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Immune checkpoint blockade (ICB) has emerged as a promising therapeutic option for hepatocellular carcinoma (HCC), but resistance to ICB occurs and patient responses vary. Here, we uncover protein arginine methyltransferase 3 (PRMT3) as a driver for immunotherapy resistance in HCC. We show that PRMT3 expression is induced by ICB-activated T cells via an interferon-gamma (IFNγ)-STAT1 signaling pathway, and higher PRMT3 expression levels correlate with reduced numbers of tumor-infiltrating CD8+ T cells and poorer response to ICB. Genetic depletion or pharmacological inhibition of PRMT3 elicits an influx of T cells into tumors and reduces tumor size in HCC mouse models. Mechanistically, PRMT3 methylates HSP60 at R446 to induce HSP60 oligomerization and maintain mitochondrial homeostasis. Targeting PRMT3-dependent HSP60 methylation disrupts mitochondrial integrity and increases mitochondrial DNA (mtDNA) leakage, which results in cGAS/STING-mediated anti-tumor immunity. Lastly, blocking PRMT3 functions synergize with PD-1 blockade in HCC mouse models. Our study thus identifies PRMT3 as a potential biomarker and therapeutic target to overcome immunotherapy resistance in HCC. Resistance to immune checkpoint blockade (ICB) is a challenge in hepatocellular carcinoma therapy. Here, the authors show that targeting PRMT3 markedly improves the anticancer efficacy of ICB via interrupting HSP60 methylation and oligomerization, damaging mitochondrial integrity, and thereby activating cGAS/STING-mediated anti-tumor immunity. [ABSTRACT FROM AUTHOR]

Seamless study designs have the potential to accelerate clinical development. The use of innovative seamless designs has been increasing in the oncology area; however, while the concept of seamless designs becomes more popular and accepted, many challenges remain in both the design and conduct of these trials. This may be especially true when seamless designs are used in late phase development supporting regulatory decision-making. The Innovative Design Scientific Working Group (IDSWG) Oncology team conducted a survey to understand the current use of seamless study designs for registration purposes in oncology clinical development. The survey was designed to provide insights into the benefits and to identify the roadblocks. A total of 16 questions were included in the survey that was distributed using the ASA Biopharmaceutical Section and IDSWG email listings from August to September 2022. A total of 51 responses were received, with 39 (76%) respondents indicating that their organizations had seamless oncology studies in planning or implementation for registration purposes. Detailed survey results are presented in the manuscript. Overall, while seamless designs offer advantages in terms of timeline reduction and cost saving, they also present challenges related to additional complexity and the need for efficient surrogate clinical endpoints in oncology drug development. [ABSTRACT FROM AUTHOR]

Atherosclerosis cardiovascular disease (ASCVD) has become one of the leading death causes in humans. Low-density lipoprotein (LDL) is an important biomarker for assessing ASCVD risk level. Thus, monitoring LDL levels can be an important means for early diagnosis of ASCVD. Herein, a novel electrochemical aptasensor for determination LDL was designed based on nitrogen-doped reduced graphene oxide-hemin-manganese oxide nanoparticles (NrGO-H-Mn3O4 NPs) integrated with clustered regularly interspaced short palindromic repeats and associated proteins (CRISPR/Cas12a) system. NrGO-H-Mn3O4 NPs not only have a large surface area and remarkable enhanced electrical conductivity but also the interconversion of different valence states of iron in hemin can provide an electrical signal. Nonspecific single-stranded DNA (ssDNA) was bound to NrGO-H-Mn3O4 NPs to form a signaling probe and was immobilized on the electrode surface. The CRISPR/Cas12a system has excellent trans-cleavage activity, which can be used to cleave ssDNA, thus detaching the NrGO-H-Mn3O4 NPs from the sensing interface and attenuating the electrical signal. Significant signal change triggered by the target was ultimately obtained, thus achieving sensitive detection of the LDL in range from 0.005 to 1000.0 nM with the detection limit of 0.005 nM. The proposed sensor exhibited good stability, selectivity, and stability and achieved reliable detection of LDL in serum samples, demonstrating its promising application prospects for the diagnostic application of LDL. [ABSTRACT FROM AUTHOR]

The reasonable construction of rare animal habitat ecological networks is an important way to maintain the stable development of wild species, and it is of great significance in biodiversity conservation. Based on the habitat suitability perspective, the maximum entropy (MaxEnt) model was used to identify the ecological sources of the Giant Panda National Park; the minimum cumulative resistance (MCR) model and hydrological analysis principles were used to extract the ecological corridors and ecological nodes; the ant colony algorithm and kernel density analysis were introduced to identify the range of corridors and key restoration points; and the ecological network of giant panda habitat was constructed. We evaluated and analyzed the constructed ecological network and pointed out the key areas for ecological protection and restoration. The results show that the habitat suitability of the region as a whole is low, and the spatial differences are obvious. There is a pattern of high values in the east and north and low values in the west and south. 24 ecological sources were extracted, with a total area of 1737.63 km2, mainly distributed in the central and northern regions. 55 ecological corridors were extracted, with a total length of 1667.816 km and a total area of 1312.08 km2, which were widely distributed in the central part of the study area in a network pattern. 23 key ecological corridors and 14 key restoration points were identified, which are areas that should be considered in the construction of national parks in the future. Based on the ecological network characteristics of Giant Panda National Park, the future direction of future ecological restoration in the region was proposed. The research results are expected to provide a reference and basis for the construction of Giant Panda National Park and biodiversity protection. [ABSTRACT FROM AUTHOR]

Objectives: To compare the diagnostic performance of machine learning (ML)–based computed tomography–derived fractional flow reserve (CT-FFR) and cardiac magnetic resonance (MR) perfusion mapping for functional assessment of coronary stenosis. Methods: Between October 2020 and March 2022, consecutive participants with stable coronary artery disease (CAD) were prospectively enrolled and underwent coronary CTA, cardiac MR, and invasive fractional flow reserve (FFR) within 2 weeks. Cardiac MR perfusion analysis was quantified by stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). Hemodynamically significant stenosis was defined as FFR ≤ 0.8 or > 90% stenosis on invasive coronary angiography (ICA). The diagnostic performance of CT-FFR, MBF, and MPR was compared, using invasive FFR as a reference. Results: The study protocol was completed in 110 participants (mean age, 62 years ± 8; 73 men), and hemodynamically significant stenosis was detected in 36 (33%). Among the quantitative perfusion indices, MPR had the largest area under receiver operating characteristic curve (AUC) (0.90) for identifying hemodynamically significant stenosis, which is in comparison with ML-based CT-FFR on the vessel level (AUC 0.89, p = 0.71), with comparable sensitivity (89% vs 79%, p = 0.20), specificity (87% vs 84%, p = 0.48), and accuracy (88% vs 83%, p = 0.24). However, MPR outperformed ML-based CT-FFR on the patient level (AUC 0.96 vs 0.86, p = 0.03), with improved specificity (95% vs 82%, p = 0.01) and accuracy (95% vs 81%, p < 0.01). Conclusion: ML-based CT-FFR and quantitative cardiac MR showed comparable diagnostic performance in detecting vessel-specific hemodynamically significant stenosis, whereas quantitative perfusion mapping had a favorable performance in per-patient analysis. Clinical relevance statement: ML-based CT-FFR and MPR derived from cardiac MR performed well in diagnosing vessel-specific hemodynamically significant stenosis, both of which showed no statistical discrepancy with each other. Key Points: • Both machine learning (ML)–based computed tomography–derived fractional flow reserve (CT-FFR) and quantitative perfusion cardiac MR performed well in the detection of hemodynamically significant stenosis. • Compared with stress myocardial blood flow (MBF) from quantitative perfusion cardiac MR, myocardial perfusion reserve (MPR) provided higher diagnostic performance for detecting hemodynamically significant coronary artery stenosis. • ML-based CT-FFR and MPR from quantitative cardiac MR perfusion yielded similar diagnostic performance in assessing vessel-specific hemodynamically significant stenosis, whereas MPR had a favorable performance in per-patient analysis. [ABSTRACT FROM AUTHOR]

