Search

Understanding and treating human diseases require valid animal models. Leveraging the genetic diversity in rhesus macaque populations across eight primate centers in the United States, we conduct targeted-sequencing on 1845 individuals for 374 genes linked to inherited human retinal and neurodevelopmental diseases. We identify over 47,000 single nucleotide variants, a substantial proportion of which are shared with human populations. By combining rhesus and human allele frequencies with established variant prediction methods, we develop a machine learning-based score that outperforms established methods in predicting missense variant pathogenicity. Remarkably, we find a marked number of loss-of-function variants and putative deleterious variants, which may lead to the development of rhesus disease models. Through phenotyping of macaques carrying a pathogenic OPA1:p.A8S variant, we identify a genetic model of autosomal dominant optic atrophy. Finally, we present a public website housing variant and genotype data from over two thousand rhesus macaques.

Xuejiao Liu, Lei Teng, Junzhu Dai, Hongxue Shao, Rui Chen, Haixiang Li, Jing Li, Huichao Zou Department of Pain Medicine, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, People’s Republic of ChinaCorrespondence: Huichao Zou, Email zouhuichao@hrbmu.edu.cnBackground: The neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR) are inflammatory biomarkers. Until now, it is unknown the impact of opioid dosage on perioperative immunity in glioma patients. The aim of this study was to explore the effect of intraoperative opioid dosage on perioperative immune perturbations using NLR and LMR as inflammatory biomarkers and evaluate the correlation between inflammatory biomarkers and pathological grade of glioma.Methods: The study included 208 patients with primary glioma who underwent glioma resection from February 2012 to November 2019 at Harbin Medical University Cancer Hospital. Complete blood count (CBC) was collected at 3 time points: one week before surgery, and 24 hours and one week after surgery. Patients were divided into high-dose and low-dose groups, based on the median value of intraoperative opioid dose. The relationships between perioperative NLR, LMR and intraoperative opioid dosage were analyzed using repeated measurement analysis of variance (ANOVA). Correlations between preoperative various factors and pathological grade were analyzed by Spearman analysis. Receiver operating characteristic (ROC) curves were performed to assess the predictive performance of the NLR and LMR for pathological grade.Results: The NLR (P=0.020) and lower LMR (P=0.037) were statistically significant different between high-dose and low-dose groups one week after surgery. The area under the curve (AUC) of the NLR to identify poor diagnosis was 0.685, which was superior to the LMR (AUC: 0.607) and indicated a correlation between the NLR with pathological grade. The preoperative NLR (P=0.000), LMR (P=0.009), age (P=0.000) and tumor size (P=0.001) e

A magnon is a collective excitation of the spin structure in a magnetic insulator and can transmit spin angular momentum with negligible dissipation. This quantum of a spin wave has always been manipulated through magnetic dipoles (that is, by breaking time-reversal symmetry). Here we report the experimental observation of chiral spin transport in multiferroic BiFeO3 and its control by reversing the ferroelectric polarization (that is, by breaking spatial inversion symmetry). The ferroelectrically controlled magnons show up to 18% modulation at room temperature. The spin torque that the magnons in BiFeO3 carry can be used to efficiently switch the magnetization of adjacent magnets, with a spin-torque efficiency comparable to the spin Hall effect in heavy metals. Utilizing such controllable magnon generation and transmission in BiFeO3, an all-oxide, energy-scalable logic is demonstrated composed of spin-orbit injection, detection and magnetoelectric control. Our observations open a new chapter of multiferroic magnons and pave another path towards low-dissipation nanoelectronics.

The development of the retina is under tight temporal and spatial control. To gain insights into the molecular basis of this process, we generate a single-nuclei dual-omic atlas of the human developing retina with approximately 220,000 nuclei from 14 human embryos and fetuses aged between 8 and 23-weeks post-conception with matched macular and peripheral tissues. This atlas captures all major cell classes in the retina, along with a large proportion of progenitors and cell-type-specific precursors. Cell trajectory analysis reveals a transition from continuous progression in early progenitors to a hierarchical development during the later stages of cell type specification. Both known and unrecorded candidate transcription factors, along with gene regulatory networks that drive the transitions of various cell fates, are identified. Comparisons between the macular and peripheral retinae indicate a largely consistent yet distinct developmental pattern. This atlas offers unparalleled resolution into the transcriptional and chromatin accessibility landscapes during development, providing an invaluable resource for deeper insights into retinal development and associated diseases.

As the industrial sector develops, the performance requirements for aluminum alloys are also constantly improving. The study explores how aging and rolling treatment affect aluminum alloys' precipitates and mechanical properties by controlling the parameters of aging process and rolling deformation variables. 7N01 aluminum alloy was selected as the experimental object, and the samples were treated with non-aging, natural aging, artificial aging, and rolling deformation. How aging processes and rolling deforming affected alloys’ mechanical properties was evaluated through performance testing (mechanical and tensile testing) and material fiber characterization methods (advanced electronic instruments). These results confirmed that the combination of three aging pre-treatments + R20 % + 120 °C re-aging could significantly improve the hardness of aluminum alloys and maintain high ductility. As the deformation decreased, the time for the sample to reach the hardness peak was shorter and the hardness was higher. The 20 % deformation sample’s strength was better than the 80 % deformation sample’s. This confirmed that appropriate aging process and deformation combinations could improve the strength and hardness of aluminum alloys. These results have certain guiding significance for optimizing the aging process of mechanical extruded aluminum alloys and provide reference for the study of related alloy properties.

Background: Menopausal hormone therapy (MHT), a common treatment to relieve symptoms of menopause, is associated with a lower risk of colorectal cancer (CRC). To inform CRC risk prediction and MHT risk-benefit assessment, we aimed to evaluate the joint association of a polygenic risk score (PRS) for CRC and MHT on CRC risk. Methods: We used data from 28,486 postmenopausal women (11,519 cases and 16,967 controls) of European descent. A PRS based on 141 CRC-associated genetic variants was modeled as a categorical variable in quartiles. Multiplicative interaction between PRS and MHT use was evaluated using logistic regression. Additive interaction was measured using the relative excess risk due to interaction (RERI). 30-year cumulative risks of CRC for 50-year-old women according to MHT use and PRS were calculated. Results: The reduction in odds ratios by MHT use was larger in women within the highest quartile of PRS compared to that in women within the lowest quartile of PRS (p-value = 2.7 × 10−8). At the highest quartile of PRS, the 30-year CRC risk was statistically significantly lower for women taking any MHT than for women not taking any MHT, 3.7% (3.3%–4.0%) vs 6.1% (5.7%–6.5%) (difference 2.4%, P-value = 1.83 × 10−14); these differences were also statistically significant but smaller in magnitude in the lowest PRS quartile, 1.6% (1.4%–1.8%) vs 2.2% (1.9%–2.4%) (difference 0.6%, P-value = 1.01 × 10−3), indicating 4 times greater reduction in absolute risk associated with any MHT use in the highest compared to the lowest quartile of genetic CRC risk. Conclusions: MHT use has a greater impact on the reduction of CRC risk for women at higher genetic risk. These findings have implications for the development of risk prediction models for CRC and potentially for the consideration of genetic information in the risk-benefit assessment of MHT use.

