Structural annotation of unknown molecules in a miniaturized mass spectrometer based on a transformer enabled fragment tree method.

Structural annotation of small molecules in tandem mass spectrometry has always been a central challenge in mass spectrometry analysis, especially using a miniaturized mass spectrometer for on-site testing. Here, we propose the Transformer enabled Fragment Tree (TeFT) method, which combines various types of fragmentation tree models and a deep learning Transformer module. It is aimed to generate the specific structure of molecules de novo solely from mass spectrometry spectra. The evaluation results on different open-source databases indicated that the proposed model achieved remarkable results in that the majority of molecular structures of compounds in the test can be successfully recognized. Also, the TeFT has been validated on a miniaturized mass spectrometer with low-resolution spectra for 16 flavonoid alcohols, achieving complete structure prediction for 8 substances. Finally, TeFT confirmed the structure of the compound contained in a Chinese medicine substance called the Anweiyang capsule. These results indicate that the TeFT method is suitable for annotating fragmentation peaks with clear fragmentation rules, particularly when applied to on-site mass spectrometry with lower mass resolution.

Molecularly Thin 2D Organic Single Crystals: A New Platform for High-Performance Polarization-Sensitive Phototransistors.

The inherent linear dichroism (LD), high absorption, and solution processability of organic semiconductors hold immense potential to revolutionize polarized light detection. However, the disordered molecular packing inherent to polycrystalline thin films obscures their intrinsic diattenuation, resulting in diminished polarization sensitivity. In this study, we develop filter-free organic polarization-sensitive phototransistors (PSPs) with both a high linear dichroic ratio (LDR) and exceptional photosensitivity utilizing molecularly thin dithieno[3,2-b:2',3'-d]thiophene derivatives (DTT-8) two-dimensional molecular crystals (2DMCs) as the active layer. The orderly molecular packing in 2DMCs amplifies the inherent LD, and their molecular-scale thickness enables complete channel depletion, significantly reducing the dark current. As a result, PSPs with an impressive LDR of 3.15 and a photosensitivity reaching 3.02 × 106 are obtained. These findings present a practical demonstration of using the polarization angle as an encryption key in optical communication, showcasing the potential of 2DMCs as a viable and promising category of semiconductors for filter-free, polarization-sensitive photodetectors.

Multiscale Deep Learning for Detection and Recognition: A Comprehensive Survey.

Recently, the multiscale problem in computer vision has gradually attracted people's attention. This article focuses on multiscale representation for object detection and recognition, comprehensively introduces the development of multiscale deep learning, and constructs an easy-to-understand, but powerful knowledge structure. First, we give the definition of scale, explain the multiscale mechanism of human vision, and then lead to the multiscale problem discussed in computer vision. Second, advanced multiscale representation methods are introduced, including pyramid representation, scale-space representation, and multiscale geometric representation. Third, the theory of multiscale deep learning is presented, which mainly discusses the multiscale modeling in convolutional neural networks (CNNs) and Vision Transformers (ViTs). Fourth, we compare the performance of multiple multiscale methods on different tasks, illustrating the effectiveness of different multiscale structural designs. Finally, based on the in-depth understanding of the existing methods, we point out several open issues and future directions for multiscale deep learning.

Overcoming the Unfavorable Effects of "Boltzmann Tyranny:" Ultra-Low Subthreshold Swing in Organic Phototransistors via One-Transistor-One-Memristor Architecture.

Organic phototransistors (OPTs), as photosensitive organic field-effect transistors (OFETs), have gained significant attention due to their pivotal roles in imaging, optical communication, and night vision. However, their performance is fundamentally limited by the Boltzmann distribution of charge carriers, which constrains the average subthreshold swing (SSave ) to a minimum of 60 mV/decade at room temperature. In this study, an innovative one-transistor-one-memristor (1T1R) architecture is proposed to overcome the Boltzmann limit in conventional OFETs. By replacing the source electrode in an OFET with a memristor, the 1T1R device exploits the memristor's sharp resistance state transitions to achieve an ultra-low SSave of 18 mV/decade. Consequently, the 1T1R devices demonstrate remarkable sensitivity to photo illumination, with a high specific detectivity of 3.9 × 109  cm W-1 Hz1/2 , outperforming conventional OPTs (4.9 × 104  cm W-1 Hz1/2 ) by more than four orders of magnitude. The 1T1R architecture presents a potentially universal solution for overcoming the detrimental effects of "Boltzmann tyranny," setting the stage for the development of ultra-low SSave devices in various optoelectronic applications.

