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Dynamic Scene Graph Generation (DSGG) focuses on identifying visual relationships within the spatial-temporal domain of videos. Conventional approaches often employ multi-stage pipelines, which typically consist of object detection, temporal association, and multi-relation classification. However, these methods exhibit inherent limitations due to the separation of multiple stages, and independent optimization of these sub-problems may yield sub-optimal solutions. To remedy these limitations, we propose a one-stage end-to-end framework, termed OED, which streamlines the DSGG pipeline. This framework reformulates the task as a set prediction problem and leverages pair-wise features to represent each subject-object pair within the scene graph. Moreover, another challenge of DSGG is capturing temporal dependencies, we introduce a Progressively Refined Module (PRM) for aggregating temporal context without the constraints of additional trackers or handcrafted trajectories, enabling end-to-end optimization of the network. Extensive experiments conducted on the Action Genome benchmark demonstrate the effectiveness of our design. The code and models are available at \url{https://github.com/guanw-pku/OED}., Comment: Accepted by CVPR'24

We present Hunyuan-DiT, a text-to-image diffusion transformer with fine-grained understanding of both English and Chinese. To construct Hunyuan-DiT, we carefully design the transformer structure, text encoder, and positional encoding. We also build from scratch a whole data pipeline to update and evaluate data for iterative model optimization. For fine-grained language understanding, we train a Multimodal Large Language Model to refine the captions of the images. Finally, Hunyuan-DiT can perform multi-turn multimodal dialogue with users, generating and refining images according to the context. Through our holistic human evaluation protocol with more than 50 professional human evaluators, Hunyuan-DiT sets a new state-of-the-art in Chinese-to-image generation compared with other open-source models. Code and pretrained models are publicly available at github.com/Tencent/HunyuanDiT, Comment: Project Page: https://dit.hunyuan.tencent.com

Understanding the genetic basis of pest adaptive evolution and the risk of adaptation in response to climate change is essential for the development of sustainable agricultural practices. However, the genetic basis of climatic adaptation for the Asian corn borer (ACB), Ostrinia furnacalis, the main pest of corn in Asia and Oceania, is poorly understood. Here, we revealed the genomic loci underlying the climatic adaptation and evolution in ACB by integrating population genomic and environmental factors. We assembled a 471-Mb chromosome-scale reference genome of ACB and re-sequenced 423 individuals covering 27 representative geographic areas. We inferred that the ACB effective population size changes tracked with the global temperature and followed by a recent decline. Based on an integrated analysis of whole-genome selection scans and genome-wide genotype-environment association studies, we revealed the genetic basis of ACB adaption to diverse climates. For diapause traits, we identified a major-effect association locus containing a circadian clock gene (period) by analyzing a diapause-segregating population. Moreover, our predictions indicated that the northern populations were more ecologically resilient to climate change than the southern populations. Together, our results revealed the genomic basis for ACB environmental adaptation and provided potential candidate genes for future evolutionary studies and genetic adaptation to climate change, intending to maintain the efficacy and sustainability of novel control techniques.

Understanding the genetic basis of pest adaptive evolution and the risk of adaptation in response to climate change is essential for the development of sustainable agricultural practices. However, the genetic basis of climatic adaptation for the Asian corn borer (ACB), Ostrinia furnacalis, the main pest of corn in Asia and Oceania, is poorly understood. Here, we revealed the genomic loci underlying the climatic adaptation and evolution in ACB by integrating population genomic and environmental factors. We assembled a 471-Mb chromosome-scale reference genome of ACB and re-sequenced 423 individuals covering 27 representative geographic areas. We inferred that the ACB effective population size changes tracked with the global temperature and followed by a recent decline. Based on an integrated analysis of whole-genome selection scans and genome-wide genotype-environment association studies, we revealed the genetic basis of ACB adaption to diverse climates. For diapause traits, we identified a major-effect association locus containing a circadian clock gene (period) by analyzing a diapause-segregating population. Moreover, our predictions indicated that the northern populations were more ecologically resilient to climate change than the southern populations. Together, our results revealed the genomic basis for ACB environmental adaptation and provided potential candidate genes for future evolutionary studies and genetic adaptation to climate change, intending to maintain the efficacy and sustainability of novel control techniques.