Clear cell renal cell carcinoma (ccRCC) is characterized by a high incidence and mortality rate. Despite advancements in therapeutic interventions, the prognosis for renal cancer patients remains suboptimal. Of late, methylation modifications have emerged as promising molecular targets for tumor assessment and treatment, yet their potential has not been fully investigated in the context of ccRCC. Transcriptomic and clinical data were extracted from The Cancer Genome Atlas, Gene Expression Omnibus, and ArrayExpress databases, leading to the identification of 57 methylation-related genes (MRGs). Utilizing DESeq2 analysis, Cox regression analysis, and the LASSO regression algorithm, a Methylation-Related Risk Score (MARS) was constructed. Cluster analysis, Gene Ontology (GO) analysis, clinical feature analysis, immune infiltration analysis, and mutation analysis were further employed to evaluate the model. Our investigation identified six pivotal prognostic MRGs and established a risk score predicated on m6A/m5C/m1A/m7G regulatory factors. This score was validated across two external cohorts and can be utilized to assess individual immune infiltration statuses and predict responses to immunotherapy. Moreover, cluster analysis delineated two distinct m6A/m5C/m1A/m7G gene clusters. We have developed and validated a robust prognostic signature based on genes associated with m6A, m5C, m1A, and m7G modifications. This gene signature demonstrates significant prognostic value in assessing survival outcomes, clinical characteristics, immune infiltration, and responses to immunotherapy in ccRCC patients. This finding provides valuable insights for refining precision treatment strategies. [ABSTRACT FROM AUTHOR]

In the context of the "dual carbon" goal, in order to solve the problem of high value utilization of modified magnesium and coal-based solid waste resources and efficient storage of carbon dioxide, CO2 gas was introduced into the modified magnesium-coal based solid waste backfill material under normal temperature and pressure conditions to prepare a new type of solid waste carbon fixation backfill material (CO2-MCSB). The preparation and flow performance study of CO2-MCSB provides a new technological approach combining solid waste resource utilization and CO2 storage, which can provide new theoretical references for solid waste backfill in mines. The effects of stirring rate, stirring time, and aeration rate on its fluidity and rheological properties were investigated through micro slump, rheological testing, and microstructure testing. The research results indicate that: (1) the slump range of freshly mixed CO2-MCSB slurry is 125 ~ 146 mm, and the expansion range is 9.5 ~ 39.5 cm, fully meeting the requirements of mine backfill fluidity. (2) The newly mixed CO2-MCSB slurry exhibits swelling fluid, and after ventilation, there is a significant thickening phenomenon, resulting in a significant decrease in flowability. (3) Microscopic analysis shows that the calcium carbonate generated by the carbonation reaction of the slurry after aeration fills the gaps, thereby increasing the probability of collision between internal particles and increasing the critical resistance for flow to begin. (4) There is a significant linear relationship between the flowability and rheological parameters of freshly mixed CO2-MCSB slurry, showing a negative correlation. (5) In the backfill application process, the stirring rate of 200 r/min, the aeration stirring time of 5 min, and the aeration rate of 5 L/min can be controlled to achieve good flow performance of CO2-MCSB. [ABSTRACT FROM AUTHOR]

The teaching objectives of traditional approaches in higher education emphasize mostly students' mastery of knowledge and have insufficient directionality to social needs. In this study, we developed a BOPPPS (Bridge-in, Objectives, Pre-assessment, Participatory Learning, Post-assessment and Summary) teaching model combined with the OBE (Outcome Based Education) concept to enhance the teaching outcomes. Firstly, based on the graduation requirements and professional training objectives of students, we divided the course objectives into three dimensions (knowledge, ability and quality), and further specified into index points. Then, the teaching content of each chapter was set to correspond with the index points. Finally, the BOPPPS teaching model was used to meet each requirement. Clinical biochemistry testing course was used as a model to assess the effects of the teaching reform. After the class, the teaching effect was analyzed based on the questionnaire surveys from the students and their scores of both the chapter and final examinations. The results showed that compared with the traditional approach, the BOPPPS teaching model combined with the OBE concept has demonstrated a notable enhancement in student engagement, and significant improvement of their mastery of knowledge, application skills, and problem-solving abilities. The examination scores of the BOPPPS group were markedly higher than those of the traditional group. Moreover, the difference between the two groups diverse assessment scores was much bigger than that between the two group examination scores. Our study indicates that the BOPPPS teaching model combined with the OBE concept is a highly effective teaching model for enhancing the learning effectiveness of students. [ABSTRACT FROM AUTHOR]

Health condition assessment is the basis for formulating and optimizing maintenance strategies of complex systems, which is crucial for ensuring the safe and stable operation of these systems. In complex system health condition assessment, it is not only necessary for the model to handle various uncertainties to ensure the accuracy of assessment results, but also to have a transparent and reasonable assessment process and interpretable, traceable assessment results. belief rule base (BRB) has been widely used as an interpretable modeling method in health condition assessment. However, BRB-based models currently face two issues: (1) inaccuracies in expert-provided parameters that can affect the model's accuracy, and (2) after model optimization, interpretability may be reduced. Therefore, this paper proposes a new method for complex system health condition assessment called interpretable BRB with reference value optimization (I-BRB). Firstly, to address the issue of inaccurate reference values, a reference value optimization algorithm with interpretability constraints is designed, which optimizes the reference values without compromising expert knowledge. Secondly, the remaining parameters are optimized using the projection covariance matrix adaptation evolution strategy (P-CMA-ES) with interpretability constraints to improve the model's accuracy. Finally, a case study evaluating the bearing components of a flywheel system is conducted to validate the proposed method. Experimental results demonstrate that I-BRB achieves higher accuracy in health condition assessment. [ABSTRACT FROM AUTHOR]

At present, in addressing security challenges within 5 G-era Vehicular Ad-Hoc Networks (VANET), a new protocol has been designed in this paper, named V2X-GKA (Dynamic Group Authentication and Key Agreement Protocol based on C-V2X). V2X-GKA utilizes cryptographic techniques such as ECDLP and DBDH to effectively mitigate risks associated with certificate forgery and key theft. Through the integration of authentication and group key agreement mechanisms, the protocol facilitates dynamic member management and secure key updates without necessitating complete protocol re-execution. This approach not only ensures both forward and backward security but also optimizes algorithmic processes, rendering it highly suitable for real-time, high-speed mobile environments. Through necessary security analysis, it is confirmed that the V2X-GKA scheme in this paper meets the existing security objectives. Compared with other similar schemes, it exhibits a certain degree of completeness and superiority, particularly in open-channel scenarios. [ABSTRACT FROM AUTHOR]

Traditional medical imaging and biomechanical studies have challenges in analyzing the long-term evolution process of abdominal aortic aneurysm (AAA). The homogenized constrained mixture theory (HCMT) allows for quantitative analysis of the changes in the multidimensional morphology and composition of AAA. However, the accuracy of HCMT still requires further clinical verification. This study aims to establish a patient-specific AAA growth model based on HCMT, simulate the long-term growth and remodeling (G&R) process of AAA, and validate the feasibility and accuracy of the method using two additional AAA cases with five follow-up datasets. The media and adventitia layers of AAA were modeled as mixtures composed of elastin, collagen fibers, and smooth muscle cells (SMCs). The strain energy function was used to describe the continuous deposition and degradation effect of the mixture during the AAA evolution. Multiple sets of growth parameters were applied to finite element simulations, and the simulation results were compared with the follow-up data for gradually selecting the optimal growth parameters. Two additional AAA patients with different growth rates were used for validating this method, the optimal growth parameters were obtained using the first two follow-up imaging data, and the growth model was applied to simulate the subsequent four time points. The differences between the simulated diameters and the follow-up diameters of AAA were compared to validate the accuracy of the mechanistic model. The growth parameters, especially the stress-mediated substance deposition gain factor, are highly related to the AAA G&R process. When setting the optimal growth parameters to simulate AAA growth, the proportion of simulation results within the distance of less than 0.5 mm from the baseline models is above 80%. For the validating cases, the mean difference rates between the simulated diameter and the real-world diameter are within 2.5%, which basically meets the clinical demand for quantitatively predicting the AAA growth in maximum diameters. This study simulated the growth process of AAA, and validated the accuracy of this mechanistic model. This method was proved to be used to predict the G&R process of AAA caused by dynamic changes in the mixtures of the AAA vessel wall during long-term, assisting accurately and quantitatively predicting the multidimensional morphological development and mixtures evolution process of AAA in the clinic. [ABSTRACT FROM AUTHOR]