In the landfills, the degradation of municipal solid wastes (MSWs) generates greenhouse gases and releases heat (i.e., self-heating). Although vegetation has been commonly found in the landfill cover system, there is no model to predict nonisothermal gas conductivity function (GCF), and how the elevation of temperature would affect gas transport of unsaturated rooted soils remains unknown. This study aims to propose a new model to predict the nonisothermal GCFs of unsaturated rooted soils considering temperature effects on soil thermodynamic properties and soil water retention curves. The newly proposed model was validated against the measured GCFs of unsaturated rooted sandy soil under different temperatures and degrees of saturation (Sr) by a tailor-designed experimental apparatus. Test results showed that the gas conductivity (Kg) increased by about one order of magnitude with the reductions in Sr. There existed a threshold Sr (approximately 0.65), above which the gas phase became discontinuous, causing a significant drop in Kg. Moreover, Kg reduced with increasing temperature, mainly because of increased gas dynamic viscosity; but the preferential path of gas transport induced by root shrinkage due to temperature elevation might counteract these effects to a certain extent. Unlike existing models requiring experimental data at different temperatures, the newly proposed model needs only one set of data measured at ambient temperature. The proposed GCF model could satisfactorily capture the temperature effects on unsaturated rooted soils (with average R2 and average RMSE of 0.903 and 1.88×10−7 m/s, respectively), especially for temperature range less than 323.15 K (50 °C) before potential root death. © 2024 Elsevier Ltd

The [O III] $\lambda\lambda\ 4960,5008$ emission lines in the optical spectra of galaxies and quasars have been widely used to investigate the possible variation of the fine-structure constant $\alpha$ over cosmic time. In this work, we utilize the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) quasar survey, for the first time, to measure the relative $\alpha$ variation $\Delta\alpha/\alpha$ in time through the [O III] doublet method. From the LAMOST Data Release 9 quasar catalog, we refine a sample of 209 quasar spectra with strong and narrow [O III] emission lines over a redshift range of $0

Background: Menopausal hormone therapy (MHT), a common treatment to relieve symptoms of menopause, is associated with a lower risk of colorectal cancer (CRC). To inform CRC risk prediction and MHT risk-benefit assessment, we aimed to evaluate the joint association of a polygenic risk score (PRS) for CRC and MHT on CRC risk. Methods: We used data from 28,486 postmenopausal women (11,519 cases and 16,967 controls) of European descent. A PRS based on 141 CRC-associated genetic variants was modeled as a categorical variable in quartiles. Multiplicative interaction between PRS and MHT use was evaluated using logistic regression. Additive interaction was measured using the relative excess risk due to interaction (RERI). 30-year cumulative risks of CRC for 50-year-old women according to MHT use and PRS were calculated. Results: The reduction in odds ratios by MHT use was larger in women within the highest quartile of PRS compared to that in women within the lowest quartile of PRS (p-value = 2.7 × 10−8). At the highest quartile of PRS, the 30-year CRC risk was statistically significantly lower for women taking any MHT than for women not taking any MHT, 3.7% (3.3%–4.0%) vs 6.1% (5.7%–6.5%) (difference 2.4%, P-value = 1.83 × 10−14); these differences were also statistically significant but smaller in magnitude in the lowest PRS quartile, 1.6% (1.4%–1.8%) vs 2.2% (1.9%–2.4%) (difference 0.6%, P-value = 1.01 × 10−3), indicating 4 times greater reduction in absolute risk associated with any MHT use in the highest compared to the lowest quartile of genetic CRC risk. Conclusions: MHT use has a greater impact on the reduction of CRC risk for women at higher genetic risk. These findings have implications for the development of risk prediction models for CRC and potentially for the consideration of genetic information in the risk-benefit assessment of MHT use.

Background: Menopausal hormone therapy (MHT), a common treatment to relieve symptoms of menopause, is associated with a lower risk of colorectal cancer (CRC). To inform CRC risk prediction and MHT risk-benefit assessment, we aimed to evaluate the joint association of a polygenic risk score (PRS) for CRC and MHT on CRC risk. Methods: We used data from 28,486 postmenopausal women (11,519 cases and 16,967 controls) of European descent. A PRS based on 141 CRC-associated genetic variants was modeled as a categorical variable in quartiles. Multiplicative interaction between PRS and MHT use was evaluated using logistic regression. Additive interaction was measured using the relative excess risk due to interaction (RERI). 30-year cumulative risks of CRC for 50-year-old women according to MHT use and PRS were calculated. Results: The reduction in odds ratios by MHT use was larger in women within the highest quartile of PRS compared to that in women within the lowest quartile of PRS (p-value = 2.7 × 10−8). At the highest quartile of PRS, the 30-year CRC risk was statistically significantly lower for women taking any MHT than for women not taking any MHT, 3.7% (3.3%–4.0%) vs 6.1% (5.7%–6.5%) (difference 2.4%, P-value = 1.83 × 10−14); these differences were also statistically significant but smaller in magnitude in the lowest PRS quartile, 1.6% (1.4%–1.8%) vs 2.2% (1.9%–2.4%) (difference 0.6%, P-value = 1.01 × 10−3), indicating 4 times greater reduction in absolute risk associated with any MHT use in the highest compared to the lowest quartile of genetic CRC risk. Conclusions: MHT use has a greater impact on the reduction of CRC risk for women at higher genetic risk. These findings have implications for the development of risk prediction models for CRC and potentially for the consideration of genetic information in the risk-benefit assessment of MHT use.

Haojun Ma,1– 3,* Rui Chen,1,* Nannan Han,1– 3 Hanming Ge,1 Shilin Li,1 Yanfei Wang,1 Xudong Yan,1,3 Chengxue Du,1,3 Yanjun Gao,4 Gejuan Zhang,1– 3 Mingze Chang1– 3 1Department of Neurology, The Affiliated Hospital of Northwest University, Xi’an No.3 hospital, Xi’an, People’s Republic of China; 2Xi’an Key Laboratory of Cardiovascular and Cerebrovascular Diseases, The Affiliated Hospital of Northwest University, Xi’an No.3 hospital, Xi’an, People’s Republic of China; 3Neurological Intensive Care Unit, The Affiliated Hospital of Northwest University, Xi’an No.3 hospital, Xi’an, People’s Republic of China; 4Department of Radiology, The Affiliated Hospital of Northwest University, Xi’an No.3 hospital, Xi’an, People’s Republic of China*These authors contributed equally to this workCorrespondence: Mingze Chang, Department of Neurology, The Affiliated Hospital of Northwest University, Xi’an No.3 hospital, Xi’an, 710069, People’s Republic of China, Email changmingze191@163.comBackground: Stroke, particularly due to large vessel occlusion (LVO), is a major cause of mortality and disability globally. Endovascular therapy (ET) significantly improves outcomes for acute ischemic stroke (AIS) patients, but complications such as stroke-associated pneumonia (SAP) increase mortality and healthcare costs. This study investigates the association between blood-brain barrier (BBB) disruption and the increased risk of SAP and explores the relationship between BBB disruption and medium-term functional outcomes.Methods: The retrospective cohort study was performed on AIS patients enrolled between January 2019 to February 2023 who underwent ET. Patients were divided into two groups: BBB disruption and without BBB disruption. Multiple logistic regression model was conducted to measure the association between BBB disruption and SAP. Mediation analysis was used to estimate the potential mediation effects on the associations of

Carbon emissions from landfill construction and management have become a global concern. Life cycle analysis (LCA) has been widely used to assess the environmental impacts of engineered infrastructures over their lifetimes. LCA has also been applied to landfill leachate and gas management, but rarely to landfill final cover systems. This paper reports the results of an LCA of the following landfill final cover systems: compacted clay cover, geomembrane cover, cover with capillary effects (CCBE), dual capillary barrier cover, three-layer landfill cover system using natural soils, three-layer cover using recycled concrete aggregate (RCA) and biochar-amended three-layer landfill cover system using RCA. The LCA assessment of landfill cover considers the cost, carbon emissions and carbon sequestration during the production, construction and operation phases. The effects of landfill cover on global warming, freshwater eutrophication, terrestrial ecotoxicity, freshwater ecotoxicity, marine ecotoxicity and fossil resource scarcity are also evaluated. In addition, the sensitivities of cost and carbon emission to the use of electric-powered machines and transportation distance are analysed. It is revealed that the three-layer cover system using RCA and biochar has the lowest unit cost and carbon emission of all of the covers, up to 88 % and 66 % lower, respectively, than those of the other six covers. In addition, this cover system has the highest carbon sequestration rate, with a value of 47.9 kg CO2/(y·m2), four times higher than that of the compacted clay cover. Finally, this sustainable cover mitigates global warming and reduces adverse environmental impacts by up to 82 %. Therefore, the biochar amended three-layer cover system using RCA without geomembrane offers the greatest economic benefits, performs effectively in terms of the pursuit of carbon neutrality and promotes sustainable development. © 2023 Elsevier B.V.