Jiangfen Wu,1 Lingyan Ren,2 Xinyi Huang,3 Li Hu,2 Liangliang Zhang,2 Dan Xie,1 Zhimin Li,4 Naijian Han,4 Shengwen Huang1,2,5 1GuiZhou University Medical College, Guiyang, Guizhou, People’s Republic of China; 2Prenatal Diagnosis Center, Guizhou Provincial People’s Hospital, Guiyang, People’s Republic of China; 3School of Medical and Life Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China; 4Annoroad Gene Technology (Beijing) Co., Ltd, Beijing, People’s Republic of China; 5NHC Key Laboratory of Pulmonary Immunological Diseases, Guizhou Provincial, People’s Hospital, Guiyang, People’s Republic of ChinaCorrespondence: Shengwen Huang, Guizhou Provincial People’s Hospital, Nanming District, Guiyang, People’s Republic of China, Tel +8615185134701, Fax +86-851-85937194, Email hsw713@sina.comBackground: Duchenne muscular dystrophy (DMD), an X-linked recessive neuromuscular disorder, is caused by pathogenic variants in the DMD gene encoding a large structural protein in muscle cells.Methods: Two probands, a 6-year old boy and a 1-month old infant, respectively, were clinically diagnosed with DMD based on elevated levels of creatine kinase and creatine kinase isoenzyme. CNVplex and whole exome sequencing (WES) were performed for causal variants, and Sanger sequencing was used for verification.Results: CNVplex found no large deletions or duplications in the DMD gene in both patients, but WES discovered a single-nucleotide deletion in exon 48 (NM_004006.2:c.6963del, p.Asp2322ThrfsTer16) in the proband of pedigree 1, and a nonsense mutation in exon 27 (NM_004006.2:c.3637A>T, p.K1213Ter) in the proband of pedigree 2.Conclusion: The results of our study expand the mutation spectrum of DMD and enrich our understanding of the clinical characteristics of DMD. Genetic counseling was provided for the two families involved in this study.Keywords: DMD gene, exon 48, exon 27, whole exome sequencing, Duchenne muscular dystro

Organic-inorganic halide perovskites (OIHPs) show high promise in optical and electronic applications such as solar cells, light-emitting diodes, and nonlinear optics. However, the fundamental knowledge of the atomic-scale microstructures in OIHP thin films is limited due to the challenge in characterizing them using transmission electron microscopy (TEM). Here a solution-phase "release-and-transfer" method is demonstrated, which entails the lifting of OIHP films from their original substrates while maintaining the film integrity, followed by a sequential transfer onto a TEM grid. The freestanding nature of the OIHP films with a nanoscale thickness, prepared as such, allows a direct TEM observation in the plan view, complementing those typical cross-sectional views enabled by focus-ion-beam specimen fabrication. Using low-dose scanning TEM, the atomic-scale microstructure of transferred OIHP films is confirmed to be generally maintained, while the microstrain existing in original films is largely relaxed. This "release-and-transfer" method is generic to both standard 3D and low-dimensional OIHPs. Based on a simple layer-by-layer transfer, the fabrication of a 2D-3D planar heterojunction with a good interfacial contact and optoelectronic properties is achieved. This unique methodology offers new opportunities to accelerate the fundamental and practical developments of OIHP materials and devices. A solution-phase release-and-transfer method for acquiring freestanding organic-inorganic halide perovskite thin films, which are suitable for the atomic-scale scanning transmission electron microscopy investigation, is demonstrated. Also, the method is generic to both 3D and 2D perovskites, unlocking a new fabrication route for perovskite planar heterojunctions.image

The authors regret an error in the published article in which the name Songhua Cai was incorrectly given as Songhhua Cai.

We demonstrate rational design of graphene-supported single and dual metal atom catalysts (SACs and DACs) for photocatalytic applications, such as singlet oxygen (O-1(2)) sensitization and H2O2 production. Here we combine density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations with experimental verifications. We found a synergistic effect between triplet sensitization and triplet-triplet (Dexter) energy transfer; both play a role in the photocatalytic activity through the volcano plot of 3d transition metal SACs. More specifically, FeN4-SAC exhibits a low ISC energy gap (Delta E-ISC) of 0.039 eV, compared with 0.108 eV for FeNiN8-DACs, both possessing a high Bader charge transfer of 0.366 e(-) and 0.405 e(-), respectively. Guided by these computational results, we synthesized a series of SACs and a DAC and confirmed their structures with scanning transmission electron microscopy (STEM) along with the X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine structure (EXAFS). We then confirm their band structures with low-energy inverse photoemission spectroscopy (LEIPS) and UV-vis-NIR. Subsequently, we synthesized the catalysts for the photooxygenation of anthracene and two-electron oxygen reduction reaction (ORR) to measure their photocatalytic activity. We found that H2O2 production through the two-electron ORR competes with the O-1(2) generation through Dexter energy transfer. FeN4-SAC demonstrates a high photooxygenation conversion of 86% and a high O-1(2) quantum yield of 1.04, obtained from electron spin resonance (ESR) spectroscopy, with low H2O2 production. In contrast, NiN4-SAC exhibits a low O-1(2) generation and a high H2O2 production mainly because of the high Gibbs free energy of the OOH{*} intermediate. This work proposes an effective DFT-guided strategy for designing SACs and DACs for various photocatalytic applications.

To overcome the crosstalk problem originating from the sneak path current, a simple structure of Cu/Al2O3/Cu memristor was fabricated in this paper. Results showed that the device presented good bipolar resistive switching characteristic (BRS) as well as the endurance and retention performance. Most importantly, the complimentary resistance switching characteristic (CRS) appeared in this device by adjusting the testing parameters, which is important in crossbar array applications. © 2023 IEEE.

