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Yingze Hou,1,2 Can Zhu,3 Ge Ban,1 Zhean Shen,4 Yingbing Liang,5 Kun Chen,1 Chenbo Wang,1 Heng Shi4 1School of Intelligent Medical Engineering, Sanquan College of Xinxiang Medical University, Xinxiang, 453003, People’s Republic of China; 2Clinical Medical College, Sanquan College of Xinxiang Medical University, Xinxiang, 453003, People’s Republic of China; 3Department of Urology, The Second Clinical Medical College of Anhui Medical University, Hefei, 230032, People’s Republic of China; 4Heart Center, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, 310000, People’s Republic of China; 5Department of Chemistry and Biotechnology, Graduate School of Engineering Tottori University Koyama-Minami 4-101, Tottori, 680-8552, JapanCorrespondence: Ge Ban, School of Intelligent Medical Engineering, Sanquan College of Xinxiang Medical University, Xinxiang, 453003, People’s Republic of China, Email bange119@qq.comAbstract: Nanoscale metal-organic frameworks (MOFs) offer high biocompatibility, nanomaterial permeability, substantial specific surface area, and well-defined pores. These properties make MOFs valuable in biomedical applications, including biological targeting and drug delivery. They also play a critical role in tumor diagnosis and treatment, including tumor cell targeting, identification, imaging, and therapeutic methods such as drug delivery, photothermal effects, photodynamic therapy, and immunogenic cell death. The diversity of MOFs with different metal centers, organics, and surface modifications underscores their multifaceted contributions to tumor research and treatment. This review is a summary of these roles and mechanisms. The final section of this review summarizes the current state of the field and discusses prospects that may bring MOFs closer to pharmaceutical applications.Keywords: metal-organic frame, oncotherapy, photothermal effect, tumor immunity, drug delivery

BACKGROUND: Low protein intake (LPI) has been suggested as a treatment for chronic kidney disease (CKD). However, protein intake is essential for bone health. METHODS: We studied the database of the National Health and Nutrition Examination Survey, 2005-2010. Basic variables, metabolic diseases, and bone density of different femoral areas were stratified into four subgroups according to different protein intake (DPI) (that is, <0.8, 0.8-1.0, 1.0-1.2, and >1.2 g/kg/day). RESULTS: Significant differences were found among all lumbar area bone mineral density (BMD) and T-scores (p < 0.0001). There was an apparent trend between a decreasing BMD in the CKD groups with increasing DPI in all single lumbar spines (L1, L2, L3, and L4) and all L spines (L1-L4). Compared with DPI (0.8-1.0 g/day/kg), higher risks of osteoporosis were noticed in the subgroup of >1.2 g/day/kg over L2 (relative risk (RR)=1.326, 95% confidence interval (CI)=1.062-1.656), subgroup >1.2 g/day/kg over L3 (RR = 1.31, 95%CI = 1.057-1.622), subgroup <0.8 g/day/kg over L4 (RR = 1.276, 95%CI = 1.015-1.605), subgroup <0.8 g/day/kg over all L spines (RR = 11.275, 95%CI = 1.051-1.548), and subgroup >1.2 g/day/kg over all L spines (RR = 0.333, 95%CI = 1.098-1.618). However, a higher risk of osteoporosis was observed only in the non-CKD group. There was an apparent trend of higher DPI coexisting with lower BMD and T scores in patients with CKD. For osteoporosis (reference:0.8-1.0 g/day/kg), lower (<0.8 g/day/kg) or higher DPI (>1.2 g/day/kg) was associated with higher risks in the non-CKD group, but not in the CKD group. CONCLUSIONS: In the CKD group, LPI for renal protection was safe without threatening L spine bone density and without causing a higher risk of osteoporosis.

In lactating mothers, the high calcium (Ca2+) demand for milk production triggers significant bone loss1. Although oestrogen normally counteracts excessive bone resorption by promoting bone formation, this sex steroid drops precipitously during this postpartum period. Here we report that brain-derived cellular communication network factor 3 (CCN3) secreted from KISS1 neurons of the arcuate nucleus (ARCKISS1) fills this void and functions as a potent osteoanabolic factor to build bone in lactating females. We began by showing that our previously reported female-specific, dense bone phenotype2 originates from a humoral factor that promotes bone mass and acts on skeletal stem cells to increase their frequency and osteochondrogenic potential. This circulatory factor was then identified as CCN3, a brain-derived hormone from ARCKISS1 neurons that is able to stimulate mouse and human skeletal stem cell activity, increase bone remodelling and accelerate fracture repair in young and old mice of both sexes. The role of CCN3 in normal female physiology was revealed after detecting a burst of CCN3 expression in ARCKISS1 neurons coincident with lactation. After reducing CCN3 in ARCKISS1 neurons, lactating mothers lost bone and failed to sustain their progeny when challenged with a low-calcium diet. Our findings establish CCN3 as a potentially new therapeutic osteoanabolic hormone for both sexes and define a new maternal brain hormone for ensuring species survival in mammals.

The use of diselenides and alkyl halides in reductive conditions represents a potent approach for synthesizing diverse un-symmetrical selenides. Nonetheless, current methods suffer from drawbacks due to the requirement of stoichiometric re-ductants. In this research, we introduce a new and straightforward photoinduced N-heterocyclic nitrenium catalysis as an alternative. This catalytic process enables single electron reduction of Se-Se bonds, leading to the generation of nucleophilic selenolates. Consequently, unsymmetrical selenides can be synthesized efficiently from readily available diselenides and alkyl chlorides.

