Unveiling dissociation mechanisms and binding patterns in the UHRF1-DPPA3 complex via multi-replica molecular dynamics simulations.

CONTEXT: Ubiquitin-like with PHD and RING finger domain containing protein 1 (UHRF1) is responsible for preserving the stability of genomic methylation through the recruitment of DNA methyltransferase 1 (DNMT1). However, the interaction between Developmental pluripotency associated 3 (DPPA3) and the pre-PHD-PHD (PPHD) domain of UHRF1 hinders the nuclear localization of UHRF1. This disruption has implications for potential cancer treatment strategies. Drugs that mimic the binding pattern between DPPA3 and PPHD could offer a promising approach to cancer treatment. Our study reveals that DPPA3 undergoes dissociation from the C-terminal through three different modes of helix unfolding. Furthermore, we have identified key residue pairs involved in this dissociation process and potential drug-targeting residues. These findings offer valuable insights into the dissociation mechanism of DPPA3 from PPHD and have the potential to inform the design of novel drugs targeting UHRF1 for cancer therapy. METHODS: To comprehend the dissociation process and binding patterns of PPHD-DPPA3, we employed enhanced sampling techniques, including steered molecular dynamics (SMD) and conventional molecular dynamics (cMD). Additionally, we utilized self-organizing maps (SOM) and time-resolved force distribution analysis (TRFDA) methodologies. The Gromacs software was used for performing molecular dynamics simulations, and the AMBER FF14SB force field was applied to the protein.

Correctable Landmark Discovery Via Large Models for Vision-Language Navigation.

Vision-Language Navigation (VLN) requires the agent to follow language instructions to reach a target position. A key factor for successful navigation is to align the landmarks implied in the instruction with diverse visual observations. However, previous VLN agents fail to perform accurate modality alignment especially in unexplored scenes, since they learn from limited navigation data and lack sufficient open-world alignment knowledge. In this work, we propose a new VLN paradigm, called COrrectable LaNdmark DiScOvery via Large ModEls (CONSOLE). In CONSOLE, we cast VLN as an open-world sequential landmark discovery problem, by introducing a novel correctable landmark discovery scheme based on two large models ChatGPT and CLIP. Specifically, we use ChatGPT to provide rich open-world landmark cooccurrence commonsense, and conduct CLIP-driven landmark discovery based on these commonsense priors. To mitigate the noise in the priors due to the lack of visual constraints, we introduce a learnable cooccurrence scoring module, which corrects the importance of each cooccurrence according to actual observations for accurate landmark discovery. We further design an observation enhancement strategy for an elegant combination of our framework with different VLN agents, where we utilize the corrected landmark features to obtain enhanced observation features for action decision. Extensive experimental results on multiple popular VLN benchmarks (R2R, REVERIE, R4R, RxR) show the significant superiority of CONSOLE over strong baselines. Especially, our CONSOLE establishes the new state-of-the-art results on R2R and R4R in unseen scenarios.

Insights into the Dissociation Process and Binding Pattern of the BRCT7/8-PHF8 Complex.

DNA topoisomerase 2-binding protein 1 (Topbp1) plays a crucial role in activating the ataxia-telangiectasia mutated and rad3-related (ATR) complex to initiate DNA damage repair responses. For this process to occur, it is necessary for PHF8 to dissociate from Topbp1. Topbp1 binds to the acidic patch sequence (APS) of PHF8 through its C-terminal BRCT7/8 domain, and disrupting this interaction could be a promising strategy for cancer treatment. To investigate the dissociation process and binding pattern of BRCT7/8-PHF8, we employed enhanced sampling techniques, such as steered molecular dynamics (SMD) simulations and accelerated molecular dynamics (aMD) simulations, along with self-organizing maps (SOM) and time-resolved force distribution analysis (TRFDA) methodologies. Our results demonstrate that the dissociation of PHF8 from BRCT7/8 starts from the N-terminus, leading to the unfolding of the N-terminal helix. Additionally, we identified critical residues that play a pivotal role in this dissociation process. These findings provide valuable insights into the disassociation of PHF8 from BRCT7/8, which could potentially guide the development of novel drugs targeting Topbp1 for cancer therapy.

