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Abstract Background Macrophage activation syndrome (MAS), an example of secondary hemophagocytic lymphohistiocytosis, is a potentially fatal complication of rheumatic diseases. We aimed to study the clinical and laboratory characteristics, treatment schemes, and outcomes of different rheumatic disorders associated with MAS in children. Early warning indicators of MAS have also been investigated to enable clinicians to make a prompt and accurate diagnosis. Methods Fifty-five patients with rheumatic diseases complicated by MAS were enrolled between January 2017 and December 2022. Clinical and laboratory data were collected before disease onset, at diagnosis, and after treatment with MAS, and data were compared between patients with systemic juvenile idiopathic arthritis (sJIA), Kawasaki disease (KD), and systemic lupus erythematosus (SLE). A random forest model was established to show the importance score of each variable with a significant difference. Results Most (81.8%) instances of MAS occurred during the initial diagnosis of the underlying disease. Compared to the active stage of sJIA, the platelet count, erythrocyte sedimentation rate, and fibrinogen level in sJIA-MAS were significantly decreased, whereas ferritin, ferritin/erythrocyte sedimentation rate, aspartate aminotransferase, alanine aminotransferase, lactate dehydrogenase, and D-dimer levels were significantly increased. Ferritin level, ferritin/erythrocyte sedimentation rate, and platelet count had the greatest predictive value for sJIA-MAS. The level of IL-18 in the sJIA-MAS group was significantly higher than in the active sJIA group, whereas IL-6 levels were significantly lower. Most patients with MAS were treated with methylprednisolone pulse combined with cyclosporine, and no deaths occurred. Conclusions Thrombocytopenia, ferritin levels, the ferritin/erythrocyte sedimentation rate, and elevated aspartate aminotransferase levels can predict the occurrence of MAS in patients with sJIA. Additionally, our analysis indicates that IL-18 plays an important role in the pathogenesis of MAS in sJIA-MAS.

Leveraging a fresh comprehension of electrolytic polishing (EP) mechanisms, this study introduces a pivotal factor - viscosity/water content ratio (VWR) - for the first time, rooted in acidity ratio theory. The present study aligns with published works to affirm the validity of the VWR indicator and its correlation with surface roughness. A noteworthy contribution lies in the design of EP solutions that can serve as a guide for stainless steels. It strikes a balance between low cost, high efficiency, long service life and desirable surface roughness. This involves regulating the dissolution-ionization-deposition process at the metal/solution interface.

Abstract Detecting the water deficit status of vertical greenery plants rapidly and accurately is a significant challenge in the process of cultivating and planting greenery plants. Currently, the mainstream method involves utilizing a single target detection algorithm for this task. However, in complex real-world scenarios, the accuracy of detection is influenced by factors such as image quality and background environment. Therefore, we propose a multi-stage progressive detection method aimed at enhancing detection accuracy by gradually filtering, processing, and detecting images through a multi-stage architecture. Additionally, to reduce the additional computational load brought by multiple stages and improve overall detection efficiency, we introduce a Swin Transformer based on mobile windows and hierarchical representations for feature extraction, along with global feature modeling through a self-attention mechanism. The experimental results demonstrate that our multi-stage detection approach achieves high accuracy in vertical greenery plants detection tasks, with an average precision of 93.5%. This represents an improvement of 19.2%, 17.3%, 13.8%, and 9.2% compared to Mask R-CNN (74.3%), YOLOv7 (76.2%), DETR (79.7%), and Deformable DETR (84.3%), respectively.

C12H12N2O8Mn, orthorhombic, Pbca (no. 61), a = 11.5651(15) Å, b = 9.4169(12) Å, c = 25.634(3) Å, V = 2791.7(6) Å3, Z = 8, Rgt(F) = 0.0402, wRref(F2) = 0.1166, T = 296.15 K.

Abstract As the core components of fifth‐generation (5G) communication technology, optical modules should be consistently miniaturized in size while improving their level of integration. This inevitably leads to a dramatic spike in power consumption and a consequent increase in heat flow density when operating in a confined space. To ensure a successful start‐up and operation of 5G optical modules, active cooling and precise temperature control via the Peltier effect in confined space is essential yet challenging. In this work, p‐type Bi0.5Sb1.5Te3 and n‐type Bi2Te2.7Se0.3 bulk thermoelectric (TE) materials are used, and a micro thermoelectric thermostat (micro‐TET) (device size, 2 × 9.3 × 1.1 mm3; leg size, 0.4 × 0.4 × 0.5 mm3; number of legs, 44) is successfully integrated into a 5G optical module with Quad Small Form Pluggable 28 interface. As a result, the internal temperature of this kind of optical module is always maintained at 45.7°C and the optical power is up to 7.4 dBm. Furthermore, a multifactor design roadmap is created based on a 3D numerical model using the ANSYS finite element method, taking into account the number of legs (N), leg width (W), leg length (L), filling atmosphere, electric contact resistance (Rec), thermal contact resistance (Rtc), ambient temperature (Ta), and the heat generated by the laser source (QL). It facilitates the integrated fabrication of micro‐TET, and shows the way to enhance packaging and performance under different operating conditions. According to the roadmap, the micro‐TET (2 × 9.3 × 1 mm3, W = 0.3 mm, L = 0.4 mm, N = 68 legs) is fabricated and consumes only 0.89 W in cooling mode (QL = 0.7 W, Ta = 80°C) and 0.36 W in heating mode (Ta = 0°C) to maintain the laser temperature of 50°C. This research will hopefully be applied to other microprocessors for precise temperature control and integrated manufacturing.

Congenital hypogonadotropic hypogonadism (CHH) is a rare genetic disorder, resulting from impaired production, secretion, or action of gonadotropin-releasing hormone (GnRH). Variants of the KISS1R gene can result in CHH. Herein we describe a Chinese boy with CHH, caused by a novel, compound heterozygous variant in KISS1R. A male infant presented to the pediatric urological surgeon at three months of age for micropenis. Laboratory investigations done at this time revealed low levels of serum gonadotropins and testosterone, suggesting a lack of minipuberty. Topical application of dihydrotestosterone gel was recommended, but the parents refused treatment. The child was brought to our hospital at 3.3 years of age for the same complaint. A diagnosis of CHH was considered, and next generation sequencing revealed a compound heterozygous variant including a novel c.182C>A (p.S61*) and a c.418C>T (p.R140C) in KISS1R. We describe a novel compound heterozygous variant in the KISS1R in a boy with CHH, born to non-consanguineous Chinese parents. This report adds to the spectrum of variants in KISS1R seen in children with CHH.

Fabricating SiC matrix with controllable Si/C ratio for C/C–SiC composites via precursor infiltration and pyrolysis is difficult to realize due to the absence of suitable precursors. Here, SiC precursors with Si-rich (Si/C = 1.23), near-stoichiometric (Si/C = 1.01), and C-rich (Si/C = 0.88) pyrolyzed ceramics at 1200 ℃ were successfully synthesized by the novel modification of polymethysilane. The structure and ceramization of synthesized SiC precursors were studied, and C/C–SiC composites with similar Si/C ratio in SiC matrix were also fabricated. The results revealed that the defects and Young’s modulus of SiC matrix and the thermal residual stress on pyrolytic carbon coated carbon fiber were determined by the Si/C ratio. As a result, the Si/C ratio exerted significant effects on the crack deflection behavior and the mechanical properties of the composites. The composites with near-stoichiometric SiC matrix showed excellent mechanical properties because of the effective crack deflection and the moderate Young’s modulus of their SiC matrix. The flexural strength, flexural modulus, and compressive strength were 423±27 MPa, 33.41±4.52 GPa, and 393±8 MPa, respectively. Meanwhile, after a heat treatment at 1600 ℃, the role of carbon fiber toughening was improved and accompanied by a decrease in mechanical properties for all composites, which was attributed to the increased defects in SiC matrix and the damaged interfaces by the thermal residual stress.

