Multi-modal long document classification based on Hierarchical Prompt and Multi-modal Transformer.

In the realm of long document classification (LDC), previous research has predominantly focused on modeling unimodal texts, overlooking the potential of multi-modal documents incorporating images. To address this gap, we introduce an innovative approach for multi-modal long document classification based on the Hierarchical Prompt and Multi-modal Transformer (HPMT). The proposed HPMT method facilitates multi-modal interactions at both the section and sentence levels, enabling a comprehensive capture of hierarchical structural features and complex multi-modal associations of long documents. Specifically, a Multi-scale Multi-modal Transformer (MsMMT) is tailored to capture the multi-granularity correlations between sentences and images. This is achieved through the incorporation of multi-scale convolutional kernels on sentence features, enhancing the model's ability to discern intricate patterns. Furthermore, to facilitate cross-level information interaction and promote learning of specific features at different levels, we introduce a Hierarchical Prompt (HierPrompt) block. This block incorporates section-level prompts and sentence-level prompts, both derived from a global prompt via distinct projection networks. Extensive experiments are conducted on four challenging multi-modal long document datasets. The results conclusively demonstrate the superiority of our proposed method, showcasing its performance advantages over existing techniques.

GSH-activable heterotrimeric nano-prodrug for precise synergistic therapy of TNBC.

Combination chemotherapy is an effective approach for triple-negative breast cancer (TNBC) therapy, especially when drugs are administered at specific optimal ratios. However, at present, strategies involving precise and controllable ratios based on effective loading and release of drugs are unavailable. Herein, we designed and synthesized a glutathione (GSH)--responsive heterotrimeric prodrug and formulated it with an amphiphilic polymer to obtain nanoparticles (DSSC2 NPs) for precise synergistic chemotherapy of TNBC. The heterotrimeric prodrug was prepared using docetaxel (DTX) and curcumin (CUR) at the optimal synergistic ratio of 1: 2. DTX and CUR were covalently conjugated by disulfide linkers. Compared with control NPs, DSSC2 NPs had quantitative/ratiometric drug loading, high drug co-loading capacity, better colloidal stability, and less premature drug leakage. After systemic administration, DSSC2 NPs selectively accumulated in tumor tissues and released the encapsulated drugs triggered by high levels of GSH in cancer cells. In vitro and in vivo experiments validated that DSSC2 NPs released DTX and CUR at the predefined ratio and had a highly synergistic therapeutic effect on tumor suppression in TNBC, which can be attributed to ratiometric drug delivery and synchronous drug activation. Altogether, the heterotrimeric prodrug delivery system developed in this study represents an effective and novel approach for combination chemotherapy.

[Development and Clinical Application of Multifunctional Inflation-Free Lumpectomy Aid].

To investigate the value of self-developed air-free laparoscopic auxiliary instruments in the clinical application of thyroid diseases. The clinical data of 70 transaxillary and 45 transareolar air-free laparoscopic surgeries for thyroid cancer and 40 conventional open surgeries were retrospectively compared. The transaxillary and transareolar laparoscopic groups had significantly longer operative times than the open group, while the postoperative satisfaction was higher in the endoscopic group than in the open group. This set of instruments has advantage of novel design, scientific structure, safe application. It can be compatible with a variety of thyroid and breast air-free laparoscopic procedures, which can promote the development and popularization of laparoscopic technology.

Beyond low-pass filtering on large-scale graphs via Adaptive Filtering Graph Neural Networks.

Graph Neural Networks (GNNs) have emerged as a crucial deep learning framework for graph-structured data. However, existing GNNs suffer from the scalability limitation, which hinders their practical implementation in industrial settings. Many scalable GNNs have been proposed to address this limitation. However, they have been proven to act as low-pass graph filters, which discard the valuable middle- and high-frequency information. This paper proposes a novel graph neural network named Adaptive Filtering Graph Neural Networks (AFGNN), which can capture all frequency information on large-scale graphs. AFGNN consists of two stages. The first stage utilizes low-, middle-, and high-pass graph filters to extract comprehensive frequency information without introducing additional parameters. This computation is a one-time task and is pre-computed before training, ensuring its scalability. The second stage incorporates a node-level attention-based feature combination, enabling the generation of customized graph filters for each node, contrary to existing spectral GNNs that employ uniform graph filters for the entire graph. AFGNN is suitable for mini-batch training, and can enhance scalability and efficiently capture all frequency information from large-scale graphs. We evaluate AFGNN by comparing its ability to capture all frequency information with spectral GNNs, and its scalability with scalable GNNs. Experimental results illustrate that AFGNN surpasses both scalable GNNs and spectral GNNs, highlighting its superiority.

