Cross-Modal Consistency with Aesthetic Similarity for Multimodal False Information Detection

With the explosive growth of false information on social media platforms, the automatic detection of multimodalfalse information has received increasing attention. Recent research has significantly contributed to multimodalinformation exchange and fusion, with many methods attempting to integrate unimodal features to generatemultimodal news representations. However, they still need to fully explore the hierarchical and complex semanticcorrelations between different modal contents, severely limiting their performance detecting multimodal falseinformation. This work proposes a two-stage detection framework for multimodal false information detection,called ASMFD, which is based on image aesthetic similarity to segment and explores the consistency andinconsistency features of images and texts. Specifically, we first use the Contrastive Language-Image Pre-training(CLIP) model to learn the relationship between text and images through label awareness and train an imageaesthetic attribute scorer using an aesthetic attribute dataset. Then, we calculate the aesthetic similarity betweenthe image and related images and use this similarity as a threshold to divide the multimodal correlation matrixinto consistency and inconsistencymatrices. Finally, the fusionmodule is designed to identify essential features fordetectingmultimodal false information. In extensive experiments on four datasets, the performance of the ASMFDis superior to state-of-the-art baseline methods.

A lightweight false alarm suppression method in heterogeneous change detection

Overlooking the issue of false alarm suppression in heterogeneous change detection leads to inferior detection per-formance.This paper proposes a method to handle false alarms in heterogeneous change detection.A lightweight network of two channels is bulit based on the combination of convolutional neural network(CNN)and graph convolutional network(GCN).CNNs learn feature difference maps of multitemporal images,and attention modules adaptively fuse CNN-based and graph-based features for different scales.GCNs with a new kernel filter adaptively distinguish between nodes with the same and those with different labels,generating change maps.Experimental evaluation on two datasets validates the efficacy of the pro-posed method in addressing false alarms.

False Positive HIV “Combo” Screening Test in a Hemodialysis Patient in Saudi Arabia: A Case Report and Review of the Literature

Introduction: HIV screening tests are routinely conducted on dialysis patients as the constant exposure of their blood during the dialysis process makes them a reasonable risk for blood-borne infections. However, in low prevalence settings, where HIV rates are <0.1% of the population, false positive results are more likely. This results in apprehension in the dialysis unit as breaches in infectious disease protocols could be presumed. This is illustrated in the case report below. Case Summary: A 62-year-old male Saudi end-stage kidney disease patient secondary to DM nephropathy began dialysis a year before presentation in a hemodialysis center in Saudi Arabia. Routine screening tests done at the start of dialysis revealed negative Hepatitis C, HIV 1 and 2 screening but a positive Hepatitis B surface antigen screen. The patient went for holiday dialysis at another facility and had a routine fourth-generation HIV test done which was positive. A confirmatory HIV PCR test was negative. Conclusion: This case highlights the need for caution in interpreting highly sensitive and specific HIV screening tests in a low-prevalence setting. Routine screening beyond the national recommendation may not be necessary in low-prevalence areas.

Residual-Based False Data Injection Attacks Against Multi-Sensor Estimation Systems

This paper investigates the security issue of multisensor remote estimation systems.An optimal stealthy false data injection(FDI)attack scheme based on historical and current residuals,which only tampers with the measurement residuals of partial sensors due to limited attack resources,is proposed to maximally degrade system estimation performance.The attack stealthiness condition is given,and then the estimation error covariance in compromised state is derived to quantify the system performance under attack.The optimal attack strategy is obtained by solving several convex optimization problems which maximize the trace of the compromised estimation error covariance subject to the stealthiness condition.Moreover,due to the constraint of attack resources,the selection principle of the attacked sensor is provided to determine which sensor is attacked so as to hold the most impact on system performance.Finally,simulation results are presented to verify the theoretical analysis.

