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Abstract Most current RM approaches are developed for fixed bottlenecks. However, the number and locations of bottlenecks are usually uncertain and even time‐varying due to some unexpected phenomena, such as severe accidents and temporal lane closures. Thus, the RM approach should be able to enhance traffic flow stability by effectively handling the time‐delay effect and fluctuations in traffic flow rate caused by uncertain bottlenecks. This study proposed a novel approach called deep reinforcement learning with curriculum learning (DRLCL) to improve ramp metering efficacy under uncertain bottleneck conditions. The curriculum learning method transfers an optimal control policy from a simple on‐ramp bottleneck case to more challenging bottleneck tasks, while DRLCL agents explore and learn from the tasks gradually. Four RM control tasks were developed in the modified cell transmission model, including typical on‐ramp bottleneck, fixed downstream bottleneck, random‐location bottleneck, and multiple bottlenecks. With curriculum learning, the entire training process was reduced by 45.1% to 64.5%, while maintaining a similar maximum reward level compared to DRL‐based RM control with full learning from scratch. Specifically, the results also demonstrated that the proposed DRLCL‐based RM outperformed the feedback‐based RM due to its stronger predictive ability, faster response, and higher action precision.

The large-diameter and long-distance drilling in coal mines can serve as a transportation channel to replace traditional underground tunnels. Compared with conventional large-diameter drilling, the main differences and difficulties lie in “point-to-point” precise drilling, efficient and rapid reaming of large-diameter and long-distance classification, and full hole section casing laying and grouting for pipe fixation. This article introduces the key technology for drilling holes through large-diameter and long-distance boreholes in coal mines. We will conduct in-depth research on five aspects, including RMRS obstacle avoidance and docking technology, graded hole reaming technology, slag removal process technology, full hole section casing laying technology, and grouting and pipe fixing technology, in order to achieve efficient drilling of large-diameter and long-distance through holes. In the guide straight holes drilled at multiple key points of the drilling design direction track, RMRS technology is used for advanced guidance to achieve directional obstacle avoidance and precise point-to-point penetration; reasonably optimize the drilling sequence and adopt the back pull drilling process to improve the efficiency of drilling; utilizing the combined slag removal process of “intra hole circulation + mechanical assistance” to improve the slag removal efficiency of borehole drilling; optional ball centralizer, using a combination of “forward pull + backward push” to achieve long-distance full hole section casing laying; adopting the “annular pressure grouting method” to ensure the quality of grouting and pipe fixation. Based on the above key technologies, a large-diameter long-distance water transmission pipeline was constructed between Daliuta Well and Huojitu Well, with a total length of 1 932 m, a final borehole diameter of 330 mm, and a water transmission casing size for the entire borehole section ϕ219 mm×8.94 mm; the pilot hole successfully passed diagonally and safely between two bridge piers. The horizontal projection displacement deviation between the actual penetration point Bs and the design penetration point B was 0.4 m, the vertical profile displacement deviation was 0.25 m, and the spatial displacement deviation was 0.47 m, basically achieving point-to-point connectivity; the qualification rate of acoustic amplitude logging for grouting pipes is 95%, and the actual water delivery capacity of the pipeline exceeds 150 m3/h.

Abstract Lateral momentum conservation is typically kept in a non-absorptive rotationally symmetric system through mirror symmetry via Noether’s theorem when illuminated by a homogeneous light wave. Therefore, it is still very challenging to break the mirror symmetry and generate a lateral optical force (LOF) in the rotationally symmetric system. Here, we report a general dynamic action in the SO(2) rotationally symmetric system, originating from the polarization-tuned mirror symmetry breaking (MSB) of the light scattering. We demonstrate theoretically and experimentally that MSB can be generally applied to the SO(2) rotationally symmetric system and tuned sinusoidally by polarization orientation, leading to a highly tunable and highly efficient LOF (9.22 pN/mW/μm−2) perpendicular to the propagation direction. The proposed MSB mechanism and LOF not only complete the sets of MSB of light-matter interaction and non-conservative force only using a plane wave but also provide extra polarization manipulation freedom.

Objective Epidemiological studies on spinal cord tumors are rare, and studies on primary intramedullary tumors are even rarer. The incidence and survival of patients with primary intramedullary spinal cord tumors have not been well documented. We aimed to study the incidence and survival of patients with primary spinal cord malignant and borderline malignant tumors based on data from the Surveillance, Epidemiology, and End Results (SEER) database and provide information for revealing the epidemiology and exploring the prognosis of patients with primary intramedullary tumors. Methods Patients in the SEER database with microscopically diagnosed malignant and borderline malignant primary spinal cord tumors from 2000 and 2019 were included in this study. We analyzed the distribution of patients according to the demographic and clinical characteristics. Then, we extracted the incidence rate and 5-year relative survival for the whole cohort and different subgroups of the cohort. Finally, multivariate Cox proportional hazards models were used to analyze the independent prognostic factors associated with overall survival. Results A total of 5,211 patients with malignant and borderline malignant primary spinal cord tumors were included in this cohort study. Ependymoma, astrocytoma (including oligodendrogliomas and glioblastoma), lymphoma and hemangioblastoma were the most common pathological types. The age-adjusted incidence rates of primary spinal cord ependymoma was 0.18 per 100,000. The incidence rate for females was significantly lower than that for males. The incidence rate was highest in Caucasian. The incidence rate of ependymoma was significantly higher than that of other pathological types. The incidence of astrocytoma was highest among people aged 0–19 years, the incidence of ependymoma was highest among people aged 40–59 years, and the incidence of lymphoma was highest among people aged 60 years or older. The 5-year observed survival and relative survival rates for the whole cohort were 82.80% and 86.00%, respectively. Patients diagnosed with ependymoma had significantly better survival than their counterparts. We also found the impact of surgery and chemotherapy on the prognosis of patients with different tumors varies a lot. Conclusion We conducted a population-based analysis of malignant and borderline malignant primary spinal cord tumors with the aim of revealing the epidemiology and survival of patients with primary intramedullary spinal cord tumors. Despite some shortcomings, this study provides valuable information to help us better understand the epidemiological characteristics of primary intramedullary spinal cord tumors.

Abstract Sensors with a small footprint and real-time detection capabilities are crucial in robotic surgery and smart wearable equipment. Reducing device footprint while maintaining its high performance is a major challenge and a significant limitation to their development. Here, we proposed a monolithic integrated micro-scale sensor, which can be used for vector force detection. This sensor combines an optical source, four photodetectors, and a hemispherical silicone elastomer component on the same sapphire-based AlGaInP wafer. The chip-scale optical coupling is achieved by employing the laser lift-off techniques and the flip-chip bonding to a processed sapphire substrate. This hemispherical structure device can detect normal and shear forces as low as 1 mN within a measurement range of 0–220 mN for normal force and 0–15 mN for shear force. After packaging, the sensor is capable of detecting forces over a broader range, with measurement capabilities extending up to 10 N for normal forces and 0.2 N for shear forces. It has an accuracy of detecting a minimum normal force of 25 mN and a minimum shear force of 20 mN. Furthermore, this sensor has been validated to have a compact footprint of approximately 1.5 mm2, while maintaining high real-time response. We also demonstrate its promising potential by combining this sensor with fine surface texture perception in the fields of compact medical robot interaction and wearable devices.

