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Abstract Hole-conductor-free printable mesoscopic perovskite solar cells (p-MPSCs) have attracted widespread attention for their low cost, up-scalability, and exceptional stability. However, the high defect density of perovskite and the absence of interfacial barrier layer between perovskite and carbon electrode cause profound open-circuit voltage (V OC) loss, which results in uncompetitive power conversion efficiency (PCE). Herein, an anion-cation synergy of decylammonium sulfate (DA2SO4) is utilized for suppressing V OC loss of p-MPSCs via a facile post-treatment method. DA+ cations transform the perovskite adjacent to carbon electrode into wide-bandgap 2D perovskite for blocking electrons, while the SO4 2− anions interact with undercoordinated lead centers for reducing defect density. As a result, the modified device delivers an enhanced PCE from 17.78% to 19.59%, with an improved V OC from 0.98 V to 1.06 V. Meanwhile, the modified device without any encapsulation exhibits excellent moisture stability with the PCE remained almost 99% of the initial value after 528 h aging in 75% RH air at room temperature.

Background: Significant progress has been achieved in the management of multiple myeloma (MM) by implementing high-dose therapy and stem cell transplantation. Moreover, the prognosis of patients has been enhanced due to the introduction of novel immunomodulatory drugs and the emergence of new targeted therapies. However, predicting the survival rates of patients with multiple myeloma is still tricky. According to recent researches, platelets have a significant impact in affecting the biological activity of tumors and are essential parts of the tumor microenvironment. Nonetheless, it is still unclear how platelet-related genes (PRGs) connect to the prognosis of multiple myeloma.Methods: We analyzed the expression of platelet-related genes and their prognostic value in multiple myeloma patients in this study. We also created a nomogram combining clinical metrics. Furthermore, we investigated disparities in the biological characteristics, immunological microenvironment, and reaction to immunotherapy, along with analyzing the drug susceptibility within diverse risk groups.Results: By using the platelet-related risk model, we were able to predict patients’ prognosis more accurately. Subjects in the high-risk cohort exhibited inferior survival outcomes, both in the training and validation datasets, as compared to those in the low-risk cohort (p < 0.05). Moreover, there were differences in the immunological microenvironments, biological processes, clinical features, and chemotherapeutic drug sensitivity between the groups at high and low risk. Using multivariable Cox regression analyses, platelet-related risk score was shown to be an independent prognostic influence in MM (p < 0.001, hazard ratio (HR) = 2.001%, 95% confidence interval (CI): 1.467–2.730). Furthermore, the capacity to predict survival was further improved when a combined nomogram was utilized. In training cohort, this outperformed the predictive value of International staging system (ISS) alone from a 5-years area under curve (AUC) = 0.668 (95% CI: 0.611–0.725) to an AUC = 0.721 (95% CI: 0.665–0.778).Conclusion: Our study revealed the potential benefits of PRGs in terms of survival prognosis of MM patients. Furthermore, we verified its potential as a drug target for MM patients. These findings open up novel possibilities for prognostic evaluation and treatment choices for MM.

Ocean exploration is crucial for utilizing its extensive resources. Images captured by underwater robots suffer from issues such as color distortion and reduced contrast. To address the issue, an innovative enhancement algorithm is proposed, which integrates Transformer and Convolutional Neural Network (CNN) in a parallel fusion manner. Firstly, a novel transformer model is introduced to capture local features, employing peak-signal-to-noise ratio (PSNR) attention and linear operations. Subsequently, to extract global features, both temporal and frequency domain features are incorporated to construct the convolutional neural network. Finally, the image’s high and low frequency information are utilized to fuse different features. To demonstrate the algorithm’s effectiveness, underwater images with various levels of color distortion are selected for both qualitative and quantitative analyses. The experimental results demonstrate that our approach outperforms other mainstream methods, achieving superior PSNR and structural similarity index measure (SSIM) metrics and yielding a detection performance improvement of over ten percent.