Kinematic Control of Serial Manipulators Under False Data Injection Attack

With advanced communication technologies,cyberphysical systems such as networked industrial control systems can be monitored and controlled by a remote control center via communication networks.While lots of benefits can be achieved with such a configuration,it also brings the concern of cyber attacks to the industrial control systems,such as networked manipulators that are widely adopted in industrial automation.For such systems,a false data injection attack on a control-center-to-manipulator(CC-M)communication channel is undesirable,and has negative effects on the manufacture quality.In this paper,we propose a resilient remote kinematic control method for serial manipulators undergoing a false data injection attack by leveraging the kinematic model.Theoretical analysis shows that the proposed method can guarantee asymptotic convergence of the regulation error to zero in the presence of a type of false data injection attack.The efficacy of the proposed method is validated via simulations.

Water Extract of Rice False Smut Balls Activates Nrf2/HO-1 and Apoptosis Pathways,Causing Liver Injury

Ustiloxins are vital cyclopeptide mycotoxins originally isolated from rice false smut balls that form in rice spikelets infected by the fungal pathogen Ustilaginoidea virens.The toxicity of the water extract of rice false smut balls(RBWE) remains to be investigated.Studies have shown that RBWE may be toxic to animals,but toxicological evidence is still lacking.In this study,we found that the IC50 values of RBWE to BNL CL.2 cells at 24 and 48 h were 40.02 and 30.11 μg/m L,respectively,with positive correlations with dose toxicity and time toxicity.After treatment with RBWE,the number of BNL CL.2 cells decreased significantly,and the morphology of BNL CL.2 cells showed atrophy and wall detachment.RBWE induced DNA presynthesis phase arrest of BNL CL.2 cells,increased the proportion of apoptotic cells and inhibited cell proliferation.RBWE up-regulated reactive oxygen species(ROS) levels and lowered mitochondrial membrane potentials.Additionally,Western blot and q RT-PCR results suggested that RBWE exerted the above effects by promoting the Nrf2/HO-1 and caspase-induced apoptosis pathways in vitro and in vivo.The contents of alanine aminotransferase,aspartate aminotransferase,alkaline phosphatase,and total bile acids in the serum of mice from Institute of Cancer were significantly up-regulated by RBWE.At the same time,RBWE can lead to increases in ROS and malondialdehyde contents,decreases in contents of oxidized glutathione,glutathione and reduced glutathione,as well as decrease in catalase and superoxide dismutase activities in mouse liver tissues,demonstrating that oxidative stress occurred in mice.Moreover,liver damage was further detected by haematoxylin-eosin staining and electron microscopy to verify the damage to the mice caused by RBWE.In general,RBWE may cause hepatotoxicity in vivo and in vitro via the apoptosis pathway,which provides a reference for hepatotoxicity and its mechanism of action.

False positive detection of serum cryptococcal antigens due to insufficient sample dilution:A case series

At present,with the development of technology,the detection of cryptococcal antigen(CRAG)plays an increasingly important role in the diagnosis of cryptococcosis.However,the three major CRAG detection technologies,latex agglutination test(LA),lateral flow assay(LFA)and Enzyme-linked Immunosorbent Assay,have certain limitations.Although these techniques do not often lead to false-positive results,once this result occurs in a particular group of patients(such as human immunodeficiency virus patients),it might lead to severe consequences.

SCADA Data-Based Support Vector Machine for False Alarm Identification for Wind Turbine Management

Maintenance operations have a critical influence on power gen-eration by wind turbines(WT).Advanced algorithms must analyze large volume of data from condition monitoring systems(CMS)to determine the actual working conditions and avoid false alarms.This paper proposes different support vector machine(SVM)algorithms for the prediction and detection of false alarms.K-Fold cross-validation(CV)is applied to evaluate the classification reliability of these algorithms.Supervisory Control and Data Acquisition(SCADA)data from an operating WT are applied to test the proposed approach.The results from the quadratic SVM showed an accuracy rate of 98.6%.Misclassifications from the confusion matrix,alarm log and maintenance records are analyzed to obtain quantitative information and determine if it is a false alarm.The classifier reduces the number of false alarms called misclassifications by 25%.These results demonstrate that the proposed approach presents high reliability and accuracy in false alarm identification.

