Open-world instance-level scene understanding aims to locate and recognize unseen object categories that are not present in the annotated dataset. This task is challenging because the model needs to both localize novel 3D objects and infer their semantic categories. A key factor for the recent progress in 2D open-world perception is the availability of large-scale image-text pairs from the Internet, which cover a wide range of vocabulary concepts. However, this success is hard to replicate in 3D scenarios due to the scarcity of 3D-text pairs. To address this challenge, we propose to harness pre-trained vision-language (VL) foundation models that encode extensive knowledge from image-text pairs to generate captions for multi-view images of 3D scenes. This allows us to establish explicit associations between 3D shapes and semantic-rich captions. Moreover, to enhance the fine-grained visual-semantic representation learning from captions for object-level categorization, we design hierarchical point-caption association methods to learn semantic-aware embeddings that exploit the 3D geometry between 3D points and multi-view images. In addition, to tackle the localization challenge for novel classes in the open-world setting, we develop debiased instance localization, which involves training object grouping modules on unlabeled data using instance-level pseudo supervision. This significantly improves the generalization capabilities of instance grouping and, thus, the ability to accurately locate novel objects. We conduct extensive experiments on 3D semantic, instance, and panoptic segmentation tasks, covering indoor and outdoor scenes across three datasets. Our method outperforms baseline methods by a significant margin in semantic segmentation (e.g., 34.5%â¼65.3%), instance segmentation (e.g., 21.8%â¼54.0%), and panoptic segmentation (e.g., 14.7%â¼43.3%). Code will be available.
Low-coverage whole-genome sequencing (LCS) offers a cost-effective alternative for sturgeon breeding, especially given the lack of SNP chips and the high costs associated with whole-genome sequencing. In this study, the efficiency of LCS for genotype imputation and genomic prediction was assessed in 643 sequenced Russian sturgeons (â¼13.68×). The results showed that using BaseVar+STITCH at a sequencing depth of 2× with a sample size larger than 300 resulted in the highest genotyping accuracy. In addition, when the sequencing depth reached 0.5× and SNP density was reduced to 50 K through linkage disequilibrium pruning, the prediction accuracy was comparable to that of whole sequencing depth. Furthermore, an incremental feature selection method has the potential to improve prediction accuracy. This study suggests that the combination of LCS and imputation can be a cost-effective strategy, contributing to the genetic improvement of economic traits and promoting genetic gains in aquaculture species.
RATIONALE & OBJECTIVE: Kidney stone disease (KSD), a significant healthcare problem within both developed and developing countries, has been associated with genetic risk factors. As well, an association between physical activity and KSD risk has been hypothesized but studies have yielded inconsistent findings. This study aimed to investigate the association between the intensity of physical activity and the incidence of KSD accounting for genetic risk. STUDY DESIGN: Prospective cohort study. SETTING & PARTICIPANTS: A total of 80,473 participants from the UK Biobank Study. EXPOSURES: Physical activity levels, including total physical activity (TPA), moderate-to-vigorous intensity physical activity (MVPA), and light-intensity physical activity (LPA), were measured using accelerometers and quantified using a machine learning model. A polygenic risk score (PRS) for KSD was also constructed. OUTCOMES: Individuals with KSD were identified using the International Classification of Diseases 10th Edition, and procedure codes for KSD surgery. ANALYTICAL APPROACH: A Fine and Gray survival model was used to estimate the associations of incident KSD with TPA, MVPA, LPA, and PRS (as categorical variables). Restricted cubic splines were used to examine potential non-linear associations within the fully adjusted models. RESULTS: During an average follow-up of 6.19 years, 421 participants developed KSD. Participants in the highest quartiles of TPA, MVPA, and LPA had lower adjusted rates of KSD compared to those in the lowest quartiles: HRs (95% confidence interval) of 0.50 (0.44, 0.56), 0.57 (0.51, 0.64), and 0.66 (0.59, 0.74), respectively. TPA, MVPA, and LPA were associated with lower risk of KSD in participants with low and high genetic predisposition for KSD. LIMITATIONS: Selection bias as participants who provided accelerometry data may have been more adherent to health care. CONCLUSION: Physical activity was negatively associated with the risk of KSD, regardless of the genetic risk. Future large studies are warranted to confirm and explain the mechanisms underlying these associations.