An automatic analysis and quality assurance method for lymphocyte subset identification.

OBJECTIVES: Lymphocyte subsets are the predictors of disease diagnosis, treatment, and prognosis. Determination of lymphocyte subsets is usually carried out by flow cytometry. Despite recent advances in flow cytometry analysis, most flow cytometry data can be challenging with manual gating, which is labor-intensive, time-consuming, and error-prone. This study aimed to develop an automated method to identify lymphocyte subsets. METHODS: We propose a knowledge-driven combined with data-driven method which can gate automatically to achieve subset identification. To improve accuracy and stability, we have implemented a Loop Adjustment Gating to optimize the gating result of the lymphocyte population. Furthermore, we have incorporated an anomaly detection mechanism to issue warnings for samples that might not have been successfully analyzed, ensuring the quality of the results. RESULTS: The evaluation showed a 99.2 % correlation between our method results and manual analysis with a dataset of 2,000 individual cases from lymphocyte subset assays. Our proposed method attained 97.7 % accuracy for all cases and 100 % for the high-confidence cases. With our automated method, 99.1 % of manual labor can be saved when reviewing only the low-confidence cases, while the average turnaround time required is only 29 s, reducing by 83.7 %. CONCLUSIONS: Our proposed method can achieve high accuracy in flow cytometry data from lymphocyte subset assays. Additionally, it can save manual labor and reduce the turnaround time, making it have the potential for application in the laboratory.

DNA Family: Boosting Weight-Sharing NAS With Block-Wise Supervisions.

Neural Architecture Search (NAS), aiming at automatically designing neural architectures by machines, has been considered a key step toward automatic machine learning. One notable NAS branch is the weight-sharing NAS, which significantly improves search efficiency and allows NAS algorithms to run on ordinary computers. Despite receiving high expectations, this category of methods suffers from low search effectiveness. By employing a generalization boundedness tool, we demonstrate that the devil behind this drawback is the untrustworthy architecture rating with the oversized search space of the possible architectures. Addressing this problem, we modularize a large search space into blocks with small search spaces and develop a family of models with the distilling neural architecture (DNA) techniques. These proposed models, namely a DNA family, are capable of resolving multiple dilemmas of the weight-sharing NAS, such as scalability, efficiency, and multi-modal compatibility. Our proposed DNA models can rate all architecture candidates, as opposed to previous works that can only access a sub- search space using heuristic algorithms. Moreover, under a certain computational complexity constraint, our method can seek architectures with different depths and widths. Extensive experimental evaluations show that our models achieve state-of-the-art top-1 accuracy of 78.9% and 83.6% on ImageNet for a mobile convolutional network and a small vision transformer, respectively. Additionally, we provide in-depth empirical analysis and insights into neural architecture ratings.

Construction and effect evaluation of prediction model for red blood cell transfusion requirement in cesarean section based on artificial intelligence.

OBJECTIVES: This study intends to build an artificial intelligence model for obstetric cesarean section surgery to evaluate the intraoperative blood transfusion volume before operation, and compare the model prediction results with the actual results to evaluate the accuracy of the artificial intelligence prediction model for intraoperative red blood cell transfusion in obstetrics. The advantages and disadvantages of intraoperative blood demand and identification of high-risk groups for blood transfusion provide data support and improvement suggestions for the realization of accurate blood management of obstetric cesarean section patients during the perioperative period. METHODS: Using a machine learning algorithm, an intraoperative blood transfusion prediction model was trained. The differences between the predicted results and the actual results were compared by means of blood transfusion or not, blood transfusion volume, and blood transfusion volume targeting postoperative hemoglobin (Hb). RESULTS: Area under curve of the model is 0.89. The accuracy of the model for blood transfusion was 96.85%. The statistical standard for the accuracy of the model blood transfusion volume is the calculation of 1U absolute error, the accuracy rate is 86.56%, and the accuracy rate of the blood transfusion population is 45.00%. In the simulation prediction results, 93.67% of the predicted and actual cases in no blood transfusion surgery; 63.45% of the same predicted blood transfusion in blood transfusion surgery, and only 20.00% of the blood transfusion volume is the same. CONCLUSIONS: In conclusion, this study used machine learning algorithm to process, analyze and predict the results of a large sample of cesarean section clinical data, and found that the important predictors of blood transfusion during cesarean section included preoperative RBC, surgical method, the site of surgery, coagulation-related indicators, and other factors. At the same time, it was found that the overall accuracy of the AI model was higher than actual blood using. Although the prediction of blood transfusion volume was not well matched with the actual blood using, the model provided a perspective of preoperative identification of high blood transfusion risks. The results can provide good auxiliary decision support for preoperative evaluation of obstetric cesarean section, and then promote the realization of accurate perioperative blood management for obstetric cesarean section patients.