Coot Optimization with Deep Learning-Based False Data Injection Attack Recognition

The recent developments in smart cities pose major security issues for the Internet of Things(IoT)devices.These security issues directly result from inappropriate security management protocols and their implementation by IoT gadget developers.Cyber-attackers take advantage of such gadgets’vulnerabilities through various attacks such as injection and Distributed Denial of Service(DDoS)attacks.In this background,Intrusion Detection(ID)is the only way to identify the attacks and mitigate their damage.The recent advancements in Machine Learning(ML)and Deep Learning(DL)models are useful in effectively classifying cyber-attacks.The current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition(COADL-FDIAR)model for the IoT environment.The presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT environment.To accomplish this,the COADL-FDIAR model initially preprocesses the input data and selects the features with the help of the Chi-square test.To detect and classify false data injection attacks,the Stacked Long Short-Term Memory(SLSTM)model is exploited in this study.Finally,the COA algorithm effectively adjusts the SLTSM model’s hyperparameters effectively and accomplishes a superior recognition efficiency.The proposed COADL-FDIAR model was experimentally validated using a standard dataset,and the outcomes were scrutinized under distinct aspects.The comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.

A PCR-Based Method for Detecting Pathogen of Rice False Smut

Rice false smut is a destructive disease that affects rice grain badly.The disease seriously affects the yield and quality of rice in Heilongjiang Province.In this paper,a pair of specific primers was designed to detect the false smut pathogen rapidly and efficiently.The results showed that the pair of primers had strong specificity for false smut pathogen.In addition,the sensitivity of this primer to the genomic DNA of rice false smut pathogen in PCR reaction was 1 pg.By using these primers,the rice false smut pathogen could be detected within 48 h after inoculation,and a PCR reaction system with good specificity and high sensitivity was established.

Effect of False Windows on Light Exposure and Sleep Quality in Hospitalized Patients

Purpose: We aimed to investigate the effects of installing false windows next to hospital beds without windows on the amount of light received by patients and their sleep quality. Methods: The study included patients admitted to the Department of Neurology at our hospital between September 2020 and August 2021. An Actigraph device was fitted to patients’ wrist and their beds to measure the amount of light received and sleep quality. Patients were divided into three groups: bed with a window, aisle bed with a false window, and aisle bed without a window. Mean sleep efficiency (%), mean steps (per day), and the amount of light (lux) received by the patients and beds were measured. Results: Valid data were obtained for 48 participants (median age, 66.5 years). There were 23 patients in beds with a window, 13 patients in aisle beds without a false window, and 12 in aisle beds with a false window. No statistically significant differences were found in terms of mean sleep efficiency, number of steps taken, and the amount of light received by the patients (P > 0.05);however, difference in the mean amount of light received by the beds at the location of the bed was statistically significant (P Conclusion: The amount of light that the patient receives is not necessarily affected by the location of the bed or the presence of a false window.

Analysis on Quantitative Trait Loci for Resistance to Rice False Smut under Different Environmental Conditions

A recombinant inbred line （RILs） population with 157 lines derived from an inter-subspecies cross of Daguandao （japonica）/IR28 （indica） by the single seed de-scent method was used to detect quantitative trait loci （QTLs） conferring resistance to rice false smut caused by Ustilaginoidea virens（Cooke） Takahashi in Nanjing and Yangzhou. The disease rate index of the two parents and 157 RILs caused by rice false smut were scored and the QTLs for rice false smut resistance were detected accordingly by QTL Cartographer software. Eight QTLs control ing false smut resis-tance were detected on chromosomes 1, 2, 4, 8, 10, 11 and 12, respectively, with the phenotypic variance of 8.6%-22.5%. There were five QTLs detected in Nanjing and Yangzhou, respectively, and only two QTLs were found in both two years, the phenotypic variation was explained by individual QTL ranged from 18.0% to 18.9% for these two QTLs, and the additive effects of these two QTLs contributed to the 8.0%-14.6% decrease of disease index and therefore the disease resistance increased. The direction of the additive effects at six loci qFsr1, qFsr2, qFsr8, qFsr10a, qFsr11 and qFsr12 coincided with that predicted by phenotypes of the parents, and the IR28 al eles at these loci had positive effect against rice false smut while the negative effects were found in Daguandao al eles at qFsr4 and qFsr10b. Both qFsr10a and qFsr11 should be useful in rice breeding for resistance to rice false smut in marker-assisted selection （MAS） program.