With recent advances in multi-modal foundation models, the previously text-only large language models (LLM) have evolved to incorporate visual input, opening up unprecedented opportunities for various applications in visualization. Our work explores the utilization of the visual perception ability of multi-modal LLMs to develop Autonomous Visualization Agents (AVAs) that can interpret and accomplish user-defined visualization objectives through natural language. We propose the first framework for the design of AVAs and present several usage scenarios intended to demonstrate the general applicability of the proposed paradigm. The addition of visual perception allows AVAs to act as the virtual visualization assistant for domain experts who may lack the knowledge or expertise in fine-tuning visualization outputs. Our preliminary exploration and proof-of-concept agents suggest that this approach can be widely applicable whenever the choices of appropriate visualization parameters require the interpretation of previous visual output. Feedback from unstructured interviews with experts in AI research, medical visualization, and radiology has been incorporated, highlighting the practicality and potential of AVAs. Our study indicates that AVAs represent a general paradigm for designing intelligent visualization systems that can achieve high-level visualization goals, which pave the way for developing expert-level visualization agents in the future.

Neural network have achieved remarkable successes in many scientific fields. However, the interpretability of the neural network model is still a major bottlenecks to deploy such technique into our daily life. The challenge can dive into the non-linear behavior of the neural network, which rises a critical question that how a model use input feature to make a decision. The classical approach to address this challenge is feature attribution, which assigns an important score to each input feature and reveal its importance of current prediction. However, current feature attribution approaches often indicate the importance of each input feature without detail of how they are actually processed by a model internally. These attribution approaches often raise a concern that whether they highlight correct features for a model prediction. For a neural network model, the non-linear behavior is often caused by non-linear activation units of a model. However, the computation behavior of a prediction from a neural network model is locally linear, because one prediction has only one activation pattern. Base on the observation, we propose an instance-wise linearization approach to reformulates the forward computation process of a neural network prediction. This approach reformulates different layers of convolution neural networks into linear matrix multiplication. Aggregating all layers' computation, a prediction complex convolution neural network operations can be described as a linear matrix multiplication $F(x) = W \cdot x + b$. This equation can not only provides a feature attribution map that highlights the important of the input features but also tells how each input feature contributes to a prediction exactly. Furthermore, we discuss the application of this technique in both supervise classification and unsupervised neural network learning parametric t-SNE dimension reduction.

This paper studies informal risk-sharing regimes in a unified framework by examining intertemporal consumption behavior of rural households in Tanzania. We exploit  a theoretically-consistent link between interest rates and cross-sectional consumption moments to test alternative risk-sharing models without requiring data on interest rates or assuming a restriction to eliminate the need for such data, which are often unavailable in developing economies. We specify tests that allow us to distinguish among models even with temporal dependence in income shocks. Our analysis shows that the consumption pattern in rural Tanzania is consistent with the self-insurance regime, and that risk aversion varies substantially across districts. Imposing a strict condition on interest rates, as often done in prior literature, misses their intertemporal heterogeneity and biases the estimation of risk aversion.

This paper studies informal risk-sharing regimes in a unified framework by examining intertemporal consumption behavior of rural households in Tanzania. We exploit  a theoretically-consistent link between interest rates and cross-sectional consumption moments to test alternative risk-sharing models without requiring data on interest rates or assuming a restriction to eliminate the need for such data, which are often unavailable in developing economies. We specify tests that allow us to distinguish among models even with temporal dependence in income shocks. Our analysis shows that the consumption pattern in rural Tanzania is consistent with the self-insurance regime, and that risk aversion varies substantially across districts. Imposing a strict condition on interest rates, as often done in prior literature, misses their intertemporal heterogeneity and biases the estimation of risk aversion.

Temporal video segmentation and classification have been advanced greatly by public benchmarks in recent years. However, such research still mainly focuses on human actions, failing to describe videos in a holistic view. In addition, previous research tends to pay much attention to visual information yet ignores the multi-modal nature of videos. To fill this gap, we construct the Tencent `Ads Video Segmentation'~(TAVS) dataset in the ads domain to escalate multi-modal video analysis to a new level. TAVS describes videos from three independent perspectives as `presentation form', `place', and `style', and contains rich multi-modal information such as video, audio, and text. TAVS is organized hierarchically in semantic aspects for comprehensive temporal video segmentation with three levels of categories for multi-label classification, e.g., `place' - `working place' - `office'. Therefore, TAVS is distinguished from previous temporal segmentation datasets due to its multi-modal information, holistic view of categories, and hierarchical granularities. It includes 12,000 videos, 82 classes, 33,900 segments, 121,100 shots, and 168,500 labels. Accompanied with TAVS, we also present a strong multi-modal video segmentation baseline coupled with multi-label class prediction. Extensive experiments are conducted to evaluate our proposed method as well as existing representative methods to reveal key challenges of our dataset TAVS.