Honghua Shen,1,2 Yiming Xu,2 Yin Zhang,2 Lei Ren,2 Rui Chen1 1Department of Respiratory Medicine, the Second Affiliated Hospital of Soochow University, Suzhou, 215004, People’s Republic of China; 2Department of Respiratory Rehabilitation, the Fourth Rehabilitation Hospital of Shanghai, Shanghai, 200040, People’s Republic of ChinaCorrespondence: Rui Chen, Department of Respiratory Medicine, the Second Affiliated Hospital of Soochow University, Suzhou, 215004, People’s Republic of China, Email chenruigood@126.comPurpose: We assess the predictive value of diaphragm excursion (DE) in enhancing exercise tolerance following pulmonary rehabilitation (PR) among patients with COPD-OSA overlap syndrome.Material and Methods: This prospective cohort study enrolled 63 patients diagnosed with COPD-OSA overlap syndrome who actively participated in a PR program from January 2021 to May 2023. Among these, 58 patients successfully completed the 20-week PR program, with exercise tolerance assessed through the measurement of six-minute walk distance (6MWD), and DE evaluated by ultrasonography. The responder to PR in terms of exercise ability was defined as a patient who showed an increase of > 30m in 6MWD. The cutoff value for predicting PR response based on DE was determined using receiver operating characteristic (ROC) curves.Results: Following the PR program, significant improvements were observed in mMRC, 6MWD, DE during deep breathing, and diaphragm thickness fraction (DTF). Of the participants, 33 patients (57%) were classified as responders, while 25 patients (43%) were considered non-responders. Baseline values of FEV1% predicted, 6MWD, DE during deep breathing, DTF, and PaO2 exhibited a significant elevation in responders as compared to non-responders. The changes of 6MWD were positively associated with the baseline values of DTF and DE during deep breathing, FEV1% predicted and PaO2, while negatively correlated with baseline value of mMRC. The predictive performance

Globe-LFMC 2.0, an updated version of Globe-LFMC, is a comprehensive dataset of over 280,000 Live Fuel Moisture Content (LFMC) measurements. These measurements were gathered through field campaigns conducted in 15 countries spanning 47 years. In contrast to its prior version, Globe-LFMC 2.0 incorporates over 120,000 additional data entries, introduces more than 800 new sampling sites, and comprises LFMC values obtained from samples collected until the calendar year 2023. Each entry within the dataset provides essential information, including date, geographical coordinates, plant species, functional type, and, where available, topographical details. Moreover, the dataset encompasses insights into the sampling and weighing procedures, as well as information about land cover type and meteorological conditions at the time and location of each sampling event. Globe-LFMC 2.0 can facilitate advanced LFMC research, supporting studies on wildfire behaviour, physiological traits, ecological dynamics, and land surface modelling, whether remote sensing-based or otherwise. This dataset represents a valuable resource for researchers exploring the diverse LFMC aspects, contributing to the broader field of environmental and ecological research.

Chenqi Chang,1 Heng Liu,2 Xiaotong Li,2 Dandan Song,1 Yue Liu,1 Chang Lu,1 Yu Zhen,1 Ying Chen,1 Jinguo Xu,1 Weidong Li,1 Xiaobin Jia,3 Zhipeng Chen,1 Rui Chen1 1School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, People’s Republic of China; 2Yunnan Provincial Key Laboratory of Entomological Biopharmaceutical R&D, College of Pharmacy, Dali University, Dali, 671003, People’s Republic of China; 3School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, 211198, People’s Republic of ChinaCorrespondence: Rui Chen; Zhipeng Chen, Email chenrui@njucm.edu.cn; czpcpu2000@hotmail.comIntroduction: Enhancing the efficacy of berberine (BBR) in the treatment of ulcerative colitis (UC) through the development of dopamine-coated berberine nanoparticles (PDA@BBR NPs) with ROS-responsive and adhesive properties.Methods: Berberine nanoparticles (BBR NPs) were synthesized using the nonsolvent precipitation method, and their surfaces were coated with polydopamine (PDA) through oxidative polymerization. The PDA@BBR NPs were characterized by transmission electron microscopy (TEM), size analysis, and zeta potential analysis. Drug loading and encapsulation efficiency were analyzed using fluorescence spectroscopy. The responsiveness of these nanoparticles to reactive oxygen species (ROS) was assessed in vitro, while their adhesive properties and therapeutic efficacy on UC were evaluated in vivo.Results: Physicochemical property studies showed that PDA coated BBR NPs nanoparticles have good dispersion and stability. In vitro results showed that PDA@BBR NPs could prolong the retention time of the drug at the colonic site and could realize the gradual drug release under ROS environment. In addition, animal studies showed that PDA@BBR NPs exhibited significant anti-inflammatory effects on DSS-induced colitis and effectively reduced intestinal mucosal damage.Conclusion: PDA@BBR NPs are ROS-responsive nanoparticles that adhere well and have

Background: Menopausal hormone therapy (MHT), a common treatment to relieve symptoms of menopause, is associated with a lower risk of colorectal cancer (CRC). To inform CRC risk prediction and MHT risk-benefit assessment, we aimed to evaluate the joint association of a polygenic risk score (PRS) for CRC and MHT on CRC risk. Methods: We used data from 28,486 postmenopausal women (11,519 cases and 16,967 controls) of European descent. A PRS based on 141 CRC-associated genetic variants was modeled as a categorical variable in quartiles. Multiplicative interaction between PRS and MHT use was evaluated using logistic regression. Additive interaction was measured using the relative excess risk due to interaction (RERI). 30-year cumulative risks of CRC for 50-year-old women according to MHT use and PRS were calculated. Results: The reduction in odds ratios by MHT use was larger in women within the highest quartile of PRS compared to that in women within the lowest quartile of PRS (p-value = 2.7 × 10−8). At the highest quartile of PRS, the 30-year CRC risk was statistically significantly lower for women taking any MHT than for women not taking any MHT, 3.7% (3.3%–4.0%) vs 6.1% (5.7%–6.5%) (difference 2.4%, P-value = 1.83 × 10−14); these differences were also statistically significant but smaller in magnitude in the lowest PRS quartile, 1.6% (1.4%–1.8%) vs 2.2% (1.9%–2.4%) (difference 0.6%, P-value = 1.01 × 10−3), indicating 4 times greater reduction in absolute risk associated with any MHT use in the highest compared to the lowest quartile of genetic CRC risk. Conclusions: MHT use has a greater impact on the reduction of CRC risk for women at higher genetic risk. These findings have implications for the development of risk prediction models for CRC and potentially for the consideration of genetic information in the risk-benefit assessment of MHT use.

Background: Menopausal hormone therapy (MHT), a common treatment to relieve symptoms of menopause, is associated with a lower risk of colorectal cancer (CRC). To inform CRC risk prediction and MHT risk-benefit assessment, we aimed to evaluate the joint association of a polygenic risk score (PRS) for CRC and MHT on CRC risk. Methods: We used data from 28,486 postmenopausal women (11,519 cases and 16,967 controls) of European descent. A PRS based on 141 CRC-associated genetic variants was modeled as a categorical variable in quartiles. Multiplicative interaction between PRS and MHT use was evaluated using logistic regression. Additive interaction was measured using the relative excess risk due to interaction (RERI). 30-year cumulative risks of CRC for 50-year-old women according to MHT use and PRS were calculated. Results: The reduction in odds ratios by MHT use was larger in women within the highest quartile of PRS compared to that in women within the lowest quartile of PRS (p-value = 2.7 × 10−8). At the highest quartile of PRS, the 30-year CRC risk was statistically significantly lower for women taking any MHT than for women not taking any MHT, 3.7% (3.3%–4.0%) vs 6.1% (5.7%–6.5%) (difference 2.4%, P-value = 1.83 × 10−14); these differences were also statistically significant but smaller in magnitude in the lowest PRS quartile, 1.6% (1.4%–1.8%) vs 2.2% (1.9%–2.4%) (difference 0.6%, P-value = 1.01 × 10−3), indicating 4 times greater reduction in absolute risk associated with any MHT use in the highest compared to the lowest quartile of genetic CRC risk. Conclusions: MHT use has a greater impact on the reduction of CRC risk for women at higher genetic risk. These findings have implications for the development of risk prediction models for CRC and potentially for the consideration of genetic information in the risk-benefit assessment of MHT use.