Nonlocal effective interactions are inherent to non-relativistic quantum many-body systems, but their systematic resummation poses a significant challenge known as the ``vertex problem" in many-body perturbation theory. We introduce a renormalization scheme based on a projection-based renormalization condition that selectively resums the most essential nonlocal contributions to the effective interaction vertex, avoiding the computational complexity of the full vertex function. This enables us to derive a renormalized Feynman diagrammatic series with large parameters canceled by counter-diagrams, efficiently generated using a perturbative expansion of the parquet equations and computed using a diagrammatic Monte Carlo algorithm. Applying our approach to a 3D Yukawa Fermi liquid, we demonstrate that the renormalized perturbation theory remains predictive even in the strongly correlated regime and uncover significant sign cancellations between different channels contributing to the scattering amplitude. Our work establishes a novel framework for investigating strong correlations in quantum many-body systems, offering a systematic approach to explore nonlocal theories for challenging systems like the electron liquid in material science., Comment: 13 pages, 7 figures

We propose a computational graph representation of high-order Feynman diagrams in Quantum Field Theory (QFT), applicable to any combination of spatial, temporal, momentum, and frequency domains. Utilizing the Dyson-Schwinger and parquet equations, our approach effectively organizes these diagrams into a fractal structure of tensor operations, significantly reducing computational redundancy. This approach not only streamlines the evaluation of complex diagrams but also facilitates an efficient implementation of the field-theoretic renormalization scheme, crucial for enhancing perturbative QFT calculations. Key to this advancement is the integration of Taylor-mode automatic differentiation, a key technique employed in machine learning packages to compute higher-order derivatives efficiently on computational graphs. To operationalize these concepts, we develop a Feynman diagram compiler that optimizes diagrams for various computational platforms, utilizing machine learning frameworks. Demonstrating this methodology's effectiveness, we apply it to the three-dimensional uniform electron gas problem, achieving unprecedented accuracy in calculating the quasiparticle effective mass at metal density. Our work demonstrates the synergy between QFT and machine learning, establishing a new avenue for applying AI techniques to complex quantum many-body problems.

We propose a computational graph representation of high-order Feynman diagrams in Quantum Field Theory (QFT), applicable to any combination of spatial, temporal, momentum, and frequency domains. Utilizing the Dyson-Schwinger and parquet equations, our approach effectively organizes these diagrams into a fractal structure of tensor operations, significantly reducing computational redundancy. This approach not only streamlines the evaluation of complex diagrams but also facilitates an efficient implementation of the field-theoretic renormalization scheme, crucial for enhancing perturbative QFT calculations. Key to this advancement is the integration of Taylor-mode automatic differentiation, a key technique employed in machine learning packages to compute higher-order derivatives efficiently on computational graphs. To operationalize these concepts, we develop a Feynman diagram compiler that optimizes diagrams for various computational platforms, utilizing machine learning frameworks. Demonstrating this methodology's effectiveness, we apply it to the three-dimensional uniform electron gas problem, achieving unprecedented accuracy in calculating the quasiparticle effective mass at metal density. Our work demonstrates the synergy between QFT and machine learning, establishing a new avenue for applying AI techniques to complex quantum many-body problems.

Data assimilation is a vital component in modern global medium-range weather forecasting systems to obtain the best estimation of the atmospheric state by combining the short-term forecast and observations. Recently, AI-based data assimilation approaches have attracted increasing attention for their significant advantages over traditional techniques in terms of computational consumption. However, existing AI-based data assimilation methods can only handle observations with a specific resolution, lacking the compatibility and generalization ability to assimilate observations with other resolutions. Considering that complex real-world observations often have different resolutions, we propose the \textit{\textbf{Fourier Neural Processes}} (FNP) for \textit{arbitrary-resolution data assimilation} in this paper. Leveraging the efficiency of the designed modules and flexible structure of neural processes, FNP achieves state-of-the-art results in assimilating observations with varying resolutions, and also exhibits increasing advantages over the counterparts as the resolution and the amount of observations increase. Moreover, our FNP trained on a fixed resolution can directly handle the assimilation of observations with out-of-distribution resolutions and the observational information reconstruction task without additional fine-tuning, demonstrating its excellent generalization ability across data resolutions as well as across tasks.

In order to effectively improve safety and stability during transportation, a shock absorber with buffering and self-locking functions was designed. Based on the rigid flexible coupling technology, the working state and dynamic response of the shock absorber were simulated and analyzed. The linear spring was treated as a flexible body, and the cam button, guide pillar, and base were treated as rigid bodies. The modal neutral files required for the rigid flexible coupling dynamic analysis were generated. Based on ADAMS, a dynamic simulation model was established, replacing a part of the rigid model with a flexible model and conducting load response analysis on it. To further improve the vibration reduction effect, the performance of the contact material of the shock absorber was analyzed. Through the calculation of dynamic characteristics, it could be known that the self-locking mechanism had no dead points, and the vibration reduction force response stability was high, meeting the working requirements.