Abstract In-sensor processing has the potential to reduce the energy consumption and hardware complexity of motion detection and recognition. However, the state-of-the-art all-in-one array integration technologies with simultaneous broadband spectrum image capture (sensory), image memory (storage) and image processing (computation) functions are still insufficient. Here, macroscale (2 × 2 mm2) integration of a rippled-assisted optoelectronic array (18 × 18 pixels) for all-day motion detection and recognition. The rippled-assisted optoelectronic array exhibits remarkable uniformity in the memory window, optically stimulated non-volatile positive and negative photoconductance. Importantly, the array achieves an extensive optical storage dynamic range exceeding 106, and exceptionally high room-temperature mobility up to 406.7 cm2 V−1 s−1, four times higher than the International Roadmap for Device and Systems 2028 target. Additionally, the spectral range of each rippled-assisted optoelectronic processor covers visible to near-infrared (405 nm–940 nm), achieving function of motion detection and recognition.

Abstract Inflammation, caused by accumulation of inflammatory cytokines from immunocytes, is prevalent in a variety of diseases. Electro-stimulation emerges as a promising candidate for inflammatory inhibition. Although electroacupuncture is free from surgical injury, it faces the challenges of imprecise pathways/current spikes, and insufficiently defined mechanisms, while non-optimal pathway or spike would require high current amplitude, which makes electro-stimulation usually accompanied by damage and complications. Here, we propose a neuromorphic electro-stimulation based on atomically thin semiconductor floating-gate memory interdigital circuit. Direct stimulation is achieved by wrapping sympathetic chain with flexible electrodes and floating-gate memory are programmable to fire bionic spikes, thus minimizing nerve damage. A substantial decrease (73.5%) in inflammatory cytokine IL-6 occurred, which also enabled better efficacy than commercial stimulator at record-low currents with damage-free to sympathetic neurons. Additionally, using transgenic mice, the anti-inflammation effect is determined by β2 adrenergic signaling from myeloid cell lineage (monocytes/macrophages and granulocytes).

Abstract Background Digital histopathology provides valuable information for clinical decision-making. We hypothesized that a deep risk network (DeepRisk) based on digital pathology signature (DPS) derived from whole-slide images could improve the prognostic value of the tumor, node, and metastasis (TNM) staging system and offer chemotherapeutic benefits for gastric cancer (GC). Methods DeepRisk is a multi-scale, attention-based learning model developed on 1120 GCs in the Zhongshan dataset and validated with two external datasets. Then, we assessed its association with prognosis and treatment response. The multi-omics analysis and multiplex Immunohistochemistry were conducted to evaluate the potential pathogenesis and spatial immune contexture underlying DPS. Results Multivariate analysis indicated that the DPS was an independent prognosticator with a better C-index (0.84 for overall survival and 0.71 for disease-free survival). Patients with low-DPS after neoadjuvant chemotherapy responded favorably to treatment. Spatial analysis indicated that exhausted immune clusters and increased infiltration of CD11b+CD11c+ immune cells were present at the invasive margin of high-DPS group. Multi-omics data from the Cancer Genome Atlas-Stomach adenocarcinoma (TCGA-STAD) hint at the relevance of DPS to myeloid derived suppressor cells infiltration and immune suppression. Conclusion DeepRisk network is a reliable tool that enhances prognostic value of TNM staging and aid in precise treatment, providing insights into the underlying pathogenic mechanisms.

Background: The lifestyles of preschoolers have become physically inactive and sedentary, their eating habits unhealthy, and their sleep routines increasingly disturbed. Parental involvement appears crucial to combat the unhealthy lifestyle of preschoolers. Because of the recognized barriers to traditional face-to-face interventions, easy access and lower costs make electronic health (eHealth) interventions appealing. However, whether parent-based eHealth intervention may be harnessed to improve the aforementioned lifestyle behaviors of preschoolers is currently unclear, a gap that this systematic review intends to address. This study aims to systematically review the current literature concerning the effectiveness of parent-based eHealth intervention on the physical activity, dietary behaviors, and sleep of preschoolers. Method: This systematic review conforms to the Preferred Reporting Items for Systematic Reviews and Meta-analysis statement. Six databases (EMBASE, PubMed, MEDLINE, Web of Science, SPORTDiscus, and PsycINFO) were retrieved for the period from January 2000 to December 2022. Studies were eligible if 1 they were quantitative study design; 2 eHealth interventions in which parents were the change agents targeted children aged 3–6 years; 3 interventions examined the effectiveness of eHealth or incorporated eHealth as one of the intervention modalities; 4 at least one variable included in either primary or secondary outcome had to concentrate on the physical activity, diet, and sleep of preschoolers; 5 publication type was limited to the English language and peer-reviewed journal articles; 6 study settings were confined to family- or parent-based ones. The risk of bias was assessed, based upon Version 2 of the Cochrane risk-of-tool for randomized trials (RoB2). Results: Twelve studies were screened. No significant group-by-time improvement in physical activity was found in studies related to physical activity outcomes. Two studies reported a significant difference between groups concerning motor ability, with one study indicating improved object control with the other reporting improvement in both object control and locomotor skills. Of the studies related to dietary behavior outcomes, six studies reported a significant difference at the posttest compared to the control group, in terms of vegetable and fruit intake, sugar-sweetened drinks, reduced candy consumption, and improved non-core food. Three studies reported a significant difference between groups in sleep duration at the end of the posttest, with the result of one study limited to preference-only participants. None of the reviewed studies found a significant difference between groups for sleep problems. Conclusion: Parent-based eHealth interventions were not significantly effective in improving physical activity and reducing sleep problems in preschoolers, but the majority of studies have found that this type of intervention significantly improves the dietary behaviors and sleep duration of preschoolers. High-quality, robustly designed studies to balance the intervention dosage and sequence are needed to investigate the effectiveness of parent-based eHealth intervention on physical activity, dietary behaviors, and sleep in preschoolers, particularly those raised in other cultural background, which may significantly impact their lifestyle. Trial registration: International Prospective Register of Systematic Review (PROSPERO): CRD42023418861.

Abstract Background Neoadjuvant chemotherapy (NAC) has become a standard treatment strategy for breast cancer (BC). However, owing to the high heterogeneity of these tumors, it is unclear which patient population most likely benefit from NAC. Multi-omics offer an improved approach to uncovering genomic and transcriptomic changes before and after NAC in BC and to identifying molecular features associated with NAC sensitivity. Methods We performed whole-exome and RNA sequencing on 233 samples (including matched pre- and post-treatment tumors) from 50 BC patients with rigorously defined responses to NAC and analyzed changes in the multi-omics landscape. Molecular features associated with NAC response were identified and validated in a larger internal, and two external validation cohorts, as well as in vitro experiments. Results The most frequently altered genes were TP53, TTN, and MUC16 in both pre- and post-treatment tumors. In comparison with pre-treatment tumors, there was a significant decrease in C > A transversion mutations in post-treatment tumors (P = 0.020). NAC significantly decreased the mutation rate (P = 0.006) of the DNA repair pathway and gene expression levels (FDR = 0.007) in this pathway. NAC also significantly changed the expression level of immune checkpoint genes and the abundance of tumor-infiltrating immune and stroma cells, including B cells, activated dendritic cells, γδT cells, M2 macrophages and endothelial cells. Furthermore, there was a higher rate of C > T substitutions in NAC nonresponsive tumors than responsive ones, especially when the substitution site was flanked by C and G. Importantly, there was a unique amplified region at 8p11.23 (containing ADGRA2 and ADRB3) and a deleted region at 3p13 (harboring FOXP1) in NAC nonresponsive and responsive tumors, respectively. Particularly, the CDKAL1 missense variant P409L (p.Pro409Leu, c.1226C > T) decreased BC cell sensitivity to docetaxel, and ADGRA2 or ADRB3 gene amplifications were associated with worse NAC response and poor prognosis in BC patients. Conclusions Our study has revealed genomic and transcriptomic landscape changes following NAC in BC, and identified novel biomarkers (CDKAL1 P409L , ADGRA2 and ADRB3) underlying chemotherapy resistance and poor prognosis, which could guide the development of personalized treatments for BC.

