Relationship of Retinal Nerve Fiber Layer Thickness and Retinal Vessel Calibers with Cognitive Impairment in the Asymptomatic Polyvascular Abnormalities Population

Objective Cognitive impairment(CI)in older individuals has a high morbidity rate worldwide,with poor diagnostic methods and susceptible population identification.This study aimed to investigate the relationship between different retinal metrics and CI in a particular population,emphasizing polyvascular status.Methods We collected information from the Asymptomatic Polyvascular Abnormalities Community Study on retinal vessel calibers,retinal nerve fiber layer(RNFL)thickness,and cognitive function of 3,785participants,aged 40 years or older.Logistic regression was used to analyze the relationship between retinal metrics and cognitive function.Subgroups stratified by different vascular statuses were also analyzed.Results RNFL thickness was significantly thinner in the CI group(odds ratio:0.973,95%confidence interval:0.953–0.994).In the subgroup analysis,the difference still existed in the non-intracranial arterial stenosis,non-extracranial carotid arterial stenosis,and peripheral arterial disease subgroups(P<0.05).Conclusion A thin RNFL is associated with CI,especially in people with non-large vessel stenosis.The underlying small vessel change in RNFL and CI should be investigated in the future.

Analysis of the Efficacy of Prenatal B-Ultrasound in Diagnosing Fetal Abnormalities

Objective:To explore the positive significance of using prenatal B-ultrasound in diagnosing fetal abnormalities.Methods:A total of 200 pregnant women who visited Shaanxi Provincial People’s Hospital between January 2023 and January 2024 were recruited as the research subjects.All pregnant women received prenatal examinations.A retrospective analysis was carried out to analyze the positive significance of prenatal B-ultrasound examination in the diagnosis of fetal abnormalities.Results:Prenatal B-ultrasound examination detected 10 cases of fetal abnormalities,with a detection rate of 5.00%.When compared with the postnatal examination results of 5.50%,the difference was insignificant(P>0.05).Moreover,comparing the fetal limb abnormalities and cardiovascular abnormalities in prenatal B-ultrasound examination and postnatal examination,one case of congenital heart disease was missed in the prenatal B-ultrasound examination,and the others were consistent with the postnatal examination results,with a coincidence rate of 90.91%,indicating a high compliance rate.Conclusion:Fetal abnormalities have a great impact on mothers,babies,and families,and it is particularly important to strengthen diagnosis during this process.Prenatal B-ultrasound examination can improve the accuracy of diagnosis of fetal abnormalities and can be promoted in clinical practice as a basis for screening fetal abnormalities.

融合IMR-WGAN的时序数据修复方法

工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小迭代修复和改进WGAN混合模型的时序数据修复方法.首先,在预处理阶段,保留异常数据,进行信息标注等处理,从而充分挖掘异常值与真实值之间的特征约束.其次,在噪声模块提出了近邻参数裁剪规则,用于修正最小迭代修复公式生成的噪声向量.将其传递至模拟分布模块的生成器中,同时设计了一个动态时间注意力网络层,用于提取时序特征权重并与门控循环单元串联组合捕捉不同步长的特征依赖,并引入递归多步预测原理共同提升模型的表达能力;在判别器中设计了Abnormal and Truth奖励机制和Weighted Mean Square Error损失函数共同反向优化生成器修复数据的细节和质量.最后,在公开数据集和真实数据集上的实验结果表明,该方法的修复准确度与模型稳定性显著优于现有方法.

Coagulation abnormalities and their relationship with bleeding manifestations in patients with dengue-A single center observational study