Recycled concrete aggregate (RCA) can be used in landfill and slope cover systems, where its unsaturated hydraulic properties play an important role. The hydraulic behavior of RCAs can be potentially controlled through mixing coarse and fine aggregates by a pre-determined proportion, which lacks experimental validation. In this study, water retention curve (WRC) and permeability function (PF) of RCAs with different fine aggregate contents (fc) were investigated. Experiments on 9 RCAs with different gradations reveal that when fc is between 20% and 30%, a second air entry value appears, and the shape of both WRC and PF becomes bimodal, which is probably resulted from an uneven dual-peak pore size distribution. With an increasing fc, the first air entry value and the residual water content increases. The unsaturated coefficient of permeability decreases with the increasing fc for a low suction level ( 0.1 kPa), but increases with the raise of fc for high suction ( 10 kPa). Overall, adopting a properly mixed RCA with fc value of less than 10% or over 40%, instead of a narrowly graded RCA, is recommended for coarse- and fine-grained layers in a capillary barrier, respectively.

Precise identification of spatial unit functional features in the city is a pre-condition for urban planning and policy-making. However, inferring unknown attributes of urban spatial units from data mining of spatial interaction remains a challenge in geographic information science. Although neural-network approaches have been widely applied to this field, urban dynamics, spatial semantics, and their relationship with urban functional features have not been deeply discussed. To this end, we proposed semantic-enhanced graph convolutional neural networks (GCNNs) to facilitate the multi-scale embedding of urban spatial units, based on which the identification of urban land use is achieved by leveraging the characteristics of human mobility extracted from the largest mobile phone datasets to date. Given the heterogeneity of multi-modal spatial data, we introduced the combination of a systematic data-alignment method and a generative feature-fusion method for the robust construction of heterogeneous graphs, providing an adaptive solution to improve GCNNs’ performance in node-classification tasks. Our work explicitly examined the scale effect on GCNN backbones, for the first time. The results prove that large-scale tasks are more sensitive to the directionality of spatial interaction, and small-scale tasks are more sensitive to the adjacency of spatial interaction. Quantitative experiments conducted in Shenzhen demonstrate the superior performance of our proposed framework compared to state-of-the-art methods. The best accuracy is achieved by the inductive GraphSAGE model at the scale of 250 m, exceeding the baseline by 25.4%. Furthermore, we innovatively explained the role of spatial-interaction factors in the identification of urban land use through the deep learning method. © 2024 by the authors.

Safety Index Synthesis (SIS) is critical for deriving safe control laws. Recent works propose to synthesize a safety index (SI) via nonlinear programming and derive a safe control law such that the system 1) achieves forward invariant (FI) with some safe set and 2) guarantees finite time convergence (FTC) to that safe set. However, real-world system dynamics can vary during run-time, making the control law infeasible and invalidating the initial SI. Since the full SIS nonlinear programming is computationally expensive, it is infeasible to re-synthesize the SI each time the dynamics are perturbed. To address that, this paper proposes an efficient approach to adapting the SI to varying system dynamics and maintaining the feasibility of the safe control law. The proposed method leverages determinant gradient ascend and derives a closed-form update to safety index parameters, enabling real-time adaptation performance. A numerical study validates the effectiveness of our approach., Comment: Accepted to American Control Conference (ACC) 2024

Material properties, crucial design parameters in landfill cover systems, can exhibit spatial variability due to non-uniform particle size and uneven compaction, affecting cover performance. Additionally, cover thickness, another key design parameter, can induce stress variation and influence soil-water retention capability. Previous designs always ignored these uncertainties and stress effects, potentially leading to unreasonable design schemes. This study aims to provide design recommendations for a three-layer landfill cover system under heavy rainfall, considering these factors simultaneously by the random finite element method (RFEM). The stress effects are captured using a stress-dependent soil-water retention curve (SDSWRC) and the hysteresis in the SDSWRC is considered in probabilistic analyses. For practical applications, uncertainties in two easily controllable particle size parameters, D10 and D60, are depicted by copula-based cross-correlated random fields. Based on empirical equations, the spatial variability of SDSWRC and water permeability function (WPF) can be further characterised by D10 and D60 random fields. It is found that considering SDSWRC and its spatial variability (e.g., shape) can overestimate percolation by at least 1.7 times compared to deterministic analysis using SWRC. The spatial variability of SDSWRC shape has a more significant impact than spatially variable saturated water permeability on cover system performance. Among candidate copulas, the CClayton copula (survival copula of Clayton copula) yields the most conservative results due to its upper tail dependence. The particle size and thickness of the bottom layer influence the cover system performance more than the upper two layers. A bottom layer with D10

Hydrochar is an urban soil amender that supports plant growth on slopes. It is a biomass-derived carbon-rich material produced by the hydrothermal carbonation process; thus, it has a different pore structure from biochar produced by pyrolysis. The effects of hydrochar on the hydraulic properties of unsaturated compacted soils and the underlying pore-level soil-hydrochar interaction mechanisms are unclear. In this study, silty-clay sand was amended by grass-derived hydrochars produced at two hydrothermal carbonisation temperatures (180 and 240 degrees C, denoted as H-180 and H-240, respectively) with different mass proportions (fH\textbackslash{}documentclass[12pt]{minimal} \textbackslash{}usepackage{amsmath} \textbackslash{}usepackage{wasysym} \textbackslash{}usepackage{amsfonts} \textbackslash{}usepackage{amssymb} \textbackslash{}usepackage{amsbsy} \textbackslash{}usepackage{mathrsfs} \textbackslash{}usepackage{upgreek} \textbackslash{}setlength{\textbackslash{}oddsidemargin}{-69pt} \textbackslash{}begin{document}$${f}_{{\textbackslash{}text{H\ }}}$$\textbackslash{}end{document}). The pore and throat size distributions, water retention curves (WRCs) and hydraulic conductivity functions (HCFs) of the soils with and without amendment were measured. The results showed that hydrochar improved the soil water retention capability as the smaller hydrochar particles filled the soil pores with diameters larger than 290 mu m. Compared with the increase in air-entry value made by H-180, the one made by H-240 was more substantial as this type of hydrochar altered the soil pores to be smaller in size. Hydrochar also increased the hydraulic conductivity of the soil by half to one order of magnitude due to the increase in the throat frequency and the presence of hydrochar intra-pores. One exception was the amendment by H-180 at fH\textbackslash{}documentclass[12pt]{minimal} \textbackslash{}usepackage{amsmath} \textbackslash{}usepackage{wasysym} \textbackslash{}usepackage{amsfonts

