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Cancer immunotherapy has drawn much attention because it can restart the recognition and killing function of the immune system to normalize the antitumor immune response. However, the role of radiotherapy and chemotherapy in cancer treatment cannot be ignored. Due to cancer heterogeneity, combined therapy has become a new trend, and its efficacy has been confirmed in many studies. This review discussed the clinical implications and the underlying mechanisms of cancer immunotherapy in combination with radiotherapy or chemotherapy, offering an outline for clinicians as well as inspiration for future research.

Emergence agitation is a common paediatric complication after inhalational anaesthesia. Intranasal dexmedetomidine can prevent emergence agitation effectively, but the optimal dose is uncertain.The aim of our study was to investigate the 95% effective dose (ED 95 ) of intranasal dexmedetomidine for the prevention of emergence agitation after inhalational anaesthesia for paediatric ambulatory surgery.A prospective, randomised, placebo-controlled, double-blind, clinical trial.The study was conducted in Guangzhou Women and Children's Medical Center in China from August 2017 to December 2018.Three hundred and eighteen children scheduled for ambulatory surgery were enrolled into two age groups of less than 3 years and at least 3 years.The children in each age group were randomised into five equal subgroups to receive either intranasal dexmedetomidine 0.5, 1.0, 1.5 or 2.0 μg kg -1 (Groups D 0.5 , D 1.0 , D 1.5 and D 2.0 ), or intranasal isotonic saline (group C) after induction.The primary outcome was the ED 95 dose of intranasal dexmedetomidine for preventing emergence agitation after inhalational anaesthesia for paediatric ambulatory surgery.The incidences of emergence agitation for Groups C, D 0.5 , D 1.0 , D 1.5 and D 2.0 were 63, 40, 23, 13 and 3% in children less than 3 years, and 43, 27, 17, 7 and 3% in children at least 3 years. The ED 95 of intranasal dexmedetomidine for preventing emergence agitation was 1.99 μg kg -1 [95% confidence interval (CI), 1.83 to 3.80 μg kg -1 ] in children less than 3 years, and 1.78 μg kg -1 (95% CI, 0.93 to 4.29 μg kg -1 ) in children at least 3 years. LMA removal time for groups D 1.5 and D 2.0 was 9.6 ± 2.2 and 9.7 ± 2.5 min, respectively, for children less than 3 years, and 9.4 ± 2.0 and 9.9 ± 2.7 min in children at least 3 years, respectively. Length of stay in the postanaesthesia care unit for Groups D 1.5 and D 2.0 was 34.3 ± 9.6 and 37.1 ± 11.2 min, respectively, in children less than 3 years, and 34.7 ± 10.2 and 37.3 ± 8.3 min in children at least 3 years, respectively. These times were longer in the D 1.5 and D 2.0 subgroups than in the control subgroup in the two age groups of less than 3 years and at least 3 years, respectively: 7.2 ± 1.9 min in children less than 3 years and 7.3 ± 2.5 min in children at least 3 years for LMA removal time, 22.2 ± 7.9 min in children less than 3 years and 22.0 ± 7.7 min in children at least 3 years for PACU stay time in control subgroup, respectively ( P 0.05).Intranasal dexmedetomidine prevented emergence agitation after paediatric surgery in a dose-dependent manner. The optimal dose of intranasal dexmedetomidine for preventing emergence agitation was higher in younger children.chictr.org.cn: ChiCTR-IOR-17012415.

Against the backdrop of the global aging trend, the proportion of the elderly population is severely increasing in the urban areas of underdeveloped regions. Despite evidence that urban forest parks are effective at enhancing the physical and mental well-being of the elderly, little has been done to investigate the connection between urban forest parks and the elderly in underdeveloped regions, and landscape studies in particular are lacking. This study attempted to address this gap, using a subjective evaluation method in which 725 elderly respondents were engaged in a questionnaire survey on their soundscape preferences in the urban forest parks of an underdeveloped city in China. The results revealed the elderly people’s preferences for soundscapes, and a further analysis demonstrated the relationships between these preferences and landscape features. The effects of personal traits and living situations on soundscape preferences were determined by analyzing the impacts of living conditions, occupation, and education on soundscape preferences. By building a model with regression coefficients, the most powerful factors influencing soundscape choice were investigated. It was found that (1) the types of sound sources preferred by the elderly, in descending order, were natural sound, livestock sound, bird song, musical sound, other sounds. (2) The differences among education, occupation, and age all affected the participants’ soundscape preferences, i.e., the mean values of the soundscape preferences among older adults varied with education, occupation, and age. The mean value of soundscape preference was higher among older adults who had received higher education, were government officials and business managers, and belonged to higher age groups. (3) Among the various factors influencing the soundscape preference of the elderly, the most influential factors were the length of time spent in the waterfront environment, the time spent in the forest park, and the importance of road signs. (4) The preference for soundscapes was strongly connected with happiness in life. (5) Wearing a mask significantly reduced soundscape perception scores under epidemic conditions, while vaccinated individuals were more tolerant of various noises. Recommendations for landscape design to improve the soundscape perception of elderly people are accordingly provided.