PurposeTo assess the prognostic significance of β2-microglobulin decline index (β2M DI) in multiple myeloma (MM).Methods150 MM patients diagnosed with MM were enrolled in this study. Cox proportional hazards model was used to analyze the uni- and multivariate prognosis in training cohort (n=105). A new combined prognostic model containing β2M DI was built up based on the data in training cohort. The validation group was used to verify the model.Resultsβ2M DI showed significant correlation with prognosis in both uni- and multivariate analyses and had a good correlation with complete response (CR) rate and deep remission rate. The ROC and calibration curves in validation cohort (n=45) indicated a good predictive performance of the new model. Based on the median risk score of the training group, we classified patients into high- and low- risk groups. In both training and validation groups, patients in the low-risk group had longer overall survival (OS) time than that in the high-risk group (p

Background: Osimertinib resistance is a major problem in the course of targeted therapy for non-small cell lung cancer (NSCLC) patients. To develop and validate a multisequence MRI-based radiomics nomogram for early prediction of osimertinib resistance in NSCLC with brain metastases (BM). Methods: Pretreatment brain MRI of 251 NSCLC patients proven with BM were retrospectively enrolled from two centers (training cohort: 196 patients; testing cohort: 55 patients). According to the gene test result of osimertinib resistance, patients were labeled as resistance and non-resistance groups (training cohort: 65 versus 131 patients; testing cohort: 25 versus 30 patients). Radiomics features were extracted from T2WI, T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (T1-CE) sequences separately and radiomics score (rad-score) were built from the four sequences. Then a multisequence MRI-based nomogram was developed and the predictive ability was evaluated by ROC curves and calibration curves. Results: The rad-scores of the four sequences has significant differences between resistance and non-resistance groups in both training and testing cohorts. The nomogram achieved the highest predictive ability with area under the curve (AUC) of 0.989 (95 % confidence interval, 0.976–1.000) and 0.923 (95 % confidence interval, 0.851–0.995) in the training and testing cohort respectively. The calibration curves showed excellent concordance between the predicted and actual probability of osimertinib resistance using the radiomics nomogram. Conclusions: The multisequence MRI-based radiomics nomogram can be used as a noninvasive auxiliary tool to identify candidates who were resistant to osimertinib, which could guide clinical therapy for NSCLC patients with BM.

Abstract Dyslipidemia is one of the known risk factors for cardiovascular disease, and its prevalence is increasing worldwide. At present, the study of dyslipidemia has gradually shifted from simple environmental or genetic factors to environment-gene interactions. In order to further explore the etiology and mechanism of dyslipidemia, we used occupational stress(OS) and LYPLAL1, APOC3 and SOD2 gene as research variables to explore their association with dyslipidemia.Here we used a case-control study to include Han workers from a coal mining enterprise in China to determine the association between study variables and dyslipidemia. Monofactor analysis showed that smoking, drinking, physical activity level, DASH diet score, sleep quality, BMI, hypertension, hyperuricemia, shift work, OS were significantly different between the two groups (P 0.05). GMDR model showed that the rs12137855-rs2854116-rs4880-OS four-factor model had the highest cross-validation consistency and training-validation accuracy (P

Based on the two-tier stochastic frontier analysis method, this paper constructs a basic model to analyze the environmental treatment investment of enterprises, and discusses the influence of the gambling power between enterprises and governments on the input of environmental pollution control. The data in this paper are from the Chinese Private Enterprise Survey (CPES). And empirical results show that the gambling power between enterprises and government affects the investment of pollution control to a great extent. The influence proportion of the gambling power of the enterprise on the investment of environmental pollution control is 84.44%, which is much higher than that of the government (15.56%). The enterprise is the executor with more real information, and can even reduce the investment of pollution control by concealing its actual situation and so on, hence the enterprise occupies the initiative in the game process. Although enterprises are still in the dominant position in the game, their advantages are continually shrinking. With the expansion of the scale of the enterprise and the high level of entrepreneur education, the game advantage of the enterprise is continuously decreasing.