Intrusion detection in industrial control systems (ICS) is crucial for maintaining the security of physical information systems. However, the existing models predominantly rely on black-box approaches, which exhibit limitations in result credibility and the ability to adapt to complex and dynamic environments. Consequently, this paper proposes an online updatable extended belief rule base model (O-EBRB) for intrusion detection in ICS. Firstly, an industrial intrusion detection model rooted in the extended belief rule base (EBRB) is established. This model excels in concurrently processing both quantitative and qualitative data, ensuring the reliability of its outcomes. Subsequently, a novel domain-based rule update methodology for integrating new observation data is proposed. By incorporating or merging fresh data into the original model, it enhances the model's adaptability in dynamic settings. Finally, employing the domain-based rule weight calculation approach, the model continues to effectively compute model parameters even with the continuous expansion of rules. Through extensive experimentation on two real-world industrial intrusion detection datasets, the results demonstrate the effectiveness of the proposed model in handling information and its robust performance in dynamic environments. [ABSTRACT FROM AUTHOR]

Adversarial patch attacks in the physical world are a major threat to the application of deep learning. However, current research on adversarial patch defense algorithms focuses on image pre-processing defenses, it has been demonstrated that this defense reduces the classification accuracy of clean images and is unable to defend against physically realizable attacks. In this paper, we propose a defense patch GNN (DPG), using a new perspective for defending against adversarial patch attacks. First, we extract the input image features with the feature extraction to obtain a feature set. Then downsampling the feature set by applying the global average pooling layer to reduce the perturbation of the features by the adversarial patch. Finally, this paper proposes a graph-structured feature subspace to robust the feature performance. In addition, we design an optimization algorithm based on stochastic gradient descent (SGD), which can significantly increase the mode's generalization ability. We demonstrate empirically the superior robustness of the DPG model on existing adversarial patch attacks. DPG shows without any accuracy loss in the prediction of clean images. [ABSTRACT FROM AUTHOR]

Mid-infrared wavelength switchable and dual-wavelength random laser output has many potential applications. A polarization-maintaining random fiber Bragg grating (PMRFBG) array based on the photonic localization effect of longitudinal invariant transverse disorder in fiber structure is proposed, which can be used as random feedback of dual-wavelength and wavelength switchable output of random fiber laser (RFL). The random fiber Bragg grating (RFBG) array was designed on the panda-type polarization-maintaining fiber (PMF), and the two center wavelengths were 2 151.60 nm and 2 152.22 nm, respectively. The RFBG array was designed on the bow tie-type PMF, and the two center wavelengths were obtained, which were 2 153.08 nm and 2 153.96 nm, respectively. The RFBG array with a center wavelength of 2 139.27 nm was designed on single-mode fiber (SMF). The length of individual fiber Bragg grating (FBG) and PMRFBG, the refractive index modulation depth, the number of cascaded gratings, and the distance between gratings have different effects on the full width at half maximum (FWHM) and reflectance of the RFBG and PMRFBG array, but not on the central wavelength, as obtained by simulation using the transmission matrix method. The designed PMRFBG array provides theoretical support for the design of the feedback mechanism of RFL. [ABSTRACT FROM AUTHOR]

In this experiment, the eutrophication system was established by adding sucrose and yeast powder, and the pH and dissolved oxygen were measured in a bioreactor in real time to study the effect of aerobic environment on the fermentation process of Polygonati Rhizoma extract by Lactiplantibacillus plantarum. To further analyze metabolic changes, UPLC-Q-Exactive MS was used for metabolomic analysis and metabolic profiling. Multivariate analysis was performed using principal component analysis and Orthogonal projections to latent structures discriminant analysis. Finally, 313 differential metabolites were selected, 196 of which were annotated through database matching. After fermentation, the content of short-chain fatty acids, lactic acid, and their derivatives increased significantly, and there were 13 kinds and 4 kinds, respectively. Both compounds and their derivatives are beneficial to the intestinal flora. Consequently, incorporating L. plantarum into the aerobic fermentation process of Polygonati Rhizoma extract within the eutrophic system is potentially advantageous in enhancing the impact of its fermentation solution on the gut microbiota and its effects on human health. Our findings for this kind of edible and medicinal material research and development offer useful insights.Highlights: First report of polygonati Rhizoma fermentation in eutrophic bioreactor system. Fermentative components were characterized by UHPLC-QE-MS/MS and multi-data analysis. The different metabolites were obtained by comparing before and after fermentation. Short-chain fatty acids, lactic acid and their derivatives contents increased. [ABSTRACT FROM AUTHOR]

Spin- and charge- stripe order has been extensively studied in the superconducting cuprates, among which underdoped La2−xSrxCuO4 (LSCO) is an archetype with static spin stripes at low temperatures. An intriguing, but not completely understood, phenomenon in LSCO is that the stripes are tilted away from the high-symmetry Cu-Cu directions. Using high-resolution neutron scattering on LSCO with x = 0.12, we find two coexisting phases at low temperatures, one with static spin stripes and the other with fluctuating ones, both sharing the same tilt angle. Our numerical calculations using the doped Hubbard model elucidate the tilting's origin, attributing it to anisotropic next-nearest neighbor hopping t ′ , consistent with the material's slight orthorhombicity. Our results underscore the model's success in describing specific details of the ground state of this real material and highlight the role of t ′ in the Hamiltonian, revealing the delicate interplay between stripes and superconductivity across theoretical and experimental contexts. Cuprate superconductors have interesting charge and spin ordering patterns in the form of stripes. By combining experimental and numerical tools, the authors reveal that the stripes are always tilted from the underlying lattice directions, with the origin well explained by the anisotropy of the next-nearest neighbor electron hopping. [ABSTRACT FROM AUTHOR]

The integration of micro-arc oxidation (MAO) film and chemical conversion (PCC) technology is a promising surface treatment for magnesium alloys. In this study, a MAO treatment of PCC film layers in a Na2SiO3 electrolyte system was used. The composition, structure, corrosion resistance, and formation mechanism of composite films were thoroughly investigated and discussed. Results showed that the composite-coated samples could effectively retard the corrosion of magnesium alloy. Additionally, the MAO treatment of zinc phosphate PCC coatings led to a reduction in pore size and enhanced coating compactness. The electrochemical test results showed that the composite coating has a higher impedance modulus in the low-frequency region (|Z|MAO/PCC-5 (2.38 × 105 Ω cm2) >|Z|MAO (1.31 × 104 Ω cm2) >|Z|PCC-5 (1.54 × 103 Ω cm2) >|Z|AZ91D (2.08 × 102 Ω cm2)), which improves the corrosion resistance of both the single MAO coating and the PCC coating. Meanwhile, the introduction of zinc effectively improves the antimicrobial properties of the coatings. This study provides a new idea for the preparation of anticorrosive and antimicrobial composite coatings on light metal surfaces. [ABSTRACT FROM AUTHOR]