Interactions of collective excitations often lead to rich emergent phenomena in many-particle quantum systems. In ordered magnets, the elementary excitations are spin waves (magnons), which obey Bose-Einstein statistics. Similar to the Cooper pairs in superconductors, magnons can be paired into bound states under attractive interactions. Even more interestingly, the Zeeman coupling to a magnetic field acts as a chemical potential that can tune the particle density through a quantum critical point (QCP), beyond which a ``hidden order'' is predicted to exist. However, experimental confirmation of this QCP and the associated new state of matter remain elusive. Here we report direct observation of the Bose-Einstein condensation (BEC) of the two-magnon bound state in Na$_2$BaNi(PO$_4$)$_2$. Comprehensive thermodynamic measurements confirmed the existence of a two-dimensional BEC-QCP at the saturation field. Inelastic neutron scattering experiments were performed to accurately establish the magnetic exchange model. An exact solution of the model found stable 2-magnon bound states that were further confirmed by an electron spin resonance (ESR) experiment, demonstrating that the QCP is due to the pair condensation and the phase below saturation field is the long-sought-after spin nematic (SN) phase., 53 pages, 28 figures

The preparation of fiber-reinforced phosphorus building gypsum composite materials (FRPGCs) is an important approach to enlarge the utilization of phosphogypsum resources. Through reinforcing phosphorus building gypsum (PBG) with basalt fiber (BF), this article probes into the effects of the length and fiber content of BF on the working performance and mechanical properties of basalt-fiber-reinforced phosphorus building gypsum composite materials (BRPGCs) and accesses the toughness of BRPGCs under bending loads using residual strength. The results showed that the addition of BF could significantly promote the mechanical properties of BRPGCs. However, due to the adverse effect of fibers on the working performance of BRPGCs, the fiber content was constrained. After adding 1.2% of 6 mm BF, the bending strength and compressive strength of FRPGCs reached maximum values of 10.98 MPa and 29.83 MPa, respectively. Under a bending load, BRPGCs exhibited an apparent ductile behavior. The P-δ curve presented five stages, with an evident phase of strength stability after cracking. A larger fiber content was conducive to the toughness of BRPGCs. When 1.6% of 6 mm BF was added, the residual strength of FRPGCs could reach 6.77 MPa.

Due to the large number of waterborne bacteria presenting in drinking water, their rapid and accurate identification has become a global priority. The surface plasmon resonance (SPR) biosensor with prism (BK7)-silver(Ag)-MXene(Ti3T2Cx)-graphene- affinity-sensing medium is examined in this paper, in which the sensing medium includes pure water, vibrio cholera (V. cholera), and escherichia coli (E. coli). For the Ag-affinity-sensing medium, the maximum sensitivity is obtained by E. coli, followed by V. cholera, and the minimum is pure water. Based on the fixed-parameter scanning (FPS) method, the highest sensitivity is 246.2 °/RIU by the MXene and graphene with monolayer, and with E. coli sensing medium. Therefore, the algorithm of improved differential evolution (IDE) is obtained. By the IDE algorithm, after three iterations, the maximum fitness value (sensitivity) of the SPR biosensor achieves 246.6 °/RIU by using the structure of Ag (61 nm)-MXene (monolayer)-graphene (monolayer)-affinity (4 nm)-E. coli. Compared with the FPS and differential evolution (DE) algorithm, the highest sensitivity is more accurate and efficient, and with fewer iterations. The performance optimization of multilayer SPR biosensors provides an efficient platform.

Feature selection (FS) plays an important role in machine learning, which extracts important features and accelerates the learning process. In this paper, we propose a deep FS method that simultaneously conducts feature selection and differentiable $ k $-NN graph learning based on the Dirichlet Energy. The Dirichlet Energy identifies important features by measuring their smoothness on the graph structure, and facilitates the learning of a new graph that reflects the inherent structure in the new feature subspace during the training process using selected features. We employ the Gumbel Softmax technique and the Optimal Transport theory to address the non-differentiability issues of learning discrete FS results and learning $ k $-NN graphs in neural networks, which theoretically makes our model applicable to other graph neural networks. Furthermore, the proposed framework is interpretable, since all modules are designed algorithmically. We validate the effectiveness of our model with extensive experiments on both synthetic and real-world datasets.

This paper investigates the differences in data organization between contrastive and supervised learning methods, focusing on the concept of locally dense clusters. We introduce a novel metric, Relative Local Density (RLD), to quantitatively measure local density within clusters. Visual examples are provided to highlight the distinctions between locally dense clusters and globally dense ones. By comparing the clusters formed by contrastive and supervised learning, we reveal that contrastive learning generates locally dense clusters without global density, while supervised learning creates clusters with both local and global density. We further explore the use of a Graph Convolutional Network (GCN) classifier as an alternative to linear classifiers for handling locally dense clusters. Finally, we utilize t-SNE visualizations to substantiate the differences between the features generated by contrastive and supervised learning methods. We conclude by proposing future research directions, including the development of efficient classifiers tailored to contrastive learning and the creation of innovative augmentation algorithms.