In order to ensure good stability and safety of the tower crane under long-term heavy load conditions, modal analysis and fatigue analysis were conducted on its key components, including standard section and frame section. The connection points of each truss structure were regarded as indivisible common nodes, and the model was simplified and parameterized, resulting in improved mesh quality and computational efficiency. Based on Pro/E, standard section and frame section models were established and imported into Workbench through intermediate data formats for simulation calculations in modal and static structural modules. By calculation, the natural frequency and modal shape of the standard section, as well as the lifespan, damage, and safety factor of the frame section, were obtained. It can be seen that the standard section has good structural stability and can maintain relative balance under excitation in different directions and degrees of freedom. If the safety factor of the web components is high and the service life is improved from a structural perspective, it can be considered to increase the thickness of the chord members.

We conducted an eye-tracking user study with 13 participants to investigate the influence of stimulus-question ordering and question modality on participants using visual question-answering (VQA) tasks. We examined cognitive load, task performance, and gaze allocations across five distinct experimental designs, aiming to identify setups that minimize the cognitive burden on participants. The collected performance and gaze data were analyzed using quantitative and qualitative methods. Our results indicate a significant impact of stimulus-question ordering on cognitive load and task performance, as well as a noteworthy effect of question modality on task performance. These findings offer insights for the experimental design of controlled user studies in visualization research., Comment: Accepted at ETVIS 2024

We present a generalization of the discrete Lehmann representation (DLR) to three-point correlation and vertex functions in imaginary time and Matsubara frequency. The representation takes the form of a linear combination of judiciously chosen exponentials in imaginary time, and products of simple poles in Matsubara frequency, which are universal for a given temperature and energy cutoff. We present a systematic algorithm to generate compact sampling grids, from which the coefficients of such an expansion can be obtained by solving a linear system. We show that the explicit form of the representation can be used to evaluate diagrammatic expressions involving infinite Matsubara sums, such as polarization functions or self-energies, with controllable, high-order accuracy. This collection of techniques establishes a framework through which methods involving three-point objects can be implemented robustly, with a substantially reduced computational cost and memory footprint.

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (P interaction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications.

Jiali Yao, Kun Chen, Hongjie Tong, Ruixin Liu Department of Critical Care Medicine, Jinhua Hospital Affiliated to Zhejiang University, Zhejiang, People’s Republic of ChinaCorrespondence: Kun Chen; Hongjie Tong, Department of Critical Care Medicine, Jinhua Hospital Affiliated to Zhejiang University, 365 Renmin East Road, Wucheng District, Zhejiang, 321000, People’s Republic of China, Email 13957970707@sina.com; 444370182@qq.comPurpose: Sepsis patients with persistent inflammation, immunosuppression, and catabolism syndrome (PICS) have a poor prognosis, and early detection of biomarkers to predict its prognosis is particularly important. The purpose of this study is to investigate the predictive value of glycemic variability (GV) and high density lipid-cholesterol (HDL-C) for secondary PICS in patients with sepsis.Patients and Methods: One hundred and sixty-five patients with sepsis were enrolled in the retrospective study and divided into sepsis secondary PICS and non-PICS group. The daily blood glucose levels from 1 to 7 days after the diagnosis of sepsis were recorded, and GV was calculated. Logistic regression was used to analyze the risk factors of sepsis with PICS, receiver operating characteristic (ROC) curve was drawn to evaluate the predictive value of GV, and HDL-C for the prognosis of sepsis with PICS.Results: In a study of 165 patients, PICS group tended to have higher GV and lower HDL-C levels than those in the non-PICS group. Logistic regression analysis identified GV and HDL-C as independent risk factors for the secondary PICS of sepsis. The results of ROC curve showed that GV and HDL-C had a certain predictive value for the secondary PICS of sepsis, the sensitivity of GV was 77.5%, specificity of 81.8%, the sensitivity of HDL-C was 76.6%, and specificity of 74.8%.Conclusion: GV and HDL are risk factors for PICS secondary to sepsis and have predictive value for patients’ prognosis.Keywords: glycemic variability, high density lipid-cholesterol, sep

Machine learning (ML) has been used for many clinical decision-making processes and diagnostic procedures in bioinformatics applications. We examined eight algorithms, including linear discriminant analysis (LDA), logistic regression (LR), k-nearest neighbor (KNN), random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), Naïve Bayes classifier (NB), and artificial neural network (ANN) models, to evaluate their classification and prediction capabilities for four tissue types in Wolfiporia extensa using their monosaccharide composition profiles. All 8 ML-based models were assessed as exemplary models with AUC exceeding 0.8. Five models, namely LDA, KNN, RF, GBM, and ANN, performed excellently in the four-tissue-type classification (AUC > 0.9). Additionally, all eight models were evaluated as good predictive models with AUC value >0.8 in the three-tissue-type classification. Notably, all 8 ML-based methods outperformed the single linear discriminant analysis (LDA) plotting method. For large sample sizes, the ML-based methods perform better than traditional regression techniques and could potentially increase the accuracy in identifying tissue samples of W. extensa., QC 20230904

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (Pinteraction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications., Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (Pinteraction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications.