Human-Object Interactions (HOI) detection, which aims to localize a human and a relevant object while recognizing their interaction, is crucial for understanding a still image. Recently, transformer-based models have significantly advanced the progress of HOI detection. However, the capability of these models has not been fully explored since the Object Query of the model is always simply initialized as just zeros, which would affect the performance. In this paper, we try to study the issue of promoting transformer-based HOI detectors by initializing the Object Query with category-aware semantic information. To this end, we innovatively propose the Category-Aware Transformer Network (CATN). Specifically, the Object Query would be initialized via category priors represented by an external object detection model to yield better performance. Moreover, such category priors can be further used for enhancing the representation ability of features via the attention mechanism. We have firstly verified our idea via the Oracle experiment by initializing the Object Query with the groundtruth category information. And then extensive experiments have been conducted to show that a HOI detection model equipped with our idea outperforms the baseline by a large margin to achieve a new state-of-the-art result., Comment: Accepted by CVPR2022

While Human-Object Interaction(HOI) Detection has achieved tremendous advances in recent, it still remains challenging due to complex interactions with multiple humans and objects occurring in images, which would inevitably lead to ambiguities. Most existing methods either generate all human-object pair candidates and infer their relationships by cropped local features successively in a two-stage manner, or directly predict interaction points in a one-stage procedure. However, the lack of spatial configurations or reasoning steps of two- or one- stage methods respectively limits their performance in such complex scenes. To avoid this ambiguity, we propose a novel actor-centric framework. The main ideas are that when inferring interactions: 1) the non-local features of the entire image guided by actor position are obtained to model the relationship between the actor and context, and then 2) we use an object branch to generate pixel-wise interaction area prediction, where the interaction area denotes the object central area. Moreover, we also use an actor branch to get interaction prediction of the actor and propose a novel composition strategy based on center-point indexing to generate the final HOI prediction. Thanks to the usage of the non-local features and the partly-coupled property of the human-objects composition strategy, our proposed framework can detect HOI more accurately especially for complex images. Extensive experimental results show that our method achieves the state-of-the-art on the challenging V-COCO and HICO-DET benchmarks and is more robust especially in multiple persons and/or objects scenes., Comment: 11 pages

Deep learning approaches have provided state-of-the-art performance in many applications by relying on large and overparameterized neural networks. However, such networks have been shown to be very brittle and are difficult to deploy on resource-limited platforms. Model pruning, i.e., reducing the size of the network, is a widely adopted strategy that can lead to a more robust and compact model. Many heuristics exist for model pruning, but empirical studies show that some heuristics improve performance whereas others can make models more brittle or have other side effects. This work aims to shed light on how different pruning methods alter the network's internal feature representation and the corresponding impact on model performance. To facilitate a comprehensive comparison and characterization of the high-dimensional model feature space, we introduce a visual geometric analysis of feature representations. We decomposed and evaluated a set of critical geometric concepts from the common adopted classification loss, and used them to design a visualization system to compare and highlight the impact of pruning on model performance and feature representation. The proposed tool provides an environment for in-depth comparison of pruning methods and a comprehensive understanding of how model response to common data corruption. By leveraging the proposed visualization, machine learning researchers can reveal the similarities between pruning methods and redundant in robustness evaluation benchmarks, obtain geometric insights about the differences between pruned models that achieve superior robustness performance, and identify samples that are robust or fragile to model pruning and common data corruption to model pruning and data corruption but also obtain insights and explanations on how some pruned models achieve superior robustness performance.

Human-Object Interaction (HOI) detection is a fundamental task in high-level human-centric scene understanding. We propose PhraseHOI, containing a HOI branch and a novel phrase branch, to leverage language prior and improve relation expression. Specifically, the phrase branch is supervised by semantic embeddings, whose ground truths are automatically converted from the original HOI annotations without extra human efforts. Meanwhile, a novel label composition method is proposed to deal with the long-tailed problem in HOI, which composites novel phrase labels by semantic neighbors. Further, to optimize the phrase branch, a loss composed of a distilling loss and a balanced triplet loss is proposed. Extensive experiments are conducted to prove the effectiveness of the proposed PhraseHOI, which achieves significant improvement over the baseline and surpasses previous state-of-the-art methods on Full and NonRare on the challenging HICO-DET benchmark., Comment: Accepted to AAAI2022

Aim: Accurate and reliable quantification of oligonucleotides can be difficult, which has led to an increased focus on bioanalytical methods for more robust analyses. Recent advances toward mitigating sample losses on liquid chromatography (LC) systems have produced recovery advantages for oligonucleotide separations. Results & methodology: LC instruments and columns constructed from MP35N metal alloy and stainless steel columns were compared against LC hardware modified with hybrid inorganic-organic silica surfaces. Designed to minimize metal-analyte adsorption, these surfaces demonstrated a 73% increase in 25-mer phosphorothioate oligonucleotide recovery using ion-pairing reversed-phase LC versus standard LC surfaces, most particularly upon initial use. Conclusion: Hybrid silica chromatographic surfaces improve the performance, detection limits and reproducibility of oligonucleotide bioanalytical assays.