Self-ratiometric fluorescent platform based on upconversion nanoparticles for on-site detection of chlorpyrifos.

Monitoring organophosphorus pesticides is significant for food safety assessment. Herein, we developed upconversion nanoparticles (UCNPs)-based self-ratiometric fluorescent platform for the detection of chlorpyrifos. The UCNPs have the ability to confine the detection and reference functions in one nanoparticle. Specifically, the blue upconversion (UC) emission (448 nm) in the shell layer of UCNPs is quenched by the product of the acetylcholinesterase-mediated reaction, while the red UC emission (652 nm) from the core remains constant as a self-calibrated reference signal. Employing the inhibition property of chlorpyrifos, self-proportional fluorescence is employed to detect chlorpyrifos. As proof-of-concept, test strips are fabricated by loading the UCNPs onto filter paper. Combined with the smartphone and image-processing algorithm, chlorpyrifos quantitative testing is achieved with a detection limit of 14.4843 ng mL-1. This portable platform displays anti-interference capability and high stability in the complicated matrix, making it an effective candidate for on-site application.

Decoding the aroma characteristics of icewine by partial least-squares regression, aroma reconstitution, and omission studies.

The appeal of icewine is attributable to its distinct aroma characteristics, such as 'honey', 'caramel', and 'dried fruit', but little is known about the chemical basis of these aroma attributes. A set of icewines with different aroma intensities were selected by a panel of wine experts. Detailed volatile compound analyses and sensory descriptive analyses were performed on the selected icewines. Using partial least-squares regression, several lactones, esters, terpenes, furanones, and ß-damascenone were positively correlated with 'honey', 'caramel', and 'dried fruit' aromas. Aroma reconstitution studies confirmed that terpenes could significantly enhance the 'honey' aroma, but weaken the 'caramel' aroma, while lactones and furanones could significantly enhance the 'caramel' and 'dried fruit' aromas. In addition, this study demonstrated that terpenes, lactones, and furanones interacted synergistically with each other to cause the sensory perception of the characteristic aromas of icewine.

Moderate mass loss enhances flight performance via alteration of flight kinematics and postures in a passerine bird.

Many birds experience fluctuations in body mass throughout the annual life cycle. The flight efficiency hypothesis posits that adaptive mass loss can enhance avian flight ability. However, whether birds can increase additional wing loading following mass loss and how birds adjust flight kinematics and postures remain largely unexplored. We investigated physiological changes in body condition in breeding female Eurasian tree sparrows (Passer montanus) through a dietary restriction experiment and determined the changes in flight kinematics and postures. Body mass decreased significantly, but the external maximum load and mass-corrected total load increased significantly after 3 days of dietary restriction. After 6 days of dietary restriction (DR6), hematocrit, pectoralis and hepatic fat content, take-off speed, theoretical maximum range speed and maximum power speed declined significantly. Notably, the load capacity and power margin remained unchanged relative to the control group. The wing stroke amplitude and relative downstroke duration were not affected by the interaction between diet restriction and extra load. Wing stroke amplitude significantly increased after DR6 treatment, while the relative downstroke duration significantly decreased. The stroke plane angle significantly increased after DR6 treatment only in the load-free condition. In addition, the sparrows adjusted their body angle and stroke plane angle in response to the extra load, but stroke amplitude and wingbeat frequency remained unchanged. Therefore, birds can maintain and even enhance their flight performance by adjusting flight kinematics and postures after a short-term mass loss.

Global and Local Interactive Perception Network for Referring Image Segmentation.

The effective modal fusion and perception between the language and the image are necessary for inferring the reference instance in the referring image segmentation (RIS) task. In this article, we propose a novel RIS network, the global and local interactive perception network (GLIPN), to enhance the quality of modal fusion between the language and the image from the local and global perspectives. The core of GLIPN is the global and local interactive perception (GLIP) scheme. Specifically, the GLIP scheme contains the local perception module (LPM) and the global perception module (GPM). The LPM is designed to enhance the local modal fusion by the correspondence between word and image local semantics. The GPM is designed to inject the global structured semantics of images into the modal fusion process, which can better guide the word embedding to perceive the whole image's global structure. Combined with the local-global context semantics fusion, extensive experiments on several benchmark datasets demonstrate the advantage of the proposed GLIPN over most state-of-the-art approaches.