In principle, the local cosmic void can be simply modeled by the spherically symmetric Lemaitre-Tolman-Bondi (LTB) metric. In practice, the real local cosmic void is probably not spherically symmetric. In this paper, to reconstruct a more realistic profile of the local cosmic void, we divide it into several segments. Each segment with certain solid angle is modeled by its own LTB metric. Meanwhile, we divide the 1048 type Ia supernovae (SNIa) of the Pantheon Survey into corresponding subsets according to their distribution in the galactic coordinate system. Obviously, each SNIa subset can only be used to reconstruct the profile of one segment. Finally, we can patch together an irregular profile for the local cosmic void with the whole Pantheon sample. Note that, the paucity of each data subset lead us to focus on the inner part of each void segment and assume that the half radii of the void segments are sufficient to constrain the whole segment. We find that, despite $2\sigma$ signals of anisotropy limited to the depth of the void segments, the constraints on every void segment are consistent with $\Lambda$CDM model at $95\%$ CL. Moreover, our constraints are too weak to challenge the cosmic homogeneity and isotropy., Comment: 12 pages, 9 figures

Mining the spatial and temporal correlation of wind farm output data is beneficial for enhancing the precision of ultra-short-term wind power prediction. However, if the wind farms are owned by separate entities, they may be reluctant to share their data directly due to privacy concerns as well as business management regulation policies. Although cryptographic approaches have been designed to protect privacy in the process of data sharing, it is still a challenging problem to encrypt the original data while extracting the nonlinear relationship among multiple wind farms in the machine learning process. This paper presents pwXGBoost, a technique based on the machine learning tree model and secure multi-party computation (SMPC) that can successfully extract complicated relationships while preserving data privacy. A maximum mean discrepancy (MMD) based scheme is proposed to effectively choose adjacent candidate wind farms to participate in the collaborative model training, therefore improving the accuracy and reducing the burden of data acquisition. The proposed method was evaluated on real world data collected from a cluster of wind farms in Inner Mongolia, China, demonstrating that it is capable of achieving considerable efficiency and performance improvements while preserving privacy

We present a novel, web-based visual eye-tracking analytics tool called Gazealytics. Our open-source toolkit features a unified combination of gaze analytics features that support flexible exploratory analysis, along with annotation of areas of interest (AOI) and filter options based on multiple criteria to visually analyse eye tracking data across time and space. Gazealytics features coordinated views unifying spatiotemporal exploration of fixations and scanpaths for various analytical tasks. A novel matrix representation allows analysis of relationships between such spatial or temporal features. Data can be grouped across samples, user-defined AOIs or time windows of interest (TWIs) to support aggregate or filtered analysis of gaze activity. This approach exceeds the capabilities of existing systems by supporting flexible comparison between and within subjects, hypothesis generation, data analysis and communication of insights. We demonstrate in a walkthrough that Gazealytics supports multiple types of eye tracking datasets and analytical tasks.

We investigate reading strategies for node-link diagrams that wrap around the boundaries in a flattened torus topology by examining eye tracking data recorded in a previous controlled study. Prior work showed that torus drawing affords greater flexibility in clutter reduction than traditional node-link representations, but impedes link-and-path exploration tasks, while repeating tiles around boundaries aids comprehension. However, it remains unclear what strategies users apply in different wrapping settings. This is important for design implications for future work on more effective wrapped visualizations for network applications, and cyclic data that could benefit from wrapping. We perform visual-exploratory data analysis of gaze data, and conduct statistical tests derived from the patterns identified. Results show distinguishable gaze behaviors, with more visual glances and transitions between areas of interest in the non-replicated layout. Full-context has more successful visual searches than partial-context, but the gaze allocation indicates that the layout could be more space-efficient.

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (Pinteraction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications., Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (Pinteraction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications.

The weather forecasting system is important for science and society, and significant achievements have been made in applying artificial intelligence (AI) to medium-range weather forecasting. However, existing AI-based weather forecasting models rely on analysis or reanalysis products from traditional numerical weather prediction (NWP) systems as initial conditions for making predictions. Initial states are typically generated by traditional data assimilation components, which are computational expensive and time-consuming. Here we present an AI-based data assimilation model, i.e., Adas, for global weather variables. By introducing the confidence matrix, Adas employs gated convolution to handle sparse observations and gated cross-attention for capturing the interactions between the background and observations. Further, we combine Adas with the advanced AI-based forecasting model (i.e., FengWu) to construct the first end-to-end AI-based global weather forecasting system: FengWu-Adas. We demonstrate that Adas can assimilate global observations to produce high-quality analysis, enabling the system operate stably for long term. Moreover, we are the first to apply the methods to real-world scenarios, which is more challenging and has considerable practical application potential. We have also achieved the forecasts based on the analyses generated by AI with a skillful forecast lead time exceeding that of the IFS for the first time.

The multiplexing capability of fluorescence microscopy is severely limited by the broad fluorescence spectral width. Spectral imaging offers potential solutions, yet typical approaches to disperse the local emission spectra notably impede the attainable throughput. Here we show that using a single, fixed fluorescence emission detection band, through frame-synchronized fast scanning of the excitation wavelength from a white lamp via an acousto-optic tunable filter, up to six subcellular targets, labeled by common fluorophores of substantial spectral overlap, can be simultaneously imaged in live cells with low (~1%) crosstalks and high temporal resolutions (down to ~10 ms). The demonstrated capability to quantify the abundances of different fluorophores in the same sample through unmixing the excitation spectra next enables us to devise novel, quantitative imaging schemes for both bi-state and Förster resonance energy transfer fluorescent biosensors in live cells. We thus achieve high sensitivities and spatiotemporal resolutions in quantifying the mitochondrial matrix pH and intracellular macromolecular crowding, and further demonstrate, for the first time, the multiplexing of absolute pH imaging with three additional target organelles/proteins to elucidate the complex, Parkin-mediated mitophagy pathway. Together, excitation spectral microscopy provides exceptional opportunities for highly multiplexed fluorescence imaging. The prospect of acquiring fast spectral images without the need for fluorescence dispersion or care for the spectral response of the detector offers tremendous potential.