Microstructure and Biomechanical Properties in Selective Laser Melting of Porous Metal Implants.

Two kinds of porous structure design strategies, ring-support (RS) and column-support (CS), are proposed for human implants. The accurate design of porosity is realized by adjusting the pore characteristics, such as strut diameter, pore diameter, and unit size. Porous specimens with porosity of 50%, 60%, 70%, and 80% were prepared by selective laser melting. The three-dimensional pore structure is basically consistent with the design characteristics, and the measured porosity is slightly lower than design value. The microstructure, microhardness, and friction and wear properties of the samples were studied. The results show that the performance along the scanning orientation is slightly better than that along the forming orientation. The compression and dynamic elastic modulus of porous specimens with different structures and porosities were analyzed. The CS porous with 60-80% porosity has suitable compressive strength and elastic modulus, which is close to that of human tissue, and effectively avoids the stress shielding phenomenon.

Fine-Grained Visual-Text Prompt-Driven Self-Training for Open-Vocabulary Object Detection.

Inspired by the success of vision-language methods (VLMs) in zero-shot classification, recent works attempt to extend this line of work into object detection by leveraging the localization ability of pretrained VLMs and generating pseudolabels for unseen classes in a self-training manner. However, since the current VLMs are usually pretrained with aligning sentence embedding with global image embedding, the direct use of them lacks fine-grained alignment for object instances, which is the core of detection. In this article, we propose a simple but effective fine-grained visual-text prompt-driven self-training paradigm for open-vocabulary detection (VTP-OVD) that introduces a fine-grained visual-text prompt adapting stage to enhance the current self-training paradigm with a more powerful fine-grained alignment. During the adapting stage, we enable VLM to obtain fine-grained alignment using learnable text prompts to resolve an auxiliary dense pixelwise prediction task. Furthermore, we propose a visual prompt module to provide the prior task information (i.e., the categories need to be predicted) for the vision branch to better adapt the pretrained VLM to the downstream tasks. Experiments show that our method achieves the state-of-the-art performance for open-vocabulary object detection, e.g., 31.5% mAP on unseen classes of COCO.

Towards Causality-Aware Inferring: A Sequential Discriminative Approach for Medical Diagnosis.

Medical diagnosis assistant (MDA) aims to build an interactive diagnostic agent to sequentially inquire about symptoms for discriminating diseases. However, since the dialogue records for building a patient simulator are collected passively, the collected records might be deteriorated by some task-unrelated biases, such as the preference of the collectors. These biases might hinder the diagnostic agent to capture transportable knowledge from the simulator. This work identifies and resolves two representative non-causal biases, i.e., (i) default-answer bias and (ii) distributional inquiry bias. Specifically, Bias (i) originates from the patient simulator which tries to answer the unrecorded inquiries with some biased default answers. To eliminate this bias and improve upon a well-known causal inference technique, i.e., propensity score matching, we propose a novel propensity latent matching in building a patient simulator to effectively answer unrecorded inquiries; Bias (ii) inherently comes along with the passively collected data that the agent might learn by remembering what to inquire within the training data while not able to generalize to the out-of-distribution cases. To this end, we propose a progressive assurance agent, which includes the dual processes accounting for symptom inquiry and disease diagnosis respectively. The diagnosis process pictures the patient mentally and probabilistically by intervention to eliminate the effect of the inquiry behavior. And the inquiry process is driven by the diagnosis process to inquire about symptoms to enhance the diagnostic confidence which alters as the patient distribution changes. In this cooperative manner, our proposed agent can improve upon the out-of-distribution generalization significantly. Extensive experiments demonstrate that our framework achieves new state-of-the-art performance and possesses the advantage of transportability.