Objective:To evaluate coagulation abnormalities and their relationship with bleeding manifestations among patients with dengue.Methods:This observational study was conducted on 292 adult dengue patients who were admitted to a tertiary care hospital of Western India from July 2021 to June 2022.Coagulation tests including prothrombin time(PT),international normalized ratio(INR),activated partial thromboplastin time(aPTT),fibrinogen,and D-dimer were performed.Patients were monitored for bleeding manifestations.Results:Coagulation abnormalities were reported in 42.8%of the patients.Overall,prolonged aPTT was the most common coagulation abnormality(40.8%),followed by low fibrinogen(38.7%),raised D-dimer(31.2%),raised INR(26.0%)and prolonged PT(19.2%).Bleeding manifestations were present in 19.9%patients.PT,INR,aPTT and D-dimer levels were significantly higher(P<0.01)and fibrinogen level was significantly lower(P<0.001)in patients with bleeding compared to patients without bleeding.Patients with bleeding had a significantly higher rate of all coagulation abnormalities than patients without bleeding(P<0.01).Conclusions:Patients with bleeding showed a significantly higher frequency of coagulation abnormalities compared to patients without bleeding.Patients with dengue should be assessed for coagulation abnormalities.

Integrated ribosome and proteome analyses reveal insights into sevoflurane-induced long-term social behavior and cognitive dysfunctions through ADNP inhibition in neonatal mice

A growing number of studies have demonstrated that repeated exposure to sevoflurane during development results in persistent social abnormalities and cognitive impairment.Davunetide,an active fragment of the activity-dependent neuroprotective protein(ADNP),has been implicated in social and cognitive protection.However,the potential of davunetide to attenuate social deficits following sevoflurane exposure and the underlying developmental mechanisms remain poorly understood.In this study,ribosome and proteome profiles were analyzed to investigate the molecular basis of sevoflurane-induced social deficits in neonatal mice.The neuropathological basis was also explored using Golgi staining,morphological analysis,western blotting,electrophysiological analysis,and behavioral analysis.Results indicated that ADNP was significantly down-regulated following developmental exposure to sevoflurane.In adulthood,anterior cingulate cortex(ACC)neurons exposed to sevoflurane exhibited a decrease in dendrite number,total dendrite length,and spine density.Furthermore,the expression levels of Homer,PSD95,synaptophysin,and vglut2 were significantly reduced in the sevoflurane group.Patch-clamp recordings indicated reductions in both the frequency and amplitude of miniature excitatory postsynaptic currents(mEPSCs).Notably,davunetide significantly ameliorated the synaptic defects,social behavior deficits,and cognitive impairments induced by sevoflurane.Mechanistic analysis revealed that loss of ADNP led to dysregulation of Ca^(2+)activity via the Wnt/β-catenin signaling,resulting in decreased expression of synaptic proteins.Suppression of Wnt signaling was restored in the davunetide-treated group.Thus,ADNP was identified as a promising therapeutic target for the prevention and treatment of neurodevelopmental toxicity caused by general anesthetics.This study provides important insights into the mechanisms underlying social and cognitive disturbances caused by sevoflurane exposure in neonatal mice and elucidates the regulatory pathways involved.

Prevalence of Ventilatory Function Abnormalities in Residents of Attecoube Lagune, Abidjan (Côte d’Ivoire)

Introduction: Studies of abnormal ventilatory function in Côte d’Ivoire have been carried out in the workplace and in schools. The objective of this study was to determine the prevalence of respiratory symptoms and ventilatory function abnormalities in the population of the lagoon district of Attécoubé in Abidjan. Material and Methods: A cross-sectional study was carried out on 170 people in the municipality of Attécoubé Lagune. A questionnaire was used to collect information on sociodemographic, clinical, and environmental characteristics. A basic spirometry and a beta mimetic test were carried out on all the subjects surveyed. Data analysis was done with the stata 15.1 software. Results: The study population was composed of 103 women and 67 men with a sex ratio (M/F) of 0.65. The average age was 35.92 ± 15.28 years. The most frequent respiratory symptoms were chest tightness (29.41%), dyspnea (28.82%), sneezing (22.94%) and cough (22.35%). The prevalence of ventilatory function abnormalities was 43.24% among residents of Attécoubé Lagune and the most frequent abnormality was ventilatory restriction (35.15%) followed by obstruction (4.85%). The risk factor for ventilatory function abnormalities was heavy pollution [OR = 2.66;IC: 1.053 - 6.743;P = 0.038]. Conclusion: Residents of the Attécoubé Lagune district had many respiratory symptoms and a high prevalence of ventilatory function abnormalities. Improving air quality is urgently needed in this municipality.