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Previous studies on resting-state electroencephalographic responses in patients with depressive disorders have identified electroencephalogram (EEG) parameters as potential biomarkers for the early detection and diagnosis of depressive disorders. However, these studies did not investigate the relationship between resting-state EEG microstates and the early detection of depressive symptoms in preclinical individuals. To explore the possible association between resting-state EEG microstate temporal dynamics and depressive symptoms among college students, EEG microstate analysis was performed on eyes-closed resting-state EEG data for approximately 5 min from 34 undergraduates with high intensity of depressive symptoms and 34 age- and sex-matched controls with low intensity of depressive symptoms. Five microstate classes (A-E) were identified to best explain the datasets of both groups. Compared to controls, the mean duration, occurrence, and coverage of microstate class B increased significantly, whereas the occurrence and coverage of microstate classes D and E decreased significantly in individuals with high intensity of depressive symptoms. Additionally, the presence of microstate class B was positively correlated with participants' Beck Depression Inventory-II (BDI-II) scores, and the presence of microstate classes D and E were negatively correlated with their BDI-II scores. Further, individuals with high intensity of depressive symptoms had higher transition probabilities of A→B, B→A, B→C, B→D, and C→B, with lower transition probabilities of A→D, A→E, D→A, D→E, E→A, E→C, and E→D than controls. These results highlight resting-state EEG microstate temporal dynamics as potential biomarkers for the early detection and timely treatment of depression in college students.

Precipitation forecasting is an important mission in weather science. In recent years, data-driven precipitation forecasting techniques could complement numerical prediction, such as precipitation nowcasting, monthly precipitation projection and extreme precipitation event identification. In data-driven precipitation forecasting, the predictive uncertainty arises mainly from data and model uncertainties. Current deep learning forecasting methods could model the parametric uncertainty by random sampling from the parameters. However, the data uncertainty is usually ignored in the forecasting process and the derivation of predictive uncertainty is incomplete. In this study, the input data uncertainty, target data uncertainty and model uncertainty are jointly modeled in a deep learning precipitation forecasting framework to estimate the predictive uncertainty. Specifically, the data uncertainty is estimated a priori and the input uncertainty is propagated forward through model weights according to the law of error propagation. The model uncertainty is considered by sampling from the parameters and is coupled with input and target data uncertainties in the objective function during the training process. Finally, the predictive uncertainty is produced by propagating the input uncertainty in the testing process. The experimental results indicate that the proposed joint uncertainty modeling framework for precipitation forecasting exhibits better forecasting accuracy (improving RMSE by 1 %–2 % and R2 by 1 %–7 % on average) relative to several existing methods, and could reduce the predictive uncertainty by ∼28 % relative to the approach of Loquercio et al. (2020). The incorporation of data uncertainty in the objective function changes the distributions of model weights of the forecasting model and the proposed method can slightly smooth the model weights, leading to the reduction of predictive uncertainty relative to the method of Loquercio et al. (2020). The predictive accuracy is improved in the proposed method by incorporating the target data uncertainty and reducing the forecasting error of extreme precipitation. The developed joint uncertainty modeling method can be regarded as a general uncertainty modeling approach to estimate predictive uncertainty from data and model in forecasting applications.

Objective: The growth differentiation factor 8 (GDF8) gene plays a key role in bone formation, resorption, and skeletal muscle development in mammals. Here, we studied the genetic variants of GDF8 and their contribution to body conformation traits in Chinese Dabieshan cattle.Methods: Single nucleotide polymorphisms (SNPs) were identified in the bovine GDF8 gene by DNA sequencing. Phylogenetic analysis, motif analysis, and genetic diversity analysis were conducted using bioinformatics software. Association analysis between five SNPs, haplotype combinations, and body conformation traits was conducted in 380 individuals.Results: The GDF8 was highly conserved in seven species, and the GDF8 sequence of cattle was most similar to the sequences of sheep and goat based on the phylogenetic analysis. The motif analysis showed that there were 12 significant motifs in GDF8. Genetic diversity analysis indicated that the polymorphism information content of the five studied SNPs was within 0.25 to 0.5. Haplotype analysis revealed a total of 12 different haplotypes and those with a frequency of 0.330) between the following SNPs: g.5070C>A, g.5076T>C, and g.5148A>C. Association analysis indicated these five SNPs were associated with some of the body conformation traits (p

Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive, safe, and efficacious treatment for major depressive disorder (MDD). However, the antidepressant efficacy of rTMS greatly varies across individual patients. Thus, markers that can be used to predict the outcome of rTMS treatment at the individual level must be identified. Thalamo-cortical connectivity was abnormal in patients with MDD, and was normalized after rTMS treatment. In the current study, we investigated whether the resting-state functional and structural thalamo-cortical connectivity could be utilized to predict the rTMS treatment efficacy by employing support vector machine regression analysis. Results showed that the Hamilton Depression Scale scores of patients with MDD decreased after rTMS treatment. The functional connectivity of mediodorsal nucleus with prefrontal cortex predicted the rTMS treatment improvement, whereas the functional connectivity of other thalamic nuclei with cerebral cortex did not predict the treatment efficacy. The brain areas that contributed the most to the prediction were dorsal lateral prefrontal cortex, ventral lateral, and orbital and medial prefrontal areas. The improvement in the outcome of rTMS treatment could also be predicted by the thalamo-prefrontal structural connectivity. No statistically significantly difference in thalamo-cortical connectivity was observed between early improvers and early non-improvers. These results suggested that the thalamo-prefrontal connectivity can predict the rTMS treatment improvement. This study highlighted the crucial role of the thalamo-prefrontal connectivity as a neuroimaging marker in the treatment of depression via rTMS.