With the rapid development of deep learning, convolutional neural networks have achieved milestones in synthetic aperture radar (SAR) image object detection. However, object detection in SAR images is still a great challenge due to the difficulty in distinguishing targets from complex backgrounds. At the same time, most of the targets in SAR images are small and unevenly distributed, which makes it challenging to extract sufficient feature information. To solve these issues mentioned above, an efficient object detection network for SAR images based on Swin transformer and YOLOv7 is proposed in this article. First, we design a novel feature aggregation module Petty-specialized feature aggregation (PS-FPN) to enrich small targets’ semantic and spatial features while keeping the model lightweight. PS-FPN module uses the fusion of deep and shallow features by using cross-layer feature aggregation and single-branch feature aggregation to enhance the detection of small targets. Second, a novel attention mechanism strategy mix-attention is proposed to find more attention regions. Finally, we add one more prediction head to extract shallow features that effectively preserve small targets’ feature information. To verify the effectiveness of the proposed algorithm, extensive experiments are carried out on several challenging SAR image datasets. The results show that, compared with other state-of-the-art detectors, the proposed method can achieve significant performance based on lightweight detection.

ObjectiveTo develop a robust machine learning prediction model for the automatic screening and diagnosis of obstructive sleep apnea (OSA) using five advanced algorithms, namely Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) to provide substantial support for early clinical diagnosis and intervention.MethodsWe conducted a retrospective analysis of clinical data from 439 patients who underwent polysomnography at the Affiliated Hospital of Xuzhou Medical University between October 2019 and October 2022. Predictor variables such as demographic information [age, sex, height, weight, body mass index (BMI)], medical history, and Epworth Sleepiness Scale (ESS) were used. Univariate analysis was used to identify variables with significant differences, and the dataset was then divided into training and validation sets in a 4:1 ratio. The training set was established to predict OSA severity grading. The validation set was used to assess model performance using the area under the curve (AUC). Additionally, a separate analysis was conducted, categorizing the normal population as one group and patients with moderate-to-severe OSA as another. The same univariate analysis was applied, and the dataset was divided into training and validation sets in a 4:1 ratio. The training set was used to build a prediction model for screening moderate-to-severe OSA, while the validation set was used to verify the model's performance.ResultsAmong the four groups, the LightGBM model outperformed others, with the top five feature importance rankings of ESS total score, BMI, sex, hypertension, and gastroesophageal reflux (GERD), where Age, ESS total score and BMI played the most significant roles. In the dichotomous model, RF is the best performer of the five models respectively. The top five ranked feature importance of the best-performing RF models were ESS total score, BMI, GERD, age and Dry mouth, with ESS total score and BMI being particularly pivotal.ConclusionMachine learning-based prediction models for OSA disease grading and screening prove instrumental in the early identification of patients with moderate-to-severe OSA, revealing pertinent risk factors and facilitating timely interventions to counter pathological changes induced by OSA. Notably, ESS total score and BMI emerge as the most critical features for predicting OSA, emphasizing their significance in clinical assessments. The dataset will be publicly available on my Github.

During the COVID-19 outbreak, there was a sharp increase in generalized anxiety disorder (GAD). Acupuncture therapy has the advantages of accurate clinical efficacy, safety and reliability, few adverse reactions, and no dependence, and is gradually becoming one of the emerging therapies for treating GAD. We present a study protocol for a randomized clinical trial with the aim of exploring the mechanism of brain plasticity in patients with GAD and evaluate the effectiveness and reliability of acupuncture treatment. Transcranial magnetic stimulation (TMS) will be used to assess cortical excitability in GAD patients and healthy people. Sixty-six GAD patients meeting the inclusion criteria will be randomly divided into two groups: TA group, (treatment with acupuncture and basic western medicine treatment) and SA group (sham acupuncture and basic western medicine treatment). Twenty healthy people will be recruited as the control group (HC). The parameters that will be evaluated are amplitude of motor evoked potentials (MEPs), cortical resting period (CSP), resting motor threshold (RMT), and Hamilton Anxiety Scale (HAMA) score. Secondary results will include blood analysis of γ-aminobutyric acid (GABA), glutamate (Glu), glutamine (Gln), serotonin (5-HT), and brain-derived nerve growth factor (BDNF). Outcomes will be assessed at baseline and after the intervention (week 8). This study protocol is the first clinical trial designed to detect differences in cerebral cortical excitability between healthy subjects and patients with GAD, and the comparison of clinical efficacy and reliability before and after acupuncture intervention is also one of the main contents of the protocol. We hope to find a suitable non-pharmacological alternative treatment for patients with GAD.

This paper introduces a post-disaster load restoration approach for the distribution grid, utilizing network reconfiguration (NR) and dispatching of repair crews (RCs) to significantly enhance grid resilience. We propose an RC–NR coordinated model that leverages diverse flexible resources within the active distribution network (ADN), aimed at not only enhancing the grid’s resilience level but also efficiently mending the fault lines. The model introduces fault repairing and sequential NR coupled constraints to devise an optimal resilience strategy within temporal domain cooperation, focusing on minimizing repair and penalty costs associated with the restoration process. To tackle the challenge of computational complexity, the nonlinear model is reformulated into a mixed-integer second-order cone programming model. The efficacy of the approach is validated through case studies on an IEEE 33-bus system, in which simulation results demonstrate a considerable improvement in grid resilience, achieving optimal load recovery with reduced restoration time and costs. The proposed approach outperforms traditional methods with optimal repair sequence and RC scheduling, aligned with NR efforts, and contributes to an improved system resilience level.

Abstract Flexible piezoelectric pressure sensors (FPPS) received a lot of attention because of their prominent performance in body motion detection and energy recovery. However, it has faced a huge challenge in manufacturing FPPS with high sensitivity, low cost, and high flexibility. Herein, a ternary flexible nanofiber composite with Sb nanosheets and BaTiO3 nanoparticles as the fillers of P(VDF‐TrFE) is prepared by electrospinning. An impressive open‐circuit voltage of 17.1 V and short‐circuit current of 4.4 µA is obtained based on the composite nanofiber mat at a pressure of 128 kPa with 2 Hz frequency. The FPPS not only exhibit a high sensitivity of 96 mV kPa−1 but also an ultrafast response time of 2 ms. Excellent flexibility and reliability are demonstrated by the unchanged open‐circuit voltage after 2400 cycles. The enhanced FPPS performance is owed to the interfacial polarization effect at the inorganic filler‐organic matrix interface, which also enhances the ferroelectric and dielectric properties. The present FPPS is further confirmed to be a real‐time motion monitor and voice recognizer, which can distinguish various actions and sounds according to distinct output voltage signals and is expected to be used in virtual reality devices, robotic electronic skin, haptic simulation, artificial throat, etc.