Coping with extremes: Alternations in diet, gut microbiota, and hepatic metabolic functions in a highland passerine.

In wild animals, diet and gut microbiota interactions are critical moderators of metabolic functions and are highly contingent on habitat conditions. Challenged by the extreme conditions of high-altitude environments, the strategies implemented by highland animals to adjust their diet and gut microbial composition and modulate their metabolic substrates remain largely unexplored. By employing a typical human commensal species, the Eurasian tree sparrow (Passer montanus, ETS), as a model species, we studied the differences in diet, digestive tract morphology and enzyme activity, gut microbiota, and metabolic energy profiling between highland (the Qinghai-Tibet Plateau, QTP; 3230 m) and lowland (Shijiazhuang, Hebei; 80 m) populations. Our results showed that highland ETSs had enlarged digestive organs and longer small intestinal villi, while no differences in key digestive enzyme activities were observed between the two populations. The 18S rRNA sequencing results revealed that the dietary composition of highland ETSs were more animal-based and less plant-based than those of the lowland ones. Furthermore, 16S rRNA sequencing results suggested that the intestinal microbial communities were structurally segregated between populations. PICRUSt metagenome predictions further indicated that the expression patterns of microbial genes involved in material and energy metabolism, immune system and infection, and xenobiotic biodegradation were strikingly different between the two populations. Analysis of liver metabolomics revealed significant metabolic differences between highland and lowland ETSs in terms of substrate utilization, as well as distinct sex-specific alterations in glycerophospholipids. Furthermore, the interplay between diet, liver metabolism, and gut microbiota suggests a dietary shift resulting in corresponding changes in gut microbiota and metabolic functions. Our findings indicate that highland ETSs have evolved to optimize digestion and absorption, rely on more protein-rich foods, and possess gut microbiota tailored to their dietary composition, likely adaptive physiological and ecological strategies adopted to cope with extreme highland environments.

Clinical effect of endovascular repair of complex aortic lesions using optimized Octopus surgery.

Objective: Complex aortic lesions, especially those involving branches of the visceral artery, remain a challenge to treat. A single-center study using the Octopus technique to evaluate the safety and short-term effects of endovascular repair of complex aortic lesions was reported and documented. Methods: The data of six cases who underwent optimized Octopus surgery in our center from August 2020 to February 2022 were analyzed retrospectively. The choice of operation scheme, operation time, operation complications, and follow-up data were analyzed among them. Results: The average age of the six patients undergoing optimized Octopus surgery was 55.1 ± 17.2 years. Two cases were diagnosed as pararenal aortic aneurysms; four cases were aortic dissection involving the visceral artery. All cases achieved technical success; all visceral arteries were reconstructed as planned. A total of 17 visceral arteries were planned to be reconstructed; five celiac arteries were embolized. Three cases of gutter endoleak were found during the operation without embolization but with follow-up observation. There were two cases of slight damage to renal function and two cases of perioperative death. Other complications, such as intestinal ischemia and spinal cord ischemia, did not occur. Follow-up ranged from 6 months to 30 months. One patient died of gastrointestinal bleeding 6 months after the operation. At the 6 months follow-up, computed tomographic angiography showed that all internal leaks had disappeared. The patency rate of the visceral artery was 100%, and no complications, such as stent displacement and occlusion, occurred during the follow-up period. Conclusion: With fenestrated and branched stent grafts technology not widely available, and off label use not a viable option, Octopus technology for treating complex aortic lesions should be considered. The Octopus technique is an up-and-coming surgical method, but we should recognize its operation difficulty, operation-related complications, and long-term prognosis. We should pay attention to and continue to optimize Octopus technology.

Deep learning-enhanced radiomics for histologic classification and grade stratification of stage IA lung adenocarcinoma: a multicenter study.