Lithium-ion batteries (LIBs) are widely used in electric vehicles (EVs) due to their superior power performance over other batteries. However, when connected in series, overcharged cells of LIBs face the risk of explosion, and undercharged cells decrease the life cycle of the battery. Eventually, the inconsistency phenomenon between cells resulting from manufacturing tolerance and usage process reduces the overall charging capacity of the battery and increases the risk of explosion after long-time use. Research has focused on synthesizing active material to achieve higher energy density and extended life cycle for LIBs while neglecting a comparative analysis of equalization technology on the performance of battery packs. In this paper, a nondissipative equalization structure is proposed to reconcile the inconsistency of series-connected LIB cells. In this structure, a circuit uses high-level equalization units to enable direct energy transfer between any two individual cells, and dual interleaved inductors in each equalization unit increase the equalization speed of a single cell in one equalization cycle by a factor of two. The circuit is compared with the classical inductor equalization circuit (CIEC), dual interleaved equalization circuit (DIEC), and parallel architecture equalization circuit (PAEC) in the states of standing, charging, and discharging, respectively, to validate the advantages of the proposed scheme. Considering the diversity of imbalance states, the state of charge (SOC) and terminal voltage are both chosen as the equalization criterion. The second-order RC model of the LIB and the adaptive unscented Kalman filter (AUKF) algorithm are employed for SOC estimation. For effective equalization, the adaptive fuzzy neural network (AFNN) is utilized to further reduce energy consumption and equalization time. The experiment results show that the AFNN algorithm reduces the total equalization time by approximately 37.4% and improves equalization efficiency by about 4.89% in contrast with the conventional mean-difference algorithm. Particularly, the experiment results of the equalization circuit verification certify that the proposed equalization structure can greatly accelerate the equalization progress and reduce the equalization loss compared to the other three equalization circuits.

Previous test-retest analysis of EEG mostly focused on eyes open and eyes closed resting-state. However, less attention was paid to the EEG during the subject-driven mental imaginary task state. In the current study, we compared the test-retest reproducibility of EEG spectrum in three mental imaginary task states (i.e. performed mental arithmetic, recalled the events of their day, and silently sang lyrics) and two resting states (i.e. eyes open and closed) during three EEG sessions. The resting state with eyes closed has the highest reproducibility, while the resting state with eyes opened has the lowest reproducibility for the spectral features of EEG signals at the sensor level. However, the reproducibility during eyes-open ranked higher among the five states at the source level. Moreover, the mental arithmetic state has the highest reproducibility among all the three task states. And its reproducibility in certain rhythms (theta, gamma, etc) was higher than the resting states. The reproducibility of the EEG spectrum was also investigated from the perspective of large-scale brain networks. The dorsal attention network showed the highest reproducibility in a wide frequency range of the alpha and beta rhythms. Our study suggests the importance of task selection based on the target brain region and the target frequency band. This may provide some suggestions for future researchers to choose appropriate experimental paradigms and provide a guideline on EEG study for the basic and clinical applications.

ObjectiveExisting guidelines for ultrasound-guided fine-needle aspiration biopsy lack specifications on sampling sites, but the number of biopsies improves diagnostic reliability. We propose the use of class activation maps (CAMs) and our modified malignancy-specific heat maps that locate important deep representations of thyroid nodules for class predictions.MethodsWe applied adversarial noise perturbations to the segmented concentric “hot” nodular regions of equal sizes to differentiate regional importance for the malignancy diagnostic performances of an accurate ultrasound-based artificial intelligence computer-aided diagnosis (AI-CADx) system using 2,602 retrospectively collected thyroid nodules with known histopathological diagnosis.ResultsThe AI system demonstrated high diagnostic performance with an area under the curve (AUC) value of 0.9302 and good nodule identification capability with a median dice coefficient >0.9 when compared to radiologists’ segmentations. Experiments confirmed that the CAM-based heat maps reflect the differentiable importance of different nodular regions for an AI-CADx system to make its predictions. No less importantly, the hot regions in malignancy heat maps of ultrasound images in comparison with the inactivated regions of the same 100 malignant nodules randomly selected from the dataset had higher summed frequency-weighted feature scores of 6.04 versus 4.96 rated by radiologists with more than 15 years of ultrasound examination experience according to widely used ultrasound-based risk stratification American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) in terms of nodule composition, echogenicity, and echogenic foci, excluding shape and margin attributes, which could only be evaluated on the whole rather than on the sub-nodular component levels. In addition, we show examples demonstrating good spatial correspondence of highlighted regions of malignancy heat map to malignant tumor cell-rich regions in hematoxylin and eosin-stained histopathological images.ConclusionOur proposed CAM-based ultrasonographic malignancy heat map provides quantitative visualization of malignancy heterogeneity within a tumor, and it is of clinical interest to investigate in the future its usefulness to improve fine-needle aspiration biopsy (FNAB) sampling reliability by targeting potentially more suspicious sub-nodular regions.