Entity-Graph Enhanced Cross-Modal Pretraining for Instance-Level Product Retrieval.

Our goal in this research is to study a more realistic environment in which we can conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories. We first contribute the Product1M datasets and define two real practical instance-level retrieval tasks that enable evaluations on price comparison and personalized recommendations. For both instance-level tasks, accurately identifying the intended product target mentioned in visual-linguistic data and mitigating the impact of irrelevant content are quite challenging. To address this, we devise a more effective cross-modal pretraining model capable of adaptively incorporating key concept information from multi-modal data. This is accomplished by utilizing an entity graph, where nodes represented entities and edges denoted the similarity relations between them. Specifically, a novel Entity-Graph Enhanced Cross-Modal Pretraining (EGE-CMP) model is proposed for instance-level commodity retrieval, which explicitly injects entity knowledge in both node-based and subgraph-based ways into the multi-modal networks via a self-supervised hybrid-stream transformer. This could reduce the confusion between different object contents, thereby effectively guiding the network to focus on entities with real semantics. Experimental results sufficiently verify the efficacy and generalizability of our EGE-CMP, outperforming several SOTA cross-modal baselines like CLIP Radford et al. 2021, UNITER Chen et al. 2020 and CAPTURE Zhan et al. 2021.

Discourse-Aware Graph Networks for Textual Logical Reasoning.

Textual logical reasoning, especially question-answering (QA) tasks with logical reasoning, requires awareness of particular logical structures. The passage-level logical relations represent entailment or contradiction between propositional units (e.g., a concluding sentence). However, such structures are unexplored as current QA systems focus on entity-based relations. In this work, we propose logic structural-constraint modeling to solve the logical reasoning QA and introduce discourse-aware graph networks (DAGNs). The networks first construct logic graphs leveraging in-line discourse connectives and generic logic theories, then learn logic representations by end-to-end evolving the logic relations with an edge-reasoning mechanism and updating the graph features. This pipeline is applied to a general encoder, whose fundamental features are joined with the high-level logic features for answer prediction. Experiments on three textual logical reasoning datasets demonstrate the reasonability of the logical structures built in DAGNs and the effectiveness of the learned logic features. Moreover, zero-shot transfer results show the features' generality to unseen logical texts.

Towards Deviation-Robust Agent Navigation via Perturbation-Aware Contrastive Learning.

Vision-and-language navigation (VLN) asks an agent to follow a given language instruction to navigate through a real 3D environment. Despite significant advances, conventional VLN agents are trained typically under disturbance-free environments and may easily fail in real-world navigation scenarios, since they are unaware of how to deal with various possible disturbances, such as sudden obstacles or human interruptions, which widely exist and may usually cause an unexpected route deviation. In this paper, we present a model-agnostic training paradigm, called Progressive Perturbation-aware Contrastive Learning (PROPER) to enhance the generalization ability of existing VLN agents to the real world, by requiring them to learn towards deviation-robust navigation. Specifically, a simple yet effective path perturbation scheme is introduced to implement the route deviation, with which the agent is required to still navigate successfully following the original instruction. Since directly enforcing the agent to learn perturbed trajectories may lead to insufficient and inefficient training, a progressively perturbed trajectory augmentation strategy is designed, where the agent can self-adaptively learn to navigate under perturbation with the improvement of its navigation performance for each specific trajectory. For encouraging the agent to well capture the difference brought by perturbation and adapt to both perturbation-free and perturbation-based environments, a perturbation-aware contrastive learning mechanism is further developed by contrasting perturbation-free trajectory encodings and perturbation-based counterparts. Extensive experiments on the standard Room-to-Room (R2R) benchmark show that PROPER can benefit multiple state-of-the-art VLN baselines in perturbation-free scenarios. We further collect the perturbed path data to construct an introspection subset based on the R2R, called Path-Perturbed R2R (PP-R2R). The results on PP-R2R show unsatisfying robustness of popular VLN agents and the capability of PROPER in improving the navigation robustness under deviation.