Correlation between Reasons for Prescription and Karyotype Results in Patients Referred for Suspected Chromosomal Abnormalities

Karyotype prescription is based on clinical signs (or reasons for karyotype prescription) which are phenotypic manifestations associated with chromosomal abnormalities. The aim of this study was to establish a correspondence between karyotype indications and their results in patients. This was a retrospective study that was carried out in the Histology-Embryology-Cytogenetics laboratory of the University Hospital of Cocody-Abidjan from 2014 to 2019. 58 patient files were identified and included the indication or reason for prescribing a constitutional karyotype and the biological result obtained. An individual data sheet was used to collect the data. 17 reasons for prescription were identified and divided into 2 groups. Sexual ambiguity was the most frequent reason (29.3%). The first group (G1) represented the 10 reasons for which the karyotype results were normal. The second group (G2) corresponded of the 7 motives with normal or abnormal karyotype results. Several anomalies were listed according to these reasons: inversions, mosaics (anomalies of number and structure) and trisomy 21. The last was the most frequent chromosomal anomaly (69.24%). It was found in several reasons for karyotype prescription: malformations, neurological disorders, suspected trisomy and cardiac pathology. Several factors could explain these results, among which are the limits of the karyotype and the non-genetic causes that can induce these abnormal phenotypes. Complementary examinations to the karyotype are molecular cytogenetic techniques, notably fluorescence in situ hybridization (FISH) and array comparative genomic hybridization (Array-CGH).

Improving Federated Learning through Abnormal Client Detection and Incentive

Data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central server.Multiple reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate,but because the clients are independent,the central server cannot fully control their behavior.The central server has no way of knowing the correctness of the model parameters provided by each client in this round,so clients may purposefully or unwittingly submit anomalous data,leading to abnormal behavior,such as becoming malicious attackers or defective clients.To reduce their negative consequences,it is crucial to quickly detect these abnormalities and incentivize them.In this paper,we propose a Federated Learning framework for Detecting and Incentivizing Abnormal Clients(FL-DIAC)to accomplish efficient and security federated learning.We build a detector that introduces an auto-encoder for anomaly detection and use it to perform anomaly identification and prevent the involvement of abnormal clients,in particular for the anomaly client detection problem.Among them,before the model parameters are input to the detector,we propose a Fourier transform-based anomaly data detectionmethod for dimensionality reduction in order to reduce the computational complexity.Additionally,we create a credit scorebased incentive structure to encourage clients to participate in training in order tomake clients actively participate.Three training models(CNN,MLP,and ResNet-18)and three datasets(MNIST,Fashion MNIST,and CIFAR-10)have been used in experiments.According to theoretical analysis and experimental findings,the FL-DIAC is superior to other federated learning schemes of the same type in terms of effectiveness.

First record of abnormal body coloration in a rockfish Sebastes koreanus(Scorpaenoidei:Sebastidae)from coastal water of China based on morphological characteristics and DNA barcoding

The first record of abnormal body coloration in Sebastes koreanus Kim and Lee,1994,from the Yellow Sea of China,was documented based on morphological characteristics and DNA barcoding.The two rockfish specimens were collected from the coastal waters of Qingdao,China,and the whole body and all fins of them were red.Of the two red-colored rockfish,there were tiny deep red spots on each fin,2 red radial stripes behind and below the eyes and 1 large deep red blotch on the opercula,while the similar stripe and spot patterns are also present in the S.koreanus specimens with normal body coloration.The countable characteristics of the two specimens are in the range of the morphometry of S.koreanus.To further clarify the species identity and taxonomic status of the two specimens,DNA barcode analysis was carried out.The genetic distance between the red-colored rockfish and S.koreanus was 0,and the minimum net genetic distances between the red-colored rockfish and other Sebastes species except for S.koreanus were 3.0%,which exceeds the threshold of species delimitation.The phylogenetic analysis showed that the DNA barcoding sequences of the two red-colored rockfish clustered with the S.koreanus sequences.The above results of DNA barcode analysis also support that the two red-colored rockfish could be identified as the species of S.koreanus.The mechanism of color variation in S.koreanus is desirable for further research and the species could be an ideal model to study the color-driven speciation of the rockfishes.