BackgroundThe global COVID-19 pandemic is still ongoing, and cross-country and cross-period variation in COVID-19 age-adjusted case fatality rates (CFRs) has not been clarified. Here, we aimed to identify the country-specific effects of booster vaccination and other features that may affect heterogeneity in age-adjusted CFRs with a worldwide scope, and to predict the benefit of increasing booster vaccination rate on future CFR.MethodCross-temporal and cross-country variations in CFR were identified in 32 countries using the latest available database, with multi-feature (vaccination coverage, demographic characteristics, disease burden, behavioral risks, environmental risks, health services and trust) using Extreme Gradient Boosting (XGBoost) algorithm and SHapley Additive exPlanations (SHAP). After that, country-specific risk features that affect age-adjusted CFRs were identified. The benefit of booster on age-adjusted CFR was simulated by increasing booster vaccination by 1–30% in each country.ResultsOverall COVID-19 age-adjusted CFRs across 32 countries ranged from 110 deaths per 100,000 cases to 5,112 deaths per 100,000 cases from February 4, 2020 to Jan 31, 2022, which were divided into countries with age-adjusted CFRs higher than the crude CFRs and countries with age-adjusted CFRs lower than the crude CFRs (n = 9 and n = 23) when compared with the crude CFR. The effect of booster vaccination on age-adjusted CFRs becomes more important from Alpha to Omicron period (importance scores: 0.03–0.23). The Omicron period model showed that the key risk factors for countries with higher age-adjusted CFR than crude CFR are low GDP per capita and low booster vaccination rates, while the key risk factors for countries with higher age-adjusted CFR than crude CFR were high dietary risks and low physical activity. Increasing booster vaccination rates by 7% would reduce CFRs in all countries with age-adjusted CFRs higher than the crude CFRs.ConclusionBooster vaccination still plays an important role in reducing age-adjusted CFRs, while there are multidimensional concurrent risk factors and precise joint intervention strategies and preparations based on country-specific risks are also essential.

Nothing ventured, nothing gained. To succeed one must take risks, and more importantly, take risks wisely, which depends on individual ability to exploit risk. Here, we explore neural substrates for the ability to exploit risk by using voxel-based morphometry (VBM). First, we carried out structural magnetic resonance imaging and measured individual risk-taking propensity and corresponding earnings by administrating the Balloon Analogue Risk Task in 1,389 participants. Behavior analysis revealed an inverted-U-shaped relation between risk-taking propensity and earnings, that earnings initially increased and then decreased as risk-taking propensity increased. Then individual ability to exploit risk was estimated by calculating the difference between individual actual earnings and the average earnings of the group at the same level of risk-taking propensity. VBM analysis revealed that individual ability to exploit risk was positively correlated with the gray matter volumes of three clusters located in the right orbitofrontal cortex, left dorsolateral prefrontal cortex (dlPFC), and right dlPFC, respectively. These findings highlight the neural substrates for the ability to exploit risk and implicate that precise valuation, adaptive learning, and self-control may underpin the ability to exploit risk, which expand our understanding of the ability to exploit risk and its neural substrates.

The univariate obesity–brain associations have been extensively explored, while little is known about the multivariate associations between obesity and resting-state functional connectivity. We therefore utilized machine learning and resting-state functional connectivity to develop and validate predictive models of 4 obesity phenotypes (i.e. body fat percentage, body mass index, waist circumference, and waist–height ratio) in 3 large neuroimaging datasets (n = 2,992). Preliminary evidence suggested that the resting-state functional connectomes effectively predicted obesity/weight status defined by each obesity phenotype with good generalizability to longitudinal and independent datasets. However, the differences between resting-state functional connectivity patterns characterizing different obesity phenotypes indicated that the obesity–brain associations varied according to the type of measure of obesity. The shared structure among resting-state functional connectivity patterns revealed reproducible neuroimaging biomarkers of obesity, primarily comprising the connectomes within the visual cortex and between the visual cortex and inferior parietal lobule, visual cortex and orbital gyrus, and amygdala and orbital gyrus, which further suggested that the dysfunctions in the perception, attention and value encoding of visual information (e.g. visual food cues) and abnormalities in the reward circuit may act as crucial neurobiological bases of obesity. The recruitment of multiple obesity phenotypes is indispensable in future studies seeking reproducible obesity–brain associations.

Background：Kidney renal clear cell carcinoma (KIRC) is a prevalent type of renal malignancy characterized by high mortality rates and poor response to treatment. N1-methyladenosine (m1A) is a type of RNA methylation modification that has received considerable attention due to its crucial roles in various biological functions. With the advancement of genomics and molecular biology, m1A-related genes (m1A RGs) have been confirmed to be intimately connected with the development and occurrence of various tumors. Nevertheless, the role of m1A RGs in KIRC remains poorly understood. Methods：This study aims to investigate the prognostic significance of 10 major m1A RGs in KIRC patients, utilizing data from The Cancer Genome Atlas (TCGA) dataset. A prognostic model was constructed using Lasso regression analysis, and risk scores were calculated. KIRC patients were classified into high- and low-risk groups based on the median of the average risk score. The prognostic value of the model was evaluated using two independent datasets, GSE537574 and GSE265745, by assessing the sensitivity and specificity using Kaplan-Meier survival analysis and receiver operating characteristic curves. Additionally, gene set enrichment analysis was conducted to explore the possible biological behavior and pathways of m1A RGs. Ultimately, 5 m1A RGs were identified to construct the prognostic model. Furthermore, nomogram and decision curve analyses were performed to evaluate the model's predictive performance and clinical application value. Results：Our study demonstrates that the expression of m1A RGs might serve as a prognostic biomarker for KIRC patients and provides a new perspective for cancer prognosis screening in clinical practice.