Carbon Catabolite Repressor UvCreA is Required for Development and Pathogenicity in Ustilaginoidea virens

The rice false smut disease, caused by Ustilaginoidea virens, has emerged as a significantglobal threat to rice production. The mechanism of carbon catabolite repression plays a crucial role in theefficient utilization of carbon nutrients and enzyme regulation in the presence of complex nutritionalconditions. Although significant progress has been made in understanding carbon catabolite repression infungi such as Aspergillus nidulans and Magnaporthe oryzae, its role in U. virens remains unclear. Toaddress this knowledge gap, we identified UvCreA, a pivotal component of carbon catabolite repression,in U. virens. Our investigation revealed that UvCreA localized to the nucleus. Deletion of UvCreA resultedin decreased growth and pathogenicity in U. virens. Through RNA-seq analysis, it was found that theknockout of UvCreA led to the up-regulation of 514 genes and down-regulation of 640 genes. Moreover,UvCreA was found to be involved in the transcriptional regulation of pathogenic genes and genesassociated with carbon metabolism in U. virens. In summary, our findings indicated that UvCreA isimportant in fungal development, virulence, and the utilization of carbon sources through transcriptionalregulation, thus making it a critical element of carbon catabolite repression.

Interaction Between Ustilaginoidea virens and Rice and Its Sustainable Control

Ustilaginoidea virens is a common rice pathogen that can easily lead to a decline in rice quality and the production of toxins pose potential risks to human health.In this review,we present a comprehensive literature review of research since the discovery of rice false smut.We provide a comprehensive and,at times,critical overview of the main results and findings from related research,and propose future research directions.Firstly,we delve into the interaction between U.virens and rice,including the regulation of transcription factors,the process of U.virens infecting rice panicles,and the plant immune response caused by rice infection.Following that,we discuss the identification and characterization of mycotoxins produced by the pathogenic fungus,as well as strategies for disease management.We emphasize the importance of comprehensive agricultural prevention and control methods for the sustainable management of U.virens.This knowledge will update our understanding of the interaction between U.virens and rice plants,offering a valuable perspective for those interested in U.virens.

Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnosis increases the patient’s chances of recovery.However,issues like overfitting and inconsistent accuracy across datasets remain challenges.In a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib detection.The aim was to create a robust detection mechanism that consistently performs well.Metrics such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for evaluation.The findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated categories.It demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib detection.These insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG signals.The comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib detection.Moreover,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

Secure Tracking Control via Fixed-Time Convergent Reinforcement Learning for a UAV CPS

Dear Editor,This letter is concerned with the secure tracking control problem in the unmanned aerial vehicle(UAV) system by fixed-time convergent reinforcement learning(RL). By virtue of the zero-sum game,the false data injection(FDI) attacker and secure controller are viewed as game players.

Adaptive detection of range-spread targets in homogeneous and partially homogeneous clutter plus subspace interference

Adaptive detection of range-spread targets is considered in the presence of subspace interference plus Gaussian clutter with unknown covariance matrix.The target signal and interference are supposed to lie in two linearly independent subspaces with deterministic but unknown coordinates.Relying on the two-step criteria,two adaptive detectors based on Gradient tests are proposed,in homogeneous and partially homogeneous clutter plus subspace interference,respectively.Both of the proposed detectors exhibit theoretically constant false alarm rate property against unknown clutter covariance matrix as well as the power level.Numerical results show that,the proposed detectors have better performance than their existing counterparts,especially for mismatches in the signal steering vectors.