Abstract Background We aimed to examine sex-specific associations between sex- and thyroid-related hormones and the risk of metabolic dysfunction-associated fatty liver disease (MAFLD) in patients with type 2 diabetes mellitus (T2DM). Methods Cross-sectional analyses of baseline information from an ongoing cohort of 432 T2DM patients (185 women and 247 men) in Xiamen, China were conducted. Plasma sex-related hormones, including estradiol (E2), follicle-stimulating hormone (FSH), luteinizing hormone (LH), prolactin (PRL), progesterone, and total testosterone (TT), and thyroid-related hormones, including free triiodothyronine (FT3), free thyroxine (FT4), thyroid-stimulating hormone (TSH), and parathyroid hormone (PTH), were measured using chemiluminescent immunoassays. MAFLD was defined as the presence of hepatic steatosis (diagnosed by either hepatic ultrasonography scanning or fatty liver index (FLI) score > 60) since all subjects had T2DM in the present study. Results Prevalence of MAFLD was 65.6% in men and 61.1% in women with T2DM (P = 0.335). For men, those with MAFLD showed significantly decreased levels of FSH (median (interquartile range (IQR)):7.2 (4.9–11.1) vs. 9.8 (7.1–12.4) mIU/ml) and TT (13.2 (10.4–16.5) vs. 16.7 (12.8–21.6) nmol/L) as well as increased level of FT3 (mean ± standard deviation (SD):4.63 ± 0.68 vs. 4.39 ± 0.85 pmol/L) than those without MAFLD (all p-values

Abstract Various environmental factors could induce groundwater hydrochemical evolution, which should be considered when addressing groundwater environmental issues. However, the complex interactions among the environmental controls remain unknown for a groundwater flow system spanning multiple geological units. To fill this gap, we conducted a study on the groundwater hydrochemical evolution in the fluvial corridor of the Fen River, Northern China, utilizing a combination of hydrogeochemical and multiple isotope methods. Results reveal that the groundwater in the corridor has significantly degraded due to high concentrations of $${\text{SO}}_{4}^{2 - }$$ SO 4 2 - , $${\text{NO}}_{3}^{ - }$$ NO 3 - , Cl−, or F−. We find an unordered evolution of the hydrochemical composition of groundwater along this corridor. These evolutions are driven by mineral dissolution/precipitation, dedolomitization, and cation exchange processes. Human activities play a significant role, with notable contributions including $${\text{NO}}_{3}^{ - }$$ NO 3 - fluxes from agricultural fertilizers, manure, and sewage, as well as $${\text{SO}}_{4}^{2 - }$$ SO 4 2 - fluxes arising from coal mining activities. The combination between karst spring areas and faults/uplifts, between coal-bearing strata and mining activities fosters the mixing of karst water/mine water with shallow groundwater, promoting dramatic hydrochemical evolution of groundwater. The flat terrain and the natural blockage formed by mountains significantly enhance water–rock interactions and groundwater evaporation by slowing groundwater flow. The contribution of evaporation on groundwater salinity ranges from ~ 0.2% to 4.8%, highlighting its importance in the groundwater hydrochemical evolution. This study unravels the multifaceted nature of groundwater hydrochemical evolution. It emphasizes the four types of environmental controls, including hydrogeochemical processes, climate factors, human activities, and variations in geological settings, can be equally important, which is usually ignored. The findings enrich our understanding of groundwater evolution and highlight the challenges encountered in regions composed of diverse geological units.

ObjectiveTo evaluate the epidemiology characteristics of Herpes simplex virus type 2 (HSV-2), Epstein-Barr virus (EBV) and Cytomegalovirus (CMV) infection in children from January 2018 to December 2023, in Nanjing, China.MethodsWe conducted a retrospective analysis of 21,210, 49,494 and 32,457 outpatients and inpatients aged 1 day to 17 years who were subjected to the three herpesviruses (HSV-2, EBV, and CMV) nucleic acid testing from January 2018 to December 2023, respectively. Demographic information, laboratory findings, etc. were collected and analyzed. HSV-2, EBV and CMV nucleic acid testing were performed by real-time PCR.ResultsThe total rate of detection of the three herpesviruses for all specimens was 0.32% (67/21,210), 14.99% (7419/49,494), and 8.88% (2881/32,457), respectively. A declining trend in the incidence of viral infections over the years was observed for the three herpesviruses (all P

Abstract Only microstructures are used to improve the sensitivity of iontronic pressure sensors. By modulating the compressive modulus, a breakthrough in the sensitivity of the iontronic pressure sensor is achieved. Furthermore, it allows for programmatic tailoring of sensor performance according to the requirements of different applications. Such a new strategy pushes the sensitivity up to a record‐high of 25 548.24 kPa−1 and expands the linear pressure range from 15 to 127 kPa. Additionally, the sensor demonstrates excellent mechanical stability over 10 000 compression‐release cycles. Based on this, a well‐controlled robotic hand that precisely tracks the pressure behavior inside a balloon to autonomously regulate the gripping angle is developed. This paves the way for the application of iontronic pressure sensors in precise sensing scenarios.

Abstract N6‐methyladenosine (m6A) is the most abundant modification of RNA in eukaryotic cells. Previous studies have shown that m6A is pivotal in diverse diseases especially cancer. m6A corelates with the initiation, progression, resistance, invasion, and metastasis of cancer. However, despite these insights, a comprehensive understanding of its specific roles and mechanisms within the complex landscape of cancer is still elusive. This review begins by outlining the key regulatory proteins of m6A modification and their posttranslational modifications (PTMs), as well as the role in chromatin accessibility and transcriptional activity within cancer cells. Additionally, it highlights that m6A modifications impact cancer progression by modulating programmed cell death mechanisms and affecting the tumor microenvironment through various cancer‐associated immune cells. Furthermore, the review discusses how microorganisms can induce enduring epigenetic changes and oncogenic effect in microorganism‐associated cancers by altering m6A modifications. Last, it delves into the role of m6A modification in cancer immunotherapy, encompassing RNA therapy, immune checkpoint blockade, cytokine therapy, adoptive cell transfer therapy, and direct targeting of m6A regulators. Overall, this review clarifies the multifaceted role of m6A modification in cancer and explores targeted therapies aimed at manipulating m6A modification, aiming to advance cancer research and improve patient outcomes.

Abstract Biodetoxification fungus selectively degrades toxic inhibitors generated from pretreatment of lignocellulose without consuming fermentable sugars. However, one barrier for practical application is the sustained cell viability in the consequent fermentation step to compete the fermentable sugars with fermenting strains, resulting in sugar loss and reduced target product yield. This study investigated the competitive growth property between the biodetoxification fungus Paecilomyces variotii FN89 and the L-lactic acid bacterium Pediococcus acidilactici ZY271 under varying temperature and lactic acid osmatic stress. The results show that the L-lactic acid bacterium Ped. acidilactici ZY271 showed less thermotolerance to Pae. variotii FN89 at high temperature of 45 °C to 50 °C in both synthetic medium and wheat straw hydrolysate. In the higher temperature environment, the growth of the biodetoxification strian failed to compete with the lactic acid fermentation strain and was quickly eliminated from the fermentation system. The high temperature fermentation facilitated a fast transition from the detoxification stage to the fermentation stage for higher production of L-lactic acid.