Background: Preoperative prediction models for histologic subtype and grade of stage IA lung adenocarcinoma (LUAD) according to the update of the WHO Classification of Tumors of the Lung in 2021 and the 2020 new grade system are yet to be explored. We aim to develop the noninvasive pathology and grade evaluation approach for patients with stage IA LUAD via CT-based radiomics approach and evaluate their performance in clinical practice. Methods: Chest CT scans were retrospectively collected from patients who were diagnosed with stage IA LUAD and underwent complete resection at two hospitals. A deep learning segmentation algorithm was first applied to assist lesion delineation. Expansion strategies such as bounding-box annotations were further applied. Radiomics features were then extracted and selected followed by radiomics modeling based on four classic machine learning algorithms for histologic subtype classification and grade stratification. The area under the receiver operating characteristic curve (AUC) was used to evaluate model performance. Results: The study included 294 and 145 patients with stage IA LUAD from two hospitals for radiomics analysis, respectively. For classification of four histological subtypes, multilayer perceptron (MLP) algorithm presented no annotation strategy preference and achieved the average AUC of 0.855, 0.922, and 0.720 on internal, independent, and external test sets with 1-pixel expansion annotation. Bounding-box annotation strategy also enabled MLP an acceptable and stable accuracy among test sets. Meanwhile, logistic regression was selected for grade stratification and achieved the average AUC of 0.928, 0.837, and 0.748 on internal, independent, and external test sets with optimal annotation strategies. Conclusions: DL-enhanced radiomics models had great potential to predict the fine histological subtypes and grades of early-stage LUADs based on CT images, which might serve as a promising noninvasive approach for the diagnosis and management of early LUADs.

Determination of major and trace elements in plant samples by inductively coupled plasma optical emission spectrometry with deep eutectic solvent extraction based on choline chloride and carboxylic acids.

A rapid, precise, and environmentally friendly approach utilizing an ultrasound assisted deep eutectic solvent-based extraction method was developed for the extraction of Ca, Fe, K, Mg, Mn, Na, P, S and Zn from plant samples. The investigation was conducted on deep eutectic solvents that are based on choline chloride and carboxylic acids. The determination of target analytes in the extracts was carried out using inductively coupled plasma-optical emission spectrometry (ICP-OES). The DESs based on malic acid were found to exhibit the highest extraction recovery values (95-106%). The parameters affecting the extraction of target analytes were optimized using standard reference materials. The target analytes can be effectively extracted from plant samples using 0.5 g of DESs and ultrasonication for 40 minutes. The determination results of the reference samples indicated that the relative error (RE) was below 15.1%, and the relative standard deviation (RSD) was less than 6.3%, demonstrating excellent accuracy and precision. The proposed method was employed to quantify target analytes in actual plant samples. The accuracy of this method was not significantly different from that of the microwave digestion method. The proposed method has been demonstrated to be a valid approach for the determination of target elements in actual plant samples.

An innovative ensemble model based on deep learning for predicting COVID-19 infection.

Nowadays, global public health crises are occurring more frequently, and accurate prediction of these diseases can reduce the burden on the healthcare system. Taking COVID-19 as an example, accurate prediction of infection can assist experts in effectively allocating medical resources and diagnosing diseases. Currently, scholars worldwide use single model approaches or epidemiology models more often to predict the outbreak trend of COVID-19, resulting in poor prediction accuracy. Although a few studies have employed ensemble models, there is still room for improvement in their performance. In addition, there are only a few models that use the laboratory results of patients to predict COVID-19 infection. To address these issues, research efforts should focus on improving disease prediction performance and expanding the use of medical disease prediction models. In this paper, we propose an innovative deep learning model Whale Optimization Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM) and Artificial Neural Network (ANN) called WOCLSA which incorporates three models ANN, CNN and LSTM. The WOCLSA model utilizes the Whale Optimization Algorithm to optimize the neuron number, dropout and batch size parameters in the integrated model of ANN, CNN and LSTM, thereby finding the global optimal solution parameters. WOCLSA employs 18 patient indicators as predictors, and compares its results with three other ensemble deep learning models. All models were validated with train-test split approaches. We evaluate and compare our proposed model and other models using accuracy, F1 score, recall, AUC and precision metrics. Through many studies and tests, our results show that our prediction models can identify patients with COVID-19 infection at the AUC of 91%, 91%, and 93% respectively. Other prediction results achieve a respectable accuracy of 92.82%, 92.79%, and 91.66% respectively, f1-score of 93.41%, 92.79%, and 92.33% respectively, precision of 93.41%, 92.79%, and 92.33% respectively, recall of 93.41%, 92.79%, and 92.33% respectively. All of these exceed 91%, surpassing those of comparable models. The execution time of WOCLSA is also an advantage. Therefore, the WOCLSA ensemble model can be used to assist in verifying laboratory research results and predict and to judge various diseases in public health events.

Will "Air Eutrophication" Increase the Risk of Ecological Threat to Public Health?