Abstract Wavelength-selective thermal emitters (WS-TEs) have been frequently designed to achieve desired target emissivity spectra, as a typical emissivity engineering, for broad applications such as thermal camouflage, radiative cooling, and gas sensing, etc. However, previous designs require prior knowledge of materials or structures for different applications and the designed WS-TEs usually vary from applications to applications in terms of materials and structures, thus lacking of a general design framework for emissivity engineering across different applications. Moreover, previous designs fail to tackle the simultaneous design of both materials and structures, as they either fix materials to design structures or fix structures to select suitable materials. Herein, we employ the deep Q-learning network algorithm, a reinforcement learning method based on deep learning framework, to design multilayer WS-TEs. To demonstrate the general validity, three WS-TEs are designed for various applications, including thermal camouflage, radiative cooling and gas sensing, which are then fabricated and measured. The merits of the deep Q-learning algorithm include that it can (1) offer a general design framework for WS-TEs beyond one-dimensional multilayer structures; (2) autonomously select suitable materials from a self-built material library and (3) autonomously optimize structural parameters for the target emissivity spectra. The present framework is demonstrated to be feasible and efficient in designing WS-TEs across different applications, and the design parameters are highly scalable in materials, structures, dimensions, and the target functions, offering a general framework for emissivity engineering and paving the way for efficient design of nonlinear optimization problems beyond thermal metamaterials.

Gravity Recovery and Climate Experiment (GRACE) satellite data monitors changes in terrestrial water storage, including groundwater, at a regional scale. However, the coarse spatial resolution limits its applicability to small watershed areas. This study introduces a novel ensemble learning-based model using meteorological and topographical data to enhance spatial resolution. The effectiveness was evaluated using groundwater-level observation data from the Henan rainstorm-affected area in July 2021. The factors influencing Groundwater Storage Anomalies (GWSA) were explored using Permutation Importance (PI) and other methods. The results demonstrate that feature engineering and Blender ensemble learning improve downscaling accuracy; the Root Mean Square Error (RMSE) can be reduced by up to 18.95%. Furthermore, Blender ensemble learning decreased the RMSE by 3.58%, achieving an R-Square (R2) value of 0.7924. Restricting the downscaling inversion to June–August data greatly enhanced the accuracy, as evidenced by a holdout dataset test with an R2 value of 0.8247. The overall GWSA variation from January to August exhibited ‘slow rise, slow fall, sharp fall, and sharp rise.’ Additionally, heavy rain exhibits a lag effect on the groundwater supply. Meteorological and topographical factors drive fluctuations in GWSA values and changes in spatial distribution. Human activities also have a significant impact.

AbstractContext Punicalagin has myocardial protection; the mechanism of punicalagin on ventricular remodeling (VR) after acute myocardial infarction (AMI) remains unclear.Objective These studies explore the role and mechanism of punicalagin in preventing and treating VR after AMI.Materials and methods Molecular docking was used to predict the targets of punicalagin. After 2 weeks of AMI model, the SD rats were randomly divided into model, and punicalagin (200, 400 mg/kg, gavage) groups for 4 weeks. Thoracotomy with perforation but no ligature was performed on rats in control group. The protein expression of nucleotide-binding oligomerization domain-like receptor family pyrin domain-containing 3 (NLRP3), apoptosis speck-like protein (ASC), caspase-1, gasdermin D (GSDMD), and GSDMD-N, the mRNA expression of NLRP3, caspase-1, GSDMD, interleukin-1β (IL-1β) and IL-18 were evaluated.Results Punicalagin had binding activities with NLRP3 (Vina score, −5.8), caspase-1 (Vina score, −6.7), and GSDMD (Vina score, −6.7). Punicalagin could improve cardiac function, alleviate cardiac pathological changes, minimize the excessive accumulation of collagen in the left ventricular myocardium (p

Looseness of high-strength wind turbine bolts is one of the main types of mechanical failure that threaten the quality and safety of wind turbines, and how to non-destructively detect bolt loosening is essential to accurate assessment of operational reliability of wind turbine structures. Therefore, to address the issue of looseness detection of high-strength wind turbine bolts, this paper proposes a non-destructive detection method based on digital image correlation (DIC). Firstly, the mathematical relationships between the inplane displacement component of the bolt’s nut surface, the bolt’s preload force loss and the bolt loosening angle are both deduced theoretically. Then, experimental measurements are respectively conducted with DIC with different small bolt loosening angles. The results show that the bolt loosening angle detection method based on DIC has a detection accuracy of over 95%, and the bolt’s preload force loss evaluated by the deduced relationship has a good agreement with the empirical value. Therefore, the proposed DIC-based bolt loosening angle detection method can meet the requirements of engineering inspection, and can achieve quantitative assessment of preload forces loss of wind turbine bolt.

Motor imagery (MI) brain-computer interface (BCI) assist users in establishing direct communication between their brain and external devices by decoding the movement intention of human electroencephalogram (EEG) signals. However, cerebral cortical potentials are highly rhythmic and sub-band features, different experimental situations and subjects have different categories of semantic information in specific sample target spaces. Feature fusion can lead to more discriminative features, but simple fusion of features from different embedding spaces leading to the model global loss is not easily convergent and ignores the complementarity of features. Considering the similarity and category contribution of different sub-band features, we propose a multi-band centroid contrastive reconstruction fusion network (MB-CCRF). We obtain multi-band spatio-temporal features by frequency division, preserving the task-related rhythmic features of different EEG signals; use a multi-stream cross-layer connected convolutional network to perform a deep feature representation for each sub-band separately; propose a centroid contrastive reconstruction fusion module, which maps different sub-band and category features into the same shared embedding space by comparing with category prototypes, reconstructing the feature semantic structure to ensure that the global loss of the fused features converges more easily. Finally, we use a learning mechanism to model the similarity between channel features and use it as the weight of fused sub-band features, thus enhancing the more discriminative features, suppressing the useless features. The experimental accuracy is 79.96% in the BCI competition Ⅳ-Ⅱa dataset. Moreover, the classification effect of sub-band features of different subjects is verified by comparison tests, the category propensity of different sub-band features is verified by confusion matrix tests and the distribution in different classes of each sub-band feature and fused feature are showed by visual analysis, revealing the importance of different sub-band features for the EEG-based MI classification task.

Objective: To investigate the correlation between changes in tumor burden and the dose of intraoperative radiation therapy (IORT) for unresectable pancreatic cancer within 60 days of treatment evaluated using imaging parameters. Methods: A total of 32 cases of unrespectable pancreatic cancer treated with IORT at the Sichuan Cancer Hospital from April 2017 to July 2020 were retrospectively included. The imaging parameters of the tumor were recorded 7 days before IORT and 60 days after IORT: the longest axis (LA), shortest axis (SA), largest cross-sectional area (A), absolute value changes of each parameter (Δ), and change rate (Δ%). Different tumor load groups (shrinking/stabilizing/increasing) were differentiated, and the relationship between each parameter, IORT radiation dose, and tumor response time was analyzed. Results: The LA of unresectable pancreatic cancer after IORT was lower than that before IORT, with significant differences observed. The SA and A values of unresectable pancreatic cancer after IORT were lower than those before IORT, with no statistical significance observed. Based on ΔLA of the pancreatic tumor, the three groups, shrinkage, stability, and enlargement, were divided, with a corresponding tumor response time of (39.57±11.77) d, (38.08±12.87) d, (41.17±42) d, respectively. There were no significant differences in LA, SA, and A among the three groups before IORT, while significant differences were observed in LA, SA, and A after IORT. The ΔLA of the three groups was statistically significant before and after IORT. There was a moderate negative correlation between ΔLA and IORT dose levels in unrespectable pancreatic cancer (r=−0.47,). There was a moderate and high correlation between ΔLA and IORT dose in the stable group and ΔA and IORT dose in the enlargement group, respectively (r=0.66 vs. 0.90). There was no significant correlation between the response time and imaging parameters in these groups, except for a moderate negative correlation between ΔSA and response time in the stability group (r=−0.68). Conclusion: ΔLA can be used to evaluate changes in tumor load within 60 days of IORT for unresectable pancreatic cancer. The efficacy of pancreatic cancer could be correlated with the IORT irradiation dose, whereas the tumor burden could not be affected by the tumor response time.