Aim: Accurate and reliable quantification of oligonucleotides can be difficult, which has led to an increased focus on bioanalytical methods for more robust analyses. Recent advances toward mitigating sample losses on liquid chromatography (LC) systems have produced recovery advantages for oligonucleotide separations. Results & methodology: LC instruments and columns constructed from MP35N metal alloy and stainless steel columns were compared against LC hardware modified with hybrid inorganic-organic silica surfaces. Designed to minimize metal-analyte adsorption, these surfaces demonstrated a 73% increase in 25-mer phosphorothioate oligonucleotide recovery using ion-pairing reversed-phase LC versus standard LC surfaces, most particularly upon initial use. Conclusion: Hybrid silica chromatographic surfaces improve the performance, detection limits and reproducibility of oligonucleotide bioanalytical assays.

Biodiversity loss affects ecosystem functioning. Top down effects of amphibian declines on the trophic food web of the forest floor are poorly understood. Here we quantify and explain the effects of disease-driven loss of salamanders on the dynamics of forest leaf litter. Using paired mesocosms, within a Belgian forest, we tested the effect of fire salamander (Salamandra salamandra) presence on the trophic cascade that results in the decomposition rate of good quality (maple - Acer pseudoplatanus) and poorer quality (oak - Quercus robur) leaf litter, over an 18 month period. The presence of salamanders reduced decomposition rate of Quercus litter up to 20%. This was associated with a significantly higher predation rate on detritivores, which altered the functional composition of the invertebrate community. Functional composition analysis of the litter microbiome showed less bacteria associated with leaf litter degradation on the Quercus litter in the presence of salamanders. Salamanders thus influence ecosystem functions through trophic cascades and promote the retention of the leaf litter fraction in poorer quality litter.

Multi-modal Ads Video Understanding Challenge is the first grand challenge aiming to comprehensively understand ads videos. Our challenge includes two tasks: video structuring in the temporal dimension and multi-modal video classification. It asks the participants to accurately predict both the scene boundaries and the multi-label categories of each scene based on a fine-grained and ads-related category hierarchy. Therefore, our task has four distinguishing features from previous ones: ads domain, multi-modal information, temporal segmentation, and multi-label classification. It will advance the foundation of ads video understanding and have a significant impact on many ads applications like video recommendation. This paper presents an overview of our challenge, including the background of ads videos, an elaborate description of task and dataset, evaluation protocol, and our proposed baseline. By ablating the key components of our baseline, we would like to reveal the main challenges of this task and provide useful guidance for future research of this area. In this paper, we give an extended version of our challenge overview. The dataset will be publicly available at https://algo.qq.com/., Comment: 8-page extended version of our challenge paper in ACM MM 2021. It presents the overview of grand challenge "Multi-modal Ads Video Understanding" in ACM MM 2021. Our grand challenge is also the Tencent Advertising Algorithm Competition (TAAC) 2021

This dissertation consists of three chapters regarding international trade and economic development. In the first two chapters I explore how China’s economic rise to the global stage affects resource allocations inside and outside the country, and in the third chapter I present a new method to infer risk sharing regimes pertinent to studying consumption behavior in developing countries. The first chapter studies how the "China shock"---the remarkable growth in China's productivity and trade activities since its accession to the World Trade Organization (WTO)---affects China's labor market and real exchange rate dynamics. I apply a dynamic trade and spatial equilibrium model to jointly explain two distinctive features of China's economic growth: the structural transformation, as characterized by the reallocation of labor from agriculture to manufacturing and services, and the sluggish appreciation of the real exchange rate, a puzzle from the perspective of a standard international economics model. The model highlights the role of the subsistence sector in shaping the patterns of the structural transformation and real exchange rate dynamics. Using inter-regional trade and migration data, I calibrate the model to decompose the ``China shock" into productivity shocks and trade shocks and show that the two features above arise naturally from the interaction between the labor market and observed shocks to productivity and trade costs. I find that while productivity growth is the primary source of the structural transformation, the accession to the WTO explains about 35% of the rise in the employment share and 20% of the increase in the real wage in the manufacturing sector. Welfare gains from the "WTO entry" are 27% on average and would be larger if complemented by relaxing labor restrictions further. By accounting for trade costs, the subsistence sector, and labor market frictions, the model generates dynamics for China's real exchange rate consistent with the data.The s