Diagnostic challenges from conflicting results of tests and imaging

Accurate diagnosis is the foundation of clinical care but accurate diagnosis is not easily reached in some cases.In rare instances,even a sophisticated multidisciplinary team at an academic medical center cannot reliably reach an accurate diagnosis after extensive testing and imaging,and has to wait until histological diagnosis or even autopsy results are available.The underlying reason of challenging diagnoses is mostly conflicting data from history,tests,and imaging that point to different diagnoses.In this issue of World Journal of Clinical Cases,Huffaker et al reported such a challenging case of a tricuspid mass in a patient with Li-Fraumeni syndrome.The case by Huffaker et al powerfully illustrates the occasional diagnostic challenges inherent in our current diagnostic approach and the current technology.Clinicians should realize that in rare situations,agnosticism in diagnosis is unavoidable but a treatment has to be initiated so long as the principle of primum non nocere is upheld.

Carotid versus axillary artery cannulation for descending aorta remodeling in type A acute aortic dissection

BACKGROUND Arterial cannulation sites for the surgical repair of type A aortic dissection(AAD)have evolved from right axillary artery(AA)cannulation to bilateral carotid artery(CA)based of femoral artery(FA)cannulation.Postoperative descending aorta remodeling is closely linked to the false lumen area ratio(FLAR),defined as false lumen area/aortic area,as well as to the incidence of renal replacement therapy(RRT).AIM To investigate the effect of the updated arterial cannulation strategy on descending aortic remodeling.METHODS A total of 443 AAD patients who received FA combined cannulation between March 2015 and March 2023 were included in the study.Of these,209 received right AA cannulation and 234 received bilateral CA cannulation.The primary outcome was the change in FLAR,as calculated from computed tomography angiography in three segments of the descending aorta:Thoracic(S1),upper abdominal(S2),and lower abdominal(S3).Secondary outcomes were the incidence of RRT and the serum inflammation response,as observed by the levels of high sensitivity C reaction protein(hs-CRP)and Interleukin-6(IL-6).RESULTS The postoperative/preoperative ratio of FLAR in S2 and S3 was higher in the AA group compared to the CA group(S2:0.80±0.08 vs 0.75±0.07,P<0.001;S3:0.57±0.12 vs 0.50±0.12,P<0.001,respectively).The AA group also had a significantly higher incidence of RRT(19.1%vs 8.5%,P=0.001;odds ratio:2.533,95%CI:1.427-4.493)and higher levels of inflammation cytokines 24 h after the procedure[hr-CRP:117±17 vs 104±15 mg/L;IL-6:129(103,166)vs 83(69,101)pg/mL;both P<0.001]compared to the CA group.CONCLUSION The CA cannulation strategy was associated with better abdominal aorta remodeling after AAD repair compared to AA cannulation,as observed by a greater change in FLAR and lower incidence of RRT.

Analysis of Quantitative Trait Loci for Pathogenic Strain Pi-1 of Resistance to Rice False Smut in Rice(Oryza sativa)

In order to identify the resistant gene of rice false smut in rice, a recombi- nant inbred line （RILs） population with 157 lines derived from an inter-subspecies cross of Daguandao/IR28 by the single seed descent method was used to detect quantitative trait loci （QTLs） conferring resistance to strain Pi-1 of rice false smut caused by Usti/aginoiclea virens （Cooke） Takahashi in 2012 and 2013. The disease rate indexes of the two parents and 157 RILs caused by the strain Pi-1 of rice false smut were scored and the QTLs for rice false smut resistance were detected accordingly by QTL Cartographer software. Seven QTLs controlling false smut re- sistance were detected on chromosomes 2, 7, 8, 11 and 12, respectively, with the phenotypic variance of 8.5%-17.2%. There were four QTLs detected in 2012 and 2013, respectively, and only one QTL was found in both two years, the phenotypic variation explained by this individual QTL was 13.5% and 17.2%, and the additive effects of this QTL contributed to the 9.9% and 14.3% decrease of disease index and therefore the disease resistance increased. The direction of the additive effects at five loci qFsr2a, qFsr8a, qFsr8b, qFsr11 and qFsr12 coincided with that predicted by phenotypes of the parents, and the IR28 alleles at these loci had positive effect against rice false smut while the negative effects were found in Daguandao alleles at qFsr2b and qFsr7. The qFsr11 should be useful in rice breeding for resistance to rice false smut in marker-assisted selection （MAS） program.