Abstract High‐performance organic neuromorphic devices with miniaturized device size and computing capability are essential elements for developing brain‐inspired humanoid intelligence technique. However, due to the structural inhomogeneity of most organic materials, downscaling of such devices to nanoscale and their high‐density integration into compact matrices with reliable device performance remain challenging at the moment. Herein, based on the design of a semicrystalline polymer PBFCL10 with ordered structure to regulate dense and uniform formation of conductive nanofilaments, we realize an organic synapse with the smallest device dimension of 50 nm and highest integration size of 1 Kb reported thus far. The as‐fabricated PBFCL10 synapses can switch between 32 conductance states linearly with a high cycle‐to‐cycle uniformity of 98.89% and device‐to‐device uniformity of 99.71%, which are the best results of organic devices. A mixed-signal neuromorphic hardware system based on the organic neuromatrix and FPGA controller is implemented to execute spiking‐plasticity‐related algorithm for decision-making tasks.

Abstract Background Broussonetia papyrifera (L.) L’Hér. ex Vent. has the characteristics of strong stress resistance, high crude protein content, and pruning tolerance. It is an ecological, economic, and medicinal plant. Polyploid plants usually perform better than their corresponding diploid plants in terms of nutrients, active substances, and stress resistance. Results In this study, the leaves, calli, and seeds of diploid B. papyrifera were used for tetraploid induction by colchicine. The induction effect of colchicine on B. papyrifera was summarized through the early morphology, chromosome count and flow cytometry. It was concluded that the best induction effect (18.6%) was obtained when the leaves of B. papyrifera were treated in liquid MS (Murashige and Skoog) medium containing 450 mg·L-1 colchicine for 3 d. The comparative analysis of the growth characteristics of diploid and tetraploid B. papyrifera showed that tetraploid B. papyrifera has larger ground diameter, larger stomata, thicker palisade tissue and thicker sponge tissue than diploid B. papyrifera. In addition, the measurement of photosynthetic features also showed that tetraploids had higher chlorophyll content and higher photosynthetic rates. Conclusion This study showed that tetraploid B. papyrifera could be obtained by treating leaves, callus and seeds with liquid and solid colchicine, but the induction efficiency was different. Moreover, there were differences in stomata, leaf cell structure and photosynthetic features between tetraploid B. papyrifera and its corresponding diploid. The induced tetraploid B. papyrifera can provide a technical basis and breeding material for the creation of B. papyrifera germplasm resources in the future.

Highlights Animal models are applied to evaluate the biosecurity and biocompatibility of the zinc-ion batteries with the electrolytes of different zinc salts. Leakage scene simulations and histological analysis are employed in investigating the tissue response after battery implantations, in which ZnSO4 exhibits higher biosecurity. Sn hetero nucleus is introduced to stabilize the zinc anode, which not only facilitates the planar zinc deposition, but also contributes to higher hydrogen evolution overpotential.

Abstract Background Myocardial bridges are congenital anatomical abnormalities in which myocardium covers a segment of coronary arteries, leading to stenocardia, myocardial ischemia, and sudden cardiac death in severe cases. However, automatic diagnosis of myocardial bridge presents significant challenges. Method A novel framework of myocardial bridge detection with x-ray angiography sequence is proposed, which can realize automatic detection of vessel stenosis and myocardial bridge. Firstly, we employ a novel neural network model for coronary vessel segmentation, which consists of both CNNs and transformer structures to effectively extract both local and global information of the vessels. Secondly, we describe the vessel segment information, establish the vessel tree in the image, and fuse the vessel tree information between sequences. Finally, based on vessel stenosis detection, we realize automatic detection of the myocardial bridge by querying the blood vessels between the image sequence information. Results In experiment, we evaluate the segmentation results using two metrics, Dice and ASD, and achieve scores of 0.917 and 1.39, respectively. In the stenosis detection, we achieve an average accuracy rate of 92.7% in stenosis detection among 262 stenoses. In multi-frame image processing, vessels in different frames can be well-matched, and the accuracy of myocardial bridge detection achieves 75%. Conclusions Our experimental results demonstrate that the algorithm can automatically detect stenosis and myocardial bridge, providing a new idea for subsequent automatic diagnosis of coronary vessels.

Objective: Determining the tumor margin is the main problem in penoscrotal extramammary Paget's disease (PEMPD) surgery. This study aimed to explore the role of modified slow Mohs circular skin biopsy in determining the tumor margin in patients with PEMPD before operation. Methods: Thirty-six PEMPD patients were divided into the control group and the observation group according to the simple randomization method. The control group, which consisted of 16 patients, underwent wide local excision, and the observation group, which consisted of 20 patients, underwent a modified slow Mohs circular skin biopsy to determine the tumor margin before surgery. The duration of surgery, number of frozen sections, satisfaction with the appearance of the skin, tumor recurrence rate, and incidence of functional sequelae were statistically analyzed. Results: The number of frozen sections, duration of surgery, incidence of functional sequelae, and tumor recurrence rate in the observation group were lower than in the control group. The satisfaction with the appearance of the skin was higher in the observation group than in the control group, and the difference was statistically significant (P

Primary sarcomas of the jaw are very rare tumor with unclear mechanism of tumorigenesis. Identification of genetic alterations contributes to better understanding of tumorigenesis and extension of tumor spectrum, as well as potential therapeutic targets application. Herein, we firstly report a case of primary sarcoma in the mandible with novel SLMAP-BRAF fusion. Morphologically, the tumor was composed of histiocyte-like cells, larger epithelioid cells, spindle cells and osteoclast-like giant cells with moderate atypia. Focally, it mimicked tenosynovial giant cell tumor or biphasic synovial sarcoma, and even giant cell tumor of bone. SATB2 was diffusely expressed, while p63 and p16 were locally positive with loss expression of p16 in histiocyte-like and larger epithelioid cells. SLMAP-BRAF (S11:B10) fusion was detected by both DNA and RNA NGS, and further verified by sanger sequencing, DNA electrophoresis and FISH. Then a descriptive diagnosis of BRAF rearrangement sarcoma with moderate-grade malignancy (non-specific type) was given according to the biological behavior, morphological features and gene alteration. The patient finished six cycles of chemotherapy after hemimaxillectomy. Within 7 months of follow-up, no tumor recurrence or metastasis was observed. Our case has enriched the spectrum of jaw bone tumor and BRAF rearrangement tumor.