CoT: Contourlet Transformer for Hierarchical Semantic Segmentation.

The Transformer-convolutional neural network (CNN) hybrid learning approach is gaining traction for balancing deep and shallow image features for hierarchical semantic segmentation. However, they are still confronted with a contradiction between comprehensive semantic understanding and meticulous detail extraction. To solve this problem, this article proposes a novel Transformer-CNN hybrid hierarchical network, dubbed contourlet transformer (CoT). In the CoT framework, the semantic representation process of the Transformer is unavoidably peppered with sparsely distributed points that, while not desired, demand finer detail. Therefore, we design a deep detail representation (DDR) structure to investigate their fine-grained features. First, through contourlet transform (CT), we distill the high-frequency directional components from the raw image, yielding localized features that accommodate the inductive bias of CNN. Second, a CNN deep sparse learning (DSL) module takes them as input to represent the underlying detailed features. This memory-and energy-efficient learning method can keep the same sparse pattern between input and output. Finally, the decoder hierarchically fuses the detailed features with the semantic features via an image reconstruction-like fashion. Experiments demonstrate that CoT achieves competitive performance on three benchmark datasets: PASCAL Context 57.21% mean intersection over union (mIoU), ADE20K (54.16% mIoU), and Cityscapes (84.23% mIoU). Furthermore, we conducted robustness studies to validate its resistance against various sorts of corruption. Our code is available at: https://github.com/yilinshao/CoT-Contourlet-Transformer.

Whole-genome variants dataset of 209 local chickens from China.

Compared to commercial chickens, local breeds exhibit better in meat quality and flavour, but the productivity (e.g., growth rate, body weight) of local chicken breeds is rather low. Genetic analysis based on whole-genome sequencing contributes to elucidating the genetic markers or putative candidate genes related to some economic traits, facilitating the improvement of production performance, the acceleration of breeding progress, and the conservation of genetic resources. Here, a total of 209 local chickens from 13 breeds were investigated, and the observation of approximately 91.4% high-quality sequences (Q30 > 90%) and a mapping rate over 99% for each individual indicated good results of this study, as confirmed by a genome coverage of 97.6%. Over 19 million single nucleotide polymorphisms (SNPs) and 1.98 million insertion-deletions (InDels) were identified using the reference genome (GRCg7b), further contributing to the public database. This dataset provides valuable resources for studying genetic diversity and adaptation and for the cultivation of new chicken breeds/lines.

Learning Cross-Domain Features With Dual-Path Signal Transformer.

The past decade has witnessed the rapid development of deep neural networks (DNNs) for automatic modulation classification (AMC). However, most of the available works learn signal features from only a single domain via DNNs, which is not reliable enough to work in uncertain and complex electromagnetic environments. In this brief, a new cross-domain signal transformer (CDSiT) is proposed for AMC, to explore the latent association between different domains of signals. By constructing a signal fusion bottleneck (SFB), CDSiT can implicitly fuse and classify signal features with complementary structures in different domains. Extensive experiments are performed on RadioML2016.10A and RadioML2018.01A, and the results show that CDSiT outperforms its counterparts, particularly for some modulation modes that are difficult to classify before. Through ablation experiences, we also verify the effectiveness of each module in CDSiT.

Association of preserved ratio impaired spirometry with mortality: a systematic review and meta-analysis.