The multiplexing capability of fluorescence microscopy is severely limited by the broad fluorescence spectral width. Spectral imaging offers potential solutions, yet typical approaches to disperse the local emission spectra notably impede the attainable throughput. Here we show that using a single, fixed fluorescence emission detection band, through frame-synchronized fast scanning of the excitation wavelength from a white lamp via an acousto-optic tunable filter, up to six subcellular targets, labeled by common fluorophores of substantial spectral overlap, can be simultaneously imaged in live cells with low (~1%) crosstalks and high temporal resolutions (down to ~10 ms). The demonstrated capability to quantify the abundances of different fluorophores in the same sample through unmixing the excitation spectra next enables us to devise novel, quantitative imaging schemes for both bi-state and Förster resonance energy transfer fluorescent biosensors in live cells. We thus achieve high sensitivities and spatiotemporal resolutions in quantifying the mitochondrial matrix pH and intracellular macromolecular crowding, and further demonstrate, for the first time, the multiplexing of absolute pH imaging with three additional target organelles/proteins to elucidate the complex, Parkin-mediated mitophagy pathway. Together, excitation spectral microscopy provides exceptional opportunities for highly multiplexed fluorescence imaging. The prospect of acquiring fast spectral images without the need for fluorescence dispersion or care for the spectral response of the detector offers tremendous potential.

Design of high-performance pH-universal electrocatalysts is critical to practical large-scale hydrogen generation as a carbon-neutral fuel, yet challenging. Herein, we report an unique motif with crystalline nickel tellurium nanorods enclosed by amorphous rhodium hydroxide (a-Rh(OH)3/NiTe), formed through a hydrothermal synthesis and a subsequent chemical etching process, to address this challenge. The as-prepared a-Rh(OH)3/NiTe cathode enables a current density of 100 mA cm−2 with low overpotentials of 51, 109, and 64 mV for HER in alkaline, neutral and acidic media, respectively. As revealed by density functional theory (DFT) calculations, the electronic interactions between a-Rh(OH)3 and NiTe enhance the performance of Rh active sites. More importantly, the motif possesses superhydrophilicity and aerophobicity features, which not only facilitates the access to electrolytes but also ensures the fast release of hydrogen bubbles, endowing the electrocatalyst with advanced pH-universal HER activity. This work provides insights for the design of highly efficient electrocatalysts for hydrogen evolution at both molecular and mesoscopic levels.

We propose to develop a wide-field and ultra-high-precision photometric survey mission, temporarily named "Earth 2.0 (ET)". This mission is designed to measure, for the first time, the occurrence rate and the orbital distributions of Earth-sized planets. ET consists of seven 30cm telescopes, to be launched to the Earth-Sun's L2 point. Six of these are transit telescopes with a field of view of 500 square degrees. Staring in the direction that encompasses the original Kepler field for four continuous years, this monitoring will return tens of thousands of transiting planets, including the elusive Earth twins orbiting solar-type stars. The seventh telescope is a 30cm microlensing telescope that will monitor an area of 4 square degrees toward the galactic bulge. This, combined with simultaneous ground-based KMTNet observations, will measure masses for hundreds of long-period and free-floating planets. Together, the transit and the microlensing telescopes will revolutionize our understandings of terrestrial planets across a large swath of orbital distances and free space. In addition, the survey data will also facilitate studies in the fields of asteroseismology, Galactic archeology, time-domain sciences, and black holes in binaries.

The endoplasmic reticulum (ER)-to-Golgi transport is critical to protein secretion and intracellular sorting. Here, we report a highly elongated tubular ER-Golgi intermediate compartment (t-ERGIC) that selectively expedites the ER-to-Golgi transport for soluble cargoes of the receptor SURF4. Lacking the canonical ERGIC marker ERGIC-53 yet positive for the small GTPases Rab1A/B, the t-ERGIC is further marked by its extraordinarily elongated and thinned shape. With its large surface-to-volume ratio, high intracellular traveling speeds, and ER-Golgi recycling capabilities, the t-ERGIC accelerates the trafficking of SURF4-bound cargoes. The biogenesis and cargo selectivity of t-ERGIC both depend on SURF4, which recognizes the N terminus of soluble cargoes and co-clusters with the selected cargoes to expand the ER-exit site. In the steady state, the t-ERGIC-mediated fast ER-to-Golgi transport is antagonized by the KDEL-mediated ER retrieval. Together, our results argue that specific cargo-receptor interactions give rise to distinct transport carriers that regulate the trafficking kinetics.