Eravacycline (ERV) has emerged as a therapeutic option for the treatment of carbapenem-resistant pathogens. However, the advent of heteroresistance (HR) to ERV poses a challenge to these therapeutic strategies. This study aimed to investigate ERV HR prevalence among common clinical isolates and further characterize ERV HR in carbapenem-resistant Klebsiella pneumoniae (CRKP). A total of 280 clinical pathogens from two centers were selected for HR and analyzed using population analysis profiling (PAP) and modified E-tests. The PAP assay revealed an overall ERV HR prevalence of 0.7% (2/280), with intermediate heterogeneity observed in 24.3% (68/280) of strains. The proportion of heteroresistant strains was 18.3% according to modified E-test results. A time-killing assay demonstrated that CRKP CFU increased significantly after 10 h of ERV treatment, contributing to the reduced bactericidal effect of ERV in vitro. Interestingly, dual treatment with ERV and polymyxin B effectively inhibited the total CFU, simultaneously reducing the required polymyxin B concentration. Furthermore, fitness cost measurements revealed a growth trade-off in CRKP upon acquiring drug resistance, highlighting fitness costs as crucial factors in the emergence of ERV HR in CRKP. Overall, the findings of the current study suggest that ERV HR in clinical strains presents a potential obstacle in its clinical application.

Abstract Background Several observational studies have suggested an association between low serum bilirubin levels and Behçet's disease uveitis. However, the causal inference between bilirubin level and juvenile idiopathic arthritis‐associated uveitis (JIAU) remains ambiguous. We investigated the potential causal relationship between serum bilirubin levels and JIAU using a bidirectional two‐sample Mendelian randomization (MR) framework. Methods We systemically integrated summary‐level data from published large‐scale genome‐wide association studies on bilirubin level and JIAU in a Caucasian British population. To determine the causal effect of bilirubin level on JIAU, we constructed strong instrumental variables using 47 and 80 single‐nucleotide polymorphisms (SNPs) specific to direct bilirubin and total bilirubin levels, respectively. For reverse causal inference, seven SNPs associated with JIAU were included in our study. Multiple complementary methods were further performed to evaluate the robustness of MR estimates. Results The inverse‐variance weighted estimate did not show any significant causal associations of genetically predicted serum direct or total bilirubin level with the risk of JIAU (odds ratio [OR]: 1.010, 95% confidence interval [CI]: 0.750–1.359, p = 0.947; OR: 0.867, 95% CI: 0.688–1.093; p = 0.227, respectively). MR–Egger and weighted median methods also obtained similar associations. Additionally, the results of reverse MR analyses using JIAU as exposure showed no associations of genetically predicted risk of JIAU with serum bilirubin levels (p > 0.05). In sensitivity analysis, the causal estimate between serum bilirubin levels and JIAU did not differ when SNPs associated with possible confounders were omitted. Conclusion Genetic evidence from our bidirectional analysis did not support a causal association between serum bilirubin levels and JIAU risk in the Caucasian British population. Future large‐scale studies should be conducted to validate these findings and explore any causal effects on the disease process.

The size of the reference group is among the most critical determinants of genomic estimated breeding values (GEBVs) accuracy. However, small- and medium-sized pig farms often need help accumulating adequate reference data, posing significant challenges to breeding programs. To solve this problem, exploring the potential benefits of combining reference groups of different sizes is necessary to improve GEBV accuracy. The primary objective of this investigation was to assess a more effective statistical model for combined multi-populations and its potential to enhance the accuracy of GEBVs for small and medium populations. Three populations were simulated using the QMSim software, each consisting of different sizes (300, 600, and 1 500, respectively). To assess the impact of heritability on the accuracy of GEBVs, four different levels of heritability (0.05, 0.15, 0.35, and 0.5) were simulated. Simultaneously, to investigate the impact of kinship on multi-populations, the study created four distinct scenarios for the three sizes of populations. These scenarios included: (1) the three groups are all independent, (2) the large group and the small group with a familial connection (n = 1 800), a middle group (n = 600) acting independently with no kinship, (3) the large group with a familial connection to the middle group (n = 2 100) but no connection to the small group (n = 300), and (4) the small group with a familial connection to the middle group (n = 900), while the large group (n = 1 500) acted independently with no kinship. This study evaluates and compares the accuracy of predicting breeding values using four different methods, including genomic best linear unbiased prediction (GBLUP), single-stepGBLUP (ssGBLUP), and two Bayesian models (Bayes A and Bayes B), with varying sizes of reference groups. In each scenario, three different prediction strategies were compared: (1) Merging all three different sizes of populations for predicting, (2) predicting each independent population separately, and (3) the other two populations predict the population. Our findings reveal that combining populations enhances the Bayesian models, with Bayes B yielding the highest accuracy. In independent populations, the best linear unbiased prediction (BLUP) models demonstrated the highest accuracy. However, in cases where populations were related and the heritability was high, the Bayes B model exhibited the highest overall accuracy (slightly higher than BLUP models) in the independent population. Our results underscore the importance of considering population combinations when using genetic models to predict breeding values, particularly for pig farmers with limited resources.

Global warming is a significant challenge, and carbon stocks in terrestrial ecosystems are crucial for reducing the greenhouse effect and increasing sinks. A study was conducted in Zhengzhou City from 2000 to 2020 using Landsat image spectral reflectance to analyze changes in carbon stock. Environmental variables such as surface moisture, salinity, vegetation index, brightness, and soil texture were constructed. Multiple linear regression (MLR), support vector machine regression (SVR), random forest regression (RFR), and long short-term memory (LSTM) models were used to invert the carbon stock. The results showed that the NDCS index, constructed using Landsat's blue band and NIR band, was the best inversion variable for carbon stock, with the clay index (CI) playing a primary role. The LSTM algorithm had the best fitting effect on carbon stock, with an R2 of 0.84 and RMSE of 3.56. The carbon stock in Zhengzhou City decreased by 13.93% between 2000 and 2020, possibly due to the large-scale reduction of arable land. Future land-use planning should focus on protecting arable land, optimizing land-use patterns, and enhancing the ecosystem's carbon sequestration capacity.

Summary: As a primary target of severe acute respiratory syndrome coronavirus 2, lung exhibits heterogeneous histopathological changes following infection. However, comprehensive insight into their protein basis with spatial resolution remains deficient, which hinders further understanding of coronavirus disease 2019 (COVID-19)-related pulmonary injury. Here, we generate a region-resolved proteomic atlas of hallmark pathological pulmonary structures by integrating histological examination, laser microdissection, and ultrasensitive proteomics. Over 10,000 proteins are quantified across 71 post-mortem specimens. We identify a spectrum of pathway dysregulations in alveolar epithelium, bronchial epithelium, and blood vessels compared with non-COVID-19 controls, providing evidence for transitional-state pneumocyte hyperplasia. Additionally, our data reveal the region-specific enrichment of functional markers in bronchiole mucus plugs, pulmonary fibrosis, airspace inflammation, and alveolar type 2 cells, uncovering their distinctive features. Furthermore, we detect increased protein expression associated with viral entry and inflammatory response across multiple regions, suggesting potential therapeutic targets. Collectively, this study provides a distinct perspective for deciphering COVID-19-caused pulmonary dysfunction by spatial proteomics.