Aquatic eutrophication, often with anthropogenic causes, facilitates blooms of cyanobacteria including cyanotoxin producing species, which profoundly impact aquatic ecosystems and human health. An emerging concern is that aquatic eutrophication may interact with other environmental changes and thereby lead to unexpected cascading effects on terrestrial systems. Here, we synthesize recent evidence showing the possibility that accelerating eutrophication will spill over from aquatic ecosystems to the atmosphere via "air eutrophication", a novel concept that refers to a process promoting the growth of airborne algae, some of them with the capacity to produce toxic compounds for humans and other organisms. Being catalyzed by various anthropogenic forcings─including aquatic eutrophication, climate warming, air contamination, and artificial light at night─accelerated air eutrophication may be expected in the future, posing a potentially increasing risk of threat to public health and the environment. So far knowledge of this topic is sparse, and we therefore consider air eutrophication a potentially important research field and propose an agenda of cross-discipline research. As a contribution, we have calculated a tolerable daily intake of 17 ng m-3 day-1 for the nasal intake of microcystins by humans.

A Dual-Masked Deep Structural Clustering Network With Adaptive Bidirectional Information Delivery.

Structured clustering networks, which alleviate the oversmoothing issue by delivering hidden features from autoencoder (AE) to graph convolutional networks (GCNs), involve two shortcomings for the clustering task. For one thing, they used vanilla structure to learn clustering representations without considering feature and structure corruption; for another thing, they exhibit network degradation and vanishing gradient issues after stacking multilayer GCNs. In this article, we propose a clustering method called dual-masked deep structural clustering network (DMDSC) with adaptive bidirectional information delivery (ABID). Specifically, DMDSC enables generative self-supervised learning to mine deeper interstructure and interfeature correlations by simultaneously reconstructing corrupted structures and features. Furthermore, DMDSC develops an ABID module to establish an information transfer channel between each pairwise layer of AE and GCNs to alleviate the oversmoothing and vanishing gradient problems. Numerous experiments on six benchmark datasets have shown that the proposed DMDSC outperforms the most advanced deep clustering algorithms.

Global face mask pollution: threats to the environment and wildlife, and potential solutions.

Face masks are an indispensable low-cost public healthcare necessity for containing viral transmission. After the coronavirus disease (COVID-19) became a pandemic, there was an unprecedented demand for, and subsequent increase in face mask production and use, leading to global ecological challenges, including excessive resource consumption and significant environmental pollution. Here, we review the global demand volume for face masks and the associated energy consumption and pollution potential throughout their life cycle. First, the production and distribution processes consume petroleum-based raw materials and other energy sources and release greenhouse gases. Second, most methods of mask waste disposal result in secondary microplastic pollution and the release of toxic gases and organic substances. Third, face masks discarded in outdoor environments represent a new plastic pollutant and pose significant challenges to the environment and wildlife in various ecosystems. Therefore, the long-term impacts on environmental and wildlife health aspects related to the production, use, and disposal of face masks should be considered and urgently investigated. Here, we propose five reasonable countermeasures to alleviate these global-scale ecological crises induced by mask use during and following the COVID-19 pandemic era: increasing public awareness; improving mask waste management; innovating waste disposal methods; developing biodegradable masks; and formulating relevant policies and regulations. Implementation of these measures will help address the pollution caused by face masks.

Discovery of quinazolin-4(3H)-one derivatives as novel AChE inhibitors with anti-inflammatory activities.

A series of quinazolin-4(3H)-one derivatives was designed through scaffold-hopping strategy and synthesized as novel multifunctional anti-AD agents demonstrating both cholinesterase inhibition and anti-inflammatory activities. Their inhibitory activities against acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) were evaluated, and the enzyme kinetics study as well as detailed binding mode via molecular docking were performed for selected compounds. MR2938 (B12) displayed promising AChE inhibitory activity with an IC50 value of 5.04 µM and suppressed NO production obviously (IC50 = 3.29 µM). Besides, it was able to decrease the mRNA levels of pro-inflammatory cytokines IL-1ß, TNF-α, IL-6 and CCL2 at 1.25 µM. Further mechanism study suggested that MR2938 suppressed the neuroinflammation through blocking MAPK/JNK and NF-κB signaling pathways. All these results indicate that MR2938 is a good starting point to develop multifunctional anti-AD lead compounds.

Physiological but not morphological adjustments along latitudinal gradients in a human commensal species, the Eurasian tree sparrow.