To assess the agreement and repeatability of scotopic pupil size measurement using 2WIN-S (Adaptica, Padova, Italy) portable refractor in Chinese adults. This prospective non-randomized open-label controlled study assessed the scotopic pupil size of 100 right eyes using OPD-Scan III (Optical path difference) (Nidek Technologies, Gamagori, Japan) and 2WIN-S. OPD-Scan III and 2WIN-S measure pupil size using infrared light and detector, while 2WIN-S measures bilateral eyes simultaneously, OPD-Scan III measures unilateral eyes individually. Participants were first measured once using OPD-Scan III and two consecutive measurements were performed using 2WIN-S after 15 min of rest interval. The primary outcome was to evaluate the agreement between 2WIN-S and OPD-Scan III, and the secondary outcome was to evaluate the repeatability of 2WIN-S. Scotopic pupil size of 100 right eyes of 100 adults (28 male and 72 female) aged 18–53 years (mean 36 ± 12 years) was assessed using OPD-Scan III and 2WIN-S, respectively. The mean scotopic pupil size of OPD-Scan III and 2WIN-S was recorded to be 6.24 ± 0.88 mm and 6.27 ± 0.81 mm, respectively. For the mean scotopic pupil size of OPD-Scan III and 2WIN-S the difference was − 0.03 mm (95%CI − 0.10 to 0.04 mm), p = 0.445, the 95% limits of agreement (LOA) was − 0.71 to 0.66 mm. ICC between the two devices was 0.92 (95% CI 0.88–0.94) (ICC > 0.9 indicates excellent consistency). Coefficients of repeatability (CoR) of 2WIN-S was 0.37, which has a high repeatability. For the mean scotopic pupil size of 2WIN-S of the repeated measurements, the difference was -0.04 mm (95%CI − 0.08 to 0.01 mm), p = 0.019, the 95% limits of agreement (LOA) was − 0.41 to 0.32 mm, with a narrow LOA. However, the majority of the variations were less than ± 0.50 mm (98% of scotopic pupil size measurements were below this threshold), within the clinically acceptable range (± 0.50 mm). Our study showed excellent agreement between 2WIN-S and OPD-Scan III (ICC > 0.9) and a good repeatability of 2WIN-S (CoR = 0.37). This study suggests a novel technique for measuring pupillary responses in low light conditions, which can be considered an alternative to OPD-Scan III in clinical settings. [ABSTRACT FROM AUTHOR]

Spinal cord injury (SCI) is a serious disabling central nervous system injury that can lead to motor, sensory, and autonomic dysfunction below the injury level. SCI can be divided into primary injury and secondary injury according to pathological process. Primary injury is mostly irreversible, while secondary injury is a dynamic regulatory process. Apoptosis is an important pathological event of secondary injury and has a significant effect on the recovery of nerve function after SCI. Nerve cell death can further aggravate the microenvironment of the injured site, leading to neurological dysfunction and thus affect the clinical outcome of patients. Therefore, apoptosis plays a crucial role in the pathological progression of secondary SCI, while inhibiting apoptosis may be a promising therapeutic strategy for SCI. This review will summarize and explore the factors that lead to cell death after SCI, the influence of cross talk between signaling pathways and pathways involved in apoptosis and discuss the influence of apoptosis on SCI, and the therapeutic significance of targeting apoptosis on SCI. This review helps us to understand the role of apoptosis in secondary SCI and provides a theoretical basis for the treatment of SCI based on apoptosis. [ABSTRACT FROM AUTHOR]

Pedestrian detection in densely populated scenes, particularly in the presence of occlusions, remains a challenging issue in computer vision. Existing approaches often address detection leakage by enhancing model architectures or incorporating attention mechanisms; However, small-scale pedestrians have fewer features and are easily overfitted to the dataset and these approaches still face challenges in accurately detecting pedestrians with small target sizes. To tackle this issue, this research rethinks the occlusion region through small-scale pedestrian detection and proposes the You Only Look Once model for efficient pedestrian detection(YOLO-EPD). Firstly, we find that Standard Convolution and Dilated Convolution do not fit well with pedestrian targets with different scales due to a single receptive field, and we propose the Selective Content Aware Downsampling (SCAD) module, which is integrated into the backbone to attain enhanced feature extraction. In addition, to address the issue of missed detections resulting from insufficient feature extraction for small-scale pedestrian detection, we propose the Crowded Multi-Head Attention (CMHA) module, which makes full use of multi-layer information. Finally, for the challenge of optimizing the performance and effectiveness of small-object detection, we design Unified Channel-Task Distillation (UCTD) with channel attention and a Lightweight head (Lhead) using parameter sharing to keep it lightweight. Experimental results validate the superiority of YOLO-EPD, achieving a remarkable 91.1% Average Precision (AP) on the Widerperson dataset, while concurrently reducing parameters and computational overhead by 40%. The experimental findings demonstrate that YOLO-EPD greatly accelerates the convergence of model training and achieves better real-time performance in real-world dense scenarios. [ABSTRACT FROM AUTHOR]

In current researches on belief rule base (BRB), input parameters are tended to be expressed in the form of quantitative values through expert knowledge combined with optimization methods. A singular quantitative value fails to capture the statistical properties, leading to irrational outcomes. Therefore, an attempt on attribute weights is made in this paper, and a new model with probability distribution attribute weights (pdw) called BRB-pdw is proposed. The combination of two attributes is in detail discussed, where attribute weights are described as random variables with specific probability distribution. To characterize the output of probability distribution attribute weight, a new concept of expectation of activation weight is proposed. In addition, the BRB-pdw is extended to multiple attributes to demonstrate its universality. Furthermore, fundamental properties and characteristics of the BRB-pdw are further validated by rigorous mathematical derivation. Finally, practicability of the BRB-pdw is validated with NASA lithium battery open dataset, and experiments show that the BRB-pdw model is more robust while maintaining precision. [ABSTRACT FROM AUTHOR]

In China, significant progress has been made in the exploration and development of coalbed methane (CBM) in medium- to high-rank coals. However, the exploration and development potential of CBM in low-rank coals in the Zhalainuoer coalfield is unknown. In this study, various testing methods were utilized, including low temperature N2/CO2 adsorption, field emission scanning electron microscopy, and nuclear magnetic resonance, to investigate the pore structure characteristics of low-rank coals in the Zhalainuoer coalfield, so as to further evaluate the occurrence space of CBM therein. The results revealed that the prevalent pore types in the low-rank coals were "ink bottle" shaped pores and semi-closed pores, and micropores provide the main specific surface area (SSA) and total pore volume (TPV). Moreover, there is no significant correlation between vitrinite content and fractal dimension, while pore SSA and TPV are correlated positively with D1 but negatively with D2. The coalification degree significantly impacts the pore characteristics of the coal reservoirs. With coalification degree increasing, the SSA and TPV of micropores and transition pores generally exhibited a pattern of initially decreasing and then increasing. These research findings establish a theoretical foundation for the exploration and development of CBM in the Zhalainuoer coalfield. [ABSTRACT FROM AUTHOR]

To prevent adverse consequences for performance deterioration, it is of great importance to evaluate performance promptly and implement precautions at the right time. Therefore, aiming at the performance evaluation problem of complex systems, a model based on the hierarchical evidential reasoning (HER) rule is proposed to integrate multi-layer uncertain information. The multi-layer indicators collected from complex systems are integrated into a belief structure, considering adaptive weights and adaptive reliability. Besides, complex systems are susceptible to both internal and external disturbances, resulting in performance fluctuations. Therefore, a disturbance analysis method for the HER rule is introduced to measure the influence of disturbance on the system. Finally, a bearing system is examined as a case study to assess the rationality of the HER rule and the effect of disturbances on the whole system. [ABSTRACT FROM AUTHOR]