BACKGROUND: Preserved ratio impaired spirometry (PRISm) is prevalent within the general population. Increased mortality has been reported among subjects with PRISm, but the evidence has never been summarised. This systematic review aims to synthesise evidence on the association between PRISm and the risk of all-cause, cardiovascular and respiratory-related mortality. METHODS: We systematically searched MEDLINE, Embase and Web of Science for population-based cohort studies from inception to April 2023 using the terms related to impaired spirometry and mortality. Titles and abstracts were screened to identify eligible studies that reported mortality estimates for individuals with PRISm. We excluded studies that adopted other definitions of impaired spirometry, had a specific study setting (e.g. HIV patients), had an insufficient follow-up period (<1 year) or reported duplicated data. Random-effects meta-analysis was used to produce pooled hazard ratio (HR) with 95% confidence intervals. Between-study heterogeneity was assessed with I2. RESULTS: Eight studies met the inclusion criteria involving 40 699 individuals with PRISm. All included studies reported increased risk of all-cause mortality among adults with PRISm. Meta-analysis showed that PRISm was associated with an increased risk of all-cause mortality (pooled HR 1.71, 95% CI 1.51-1.93; I2=64%), cardiovascular mortality (pooled HR 1.57, 95% CI 1.44-1.72; I2=35%) and respiratory-related mortality (pooled HR 1.97, 95% CI 1.55-2.49; I2=0%). CONCLUSIONS: Individuals with PRISm have a significantly increased risk of mortality compared with those with normal spirometry.

Safety, tolerability, and immunogenicity of a CpG/Alum adjuvanted SARS-CoV-2 recombinant protein vaccine (ZR202-CoV) in healthy adults: Preliminary report of a phase 1, randomized, double-blind, placebo-controlled, dose-escalation trial.

This was a phase 1 dose-escalation study of ZR202-CoV, a recombinant protein vaccine candidate containing a pre-fusion format of the spike (S)-protein (S-trimer) combined with the dual-adjuvant system of Alum/CpG. A total of 230 participants were screened and 72 healthy adults aged 18-59 years were enrolled and randomized to receive two doses at a 28-day interval of three different ZR202-CoV formulations or normal saline. We assessed the safety for 28 days after each vaccination and collected blood samples for immunogenicity evaluation. All formulations of ZR202-CoV were well-tolerated, with no observed solicited adverse events ≥ Grade 3 within 7 days after vaccination. No unsolicited adverse events ≥ Grade 3, or serious adverse events related to vaccination occurred as determined by the investigator. After the first dose, detectable immune responses were observed in all subjects. All subjects that received ZR202-CoV seroconverted at 14 days after the second dose by S-binding IgG antibody, pseudovirus and live-virus based neutralizing antibody assays. S-binding response (GMCs: 2708.7 ~ 4050.0 BAU/mL) and neutralizing activity by pseudovirus (GMCs: 363.1 ~ 627.0 IU/mL) and live virus SARS-CoV-2 (GMT: 101.7 ~ 175.0) peaked at 14 days after the second dose of ZR202-CoV. The magnitudes of immune responses compared favorably with COVID-19 vaccines with reported protective efficacy.

Multiresolution Interpretable Contourlet Graph Network for Image Classification.

Modeling contextual relationships in images as graph inference is an interesting and promising research topic. However, existing approaches only perform graph modeling of entities, ignoring the intrinsic geometric features of images. To overcome this problem, a novel multiresolution interpretable contourlet graph network (MICGNet) is proposed in this article. MICGNet delicately balances graph representation learning with the multiscale and multidirectional features of images, where contourlet is used to capture the hyperplanar directional singularities of images and multilevel sparse contourlet coefficients are encoded into graph for further graph representation learning. This process provides interpretable theoretical support for optimizing the model structure. Specifically, first, the superpixel-based region graph is constructed. Then, the region graph is applied to code the nonsubsampled contourlet transform (NSCT) coefficients of the image, which are considered as node features. Considering the statistical properties of the NSCT coefficients, we calculate the node similarity, i.e., the adjacency matrix, using Mahalanobis distance. Next, graph convolutional networks (GCNs) are employed to further learn more abstract multilevel NSCT-enhanced graph representations. Finally, the learnable graph assignment matrix is designed to get the geometric association representations, which accomplish the assignment of graph representations to grid feature maps. We conduct comparative experiments on six publicly available datasets, and the experimental analysis shows that MICGNet is significantly more effective and efficient than other algorithms of recent years.

Association of spirometric restriction with mortality in the silicotics: a cohort study.