To clarify the wind-driven post-bloom dispersion range of Microcystis, which originally clustered on the water surface, an Individual-Based Model (IBM) of Microcystis movement considering the combined effects of wind and light was developed based on actual hydrodynamic data and Microcystis biomass. After calibrating the effects of hydrodynamics and light, 66 cases of short-term (within a week) post-bloom with satellite images from 2011 to 2017 were simulated. The results showed that there were three short-term post-bloom types: vertical reduction (VR), horizontal reduction (HR) and mixed reduction (MR). For VR type, the cyanobacterial bloom reduction rate was rapid (>160â¯km2/day), but the dispersion range of Microcystis was limited (<2â¯km/day), and a larger bloom area was likely to form in the original location when wind speed decreased. For HR type, the cyanobacterial bloom reduction rate was slow (<10â¯km2/day), but Microcystis exhibited a broad dispersion range (>4â¯km/day), often leading to smaller, thicker, and longer-lasting cyanobacterial blooms downwind, albeit with a lower probability of occurrence. The characteristics of MR lay between the two aforementioned types.
Background: The thyroid gland exhibits a subtle interconnection with the lungs. We further investigated the correlation between thyroid hormone sensitivity and lung function in euthyroid individuals. Methods: Data on spirometry and mortality for participants aged 19-79 years were extracted from the NHANES database. Obstructive lung function was defined as a forced expiratory volume in 1 s to forced vital capacity ratio (FEV1/FVC) < 0.70, while restrictive lung function was considered when FEV1/FVC ≥0.70 and baseline FVC <80 % predicted. Central and peripheral sensitivities to thyroid hormones were mainly evaluated by Thyroid Feedback Quantile-based Index (TFQI) and Free Triiodothyronine/Free thyroxine (FT3/FT4) ratio. Logistic regression and subgroup analysis were used to examine potential associations between thyroid hormone sensitivity and lung function. The association between TFQI and all-cause mortality risk was also investigated. Results: A total of 6539 participants were analyzed, 900 with obstructive lung function and 407 with restrictive lung function. The prevalence of impaired lung function, both obstructive and restrictive, increased with higher TFQI levels. Logistic regression analysis showed that increased TFQI and decreased FT3/FT4 levels were independent risk factors for obstructive and restrictive lung function (P < 0.05). After adjusting for the impact of lung function, TFQI (HR = 1.25, 95 % CI 1.00-1.56, P = 0.048) was an independent risk factor for all-cause mortality. Conclusion: Reduced sensitivity to thyroid hormones has been linked to impaired lung function. TFQI and FT3/FT4 are potential epidemiological tools to quantify the role of central and peripheral thyroid resistance in lung function.
Bridge detection in remote sensing images (RSIs) plays a crucial role in various applications, but it poses unique challenges compared to the detection of other objects. In RSIs, bridges exhibit considerable variations in terms of their spatial scales and aspect ratios. Therefore, to ensure the visibility and integrity of bridges, it is essential to perform holistic bridge detection in large-size very-high-resolution (VHR) RSIs. However, the lack of datasets with large-size VHR RSIs limits the deep learning algorithms' performance on bridge detection. Due to the limitation of GPU memory in tackling large-size images, deep learning-based object detection methods commonly adopt the cropping strategy, which inevitably results in label fragmentation and discontinuous prediction. To ameliorate the scarcity of datasets, this paper proposes a large-scale dataset named GLH-Bridge comprising 6,000 VHR RSIs sampled from diverse geographic locations across the globe. These images encompass a wide range of sizes, varying from 2,048 × 2,048 to 16,384 × 16,384 pixels, and collectively feature 59,737 bridges. These bridges span diverse backgrounds, and each of them has been manually annotated, using both an oriented bounding box (OBB) and a horizontal bounding box (HBB). Furthermore, we present an efficient network for holistic bridge detection (HBD-Net) in large-size RSIs. The HBD-Net presents a separate detector-based feature fusion (SDFF) architecture and is optimized via a shape-sensitive sample re-weighting (SSRW) strategy. The SDFF architecture performs inter-layer feature fusion (IFF) to incorporate multi-scale context in the dynamic image pyramid (DIP) of the large-size image, and the SSRW strategy is employed to ensure an equitable balance in the regression weight of bridges with various aspect ratios. Based on the proposed GLH-Bridge dataset, we establish a bridge detection benchmark including the OBB and HBB tasks, and validate the effectiveness of the proposed HBD-Net. Additionally, cross-dataset generalization experiments on two publicly available datasets illustrate the strong generalization capability of the GLH-Bridge dataset. The dataset and source code will be released at https://luo-z13.github.io/GLH-Bridge-page/.