Abstract Background Currently, automatic esophagus segmentation remains a challenging task due to its small size, low contrast, and large shape variation. We aimed to improve the performance of esophagus segmentation in deep learning by applying a strategy that involves locating the object first and then performing the segmentation task. Methods A total of 100 cases with thoracic computed tomography scans from two publicly available datasets were used in this study. A modified CenterNet, an object location network, was employed to locate the center of the esophagus for each slice. Subsequently, the 3D U-net and 2D U-net_coarse models were trained to segment the esophagus based on the predicted object center. A 2D U-net_fine model was trained based on the updated object center according to the 3D U-net model. The dice similarity coefficient and the 95% Hausdorff distance were used as quantitative evaluation indexes for the delineation performance. The characteristics of the automatically delineated esophageal contours by the 2D U-net and 3D U-net models were summarized. Additionally, the impact of the accuracy of object localization on the delineation performance was analyzed. Finally, the delineation performance in different segments of the esophagus was also summarized. Results The mean dice coefficient of the 3D U-net, 2D U-net_coarse, and 2D U-net_fine models were 0.77, 0.81, and 0.82, respectively. The 95% Hausdorff distance for the above models was 6.55, 3.57, and 3.76, respectively. Compared with the 2D U-net, the 3D U-net has a lower incidence of delineating wrong objects and a higher incidence of missing objects. After using the fine object center, the average dice coefficient was improved by 5.5% in the cases with a dice coefficient less than 0.75, while that value was only 0.3% in the cases with a dice coefficient greater than 0.75. The dice coefficients were lower for the esophagus between the orifice of the inferior and the pulmonary bifurcation compared with the other regions. Conclusion The 3D U-net model tended to delineate fewer incorrect objects but also miss more objects. Two-stage strategy with accurate object location could enhance the robustness of the segmentation model and significantly improve the esophageal delineation performance, especially for cases with poor delineation results.

Abstract The precision of workpiece machining is critically influenced by the geometric errors in the kinematics of grind robots, which directly affect their absolute positioning accuracy. To tackle this challenge, this paper introduces a logistic-tent chaotic mapping Levenberg Marquardt algorithm designed to accurately identify and compensate for this geometric error. the approach begins with the construction of a forward kinematic model and an error model specific to the robot. Then the algorithm is adopted to identify and compensate for the geometric error. The method establishes a mapping interval around the initial candidate solutions derived from iterative applications of the Levenberg Marquardt algorithm. Within this interval, the logistic-tent chaotic mapping method generates a diverse set of candidate solutions. These candidates are evaluated based on their fitness values, with the optimal solution selected for subsequent iterations. Empirical compensation experiments have validated the proposed method's precision and effectiveness, demonstrating a 6% increase in compensation accuracy and a 47.68% improvement in efficiency compared to existing state-of-the-art approaches. This process not only minimizes the truncation error inherent in the Levenberg Marquardt algorithm but also significantly enhances solution efficiency. Moreover, simulation experiments on grind processes further validate the method's ability to significantly improve the quality of workpiece machining.

Abstract Biochemical sensors have important applications in biology, chemistry, and medicine. Nevertheless, many biochemical sensors are hampered by intricate techniques, cumbersome procedures, and the need for labeling. In the past two decades, it has been discovered that liquid crystals can be used to achieve the optical amplification of biological interactions. By modifying recognition molecules, a variety of label-free biochemical sensors can be created. Consequently, biochemical sensors based on the amplification of liquid crystals have become one of the most promising sensors. This paper describes in detail the optical sensing principle of liquid crystals, sensing devices, and optical detection technologies. Meanwhile, the latest research findings are elucidated. Finally, the challenges and future research directions are discussed.

Primary atypical teratoid/rhabdoid tumors (AT/RTs) in the spinal canal are rare central nervous system (CNS) neoplasms that are challenging to diagnose and treat. To date, there has been no standard treatment regimen for these challenging malignant tumors. Thus, we conducted this research to explore potential prognostic factors and feasible treatment modalities for improving the prognosis of these tumors. Articles were retrieved from the PubMed, MEDLINE, and Embase databases, using the keywords “atypical teratoid/rhabdoid tumor,” “rhabdoid tumor,” “spine,” “spinal,” “spinal neoplasm”, and “spinal cord neoplasm.” All eligible cases demonstrated SMARCB1-deficient expression validated by pathological examination. We collected and analyzed data related to clinical presentation, radiological features, pathological characteristics, treatment modalities and prognosis via Kaplan-Meier and Cox regression analyses. Thirty-six articles comprising 58 spinal AT/RT patients were included in the study. The median progression-free survival (PFS) and overall survival (OS) were 18 and 22 months, respectively. Kaplan-Meier analysis demonstrated significant survival improvements for OS in the nonmetastasis, male, radiotherapy and intrathecal chemotherapy groups as well as for PFS in the chemotherapy and radiotherapy groups. Multivariate analysis revealed that chemotherapy and radiotherapy were prognostic factors for improved PFS, and that intrathecal chemotherapy reduced the risk of mortality. Spinal AT/RTs are uncommon malignant entities with a dismal survival rate. Although our review is limited by variability between cases, there is some evidence revealing potential risk factors and the importance of systematic chemotherapy, intrathecal chemotherapy and radiotherapy in spinal AT/RT treatment modalities.

Abstract With the rapid growth of Internet of Vehicles (IoV) technology, the performance and privacy of IoV terminals (IoVT) have become increasingly important. This paper proposes a federated learning model for IoVT classification using connection records (FLM-ICR) to address privacy concerns and poor computational performance in analyzing users' private data in IoV. FLM-ICR, in the horizontally federated learning client-server architecture, utilizes an improved multi-layer perceptron and logistic regression network as the model backbone, employs the federated momentum gradient algorithm as the local model training optimizer, and uses the federated Gaussian differential privacy algorithm to protect the security of the computation process. The experiment evaluates the model's classification performance using the confusion matrix, explores the impact of client collaboration on model performance, demonstrates the model's suitability for imbalanced data distribution, and confirms the effectiveness of federated learning for model training. FLM-ICR achieves the accuracy, precision, recall, specificity, and F1 score of 0.795, 0.735, 0.835, 0.75, and 0.782, respectively, outperforming existing research methods and balancing classification performance and privacy security, making it suitable for IoV computation and analysis of private data.

SMALL AUXIN UP RNAs (SAURs), the largest family of early auxin response genes, plays crucial roles in multiple processes, including cell expansion, leaf growth and senescence, auxin transport, tropic growth and so on. Although the rice SAUR gene family was identified in 2006, it is necessary to identify the rice SAUR gene due to the imperfection of its analysis methods. In this study, a total of 60 OsSAURs (including two pseudogenes) distributed on 10 chromosomes were identified in rice (Oryza sativa). Bioinformatics tools were used to systematically analyze the physicochemical properties, subcellular localization, motif compositions, chromosomal location, gene duplication, evolutionary relationships, auxin-responsive cis-elements of the OsSAURs. In addition, the expression profiles obtained from microarray data analysis showed that OsSAUR genes had different expression patterns in different tissues and responded to auxin treatment, indicating functional differences among members of OsSAUR gene family. In a word, this study provides basic information for SAUR gene family of rice and lays a foundation for further study on the role of SAUR in rice growth and development.