BACKGROUND: Restrictive spirometry pattern (RSP), defined as reduced forced vital capacity (FVC) in absence of airflow obstruction (AFO), is associated with increased risk of mortality in general population. However, evidence in the patients with silicosis is limited. This study was aimed to investigate the relationship between RSP and the risk of death in a silicotic cohort. METHOD: This retrospective cohort study used data from the Pneumoconiosis Clinic, Hong Kong Department of Health that containing 4315 patients aged 18-80 years and diagnosed with silicosis during 1981-2019, with a follow-up till 31 December 2019. Spirometry was carried out at the diagnostic examination of silicosis. Lung function categories were classified as normal spirometry (FEV1/FVC ≥ 0.7, FVC ≥ 80% predicted), RSP only (FEV1/FVC ≥ 0.7, FVC < 80% predicted), AFO only (FEV1/FVC < 0.7, FVC ≥ 80% predicted), and RSP&AFO mixed (FEV1/FVC < 0.7, FVC < 80% predicted). The hazard ratio (HR) and 95% confidence intervals (95% CI) were computed using a Cox proportional hazards model adjusting for age, body mass index, history of tuberculosis, smoking status, pack-years, and radiographic characteristics of silicotic nodules. RESULTS: Among the 4315 patients enrolled in the study, the prevalence of RSP was 24.1% (n = 1038), including 11.0% (n = 473) with RSP only and 13.1% (n = 565) with mixed RSP and AFO. During the follow-up period, a total of 2399 (55.6%) deaths were observed. Compared with the silicotics with normal spirometry, those with RSP only had significantly increased risk of all-cause mortality (HR = 1.63, 95% CI 1.44-1.85) and respiratory-related mortality (HR = 1.56, 95% CI 1.31-1.85). Notably, a higher risk of mortality was observed in silicotics with mixed ventilatory defects of both RSP and AFO (all-cause mortality: HR = 2.22, 95% CI 1.95-2.52; respiratory-related mortality: HR = 2.59, 95% CI 2.18-3.07) than in those with RSP only. CONCLUSION: RSP is significantly associated with increased risk of all-cause and respiratory-related mortality in the silicotics, and patients with mixed restrictive and obstructive ventilatory defect have higher risk of mortality than those with single RSP or AFO. These findings emphasize the importance of recognizing RSP in the occupational settings, especially for the silicotic patients with mixed ventilatory defect.

A Complex-Former Tracker With Dynamic Polar Spatio-Temporal Encoding.

Recently, the excellent performance of transformer has attracted the attention of the visual community. Visual transformer models usually reshape images into sequence format and encode them sequentially. However, it is difficult to explicitly represent the relative relationship in distance and direction of visual data with typical 2-D spatial structures. Also, the temporal motion properties of consecutive frames are hardly exploited when it comes to dynamic video tasks like tracking. Therefore, we propose a novel dynamic polar spatio-temporal encoding for video scenes. We use spiral functions in polar space to fully exploit the spatial dependences of distance and direction in real scenes. We then design a dynamic relative encoding mode for continuous frames to capture the continuous spatio-temporal motion characteristics among video frames. Finally, we construct a complex-former framework with the proposed encoding applied to video-tracking tasks, where the complex fusion mode (CFM) realizes the effective fusion of scenes and positions for consecutive frames. The theoretical analysis demonstrates the feasibility and effectiveness of our proposed method. The experimental results on multiple datasets validate that our method can improve tracker performance in various video scenarios.

Integration of Metabolomic and Transcriptomic Provides Insights into Anti-Inflammatory Response to trans-10-Hydroxy-2-decenoic Acid on LPS-Stimulated RAW 264.7 Cells.

Trans-10-hydroxy-2-decenoic acid (10-HDA) is a unique fatty acid found in royal jelly that possesses potential health benefits such as anti-inflammatory. However, further research is needed to fully understand its mechanisms of action and therapeutic potential for inflammation-associated diseases. In this present study, liquid chromatography-tandem mass spectrometry (LC-MS/MS) and RNA-seq analyses were conducted to comprehensively analyze the in vitro anti-inflammatory effects of 10-HDA on lipopolysaccharide (LPS)-stimulated RAW 264.7 cells. Our results demonstrated that 128 differentially expressed metabolites and 1721 differentially expressed genes were identified in the 10-HDA-treated groups compared to the LPS groups. Metabolites were significantly enriched in amino acid metabolism pathways, including methionine metabolism, glycine and serine metabolism, and tryptophan metabolism. The differentially expressed genes enrichment analysis indicated that antigen processing and presentation, NOD-like receptor signaling pathway, and arginine biosynthesis were enriched with the administration of 10-had. The correlation analysis revealed that glycerophospholipid metabolism and s-adenosylmethionine-dependent methylation processes might be involved in the response to the 10-HDA treatment. Overall, the findings from this study showed that 10-HDA might involve the modulation of certain signaling pathways involved in the inflammatory response, but further research is needed to determine the safety and efficacy as a therapeutic agent.