Template-Based Contrastive Distillation Pretraining for Math Word Problem Solving.

Since math word problem (MWP) solving aims to transform natural language problem description into executable solution equations, an MWP solver needs to not only comprehend the real-world narrative described in the problem text but also identify the relationships among the quantifiers and variables implied in the problem and maps them into a reasonable solution equation logic. Recently, although deep learning models have made great progress in MWPs, they ignore the grounding equation logic implied by the problem text. Besides, as we all know, pretrained language models (PLM) have a wealth of knowledge and high-quality semantic representations, which may help solve MWPs, but they have not been explored in the MWP-solving task. To harvest the equation logic and real-world knowledge, we propose a template-based contrastive distillation pretraining (TCDP) approach based on a PLM-based encoder to incorporate mathematical logic knowledge by multiview contrastive learning while retaining rich real-world knowledge and high-quality semantic representation via knowledge distillation. We named the pretrained PLM-based encoder by our approach as MathEncoder. Specifically, the mathematical logic is first summarized by clustering the symbolic solution templates among MWPs and then injected into the deployed PLM-based encoder by conducting supervised contrastive learning based on the symbolic solution templates, which can represent the underlying solving logic in the problems. Meanwhile, the rich knowledge and high-quality semantic representation are retained by distilling them from a well-trained PLM-based teacher encoder into our MathEncoder. To validate the effectiveness of our pretrained MathEncoder, we construct a new solver named MathSolver by replacing the GRU-based encoder with our pretrained MathEncoder in GTS, which is a state-of-the-art MWP solver. The experimental results demonstrate that our method can carry a solver's understanding ability of MWPs to a new stage by outperforming existing state-of-the-art methods on two widely adopted benchmarks Math23K and CM17K. Code will be available at https://github.com/QinJinghui/tcdp.

Knowledge Fusion Distillation: Improving Distillation with Multi-scale Attention Mechanisms.

The success of deep learning has brought breakthroughs in many fields. However, the increased performance of deep learning models is often accompanied by an increase in their depth and width, which conflicts with the storage, energy consumption, and computational power of edge devices. Knowledge distillation, as an effective model compression method, can transfer knowledge from complex teacher models to student models. Self-distillation is a special type of knowledge distillation, which does not to require a pre-trained teacher model. However, existing self-distillation methods rarely consider how to effectively use the early features of the model. Furthermore, most self-distillation methods use features from the deepest layers of the network to guide the training of the branches of the network, which we find is not the optimal choice. In this paper, we found that the feature maps obtained by early feature fusion do not serve as a good teacher to guide their own training. Based on this, we propose a selective feature fusion module and further obtain a new self-distillation method, knowledge fusion distillation. Extensive experiments on three datasets have demonstrated that our method has comparable performance to state-of-the-art distillation methods. In addition, the performance of the network can be further enhanced when fused features are integrated into the network.

Point-Guided Contrastive Learning for Monocular 3-D Object Detection.

3-D object detection is a fundamental task in the context of autonomous driving. In the literature, cheap monocular image-based methods show a significant performance drop compared to the expensive LiDAR and stereo-images-based algorithms. In this article, we aim to close this performance gap by bridging the representation capability between 2-D and 3-D domains. We propose a novel monocular 3-D object detection model using self-supervised learning and auxiliary learning, resorting to mimicking the representations over 3-D point clouds. Specifically, given a 2-D region proposal and the corresponding instance point cloud, we supervise the feature activation from our image-based convolution network to mimic the latent feature of a point-based neural network at the training stage. While state-of-the-art (SOTA) monocular 3-D detection algorithms typically convert images to pseudo-LiDAR with depth estimation and regress 3-D detection with LiDAR-based methods, our approach seeks the power of the 2-D neural network straightforwardly and essentially enhances the 2-D module capability with latent spatial-aware representations by contrastive learning. We empirically validate the performance improvement from the feature mimicking the KITTI and ApolloScape datasets and achieve the SOTA performance on the KITTI and ApolloScape leaderboard.