BackgroundCardiac stereotactic body radiotherapy (CSBRT) with photons efficaciously and safely treats cardiovascular arrhythmias. Proton therapy, with its unique physical and radiobiological properties, can offer advantages over traditional photon-based therapies in certain clinical scenarios, particularly pediatric tumors and those in anatomically challenging areas. However, dose uncertainties induced by cardiorespiratory motion are unknown.ObjectiveThis study investigated the effect of cardiorespiratory motion on intensity-modulated proton therapy (IMPT) and the effectiveness of motion-encompassing methods.MethodsWe retrospectively included 12 patients with refractory arrhythmia who underwent CSBRT with four-dimensional computed tomography (4DCT) and 4D cardiac CT (4DcCT). Proton plans were simulated using an IBA accelerator based on the 4D average CT. The prescription was 25 Gy in a single fraction, with all plans normalized to ensure that 95% of the target volume received the prescribed dose. 4D dose reconstruction was performed to generate 4D accumulated and dynamic doses. Furthermore, dose uncertainties due to the interplay effect of the substrate target and organs at risk (OARs) were assessed. The differences between internal organs at risk volume (IRV) and OARreal (manually contoured on average CT) were compared. In 4D dynamic dose, meeting prescription requirements entails V25 and D95 reaching 95% and 25 Gy, respectively.ResultsThe 4D dynamic dose significantly differed from the 3D static dose. The mean V25 and D95 were 89.23% and 24.69 Gy, respectively, in 4DCT and 94.35% and 24.99 Gy, respectively, in 4DcCT. Eleven patients in 4DCT and six in 4DcCT failed to meet the prescription requirements. Critical organs showed varying dose increases. All metrics, except for Dmean and D50, significantly changed in 4DCT; in 4DcCT, only D50 remained unchanged with regards to the target dose uncertainties induced by the interplay effect. The interplay effect was only significant for the Dmax values of several OARs. Generally, respiratory motion caused a more pronounced interplay effect than cardiac pulsation. Neither IRV nor OARreal effectively evaluated the dose discrepancies of the OARs.ConclusionsComplex cardiorespiratory motion can introduce dose uncertainties during IMPT. Motion-encompassing techniques may mitigate but cannot entirely compensate for the dose discrepancies. Individualized 4D dose assessments are recommended to verify the effectiveness and safety of CSBRT.

According to the latest consensus, many traditional diseases are considered metabolic diseases, such as cancer, type 2 diabetes, obesity, and cardiovascular disease. Currently, metabolic diseases are increasingly prevalent because of the ever-improving living standards and have become the leading threat to human health. Multiple therapy methods have been applied to treat these diseases, which improves the quality of life of many patients, but the overall effect is still unsatisfactory. Therefore, intensive research on the metabolic process and the pathogenesis of metabolic diseases is imperative. N6-methyladenosine (m6A) is an important modification of eukaryotic RNAs. It is a critical regulator of gene expression that is involved in different cellular functions and physiological processes. Many studies have indicated that m6A modification regulates the development of many metabolic processes and metabolic diseases. In this review, we summarized recent studies on the role of m6A modification in different metabolic processes and metabolic diseases. Additionally, we highlighted the potential m6A-targeted therapy for metabolic diseases, expecting to facilitate m6A-targeted strategies in the treatment of metabolic diseases.

Abstract The development of iontronic skin (I-skin) capable of ultrafast sensing in a wide pressure range, comparable to human skin, is of paramount importance for intelligent robotics. However, this remains a major challenge due to the lack of iontronic array architectures that can achieve ultrafast readout and crosstalk-free under large capacitance response generated within a wide pressure range. Here, we report a frequency-coding architecture of artificial ion mechanoreceptor skin (AIM-skin) that can provide a universal mode of iontronic array sensing and bypass the dependence of complex integrated back-end interface electronics. Notably, the successful implementation of orthogonal frequency coding in the AIM-skin with high sensitivity and ultrawide pressure range achieve ultrafast parallel readout for the spatiotemporal mechanical stimuli. Furthermore, the parallel zero-potential mechanism (PZPM) of the architecture effectively mitigates electrical crosstalk between sensing units. We have demonstrated that combhination of proposed device and deep learning has a broad application prospect in intelligent human-machine interaction and real-time dynamic robotic manipulation.

Approximately one third of the population is prone to motion sickness (MS), which is associated with the dysfunction in the integration of sensory inputs. Transcranial alternating current stimulation (tACS) has been widely used to modulate neurological functions by affecting neural oscillation. However, it has not been applied in the treatment of motion sickness. This study aims to investigate changes in brain oscillations during exposure to MS stimuli and to further explore the potential impact of tACS with the corresponding frequency and site on MS symptoms. A total of 19 subjects were recruited to be exposed to Coriolis stimuli to complete an inducing session. After that, they were randomly assigned to tACS stimulation group or sham stimulation group to complete a stimulation session. Electroencephalography (EEG), electrocardiogram, and galvanic skin response were recorded during the experiment. All the subjects suffering from obvious MS symptoms after inducing session were observed that alpha power of four channels of parieto-occipital lobe significantly decreased (P7: t =3.589, p

Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major challenge. Recently, electroencephalography (EEG) has shown potential in helping to determine the relationship between cortical neural activity and motor recovery. EEG recorded in different states could more accurately predict motor recovery than single-state recordings. Here, we design a multi-state (combining eyes closed, EC, and eyes open, EO) fusion neural network for predicting the motor recovery of patients with stroke after EEG-brain-computer-interface (BCI) rehabilitation training and use an explainable deep learning method to identify the most important features of EEG power spectral density and functional connectivity contributing to prediction. The prediction accuracy of the multi-states fusion network was 82%, significantly improved compared with a single-state model. The neural network explanation result demonstrated the important region and frequency oscillation bands. Specifically, in those two states, power spectral density and functional connectivity were shown as the regions and bands related to motor recovery in frontal, central, and occipital. Moreover, the motor recovery relation in bands, the power spectrum density shows the bands at delta and alpha bands. The functional connectivity shows the delta, theta, and alpha bands in the EC state; delta, theta, and beta mid at the EO state are related to motor recovery. Multi-state fusion neural networks, which combine multiple states of EEG signals into a single network, can increase the accuracy of predicting motor recovery after BCI training, and reveal the underlying mechanisms of motor recovery in brain activity.

This paper investigates the potential of negative stiffness suspensions for enhanced vehicle vibration isolation. By analyzing and improving traditional control algorithms, we propose and experimentally validate novel skyhook, groundhook, and hybrid control strategies for suspensions with negative stiffness characteristics. We establish pavement models, incorporate negative stiffness into suspension modeling, and develop a performance evaluation index. Our research identifies shortcomings of classical semi-active control algorithms and introduces a new band selector to combine improved control methods. Simulation results demonstrate that the proposed semi-active suspension control strategy based on negative stiffness effectively reduces body vibration and enhances vehicle ride performance.

Vehicles conduct mandatory lane changes to merge into the open lanes in temporary highway work zones, and the merging characteristics have been widely studied over the past decades. Unlike research on other traffic facilities, the data and scenarios of highway work zones are often lacking and monotonous. This study presented a vehicle trajectory dataset in highway work zones. The dataset contains ten work zone sites and three different layouts. After transforming raw videos into high-resolution trajectories, we selected eight areas and divided them into three scenarios to investigate the merging characteristics in highway work zones with different traffic states and layouts. In work zones with varying traffic conditions, we found opposite merging locations and lane speed distribution trends. In work zones with varying layouts, we found different merging location distributions, lane speed distributions, and impacts on the following vehicle’s speed. The relationship between the gap and merge speed was consistent with previous studies.