Finding reliable and easy-to-obtain predictors of severe infectious complications after shock wave lithotripsy (SWL) is a major clinical need, particular in symptom-free hydronephrosis. Therefore, we aim to prospectively investigate the predictive value of Hounsfield units (HU) in renal pelvis urine for the risk of severe infectious complications in patients with ureteral stones and symptom-free hydronephrosis after SWL. This multi-center prospective study was conducted from June 2020 to December 2023. The HU of renal pelvis urine was measured by non-enhanced computed tomography. The severe infectious complications included systemic inflammatory response syndrome, sepsis, and septic shock. Binary logistic regression models assessed the odds ratios (ORs) and 95% confidence intervals (CIs). Finally, 1,436 patients with ureteral stones were enrolled in this study. 8.9% (128/1,436) of patients experienced severe infectious complications after SWL treatment. After adjusting confounding variables, compared with the patients in the lowest renal pelvis urine density quartile, the OR (95% CI) for the highest quartile was 32.36 (13.32, 78.60). There was a positive linear association between the HU value of renal pelvis urine and the risk of severe infectious complications after SWL (P for trend < 0.001). Furthermore, this association was also observed stratified by age, gender, BMI, stone size, stone location and hydronephrosis grade (all P for interaction > 0.05). Additionally, the nonlinear association employed by restricted cubic splines is not statistically significant (nonlinear P = 0.256). The AUROC and 95%CI of renal pelvis urine density were 0.895 (0.862 to 0.927, P value < 0.001). The cut-off value was 12.0 HU with 78.59% sensitivity and 85.94% specificity. This multi-center prospective study demonstrated a positive linear association between HU in renal pelvis urine and the risk of severe infectious complications in patients with ureteral stones and symptom-free hydronephrosis after SWL, regardless of age, gender, BMI, stone size, stone location, and hydronephrosis grade. These findings might be helpful in the SWL treatment decision-making process.
Hydronephrosis , Kidney Pelvis , Lithotripsy , Ureteral Calculi , Humans , Lithotripsy/adverse effects , Male , Prospective Studies , Female , Hydronephrosis/etiology , Middle Aged , Adult , Ureteral Calculi/complications , Ureteral Calculi/therapy , Postoperative Complications/etiology , Postoperative Complications/epidemiology , Aged , Tomography, X-Ray Computed , Urine/microbiology , Risk Assessment , Sepsis/etiology , Sepsis/complications , Risk Factors , Predictive Value of Tests , Severity of Illness Index
This investigation focuses on the Goos-Hänchen (GH) and Imbert-Fedorov (IF) shifts on the surface of the uniaxial hyperbolic material hexagonal boron nitride (hBN) based on the biaxial hyperbolic material alpha-molybdenum (α-MoO3) trioxide structure, where the anisotropic axis of hBN is rotated by an angle with respect to the incident plane. The surface with the highest degree of anisotropy among the two crystals is selected in order to analyze and calculate the GH- and IF-shifts of the system, and obtain the complex beam-shift spectra. The addition of α-MoO3 substrate significantly amplified the GH shift on the system's surface, as compared to silica substrate. With the p-polarization light incident, the GH shift can reach 381.76λ0 at about 759.82 cm-1, with the s-polarization light incident, the GH shift can reach 288.84λ0 at about 906.88 cm-1, and with the c-polarization light incident, the IF shift can reach 3.76λ0 at about 751.94 cm-1. The adjustment of the IF shift, both positive and negative, as well as its asymmetric nature, can be achieved by manipulating the left and right circular polarization light and torsion angle. The aforementioned intriguing phenomena offer novel insights for the advancement of sensor technology and optical encoder design.
Benzimidazole fungicides are a class of highly effective, low-toxicity, systemic broad-spectrum fungicides developed in the 1960s and 1970s, based on the fungicidal activity of the benzimidazole ring structure. They exhibit biological activities including anticancer, antibacterial, and antiparasitic effects. Due to their particularly outstanding antibacterial properties, they are widely used in agriculture to prevent and control various plant diseases caused by fungi. The main products of benzimidazole fungicides include benomyl, carbendazim, thiabendazole, albendazole, thiophanate, thiophanate-methyl, fuberidazole, methyl (1-{[(5-cyanopentyl)amino]carbonyl}-1H-benzimidazol-2-yl) carbamate, and carbendazim salicylate. This article mainly reviews the physicochemical properties, toxicological properties, disease control efficacy, and pesticide residue and detection technologies of the aforementioned nine benzimidazole fungicides and their main metabolite (2-aminobenzimidazole). On this basis, a brief outlook on the future research directions of benzimidazole fungicides is presented.