Introduction: CRISPR/Cas9 is a gene-editing technology which could specifically cleave dsDNA and induce target gene mutation. CRISPR/Cas9 has been widely used in gene functional studies in many fields, such as medicine, biology, and agriculture due to its simple design, low cost, and high efficiency. Although it has been well developed in model fish and freshwater fish for gene function analysis, it is still novel in the studies dealing with economic crustacean species.Methods: In this study, we established a CRISPR/Cas9 system based on microinjection for M. nipponense, an important economic crustacean aquaculture species. The vitellogenin (Vg) gene and the eyeless (Ey) gene were selected as the targeted genes for mutation. Two sgRNAs were designed for Mn-Vg and Mn-Ey gene editing, respectively.Results and Discussion: For sg-Vg-1, the gastrula survival ratio was 8.69%, and the final hatching ratio was 4.83%. The blastula mutant ratio was 10%, and the hatching individual mutant ratio was 30%. For sg-Vg-2, the gastrula survival ratio was 5.85%, and the final hatching ratio was 3.89%. The blastula mutant ratio was 16.67%, and no mutant sequences were detected in hatching individuals. For sg-Ey-1, the gastrula survival ratio was 6.25%, and the final hatching ratio was 2.34%. The blastula mutant ratio was 10.00%, and the hatching individual mutant ratio was 66.67%. For sg-Ey-2, the gastrula survival ratio was 6.00%, and the final hatching ratio was 2.67%. No mutant sequence was detected in both blastula stage and hatching individuals. There were no significant morphological changes observed in the Mn-Vg group. Two deformed types were detected in sg-Ey-1-injected embryos. An evident developmental delay of the compound eye was detected in Ey-sg1-H1 in the zoea stage. The compound eyes of the Ey-sg1-H2 embryo could not form well-defined spheres, and the whole compound eye appeared to diffuse at the end of the late zoea stage. The establishment of a gene-editing platform based on CRISPR/Cas9 will not only provide an efficient and convenient method for gene function analysis but also provide a powerful tool for molecular-assisted breeding of Macrobrachium nipponense.

Knowledge bases (KBs) are ideal vehicles for tackling many challenges, such as Query Answering, Root Cause Analysis. Given that the world is changing over time, previously acquired knowledge can become outdated. Thus, we need methods to update the knowledge when new information comes and repair any identified faults in the constructed KBs. However, to the best of our knowledge, there are few research works in this area. In this paper, we propose a system called TREAT (Tacit Relation Extraction and Transformation) to automatically construct a probabilistic KB which is continuously self-updating such that the knowledge remains consistent and up to date.

In this study, we explored whether satisfaction with government management, perception of risk, and gratitude influenced public anxiety during the COVID-19 pandemic in China. Using a cross-sectional, anonymous and confidential online survey, a nationwide sample of Chinese adults (N = 876) was targeted between March 25–March 30, 2020, a period in which newly confirmed cases significantly declined in China. The anxiety level was decreased as compared to that assessed during the peak period. Multiple parallel mediation modeling demonstrated that risk perception and gratitude partially mediated the relationship between satisfaction with government management and public anxiety. Increasing satisfaction and gratitude, as well as reducing risk perception contribute to the public’s mental health. The results may shed light on the positive factors for psychological well-being during the COVID-19 pandemic and may aid potential strategies for the policy maker, the public, and the clinic to regulate negative emotions or future emerging infectious diseases.

Limited data are available on the prevalence of prescription opioid use among patients with cardiac conditions who were exposed to increased risks of cardiac events including myocardial failure and cardiac arrest. According to the U.S. National Health Interview Survey, we evaluated the prevalence of opioid use in patients with cardiac conditions who reported prescription opioid use in the past 12 months and 3 months in 2019 and 2020, respectively, and further estimated the prevalence of opioid use for acute pain or chronic pain. We also analyzed the stratified prevalence by demographical characteristics. Our results showed that there was no statistically significant change in the prevalence of opioid use in the past 12 months (26.5% in 2019 vs. 25.7% in 2020) or the past 3 months (66.6% in 2019 vs. 62.5% in 2020) before and during the COVID-19 pandemic. However, there was a significant decline in the prevalence of opioid use for acute pain, from 64.2% (95% confidence interval [CI] 57.6% to 70.3%) in 2019 to 49.6% (95% CI 40.1% to 59.0%) in 2020 (P = 0.012), particularly in the subgroups of men, non-Hispanic white people, adults with education below high school, those with an income-to-poverty ratio ranging from 1.0 to 1.9, and those covered with health insurance. Our findings suggest that monitoring opioid use in the era of living with COVID-19 is important, which will help inform healthcare providers to develop care strategies to reduce health loss for vulnerable individuals.