Traditional sequential recommendation methods assume that users' sequence data is clean enough to learn accurate sequence representations to reflect user preferences. In practice, users' sequences inevitably contain noise (e.g., accidental interactions), leading to incorrect reflections of user preferences. Consequently, some pioneer studies have explored modeling sequentiality and correlations in sequences to implicitly or explicitly reduce noise's influence. However, relying on only available intra-sequence information (i.e., sequentiality and correlations in a sequence) is insufficient and may result in over-denoising and under-denoising problems (OUPs), especially for short sequences. To improve reliability, we propose to augment sequences by inserting items before denoising. However, due to the data sparsity issue and computational costs, it is challenging to select proper items from the entire item universe to insert into proper positions in a target sequence. Motivated by the above observation, we propose a novel framework--Self-augmented Sequence Denoising for sequential Recommendation (SSDRec) with a three-stage learning paradigm to solve the above challenges. In the first stage, we empower SSDRec by a global relation encoder to learn multi-faceted inter-sequence relations in a data-driven manner. These relations serve as prior knowledge to guide subsequent stages. In the second stage, we devise a self-augmentation module to augment sequences to alleviate OUPs. Finally, we employ a hierarchical denoising module in the third stage to reduce the risk of false augmentations and pinpoint all noise in raw sequences. Extensive experiments on five real-world datasets demonstrate the superiority of \model over state-of-the-art denoising methods and its flexible applications to mainstream sequential recommendation models. The source code is available at https://github.com/zc-97/SSDRec., Comment: ICDE 2024

Fine-tuning LLMs is crucial to enhancing their task-specific performance and ensuring model behaviors are aligned with human preferences. Among various fine-tuning methods, LoRA is popular for its efficiency and ease to use, allowing end-users to easily post and adopt lightweight LoRA modules on open-source platforms to tailor their model for different customization. However, such a handy share-and-play setting opens up new attack surfaces, that the attacker can render LoRA as an attacker, such as backdoor injection, and widely distribute the adversarial LoRA to the community easily. This can result in detrimental outcomes. Despite the huge potential risks of sharing LoRA modules, this aspect however has not been fully explored. To fill the gap, in this study we thoroughly investigate the attack opportunities enabled in the growing share-and-play scenario. Specifically, we study how to inject backdoor into the LoRA module and dive deeper into LoRA's infection mechanisms. We found that training-free mechanism is possible in LoRA backdoor injection. We also discover the impact of backdoor attacks with the presence of multiple LoRA adaptions concurrently as well as LoRA based backdoor transferability. Our aim is to raise awareness of the potential risks under the emerging share-and-play scenario, so as to proactively prevent potential consequences caused by LoRA-as-an-Attack. Warning: the paper contains potential offensive content generated by models.

Recommender systems (RSs) have become an essential tool for mitigating information overload in a range of real-world applications. Recent trends in RSs have revealed a major paradigm shift, moving the spotlight from model-centric innovations to data-centric efforts (e.g., improving data quality and quantity). This evolution has given rise to the concept of data-centric recommender systems (Data-Centric RSs), marking a significant development in the field. This survey provides the first systematic overview of Data-Centric RSs, covering 1) the foundational concepts of recommendation data and Data-Centric RSs; 2) three primary issues of recommendation data; 3) recent research developed to address these issues; and 4) several potential future directions of Data-Centric RSs., Comment: 9 pages, 5 figures

Negative sampling is essential for implicit collaborative filtering to provide proper negative training signals so as to achieve desirable performance. We experimentally unveil a common limitation of all existing negative sampling methods that they can only select negative samples of a fixed hardness level, leading to the false positive problem (FPP) and false negative problem (FNP). We then propose a new paradigm called adaptive hardness negative sampling (AHNS) and discuss its three key criteria. By adaptively selecting negative samples with appropriate hardnesses during the training process, AHNS can well mitigate the impacts of FPP and FNP. Next, we present a concrete instantiation of AHNS called AHNS_{p<0}, and theoretically demonstrate that AHNS_{p<0} can fit the three criteria of AHNS well and achieve a larger lower bound of normalized discounted cumulative gain. Besides, we note that existing negative sampling methods can be regarded as more relaxed cases of AHNS. Finally, we conduct comprehensive experiments, and the results show that AHNS_{p<0} can consistently and substantially outperform several state-of-the-art competitors on multiple datasets., Comment: Accepted by AAAI2024

This paper presents GenH2R, a framework for learning generalizable vision-based human-to-robot (H2R) handover skills. The goal is to equip robots with the ability to reliably receive objects with unseen geometry handed over by humans in various complex trajectories. We acquire such generalizability by learning H2R handover at scale with a comprehensive solution including procedural simulation assets creation, automated demonstration generation, and effective imitation learning. We leverage large-scale 3D model repositories, dexterous grasp generation methods, and curve-based 3D animation to create an H2R handover simulation environment named \simabbns, surpassing the number of scenes in existing simulators by three orders of magnitude. We further introduce a distillation-friendly demonstration generation method that automatically generates a million high-quality demonstrations suitable for learning. Finally, we present a 4D imitation learning method augmented by a future forecasting objective to distill demonstrations into a visuo-motor handover policy. Experimental evaluations in both simulators and the real world demonstrate significant improvements (at least +10\% success rate) over baselines in all cases. The project page is https://GenH2R.github.io/., Comment: The project page is https://GenH2R.github.io

We present a novel generative 3D modeling system, coined CraftsMan, which can generate high-fidelity 3D geometries with highly varied shapes, regular mesh topologies, and detailed surfaces, and, notably, allows for refining the geometry in an interactive manner. Despite the significant advancements in 3D generation, existing methods still struggle with lengthy optimization processes, irregular mesh topologies, noisy surfaces, and difficulties in accommodating user edits, consequently impeding their widespread adoption and implementation in 3D modeling software. Our work is inspired by the craftsman, who usually roughs out the holistic figure of the work first and elaborates the surface details subsequently. Specifically, we employ a 3D native diffusion model, which operates on latent space learned from latent set-based 3D representations, to generate coarse geometries with regular mesh topology in seconds. In particular, this process takes as input a text prompt or a reference image and leverages a powerful multi-view (MV) diffusion model to generate multiple views of the coarse geometry, which are fed into our MV-conditioned 3D diffusion model for generating the 3D geometry, significantly improving robustness and generalizability. Following that, a normal-based geometry refiner is used to significantly enhance the surface details. This refinement can be performed automatically, or interactively with user-supplied edits. Extensive experiments demonstrate that our method achieves high efficacy in producing superior-quality 3D assets compared to existing methods. HomePage: https://craftsman3d.github.io/, Code: https://github.com/wyysf-98/CraftsMan, Comment: HomePage: https://craftsman3d.github.io/, Code: https://github.com/wyysf-98/CraftsMan

We consider solving a combinatorial optimization problem with an unknown linear constraint using a membership oracle that, given a solution, determines whether it is feasible or infeasible with absolute certainty. The goal of the decision maker is to find the best possible solution subject to a budget on the number of oracle calls. Inspired by active learning based on Support Vector Machines (SVMs), we adapt a classical framework in order to solve the problem by learning and exploiting a surrogate linear constraint. The resulting new framework includes training a linear separator on the labeled points and selecting new points to be labeled, which is achieved by applying a sampling strategy and solving a 0-1 integer linear program. Following the active learning literature, one can consider using SVM as a linear classifier and the information-based sampling strategy known as Simple margin. We improve on both sides: we propose an alternative sampling strategy based on mixed-integer quadratic programming and a linear separation method inspired by an algorithm for convex optimization in the oracle model. We conduct experiments on the pure knapsack problem and on a college study plan problem from the literature to show how different linear separation methods and sampling strategies influence the quality of the results in terms of objective value.