Objectives: Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory disorder characterized by progressive airflow limitation. This meta-analysis aims to evaluate the effectiveness of respiratory muscle training (RMT) on key pulmonary function parameters, inspiratory muscle strength and quality of life in patients with stable COPD. Methods: A comprehensive search was conducted in the databases including PubMed, Cochrane, Web of Science, Embase, and ClinicalTrials.gov, from their inception to June 12, 2023. Randomized controlled trials (RCTs) evaluating the impact of RMT on stable COPD were included for meta-analysis. Results: In total, 12 RCTs involving 453 participants were included in the meta-analysis. RMT demonstrated a significant increase in maximal inspiratory pressure (PImax, MD, 95% CI: 14.34, 8.17 to 20.51, P

Engineered geopolymer composite (EGC) exhibits ultra-high toughness, excellent crack control capability, and superior durability, making it highly promising for applications in bridge connecting slabs, wet joints of prefabricated components, and concrete structure reinforcement. However, the bond performance and failure mechanisms at the interface between EGC and existing concrete remain unclear. To elucidate the bond performance of EGC to existing concrete, direct shear tests were conducted on 15 sets of EGC–existing concrete bond specimens. This study explored the effects of existing concrete strength, interface roughness, and EGC strength on the bond performance and mechanisms. Additionally, a direct shear bond mechanical model was established to predict the interface bond strength. The results indicate that, with comparable compressive strength, the preparation of EGC can reduce the total carbon emissions by up to 127% compared to ECC. The failure mode of EGC-existing concrete bond specimens was mainly adhesive failure (except for specimen C30-III-G95), which can be categorized into serrated interfacial failure and alternating crack paths. The change in interface roughness was the primary factor leading to the transition between failure paths. The changes in interface roughness and EGC strength significantly influenced the bond performance. Under their combined effect, the interface bond strength of specimen C50-III-G95 increased by 345% compared to C50-I-G45. In contrast, the improvement in existing concrete strength had a relatively smaller effect on the increase in interface bond strength. Based on the experimental results and the bonding mechanism under direct shear stress, a direct shear bond mechanical model correlating existing concrete strength, interface roughness, and EGC strength was established. The model predictions showed good consistency with the experimental results. This study provides theoretical support and experimental data for the engineering application of EGC.

Demand prediction plays a critical role in traffic research. The key challenge of traffic demand prediction lies in modeling the complex spatial dependencies and temporal dynamics. However, there is no mature and widely accepted concept to support the solution of the above challenge. Essentially, a prediction model combined with similar objects in temporal and spatial dimensions could obtain better performance. This paper proposes a concept called the Similarity-based Principle (SP), which is applied to improve the prediction performance of deep learning models in complex traffic scenarios. For the temporal components, the long-term temporal dynamics in contemporaneous historical data for ridership are extracted by the Stacked Autoencoder (SAE) method. For the spatial components, the activity-based spatial geographic information (ABG-information) is used to capture the spatial correlation of the traffic network, which is reflected in the daily activities of humans. Specifically, the SP is applied to a Spatio-temporal Graph Convolutional Neural Network (STGCNN) model. In the case study, the Similarity-based Principle Spatio-temporal Graph Convolutional Neural Network (SP-STGCNN) model predicts demand for bicycle sharing in San Francisco. The results show that the SP effectively improves the model's performance. The prediction accuracy is enhanced by up to 10.34% compared with STGCNN. For spatial relationships, the model using the geographic information attribute performs better than that using the road information attribute and the distance attribute. It is proved that the construction of the Spatio-temporal model-based similarity principle can improve the performance.

Background: Pain is a major challenge in performing ultrasound-guided percutaneous microwave ablation (PMWA) of uterine myomas. Inadequate analgesia by local anesthetics hinders the possibility of conducting PMWA of uterine myomas in the Ambulatory Surgery Center (ASC) of the Department of Ultrasound. Objective: The superior hypogastric plexus (SHP) forms a suitable target for pain relief through the blockade, as it contains nociceptive afferent fibers from pelvic organs such as the uterus, rectum, and bladder. Superior hypogastric plexus block (SHPB) has demonstrated promise as an alternative treatment option for alleviating pelvic pain, reducing opioid consumption, and improving quality of life. This study aims to evaluate the efficacy of ultrasound-guided SHPB combined with conscious sedation as an alternative anesthesia option for ambulatory patients receiving ultrasound-guided PMWA of uterine myomas. Methods and analysis: This randomized controlled trial (RCT) will be carried out at the Department of Ultrasound, The First Affiliated Hospital of Xiamen University. Women scheduled for ultrasound-guided PMWA of uterine myomas will be eligible. 86 patients will be recruited and randomly assigned to either the intervention or control groups in a 1:1 ratio. The intervention group will undergo ultrasound-guided superior hypogastric plexus block (SHPB) combined with conscious sedation, while the control group will receive local anesthesia combined with conscious sedation. The primary outcome is the success rate of anesthesia, secondary outcomes include vasoactive drug consumption, acetaminophen consumption, sleep quality, sonographer satisfaction score, patient satisfaction score, the detained time in hospital, and adverse events. Discussions: This RCT represents the inaugural effort to specifically evaluate the safety and efficacy of ultrasound-guided SHPB combined with conscious sedation in patients undergoing ultrasound-guided PMWA of uterine myomas and will provide valuable evidence and insight into the analgesic management of this ambulatory surgery. Ethics and dissemination: This study has been approved by the Ethics Committee of the First Affiliated Hospital of Xiamen University (Scientific Research Ethics Review 2023, No. 139). The results will be submitted for publication in peer-reviewed journals.

Purpose: This study aims to develop a data-collecting package ExpressMLC and investigate the applicability of MapCHECK2 for multileaf collimator (MLC) modeling and commissioning for complex radiation treatment plans. Materials and methods: The MLC model incorporates realistic parameters to account for sophisticated MLC features. A set of 8 single-beam plans, denoted by ExpressMLC, is created for the determination of parameters. For the commissioning of the MLC model, 4 intensity modulated radiation therapy (IMRT) plans specified by the AAPM TG 119 report were transferred to a computed tomography study of MapCHECK2, recalculated, and compared to measurements on a Varian accelerator. Both per-beam and composite-beam dose verification were conducted. Results: Through sufficient characterization of the MLC model, under 3%/2 mm and 2%/2 mm criteria, MapCHECK2 can be used to accurately verify per beam dose with gamma passing rate better than 90.9% and 89.3%, respectively, while the Gafchromic EBT3 films can achieve gamma passing rate better than 89.3% and 85.7%, respectively. Under the same criteria, MapCHECK2 can achieve composite beam dose verification with a gamma passing rate better than 95.9% and 90.3%, while the Gafchromic EBT3 films can achieve a gamma passing rate better than 96.1% and 91.8%; the p -value from the Mann Whitney test between gamma passing rates of the per beam dose verification using full MapCHECK2 package calibrated MLC model and film calibrated MLC model is .44 and .47, respectively; the p -value between those of the true composite beam dose verification is .62 and .36, respectively. Conclusion: It is confirmed that the 2-dimensional (2D) diode array MapCHECK2 can be used for data collection for MLC modeling with the combination of the ExpressMLC package of plans, whose doses are sufficient for the determination of MLC parameters. It could be a fitting alternative to films to boost the efficiency of MLC modeling and commissioning without sacrificing accuracy.