Fungicides, Industrial , Fungicides, Industrial/pharmacology , Benzimidazoles/pharmacology , Benzimidazoles/metabolism , Carbamates/pharmacology , Thiophanate , Anti-Bacterial Agents
OBJECTIVES: To investigate the clinical characteristics and prognosis of pneumococcal meningitis (PM), and drug sensitivity of Streptococcus pneumoniae (SP) isolates in Chinese children. METHODS: A retrospective analysis was conducted on clinical information, laboratory data, and microbiological data of 160 hospitalized children under 15 years old with PM from January 2019 to December 2020 in 33 tertiary hospitals across the country. RESULTS: Among the 160 children with PM, there were 103 males and 57 females. The age ranged from 15 days to 15 years, with 109 cases (68.1%) aged 3 months to under 3 years. SP strains were isolated from 95 cases (59.4%) in cerebrospinal fluid cultures and from 57 cases (35.6%) in blood cultures. The positive rates of SP detection by cerebrospinal fluid metagenomic next-generation sequencing and cerebrospinal fluid SP antigen testing were 40% (35/87) and 27% (21/78), respectively. Fifty-five cases (34.4%) had one or more risk factors for purulent meningitis, 113 cases (70.6%) had one or more extra-cranial infectious foci, and 18 cases (11.3%) had underlying diseases. The most common clinical symptoms were fever (147 cases, 91.9%), followed by lethargy (98 cases, 61.3%) and vomiting (61 cases, 38.1%). Sixty-nine cases (43.1%) experienced intracranial complications during hospitalization, with subdural effusion and/or empyema being the most common complication [43 cases (26.9%)], followed by hydrocephalus in 24 cases (15.0%), brain abscess in 23 cases (14.4%), and cerebral hemorrhage in 8 cases (5.0%). Subdural effusion and/or empyema and hydrocephalus mainly occurred in children under 1 year old, with rates of 91% (39/43) and 83% (20/24), respectively. SP strains exhibited complete sensitivity to vancomycin (100%, 75/75), linezolid (100%, 56/56), and meropenem (100%, 6/6). High sensitivity rates were also observed for levofloxacin (81%, 22/27), moxifloxacin (82%, 14/17), rifampicin (96%, 25/26), and chloramphenicol (91%, 21/23). However, low sensitivity rates were found for penicillin (16%, 11/68) and clindamycin (6%, 1/17), and SP strains were completely resistant to erythromycin (100%, 31/31). The rates of discharge with cure and improvement were 22.5% (36/160) and 66.2% (106/160), respectively, while 18 cases (11.3%) had adverse outcomes. CONCLUSIONS: Pediatric PM is more common in children aged 3 months to under 3 years. Intracranial complications are more frequently observed in children under 1 year old. Fever is the most common clinical manifestation of PM, and subdural effusion/emphysema and hydrocephalus are the most frequent complications. Non-culture detection methods for cerebrospinal fluid can improve pathogen detection rates. Adverse outcomes can be noted in more than 10% of PM cases. SP strains are high sensitivity to vancomycin, linezolid, meropenem, levofloxacin, moxifloxacin, rifampicin, and chloramphenicol.