Surface integral equations (SIEs)-based boundary element methods are widely used for analyzing electromagnetic scattering scenarii. However, after discretization of SIEs, the spectrum and eigenvectors of the boundary element matrices are not usually representative of the spectrum and eigenfunctions of the underlying surface integral operators, which can be problematic for methods that rely heavily on spectral properties. To address this issue, we delineate some efficient algorithms that allow for the computation of matrix square roots and inverse square roots of the Gram matrices corresponding to the discretization scheme, which can be used for revealing the spectrum of standard electromagnetic integral operators. The algorithms, which are based on properly chosen expansions of the square root and inverse square root functions, are quite effective when applied to several of the most relevant Gram matrices used for boundary element discretizations in electromagnetics. Tables containing different sets of expansion coefficients are provided along with comparative numerical experiments that evidence advantages and disadvantages of the different approaches. In addition, to demonstrate the spectrum-revealing properties of the proposed techniques, they are applied to the discretization of the problem of scattering by a sphere for which the analytic spectrum is known., Comment: 8 pages, 9 figures

The requirements for real-world manipulation tasks are diverse and often conflicting; some tasks necessitate force constraints or collision avoidance, while others demand high-frequency feedback. Satisfying these varied requirements with a fixed state-action representation and control strategy is challenging, impeding the development of a universal robotic foundation model. In this work, we propose Meta-Control, the first LLM-enabled automatic control synthesis approach that creates customized state representations and control strategies tailored to specific tasks. Meta-Control leverages a generic hierarchical control framework to address a wide range of heterogeneous tasks. Our core insight is the decomposition of the state space into an abstract task space and a concrete tracking space. By harnessing LLM's extensive common sense and control knowledge, we enable the LLM to design these spaces, including states, dynamic models, and controllers, using pre-defined but abstract templates. Meta-Control stands out for its fully model-based nature, allowing for rigorous analysis, efficient parameter tuning, and reliable execution. It not only utilizes decades of control expertise encapsulated within LLMs to facilitate heterogeneous control but also ensures formal guarantees such as safety and stability. Our method is validated both in real-world scenarios and simulations across diverse tasks with conflicting requirements, such as collision avoidance versus convergence and compliance versus high precision. Videos and additional results are at meta-control-paper.github.io

As an alternative to expensive expert evaluation, Image Aesthetic Assessment (IAA) stands out as a crucial task in computer vision. However, traditional IAA methods are typically constrained to a single data source or task, restricting the universality and broader application. In this work, to better align with human aesthetics, we propose a Unified Multi-modal Image Aesthetic Assessment (UNIAA) framework, including a Multi-modal Large Language Model (MLLM) named UNIAA-LLaVA and a comprehensive benchmark named UNIAA-Bench. We choose MLLMs with both visual perception and language ability for IAA and establish a low-cost paradigm for transforming the existing datasets into unified and high-quality visual instruction tuning data, from which the UNIAA-LLaVA is trained. To further evaluate the IAA capability of MLLMs, we construct the UNIAA-Bench, which consists of three aesthetic levels: Perception, Description, and Assessment. Extensive experiments validate the effectiveness and rationality of UNIAA. UNIAA-LLaVA achieves competitive performance on all levels of UNIAA-Bench, compared with existing MLLMs. Specifically, our model performs better than GPT-4V in aesthetic perception and even approaches the junior-level human. We find MLLMs have great potential in IAA, yet there remains plenty of room for further improvement. The UNIAA-LLaVA and UNIAA-Bench will be released.

Recently, image-to-3D approaches have achieved significant results with a natural image as input. However, it is not always possible to access these enriched color input samples in practical applications, where only sketches are available. Existing sketch-to-3D researches suffer from limitations in broad applications due to the challenges of lacking color information and multi-view content. To overcome them, this paper proposes a novel generation paradigm Sketch3D to generate realistic 3D assets with shape aligned with the input sketch and color matching the textual description. Concretely, Sketch3D first instantiates the given sketch in the reference image through the shape-preserving generation process. Second, the reference image is leveraged to deduce a coarse 3D Gaussian prior, and multi-view style-consistent guidance images are generated based on the renderings of the 3D Gaussians. Finally, three strategies are designed to optimize 3D Gaussians, i.e., structural optimization via a distribution transfer mechanism, color optimization with a straightforward MSE loss and sketch similarity optimization with a CLIP-based geometric similarity loss. Extensive visual comparisons and quantitative analysis illustrate the advantage of our Sketch3D in generating realistic 3D assets while preserving consistency with the input.

Scalar auxiliary variable (SAV) methods are a class of linear schemes for solving gradient flows that are known for the stability of a `modified' energy. In this paper, we propose an improved SAV (iSAV) scheme that not only retains the complete linearity but also ensures rigorously the stability of the original energy. The convergence and optimal error bound are rigorously established for the iSAV scheme and discussions are made for its high-order extension. Extensive numerical experiments are done to validate the convergence, robustness and energy stability of iSAV, and some comparisons are made.

Manipulating unseen articulated objects through visual feedback is a critical but challenging task for real robots. Existing learning-based solutions mainly focus on visual affordance learning or other pre-trained visual models to guide manipulation policies, which face challenges for novel instances in real-world scenarios. In this paper, we propose a novel part-guided 3D RL framework, which can learn to manipulate articulated objects without demonstrations. We combine the strengths of 2D segmentation and 3D RL to improve the efficiency of RL policy training. To improve the stability of the policy on real robots, we design a Frame-consistent Uncertainty-aware Sampling (FUS) strategy to get a condensed and hierarchical 3D representation. In addition, a single versatile RL policy can be trained on multiple articulated object manipulation tasks simultaneously in simulation and shows great generalizability to novel categories and instances. Experimental results demonstrate the effectiveness of our framework in both simulation and real-world settings. Our code is available at https://github.com/THU-VCLab/Part-Guided-3D-RL-for-Sim2Real-Articulated-Object-Manipulation., Comment: 9 pages

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC.

We present a novel character control framework that effectively utilizes motion diffusion probabilistic models to generate high-quality and diverse character animations, responding in real-time to a variety of dynamic user-supplied control signals. At the heart of our method lies a transformer-based Conditional Autoregressive Motion Diffusion Model (CAMDM), which takes as input the character's historical motion and can generate a range of diverse potential future motions conditioned on high-level, coarse user control. To meet the demands for diversity, controllability, and computational efficiency required by a real-time controller, we incorporate several key algorithmic designs. These include separate condition tokenization, classifier-free guidance on past motion, and heuristic future trajectory extension, all designed to address the challenges associated with taming motion diffusion probabilistic models for character control. As a result, our work represents the first model that enables real-time generation of high-quality, diverse character animations based on user interactive control, supporting animating the character in multiple styles with a single unified model. We evaluate our method on a diverse set of locomotion skills, demonstrating the merits of our method over existing character controllers. Project page and source codes: https://aiganimation.github.io/CAMDM, Comment: Accepted by SIGGRAPH 2024 (Conference Track). Project page and source codes: https://aiganimation.github.io/CAMDM

The recent discoveries of ferroic properties, such as ferromagnetism and ferroelectricity, in two-dimensional van der Waals (2D vdW) materials have generated a surge of interest in the exploration of fundamental interaction in reduced dimensions and functional device applications. On one hand, ferromagnetism in 2D vdW crystals enables better understanding of fundamental interactions in ultra-thin condensed matter systems. However, existing material candidates face limitations, such as the lack of room-temperature magnetic ordering. Here we report the room-temperature ferromagnetism in Co-doped graphene-like ZnO, a novel chemically stable 2D material, down to single-atom thickness. Through magneto-optic Kerr effect and superconducting quantum interference device measurements, we identify the spontaneous magnetization in such systems at room temperature and above. X-ray characterization shows that the Co atoms form Co2+ states in graphitic ZnO. By varying the Co doping level, we observe an exotic manipulation among paramagnetic, ferromagnetic, and disordered phases. Our development of Co-doped 2D ZnO opens an appealing path to 2D ferromagnetism persisting at room temperature with unique controllability. On the other hand, the stabilization of 2D ferroelectricity overcomes the thickness limit. In contrast with conventional 3D ferroelectrics, 2D ferroelectrics show great advantages, including but not limited to, high mobility, perfect interface, allowing for non-destructive reading, being fatigueless. In this dissertation, we report the physical vapor deposition (PVD) of 2D vdW SnSe directly onto the silicon substrate. The synthesis process occurs at relatively low temperatures; the deposition temperature is 250 ℃. By varying the time of synthesis, we are able to manipulate the thickness of SnSe single crystals, which spans in a large range from 6 nm to the bulk. Out-of-plane ferroelectricity is detected in SnSe at room temperature (300 K) by means of piezoresponse forc