Safety and efficiency of autonomous driving behavior are a tradeoff. Behaviors that are too focused on safety can reduce road operation efficiency, while those that are too efficient can compromise passengers’ safety beyond their tolerance. Therefore, it is important to understand people’s characteristics and maintain a balance between safety and efficiency. Overtaking, which involves passing the preceding vehicle and improving road capacity, requires complex interaction as collisions with opposing vehicles must be avoided on a two-lane, two-way road. Overtaking to increase road capacity can induce unnecessary deceleration in oncoming vehicles, harming oncoming traffic flow. To address these concerns, a diverse dataset of natural overtaking behavior is a priority. We conduct experiments using a network connection between two multiagent driving simulators to collect a human behavior-based overtaking dataset and develop driving behavior models engaged in overtaking situations using the Extra Trees model. The behavior models are embedded in microsimulation to generate human behavior-based datasets under different conditions using a dynamic link library and component object model interfaces. To understand the interaction in an overtaking scenario by the generated datasets, we used a K-means clustering technique to analyze the different reaction behaviors between the oncoming and overtaking vehicles. The threshold for achieving a balanced combination of safety and efficiency is established using XGboost. Finally, safe overtaking behavior is analyzed using a combination of the classified driving styles and thresholds. The results show that the overtaking vehicle can safely start overtaking without endangering oncoming vehicles when both speed and distance conditions are met simultaneously; the speed is lower than 44.29 km/h and it is 407 m away from oncoming vehicles.

Lung cancer is a major public health challenge and, despite therapeutic improvements, is the first leading cause of cancer worldwide. The current cure rate from advanced cancer treatment is excessively low. Therefore, it is of great importance to identify novel, potent and less toxic anticancer agents for the treatment of lung cancer. The aim of our research is to synthesize a new biscoumarin 3,3'-((3,4,5-trifluorop -phenyl)methylene)bis(4-hydroxy-2H-chromen-2-one) (C35) as an anticancer agent. C35 was simply prepared by 4-hydroxycoumarin and 3,4,5-trifluorobenzaldehyde under ethanol and its structure was analyzed by spectroscopic analyses. The anti-proliferation effect of C35 was detected using CCK-8 assay. Migration abilities were measured by Transwell assay. The expression of correlated proteins was determined by Western blot. The results showed that C35 displayed strong cytostatic effects on lung cancer cell proliferation. In addition, C35 possessed a significant inhibition of migration by reducing the expression of matrix metalloproteinases-2 (MMP-2) and MMP-9 in lung cancer cells. Furthermore, C35 treatment suppressed the phosphorylation of p38 in lung cancer cells. Moreover, in vivo experiments were carried out, in which we treated Lewis tumor-bearing C57 mice via intraperitoneal injection of C35. Results showed that C35 inhibited tumor growth in vivo. In conclusion, our study demonstrated the anticancer activity of C35 via suppression of lung cancer cell proliferation and migration, which is possibly involved with the inhibition of the p38 pathway.

For managers of road infrastructure, culvert deterioration is a major concern since culvert failures can cause serious risks to the traveling public. The efficiency of the cost- and labor-intensive culvert inspection and maintenance process can be improved by properly identifying the key impact factors on culvert condition deterioration. Although the use of machine learning (ML) techniques to predict culvert conditions has been proven to be a promising tool for enhancing culvert management and enabling proactive scheduling of maintenance tasks, the information provided by the developed ML models has been given little attention for further use and analysis. By utilizing the predictor importance results of an evaluated decision tree (DT) culvert condition prediction model and the Mann–Whitney U test, this study provided insights to the identification of the key variables influencing culvert deterioration. According to the findings, five impact factors, including culvert span, pH, age, rise, and cover height, often have significant impact on the condition ratings of culverts made of various materials. In addition, such a statistical test-assisted factor identification process offered a way of identifying and enhancing the input variable selection for predictive ML model development.

M6A is essential for tumor occurrence and progression. The expression patterns of m6A regulators differ in various kinds of tumors. Transcriptomic expression statistics together with clinical data from a database were analyzed to distinguish patients with digestive tract tumors. Based on the expression patterns of diverse m6A regulators, patients were divided into several clusters. Survival analysis suggested significant differences in patient prognosis among the m6A clusters. The results showed overlapping of m6A expression patterns with energy metabolism and nucleotide metabolism. Functional analyses imply that m6A modifications in tumor cells probably drive metabolic reprogramming to sustain rapid proliferation of cancer cells. Our analysis highlights the m6A risk characterizes various kinds of metabolic features and predicts chemotherapy sensitivity in digestive tract tumors, providing evidence for m6A regulators as markers to predict patient outcomes.

BackgroundExisting evidence on the associations of liver steatosis and fibrosis with bone mineral density (BMD) and risk of osteopenia/osteoporosis was limited with conflicting results. We aimed to evaluate the associations of metabolic dysfunction-associated fatty liver disease (MAFLD) and hepatic fibrosis with BMD and risk of osteopenia/osteoporosis in type 2 diabetes mellitus (T2DM) patients.MethodsBaseline information of an ongoing cohort of 249 T2DM patients in Xiamen, China was analyzed. MAFLD was defined as the presence of hepatic steatosis [diagnosed by either hepatic ultrasonography scanning or fatty liver index (FLI) score >60] for T2DM patients. BMD was measured using dual-energy x-ray absorptiometry at total lumbar (L2–4), femur neck (FN), and total hip (TH) and was categorized as normal (T ≥ −1.0), osteopenia (−2.5 < T < −1.0), or osteoporosis (T ≤ −2.5) according to its minimum T-score.ResultsAmong the 249 T2DM patients, prevalence rates of MAFLD, osteopenia, and osteoporosis were 57.8%, 50.6%, and 17.7%, respectively. Patients with MAFLD had significantly higher BMD T-scores of L2–4, FN, and TH and the minimum as well as lower prevalence of osteoporosis than patients without MAFLD. Hepatic steatosis indices, including FLI score, fatty liver (FLI ≥ 60 or hepatic ultrasonography scanning), and MAFLD, were significantly and positively associated with all T-scores, while hepatic fibrosis index and FIB-4 score, but not NAFLD fibrosis score (NFS), were negatively associated with all T-scores. MAFLD was significantly associated with the decreased risk of osteopenia/osteoporosis and osteoporosis with unadjusted odds ratios (ORs) (95% CI) of 0.565 (0.324–0.987) and 0.434 (0.224–0.843) (both p-values < 0.05), respectively. As for liver fibrosis, FIB-4 score, but not NFS, was significantly associated with elevated risk of osteoporosis with an unadjusted OR (95% CI) per SD increase of FIB-4 score of 1.446 (1.080–1.936, p-value = 0.013). Adjusting for potential confounding variables, especially body mass index, in the multivariable regression analyses, all associations of hepatic steatosis and fibrosis indices with BMD and risk of osteopenia/osteoporosis were not statistically significant.ConclusionMAFLD and hepatic fibrosis were not significantly associated with BMD and risk of osteopenia/osteoporosis independent of obesity. Nevertheless, screening and management of MAFLD and osteopenia/osteoporosis were still important for the prevention of fracture in T2DM patients.