This dissertation consists of three chapters regarding international trade and economic development. In the first two chapters I explore how China’s economic rise to the global stage affects resource allocations inside and outside the country, and in the third chapter I present a new method to infer risk sharing regimes pertinent to studying consumption behavior in developing countries. The first chapter studies how the "China shock"---the remarkable growth in China's productivity and trade activities since its accession to the World Trade Organization (WTO)---affects China's labor market and real exchange rate dynamics. I apply a dynamic trade and spatial equilibrium model to jointly explain two distinctive features of China's economic growth: the structural transformation, as characterized by the reallocation of labor from agriculture to manufacturing and services, and the sluggish appreciation of the real exchange rate, a puzzle from the perspective of a standard international economics model. The model highlights the role of the subsistence sector in shaping the patterns of the structural transformation and real exchange rate dynamics. Using inter-regional trade and migration data, I calibrate the model to decompose the ``China shock" into productivity shocks and trade shocks and show that the two features above arise naturally from the interaction between the labor market and observed shocks to productivity and trade costs. I find that while productivity growth is the primary source of the structural transformation, the accession to the WTO explains about 35% of the rise in the employment share and 20% of the increase in the real wage in the manufacturing sector. Welfare gains from the "WTO entry" are 27% on average and would be larger if complemented by relaxing labor restrictions further. By accounting for trade costs, the subsistence sector, and labor market frictions, the model generates dynamics for China's real exchange rate consistent with the data.The s

Instant Catapult Steam Explosion (ICSE) is a rapid and effective technique with ability to reduce the toxic compound and improve the cellulose digestibility of lignocellulosic biomass. It has beneficial feature of very short explosion time (<0.1 s) and high explosion power density (400–1000 order of magnitude density) compared to conventional explosion. In this study, ICSE was examined for its potential to degrade aflatoxin B1 (AFB1) in corn stalk in order to improve its utilization as a toxic-free animal feed. Incubation of AFB1-containing corn stalk followed by processing with ICSE at 2.4 MPa led to approximately 100% degradation of AFB1 in 140 s of process time. The degradation of AFB1 by ICSE led to generation of 5 products including C17H8O6 (m/z 308), C17H14O6 (m/z 314), C16H8O5 (m/z 280), C17H14O7 (m/z 330) and C17H16O9 (m/z 364). Two degradation pathways were proposed and the first one involved hydrolysis, oxidation and dehydrogenation while the second consisted of dehydrogenation and decarbonylation. The toxicity of these products were found to be substantially less than that possessed by AFB1. The study demonstrates that ICSE shows great promise as a rapid technique to detoxify aflatoxin-contaminated biomass for sustainable and safe utilization of agricultural biomass as animal feed.

Wildlife diseases are contributing to the current Earth’s sixth mass extinction; one disease, chytridiomycosis, has caused mass amphibian die-offs. While global spread of a hypervirulent lineage of the fungus Batrachochytrium dendrobatidis (BdGPL) causes unprecedented loss of vertebrate diversity by decimating amphibian populations, its impact on amphibian communities is highly variable across regions. Here, we combine field data with in vitro and in vivo trials that demonstrate the presence of a markedly diverse variety of low virulence isolates of BdGPL in northern European amphibian communities. Pre-exposure to some of these low virulence isolates protects against disease following subsequent exposure to highly virulent BdGPL in midwife toads (Alytes obstetricans) and alters infection dynamics of its sister species B. salamandrivorans in newts (Triturus marmoratus), but not in salamanders (Salamandra salamandra). The key role of pathogen virulence in the complex host-pathogen-environment interaction supports efforts to limit pathogen pollution in a globalized world.

The chytrid fungus Batrachochytrium dendrobatidis has caused the most prominent loss of vertebrate diversity ever recorded, which peaked in the 1980s. Recent incursion by its sister species B. salamandrivorans in Europe raised the alarm for a new wave of declines and extinctions in western Palearctic urodeles. The European Commission has responded by restricting amphibian trade. However, private amphibian collections, the main end consumers, were exempted from the European legislation. Here, we report how invasion by a released, exotic newt coincided with B. salamandrivorans invasion at over 1000 km from the nearest natural outbreak site, causing mass mortality in indigenous marbled newts (Triturus marmoratus), and posing an acute threat to the survival of nearby populations of the most critically endangered European newt species (Montseny brook newt, Calotriton arnoldi). Disease management was initiated shortly after detection in a close collaboration between policy and science and included drastic on site measures and intensive disease surveillance. Despite these efforts, the disease is considered temporarily contained but not eradicated and continued efforts will be necessary to minimize the probability of further pathogen dispersal. This precedent demonstrates the importance of tackling wildlife diseases at an early stage using an integrated approach, involving all stakeholders and closing loopholes in existing regulations.