Empyema , Hydrocephalus , Meningitis, Pneumococcal , Subdural Effusion , Infant , Female , Male , Humans , Child , Infant, Newborn , Adolescent , Meningitis, Pneumococcal/drug therapy , Meningitis, Pneumococcal/epidemiology , Meropenem , Vancomycin , Levofloxacin , Linezolid , Moxifloxacin , Retrospective Studies , Rifampin , Streptococcus pneumoniae , Chloramphenicol
PURPOSE: Breast density has consistently been shown to be an independent risk factor for breast cancer in Western populations; however, few studies have evaluated this topic in Chinese women and there is not yet a unified view. This study investigated the association between mammographic density (MD) and breast cancer risk in Chinese women. METHODS: The PubMed, Cochrane Library, Embase, and Wanfang databases were systematically searched in June 2023 to include all studies on the association between MD and breast cancer risk in Chinese women. A total of 13,977 breast cancer cases from 14 studies were chosen, including 10 case-control/cross-sectional studies, and 4 case-only studies. For case-control/cross-sectional studies, the odds ratios (ORs) of MD were combined using random effects models, and for case-only studies, relative odds ratios (RORs) were combinations of premenopausal versus postmenopausal breast cancer cases. RESULTS: Women with BI-RADS density category II-IV in case-control/cross-sectional studies had a 0.93-fold (95% confidence interval [CI] 0.55, 1.57), 1.08-fold (95% CI 0.40, 2.94), and 1.24-fold (95% CI 0.42, 3.69) higher risk compared to women with the lowest density category. Combined RORs for premenopausal versus postmenopausal women in case-only studies were 3.84 (95% CI 2.92, 5.05), 22.65 (95% CI 7.21, 71.13), and 42.06 (95% CI 4.22, 419.52), respectively, for BI-RADS density category II-IV versus I. CONCLUSIONS: For Chinese women, breast cancer risk is weakly associated with MD; however, breast cancer risk is more strongly correlated with mammographic density in premenopausal women than postmenopausal women. Further research on the factors influencing MD in premenopausal women may provide meaningful insights into breast cancer prevention in China.
Breast Neoplasms , Female , Humans , Breast Neoplasms/diagnostic imaging , Breast Neoplasms/epidemiology , Breast Density , Mammography , Cross-Sectional Studies , Breast/diagnostic imaging , Risk Factors
PURPOSE: The relationship between thyroid hormone sensitivity and albuminuria remains unclear. We aimed to investigate the association between thyroid hormone sensitivity and the risk of albuminuria in a euthyroid population. METHODS: This cross-sectional study included 7634 euthyroid adults collected from the National Health and Nutrition Examination Survey (NHANES) between 2007 and 2012. Central sensitivity to thyroid hormones was evaluated using the thyroid-stimulating hormone index (TSHI), the thyrotrophic thyroxine resistance index (TT4RI), and the thyroid feedback quantile-based index (TFQI). Peripheral sensitivity to thyroid hormones was measured using the free triiodothyronine/free thyroxine (FT3/FT4) ratio. Furthermore, the independent relationship between sensitivity to thyroid hormones and albuminuria was assessed. RESULTS: The proportion of albuminuria increased with a higher interquartile range of TFQI levels (7.31% vs. 7.89% vs. 7.95% vs. 9.89%, P = 0.024). Furthermore, TFQI was found to be independently associated with the risk of albuminuria after adjusting for confounding factors (OR = 1.28, 95% CI 1.01-1.60, P = 0.037). Subgroup analysis revealed a significant relationship between TFQI and albuminuria, especially among individuals over 60. CONCLUSIONS: In euthyroid subjects, impaired central sensitivity to thyroid hormones is associated with albuminuria. TFQI holds significant potential as an epidemiological tool for quantifying the impact of impaired central sensitivity on the risk of albuminuria.
OBJECTIVE: Infections in patients with kidney stones after extracorporeal shockwave lithotripsy (SWL) is a common clinical issue. However, the associated factors are unclear. Therefore, we aim to develop and validate a predictive model for infections after SWL in patients with kidney stone. METHODS: Between June 2020 and May 2022, consecutive kidney stone patients were enrolled. Of them, 553 patients comprised the development cohort. One hundred sixty-five patients comprised the validation cohort. The data were prospectively collected. The stepwise selection was applied using the likelihood ratio test with Akaike's information criterion as the stopping rule; A predictive model was constructed through multivariate logistic regression. The performance was evaluated regarding discrimination, calibration, and clinical usefulness. RESULTS: Predictors of infections after SWL in treating kidney stones included older age (OR = 1.026, p = 0.041), female (OR = 2.066, p = 0.039), higher BMI (OR = 1.072, p = 0.039), lower stone density (OR = 0.995, p < 0.001), and higher grade of hydronephrosis (OR = 5.148, p < 0.001). For the validation cohort, the model showed good discrimination with an area under the receiver operating characteristic curve of 0.839 (95% CI 0.736, 0.941) and good calibration. Decision curve analysis demonstrated that the model was also clinically useful. CONCLUSION: This study indicated that age, gender, BMI, stone density, and hydronephrosis grade were significant predictors of infections after SWL in treating kidney stones. It provided evidence in optimizing prevention and perioperative treatment strategies to reduce the risk of infection after SWL.