Importance: Social determinants of health (SDOH) are known to be associated with increased risk of suicidal behaviors, but few studies utilized SDOH from unstructured electronic health record (EHR) notes. Objective: To investigate associations between suicide and recent SDOH, identified using structured and unstructured data. Design: Nested case-control study. Setting: EHR data from the US Veterans Health Administration (VHA). Participants: 6,122,785 Veterans who received care in the US VHA between October 1, 2010, and September 30, 2015. Exposures: Occurrence of SDOH over a maximum span of two years compared with no occurrence of SDOH. Main Outcomes and Measures: Cases of suicide deaths were matched with 4 controls on birth year, cohort entry date, sex, and duration of follow-up. We developed an NLP system to extract SDOH from unstructured notes. Structured data, NLP on unstructured data, and combining them yielded six, eight and nine SDOH respectively. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated using conditional logistic regression. Results: In our cohort, 8,821 Veterans committed suicide during 23,725,382 person-years of follow-up (incidence rate 37.18/100,000 person-years). Our cohort was mostly male (92.23%) and white (76.99%). Across the five common SDOH as covariates, NLP-extracted SDOH, on average, covered 80.03% of all SDOH occurrences. All SDOH, measured by structured data and NLP, were significantly associated with increased risk of suicide. The SDOH with the largest effects was legal problems (aOR=2.66, 95% CI=.46-2.89), followed by violence (aOR=2.12, 95% CI=1.98-2.27). NLP-extracted and structured SDOH were also associated with suicide. Conclusions and Relevance: NLP-extracted SDOH were always significantly associated with increased risk of suicide among Veterans, suggesting the potential of NLP in public health studies., Comment: Submitted to JAMA Network Open

Due to the absence of a library for non-linear function evaluation, so-called general-purpose secure multi-party computation (MPC) are not as ''general'' as MPC programmers expect. Prior arts either naively reuse plaintext methods, resulting in suboptimal performance and even incorrect results, or handcraft ad hoc approximations for specific functions or platforms. We propose a general technique, NFGen, that utilizes pre-computed discrete piecewise polynomials to accurately approximate generic functions using fixed-point numbers. We implement it using a performance-prediction-based code generator to support different platforms. Conducting extensive evaluations of 23 non-linear functions against six MPC protocols on two platforms, we demonstrate significant performance, accuracy, and generality improvements over existing methods., Comment: 20 pages, full version of CCS22 paper

Traditional visualisations are designed to be shown on a flat surface (screen or page) but most data is not "flat". For example, the surface of the earth exists on a sphere, however, when that surface is presented on a flat map, key information is hidden, such as geographic paths on the spherical surface being wrapped across the boundaries of the flat map. Similarly, cyclical time-series data has no beginning or end. When such cyclical data is presented on a traditional linear chart, the viewer needs to perceive continuity of the visualisation across the chart's boundaries. Mentally reconnecting the chart across such a boundary may induce additional cognitive load. More complex data such as a network diagram with hundreds or thousands of links between data points leads to a densely connected structure that is even less "flat" and needs to wrap around in multiple dimensions. To improve the usability of these visualisations, this thesis explores a novel class of interactive wrapped data visualisations, i.e., visualisations that wrap around continuously when interactively panned on a two-dimensional projection of surfaces of 3D shapes, specifically, cylinder, torus, or sphere. We start with a systematic exploration of the design space of interactive wrapped visualisations, characterising the visualisations that help people understand the relationship within the data. Subsequently, we investigate a series of wrappable visualisations for cyclical time series, network, and geographic data. We show that these interactive visualisations better preserve the spatial relations in the case of geospatial data, and better reveal the data's underlying structure in the case of abstract data such as networks and cyclical time series. Furthermore, to assist future research and development, we contribute layout algorithms and toolkits to help create pannable wrapped visualisations.

We introduce libdlr, a library implementing the recently introduced discrete Lehmann representation (DLR) of imaginary time Green's functions. The DLR basis consists of a collection of exponentials chosen by the interpolative decomposition to ensure stable and efficient recovery of Green's functions from imaginary time or Matsubara frequency samples. The library provides subroutines to build the DLR basis and grids, and to carry out various standard operations. The simplicity of the DLR makes it straightforward to incorporate into existing codes as a replacement for less efficient representations of imaginary time Green's functions, and libdlr is intended to facilitate this process. libdlr is written in Fortran, provides a C header interface, and contains a Python module pydlr. We also introduce a stand-alone Julia implementation, Lehmann.jl. Program summary Program Title: libdlr CPC Library link to program files: https://doi .org /10 .17632 /56z594pzsj .1 Developer's repository link: https://github .com /jasonkaye /libdlr Licensing provisions: Apache-2.0 Programming language: Fortran, C, Python, Julia Nature of problem: Discretization and compression of functions (Green's functions and self-energies) with an imaginary time variable. Solution method: Explicit basis functions and discretization points obtained by low rank compression of the analytical continuation kernel., Funding agency:European Research Council (ERC) 854843-FASTCORR

There are many methods for projecting spherical maps onto the plane. Interactive versions of these projections allow the user to centre the region of interest. However, the effects of such interaction have not previously been evaluated. In a study with 120 participants we find interaction provides significantly more accurate area, direction and distance estimation in such projections. The surface of 3D sphere and torus topologies provides a continuous surface for uninterrupted network layout. But how best to project spherical network layouts to 2D screens has not been studied, nor have such spherical network projections been compared to torus projections. Using the most successful interactive sphere projections from our first study, we compare spherical, standard and toroidal layouts of networks for cluster and path following tasks with 96 participants, finding benefits for both spherical and toroidal layouts over standard network layouts in terms of accuracy for cluster understanding tasks.