Abnormal State Detection in Lithium-ion Battery Using Dynamic Frequency Memory and Correlation Attention LSTM Autoencoder

This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery(LiB)time series data.As the energy sector increasingly focuses on integrating distributed energy resources,Virtual Power Plants(VPP)have become a vital new framework for energy management.LiBs are key in this context,owing to their high-efficiency energy storage capabilities essential for VPP operations.However,LiBs are prone to various abnormal states like overcharging,over-discharging,and internal short circuits,which impede power transmission efficiency.Traditional methods for detecting such abnormalities in LiB are too broad and lack precision for the dynamic and irregular nature of LiB data.In response,we introduce an innovative method:a Long Short-Term Memory(LSTM)autoencoder based on Dynamic Frequency Memory and Correlation Attention(DFMCA-LSTM-AE).This unsupervised,end-to-end approach is specifically designed for dynamically monitoring abnormal states in LiB data.The method starts with a Dynamic Frequency Fourier Transform module,which dynamically captures the frequency characteristics of time series data across three scales,incorporating a memory mechanism to reduce overgeneralization of abnormal frequencies.This is followed by integrating LSTM into both the encoder and decoder,enabling the model to effectively encode and decode the temporal relationships in the time series.Empirical tests on a real-world LiB dataset demonstrate that DFMCA-LSTM-AE outperforms existing models,achieving an average Area Under the Curve(AUC)of 90.73%and an F1 score of 83.83%.These results mark significant improvements over existing models,ranging from 2.4%–45.3%for AUC and 1.6%–28.9%for F1 score,showcasing the model’s enhanced accuracy and reliability in detecting abnormal states in LiB data.

Abnormal Traffic Detection for Internet of Things Based on an Improved Residual Network

Along with the progression of Internet of Things(IoT)technology,network terminals are becoming continuously more intelligent.IoT has been widely applied in various scenarios,including urban infrastructure,transportation,industry,personal life,and other socio-economic fields.The introduction of deep learning has brought new security challenges,like an increment in abnormal traffic,which threatens network security.Insufficient feature extraction leads to less accurate classification results.In abnormal traffic detection,the data of network traffic is high-dimensional and complex.This data not only increases the computational burden of model training but also makes information extraction more difficult.To address these issues,this paper proposes an MD-MRD-ResNeXt model for abnormal network traffic detection.To fully utilize the multi-scale information in network traffic,a Multi-scale Dilated feature extraction(MD)block is introduced.This module can effectively understand and process information at various scales and uses dilated convolution technology to significantly broaden the model’s receptive field.The proposed Max-feature-map Residual with Dual-channel pooling(MRD)block integrates the maximum feature map with the residual block.This module ensures the model focuses on key information,thereby optimizing computational efficiency and reducing unnecessary information redundancy.Experimental results show that compared to the latest methods,the proposed abnormal traffic detection model improves accuracy by about 2%.

Fortifying Smart Grids: A Holistic Assessment Strategy against Cyber Attacks and Physical Threats for Intelligent Electronic Devices

Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control instructions.In the context of the heightened security challenges within smart grids,IEDs pose significant risks due to inherent hardware and software vulner-abilities,as well as the openness and vulnerability of communication protocols.Smart grid security,distinct from traditional internet security,mainly relies on monitoring network security events at the platform layer,lacking an effective assessment mechanism for IEDs.Hence,we incorporate considerations for both cyber-attacks and physical faults,presenting security assessment indicators and methods specifically tailored for IEDs.Initially,we outline the security monitoring technology for IEDs,considering the necessary data sources for their security assessment.Subsequently,we classify IEDs and establish a comprehensive security monitoring index system,incorporating factors such as running states,network traffic,and abnormal behaviors.This index system contains 18 indicators in 3 categories.Additionally,we elucidate quantitative methods for various indicators and propose a hybrid security assessment method known as GRCW-hybrid,combining grey relational analysis(GRA),analytic hierarchy process(AHP),and entropy weight method(EWM).According to the proposed assessment method,the security risk level of IEDs can be graded into 6 levels,namely 0,1,2,3,4,and 5.The higher the level,the greater the security risk.Finally,we assess and simulate 15 scenarios in 3 categories,which are based on monitoring indicators and real-world situations encountered by IEDs.The results show that calculated security risk level based on the proposed assessment method are consistent with actual simulation.Thus,the reasonableness and effectiveness of the proposed index system and assessment method are validated.

