CAFs in OSCC are poorly understood.MethodsThirteen genes related to the arecoline were analyzed to explore the significant ones involved in arecoline-related OSCC metastasis. The GSE139869 (n = 10) and The Cancer Genome Atlas (TCGA)-OSCC data (n = 361) were mined for the identification of the differentially expressed genes. The least absolute shrinkage and selection operator (LASSO) regression was performed to identify the independent prognostic signatures. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to explore the functional enrichment of selected genes, and gene set enrichment analysis of cuproptosis-related genes was completed. Spearman’s analysis and Tumor Immune Estimation Resource (TIMER) were used to visualize the correlation between the infiltration of CAFs and the gene expression. The correlation analysis of the cells and different genes, including CAF infiltration and transcripts per million expression, was assessed. The relationship between arecoline and CAFs was confirmed by cell counting kit-8 assay (CCK-8). CancerSEA was searched to identify the single-cell phenotype.ResultArecoline-associated fibrosis-related OSCC differentially expressed genes (AFOC-DEGs), namely, PLAU, IL1A, SPP1, CCL11, TERT, and COL1A2, were screened out and selected from the Gene Expression Omnibus (GEO) database and TCGA database. AFOC-DEGs were highly expressed in OSCC, which led to poor survival of patients. Functional enrichment analysis, protein–protein interaction network construction, and Spearman’s correlation analysis all suggested that AFOC-DEGs were closely associated with cuproptosis. Cellular experiments demonstrated that arecoline stimulation could significantly increase the cell viability of CAFs. Single-sample Gene Set Enrichment Analysis (ssGSEA) results showed that GLS and MTF1 were highly expressed when fibroblasts proliferated at high enrichment levels. In addition, analysis of single-cell sequencing results suggested that OSCC cells with high expression of AFOC-DEGs were associated with OSCC metastasis.ConclusionWe found a close association between arecoline, cuproptosis, and CAFs, which might play an important role in the metastasis of OSCC.

PurposeThis study aimed to evaluate the effect and influence of the undergraduate tutor system on the undergraduate stage of Chinese 8-year medical program students in scientific research.MethodsWe collected related data from 194 medical students in the Xiangya Medical School of Central South University. The questionnaire was composed of three parts, namely, eight questions for basic information about individual and undergraduate tutor system, five questions for the subjective feeling impact of the undergraduate tutor system, and 22 questions for accessing the scientific research ability and academic results. The students were mainly divided into three groups to compare different kinds of undergraduate tutor systems, namely, single tutor for multiple students' system (group A), multiple tutors for multiple students' system (group B), and no tutor system for comparison (group C).ResultsThe type of tutorial system, the frequency of guidance, and the way of guidance were independent influence factors of the view of 8-year medical students on scientific research. Group B behaved better than group C in literature processing (P = 0.012), experimental operation (P < 0.001), statistical analysis (P < 0.001), and manuscript producing (P = 0.019). Group A and B joined in more National college students' innovation and entrepreneurship training programs (P = 0.003, P < 0.001). The most popular types of articles published by students were bioinformatics, meta-analysis, and reviews.ConclusionUndergraduate tutor system has made tremendous achievements in cultivating students' scientific research capacity; however, implement improvement should be considered to better educate students.

Nuclear magnetic resonance (NMR) spectroscopy is highly unbiased and reproducible, which provides us a powerful tool to analyze mixtures consisting of small molecules. However, the compound identification in NMR spectra of mixtures is highly challenging because of chemical shift variations of the same compound in different mixtures and peak overlapping among molecules. Here, we present a pseudo-Siamese convolutional neural network method (pSCNN) to identify compounds in mixtures for NMR spectroscopy. A data augmentation method was implemented for the superposition of several NMR spectra sampled from a spectral database with random noises. The augmented dataset was split and used to train, validate and test the pSCNN model. Two experimental NMR datasets (flavor mixtures and additional flavor mixture) were acquired to benchmark its performance in real applications. The results show that the proposed method can achieve good performances in the augmented test set (ACC = 99.80%, TPR = 99.70% and FPR = 0.10%), the flavor mixtures dataset (ACC = 97.62%, TPR = 96.44% and FPR = 2.29%) and the additional flavor mixture dataset (ACC = 91.67%, TPR = 100.00% and FPR = 10.53%). We have demonstrated that the translational invariance of convolutional neural networks can solve the chemical shift variation problem in NMR spectra. In summary, pSCNN is an off-the-shelf method to identify compounds in mixtures for NMR spectroscopy because of its accuracy in compound identification and robustness to chemical shift variation.