Dynamic Support Network for Few-Shot Class Incremental Learning.

Few-shot class-incremental learning (FSCIL) is challenged by catastrophically forgetting old classes and over-fitting new classes. Revealed by our analyses, the problems are caused by feature distribution crumbling, which leads to class confusion when continuously embedding few samples to a fixed feature space. In this study, we propose a Dynamic Support Network (DSN), which refers to an adaptively updating network with compressive node expansion to "support" the feature space. In each training session, DSN tentatively expands network nodes to enlarge feature representation capacity for incremental classes. It then dynamically compresses the expanded network by node self-activation to pursue compact feature representation, which alleviates over-fitting. Simultaneously, DSN selectively recalls old class distributions during incremental learning to support feature distributions and avoid confusion between classes. DSN with compressive node expansion and class distribution recalling provides a systematic solution for the problems of catastrophic forgetting and overfitting. Experiments on CUB, CIFAR-100, and miniImage datasets show that DSN significantly improves upon the baseline approach, achieving new state-of-the-arts.

DS-Net++: Dynamic Weight Slicing for Efficient Inference in CNNs and Vision Transformers.

Dynamic networks have shown their promising capability in reducing theoretical computation complexity by adapting their architectures to the input during inference. However, their practical runtime usually lags behind the theoretical acceleration due to inefficient sparsity. In this paper, we explore a hardware-efficient dynamic inference regime, named dynamic weight slicing, that can generalized well on multiple dimensions in both CNNs and transformers (e.g. kernel size, embedding dimension, number of heads, etc.). Instead of adaptively selecting important weight elements in a sparse way, we pre-define dense weight slices with different importance level by nested residual learning. During inference, weights are progressively sliced beginning with the most important elements to less important ones to achieve different model capacity for inputs with diverse difficulty levels. Based on this conception, we present DS-CNN++ and DS-ViT++, by carefully designing the double headed dynamic gate and the overall network architecture. We further propose dynamic idle slicing to address the drastic reduction of embedding dimension in DS-ViT++. To ensure sub-network generality and routing fairness, we propose a disentangled two-stage optimization scheme. In Stage I, in-place bootstrapping (IB) and multi-view consistency (MvCo) are proposed to stablize and improve the training of DS-CNN++ and DS-ViT++ supernet, respectively. In Stage II, sandwich gate sparsification (SGS) is proposed to assist the gate training. Extensive experiments on 4 datasets and 3 different network architectures demonstrate our methods consistently outperform the state-of-the-art static and dynamic model compression methods by a large margin (up to 6.6%). Typically, we achieves 2-4× computation reduction and up to 61.5% real-world acceleration on MobileNet, ResNet-50 and Vision Transformer, with minimal accuracy drops on ImageNet. Code release: https://github.com/changlin31/DS-Net.

Auxiliary signal-guided knowledge encoder-decoder for medical report generation.

Medical reports have significant clinical value to radiologists and specialists, especially during a pandemic like COVID. However, beyond the common difficulties faced in the natural image captioning, medical report generation specifically requires the model to describe a medical image with a fine-grained and semantic-coherence paragraph that should satisfy both medical commonsense and logic. Previous works generally extract the global image features and attempt to generate a paragraph that is similar to referenced reports; however, this approach has two limitations. Firstly, the regions of primary interest to radiologists are usually located in a small area of the global image, meaning that the remainder parts of the image could be considered as irrelevant noise in the training procedure. Secondly, there are many similar sentences used in each medical report to describe the normal regions of the image, which causes serious data bias. This deviation is likely to teach models to generate these inessential sentences on a regular basis. To address these problems, we propose an Auxiliary Signal-Guided Knowledge Encoder-Decoder (ASGK) to mimic radiologists' working patterns. Specifically, the auxiliary patches are explored to expand the widely used visual patch features before fed to the Transformer encoder, while the external linguistic signals help the decoder better master prior knowledge during the pre-training process. Our approach performs well on common benchmarks, including CX-CHR, IU X-Ray, and COVID-19 CT Report dataset (COV-CTR), demonstrating combining auxiliary signals with transformer architecture can bring a significant improvement in terms of medical report generation. The experimental results confirm that auxiliary signals driven Transformer-based models are with solid capabilities to outperform previous approaches on both medical terminology classification and paragraph generation metrics.