Vehicle Abnormal Behavior Detection Based on Dense Block and Soft Thresholding

With the rapid advancement of social economies,intelligent transportation systems are gaining increasing atten-tion.Central to these systems is the detection of abnormal vehicle behavior,which remains a critical challenge due to the complexity of urban roadways and the variability of external conditions.Current research on detecting abnormal traffic behaviors is still nascent,with significant room for improvement in recognition accuracy.To address this,this research has developed a new model for recognizing abnormal traffic behaviors.This model employs the R3D network as its core architecture,incorporating a dense block to facilitate feature reuse.This approach not only enhances performance with fewer parameters and reduced computational demands but also allows for the acquisition of new features while simplifying the overall network structure.Additionally,this research integrates a self-attentive method that dynamically adjusts to the prevailing traffic conditions,optimizing the relevance of features for the task at hand.For temporal analysis,a Bi-LSTM layer is utilized to extract and learn from time-based data nuances.This research conducted a series of comparative experiments using the UCF-Crime dataset,achieving a notable accuracy of 89.30%on our test set.Our results demonstrate that our model not only operates with fewer parameters but also achieves superior recognition accuracy compared to previous models.

Ultrasound blood flow characteristics changes in fetal umbilical artery thrombosis:A retrospective analysis

BACKGROUND Umbilical artery thrombosis(UAT)is extremely uncommon and leads to adverse perinatal outcomes.Hypercoagulation of blood in pregnant women is suspected to be an important risk for UAT.Ultrasound is an effective way to detect thrombosis.The mother can monitor her own fetal health using ultrasound,which enables her to take preventative action in case of emergency.AIM To investigate ultrasonic blood signal after UAT in the umbilical artery,and evaluate the relationship between hypercoagulability and UAT.METHODS We described a case of a newly formed UAT with markedly altered ultrasonic indices of umbilical artery blood flow,and retrospectively studied it with 18 UAT patients confirmed by histopathology from October 2019 and March 2023 in Xiamen Women and Children's Hospital.Patients’information was collected from medical archives,including maternal clinical data,neonatal outcomes,pathological findings and ultrasonic indices of umbilical artery blood flow,such as systolic-diastolic duration ratio(S/D),resistance index(RI),pulsatility index(PI)and peak systolic velocity(PSV).Ultrasound and coagulation indices were analyzed with matched samples t-test and Wilcoxon rank sum test using the statistical packages in R(version 4.2.1)including car(version 3.1-0)and stats(version 4.2.1),and visualized by ggplot2 package(version 3.3.6).RESULTS A patient with normal findings in second and third-trimester routine ultrasound scan developed UAT with severe changes in ultrasonic indices of umbilical artery blood flow(within 2.5th of reference ranges)in a short period of time.Statistical analysis of umbilical artery blood flow ultrasound indices for 19 patients with UAT showed that the decrease in S/D,RI,and PI and increase of PSV during the disease process was greater than that of non-UAT.All 18 patients delivered in our hospital showed characteristic manifestations of UAT on histological examination after delivery,most of which(16/18)showed umbilical cord abnormalities,with 15 umbilical cord torsion and 1 pseudoknot.Coagulation parameters were not significantly changed in UAT patients compared with normal pregnancy women.CONCLUSION Significant changes in ultrasound indicators after UAT were demonstrated.PSV can play important roles in the diagnosis of UAT.Hypercoagulability alone is not sufficient for the occurrence of UAT.