Hydronephrosis , Kidney Calculi , Lithotripsy , Humans , Female , Prospective Studies , Kidney Calculi/therapy , Lithotripsy/adverse effects , Patients
Background: Cellular senescence is thought to play a significant role in the onset and development of diabetic nephropathy. The goal of this study was to explore potential biomarkers associated with diabetic glomerulopathy from the perspective of senescence. Methods: Datasets about human glomerular biopsy samples related to diabetic nephropathy were systematically obtained from the Gene Expression Omnibus database. Hub senescence-associated genes were investigated by differential gene analysis and Least Absolute Shrinkage and Selection Operator analysis. Cluster analysis was employed to identify senescence molecular subtypes. A single-cell dataset was used to validate the above findings and further evaluate the senescence environment. The relationship between these genes and the glomerular filtration rate was explored based on the Nephroseq database. These gene expressions have also been explored in various kidney diseases. Results: Twelve representative senescence-associated genes (VEGFA, IQGAP2, JUN, PLAT, ETS2, ANG, MMP14, VEGFC, SERPINE2, CXCR2, PTGES, and EGF) were finally identified. Biological changes in immune inflammatory response, cell cycle regulation, metabolic regulation, and immune microenvironment have been observed across different molecular subtypes. The above results were also validated based on single-cell analysis. Additionally, we also identified several significantly altered cell communication pathways, including COLLAGEN, PTN, LAMININ, SPP1, and VEGF. Finally, almost all these genes could well predict the occurrence of diabetic glomerulopathy based on receiver operating characteristic analysis and are associated with the glomerular filtration rate. These genes are differently expressed in various kidney diseases. Conclusion: The present study identified potential senescence-associated biomarkers and further explored the heterogeneity of diabetic glomerulopathy that might provide new insights into the diagnosis, assessment, management, and personalized treatment of DN.
Diabetes Mellitus , Diabetic Nephropathies , Humans , Diabetic Nephropathies/genetics , Serpin E2 , Kidney Glomerulus , Biomarkers , Computational Biology , ras GTPase-Activating Proteins
There are two mainstream approaches for object detection: top-down and bottom-up. The state-of-the-art approaches are mainly top-down methods. In this paper, we demonstrate that bottom-up approaches show competitive performance compared with top-down approaches and have higher recall rates. Our approach, named CenterNet, detects each object as a triplet of keypoints (top-left and bottom-right corners and the center keypoint). We first group the corners according to some designed cues and confirm the object locations based on the center keypoints. The corner keypoints allow the approach to detect objects of various scales and shapes and the center keypoint reduces the confusion introduced by a large number of false-positive proposals. Our approach is an anchor-free detector because it does not need to define explicit anchor boxes. We adapt our approach to backbones with different structures, including 'hourglass'-like networks and 'pyramid'-like networks, which detect objects in single-resolution and multi-resolution feature maps, respectively. On the MS-COCO dataset, CenterNet with Res2Net-101 and Swin-Transformer achieve average precisions (APs) of 53.7% and 57.1%, respectively, outperforming all existing bottom-up detectors and achieving state-of-the-art performance. We also design a real-time CenterNet model, which achieves a good trade-off between accuracy and speed, with an AP of 43.6% at 30.5 frames per second (FPS).
Semiconductor-based step-scheme (S-scheme) heterojunctions possess many merits toward mimicking natural photosynthesis. However, their applications for solar-to-chemical energy conversion are hindered by inefficient charge utilization and unsatisfactory surface reactivity. Herein, two synergistic protocols are demonstrated to overcome these limitations based on the construction of a hollow plasmonic p-metal-n S-scheme heterojunction photoreactor with spatially separated dual noble-metal-free cocatalysts. On one side, plasmonic Au, inserted into the heterointerfaces of CuS@ZnIn2 S4 core-shell nanoboxes, not only accelerates the transfer and recombination of useless charges, enabling a more thorough separation of useful ones for CO2 reduction and H2 O oxidation but also generates hot electrons and holes, respectively injects them into ZnIn2 S4 and CuS, further increasing the number of active carriers participating in redox reactions. On the other side, Fe(OH)x and Ti3 C2 cocatalysts, separately located on the CuS and ZnIn2 S4 surface, enrich the redox sites, adjust the reduction potential and pathway for selective CO2 -to-CH4 transformation, and balance the transfer and consumption of photocarriers. As expected, significantly enhanced activity and selectivity in CH4 production are achieved by the smart design along with nearly stoichiometric ratios of reduction and oxidation products. This study paves the way for optimizing artificial photosynthetic systems via rational interfacial channel introduction and surface cocatalyst modification.