Oxygen evolution reaction (OER) is a bottleneck to photoelectrochemical (PEC) water splitting; however, there remains an impressive challenge for intrinsic charge transport for the development of integrated photoanodes. Herein, covalent triazine frameworks as conjugated molecules are grafted on the surfaces of ferroelectric BaTiO3−x (CTF/BTO) nanorod array, and then oxyhydroxide oxygen evolution cocatalyst (OEC) is constructed as an integrated photoanode. The OEC/CTF/BTO array not only achieves a high photocurrent density of 0.83 mA cm−2 at 1.23 V versus reversible hydrogen electrode (vs RHE) and low onset potential of ≈0.23 VRHE, but also optimizes outstanding stability. To disclose the origin, the enhanced PEC activity can be contributed to the integration of CTF and OEC, enhancing light-harvesting capability, boosting charge carrier mediation, and promoting water oxidation kinetics through electrochemical analysis and density functional theory calculations. This study not only provides an alternative to accelerate charge transfer, but also paves the rational design and fabrication of integrated photoanodes for boosting PEC water splitting performance., QC 20240703

The work aims to study the application of Deoxyribonucleic Acid (DNA) multi-source data storage in Digital Twins (DT). Through the investigation of the research status of DT and DNA computing, the work puts forward the concept of DNA multi-source data storage for DT. Raptor code is improved from the design direction of degree distribution function, and six degree function distribution schemes are proposed in turn in the process of describing the research method. Additionally, a quaternary dynamic Huffman coding method is applied in DNA data storage, combined with the improved concatenated code as the error correction code. Considering the content of cytosine deoxynucleotide (C) and guanine deoxynucleotide Guanine (G) and the distribution of homopolymer in DNA storage, the work proposes and verifies an improved concatenated code algorithm Deoxyribonucleic Acid-Improved Concatenated code (DNA-ICC). The results show that while the Signal-to-Noise Ratio (SNR) increases, the Bit Error Rate (BER) decreases gradually and the trend is similar. But the anti-interference ability of the degree distribution function optimized by the probability transfer method is better. The BER of DNA-ICC scheme decreases with the decrease of error probability, which is stronger than other error correction codes. Compared with the original concatenated code, it saves at least 1.65 s, and has a good control effect on homopolymer. When the size of homopolymer exceeds 4 nt, the probability of homopolymer is only 0.44%. The proposed Quaternary dynamic Huffman code and concatenated error correction code have excellent performance.

The work aims to study the application of Deoxyribonucleic Acid (DNA) multi-source data storage in Digital Twins (DT). Through the investigation of the research status of DT and DNA computing, the work puts forward the concept of DNA multi-source data storage for DT. Raptor code is improved from the design direction of degree distribution function, and six degree function distribution schemes are proposed in turn in the process of describing the research method. Additionally, a quaternary dynamic Huffman coding method is applied in DNA data storage, combined with the improved concatenated code as the error correction code. Considering the content of cytosine deoxynucleotide (C) and guanine deoxynucleotide Guanine (G) and the distribution of homopolymer in DNA storage, the work proposes and verifies an improved concatenated code algorithm Deoxyribonucleic Acid-Improved Concatenated code (DNA-ICC). The results show that while the Signal-to-Noise Ratio (SNR) increases, the Bit Error Rate (BER) decreases gradually and the trend is similar. But the anti-interference ability of the degree distribution function optimized by the probability transfer method is better. The BER of DNA-ICC scheme decreases with the decrease of error probability, which is stronger than other error correction codes. Compared with the original concatenated code, it saves at least 1.65 s, and has a good control effect on homopolymer. When the size of homopolymer exceeds 4 nt, the probability of homopolymer is only 0.44%. The proposed Quaternary dynamic Huffman code and concatenated error correction code have excellent performance.

The work aims to study the application of Deoxyribonucleic Acid (DNA) multi-source data storage in Digital Twins (DT). Through the investigation of the research status of DT and DNA computing, the work puts forward the concept of DNA multi-source data storage for DT. Raptor code is improved from the design direction of degree distribution function, and six degree function distribution schemes are proposed in turn in the process of describing the research method. Additionally, a quaternary dynamic Huffman coding method is applied in DNA data storage, combined with the improved concatenated code as the error correction code. Considering the content of cytosine deoxynucleotide (C) and guanine deoxynucleotide Guanine (G) and the distribution of homopolymer in DNA storage, the work proposes and verifies an improved concatenated code algorithm Deoxyribonucleic Acid-Improved Concatenated code (DNA-ICC). The results show that while the Signal-to-Noise Ratio (SNR) increases, the Bit Error Rate (BER) decreases gradually and the trend is similar. But the anti-interference ability of the degree distribution function optimized by the probability transfer method is better. The BER of DNA-ICC scheme decreases with the decrease of error probability, which is stronger than other error correction codes. Compared with the original concatenated code, it saves at least 1.65 s, and has a good control effect on homopolymer. When the size of homopolymer exceeds 4 nt, the probability of homopolymer is only 0.44%. The proposed Quaternary dynamic Huffman code and concatenated error correction code have excellent performance.

In this work, we adopt the one-boson-exchange model to study the $Y_cK^{(*)} (Y_c=\Lambda_c, \Sigma_c)$ interactions. After considering both of the $S-D$ wave mixing effects and the coupled channel effects, we can predict several possible open-charm molecular pentaquarks, i.e., the single $\Sigma_cK^*$ molecular states with $I(J^P)=1/2(1/2^-)$, $1/2(3/2^-)$ and $3/2(1/2^-)$, the coupled $\Lambda_cK^*/\Sigma_cK^*$ molecular states with $1/2(1/2^-)$ and $1/2(3/2^-)$, and the coupled $\Sigma_cK/\Lambda_cK^*/\Sigma_cK^*$ molecular state with $1/2(1/2^-)$. Meanwhile, we extend our study to the $Y_c\bar{K}^{(*)}$ interactions, our results suggest the $\Sigma_c\bar{K}$ system with $I(J^P)=1/2(1/2^-)$, the $\Sigma_c\bar{K}^*$ systems with $1/2(1/2^-)$, $1/2(3/2^-)$, and $3/2(3/2^-)$, the coupled $\Lambda_c\bar{K}^*/\Sigma_c\bar K^*$ system with $1/2(1/2^-)$, and the $\Sigma_c\bar{K}/\Lambda_c\bar{K}^*/\Sigma_c\bar K^*$ system with $1/2(1/2^-)$ can be the prime molecular candidates., Comment: 9 pages, 1 figure. arXiv admin note: text overlap with arXiv:2208.10196

BackgroundGenetic variants can modulate phenotypic outcomes via epigenetic intermediates, for example at methylation quantitative trait loci (mQTL). We present the first large-scale assessment of mQTL at human genomic regions selected for interindividual variation in CpG methylation, which we call correlated regions of systemic interindividual variation (CoRSIVs). These can be assayed in blood DNA and do not reflect interindividual variation in cellular composition.ResultsWe use target-capture bisulfite sequencing to assess DNA methylation at 4086 CoRSIVs in multiple tissues from each of 188 donors in the NIH Gene-Tissue Expression (GTEx) program. At CoRSIVs, DNA methylation in peripheral blood correlates with methylation and gene expression in internal organs. We also discover unprecedented mQTL at these regions. Genetic influences on CoRSIV methylation are extremely strong (median R2=0.76), cumulatively comprising over 70-fold more human mQTL than detected in the most powerful previous study. Moreover, mQTL beta coefficients at CoRSIVs are highly skewed (i.e., the major allele predicts higher methylation). Both surprising findings are independently validated in a cohort of 47 non-GTEx individuals. Genomic regions flanking CoRSIVs show long-range enrichments for LINE-1 and LTR transposable elements; the skewed beta coefficients may therefore reflect evolutionary selection of genetic variants that promote their methylation and silencing. Analyses of GWAS summary statistics show that mQTL polymorphisms at CoRSIVs are associated with metabolic and other classes of disease.ConclusionsA focus on systemic interindividual epigenetic variants, clearly enhanced in mQTL content, should likewise benefit studies attempting to link human epigenetic variation to the risk of disease.