Planning,monitoring and replanning techniques for handling abnormity in HTN-based planning and execution

A framework that integrates planning,monitoring and replanning techniques is proposed.It can devise the best solution based on the current state according to specific objectives and properly deal with the influence of abnormity on the plan execution.The framework consists of three parts:the hierarchical task network(HTN)planner based on Monte Carlo tree search(MCTS),hybrid plan monitoring based on forward and backward and norm-based replanning method selection.The HTN planner based on MCTS selects the optimal method for HTN compound task through pre-exploration.Based on specific objectives,it can identify the best solution to the current problem.The hybrid plan monitoring has the capability to detect the influence of abnormity on the effect of an executed action and the premise of an unexecuted action,thus trigger the replanning.The norm-based replanning selection method can measure the difference between the expected state and the actual state,and then select the best replanning algorithm.The experimental results reveal that our method can effectively deal with the influence of abnormity on the implementation of the plan and achieve the target task in an optimal way.

A method for cleaning wind power anomaly data by combining image processing with community detection algorithms

Current methodologies for cleaning wind power anomaly data exhibit limited capabilities in identifying abnormal data within extensive datasets and struggle to accommodate the considerable variability and intricacy of wind farm data.Consequently,a method for cleaning wind power anomaly data by combining image processing with community detection algorithms(CWPAD-IPCDA)is proposed.To precisely identify and initially clean anomalous data,wind power curve(WPC)images are converted into graph structures,which employ the Louvain community recognition algorithm and graph-theoretic methods for community detection and segmentation.Furthermore,the mathematical morphology operation(MMO)determines the main part of the initially cleaned wind power curve images and maps them back to the normal wind power points to complete the final cleaning.The CWPAD-IPCDA method was applied to clean datasets from 25 wind turbines(WTs)in two wind farms in northwest China to validate its feasibility.A comparison was conducted using density-based spatial clustering of applications with noise(DBSCAN)algorithm,an improved isolation forest algorithm,and an image-based(IB)algorithm.The experimental results demonstrate that the CWPAD-IPCDA method surpasses the other three algorithms,achieving an approximately 7.23%higher average data cleaning rate.The mean value of the sum of the squared errors(SSE)of the dataset after cleaning is approximately 6.887 lower than that of the other algorithms.Moreover,the mean of overall accuracy,as measured by the F1-score,exceeds that of the other methods by approximately 10.49%;this indicates that the CWPAD-IPCDA method is more conducive to improving the accuracy and reliability of wind power curve modeling and wind farm power forecasting.

Predictors of Adverse Pregnancy Outcomes Following Traumatic Injuries

Objective After traumatic injury in pregnant women,providing timely and appropriate management for high-risk patients is crucial for both pregnant women and fetuses.This study aimed to identify risk factors that predict adverse pregnancy outcomes after traumatic injury.Methods A retrospective cohort study including 317 pregnant patients who experienced trauma was conducted.The collected data included general demographics,injury mechanisms and adverse pregnancy outcomes.Patients were divided into two subgroups based on the absence or presence of trauma-related adverse pregnancy outcomes.Univariate and multivariate logistic regressions were conducted to estimate the associations between clinical variables and adverse pregnancy outcomes.Results A total of 41(12.93%)patients experienced adverse pregnancy outcomes within the first 24 h post-trauma.This study revealed that age>35 years(OR=14.995,95%CI:5.024–44.755,P<0.001),third trimester trauma(OR=3.878,95%CI:1.343–11.204,P=0.012),abdominal pain(OR=3.032,95%CI:1.221–7.527,P=0.017),vaginal bleeding(OR=3.226,95%CI:1.093–9.523,P=0.034),positive scan in focused assessment with sonography for trauma(FAST)positive(OR=8.496,95%CI:2.825–25.555,P<0.001),9≤injury severity score(ISS)<16(OR=3.039,95%CI:1.046–8.835,P=0.041)and ISS≥16(OR=5.553,95%CI:1.387–22.225,P=0.015)increased the probability of posttraumatic adverse pregnancy outcomes.Maternal age,gestational age at delivery,vaginal bleeding and positive FAST results were risk factors for abnormal delivery.Conclusion Advanced maternal age,third trimester,and positive FAST results should alert multidisciplinary trauma teams to closely monitor patients to prevent adverse pregnancy outcomes.