AbstractMitochondrial DNA (mtDNA) copy number (CN) is a biomarker of mitochondrial function and has been reported associated with kidney disease. However, its association with IgA nephropathy (IgAN), the most common cause of glomerulonephritis (GN), has not been evaluated. We included 664 patients with biopsy-proven IgAN and measured mtDNA-CN in peripheral blood by multiplexed real-time quantitative polymerase chain reaction (RT-qPCR). We examined the associations between mtDNA-CN and clinical variables and found that patients with higher mtDNA-CN had higher estimated glomerular filtration rate (eGFR) (r = 0.1009, p = .0092) and lower serum creatinine (SCr), blood urea nitrogen (BUN), and uric acid (UA) (r=−0.1101, −0.1023, −0.07806, respectively, all p values

Abstract We study the rotation effects of the hot and dense QCD matter in a non-perturbative regime by the gauge/gravity duality. We use the gravitational model that is designated to match the state-of-the-art lattice data on the thermal properties of (2+1)-flavor QCD and predict the location of the critical endpoint and the first-order phase transition line at large baryon chemical potential without rotation. After introducing the angular velocity via a local Lorentz boost, we investigate the thermodynamic quantities for the system under rotation in a self-consistent way. We find that the critical temperature and baryon chemical potential associated with the QCD phase transition decrease as the angular velocity increases. Moreover, some interesting phenomena are observed near the critical endpoint. We then construct the 3-dimensional phase diagram of the QCD matter in terms of temperature, baryon chemical potential, and angular velocity. As a parallel investigation, we also consider the gravitational model of SU(3) pure gluon system, for which the 2-dimensional phase diagram associated with temperature and angular velocity has been predicted. The corresponding thermodynamic quantities with rotation are investigated.

Rice, a staple food for billions around the globe, is cultivated in numerous forms. Among them, soft rice is well known, which is characterized by its tender, creamy consistency and desirable texture. In this study, we examined the physicochemical properties and fine structure of starch from two soft rice varieties, Jinong Xiangruan 1 and DGR1, cultivated in different regions in China (Baodi District, Tianjin City; Liaoning Province; and Fengyang City, Anhui Province). The aim was to understand how amylopectin content (AC) influences rice quality. This research aims to bridge the knowledge gap regarding the role of amylopectin in determining rice’s adhesive consistency and viscosity. Significant regional differences were observed in yield components such as the number of grains per panicle, seed setting rates and 1000-grain weight, with Liaoning generally showing higher performance metrics compared to other regions. Physicochemical analysis highlighted that though glue consistency and taste values showed little regional variation, AC significantly influenced rice hardness and viscosity. Rapid Visco Analyzer (RVA) profile analysis further demonstrated distinct differences in viscosity characteristics, underscoring the regional impacts on starch behavior. Additionally, molecular weight distribution and amylopectin chain length analysis, conducted via SEC-MALLS-RI and ICS ion chromatography, revealed notable differences in starch composition across varieties and locations. The findings suggest that environmental conditions play a crucial role in defining starch characteristics and, consequently, the eating quality of rice. This provides valuable insights for breeding high-quality japonica rice with broad adaptability.

Abiotic stress such as high temperature at flowering is one of many conditions reducing yield of corn (Zea mays L.). Mixing corn cultivars with diverse functional traits increases within-crop diversity and provides a potential means of mitigating yield losses under stress conditions. We conducted a three-year field study to investigate the effects of cultivar mixtures on kernel setting rate, pollen sources, and yield. This study consisted of six treatments, including two high temperature-tolerant (HTT) monocrops of WK702 and DH701, two high temperature-sensitive (HTS) monocrops of DH605 and DH662, and two HTT–HTS mixtures of WK702-DH605 and DH701-DH662. The anthesis–silking interval (ASI) was 0.9–1.6 days shorter in mixtures than in monocrops. Kernel setting rate was increased in mixtures (86.4%–88.7%) compared with those in monocrops (74.7%–84.1%) as a result of synchrony and complementarity of pollination. Grain yields of the HTT–HTS mixtures increased by 13.3%–18.7%, equivalent to 1169 to 1605 kg ha−1, in comparison with HTS corn monocrops. The results of SSR markers showed that cross-fertilization percentage in corn cultivar mixtures ranged from 29.3% to 47.8%, partially explaining yield improvement. Land equivalent ratio (LER) was 1.12 for corn mixtures and the partial land equivalent ratio (e.g., > 0.5) showed the complementary benefits in corn mixtures. The results indicated that mixing corn cultivars with diverse flowering and drought-tolerance traits increased yields via pollination synchrony.

Target tracking faces various challenges in natural scenes, such as changing lighting, scale, occlusion, etc. In response to these issues, research is based on the use of Siamese neural networks for target tracking. And this paper uses channel attention mechanisms and high-quality bounding box models to improve the target tracking algorithm. Firstly, by combining attention mechanism with twin neural networks, useful channel information is enhanced and useless channel information is suppressed. Then, based on this, the classification branch and quality prediction branch are jointly represented, and the bounding box is represented through a general distribution form to further optimize the algorithm. The algorithm optimized based on attention mechanism has a success rate of 71.2% and an accuracy of 91.3%, which has better performance compared to other comparative algorithms. The algorithm optimized based on high-quality bounding box models has a success rate of 73.6% and an accuracy of 92.5%, which further improves its performance compared to attention mechanism optimization. The improvement strategy proposed in the study has improved the robustness and accuracy of target tracking, and the improved algorithm has better robustness and performance advantages in complex tracking scenarios. Meanwhile, the practical applicability of this algorithm has been effectively verified through the simulation experiments of robot control systems.

Abstract Bounding is one of the important gaits in quadrupedal locomotion for negotiating obstacles. The authors proposed an effective approach that can learn robust bounding gaits more efficiently despite its large variation in dynamic body movements. The authors first pretrained the neural network (NN) based on data from a robot operated by conventional model‐based controllers, and then further optimised the pretrained NN via deep reinforcement learning (DRL). In particular, the authors designed a reward function considering contact points and phases to enforce the gait symmetry and periodicity, which improved the bounding performance. The NN‐based feedback controller was learned in the simulation and directly deployed on the real quadruped robot Jueying Mini successfully. A variety of environments are presented both indoors and outdoors with the authors’ approach. The authors’ approach shows efficient computing and good locomotion results by the Jueying Mini quadrupedal robot bounding over uneven terrain. The cover image is based on the Research Article Efficient learning of robust quadruped bounding using pretrained neural networks by Zhicheng Wang et al., https://doi.org/10.1049/csy2.12062.