Both atezolizumab (a PD-L1 inhibitor) plus bevacizumab (A+B) and sintilimab (a PD-1 inhibitor) plus bevacizumab (S+B) are recommended as the first-line regimen for advanced hepatocellular carcinoma (HCC) in China. Different efficacy between the two regimens combined with transvascular intervention for unresectable HCC (uHCC) remain unknown. We retrospectively analyzed uHCC patients treated in three centers by simultaneous combination of A+B or S+B with transarterial chemoembolization (TACE) and FOLFOX-based hepatic arterial infusion chemotherapy (HAIC). Objective response rate (ORR), progression-free survival (PFS), overall survival (OS) and treatment-related adverse events (TRAEs) were compared. Totally 188 patients were included, with 92 and 96 administered A+B+TACE-HAIC (ABTH) and S+B+TACE-HAIC (SBTH), respectively. ORRs (62.0 vs. 70.8%, respectively; P = 0.257) and disease control rates (88.0 vs. 93.8%, P = 0.267) were similar between groups by the mRECIST criteria. ABTH showed no survival advantage over SBTH, with median PFS times of 11.7 months and 13.0 months, respectively (HR = 0.81, 95% CI, 0.52–1.26, P = 0.35) and similar OS times (HR = 1.19, 95% CI, 0.32–4.39, P = 0.8). No significant differences were observed in grade 3–4 TRAEs between groups. Either PD-L1 or PD-1 inhibitor plus bevacizumab combined with TACE-HAIC have similarly excellent therapeutic efficacy with manageable adverse events, representing promising treatment options for uHCC. [ABSTRACT FROM AUTHOR]

The practices of prefabricated buildings illustrate the incentives in prefabricated markets as well as the decision-making behavior of stakeholders, which can influence the transformation, upgrading, and sustainability of the buildings sector. The game between the stakeholders in the prefabricated buildings market becomes complicated by the fact that they dynamically adjust their strategies in response to changing market conditions. With the aim of quantitatively studying the implications of the dynamic decision behavior of prefabricated construction stakeholders on the prefabricated construction market, this study introduces component suppliers as participating agents and government subsidies and penalties as the main measures. And a three-way model of the evolutionary game involving government subsidies and punitive schemes is constructed by this study. The essay examines the evolutionary equilibrium strategies of each game subject as well as the effect of original strategies and different parameter choices on the decision-making of each subject using MATLAB. The findings reveal that (1) decision-making behaviors among the government, component suppliers, and developers are both interrelated and constrained, but governments are the dominant agents in the evolution of prefabricated buildings. (2) Parameters such as subsidies, penalties, costs, and benefits affect the stability of the tripartite evolutionary model, which remains consistent with the previous research. (3) Component suppliers have an equally important position in the development of prefabricated buildings as recognized entities such as developers and contractors. (4) When subsidies are kept at 40% of the incremental cost and penalties at 80% of the incremental cost, the model combines a fast evolutionary rate with stable model evolution. In conclusion, the article's research findings not only validate the rationality and feasibility of component suppliers as research subjects and confirm the importance of component suppliers but also propose a more reasonable boundary value for penalties and subsidies, which provides a reference for the government to improve the incentive and punishment measures as well as the dynamic adjustment strategy of stakeholders and also provides a new perspective to achieve the goal of sustainable development of construction. [ABSTRACT FROM AUTHOR]

Coal-rock interface identification technology was pivotal in automatically adjusting the shearer's cutting drum during coal mining. However, it also served as a technical bottleneck hindering the advancement of intelligent coal mining. This study aimed to address the poor accuracy of current coal-rock identification technology on comprehensive working faces, coupled with the limited availability of coal-rock datasets. The loss function of the SegFormer model was enhanced, the model's hyperparameters and learning rate were adjusted, and an automatic recognition method was proposed for coal-rock interfaces based on FL-SegFormer. Additionally, an experimental platform was constructed to simulate the dusty environment during coal-rock cutting by the shearer, enabling the collection of coal-rock test image datasets. The morphology-based algorithms were employed to expand the coal-rock image datasets through image rotation, color dithering, and Gaussian noise injection so as to augment the diversity and applicability of the datasets. As a result, a coal-rock image dataset comprising 8424 samples was generated. The findings demonstrated that the FL-SegFormer model achieved a Mean Intersection over Union (MIoU) and mean pixel accuracy (MPA) of 97.72% and 98.83%, respectively. The FL-SegFormer model outperformed other models in terms of recognition accuracy, as evidenced by an MIoU exceeding 95.70% of the original image. Furthermore, the FL-SegFormer model using original coal-rock images was validated from No. 15205 working face of the Yulin test mine in northern Shaanxi. The calculated average error was only 1.77%, and the model operated at a rate of 46.96 frames per second, meeting the practical application and deployment requirements in underground settings. These results provided a theoretical foundation for achieving automatic and efficient mining with coal mining machines and the intelligent development of coal mines. [ABSTRACT FROM AUTHOR]

To emulate the ordered arrangement of monomer units found in natural macromolecules, single-unit monomer insertion (SUMI) have emerged as a potent technique for synthesizing sequence-controlled vinyl polymers. Specifically, numerous applications necessitate vinyl polymers encompassing both radically and cationically polymerizable monomers, posing a formidable challenge due to the distinct thiocarbonylthio end-groups required for efficient control over radical and cationic SUMIs. Herein, we present a breakthrough in the form of interconvertible radical and cationic SUMIs achieved through the manipulation of thiocarbonylthio end-groups. The transition from a trithiocarbonate (for radical SUMI) to a dithiocarbamate (for cationic SUMI) is successfully accomplished via a radical-promoted reaction with bis(thiocarbonyl) disulfide. Conversely, the reverse transformation utilizes the reaction between dithiocarbamate and bistrithiocarbonate disulfide under a cationic mechanism. Employing this strategy, we demonstrate a series of synthetic examples featuring discrete oligomers containing acrylate, maleimide, vinyl ether, and styrene, compositions unattainable through the SUMI of a single mechanism alone. To emulate the ordered arrangement of radically and cationically polymerizable monomers using single-unit monomer insertion (SUMI) remians challenging. Here the authors demonstrate sequence-defined vinyl polymers via SUMI by manipulating thiocarbonylthio end-groups. [ABSTRACT FROM AUTHOR]

We study Hill’s differential equation with potential expressed by elliptic functions which arises in some problems of physics and mathematics. Analytical method can be applied to study the local properties of the potential in asymptotic regions of the parameter space. The locations of the saddle points of the potential are determined, the locations of turning points can be determined too when they are close to a saddle point. Combined with the quadratic differential associated with the differential equation, these local data give a qualitative explanation for the asymptotic eigensolutions obtained recently. A relevant topic is about the generalisation of Floquet theorem for ODE with doubly-periodic elliptic function coefficient which bears some new features compared to the case of ODE with real valued singly-periodic coefficient. Beyond the local asymptotic regions, global properties of the elliptic potential are studied using numerical method. [ABSTRACT FROM AUTHOR]

Drawing on the dual-systems theory (DST), this study proposes a research model that focuses on how the characteristics of the key opinion leader influence consumers' purchase intention in live streaming commerce (LSC). We collected data through questionnaire surveys from samples of consumers in China (N = 467), which indicated that key opinion leader's characteristics (i.e., attractiveness, trustworthiness, expertise) are all positively related to purchase intention. Importantly, the mediator analyses indicated that the effect of systems 1 on the relationship between attractiveness and purchase intention persisted is persistent, regardless of the type of goods purchased by the consumers (utilitarian or hedonic). This finding highlights the importance of attractiveness and reveals unconscious thought (UT) of consumers when shopping in LSC. This study provides a new theoretical perspective (i.e., DST) to identify attractiveness as a determinant of purchase intention in LSC and influence purchase intention by activating systems 1, thus solving the problem of identifying key factors in stimulus–organism–response theory. Besides, our results identify and test UT in LSC. The practical implications of these findings are to provide guidance for the selection of key opinion leaders in LSC and the development of live streaming industry. [ABSTRACT FROM AUTHOR]

