Light plays an essential role in psychobiological and psychophysiological processes,such as alertness.The alerting effect is influenced by light characteristics and the timing of interventions.This meta-analysis is th...Light plays an essential role in psychobiological and psychophysiological processes,such as alertness.The alerting effect is influenced by light characteristics and the timing of interventions.This meta-analysis is the first to systematically review the effect of light intervention on alertness and to discuss the optimal protocol for light intervention.In this meta-analysis,registered at PROSPERO(Registration ID:CRD42020181485),we conducted a systematic search of the Web of Science,PubMed,and PsycINFO databases for studies published in English prior to August 2021.The outcomes included both subjective and objective alertness.Subgroup analyses considered a variety of factors,such as wavelength,correlated color temperature(CCT),light illuminance,and timing of interventions(daytime,night-time,or all day).Twenty-seven crossover studies and two parallel-group studies were included in this meta-analysis,with a total of 1210 healthy participants(636(52%)male,mean age 25.62 years).The results revealed that light intervention had a positive effect on both subjective alertness(standardized mean difference(SMD)=-0.28,95%confidence interval(CI):-0.49 to-0.06,P=0.01)and objective alertness in healthy subjects(SMD=-0.34,95%CI:-0.68 to-0.01,P=0.04).The subgroup analysis revealed that cold light was better than warm light in improving subjective alertness(SMD=-0.37,95%CI:-0.65 to-0.10,P=0.007,I2=26%)and objective alertness(SMD=-0.36,95%CI:-0.66 to-0.07,P=0.02,I2=0).Both daytime(SMD=-0.22,95%CI:-0.37 to-0.07,P=0.005,I2=74%)and night-time(SMD=-0.32,95%CI:-0.61 to-0.02,P=0.04,I2=0)light exposure improved subjective alertness.The results of this meta-analysis and systematic review indicate that light exposure is associated with significant improvement in subjective and objective alertness.In addition,light exposure with a higher CCT was more effective in improving alertness than light exposure with a lower CCT.Our results also suggest that both daytime and night-time light exposure can improve subjective alertness.展开更多
A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in th...A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%;thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.展开更多
The increasing prevalence of violent incidents in public spaces has created an urgent need for intelligent surveillance systems capable of detecting dangerous objects in real time.While traditional video surveillance ...The increasing prevalence of violent incidents in public spaces has created an urgent need for intelligent surveillance systems capable of detecting dangerous objects in real time.While traditional video surveillance relies on human monitoring,this approach suffers from limitations such as fatigue and delayed response times.This study addresses these challenges by developing an automated detection system using advanced deep learning techniques to enhance public safety.Our approach leverages state-of-the-art convolutional neural networks(CNNs),specifically You Only Look Once version 4(YOLOv4)and EfficientDet,for real-time object detection.The system was trained on a comprehensive dataset of over 50,000 images,enhanced through data augmentation techniques to improve robustness across varying lighting conditions and viewing angles.Cloud-based deployment on Amazon Web Services(AWS)ensured scalability and efficient processing.Experimental evaluations demonstrated high performance,with YOLOv4 achieving 92%accuracy and processing images in 0.45 s,while EfficientDet reached 93%accuracy with a slightly longer processing time of 0.55 s per image.Field tests in high-traffic environments such as train stations and shopping malls confirmed the system’s reliability,with a false alarm rate of only 4.5%.The integration of automatic alerts enabled rapid security responses to potential threats.The proposed CNN-based system provides an effective solution for real-time detection of dangerous objects in video surveillance,significantly improving response times and public safety.While YOLOv4 proved more suitable for speed-critical applications,EfficientDet offered marginally better accuracy.Future work will focus on optimizing the system for low-light conditions and further reducing false positives.This research contributes to the advancement of AI-driven surveillance technologies,offering a scalable framework adaptable to various security scenarios.展开更多