Genetic mechanism and main controlling factors of high-quality clastic rock reservoirs in deep and ultradeep layers:A case study of Oligocene Linhe Formation in Linhe Depression,Hetao Basin,NW China

Based on new data from cores,drilling and logging,combined with extensive rock and mineral testing analysis,a systematic analysis is conducted on the characteristics,diagenesis types,genesis and controlling factors of deep to ultra-deep abnormally high porosity clastic rock reservoirs in the Oligocene Linhe Formation in the Hetao Basin.The reservoir space of the deep to ultra-deep clastic rock reservoirs in the Linhe Formation is mainly primary pores,and the coupling of three favorable diagenetic elements,namely the rock fabric with strong compaction resistance,weak thermal compaction diagenetic dynamic field,and diagenetic environment with weak fluid compaction-weak cementation,is conducive to the preservation of primary pores.The Linhe Formation clastic rocks have a superior preexisting material composition,with an average total content of 90%for quartz,feldspar,and rigid rock fragments,and strong resistance to compaction.The geothermal gradient in Linhe Depression in the range of(2.0–2.6)°C/100 m is low,and together with the burial history of long-term shallow burial and late rapid deep burial,it forms a weak thermal compaction diagenetic dynamic field environment.The diagenetic environment of the saline lake basin is characterized by weak fluid compaction.At the same time,the paleosalinity has zoning characteristics,and weak cementation in low salinity areas is conducive to the preservation of primary pores.The hydrodynamic conditions of sedimentation,salinity differentiation of ancient water in saline lake basins,and sand body thickness jointly control the distribution of high-quality reservoirs in the Linhe Formation.

Abnormal Action Detection Based on Parameter-Efficient Transfer Learning in Laboratory Scenarios

Laboratory safety is a critical area of broad societal concern,particularly in the detection of abnormal actions.To enhance the efficiency and accuracy of detecting such actions,this paper introduces a novel method called TubeRAPT(Tubelet Transformer based onAdapter and Prefix TrainingModule).Thismethod primarily comprises three key components:the TubeR network,an adaptive clustering attention mechanism,and a prefix training module.These components work in synergy to address the challenge of knowledge preservation in models pretrained on large datasets while maintaining training efficiency.The TubeR network serves as the backbone for spatio-temporal feature extraction,while the adaptive clustering attention mechanism refines the focus on relevant information.The prefix training module facilitates efficient fine-tuning and knowledge transfer.Experimental results demonstrate the effectiveness of TubeRAPT,achieving a 68.44%mean Average Precision(mAP)on the CLA(Crazy LabActivity)small-scale dataset,marking a significant improvement of 1.53%over the previous TubeR method.This research not only showcases the potential applications of TubeRAPT in the field of abnormal action detection but also offers innovative ideas and technical support for the future development of laboratory safety monitoring technologies.The proposed method has implications for improving safety management systems in various laboratory environments,potentially reducing accidents and enhancing overall workplace safety.

Rice AGL1 determines grain size and sterile lemma identity

The grass spikelet is a unique inflorescence structure that determines grain size.Although many genetic factors have been well characterized for grain size and glume development,the underlying molecular mechanisms in rice are far from established.Here,we isolated rice gene,AGL1 that controlled grain size and determines the fate of the sterile lemma.Loss of function of AGL1 produced larger grains and reduced the size of the sterile lemma.Larger grains in the agl1 mutant were caused by a larger number of cells that were longer and wider than in the wild type.The sterile lemma in the mutant spikelet was converted to a rudimentary glume-like organ.Our findings showed that the AGL1(also named LAX1)protein positively regulated G1 expression,and negatively regulated NSG1 expression,thereby affecting the fate of the sterile lemma.Taken together,our results revealed that AGL1 played a key role in negative regulation of grain size by controlling cell proliferation and expansion,and supported the opinion that rudimentary glume and sterile lemma in rice are homologous organs.