Objectives: To compare prostate-specific membrane antigen (PSMA) PET with multiparametric MRI (mpMRI) in the diagnosis of pretreatment prostate cancer (PCa). Methods: Pubmed, Embase, Medline, Web of Science, and Cochrane Library were searched for eligible studies published before June 22, 2022. We assessed risk of bias and applicability by using QUADAS-2 tool. Data synthesis was performed with Stata 17.0 software, using the "midas" and "meqrlogit" packages. Results: We included 29 articles focusing on primary cancer detection, 18 articles about primary staging, and two articles containing them both. For PSMA PET versus mpMRI in primary PCa detection, sensitivities and specificities in the per-patient analysis were 0.90 and 0.84 (p<0.0001), and 0.66 and 0.60 (p <0.0001), and in the per-lesion analysis they were 0.79 and 0.78 (p <0.0001), and 0.84 and 0.82 (p <0.0001). For the per-patient analysis of PSMA PET versus mpMRI in primary staging, sensitivities and specificities in extracapsular extension detection were 0.59 and 0.66 (p =0.005), and 0.79 and 0.76 (p =0.0074), and in seminal vesicle infiltration (SVI) detection they were 0.51 and 0.60 (p =0.0008), and 0.93 and 0.96 (p =0.0092). For PSMA PET versus mpMRI in lymph node metastasis (LNM) detection, sensitivities and specificities in the per-patient analysis were 0.68 and 0.46 (p <0.0001), and 0.91 and 0.90 (p =0.81), and in the per-lesion analysis they were 0.67 and 0.36 (p <0.0001), and 0.99 and 0.99 (p =0.18). Conclusion: PSMA PET has higher diagnostic value than mpMRI in the detection of primary PCa. Regarding the primary staging, mpMRI has potential advantages in SVI detection, while PSMA PET has relative advantages in LNM detection. Clinical relevance statement: The integration of prostate-specific membrane antigen (PSMA) PET into the diagnostic pathway may be helpful for improving the accuracy of prostate cancer detection. However, further studies are needed to address the cost implications and evaluate its utility in specific patient populations or clinical scenarios. Moreover, we recommend the combination of PSMA PET and mpMRI for cancer staging. Key Points: • Prostate-specific membrane antigen PET has higher sensitivity and specificity for primary tumor detection in prostate cancer compared to multiparametric MRI. • Prostate-specific membrane antigen PET also has significantly better sensitivity and specificity for lymph node metastases of prostate cancer compared to multiparametric MRI. • Multiparametric MRI has better accuracy for extracapsular extension and seminal vesicle infiltration compared to ate-specific membrane antigen PET. [ABSTRACT FROM AUTHOR]

Objectives: To develop an interactive, non-invasive artificial intelligence (AI) system for malignancy risk prediction in cystic renal lesions (CRLs). Methods: In this retrospective, multicenter diagnostic study, we evaluated 715 patients. An interactive geodesic-based 3D segmentation model was created for CRLs segmentation. A CRLs classification model was developed using spatial encoder temporal decoder (SETD) architecture. The classification model combines a 3D-ResNet50 network for extracting spatial features and a gated recurrent unit (GRU) network for decoding temporal features from multi-phase CT images. We assessed the segmentation model using sensitivity (SEN), specificity (SPE), intersection over union (IOU), and dice similarity (Dice) metrics. The classification model's performance was evaluated using the area under the receiver operator characteristic curve (AUC), accuracy score (ACC), and decision curve analysis (DCA). Results: From 2012 to 2023, we included 477 CRLs (median age, 57 [IQR: 48–65]; 173 men) in the training cohort, 226 CRLs (median age, 60 [IQR: 52–69]; 77 men) in the validation cohort, and 239 CRLs (median age, 59 [IQR: 53–69]; 95 men) in the testing cohort (external validation cohort 1, cohort 2, and cohort 3). The segmentation model and SETD classifier exhibited excellent performance in both validation (AUC = 0.973, ACC = 0.916, Dice = 0.847, IOU = 0.743, SEN = 0.840, SPE = 1.000) and testing datasets (AUC = 0.998, ACC = 0.988, Dice = 0.861, IOU = 0.762, SEN = 0.876, SPE = 1.000). Conclusion: The AI system demonstrated excellent benign-malignant discriminatory ability across both validation and testing datasets and illustrated improved clinical decision-making utility. Critical relevance statement: In this era when incidental CRLs are prevalent, this interactive, non-invasive AI system will facilitate accurate diagnosis of CRLs, reducing excessive follow-up and overtreatment. Key Points: The rising prevalence of CRLs necessitates better malignancy prediction strategies. The AI system demonstrated excellent diagnostic performance in identifying malignant CRL. The AI system illustrated improved clinical decision-making utility. [ABSTRACT FROM AUTHOR]

Background: Clear cell renal cell carcinoma (ccRCC), the most common pathological subtype of kidney cancer, accounts for approximately 70% to 80% of all cases. Histone deacetylase 10 (HDAC10) belongs to the HDAC class IIb subgroup, one of the histone deacetylases (HDAC) family. Previous studies suggest that HDAC10 may regulate the development of multiple tumor types. The specific molecular mechanisms employed by HDAC10 in the etiology of ccRCC still need to be discovered. Methods: The analysis included examining HDAC10 expression levels and their clinical importance within a cohort of inpatients and ccRCC patients documented in the Tumor Genome Atlas (TCGA). Moreover, the biological functions and underlying molecular mechanisms of HDAC10 were investigated. Results: HDAC10 showed increased expression in ccRCC tumor tissues. Subsequent analysis revealed overexpression of HDAC10 was associated with advanced clinical phenotype and unfavorable prognosis. The absence of HDAC10 significantly decreased ccRCC cell proliferation and migration capabilities. Mechanistic research suggests that HDAC10 may promote RCC development by activating the Notch-1 pathway and downregulating PTEN expression levels. Conclusion: In summary, HDAC10 can modulate critical biological processes in ccRCC, including proliferation, migration, and apoptosis. Notably, the Notch-1 pathway and PTEN serve as crucial signaling pathways and target genes through which HDAC10 regulates the progression of ccRCC. These findings offer a novel outlook for ccRCC treatment. [ABSTRACT FROM AUTHOR]

This paper presents a massively parallel approach of the multilevel fast multipole algorithm (PMLFMA) on homegrown many-core SW26010 cluster of China, noted as (SW-PMLFMA), for 3-D electromagnetic scattering problems. In this approach, the multilevel fast multipole algorithm (MLFMA) octree is first partitioned among management processing elements (MPEs) of SW26010 processors following the ternary partitioning scheme using the message passing interface (MPI). Then, the computationally intensive parts of the PMLFMA on each MPI process, matrix filling, aggregation and disaggregation are accelerated by using all the 64 computing processing elements (CPEs) in the same core group of the MPE via the Athread parallel programming model. Different parallelization strategies are designed for many-core accelerators to ensure a high computational throughput. In coincidence with the special characteristic of local Lagrange interpolation, the compressed sparse row (CSR) and the compressed sparse column (CSC) sparse matrix storage format is used for storing interpolation and anterpolation matrices, respectively, together with a specially designed cache mechanism of hybrid dynamic and static buffers using the scratchpad memory (SPM) to improve data access efficiency. Numerical results are included to demonstrate the efficiency and versatility of the proposed method. The proposed parallel scheme is shown to have excellent speedup. [ABSTRACT FROM AUTHOR]

Microblade assemblages are among the most common prehistoric archaeological materials found on the Tibetan Plateau (TP) and are thought to indicate large scale migration to and settlement of the TP. Few microblade sites, however, have been systematically excavated, especially in the remotest, highest-elevation regions of the TP. The timing of the large-scale arrival, spread, and permanent settlement of people on the TP therefore remains controversial. In this paper, we report on a recently excavated site, Locality 3 of the Nwya Devu Site (ND3), located at 4600 meters above sea level (masl), near the shore of Ngoin Lake, on the interior TP. Our analyses reveal a fairly typical microblade technological orientation and two types of microblade cores: wedge-shaped and semi-conical, which are similar to those found throughout North China. Using Optically Stimulated Luminescence (OSL) dating and AMS 14C dating, the age of ND3 ranges from 11 to 10 ka. This date range indicates ND3 is the oldest microblade site yet recorded in the remote, high-elevation regions of the TP and thus provides important information about when and how hunter-gatherers using microblades began exploiting the higher altitudes of the TP. Taken together, studies at ND3 and throughout the TP suggest that a microblade adaptation is associated with the first prolonged human occupation of the plateau and that microblades played a significant role in mediating the risks and facilitating the mobility necessary to permanently inhabit the TP. [ABSTRACT FROM AUTHOR]