Abstract The development and application of artificial intelligence have led to the exploitation of low‐power and compact intelligent information‐processing systems integrated with sensing, memory, and neuromorphic computing functions. The 2D van der Waals (vdW) materials with abundant reservoirs for arbitrary stacking based on functions and enabling continued device downscaling offer an attractive alternative for continuously promoting artificial intelligence. In this study, full 2D SnS2/h‐BN/CuInP2S6 (CIPS)‐based ferroelectric field‐effect transistors (Fe‐FETs) and utilized light‐induced ferroelectric polarization reversal to achieve excellent memory properties and multi‐functional sensing‐memory‐computing vision simulations are designed. The device exhibits a high on/off current ratio of over 105, long retention time (>104 s), stable cyclic endurance (>350 cycles), and 128 multilevel current states (7‐bit). In addition, fundamental synaptic plasticity characteristics are emulated including paired‐pulse facilitation (PPF), short‐term plasticity (STP), long‐term plasticity (LTP), long‐term potentiation, and long‐term depression. A ferroelectric optoelectronic reservoir computing system for the Modified National Institute of Standards and Technology (MNIST) handwritten digital recognition achieved a high accuracy of 93.62%. Furthermore, retina‐like light adaptation and Pavlovian conditioning are successfully mimicked. These results provide a strategy for developing a multilevel memory and novel neuromorphic vision systems with integrated sensing‐memory‐processing.

BackgroundThe GABRA1 gene, encoding the GABRAR subunit α1, plays vital roles in inhibitory neurons. Previously, the GABRA1 gene has been identified to be associated with developmental and epileptic encephalopathy (DEE) and idiopathic generalized epilepsy (IGE). This study aims to explore the phenotypic spectrum of GABRA1 and molecular subregional effect analysis.MethodsTrios-based whole-exome sequencing was performed in patients with epilepsy. Previously reported GABRA1 mutations were systematically reviewed to analyze the molecular subregional effects.ResultsDe novo GABRA1 mutations were identified in six unrelated patients with heterogeneous epilepsy, including three missense mutations (p.His83Asn, p.Val207Phe, and p.Arg214Cys) and one frameshift mutation (p.Thr453Hisfs*47). The two missense mutations, p.His83Asn and p.Val207Phe, were predicted to decrease the protein stability but no hydrogen bond alteration, with which the two patients also presented with mild genetic epilepsy with febrile seizures plus and achieved seizure-free status by monotherapy. The missense variant p.Arg214Cys was predicted to decrease protein stability and destroy hydrogen bonds with surrounding residues, which was recurrently identified in three cases with severe DEE. The frameshift variant p.Thr453Hisfs*47 was located in the last fifth residue of the C-terminus and caused an extension of 47 amino acids, with which the patients presented with moderated epilepsy with generalized tonic-clonic seizures alone (GTCA) but achieved seizure-free status by four drugs. The four variants were not presented in gnomAD and were evaluated as “pathogenic/likely pathogenic” according to ACMG criteria. Analysis of all reported cases indicated that patients with mutations in the N-terminal extracellular region presented a significantly higher percentage of FS and DEE, and the patients with variants in the transmembrane region presented earlier seizure onset ages.SignificanceThis study suggested that GABRA1 variants were potentially associated with a spectrum of epilepsies, including EFS+, DEE, and GTCA. Phenotypic severity may be associated with the damaging effect of variants. The molecular subregional effects help in understanding the underlying mechanism of phenotypic variation.

BackgroundThe DLG3 gene encodes disks large membrane-associated guanylate kinase scaffold protein 3, which plays essential roles in the clustering of N-methyl-D-aspartate receptors (NMDARs) at excitatory synapses. Previously, DLG3 has been identified as the causative gene of X-linked intellectual developmental disorder—90 (XLID-90; OMIM# 300850). This study aims to explore the phenotypic spectrum of DLG3 and the genotype-phenotype correlation.MethodsTrios-based whole-exome sequencing was performed in patients with epilepsy of unknown causes. To analyze the genotype-phenotype correlations, previously reported DLG3 variants were systematically reviewed.ResultsDLG3 variants were identified in seven unrelated cases with epilepsy. These variants had no hemizygous frequencies in controls. All variants were predicted to be damaging by silico tools and alter the hydrogen bonds with surrounding residues and/or protein stability. Four cases mainly presented with generalized seizures, including generalized tonic-clonic and myoclonic seizures, and the other three cases exhibited secondary generalized tonic-clonic seizures and focal seizures. Multifocal discharges were recorded in all cases during electroencephalography monitoring, including the four cases with generalized discharges initially but multifocal discharges after drug treating. Protein-protein interaction network analysis revealed that DLG3 interacts with 52 genes with high confidence, in which the majority of disease-causing genes were associated with a wide spectrum of neurodevelopmental disorder (NDD) and epilepsy. Three patients with variants locating outside functional domains all achieved seizure-free, while the four patients with variants locating in functional domains presented poor control of seizures. Analysis of previously reported cases revealed that patients with non-null variants presented higher percentages of epilepsy than those with null variants, suggesting a genotype-phenotype correlation.SignificanceThis study suggested that DLG3 variants were associated with epilepsy with/without NDD, expanding the phenotypic spectrum of DLG3. The observed genotype-phenotype correlation potentially contributes to the understanding of the underlying mechanisms driving phenotypic variation.

Abstract Artificial lights can cause critical microbial biodeterioration of heritage monuments by promoting the outbreak of phototrophic microbiomes when they are used for touristic viewing. Here, with the ultimate aim of providing innovative solutions for the conservation and visiting of such monuments, we conducted a pioneering two-year in situ manipulative experiment to evaluate the impacts of different artificial light wavelengths (i.e., blue, green and red lights compared to white light) on the phototrophic microbiome of a millennial Chinese imperial mausoleum. Our results show that artificial light can shape the ecophysiological features of the phototrophic bacteriome in this monument and reduce its potential for further biodeterioration. In general, Cyanobacteria dominated (42.0% of the total relative abundance) the phototrophic bacteriome of this cultural relic; however, they were also very sensitive to the choice of artificial light. Compared to white light, monochromatic light, especially green light, reduced Cyanobacteria abundances (18.6%) by decreasing photosynthetic pigment abundances (42.9%); decreased the abundances of heterotrophic species belonging to Proteobacteria (4.5%) and the proportion of genes (6.1%) associated with carbon (i.e., carbon fixation), nitrogen (i.e., denitrification), and sulfur (i.e., dissimilatory sulfate reduction) cycling; and further decreased organic acid (10.1–14.1%) production of the phototrophic bacteriome, which is known to be involved in biodeterioration. Taken together, our findings constitute a major advancement in understanding how light wavelengths influence the phototrophic microbiome in cultural relics, and we found that artificial lights with certain wavelengths (e.g., green light) can help long-term conservation while allowing tourism activities.

Abstract Background This study investigates the accuracy of 3D-printed dental stents in intensity-modulated radiotherapy (IMRT) for oropharyngeal cancer (OPC) and their dosimetric effects on normal tissues. Methods We selected 60 patients with OPC who underwent IMRT in the Department of Oncology, Special Medical Center of Army Medical University. These patients were randomly assigned into 3D-printed oral stent, simple glass bottle, and nonstent groups (20 patients/group). The positioning error was analyzed with the onboard imaging system once a week after 5 fractions of IMRT. The conformity index (CI), homogeneity index (HI), radiation dose of organs at risk (OARs), and oral mucosal reaction were compared among the three groups. Results No significant difference was observed in the conformity and uniformity of the target dose and the dose received by the spinal cord, larynx, and bilateral parotid glands among the three groups (P > 0.05). Meanwhile, the dose received by the upper cheek, hard palate, and soft palate of patients was significantly lower in the 3D-printed oral stent group than in the nonstent group (P 0.05). When compared with the nonstent group, the simple glass bottle group showed a markedly lower dose received by the upper cheek (P 0.05). According to Common Terminology Criteria for Adverse Events v.5.0, the adverse response rate of the hard palate mucosa was lower in the 3D-printed oral stent group than in the simple glass bottle and nonstent groups (P