A Fast Evolutionary Knowledge Transfer Search for Multiscale Deep Neural Architecture.

The emergence of neural architecture search (NAS) algorithms has removed the constraints on manually designed neural network architectures, so that neural network development no longer requires extensive professional knowledge, trial and error. However, the extremely high computational cost limits the development of NAS algorithms. In this article, in order to reduce computational costs and to improve the efficiency and effectiveness of evolutionary NAS (ENAS) is investigated. In this article, we present a fast ENAS framework for multiscale convolutional networks based on evolutionary knowledge transfer search (EKTS). This framework is novel, in that it combines global optimization methods with local optimization methods for search, and searches a multiscale network architecture. In this article, evolutionary computation is used as a global optimization algorithm with high robustness and wide applicability for searching neural architectures. At the same time, for fast search, we combine knowledge transfer and local fast learning to improve the search speed. In addition, we explore a multiscale gray-box structure. This gray box structure combines the Bandelet transform with convolution to improve network approximation, learning, and generalization. Finally, we compare the architectures with more than 40 different neural architectures, and the results confirmed its effectiveness.

Safety and immunogenicity of quadrivalent meningococcal polysaccharide vaccine (MPV ACYW135) compared with quadrivalent meningococcal conjugate vaccine (Menactra®) in Malian children.

Affordable, polyvalent meningococcal vaccines are needed for use in emergency reactive immunization campaigns. A phase IV randomized, observer-blind, controlled study compared the safety and immunogenicity of a quadrivalent meningococcal polysaccharide vaccine (MPV-4, MPV ACYW135) and quadrivalent meningococcal ACWY conjugate vaccine (MCV-4, Menactra®). Healthy, 2- to 10-year-old children in Bamako, Mali, were randomized 1:1 to receive one dose of MPV-4 or MCV-4. Safety outcomes were evaluated for 6 months post-immunization. Immunogenicity for all serogroups was assessed for non-inferiority between MPV-4 and MCV-4 30 days post immunization by serum bactericidal antibody assay using baby rabbit complement (rSBA). From December 2020 to July 2021, 260 healthy subjects were consented and randomized. At Day 30 post-immunization, the proportions of subjects with rSBA titers ≥ 128 for all serogroups in the MPV-4 group were non-inferior to those in MCV-4 group. The proportions of subjects with rSBA ≥ 4-fold increase and rSBA titers ≥ 8 for all serogroups were similar among vaccine groups (P > .05). Geometric Mean Titers and Geometric Mean Fold Increases for all serogroups in both vaccine groups were similar (P > .05). Few local and systemic post-immunization reactions of similar severity and duration were observed within 7 days and were similar in both groups (P > .05). All resolved without sequelae. Unsolicited adverse events were similar in both groups regarding relationship to study vaccine, severity and duration. No serious adverse events were reported during the study period. MPV ACYW135 showed a non-inferior immunogenicity profile and a comparable reactogenicity profile to MCV-4 in Malian children aged 2-10 years.Clinical Trial Registration: NCT04450498.

A Patch Diversity Transformer for Domain Generalized Semantic Segmentation.

Domain generalization (DG) is one of the critical issues for deep learning in unknown domains. How to effectively represent domain-invariant context (DIC) is a difficult problem that DG needs to solve. Transformers have shown the potential to learn generalized features, since the powerful ability to learn global context. In this article, a novel method named patch diversity Transformer (PDTrans) is proposed to improve the DG for scene segmentation by learning global multidomain semantic relations. Specifically, patch photometric perturbation (PPP) is proposed to improve the representation of multidomain in the global context information, which helps the Transformer learn the relationship between multiple domains. Besides, patch statistics perturbation (PSP) is proposed to model the feature statistics of patches under different domain shifts, which enables the model to encode domain-invariant semantic features and improve generalization. PPP and PSP can help to diversify the source domain at the patch level and feature level. PDTrans learns context across diverse patches and takes advantage of self-attention to improve DG. Extensive experiments demonstrate the tremendous performance advantages of the PDTrans over state-of-the-art DG methods.