Waner Wu,1,* Na Xu,1,* Xuan Zhou,1 Liang Liu,1 Yaxian Tan,1 Jie Luo,1 Jixian Huang,2 Jiayue Qin,3 Juan Wang,3 Zhimin Li,3 Changxin Yin,1 Lingling Zhou,1 Xiaoli Liu1 1Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong, People’s Republic of China; 2Department of Hematology, Yuebei People’s Hospital, Shantou University, Shaoguan 512025, Guangdong, People’s Republic of China; 3Yiwu Cancer Research Center, Fudan University Shanghai Cancer Center, Yiwu, Zhejiang 322000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaoli Liu Email lxl2405@126.comIntroduction: While the acquisition of mutations in the ABL1 kinase domain (KD) has been identified as a common mechanism behind tyrosine kinase inhibitor (TKI) resistance, recent genetic studies have revealed that patients with TKI resistance or intolerance frequently harbor one or more genetic alterations implicated in myeloid malignancies. This suggests that additional mutations other than ABL1 KD mutations might contribute to disease progression.Methods: We performed targeted-capture sequencing of 127 known and putative cancer-related genes of 63 patients with CML using next-generation sequencing (NGS), including 42 patients with TKI resistance and 21 with TKI intolerance.Results: The differences in the number of mutations between groups had no statistical significance. This could be explained in part by not all of the patients having achieved major molecular remission in the early period as expected. Overall, 66 mutations were identified in 96.8% of the patients, most frequently in the KTM2C (31.82%), ABL1 (31.82%), FAT1 (25.76%), and ASXL1 (22.73%) genes. CUX1, KIT, and GATA2 were associated with TKI intolerance, and two of them (CUX1, GATA2) are transcription factors in which mutations were identified in 82.61% of patients with TKI intolerance. ASXL1 mutations were found more frequently in pa

Ruixiao Wang,1,* Jiayue Qin,1,* Yafeng Fan,2 Zhimin Li,1 Chongjian Chen,1 Wenzhong Su2 1Annoroad Gene Technology Co., Ltd, Beijing 100176, People’s Republic of China; 2Department of Respiration Medicine, Shanxi Cancer Hospital, Taiyuan, Shanxi 030013, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wenzhong Su; Chongjian Chen Email wenzhongsu001@163.com; cchen@annoroad.comAbstract: Anaplastic lymphoma kinase (ALK) fusion is present in approximately 2– 7% of patients with lung adenocarcinoma. ALK fusion-positive patients can benefit from targeted therapy. We herein report a 53-year-old Chinese male patient diagnosed as lung adenocarcinoma with a smoking history. Next-generation sequencing was performed to detect somatic mutations of oncogenic drivers and tumor suppressor genes in plasma-derived circulating tumor DNA using an ultra-deep 160-gene panel. A novel HPCAL1-ALK fusion variant was identified in the patient responding to ALK inhibitor treatments, and the fusion variant was also confirmed by fluorescence in situ hybridization and immunohistochemical. Our study expands the mutational spectrum of ALK fusion variants and provides options for the precise treatment of such patients.Keywords: non-small cell lung cancer, anaplastic lymphoma kinase fusion, next-generation sequencing, targeted therapy

Mixed transition metal oxides (MTMOs) have enormous potential applications in energy and environment. Their use as catalysts for the treatment of environmental pollution requires further enhancement in activity and stability. This work presents a new synthesis approach that is both convenient and effective in preparing binary metal oxide catalysts (CeCuOx) with excellent activity by achieving molecular‐level mixing to promote aliovalent substitution. It also allows a single, pure MTMO to be prepared for enhanced stability under reaction by using a bimetallic metal–organic framework (MOF) as the catalyst precursor. This approach also enables the direct manipulation of the shape and form of the MTMO catalyst by controlling the crystallization and growth of the MOF precursor. A 2D CeCuOx catalyst is investigated for the oxidation reactions of methanol, acetone, toluene, and o‐xylene. The catalyst can catalyze the complete reactions of these molecules into CO2 at temperatures below 200 °C, representing a significant improvement in performance. Furthermore, the catalyst can tolerate high moisture content without deactivation.

Mixed transition metal oxides (MTMOs) have enormous potential applications in energy and environment. Their use as catalysts for the treatment of environmental pollution requires further enhancement in activity and stability. This work presents a new synthesis approach that is both convenient and effective in preparing binary metal oxide catalysts (CeCuOx) with excellent activity by achieving molecular‐level mixing to promote aliovalent substitution. It also allows a single, pure MTMO to be prepared for enhanced stability under reaction by using a bimetallic metal–organic framework (MOF) as the catalyst precursor. This approach also enables the direct manipulation of the shape and form of the MTMO catalyst by controlling the crystallization and growth of the MOF precursor. A 2D CeCuOx catalyst is investigated for the oxidation reactions of methanol, acetone, toluene, and o‐xylene. The catalyst can catalyze the complete reactions of these molecules into CO2 at temperatures below 200 °C, representing a significant improvement in performance. Furthermore, the catalyst can tolerate high moisture content without deactivation.