Sleep health is both conceptually and operationally a composite concept containing multiple domains of sleep. In line with this, high dependence and interaction across different domains of sleep health encourage a transition in sleep health research from categorical to dimensional approaches that integrate neuroscience and sleep health. Here, we seek to identify the covariance patterns between multiple-sleep health domains and distributed intrinsic functional connectivity by applying a multivariate approach (partial least squares). This multivariate analysis revealed a composite sleep health dimension co-varying with connectivity patterns involving the attentional and thalamic networks and which appear relevant at the neuromolecular level. These findings were further replicated and generalized to several unseen independent datasets. Critically, the identified sleep-health related connectome showed diagnostic potential for insomnia disorder. These results together delineate a brain connectome biomarker for sleep health with high potential for clinical translation.

Purpose: In the critically ill, hospital-acquired bloodstream infections (HA-BSI) are associated with significant mortality. Granular data are required for optimizing management, and developing guidelines and clinical trials. Methods: We carried out a prospective international cohort study of adult patients (≥ 18years of age) with HA-BSI treated in intensive care units (ICUs) between June 2019 and February 2021. Results: 2600 patients from 333 ICUs in 52 countries were included. 78% HA-BSI were ICU-acquired. Median Sequential Organ Failure Assessment (SOFA) score was 8 [IQR 5; 11] at HA-BSI diagnosis. Most frequent sources of infection included pneumonia (26.7%) and intravascular catheters (26.4%). Most frequent pathogens were Gram-negative bacteria (59.0%), predominantly Klebsiella spp. (27.9%), Acinetobacter spp. (20.3%), Escherichia coli (15.8%), and Pseudomonas spp. (14.3%). Carbapenem resistance was present in 37.8%, 84.6%, 7.4%, and 33.2%, respectively. Difficult-to-treat resistance (DTR) waspresent in 23.5% and pan-drug resistance in 1.5%. Antimicrobial therapy was deemed adequate within 24h for 51.5%. Antimicrobial resistance was associated with longer delays to adequate antimicrobial therapy. Source control was needed in 52.5% but not achieved in 18.2%. Mortality was 37.1%, and only 16.1% had been discharged alivefrom hospital by day-28. Conclusions: HA-BSI was frequently caused by Gram-negative, carbapenem-resistant and DTR pathogens. Antimicrobial resistance led to delays in adequate antimicrobial therapy. Mortality was high, and at day-28 only a minority of the patients were discharged alive from the hospital. Prevention of antimicrobial resistance and focusing on adequate antimicrobial therapy and source control are important to optimize patient management and outcomes.

Normal sleepers may be at risk for insomnia during COVID-19. Identifying psychological factors and neural markers that predict their insomnia risk, as well as investigating possible courses of insomnia development, could lead to more precise targeted interventions for insomnia during similar public health emergencies. Insomnia severity index of 306 participants before and during COVID-19 were employed to determine the development of insomnia, while pre-COVID-19 psychometric and resting-state fMRI data were used to explore corresponding psychological and neural markers of insomnia development. Normal sleepers as a group reported a significant increase in insomnia symptoms after COVID-19 outbreak (F = 4.618, P = 0.0102, df = 2, 609.9). Depression was found to significantly contribute to worse insomnia (β = 0.066, P = 0.024). Subsequent analysis found that functional connectivity between the precentral gyrus and middle/inferior temporal gyrus mediated the association between pre-COVID-19 depression and insomnia symptoms during COVID-19. Cluster analysis identified that postoutbreak insomnia symptoms followed 3 courses (lessened, slightly worsened, and developed into mild insomnia), and pre-COVID-19 depression symptoms and functional connectivities predicted these courses. Timely identification and treatment of at-risk individuals may help avoid the development of insomnia in the face of future health-care emergencies, such as those arising from COVID-19 variants.

Immune checkpoint blockers (ICBs) have failed in all phase III glioblastoma trials. Here, we found that ICBs induce cerebral edema in some patients and mice with glioblastoma. Through single-cell RNA sequencing, intravital imaging, and CD8 + T cell blocking studies in mice, we demonstrated that this edema results from an inflammatory response following antiprogrammed death 1 (PD1) antibody treatment that disrupts the blood–tumor barrier. Used in lieu of immunosuppressive corticosteroids, the angiotensin receptor blocker losartan prevented this ICB-induced edema and reprogrammed the tumor microenvironment, curing 20% of mice which increased to 40% in combination with standard of care treatment. Using a bihemispheric tumor model, we identified a “hot” tumor immune signature prior to losartan+anti-PD1 therapy that predicted long-term survival. Our findings provide the rationale and associated biomarkers to test losartan with ICBs in glioblastoma patients.

The Coronavirus disease of 2019 (COVID-19) and measures to curb it created population-level changes in male-dominant impulsive and risky behaviors such as violent crimes and gambling. One possible explanation for this is that the pandemic has been stressful, and males, more so than females, tend to respond to stress by altering their focus on immediate versus delayed rewards, as reflected in their delay discounting rates. Delay discounting rates from healthy undergraduate students were collected twice during the pandemic. Discounting rates of males (n=190) but not of females (n=493) increased during the pandemic. Using machine learning, we show that prepandemic functional connectome predict increased discounting rates in males (n=88). Moreover, considering that delay discounting is associated with multiple psychiatric disorders, we found the same neural pattern that predicted increased discounting rates in this study, in secondary datasets of patients with major depression and schizophrenia. The findings point to sex-based differences in maladaptive delay discounting under real-world stress events, and to connectome-based neuromarkers of such effects. They can explain why there was a population-level increase in several impulsive and risky behaviors during the pandemic and point to intriguing questions about the shared underlying mechanisms of stress responses, psychiatric disorders and delay discounting.