Numerous sectors,such as education,the IT sector,and corporate organizations,transitioned to virtual meetings after the COVID-19 crisis.Organizations now seek to assess participants’fatigue levels in online meetings ...Numerous sectors,such as education,the IT sector,and corporate organizations,transitioned to virtual meetings after the COVID-19 crisis.Organizations now seek to assess participants’fatigue levels in online meetings to remain competitive.Instructors cannot effectively monitor every individual in a virtual environment,which raises significant concerns about participant fatigue.Our proposed system monitors fatigue,identifying attentive and drowsy individuals throughout the online session.We leverage Dlib’s pre-trained facial landmark detector and focus on the eye landmarks only,offering a more detailed analysis for predicting eye opening and closing of the eyes,rather than focusing on the entire face.We introduce an Eye Polygon Area(EPA)formula,which computes eye activity from Dlib eye landmarks by measuring the polygonal area of the eye opening.Unlike the Eye Aspect Ratio(EAR),which relies on a single distance ratio,EPA adapts to different eye shapes(round,narrow,or wide),providing a more reliable measure for fatigue detection.The VMFD system issues a warning if a participant remains in a fatigued condition for 36 consecutive frames.The proposed technology is tested under multiple scenarios,including low-to high-lighting conditions(50-1400 lux)and both with and without glasses.This study builds an OpenCV application in Python,evaluated using the iBUG 300-W dataset,achieving 97.5%accuracy in detecting active participants.We compare VMFD with conventional methods relying on the EAR and show that the EPA technique performs significantly better.展开更多
Background： Rhythmical massage therapy（RMT） is a massage technique used in anthroposophic medicine.Objective： The authors aimed to investigate the physiological action of RMT on the cardiovascular system by analys...Background： Rhythmical massage therapy（RMT） is a massage technique used in anthroposophic medicine.Objective： The authors aimed to investigate the physiological action of RMT on the cardiovascular system by analysing heart rate variability（HRV）.Design, setting, participants and intervention： This study was a randomised, controlled and single-blinded trial, involving 44 healthy women（mean age：（26.20 ± 4.71） years）. The subjects were randomised to one of three arms： RMT with aromatic oil（RA）, RMT without aromatic oil（RM） or standardised sham massage（SM）. In the study the subjects were exposed to a standardised stress situation followed by one of the study techniques and Holter electrocardiograms（ECGs） were recorded for 24 h.Main outcome measures： HRV parameters were calculated from linear（time and frequency domain） and nonlinear dynamics（symbolic dynamics, Poincare plot analysis） of the 24-h Holter ECG records.Results： Short-and long-term effects of massage on autonomic regulation differed significantly among the three groups. Immediately after an RMT session, stimulation of HRV was found in the groups RA and RM. The use of an aromatic oil produced greater short-term measurable changes in HRV compared with rhythmic massage alone, but after 24 h the effect was no longer distinguishable from the RM group.The lowest stimulation of HRV parameters was measured in the SM group.Conclusion： RMT causes specific and marked stimulation of the autonomic nervous system. Use of a medicinal aromatic oil had only a temporary effect on HRV, indicating that the RM causes the most relevant long-term effect. The effect is relatively specific, as the physiological effects seen in the group of subjects who received only SM were considerably less pronounced.展开更多
Building attack scenario is one of the most important aspects in network security.This paper pro-posed a system which collects intrusion alerts,clusters them as sub-attacks using alerts abstraction,ag-gregates the sim...Building attack scenario is one of the most important aspects in network security.This paper pro-posed a system which collects intrusion alerts,clusters them as sub-attacks using alerts abstraction,ag-gregates the similar sub-attacks,and then correlates and generates correlation graphs.The scenarios wererepresented by alert classes instead of alerts themselves so as to reduce the required rules and have the a-bility of detecting new variations of attacks.The proposed system is capable of passing some of the missedattacks.To evaluate system effectiveness,it was tested with different datasets which contain multi-stepattacks.Compressed and easily understandable Correlation graphs which reflect attack scenarios were gen-erated.The proposed system can correlate related alerts,uncover the attack strategies,and detect newvariations of attacks.展开更多
Background: The rapid diagnosis of mycobacterial infections is essential to implement the adequate antimicrobial therapy. This study evaluates the performance of the BacT/ALERT 3D system for isolates and identificatio...Background: The rapid diagnosis of mycobacterial infections is essential to implement the adequate antimicrobial therapy. This study evaluates the performance of the BacT/ALERT 3D system for isolates and identification of mycobacteria from clinical samples. Methods: 1011 clinical specimens from nonsterile and sterile body sites were studied from August 2010 to December 2012 at the National Reference Laboratory of Tuberculosis, IPK, Cuba. The results obtained were compared with respect to time detection of mycobacteria and contamination rates, and performance indicators of BacT/ALERT 3D were calculated. Results: The time detection of growth (TDG) for Mycobacterium tuberculosis (Mtb) and nontuberculous mycobacteria (NTM) by BacT/ALERT 3D was 16,435 and 10,956, respectively;by LJ the TDG was 33.577 for Mtb and 35.952 for NTM. By culture method used the TDG for LJ was 33,577 and 6.435 by BacT/ALERT 3D, this difference being statistically significant. The overall contamination rate (CR) for BacT/ALERT 3D was 4.6% and 7.8% for LJ. Conclusions: BacT/ALERT 3D were a suitable method for recovering mycobacteria from clinical samples. It demonstrated a shorter time to detection of mycobacteria growth;it was very useful to provide faster treatment and a better prognosis in patients AFB smear negative with HIV. The use of LJ culture and BacT/ALERT 3D System was useful to assure a total mycobacterial recovery.展开更多