In this paper, we follow Eftekhari's work to give a non-local convergence analysis of deep linear networks. Specifically, we consider optimizing deep linear networks which have a layer with one neuron under quadratic loss. We describe the convergent point of trajectories with arbitrary starting point under gradient flow, including the paths which converge to one of the saddle points or the original point. We also show specific convergence rates of trajectories that converge to the global minimizer by stages. To achieve these results, this paper mainly extends the machinery in Eftekhari's work to provably identify the rank-stable set and the global minimizer convergent set. We also give specific examples to show the necessity of our definitions. Crucially, as far as we know, our results appear to be the first to give a non-local global analysis of linear neural networks from arbitrary initialized points, rather than the lazy training regime which has dominated the literature of neural networks, and restricted benign initialization in Eftekhari's work. We also note that extending our results to general linear networks without one hidden neuron assumption remains a challenging open problem.

We propose to develop a wide-field and ultra-high-precision photometric survey mission, temporarily named "Earth 2.0 (ET)". This mission is designed to measure, for the first time, the occurrence rate and the orbital distributions of Earth-sized planets. ET consists of seven 30cm telescopes, to be launched to the Earth-Sun's L2 point. Six of these are transit telescopes with a field of view of 500 square degrees. Staring in the direction that encompasses the original Kepler field for four continuous years, this monitoring will return tens of thousands of transiting planets, including the elusive Earth twins orbiting solar-type stars. The seventh telescope is a 30cm microlensing telescope that will monitor an area of 4 square degrees toward the galactic bulge. This, combined with simultaneous ground-based KMTNet observations, will measure masses for hundreds of long-period and free-floating planets. Together, the transit and the microlensing telescopes will revolutionize our understandings of terrestrial planets across a large swath of orbital distances and free space. In addition, the survey data will also facilitate studies in the fields of asteroseismology, Galactic archeology, time-domain sciences, and black holes in binaries., Comment: 116 pages,79 figures

Statistical learning with a large number of rare binary features is commonly encountered in analyzing electronic health records (EHR) data, especially in the modeling of disease onset with prior medical diagnoses and procedures. Dealing with the resulting highly sparse and large-scale binary feature matrix is notoriously challenging as conventional methods may suffer from a lack of power in testing and inconsistency in model fitting while machine learning methods may suffer from the inability of producing interpretable results or clinically-meaningful risk factors. To improve EHR-based modeling and utilize the natural hierarchical structure of disease classification, we propose a tree-guided feature selection and logic aggregation approach for large-scale regression with rare binary features, in which dimension reduction is achieved through not only a sparsity pursuit but also an aggregation promoter with the logic operator of ``or''. We convert the combinatorial problem into a convex linearly-constrained regularized estimation, which enables scalable computation with theoretical guarantees. In a suicide risk study with EHR data, our approach is able to select and aggregate prior mental health diagnoses as guided by the diagnosis hierarchy of the International Classification of Diseases. By balancing the rarity and specificity of the EHR diagnosis records, our strategy improves both prediction and model interpretation. We identify important higher-level categories and subcategories of mental health conditions and simultaneously determine the level of specificity needed for each of them in predicting suicide risk.

Loss-of-function mutations in the histone demethylases KDM5A, KDM5B, or KDM5C are found in intellectual disability (ID) and autism spectrum disorders (ASD) patients. Here, we use the model organism Drosophila melanogaster to delineate how KDM5 contributes to ID and ASD. We show that reducing KDM5 causes intestinal barrier dysfunction and changes in social behavior that correlates with compositional changes in the gut microbiota. Therapeutic alteration of the dysbiotic microbiota through antibiotic administration or feeding with a probiotic Lactobacillus strain partially rescues the behavioral, lifespan, and cellular phenotypes observed in kdm5-deficient flies. Mechanistically, KDM5 was found to transcriptionally regulate component genes of the immune deficiency (IMD) signaling pathway and subsequent maintenance of host-commensal bacteria homeostasis in a demethylase-dependent manner. Together, our study uses a genetic approach to dissect the role of KDM5 in the gut-microbiome-brain axis and suggests that modifying the gut microbiome may provide therapeutic benefits for ID and ASD patients.

To solve tea image classification problems, this study focuses on triplet loss convolutional neural network to classify six high-mountain oolong tea classes. In the experiment, instead of using traditional deep learning training approach for local feature of tea images, an innovative image verification approach is proposed to learn the global feature of tea images by integrating the distributed tea leaves’ features of all tea sub-images and using a majority voting mechanism to do classification. The results show that the proposed approach can work for small sample size dataset and have higher accuracy than normal transfer learning approach. The average accuracy of the proposed approach achieves 99.54%.

To solve tea image classification problems, this study focuses on triplet loss convolutional neural network to classify six high-mountain oolong tea classes. In the experiment, instead of using traditional deep learning training approach for local feature of tea images, an innovative image verification approach is proposed to learn the global feature of tea images by integrating the distributed tea leaves’ features of all tea sub-images and using a majority voting mechanism to do classification. The results show that the proposed approach can work for small sample size dataset and have higher accuracy than normal transfer learning approach. The average accuracy of the proposed approach achieves 99.54%.