PurposeFoveoschisis involves the pathologic splitting of retinal layers at the fovea, which may occur congenitally in X-linked retinoschisis (XLRS) or as an acquired complication of myopia. XLRS is attributed to functional loss of the retinal adhesion protein retinoschisin 1 (RS1), but the pathophysiology of myopic foveoschisis is unclear due to the lack of animal models. Here, we characterized a novel nonhuman primate model of myopic foveoschisis through clinical examination and multimodal imaging followed by morphologic, cellular, and transcriptional profiling of retinal tissues and genetic analysis.MethodsWe identified a rhesus macaque with behavioral and anatomic features of myopic foveoschisis, and monitored disease progression over 14 months by fundus photography, fluorescein angiography, and optical coherence tomography (OCT). After necropsy, we evaluated anatomic and cellular changes by immunohistochemistry and transcriptomic changes using single-nuclei RNA-sequencing (snRNA-seq). Finally, we performed Sanger and whole exome sequencing with focus on the RS1 gene.ResultsAffected eyes demonstrated posterior hyaloid traction and progressive splitting of the outer plexiform layer on OCT. Immunohistochemistry showed increased GFAP expression in Müller glia and loss of ramified Iba-1+ microglia, suggesting macro- and microglial activation with minimal photoreceptor alterations. SnRNA-seq revealed gene expression changes predominantly in cones and retinal ganglion cells involving chromatin modification, suggestive of cellular stress at the fovea. No defects in the RS1 gene or its expression were detected.ConclusionsThis nonhuman primate model of foveoschisis reveals insights into how acquired myopic traction leads to phenotypically similar morphologic and cellular changes as congenital XLRS without alterations in RS1.

The work aims to study the application of Deoxyribonucleic Acid (DNA) multi-source data storage in Digital Twins (DT). Through the investigation of the research status of DT and DNA computing, the work puts forward the concept of DNA multi-source data storage for DT. Raptor code is improved from the design direction of degree distribution function, and six degree function distribution schemes are proposed in turn in the process of describing the research method. Additionally, a quaternary dynamic Huffman coding method is applied in DNA data storage, combined with the improved concatenated code as the error correction code. Considering the content of cytosine deoxynucleotide (C) and guanine deoxynucleotide Guanine (G) and the distribution of homopolymer in DNA storage, the work proposes and verifies an improved concatenated code algorithm Deoxyribonucleic Acid-Improved Concatenated code (DNA-ICC). The results show that while the Signal-to-Noise Ratio (SNR) increases, the Bit Error Rate (BER) decreases gradually and the trend is similar. But the anti-interference ability of the degree distribution function optimized by the probability transfer method is better. The BER of DNA-ICC scheme decreases with the decrease of error probability, which is stronger than other error correction codes. Compared with the original concatenated code, it saves at least 1.65 s, and has a good control effect on homopolymer. When the size of homopolymer exceeds 4 nt, the probability of homopolymer is only 0.44%. The proposed Quaternary dynamic Huffman code and concatenated error correction code have excellent performance.

Intelligent traffic incident detection provides benefits such as minimizing traffic accidents and fuel consumption, reducing congestion, and enhancing transportation safety. Hence, traffic incident detection has been an active research area in customer-centric intelligent transportation systems (ITS). Given that a driver’s negative emotions (e.g. anger, nervousness) are often a main cause of traffic incidents, we argue there is a close relationship between traffic incident detection and driver emotion recognition. We propose a Dual channel Dual attention Graph Attention neTworks, termed DDGAT. Specifically, the traffic channel builds a sequential-based graph, where words are nodes and their co-occurrences are edges. In contrast, the emotion channel builds a syntactic-based graph with words as nodes and semantic dependencies as edges. The first attention mechanism automatically learns the importance of neighbors in different layers for different tasks. The second attention produces the attentive graph representation for both tasks. Experiments on two benchmarking datasets including GIIE and Twitter, show the effectiveness of the proposed model over state-of-the-art baselines in terms of micro F1 and H@1, with significant improvements of 3.5%, 3.2%, 2.0%, and 1.7%. © 2023 IEEE

Guanghui Jiang,&ast; Cuicui Sheng,&ast; Luxia Yan,&ast; Zipeng Wang, Qing Wang, Rui Chen, Ying Zhao Department of Neurology, The Affiliated Huai’an Hospital of Xuzhou Medical University, Huai’an, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Ying Zhao; Rui Chen, Email zhaoying772023@163.com; chenrui5980@163.comPurpose: To explore the relationship between the serum level of S100 calcium-binding protein, beta chain (S100β) and Parkinson’s disease (PD) with depression.Patients and Methods: A total of 145 patients with PD and 60 healthy controls matched for sex, age, and years of education in our hospital were selected. Fluorescence quantitative immunochromatography was used to quantify the level of S100β in serum. Clinical manifestations were assessed by Unified Parkinson’s Disease Rating Scale part-III (UPDRS-III), Hoehn & Yahr (H-Y) stage and 17-item Hamilton Rating Scale for Depression (HAMD-17). According to the results of HAMD-17, PD patients were divided into PD with depression group and PD without depression group. The relationship between serum S100β and HAMD-17 scores in PD patients with depression was investigated through correlation analysis and multivariate regression analysis, and receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value of serum S100β.Results: The level of serum S100β in PD with depression group was significantly higher than that in PD without depression group and control group. In PD patients with depression, serum S100β level was positively correlated with UPDRS-III score, H-Y Scale and HAMD-17 score. The HAMD-17 score was positively correlated with the UPDRS-III and H-Y scales, and the increase in the HAMD-17 score was associated with women. Elevated serum S100β level and UPDRS-III score are independent risk factors for PD with depression. Analysis of receiver operating characteristic (ROC) curves showed that the serum S100

Accurate estimation of photosynthetic phenology is of great importance for understanding the response of terrestrial biosphere to climate change. The near-infrared reflectance of vegetation (NIRv) has been increasingly used to estimate photosynthetic phenology. However, topography significantly affects illumination conditions and induces uncertainty in the retrieval of NIRv photosynthetic phenology over mountainous areas. We evaluated the illumination effects on three modalities of NIRv: (i) the original NIRv, (ii) the product of NIRv and solar incident radiation (NIRvP), and (iii) the topographically corrected NIRv (TCNIRv). We assessed the impact of sun geometry on the phenological metrics for the timing of the start and end of the season (i.e., SOS and EOS) derived from satellite NIRv time series as compared with those derived from in-situ gross primary production (GPP) measured at the Brasschaat (BE-Bra) and Lägeren (CH-Lae) flux towers located, respectively, over a flat and a mountainous forest area in Europe. We observed a seasonally divergent performance of NIRv in photosynthetic phenology extraction: NIRv-derived SOS had a good consistency with GPP-derived estimates (109/114d for NIRv vs 108/115d for GPP at BE-Bra/CH-Lae), whilst it showed a positive time lag for the EOS (291/292d for NIRv vs 264/268d for GPP at BE-Bra/CH-Lae). The radiation constraint in NIRvP formulation corrected the bias in NIRv-based EOS estimates. Path length correction (PLC) also alleviates the illumination effects on the original NIRv, making TCNIRv comparable to NIRvP in estimating SOS and EOS. We conclude that illumination effects must be corrected from NIRv for the extraction of photosynthetic phenology, especially for the autumn phenology. Our study has significant implications for understanding the responses of phenology to climate change and the climate-carbon feedbacks, particularly over complex topography mountainous areas.