A conductive catecholate-based framework coordinated with unsaturated bismuth boosts CO2 electroreduction to formate.

Bismuth-based metal-organic frameworks (Bi-MOFs) have received attention in electrochemical CO2-to-formate conversion. However, the low conductivity and saturated coordination of Bi-MOFs usually lead to poor performance, which severely limits their widespread application. Herein, a conductive catecholate-based framework with Bi-enriched sites (HHTP, 2,3,6,7,10,11-hexahydroxytriphenylene) is constructed and the zigzagging corrugated topology of Bi-HHTP is first unraveled via single-crystal X-ray diffraction. Bi-HHTP possesses excellent electrical conductivity (1.65 S m-1) and unsaturated coordination Bi sites are confirmed by electron paramagnetic resonance spectroscopy. Bi-HHTP exhibited an outstanding performance for selective formate production of 95% with a maximum turnover frequency of 576 h-1 in a flow cell, which surpassed most of the previously reported Bi-MOFs. Significantly, the structure of Bi-HHTP could be well maintained after catalysis. In situ attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) confirms that the key intermediate is *COOH species. Density functional theory (DFT) calculations reveal that the rate-determining step is *COOH species generation, which is consistent with the in situ ATR-FTIR results. DFT calculations confirmed that the unsaturated coordination Bi sites acted as active sites for electrochemical CO2-to-formate conversion. This work provides new insights into the rational design of conductive, stable, and active Bi-MOFs to improve their performance towards electrochemical CO2 reduction.

Variations and driving mechanisms of desertification in the southeast section of the China-Mongolia-Russia Economic Zone.

Desertification seriously restricts sustainable development in the arid-semiarid areas of the eastern section of the China-Mongolia-Russia Economic Zone, especially in China and Mongolia. In this study, the potential range of desertification was bounded. Spatio-temporal dynamics from 2000 to 2020 were analyzed using best-performing indices (fvc, albedo and LST). Further analysis focused on the driving factors resulting in desertification. The research showed that the potential range of desertification accounted for 50.99 % of the entire region, mainly distributed in central and western parts of Inner Mongolia, and central and southern parts of Mongolia. From 2000 to 2020, desertification in the entire study area improved, with a 2.23 % decrease in the area of severe and extremely severe desertification. Among the studied countries, the grades of desertification in China decreased over the years of study; the area of desertification in Mongolia expanded. The study also indicated that the restoration regions were affected mainly by climatic factor sand human activities, and the degradation area was driven primarily by human activities. Therefore, it is essential to formulate a reasonable land policy for desertification control.

Safety and immunogenicity of a bivalent HPV16/18 vaccine in Chinese females.

As global supply is still inadequate to address the worldwide requirements for HPV vaccines, we assessed the safety and immunogenicity of a new bivalent HPV16/18 vaccine. In this randomized, double-blind, placebo-controlled, phase 2 trial, healthy 9-45-year-old Chinese females in three age cohorts (600 aged 9-17 years; 240 aged 18-26 years; 360 aged 27-45 years) were randomized 1:1 to receive three doses (0,2,6 months) of HPV16/18 vaccine or placebo. We measured neutralizing antibodies against HPV 16 and 18 at 7 months and monitored safety to 12 months in all age cohorts; 9-17-year-old girls were monitored for safety and immunogenicity to 48 months. In vaccinees, 99.8% seroconverted for HPV 16 and 18 types at 7 months; respective GMTs of 5827 (95% CI: 5249, 6468) and 4223 (3785, 4713) were significantly (p < .001) higher than controls for all comparisons. GMTs in the 9-17-year-olds, which were significantly higher than in older women at 7 months, gradually declined to 48 months but remained higher than placebo with seropositivity rates maintained at 98.5% and 97.6% against HPV 16 and 18, respectively. Adverse events occurred at similar rates after vaccine and placebo (69.8% vs. 72.5%, p = .308), including solicited local reactions and systemic adverse events which were mainly mild-to-moderate. The bivalent HPV16/18 vaccine was well tolerated and induced high levels of neutralizing antibodies in all age groups which persisted at high levels to 48 months in the 9-17-year-old age group which would be the target for HPV vaccination campaigns.