Clinical characteristics of children with lobar pneumonia and the distribution of pathogens in bronchoalveolar lavage fluid / 中国热带医学

@#Abstract: Objective　To analyze the clinical characteristics of children with lobar pneumonia and the distribution of pathogens in bronchoalveolar lavage fluid (BALF) collected from these patients, hence providing a scientific basis for their precise diagnosis and treatment. Methods　A total of 115 children diagnosed with lobar pneumonia from August 2019 to August 2022 at Suining Central Hospital were screened as the research subjects. The clinical manifestations and occurrence of complications in the patients were investigated. All the children underwent bronchoalveolar lavage after admission, and BALF samples were collected. Fluorescence quantitative PCR was adopted to detect and analyze the distribution and clinical characteristics of Streptococcus pneumoniae (SP) and other related pathogenic microorganisms in BALF specimens. Results Among the 115 pediatric patients with lobar pneumonia, the occurrence of manifestations or complications including involvement of ≥2 lung lobes, myocardial damage, pleural effusion, abnormal liver function, digestive system involvement, nervous system involvement, rash, renal function impairment, and lung atelectasis were observed in 46, 46, 39, 33, 18, 17, 11, 5, and 4 cases, respectively. The pathogen positivity rate in the BALF samples of the 115 patients was 87.0% (100/115), with 81 cases of single infection and 19 cases of mixed infection. A total of 121 strains of pathogens were isolated, including 83 strains of Mycoplasmal pneumonia (MP) (accounting for 68.6%) and SP(13.2%). The differences in the detection rates of HI, MP, RSV strains among different age groups were statistically significant (χ2=8.834, 19.454, 10.284, P<0.05), while the differences in the infection rates of SP, KP, CP, and ADV were not statistically significant (χ2=3.393, 2.67, 0.565, 0.097, P>0.05). The MP pneumonia group showed significantly higher incidence of complications such as pleural effusion, nervous system involvement, and abnormal liver function than the non-MP pneumonia group (χ2=3.925, 4.195, and 4.513, P<0.05). The highest pathogen detection rate was in winter, accounting for 33.91%. Conclusions MP is the most common pathogen in BALF of children with lobar pneumonia. There is variation in the pathogen detection rate among different age groups and seasons. Those with combined infections were more prone to complications, which is worthy of attention by clinicians.

Prototypical Graph Contrastive Learning.

Graph-level representations are critical in various real-world applications, such as predicting the properties of molecules. However, in practice, precise graph annotations are generally very expensive and time-consuming. To address this issue, graph contrastive learning constructs an instance discrimination task, which pulls together positive pairs (augmentation pairs of the same graph) and pushes away negative pairs (augmentation pairs of different graphs) for unsupervised representation learning. However, since for a query, its negatives are uniformly sampled from all graphs, existing methods suffer from the critical sampling bias issue, i.e., the negatives likely having the same semantic structure with the query, leading to performance degradation. To mitigate this sampling bias issue, in this article, we propose a prototypical graph contrastive learning (PGCL) approach. Specifically, PGCL models the underlying semantic structure of the graph data via clustering semantically similar graphs into the same group and simultaneously encourages the clustering consistency for different augmentations of the same graph. Then, given a query, it performs negative sampling via drawing the graphs from those clusters that differ from the cluster of query, which ensures the semantic difference between query and its negative samples. Moreover, for a query, PGCL further reweights its negative samples based on the distance between their prototypes (cluster centroids) and the query prototype such that those negatives having moderate prototype distance enjoy relatively large weights. This reweighting strategy is proven to be more effective than uniform sampling. Experimental results on various graph benchmarks testify the advantages of our PGCL over state-of-the-art methods. The code is publicly available at https://github.com/ha-lins/PGCL.