Peronophythora litchii (P. litchii) is the pathogenic factor of litchi downy blight. Derivatives extracted from harmine officinalis and modified, N-(2-pyridyl)-1-phenyl-9 H-pyrido [3, 4-b] indole-3-formamide (PPPIF), could inhibited P. litchii growth and development. Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) analyses indicated that PPPIF severely damaged the mitochondria, cell wall and endomembrane system of P. litchii cells, thus abnormal morphology, as well as deformed mycelia. PPPIF could suppress mycelial growth, sporulation, sporangia germination and germ tube elongation as well. PPPIF also caused a series of physiological and biochemical changes of P. litchii, including the serious deviation of extracellular pH, the obvious increase of the content of extracellular reducing sugar and malondialdehyde, the notable decrease of soluble protein and the activity of NADH oxidase. Furthermore, PPPIF seriously raised openness of membrane permeability transporter (MPTP) and markedly decreased the transmembrane potential (Δψm) and the activity of enzyme complex I–complex V in respiratory chain of mitochondria. The current research suggests that PPPIF may act upon the mitochondrion of P. litchii, then respiratory chain complex activity was blocked and energy metabolism disrupted or inhibited, resulting in the growth inhibition of P. litchii. Above results have strengthened our understanding of P. litchii resistance mechanisms and may help in the development of more potent inhibitors against plant diseases in the fields.

Abstract When people are keen to exploring the thermoelectric properties of polymer composite inorganic electrolyte aqueous flexible electronic devices, the complicated chemical configuration of transition metal chloride anion has not been paid attention and reported. Here, we demonstrate a hydrated polyvinyl alcohol (PVA)-cellulosic membrane that relies on the thermal gradient diffusion of transition metal and chloride complexe [CuCl4]2− enhanced by copper-coordinated carboxylated cellulose. The PVA-cellulosic membrane exhibits a thermopower of −26.25 mV·K−1, rendering it among the best n-type ionic thermoelectric materials under the same conditions. We attribute the enhanced thermally generated voltage to the highly-coordinated configuration of Cu2+ - Cl−, which converts the independent thermal motion of Cu2+ and Cl− to the higher ion transport heat of [CuCl4]2− anion via carboxylated cellulose, while this phenomenon do not occur in the other transition metal chlorides. This specific heat transport behavior of [CuCl4]2− has important implications in designing high-quality ionic thermoelectric materials.

Atypical teratoid/rhabdoid tumours (AT/RTs) are rare central nervous system neoplasms that frequently occur in infants and children and have a very poor prognosis. In recent years, molecular analysis of AT/RTs has shown that biallelic inactivation of SMARCB1 (INI1, SNF5, BAF47) or SMARCA4 (BRG1) frequently occurs. Here, we present a case of basal ganglia AT/RT with SMARCB1 gene deficiency and CDK6 gene amplification in a 5-year-old child. A 5-year-old boy was hospitalized due to a 1-week history of frontal and parietal headache. Magnetic resonance imaging (MRI) demonstrated a 3 cm × 2 cm × 1.5 cm heterogeneous enhanced mass located at the right basal ganglia that partially protruded into the right lateral ventricle. The lesion was successfully resected under electrophysiological monitoring and neuronavigation. The postoperative pathological examination implied an AT/RT diagnosis, with loss of SMARCB1 protein, SMARCB1 gene deficiency and CDK6 gene amplification. Unfortunately, the patient died due to respiratory and circulatory failure at 5 weeks after the operation. To date, standard regimens have not yet been established due to the lack of large-scale prospective studies for AT/RT. The p16-RB signalling pathway should be considered as a potential target for AT/RT treatment modalities. Apart from traditional regimens, targeted therapies, especially CDK4/6 inhibitors, are likely a promising therapeutic option for AT/RT treatment.

Abstract Whereas the presence of flowers on ornamental flowering plants is essential for their identification via traditional methods, ornamental flowering plants cannot be reliably identified in non‐flowering stages likewise. Here, DBALM (DNA Barcodes–Leaf Morphology), a new approach that combines DNA barcoding data with micromorphological features of the leaf epidermis and that is not limited by the flowering stage, was used to identify 16 evergreen rhododendron cultivars. First, the sequences of DNA barcodes, ITS, matK, psbA‐trnH, and rbcL, were obtained from the DNA of leaves. Phylogenetic analysis was conducted to clarify the groupings among all the samples based on the four markers. Then, microscopic features of the leaf epidermis were used to further distinguish individuals from the same clade. DNA barcoding permitted the 16 cultivars to be divided into eight groups. The microscopic features of the leaf epidermis permitted cultivars within the same clade to be distinguished. The matK + psbA‐trnH combination was the most effective barcode combination in this study. In addition, the new primer matK‐Rh_R was designed, and it increased the amplification rate of evergreen rhododendron cultivars to 100%. In sum, DBALM was capable of accurately identifying the 16 evergreen rhododendron cultivars using data collected from a single leaf in the vegetative growth stage. This method can greatly facilitate the identification and breeding of ornamental flowering plants.

Abstract The rapid advancement of nanomedicine and nanoparticle (NP) materials presents novel solutions potentially capable of revolutionizing health care by improving efficacy, bioavailability, drug targeting, and safety. NPs are intriguing when considering medical applications because of their essential and unique qualities, including a significantly higher surface to mass ratio, quantum properties, and the potential to adsorb and transport drugs and other compounds. However, NPs must overcome or navigate several biological barriers of the human body to successfully deliver drugs at precise locations. Engineering the drug carrier biointerface can help overcome the main biological barriers and optimize the drug delivery in a more personalized manner. This review discusses the significant heterogeneous biological delivery barriers and how biointerface engineering can promote drug carriers to prevail over hurdles and navigate in a more personalized manner, thus ushering in the era of Precision Medicine. We also summarize the nanomedicines' current advantages and disadvantages in drug administration, from natural/synthetic sources to clinical applications. Additionally, we explore the innovative NP designs used in both non-personalized and customized applications as well as how they can attain a precise therapeutic strategy.

Critics decry cryptocurrency mining as a huge waste of energy, while proponents insist on claiming that it is a green industry. Is Bitcoin mining really worth the energy it consumes? The high power consumption of cryptocurrency mining has become the latest global flashpoint. In this paper, we define the Mining Domestic Production (MDP) as a method to account for the final outcome of the Bitcoin mining industry’s production activities in a certain period time, calculate the carbon emission per unit output value of the Bitcoin mining industry in China, and compare it with three other traditional industries. The results show that Bitcoin mining does not always have the highest when compared with others. The contribution of this paper is that we give a new perspective on thinking whether Bitcoin mining is more efficient to make more profit, in terms of the same amount of carbon emissions per unit compared to other industries. Moreover, it could even be argued that Bitcoin may present an opportunity for some developing countries to build out their electrical capacity and generate revenue.

This work has developed a hybrid framework that combines machine learning and control approaches for legged robots to achieve new capabilities of balancing against external perturbations. The framework embeds a kernel which is a model-based, full parametric closed-loop and analytical controller as the gait pattern generator. On top of that, a neural network with symmetric partial data augmentation learns to automatically adjust the parameters for the gait kernel, and also generate compensatory actions for all joints, thus significantly augmenting the stability under unexpected perturbations. Seven Neural Network policies with different configurations were optimized to validate the effectiveness and the combined use of the modulation of the kernel parameters and the compensation for the arms and legs using residual actions. The results validated that modulating kernel parameters alongside the residual actions have improved the stability significantly. Furthermore, The performance of the proposed framework was evaluated across a set of challenging simulated scenarios, and demonstrated considerable improvements compared to the baseline in recovering from large external forces (up to 118%). Besides, regarding measurement noise and model inaccuracies, the robustness of the proposed framework has been assessed through simulations, which demonstrated the robustness in the presence of these uncertainties. Furthermore, the trained policies were validated across a set of unseen scenarios and showed the generalization to dynamic walking.