Background: One particular type of cellular death that is known as ferroptosis is caused by the excessive lipid peroxidation. It is a regulated form of cell death that can affect the response of the tumor cells. Currently, it is not known if the presence of this condition can affect the prognosis of patients with multiple myeloma (MM).Methods: In this study, we studied the expression differences and prognostic value of ferroptosis-related genes (FRGs) in MM, and established a ferroptosis risk scoring model. In order to improve the prediction accuracy and clinical applicability, a nomogram was also established. Through gene enrichment analysis, pathways closely related to high-risk groups were identified. We then explored the differences in risk stratification in drug sensitivity and immune patterns, and evaluated their value in prognostic prediction and treatment response. Lastly, we gathered MM cell lines and samples from patients to confirm the expression of marker FRGs using quantitative real-time PCR (qRT-PCR).Results: The ability to predict the survival of MM patients is a challenging issue. Through the use of a risk model derived from ferroptosis, we were able to develop a more accurate prediction of the disease’s prognosis. They were then validated by a statistical analysis, which showed that the model is an independent factor in the prognosis of MM. Patients of high ferroptosis risk scores had a much worse chance of survival than those in the low-risk groups. The calibration and power of the nomogram were also strong. We noted that the link between the ferroptosis risk score and the clinical treatment was suggested by the FRG’s significant correlation with the immune checkpoint genes and the medication sensitivity. We validated the predictive model using qRT-PCR.Conclusion: We demonstrated the association between FRGs and MM, and developed a new risk model for prognosis in MM patients. Our study sheds light on the potential clinical relevance of ferroptosis in MM and highlights its potential as a therapeutic target for patients with this disease.

Abstract Background Occupational exposure may be associated with an increased risk of developing hyperuricemia. This study sheds lights on the association between occupational exposure and hyperuricemia in steelworkers. Method A nested case–control study was conducted within a cohort of workers in steel companies to explore the association between occupational exposure and hyperuricemia. The case group consisted of a total of 641 cases of hyperuricemia identified during the study period, while 641 non-hyperuricemia subjects with the same age and gender distribution were randomly selected from the cohort as the control group. Results The incidence rate of hyperuricemia among workers in the steel company was 17.30%, with an incidence density of 81.32/1,000 person-years. In comparison to the reference group, the risks of developing hyperuricemia for steelworkers undergoing ever shifts, current shifts, heat exposure, and dust exposure were 2.18 times, 1.81 times, 1.58 times and 1.34 times higher respectively. The odds ratios (ORs) and 95% confidence intervals (CIs) were 1.87(1.12–3.13) and 2.02(1.21–3.37) for the cumulative number of days of night work at 0–1,972.80 and ≥ 1,972.80 (days), respectively. Compared to the group with the cumulative heat exposure of 0 (°C/year), the ORs (95% CI) for the risk of developing hyperuricemia in the groups with the cumulative heat exposure of 0–567.83 and ≥ 567.83 (°C/year) were 1.50(1.02–2.22) and 1.64(1.11–2.43), respectively. The OR (95% CI) for the risk of developing hyperuricemia was 1.56(1.05–2.32) at the cumulative dust exposure of ≥ 30.02 (mg/m3/year) compared to that at the cumulative dust exposure of 0 (mg/m3/year). Furthermore, there was a multiplicative interaction between heat exposure and dust exposure in the development of hyperuricemia. Conclusion Shift work, heat, and dust are independent risk factors for the development of hyperuricemia in steelworkers. Additionally, there is a multiplicative interaction between heat exposure and dust exposure in the development of hyperuricemia. Interventions for shift work, heat and dust may help to reduce the incidence rate of hyperuricemia and improve the health of steelworkers.