Incessant fire-outbreak in urban settlements has remained intractable especially in developing countries like Nigeria. This is often characterized by grave socio-economic aftermath effects. Urban fire outbreak in Nige...Incessant fire-outbreak in urban settlements has remained intractable especially in developing countries like Nigeria. This is often characterized by grave socio-economic aftermath effects. Urban fire outbreak in Nigerian cities has been on increase in recent times. The major problem faced by fire fighters in Nigerian urban centres is that there are no mechanisms to detect fire outbreaks early enough to save lives and properties. They often rely on calls made by neighbours or occupants when an outbreak occurs and this accounts for the delay in fighting fire outbreaks. This work uses Artificial Neural Networks (ANN) with backpropagation method to detect the occurrence of urban fires. The method uses smoke density, room temperature and cooking gas concentration as inputs. The work was implemented using Java programming language and results showed that it detected the occurrence of urban fires with reasonable accuracy. The work is recommended for use to minimize the effect of urban fire outbreak.展开更多
Accurate prediction of drug-induced adverse drug reactions(ADRs)is crucial for drug safety evaluation,as it directly impacts public health and safety.While various models have shown promising results in predicting ADR...Accurate prediction of drug-induced adverse drug reactions(ADRs)is crucial for drug safety evaluation,as it directly impacts public health and safety.While various models have shown promising results in predicting ADRs,their accuracy still needs improvement.Additionally,many existing models often lack interpretability when linking molecular structures to specific ADRs and frequently rely on manually selected molecular fingerprints,which can introduce bias.To address these challenges,we propose ToxBERT,an efficient transformer encoder model that leverages attention and masking mechanisms for simplified molecular input line entry system(SMILES)representations.Our results demonstrate that ToxBERT achieved area under the receiver operating characteristic curve(AUROC)scores of 0.839,0.759,and 0.664 for predicting drug-induced QT prolongation(DIQT),rhabdomyolysis,and liver injury,respectively,outperforming previous studies.Furthermore,ToxBERT can identify drug substructures that are closely associated with specific ADRs.These findings indicate that ToxBERT is not only a valuable tool for understanding the mechanisms underlying specific drug-induced ADRs but also for mitigating potential ADRs in the drug discovery pipeline.展开更多
Objective To report the development,validation,and findings of the Multi-dimensional Attention Rating Scale(MARS),a self-report tool crafted to evaluate six-dimension attention levels.Methods The MARS was developed ba...Objective To report the development,validation,and findings of the Multi-dimensional Attention Rating Scale(MARS),a self-report tool crafted to evaluate six-dimension attention levels.Methods The MARS was developed based on Classical Test Theory(CTT).Totally 202 highly educated healthy adult participants were recruited for reliability and validity tests.Reliability was measured using Cronbach's alpha and test-retest reliability.Structural validity was explored using principal component analysis.Criterion validity was analyzed by correlating MARS scores with the Toronto Hospital Alertness Test(THAT),the Attentional Control Scale(ACS),and the Attention Network Test(ANT).Results The MARS comprises 12 items spanning six distinct dimensions of attention:focused attention,sustained attention,shifting attention,selective attention,divided attention,and response inhibition.As assessed by six experts,the content validation index(CVI)was 0.95,the Cronbach's alpha for the MARS was 0.78,and the test-retest reliability was 0.81.Four factors were identified(cumulative variance contribution rate 68.79%).The total score of MARS was correlated positively with THAT(r=0.60,P<0.01)and ACS(r=0.78,P<0.01)and negatively with ANT's reaction time for alerting(r=−0.31,P=0.049).Conclusion The MARS can reliably and validly assess six-dimension attention levels in real-world settings and is expected to be a new tool for assessing multi-dimensional attention impairments in different mental disorders.展开更多
基金supported by the National Natural Science Foundation of China,No.82172530(to QT)Science and Technology Program of Guangdong,No.2018B030334001(to CRR)Guangzhou Science and Technology Project,No.202007030012(to QT).