Informal status has strong positive implications in the field of organizational behavior, but it also has some harmful effects on individuals. Based on conservation of resources (COR) theory, this study constructs a model to explore the double-edged sword effect of informal status on employees' work procrastination behavior. A questionnaire survey of 202 employees was conducted at two time points, and analysis found that informal status can reduce work procrastination by enhancing employees' career calling, but it will also increase the workload of employees and in turn, increase work procrastination. In addition, the moderating effect of Zhongyong thinking was not significant, but, its pluralistic dimension of thinking strengthened the relationship between informal status and career calling. Contrariwise, the harmony dimension weakens the relationship between informal status and career calling and workload. The findings not only broaden and complement existing research in the field, but also have certain practical implications. [ABSTRACT FROM AUTHOR]

Objective: Sleep disorders such as insomnia, apnea, and restless leg syndrome can negatively affect a person's overall health and may cause hypertension, heart failure, and coronary heart disease. Likewise, periodontitis, a gum disease, can lead to both physical and psychological health issues, exerting a considerable effect on one's overall well-being—periodontitis stands as a primary cause of tooth loss. Nevertheless, there has been insufficient research on the correlation between the amount of sleep individuals get and the occurrence of periodontitis/tooth loss among Americans. Therefore, this study aimed to assess the influence of sleep length on periodontitis in the American population. Methods: Periodontitis severity was classified (none, mild, moderate, and severe) using American Periodontal Association criteria. Sleep duration was assessed by self-reported data and categorized into three groups (deficient, adequate, and excessive). Tooth loss was assessed by the oral examination. To establish a connection between the duration of sleep and periodontitis/tooth loss, a weighted multivariable logistic regression analysis was employed. A GAM analysis and smooth curve fitting assessment were conducted to identify non-linear relationships. Subgroup, interaction, and mediation analyses were also performed. Results: The prevalence of tooth loss was significantly high, affecting 96.4% of the individuals, whereas 46.6% of the study sample experienced moderate to severe periodontitis. The average age of participants was 52.7 years. After adjusting for potential confounding factors, the analysis of weighted multivariable logistic regression revealed a significant association between sleep insufficiency and moderate/severe periodontitis (OR 1.15, 95% CI 1.01–1.30, P = 0.0298), as well as tooth loss (OR 1.16, 95% CI 1.01–1.33, P = 0.0371). Additionally, the research showed a correlation between the length of sleep and periodontitis that followed a U-shaped pattern. In addition, the analysis of mediation revealed that high blood pressure explained 7.0% (95% CI 4.0% to 12.9%; P < 0.0001) of the link between the amount of sleep and the likelihood of losing teeth. Conclusion: Sleep duration was independently correlated with moderate/severe periodontitis/tooth loss and had a non-linear relationship. [ABSTRACT FROM AUTHOR]

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Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants' T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes. A study utilizing unsupervised deep learning to generate interpretable brain imaging phenotypes from brain T1 and T2-FLAIR MRI identified 97 genetic loci enhancing understanding of brain structure genetics. [ABSTRACT FROM AUTHOR]

Spatial transcriptomics has revolutionized the study of gene expression within tissues, while preserving spatial context. However, annotating spatial spots' biological identity remains a challenge. To tackle this, we introduce Pianno, a Bayesian framework automating structural semantics annotation based on marker genes. Comprehensive evaluations underscore Pianno's remarkable prowess in precisely annotating a wide array of spatial semantics, ranging from diverse anatomical structures to intricate tumor microenvironments, as well as in estimating cell type distributions, across data generated from various spatial transcriptomics platforms. Furthermore, Pianno, in conjunction with clustering approaches, uncovers a region- and species-specific excitatory neuron subtype in the deep layer 3 of the human neocortex, shedding light on cellular evolution in the human neocortex. Overall, Pianno equips researchers with a robust and efficient tool for annotating diverse biological structures, offering new perspectives on spatial transcriptomics data. Recognising spatial spots' biological identity in spatial transcriptomics remains a challenge. Here, authors introduce Pianno, a tool that helps annotate the biological structures or cell-type constructions across diverse tissues, offering new perspectives on understanding spatial transcriptomics. [ABSTRACT FROM AUTHOR]

To enhance the quality of tobacco leaves and optimize the smoking experience, diverse strains of functional bacteria and their associated metabolites have been used in tobacco aging. Exogenous cellulase additives are frequently employed to facilitate the degradation of cellulose and other macromolecular matrices and enhance the quality of the tobacco product. However, little is known about how microbial metabolites present in exogenous enzyme additives affect tobacco quality. In this study, crude cellulase solutions, produced by a tobacco-originating bacterium Bacillus subtilis FX-1 were employed on flue-cured tobacco. The incorporation of cellulase solutions resulted in the reduction of cellulose crystallinity in tobacco and the enhancement of the overall sensory quality of tobacco. Notably, tobacco treated with cellulase obtained from laboratory flask fermentation demonstrated superior scent and flavor attributes in comparison to tobacco treated with enzymes derived from industrial bioreactor fermentation. The targeted and untargeted metabolomic analysis revealed the presence of diverse flavor-related precursors and components in the cellulase additives, encompassing sugars, alcohols, amino acids, organic acids, and others. The majority of these metabolites exhibited significantly higher levels in the flask group compared to the bioreactor group, probably contributing to a pronounced enhancement in the sensory quality of tobacco. Our findings suggest that the utilization of metabolic products derived from B. subtilis FX-1 as additives in flue-cured tobacco holds promise as a viable approach for enhancing sensory attributes, establishing a solid theoretical foundation for the potential development of innovative tobacco aging additives. Cellulase solutions obtained from Bacillus subtilis FX-1, a bacterium originating from tobacco, were applied to flue-cured tobacco during the aging process. The cellulase structure of tobacco was analyzed using 13C cross-polarization magic angle spinning NMR spectroscopy. Sensory evaluation was utilized to assess the sensory attributes and quality characteristics. Targeted and untargeted metabolomics were utilized to investigate the chemical components present in the cellulase solutions. The inclusion of cellulase additives altered the cellulose structure in tobacco and introduced various flavor-related precursors and components. The utilization of cellulase additives derived from B. subtilis FX-1 substantially improved the overall quality of the tobacco product. [ABSTRACT FROM AUTHOR]

Constructing an artificial solid electrolyte interphase (SEI) on lithium metal electrodes is a promising approach to address the rampant growth of dangerous lithium morphologies (dendritic and dead Li0) and low Coulombic efficiency that plague development of lithium metal batteries, but how Li+ transport behavior in the SEI is coupled with mechanical properties remains unknown. We demonstrate here a facile and scalable solution-processed approach to form a Li3N-rich SEI with a phase-pure crystalline structure that minimizes the diffusion energy barrier of Li+ across the SEI. Compared with a polycrystalline Li3N SEI obtained from conventional practice, the phase-pure/single crystalline Li3N-rich SEI constitutes an interphase of high mechanical strength and low Li+ diffusion barrier. We elucidate the correlation among Li+ transference number, diffusion behavior, concentration gradient, and the stability of the lithium metal electrode by integrating phase field simulations with experiments. We demonstrate improved reversibility and charge/discharge cycling behaviors for both symmetric cells and full lithium-metal batteries constructed with this Li3N-rich SEI. These studies may cast new insight into the design and engineering of an ideal artificial SEI for stable and high-performance lithium metal batteries.Constructing an artificial solid electrolyte interphase to protect the lithium metal electrode is promising but challenging. Here, authors report a facile approach to form a layer to simultaneously overcome diffusion and advection-limited ion transport to achieve dendrite-free Li plating/stripping. [ABSTRACT FROM AUTHOR]