Background: Metabolic reprogramming is an important hallmark of cancer. Glycolysis provides the conditions on which multiple myeloma (MM) thrives. Due to MM’s great heterogeneity and incurability, risk assessment and treatment choices are still difficult.Method: We constructed a glycolysis-related prognostic model by Least absolute shrinkage and selection operator (LASSO) Cox regression analysis. It was validated in two independent external cohorts, cell lines, and our clinical specimens. The model was also explored for its biological properties, immune microenvironment, and therapeutic response including immunotherapy. Finally, multiple metrics were combined to construct a nomogram to assist in personalized prediction of survival outcomes.Results: A wide range of variants and heterogeneous expression profiles of glycolysis-related genes were observed in MM. The prognostic model behaved well in differentiating between populations with various prognoses and proved to be an independent prognostic factor. This prognostic signature closely coordinated with multiple malignant features such as high-risk clinical features, immune dysfunction, stem cell-like features, cancer-related pathways, which was associated with the survival outcomes of MM. In terms of treatment, the high-risk group showed resistance to conventional drugs such as bortezomib, doxorubicin and immunotherapy. The joint scores generated by the nomogram showed higher clinical benefit than other clinical indicators. The in vitro experiments with cell lines and clinical subjects further provided convincing evidence for our study.Conclusion: We developed and validated the utility of the MM glycolysis-related prognostic model, which provides a new direction for prognosis assessment, treatment options for MM patients.

BackgroundThe current oil production determines oil workers’ occupational noise exposure. Without effective protection, noise will affect various aspects of worker’s body functions, including acting on the adrenal cortex system and resulting in renal function damage. ObjectiveTo evaluate the associations of noise exposure and its cumulative exposure level with renal function impairment of oil workers. MethodsOil workers from a collective medical examination in a hospital were selected as the study subjects. In accordance with the national standard Measurement of Physical Agents in the Workplace Part 8: Noise (GBZ/T 189.8—2007), noise exposure was measured three times at the oil workers' work site, and their average value was calculated to obtain the cumulative noise exposure (CNE). A questionnaire survey was conducted to collect general information such as socio-demographic characteristics, family history, lifestyles, and occupational history. All blood biochemical indicators were measured in the fasting state. Renal function impairment was judged based on the glomerular filtration rate. The relationship between CNE and renal function was analyzed using receiver operating characteristic curve (ROC) for workers with noise exposure. ResultsA total of 2 917 subjects were included in the study and their prevalence of renal function impairment was 14.2%. The univariate analysis results suggested statistically significant differences in the prevalence of renal function impairment among the oil workers grouped by having hypertension or not, gender, age, marital status, marital status, smoking, and alcohol consumption (P＜0.05); the prevalence of renal impairment was significantly higher in those with abnormal values of uric acid, total cholesterol, triglycerides, high-density lipoprotein, and fasting glucose than in those with normal values (P＜0.05); the oil workers with noise exposure [n=1565, 53.7%, equivalent sound level ≥80 dB(A)] showed a higher prevalence of renal function impairment than those without (P＜0.05). The results of multiple logistic regression analysis showed that being female (OR=2.811, 95%CI: 1.960-4.030), age at 31 years and above (OR31-40=3.502, 95%CI: 1.402-8.751; OR41-50=4.255, 95%CI: 1.759-10.291; OR≥51=7.179, 95%CI: 2.864-17.996), showing abnormal uric acid (OR=5.932, 95%CI: 4.486-7.843), having hypertension (OR=1.593, 95%CI: 1.230-2.063), alcohol consumption (OR=2.648, 95%CI: 1.346-5.212), and smoking (OR=1.816, 95%CI: 1.133-2.911) had higher risks of developing renal function impairment; besides, those exposed to noise had 1.351 times (95%CI: 1.073-1.702) higher risks of developing renal function impairment than non-exposed individuals. Noise-exposed oil workers in the renal impairment group had higher noise exposure intensity and CNE compared to the noise-exposed oil workers in the normal renal function group (P＜0.05), and the workers had an increased risk of renal function impairment when the CNE was >95.85 dB(A)·year versus CNE ≤ 95.85 dB(A)·year (OR=2.583, 95%CI: 1.956-3.411). ConclusionExposure to noise, higher noise exposure intensity, and higher level of CNE may be associated with developing renal function impairment in oil workers. Oil workers with CNE above 95.85 dB(A)·year are at an increased risk of renal impairment.