Human commensal species take advantage of anthropogenic conditions that are less likely to be challenged by the selective pressures of natural environments. Their morphological and physiological phenotypes can therefore dissociate from habitat characteristics. Understanding how these species adjust their morphological and physiological traits across latitudinal gradients is fundamental to uncovering the eco-physiological strategies underlying coping mechanisms. Here, we studied morphological traits in breeding Eurasian tree sparrows (ETSs; Passer montanus) among low-latitude (Yunnan and Hunan) and middle-latitude (Hebei) localities in China. We then compared body mass; lengths of bill, tarsometatarsus, wing, total body, and tail feather; and baseline and capture stress-induced levels of plasma corticosterone (CORT) and the metabolites including glucose (Glu), total triglyceride (TG), free fatty acid (FFA), total protein, and uric acid (UA). None of the measured morphological parameters varied with latitude except in the Hunan population, which demonstrated longer bills than those in other populations. Stress-induced CORT levels significantly exceeded baseline levels and decreased with increasing latitude, but total integrated CORT levels did not vary with latitude. Capture stress-induced significantly increased Glu levels and decreased TG levels, independent of site. However, the Hunan population had significantly higher baseline CORT, baseline and stress-induced FFA levels, but lower UA levels, which differed from other populations. Our results suggest that rather than morphological adjustments, physiological adjustments are mainly involved in coping mechanisms for middle-latitude adaptation in ETSs. It is worth investigating whether other avian species also exhibit such dissociation from external morphological designs while depending on physiological adjustments.

Light-induced stomatal opening in Arabidopsis is negatively regulated by chloroplast-originated OPDA signaling.

Stomatal movement is orchestrated by diverse signaling cascades and metabolic activities in guard cells. Light triggers the opening of the pores through the phototropin-mediated pathway, which leads to the activation of plasma membrane H+-ATPase and thereby facilitates potassium accumulation through Kin+ channels. However, it remains poorly understood how phototropin signaling is fine-tuned to prevent excessive stomatal opening and consequent water loss. Here, we show that the stomatal response to light is negatively regulated by 12-oxo-phytodienoic acid (OPDA), an oxylipin metabolite produced through enzymatic oxygenation of polyunsaturated fatty acids (PUFAs). We identify a set of phospholipase-encoding genes, phospholipase (PLIP)1/2/3, which are transactivated rapidly in guard cells upon illumination in a phototropin-dependent manner. These phospholipases release PUFAs from the chloroplast membrane, which is oxidized by guard-cell lipoxygenases and further metabolized to OPDA. The OPDA-deficient mutants had wider stomatal pores, whereas mutants containing elevated levels of OPDA showed the opposite effect on stomatal aperture. Transmembrane solute fluxes that drive stomatal aperture were enhanced in lox6-1 guard cells, indicating that OPDA signaling ultimately impacts on activities of proton pumps and Kin+ channels. Interestingly, the accelerated stomatal kinetics in lox6-1 leads to increased plant growth without cost in water or macronutrient use. Together, our results reveal a new role for chloroplast membrane oxylipin metabolism in stomatal regulation. Moreover, the accelerated stomatal opening kinetics in OPDA-deficient mutants benefits plant growth and water use efficiency.

The effect of precipitation timing on phylogenetic and functional community structure in a semi-arid steppe.

Changes in the amount and timing of precipitation may affect plant species coexistence. However, little is known about how these changes in precipitation structure plant communities. Here, we conducted a 6-year field precipitation manipulation experiment in the semi-arid steppe of Inner Mongolia, China, to assess the importance of species extinction and colonization in community assembly by incorporating information on phylogenetic and functional relatedness. Our results demonstrated that the decline in plant species richness under decreasing precipitation in the late and entire growing season could be attributed to a decrease in species colonization and an increase in species loss, respectively. The increase in species richness under increasing precipitation in the late growing season was mainly caused by increases in colonizing species. The loss of species that were more closely related to other residents under decreasing precipitation in the late growing season did not alter patterns of phylogenetic overdispersion, and the colonization of species that were more distantly related to residents under increasing precipitation in the late growing season shifted functional relatedness from clustering to randomness. Increasing precipitation may weaken the strength of environmental filtering induced by water stress in this semi-arid steppe and thus increase the probability of successful colonization of functionally dissimilar species relative to residents. Our study suggests that incorporating information on the functional and phylogenetic relatedness of locally lost resident species and the colonization of new species into analyses of community assembly processes can provide new insights into the general responses of plant communities to global change.