Mechanoluminescence (ML) sensing technologies open up new opportunities for intelligent sensors, self-powered displays and wearable devices. However, the emission efficiency of ML materials reported so far still fails to meet the growing application requirements due to the insufficiently understood mechano-to-photon conversion mechanism. Herein, we propose to quantify the ability of different phases to gain or lose electrons under friction (defined as triboelectric series), and reveal that the inorganic-organic interfacial triboelectricity is a key factor in determining the ML in inorganic-organic composites. A positive correlation between the difference in triboelectric series and the ML intensity is established in a series of composites, and a 20-fold increase in ML intensity is finally obtained by selecting an appropriate inorganic-organic combination. The interfacial triboelectricity-regulated ML is further demonstrated in multi-interface systems that include an inorganic phosphor-organic matrix and organic matrix-force applicator interfaces, and again confirmed by self-oxidization and reduction of emission centers under continuous mechanical stimulus. This work not only gives direct experimental evidences for the underlying mechanism of ML, but also provides guidelines for rationally designing high-efficiency ML materials. Mechanoluminescence enables sensing applications of mechanical stimuli. Here, the authors reveal the importance of interfacial triboelectricity to this phenomenon in inorganic-organic composite materials. [ABSTRACT FROM AUTHOR]

Microbubble reactors play an important role in the development of gas-liquid reaction process enhancement. However, the urgent demand for high efficiency and low energy consumption in gas-liquid reaction processes, as well as the trend towards large-scale production, have put forward higher requirements for the design and optimization of microbubble reactors. In this study, a self-priming microbubble reactor was designed and its structure parameters were optimized by (computational fluid dynamics) CFD simulations. Based on the grid division method combining structured and unstructured grids, the most suitable mesh number is selected, and the simulation calculation time is saved on the premise of ensuring the accuracy. The effects of five structural parameters on the gas content and energy loss was discussed and the optimal structural parameters of the microbubble reactor were determined as follows: the diffusion section length is 75 mm, the contraction angle is 22°, the diffusion angle is 10.5°, the inlet diameter of the gas phase is 6 mm, the inlet diameter of the liquid phase flowing into the gas chamber is 3 mm, the diffusion section inlet diameter is 5 mm. Under the condition of the same inlet flow rate, the outlet gas content of the optimized gas-liquid reactor is increased by 42.9% compared with the initial structure. In the wastewater treatment experiment, the microbubble reactor reduced the chemical oxygen demand of wastewater by 61% within three hours. This study provides significant references for the design of the self-priming microbubble reactor. [ABSTRACT FROM AUTHOR]

Potato (Solanum tuberosum L.) is an essential staple crop in China. Appropriate storage methods and technology are critical to ensure the quality of seed potatoes, which is closely related to the field performance. So far, the diffused light storage (DLS) technique has been widely applied to potato crop. In this paper, two special potato varieties ('Chuanyu 117' and 'Mira') were treated at DLS for 60 days, and the effects of growth conditions on tuber sprouts and subsequent field performance were evaluated. The results showed that the DLS treatment significantly promoted the greening of potato tubers from highland and simultaneously affected the average sprouting rate, sprout length and number of sprouts at different storage stages, which indicated obvious interacting correlations between DLS treatments and potato varieties. Especially for 'Mira', the shortest average sprout length (2.7 mm) was obtained under 40–200 lx light level, while 'Chuanyu 117' had the shortest average sprout length (1.3 mm) under 5–10 lx and its sprout length was also controlled within 2.3 mm under 200–2000 lx. In the cultivation experiment that followed, the potato tubers with 4–6 mm and 2–3 mm sprout length had the highest yield after 90 days and 110 days planting, respectively. Overall, these results demonstrated that for seed potatoes harvested from the highland area followed by storing for 30 days in the lowland, the sprout length can be better controlled at 2–3 mm to obtain short and strong sprouts when stored under 40–200 lx or 200–2000 lx diffused light for 60 days, which is especially suitable in terms of fast field emergence after planting and final tuber yield for the autumn and winter cultivation of potato in the Chengdu Plain. It is also suggested that the DLS technique on seed potatoes could be used in the mixed single and double-cropping areas in Sichuan and other areas with similar geographical and climatic conditions. The findings of this study have several important implications in the storage, transportation, processing as well as seed potato market for commercial practice. [ABSTRACT FROM AUTHOR]

To enhance the fire safety and wear resistance of epoxy, phosphorus-containing nickel phyllosilicate whiskers (FP-NiPS) were synthesized using a facile hydrothermal technology, with 9,10-dihydro-9-oxa-10-phosphaphenanthrene as the organic modifier. The impacts of FP-NiPS on the thermal stability, flame retardancy, and mechanical and tribological properties of EP composites were explored. The findings demonstrated that 5 wt % FP-NiPS elevated the limiting oxygen index of the EP composite from 23.8% to 28.4%, achieving a V-0 rating during vertical burning tests. FP-NiPS could enhance the thermal stability of epoxy resin (EP) and facilitate the development of a dense and continuous carbon layer, thereby significantly improving the fire safety of the EP composites. The FP-NiPS led to an 8.2% increase in the tensile strength and a 38.8% increase in the elastic modulus of the EP composite, showing outstanding mechanical properties. Furthermore, FP-NiPS showed remarkable potential in enhancing the wear resistance of EP. The wear rate of 1 wt % FP-NiPS is 2.34 × 10−5 mm3·N−1·m−1, a decrease of 66.7% compared to EP. This work provides a novel promising modification method to enhance the fire safety, mechanical and wear resistance properties of EP. [ABSTRACT FROM AUTHOR]

Due to the harsh operating environment and ultralong operating hours of wireless sensor networks (WSNs), node failures are inevitable. Ensuring the reliability of the data collected by the WSN necessitates the utmost importance of diagnosing faults in nodes within the WSN. Typically, the initial step in the fault diagnosis of WSN nodes involves extracting numerical features from neighboring nodes. A solitary data feature is often assigned a high weight, resulting in the failure to effectively distinguish between all types of faults. Therefore, this study introduces an enhanced variant of the traditional belief rule base (BRB), called the belief rule base with adaptive attribute weights (BRB-AAW). First, the data features are extracted as input attributes for the model. Second, a fault diagnosis model for WSN nodes, incorporating BRB-AAW, is established by integrating parameters initialized by expert knowledge with the extracted data features. Third, to optimize the model's initial parameters, the projection covariance matrix adaptive evolution strategy (P-CMA-ES) algorithm is employed. Finally, a comprehensive case study is designed to verify the accuracy and effectiveness of the proposed method. The results of the case study indicate that compared with the traditional BRB method, the accuracy of the proposed model in WSN node fault diagnosis is significantly improved. [ABSTRACT FROM AUTHOR]

With the advancement of technology, the demand for increased production efficiency has gradually risen, leading to the emergence of new trends in agricultural automation and intelligence. Precision classification models play a crucial role in helping farmers accurately identify, classify, and process various agricultural products, thereby enhancing production efficiency and maximizing the economic value of agricultural products. The current MobileNetV2 network model is capable of performing the aforementioned tasks. However, it tends to exhibit recognition biases when identifying different subcategories within agricultural product varieties. To address this challenge, this paper introduces an improved MobileNetV2 convolutional neural network model. Firstly, inspired by the Inception module in GoogLeNet, we combine the improved Inception module with the original residual module, innovatively proposing a new Res-Inception module. Additionally, to further enhance the model's accuracy in detection tasks, we introduce an efficient multi-scale cross-space learning module (EMA) and embed it into the backbone structure of the network. Experimental results on the Fruit-360 dataset demonstrate that the improved MobileNetV2 outperforms the original MobileNetV2 in agricultural product classification tasks, with an accuracy increase of 1.86%. [ABSTRACT FROM AUTHOR]