Background: Cuproptosis is a newly identified unique copper-triggered modality of mitochondrial cell death, distinct from known death mechanisms such as necroptosis, pyroptosis, and ferroptosis. Multiple myeloma (MM) is a hematologic neoplasm characterized by the malignant proliferation of plasma cells. In the development of MM, almost all patients undergo a relatively benign course from monoclonal gammopathy of undetermined significance (MGUS) to smoldering myeloma (SMM), which further progresses to active myeloma. However, the prognostic value of cuproptosis in MM remains unknown.Method: In this study, we systematically investigated the genetic variants, expression patterns, and prognostic value of cuproptosis-related genes (CRGs) in MM. CRG scores derived from the prognostic model were used to perform the risk stratification of MM patients. We then explored their differences in clinical characteristics and immune patterns and assessed their value in prognosis prediction and treatment response. Nomograms were also developed to improve predictive accuracy and clinical applicability. Finally, we collected MM cell lines and patient samples to validate marker gene expression by quantitative real-time PCR (qRT-PCR).Results: The evolution from MGUS and SMM to MM was also accompanied by differences in the CRG expression profile. Then, a well-performing cuproptosis-related risk model was developed to predict prognosis in MM and was validated in two external cohorts. The high-risk group exhibited higher clinical risk indicators. Cox regression analyses showed that the model was an independent prognostic predictor in MM. Patients in the high-risk group had significantly lower survival rates than those in the low-risk group (p < 0.001). Meanwhile, CRG scores were significantly correlated with immune infiltration, stemness index and immunotherapy sensitivity. We further revealed the close association between CRG scores and mitochondrial metabolism. Subsequently, the prediction nomogram showed good predictive power and calibration. Finally, the prognostic CRGs were further validated by qRT-PCR in vitro.Conclusion: CRGs were closely related to the immune pattern and self-renewal biology of cancer cells in MM. This prognostic model provided a new perspective for the risk stratification and treatment response prediction of MM patients.

Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous disease. Therefore, more reliable biomarkers are required to better predict the prognosis of DLBCL. Cuproptosis is a novel identified form of programmed cell death (PCD) that is different from oxidative stress-related cell death (e.g., apoptosis, ferroptosis, and necroptosis) by Tsvetkov and colleagues in a recent study released in Science. Cuproptosis is copper-dependent PCD that is closely tied to mitochondrial metabolism. However, the prognostic value of cuproptosis-related genes (CRGs) in DLBCL remains to be further elucidated. In the present study, we systematically evaluated the molecular changes of CRGs in DLBCL and found them to be associated with prognosis. Subsequently, based on the expression profiles of CRGs, we characterized the heterogeneity of DLBCL by identifying two distinct subtypes using consensus clustering. Two isoforms exhibited different survival, biological functions, chemotherapeutic drug sensitivity, and immune microenvironment. After identifying differentially expressed genes (DEGs) between CRG clusters, we built a prognostic model with the Least absolute shrinkage and selection operator (LASSO) Cox regression analysis and validated its prognostic value by Cox regression analysis, Kaplan-Meier curves, and receiver operating characteristic (ROC) curves. In addition, the risk score can predict clinical characteristics, levels of immune cell infiltration, and prognosis. Furthermore, a nomogram incorporating clinical features and risk score was generated to optimize risk stratification and quantify risk assessment. Compared to the International Prognostic Index (IPI), the nomogram has demonstrated more accuracy in survival prediction. Furthermore, we validated the prognostic gene expression levels through external experiments. In conclusion, cuproptosis-related gene signature can serve as a potential prognostic predictor in DLBCL patients and may provide new insights into cancer therapeutic targets.

Metasurfaces have recently gained increasing attention and shown great potential in replacing bulk optics with ultracompact flat metadevices, owing to their unprecedented capabilities of molding optical fields with surface‐confined configurations and exceedingly low losses. However, most existing metasurfaces are typically passive and feature well‐defined responses determined by material compositions and configurations, which severely limit the functionalities in intelligent and adaptive nanophotonics. Therefore, it is highly desired to realize active metasurfaces with dynamic modulation postfabrication. Herein, a highly efficient tunable metawaveplate at the telecom wavelength of 1.55 μm by structuring the phase‐change alloy Sb2Se3 is designed, which can switch from a half‐waveplate to a quarter‐waveplate once Sb2Se3 transits from the amorphous to the crystalline state. Capitalizing on the designed Sb2Se3 meta‐atom and the Pancharatnam–Berry phase, two switchable meta‐devices capable of actively shaping the wavefronts of the transmitted waves are proposed, including tunable beam steering and metalens with tailored focal intensity. The presented tunable metasurfaces can open new avenues for adaptive photonics with advanced functionalities.