Abstract Background Identification of factors relevant to balance performance impairments in the elderly population was critical for developing effective interventions and preventions. However, there have been very limited data available based on large scale studies. The present study identified factors that independently contributed to performance impairments in overall balance, domains of static balance, postural stability, and dynamic balance, and individual items. Methods A total of 1984 community-dwelling Chinese elderly from urban areas of Shanghai were recruited. Information on demographic characteristic, exercise, and health status were collected with a face-to-face interview. Balance performances were assessed on site by trained investigators based on the X16 balance testing scale. To identify the effectors, ordinal logistic regression analysis was applied for overall balance, static balance, postural stability, and dynamic balance. Binary logistic regression analysis was used for 16 items. Results The community-dwelling elderly residents were aged from 60 to 97 years old. With increases of age, risks of impairments in overall balance increased gradually (ORs from 1.26 to 3.20, all P

ZnS-based mechanoluminescent film has been widely used in the fields of stress visualization and stress sensing, due to its high brightness and repeatable stable luminescent characteristics. To evaluate the flexibleelastic deformation performance of ZnS-based mechanoluminescent film, both visual inspection and digital image correlation (DIC) are, respectively, employed for measuring the ZnS-based mechanoluminescent film. ZnS:Cu 2+ mechanoluminescent powders are first mixed with polydimethylsiloxane (PDMS) matrix to produce ZnS:Cu 2+–PDMS mechanoluminescent film. Then, two measurement experiments are, respectively, conducted to investigate the mechanical response and the flexible-elastic deformation performance of the prepared ZnS:Cu2+–PDMS mechanoluminescent film. On one hand, the mechanical response performance of the ZnS:Cu 2+–PDMS mechanoluminescent film is validated by visual monitoring of composite concrete fracture processes. On the other hand, the prepared ZnS:Cu 2+–PDMS mechanoluminescent film is also measured by DIC to obtain its full-field deformations and strains information. The flexible-elastic deformation performance of the ZnS:Cu 2+–PDMS mechanoluminescent film is well demonstrated by the DIC measured results.

Abstract Background Ilex metabaptista is a woody tree species with strong waterlogging tolerance and is also admired as a landscape plant with high development prospects and scientific research value. Unfortunately, populations of this species have declined due to habitat loss. Thus, it is a great challenge for us to efficiently protect I. metabaptista resources from extinction. Molecular biology research can provide the scientific basis for the conservation of species. However, the study of I. metabaptista genetics is still in its infancy. To date, no mitochondrial genome (mitogenome) in the genus Ilex has been analysed in detail. Results The mitogenome of I. metabaptista was assembled based on the reads from Illumina and Nanopore sequencing platforms; it was a typical circular DNA molecule of 529,560 bp with a GC content of 45.61% and contained 67 genes, including 42 protein-coding genes, 22 tRNA genes, and 3 rRNA genes. Repeat sequence analysis and prediction of RNA editing sites revealed a total of 286 dispersed repeats, 140 simple repeats, 18 tandem repeats, and 543 RNA editing sites. Analysis of codon usage showed that codons ending in A/T were preferred. Gene migration was observed to occur between the mitogenome and chloroplast genome via the detection of homologous fragments. In addition, Ka/Ks analysis revealed that most of the protein-coding genes in the mitogenome had undergone negative selection, and only the ccmB gene had undergone potential positive selection in most asterids. Nucleotide polymorphism analysis revealed the variation in each gene, with atp9 being the most notable. Furthermore, comparative analysis showed that the GC contents were conserved, but the sizes and structure of mitogenomes varied greatly among asterids. Phylogenetic analysis based on the mitogenomes reflected the exact evolutionary and taxonomic status of I. metabaptista. Conclusion In this study, we sequenced and annotated the mitogenome of I. metabaptista and compared it with the mitogenomes of other asterids, which provided essential background information for further understanding of the genetics of this plant and helped lay the foundation for future studies on molecular breeding of I. metabaptista.

Abstract Reservoir computing (RC) has received recent interest because reservoir weights do not need to be trained, enabling extremely low-resource consumption implementations, which could have a transformative impact on edge computing and in-situ learning where resources are severely constrained. Ideally, a natural hardware reservoir should be passive, minimal, expressive, and feasible; to date, proposed hardware reservoirs have had difficulty meeting all of these criteria. We, therefore, propose a reservoir that meets all of these criteria by leveraging the passive interactions of dipole-coupled, frustrated nanomagnets. The frustration significantly increases the number of stable reservoir states, enriching reservoir dynamics, and as such these frustrated nanomagnets fulfill all of the criteria for a natural hardware reservoir. We likewise propose a complete frustrated nanomagnet reservoir computing (NMRC) system with low-power complementary metal-oxide semiconductor (CMOS) circuitry to interface with the reservoir, and initial experimental results demonstrate the reservoir’s feasibility. The reservoir is verified with micromagnetic simulations on three separate tasks demonstrating expressivity. The proposed system is compared with a CMOS echo state network (ESN), demonstrating an overall resource decrease by a factor of over 10,000,000, demonstrating that because NMRC is naturally passive and minimal it has the potential to be extremely resource efficient.

Abstract To explore the minds of others, which is traditionally referred to as Theory of Mind (ToM), is perhaps the most fundamental ability of humans as social beings. Impairments in ToM could lead to difficulties or even deficits in social interaction. The present study focuses on two core components of ToM, the ability to infer others’ beliefs and the ability to infer others’ emotions, which we refer to as cognitive and affective ToM respectively. Charting both typical and atypical trajectories underlying the cognitive-affective ToM promises to shed light on the precision identification of mental disorders, such as depressive disorders (DD) and autism spectrum disorder (ASD). However, most prior studies failed to capture the underlying processes involved in the cognitive-affective ToM in a fine-grained manner. To address this problem, we propose an innovative conceptual framework, referred to as visual theory of mind (V-ToM), by constructing visual scenes with emotional and cognitive meanings and by depicting explicitly a four-stage process of how humans make inferences about the beliefs and emotions of others. Through recording individuals’ eye movements while looking at the visual scenes, our model enables us to accurately measure each stage involved in the computation of cognitive-affective ToM, thereby allowing us to infer about potential difficulties that might occur in each stage. Our model is based on a large sample size (n > 700) and a novel audio-visual paradigm using visual scenes containing cognitive-emotional meanings. Here we report the obtained differential features among healthy controls, DD and ASD individuals that overcome the subjectivity of conventional questionnaire-based assessment, and therefore could serve as valuable references for mental health applications based on AI-aided digital medicine.

Abstract Solar-driven photosynthesis is a sustainable process for the production of hydrogen peroxide, the efficiency of which is plagued by side reactions. Metal-free covalent organic frameworks (COFs) that can form suitable intermediates and inhibit side reactions show great promise to photo-synthesize H2O2. However, the insufficient formation and separation/transfer of photogenerated charges in such materials restricts the efficiency of H2O2 production. Herein, we provide a strategy for the design of donor-acceptor COFs to greatly boost H2O2 photosynthesis. We demonstrate that the optimal intramolecular polarity of COFs, achieved by using suitable amounts of phenyl groups as electron donors, can maximize the free charge generation, which leads to high H2O2 yield rates (605 μmol g−1 h−1) from water, oxygen and visible light without sacrificial agents. Combining in-situ characterization with computational calculations, we describe how the triazine N-sites with optimal N 2p states play a crucial role in H2O activation and selective oxidation into H2O2. We further experimentally demonstrate that H2O2 can be efficiently produced in tap, river or sea water with natural sunlight and air for water decontamination.