Mixed transition metal oxides (MTMOs) have enormous potential applications in energy and environment. Their use as catalysts for the treatment of environmental pollution requires further enhancement in activity and stability. This work presents a new synthesis approach that is both convenient and effective in preparing binary metal oxide catalysts (CeCuOx) with excellent activity by achieving molecular‐level mixing to promote aliovalent substitution. It also allows a single, pure MTMO to be prepared for enhanced stability under reaction by using a bimetallic metal–organic framework (MOF) as the catalyst precursor. This approach also enables the direct manipulation of the shape and form of the MTMO catalyst by controlling the crystallization and growth of the MOF precursor. A 2D CeCuOx catalyst is investigated for the oxidation reactions of methanol, acetone, toluene, and o‐xylene. The catalyst can catalyze the complete reactions of these molecules into CO2 at temperatures below 200 °C, representing a significant improvement in performance. Furthermore, the catalyst can tolerate high moisture content without deactivation.

Barren tip on corn ear is an important agronomic trait in maize, which is highly associated with grain yield. Understanding the genetic basis of tip-barrenness may help to reduce the ear tip-barrenness in breeding programs. In this study, ear tip-barrenness was evaluated in two environments in a F2:3 population, and it showed significant genotypic variation for ear tip-barrenness in both environments. Using mixed-model composite interval mapping method, three additive effects quantitative trait loci (QTL) for ear tip-barrenness were mapped on chromosomes 2, 3 and 6, respectively. They explained 16.6% of the phenotypic variation, and no significant QTL × Environment interactions and digenic interactions were detected. The results indicated that additive effect was the main genetic basis for ear tip-barrenness in maize. This is the first report of QTL mapped for ear tip-barrenness in maize.

Barren tip on corn ear is an important agronomic trait in maize, which is highly associated with grain yield. Understanding the genetic basis of tip-barrenness may help to reduce the ear tip-barrenness in breeding programs. In this study, ear tip-barrenness was evaluated in two environments in a F2:3 population, and it showed significant genotypic variation for ear tip-barrenness in both environments. Using mixed-model composite interval mapping method, three additive effects quantitative trait loci (QTL) for ear tip-barrenness were mapped on chromosomes 2, 3 and 6, respectively. They explained 16.6% of the phenotypic variation, and no significant QTL × Environment interactions and digenic interactions were detected. The results indicated that additive effect was the main genetic basis for ear tip-barrenness in maize. This is the first report of QTL mapped for ear tip-barrenness in maize.

Fluxes in stratified water bodies such as lakes and oceans are often controlled by turbulence and mixing at sloping boundaries. One mechanism of boundary mixing is the reflection of internal waves from slopes; when the angle  that the internal wave ray makes with the horizontal is equal to the slope angle --that is, when the ratio  = sin/sin = 1, critical reflection occurs. Critical reflection is thought to increase vertical mixing in lakes and contribute to the shaping of continental shelves in the ocean. Experiments were conducted to measure in detail the turbulence generated by near-critical and supercritical internal waves impinging on a slope. Velocity fields were measured with particle image velocimetry (PIV) and used to derive two-dimensional fields of dissipation. Harmonics were observed in the velocity fields in some cases with  1, and the angle of the harmonics agreed with theoretical predictions of others. The highest dissipation was patchily distributed near the slope, and it formed a layer whose thickness was within 20-25% of the values predicted by previous researchers. For supercritical waves (>1), high values of dissipation were not confined solely in a layer near the slope. The high dissipation also appeared in regions with reflecting waves, harmonics, and--in some cases--the incident waves. Regions of high turbulent kinetic energy coincided with regions of high dissipation for all cases. Values of /νN2 were small near the slope. Maximum values were O(1), while values based on cycle-averaged dissipation were less than 0.2. In these cases, the patchy nature of the dissipation suggests that little mixing will occur.

Questions What is the community-level consequence of biotic processes, in particular the importance of facilitation, in determining patterns of diversity in alpine plant communities of the Himalayan Hengduan Mountains? Does facilitation intensity change with environmental severity, and are these changes due to environmental severity or nurse trait effects? Location Eleven alpine plant communities dominated by cushion plants in the Himalayan Hengduan Mountains, within the mountain system of south-central China. Methods We determined plant species richness and abundance in habitats created by cushions and cushion-free areas, and assessed the cushion effects on species richness and abundance with rarefaction curves and the relative interaction index, respectively. We examined the relationship between cushion effects on diversity and habitat severity and tested if changes in the net cushion effects along the severity gradient were due to a change in the performance of species without cushions (environmental severity effect) and/or with cushions (nurse trait-mediated effect). Results The presence of cushion plants increased species richness and abundance in most studied plant communities. The net facilitation effect of cushions increased with increasing habitat severity, an effect that could be attributed mainly to a reduction in species richness in cushion-free areas with increasing severity. However, the changes in magnitude of facilitation of particular cushion species growing in high and low severity habitats could be attributed to both environmental severity and neighbour trait effects. Despite the overall positive effect of cushion plants on plant species richness, not all cushion species contributed to higher species richness. The deviation of individual cushion species from the general pattern of increasing facilitation with increasing severity indicated the species specificity of facilitation depends on the traits of nurse species. Conclusion The presence of cus