A novel copper nanoparticle variant, denoted as Cu98Ni2 NPs, which incorporate Ni atoms in an atomically dispersed manner, has been successfully synthesized via a straightforward one-pot electrochemical codeposition process. These nanoparticles were subsequently employed as an anode to facilitate the oxidation of furfural, leading to the production of hydrogen gas. Voltammetric measurements revealed that the inclusion of trace amounts of Ni atoms in the nanoparticles resulted in a pronounced synergistic electronic effect between Cu and Ni. Consequently, a 43% increase in current density at 0.1 V was observed in comparison to pure Cu NPs. Importantly, when the Cu98Ni2 NPs were irradiated with visible light, a remarkable current density enhancement factor of 505% at 0.1 V was achieved relative to that of pure Cu NPs in the absence of light. This enhancement can be attributed to localized surface plasmon resonance induced by visible light, which triggers photothermal and photoelectric effects. These effects collectively contribute to the significant overall improvement in the electrocatalytic oxidation of furfural, leading to enhanced hydrogen evolution.
In the field of synthetic biology, rapid advancements in DNA assembly and editing have made it possible to manipulate large DNA, even entire genomes. These advancements have facilitated the introduction of long metabolic pathways, the creation of large-scale disease models, and the design and assembly of synthetic mega-chromosomes. Generally, the introduction of large DNA in host cells encompasses three critical steps: design-cloning-transfer. This review provides a comprehensive overview of the three key steps involved in large DNA transfer to advance the field of synthetic genomics and large DNA engineering.
The effect of pre-rolling on the microstructure and fatigue crack (FC) propagation resistance of the Al-Cu-Li alloy was studied using tensile testing, fatigue testing, transmission electron microscopy (TEM), X-ray diffractometer (XRD), and scanning electron microscopy (SEM). The results revealed that reducing the alloy thickness through pre-rolling by up to 12% enhanced both tensile strength and yield strength, albeit at the expense of reduced elongation. In addition, the FC growth rate decreased by up to 9% pre-rolling, reaching the minimum, while the application of additional mechanical stress during the pre-rolling increases this parameter. Deformations in the Al-Cu-Li alloy with less than a 9% thickness reduction were confined to the surface layer and did not extend to the central layer. This non-uniform deformation induced a compressive stress gradient in the thickness direction and led to an inhomogeneous distribution of T1 phase, resembling the structure generated by shot peening. The superior FC propagation resistance in the 9% pre-rolled alloy could be primarily attributed to the optimum balance of compressive residual stress and work hardening.
Introduction: The swift advancement of biotechnology has presented both opportunities and challenges to our society, thrusting biosafety to the forefront of concern. Consequently, the evaluation of rescue capabilities in the event of a bioterrorism incident becomes of paramount importance. Currently, there is a notable absence of specific measurement criteria and a comprehensive evaluation system. This paper aims to establish a systematic approach towards assessing emergency response capabilities in the context of bioterrorism incidents. Methods: We employed an enhanced Delphi methodology to establish an index evaluation framework. Subsequently, the weight of the judgment matrix was ascertained via the application of the fuzzy comprehensive evaluation approach. This led to the creation of a fuzzy comprehensive evaluation model for bioterrorism rescue capability. Results: A modified Delphi study was conducted involving 11 experts across two rounds, achieving a response rate of 100%. The Kendall coordination coefficients recorded in the first and second rounds were 0.303 and 0.632, respectively (P<0.05). Upon comprehensive analysis involving score, coefficient of variation, and full score ratio, we distinguished five primary indicators and 25 secondary indicators. Subsequently, an evaluation model was developed based on the Analytic Hierarchy Process (AHP) tailored to assess the response to a rescue from bioterrorism. Discussion: The expert panel confirmed consensus on all aspects of the model, validating its comprehensive content. The succeeding course of action involves converting the assessment model to a measurable scale, affirming its functionality, and implementing it in practical evaluation tasks to further enhance the capabilities of the biological incident rescue team.