Communication is vital to complete tasks coordinately for robot swarms. In this paper, we investigate massive MIMO enabled robot swarms. Specifically, for the robot swarms, the transceiver beamforming not only needs to maximize the rate, but also has to restrict the interference on other receivers. Therefore, the transceiver design of robots is critical to optimize the sum-rate performance under the restriction of the interference on the a specific robot. Currently, only exhaustive search is able to provide the optimal solution for the problem, whereas its complexity is unacceptable. In this paper, to address the intractable issue, based on the max-plus approach, we consider each transmitter or receiver as an independent decision agent, and all robots coordinately choose the optimal joint beam combination by max-plus algorithm. In the multi-agent framework, each agent learns the policy of choosing analog beam by reinforcement learning (RL). Furthermore, to improve the learning efficiency of RL and reduce the transmission latency, we exploit the efficient ELM network to replace the deep network of deep RL, and propose a ELM-based RL method to conduct the transmission between robots in robot swarm. Analysis and simulation results reveal that, the proposed method is able to achieve a near-optimal sum-rate performance, while the complexity is acceptable.

A novel voltammetric electronic tongue (VE-tongue) system based on three nanocomposites modified working electrodes was used for the discrimination of red wine from different geographical origins. The three types of modified working electrodes were fabricated to detect glucose (Glu), tartaric acid (TA), and non-specific flavor information in a red wine sample, respectively. The electrochemical properties of three electrodes were tested by cyclic voltammetric method, and pH, accumulation time, and scan rates were optimized for Glu and TA sensors. Scanning electron microscopy (SEM), X-ray proton spectrum (XPS), and X-ray diffraction (XRD) were used for the characterization of modified materials. This sensor array was then applied to identify four kinds of red wines from different geographical origins, and the multi-frequency and potential steps (STEP) method was used to obtain flavor information regarding rice wines. The classification ability of this VE-tongue system was evaluated by using partial least squares (PLS) regression and principal component analysis (PCA), while back propagation neural network (BPNN), random forest (RF), support vector machines (SVM), deep neural network (DNN), and K-nearest neighbor (KNN) were used for the prediction. The results showed that PCA could explain about the 95.7% of the total variance, and BPNN performed best in the prediction work (the prediction accuracy was 95.8%). Therefore, the VE-tongue system with BPNN was chosen to effectively discriminate red wines from different geographical origins, and the novel VE-tongue aiming at red wine discrimination with high accuracy and lower cost was established.

Massive multiple-input multiple-output(MIMO)brings huge improvements in energy efficiency and reduces emission power by using a large number of antennas,which is known as a key technology in the fifth generation (5G)mobile communication.With the increasing number of antennas,the complexity of signal detection is increasing at the same time.According to the research status of massive MIMO signal detection,approximate message passing algorithm was proposed.The complexity of AMP was compared to some iteration algorithm like Richarson algorithm and Neumann series approximation algorithm.The results indicate that the proposed algorithm can use less iteration to achieve almost the same performance of MMSE algorithm.

With the widespread deployment and use of MTC（machine-type communication）devices,MTC has been closely integrated with the mobile communication network.Significant increase in the number of MTC devices causes more strict requirements for communication system.According to the characteristics of MTC communication and the development trend,three key areas of MTC in current LTE-Advanced network were discussed:RAN overload contro1,efficient radio resource allocation and energy savings.Several research challenges that need to be addressed for scalable support of MTC devices for R12 and beyond were summarized,typical optimization methods and solutions were introduced,and corresponding improvement directions according to the current application status were put forward.