Deep Neural Networks (DNNs) have shown superiority in solving the problems of classification and recognition in recent years. However, DNN hardware implementation is challenging due to the high computational complexity and diverse dataflow in different DNN models. 'lb mitigate this design challenge, a large body of research has focused on accelerating specific DNN models or layers and proposed dedicated designs. However, dedicated designs for specific DNN models or layers limit the design flexibility. In this work, we take advantage of the similarity among different DNN models and propose a novel Lego-based Deep Neural Network on a Chip (DNNoC) design methodology. We work on common neural computing units (e.g., multiply-accumulation and pooling) and create some neuron computing units called NeuLego processing elements (NeuLego(PE)(s)). These NeuLego(PE)(s) are then interconnected using a flexible Network-on-Chip (NoC), allowing to construct different DNN models. To support large-scale DNN models, we enhance the reusability of each NeuLego(PE) by proposing a Lego placement method. The proposed design methodology allows leveraging different DNN model implementations, helping to reduce implementation cost and time-to-market. Compared with the conventional approaches, the proposed approach can improve the average throughput by 2,802% for given DNN models. Besides, the corresponding hardware is implemented to validate the proposed design methodology, showing on average 12,523% hardware efficiency improvement by considering the throughput and area overhead simultaneously., QC 20220111

Yadong Xie,1,2,&ast; Min Li,1,3,&ast; Kun Chen,4,&ast; Haoxiang Zhu,5 Mengyao Tang,6 Chun Zhou,1 Yaoming Zheng,1 Jing Wen,2 Miaomiao Han,1 Jia Zhang,1 Keqing Zhao,1 Hui Xiao,2 Huabin Li1 1ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, 200031, People’s Republic of China; 2The Center for Microbes, Development and Health, Institut Pasteur of Shanghai; University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, People’s Republic of China; 3Department of Otolaryngology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310003, People’s Republic of China; 4Department of Otolaryngology-Head and Neck Surgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, People’s Republic of China; 5Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, 200040, People’s Republic of China; 6Department of Plastic and Reconstructive Surgery, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, 200072, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Huabin LiENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, 83 Fenyang Road, Shanghai, 200031, People’s Republic of ChinaTel + 86-21-6437-7134Email allergyli@163.comHui XiaoThe Center for Microbes, Development and Health, Institut Pasteur of Shanghai; University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, People’s Republic of ChinaTel + 86-21-5492-3127Email huixiao@ips.ac.cnBackground: Necroptosis is an inflammatory cell death associated with a variety of chronic diseases. Chronic rhinosinusitis with nasal polyps (CRSwNP) is a chronic inflammatory disease accompanied by eosinophil and neutrophil infiltration. The role of necroptosis in the pathogenesis of

Yi Zhao,1 Gang Wang,2 Wenqiong Ni,3 Zhiqiang Song,4 Kun Chen,5 Chunlei Zhang,6 Shoumin Zhang,7 Yangfeng Ding,8 Min Zheng,9 Yulin Shi,10 Zhimiao Lin,11 Xiaomin Liu,12 Xiaofei Zhang,13 Qintian Zhou,14 Kazi Deluwar Ahmed14 1Department of Dermatology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, People’s Republic of China; 2Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi’an, People’s Republic of China; 3Department of Dermatology, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, People’s Republic of China; 4Department of Dermatology, Southwest Hospital, The Third Military Medical University, Chongqing, People’s Republic of China; 5Institute of Dermatology, Chinese Academy of Medical Science and Peking Union Medical College, Nanjing, People’s Republic of China; 6Department of Dermatology, Peking University Third Hospital, Beijing, People’s Republic of China; 7Department of Dermatology, Henan Provincial People’s Hospital, Zhengzhou 450003, People’s Republic of China; 8Department of Dermatology, Shanghai Skin Disease Hospital, Shanghai, People’s Republic of China; 9Department of Dermatology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China; 10Department of Dermatology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine; Institute of Psoriasis, Tongji University School of Medicine, Shanghai, People’s Republic of China; 11Department of Dermatology and Venereology, Peking University First Hospital, Beijing, People’s Republic of China; 12Department of Dermatology, The University of Hong Kong–Shenzhen Hospital, Shenzhen, People’s Republic of China; 13Department of Clinical Epidemiology and Biostatistics, Beijing Tsinghua Changgung Hospita

Physical unclonable function (PUF) has been proposed as a promising and trustworthy solution to a variety of cryptographic applications. Here we propose a non-imaging based authentication scheme for optical PUFs materialized by random scattering media, in which the characteristic fingerprints of optical PUFs are extracted from stochastical fluctuations of the scattered light intensity with respect to laser challenges which are detected by a single-pixel detector. The randomness, uniqueness, unpredictability, and robustness of the extracted fingerprints are validated to be qualified for real authentication applications. By increasing the key length and improving the signal to noise ratio, the false accept rate of a fake PUF can be dramatically lowered to the order of 10^-28. In comparison to the conventional laser-speckle-imaging based authentication with unique identity information obtained from textures of laser speckle patterns, this non-imaging scheme can be implemented at small speckle size bellowing the Nyquist--Shannon sampling criterion of the commonly used CCD or CMOS cameras, offering benefits in system miniaturization and immunity against reverse engineering attacks simultaneously., Comment: 12 pages, 11 figures

We introduce libdlr, a library implementing the recently introduced discrete Lehmann representation (DLR) of imaginary time Green's functions. The DLR basis consists of a collection of exponentials chosen by the interpolative decomposition to ensure stable and efficient recovery of Green's functions from imaginary time or Matsbuara frequency samples. The library provides subroutines to build the DLR basis and grids, and to carry out various standard operations. The simplicity of the DLR makes it straightforward to incorporate into existing codes as a replacement for less efficient representations of imaginary time Green's functions, and libdlr is intended to facilitate this process. libdlr is written in Fortran, provides a C header interface, and contains a Python module pydlr. We also introduce a stand-alone Julia implementation, Lehmann.jl.