Renal fibrosis is an unavoidable consequence that occurs in nearly all of the nephropathies. It is characterized by a superabundant deposition and accumulation of extracellular matrix (ECM). All compartments in the kidney can be affected, including interstitium, glomeruli, vasculature, and other connective tissue, during the pathogenesis of renal fibrosis. The development of this process eventually causes destruction of renal parenchyma and end-stage renal failure, which is a devastating disease that requires renal replacement therapies. Recently, long non-coding RNAs (lncRNAs) have been emerging as key regulators governing gene expression and affecting various biological processes. These versatile roles include transcriptional regulation, organization of nuclear domains, and the regulation of RNA molecules or proteins. Current evidence proposes the involvement of lncRNAs in the pathologic process of kidney fibrosis. In this review, the biological relevance of lncRNAs in renal fibrosis will be clarified as important novel regulators and potential therapeutic targets. The biology, and subsequently the current understanding, of lncRNAs in renal fibrosis are demonstrated—highlighting the involvement of lncRNAs in kidney cell function, phenotype transition, and vascular damage and rarefaction. Finally, we discuss challenges and future prospects of lncRNAs in diagnostic markers and potential therapeutic targets, hoping to further inspire the management of renal fibrosis.

The removal of anthracene from vehicle-wash wastewater (VWW) was investigated using adsorption employing biochar (EWB) generated from eucalyptus wood waste. The EWB was characterized using analytical techniques such as surface area analysis (SAA), scanning electron microscopy (SEM), and fourier transform infrared (FT-IR). The study determined that the most effective conditions to achieve maximum anthracene adsorption (98.40%) were as follows: an initial concentration of 40 ppm, a contact time of 60 min, a pH level of 5, a temperature of 50 °C, and an adsorbent dose of 0.4 g. Isotherms and kinetic models were utilized to evaluate the adsorptive capacity of anthracene. The kinetic analysis results indicate that the anthracene adsorption process onto the EWB is controlled by a pseudo 2nd order kinetic model. The experiment's findings showed that the Langmuir isotherm model provided the most accurate fit to the data. Also, error function analysis was performed. The exothermic and spontaneous nature of the adsorption process is evident based on the values of thermodynamic parameters, including enthalpy (ΔH°), Gibbs free energy (ΔG°), and entropy (ΔS°). The outcomes were also contrasted with the capabilities of a few commercial adsorbents in real-world applications. According to this investigation, the novel EWB exhibits a significant potential, which exceeds 95%, for the extraction and recovery of anthracene from industrial wastewater. [ABSTRACT FROM AUTHOR]

Soft shells are ubiquitous in soft devices, e.g., soft robots, wearable sensors, and soft medial replicas. However, previous widely accepted methods, such as mold casting, dip coating, and additive manufacturing, are limited to thick shells due to the mold assembly and the large friction during demolding, long processing time for mold dissolution, and poor scalability, respectively. Here, a facile, robust, and scalable manufacturing technique, named flow casting, to create soft shells with complex geometries and multifunctionalities is proposed. The method involves a flow-governed layer casting process and a peel-dominated demolding process. A one-dimensional soft shell is first made with controllable thicknesses (100-400 µm) and fabricated various soft shells of intricate geometries, including three-branched, circular-shaped, and exquisite microstructures such as papillae and microgrooves on curved surfaces, with the resolution of feature sizes on the order of 100 µm. Furthermore, the versatility of this method is demonstrated with a 3D vascular phantom model for a magnetic robot transporting, microstructured cubic sleeves for enhancing the grasping ability of rigid grippers, and a stretchable optical waveguide capable of color changing by external mechanical stimuli. [ABSTRACT FROM AUTHOR]

The article provides information on a study which showed the impact of ultrafine nanostructuring on the thermal conductivity reduction of amorphous silicon nitride thin films, in which the thermal transport is impeded by the atomic disorders. Topics discussed include role played by amorphous materials in modern semiconductor devices, and results of the study.

Tight porous materials are used as pneumatic components in a wide range of industrial applications. Such porous materials contain thousands of interconnected irregular micropores, which produce a large pressure drop (ΔP) between the upstream and downstream sides of the porous material when a fluid flows through it. The relationship between the pressure drop and flow rate (i.e., ΔP-G characteristics) is a very important basic characteristic. Temperature is one of the factors that affect the ΔP-G characteristics because variations in temperature change the viscosity and density of the fluid. In this study, we experimentally analyzed the ΔP-G characteristics of tight porous materials by heating them using an electromagnetic system. First, we experimentally investigated the change in the ΔP-G curve under the condition of constant heating power. Then, based on the Darcy-Forchheimer theoiy, we introduced an experimental method to determine the average temperature of the fluid. The results show that the temperature reaches approximately 500 K in the small flow rate range, which produces considerable changes in the ΔP-G curve. As the flow rate increases, the temperature decreases, and thus, the ΔP-G curve at constant heating power converges to the curve for the room temperature. Furthermore, we compared three porous materials with different permeability coefficients and porosities and analyzed the effect of these parameters on the ΔP-G characteristics. We also performed experiments at different downstream pressures to study the effect of the average density on the ΔP-G characteristics. [ABSTRACT FROM AUTHOR]