The quadrilateral plate (QP) is an essential structure of the inner wall of the acetabulum, an important weight-bearing joint of the human body, which is often involved in acetabular fractures. The operative exposure, reduction and fixation of QP fractures have always been the difficulties in orthopedics due to the special morphological structure and anatomical features of the QP. Fortunately, there have been many effective methods and instruments developed for QP exposure, reduction and fixation by virtue of the combined efforts of numerous orthopedists. At the same time, each method presents with its own advantages and disadvantages, resulting in different prognoses. It is necessary to have a thorough understanding of the anatomy, radiology and fixation techniques of the QP in terms of patient prognosis optimization. In this paper, the anatomical features, definition and classification of QP, operative approach selection, implant internal fixation methods and efficacy were reviewed.

In the information age, data storage technology has become the key to improving computer systems. Since traditional storage technologies cannot meet the demand for massive storage, new DNA storage technology based on biomolecules attracts much attention. DNA storage refers to the technology that uses artificially synthesized deoxynucleotide chains to store and read all information, such as documents, pictures, and audio. First, data are encoded into binary number strings. Then, the four types of base, A(Adenine), T(Thymine), C(Cytosine), and G(Guanine), are used to encode the corresponding binary numbers so that the data can be used to construct the target DNA molecules in the form of deoxynucleotide chains. Subsequently, the corresponding DNA molecules are artificially synthesized, enabling the data to be stored within them. Compared with traditional storage systems, DNA storage has major advantages, such as high storage density, long duration, as well as low hardware cost, high access parallelism, and strong scalability, which satisfies the demands for big data storage. This manuscript first reviews the origin and development of DNA storage technology, then the storage principles, contents, and methods are introduced. Finally, the development of DNA storage technology is analyzed. From the initial research to the cutting edge of this field and beyond, the advantages, disadvantages, and practical applications of DNA storage technology require continuous exploration.

Seismic method is a major approach for detecting the seismic geological features ahead of the tunnel, understanding the distribution of unfavorable geology, and ensuring the safety of tunnel construction. Observation system is the key for seismic detection, many studies have been conducted to optimize the observation system; however, most of them focused on the surface seismic investigation and numerical simulation rather than in tunnel field environment (limited aperture and full space environment). How to obtain better wavefield information with limited observation aperture is a great challenge. In this study numerical simulation and instrumental techniques (GPR, DC, etc.) were implemented to further check the result of seismic detection at the 1# tailrace tunnel at the Wudongde hydropower station. In the field case, observation detectors were arranged spatially in the tunnel and source points were placed in four ways: linearly along a single side, on the tunnel face, in front of the detectors, and behind the detectors. Then, after data acquisition, the data processing is conducted to carry out the migration results. The imaging results indicate that the observation system with sources and detectors in liner arrangement (with equal interval) helps to suppress artifacts, further supporting the advantages of spatial observation system with liner observation line (detectors). Moreover, the study provides suggestions for geological prospecting in similar tunnel projects.

Shuo Wang, Yuying Liu, Xitian Hu, Xiaolei Zhang, Lei Xu, Yan Yang, Rubing Wu, Enmao Wang, Tianjie Lv Department of Cardiovasology, Shijiazhuang People’s Hospital, Shijiazhuang, People’s Republic of ChinaCorrespondence: Xitian HuDepartment of Cardiovasology, Shijiazhuang People’s Hospital, No. 9 Fangbei Road, Shijiazhuang, Hebei, 050000, People’s Republic of ChinaTel +86-17603119015Email huxitian007@163.comPurpose: Myocardial fibrosis (MF) after acute myocardial infarction (AMI) ultimately results in heart failure, which is a serious threat to human life. This study aimed to excavate critical biomarkers associated with MF after AMI.Materials and Methods: RNA-sequencing was performed to obtain differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs) and lncRNAs (DElncRNAs) in AMI and MF after AMI.Results: Abundant DEmRNAs, DEmiRNAs and DElncRNAs were identified in AMI and MF after AMI. The ceRNA network, which contained 9 lncRNA-miRNA pairs and 9 miRNA-mRNA pairs, was acquired. In AMI, all candidate markers generally exhibited the same pattern as that in our RNA-seq results; while in MF after AMI, except for CENPB, JAK2 and hsa-miR-197-3p, the expression of the others in the qRT-PCR results exhibited the same pattern as that in our RNA-seq results.Conclusion: We speculated that LINC00664/hsa-miR-197-3p/JAK2 and GAS6-AS1/SNHG22/hsa-miR-135a-5p/CENPB/BCL9L interaction pairs may serve as potential biomarkers in MF after AMI.Keywords: acute myocardial infarction, myocardial fibrosis, lncRNA, ceRNA network