文摘Light plays an essential role in psychobiological and psychophysiological processes,such as alertness.The alerting effect is influenced by light characteristics and the timing of interventions.This meta-analysis is the first to systematically review the effect of light intervention on alertness and to discuss the optimal protocol for light intervention.In this meta-analysis,registered at PROSPERO(Registration ID:CRD42020181485),we conducted a systematic search of the Web of Science,PubMed,and PsycINFO databases for studies published in English prior to August 2021.The outcomes included both subjective and objective alertness.Subgroup analyses considered a variety of factors,such as wavelength,correlated color temperature(CCT),light illuminance,and timing of interventions(daytime,night-time,or all day).Twenty-seven crossover studies and two parallel-group studies were included in this meta-analysis,with a total of 1210 healthy participants(636(52%)male,mean age 25.62 years).The results revealed that light intervention had a positive effect on both subjective alertness(standardized mean difference(SMD)=-0.28,95%confidence interval(CI):-0.49 to-0.06,P=0.01)and objective alertness in healthy subjects(SMD=-0.34,95%CI:-0.68 to-0.01,P=0.04).The subgroup analysis revealed that cold light was better than warm light in improving subjective alertness(SMD=-0.37,95%CI:-0.65 to-0.10,P=0.007,I2=26%)and objective alertness(SMD=-0.36,95%CI:-0.66 to-0.07,P=0.02,I2=0).Both daytime(SMD=-0.22,95%CI:-0.37 to-0.07,P=0.005,I2=74%)and night-time(SMD=-0.32,95%CI:-0.61 to-0.02,P=0.04,I2=0)light exposure improved subjective alertness.The results of this meta-analysis and systematic review indicate that light exposure is associated with significant improvement in subjective and objective alertness.In addition,light exposure with a higher CCT was more effective in improving alertness than light exposure with a lower CCT.Our results also suggest that both daytime and night-time light exposure can improve subjective alertness.
文摘A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%;thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.
文摘The increasing prevalence of violent incidents in public spaces has created an urgent need for intelligent surveillance systems capable of detecting dangerous objects in real time.While traditional video surveillance relies on human monitoring,this approach suffers from limitations such as fatigue and delayed response times.This study addresses these challenges by developing an automated detection system using advanced deep learning techniques to enhance public safety.Our approach leverages state-of-the-art convolutional neural networks(CNNs),specifically You Only Look Once version 4(YOLOv4)and EfficientDet,for real-time object detection.The system was trained on a comprehensive dataset of over 50,000 images,enhanced through data augmentation techniques to improve robustness across varying lighting conditions and viewing angles.Cloud-based deployment on Amazon Web Services(AWS)ensured scalability and efficient processing.Experimental evaluations demonstrated high performance,with YOLOv4 achieving 92%accuracy and processing images in 0.45 s,while EfficientDet reached 93%accuracy with a slightly longer processing time of 0.55 s per image.Field tests in high-traffic environments such as train stations and shopping malls confirmed the system’s reliability,with a false alarm rate of only 4.5%.The integration of automatic alerts enabled rapid security responses to potential threats.The proposed CNN-based system provides an effective solution for real-time detection of dangerous objects in video surveillance,significantly improving response times and public safety.While YOLOv4 proved more suitable for speed-critical applications,EfficientDet offered marginally better accuracy.Future work will focus on optimizing the system for low-light conditions and further reducing false positives.This research contributes to the advancement of AI-driven surveillance technologies,offering a scalable framework adaptable to various security scenarios.
文摘Numerous sectors,such as education,the IT sector,and corporate organizations,transitioned to virtual meetings after the COVID-19 crisis.Organizations now seek to assess participants’fatigue levels in online meetings to remain competitive.Instructors cannot effectively monitor every individual in a virtual environment,which raises significant concerns about participant fatigue.Our proposed system monitors fatigue,identifying attentive and drowsy individuals throughout the online session.We leverage Dlib’s pre-trained facial landmark detector and focus on the eye landmarks only,offering a more detailed analysis for predicting eye opening and closing of the eyes,rather than focusing on the entire face.We introduce an Eye Polygon Area(EPA)formula,which computes eye activity from Dlib eye landmarks by measuring the polygonal area of the eye opening.Unlike the Eye Aspect Ratio(EAR),which relies on a single distance ratio,EPA adapts to different eye shapes(round,narrow,or wide),providing a more reliable measure for fatigue detection.The VMFD system issues a warning if a participant remains in a fatigued condition for 36 consecutive frames.The proposed technology is tested under multiple scenarios,including low-to high-lighting conditions(50-1400 lux)and both with and without glasses.This study builds an OpenCV application in Python,evaluated using the iBUG 300-W dataset,achieving 97.5%accuracy in detecting active participants.We compare VMFD with conventional methods relying on the EAR and show that the EPA technique performs significantly better.
基金supported financially by the Dr.Hauschka Stiftung Stuttgart(Germany)the SOFTWARE AG–Stiftung(Germany)the Christophorus Stiftung in der GLS Treuhand(Germany)
文摘Background： Rhythmical massage therapy（RMT） is a massage technique used in anthroposophic medicine.Objective： The authors aimed to investigate the physiological action of RMT on the cardiovascular system by analysing heart rate variability（HRV）.Design, setting, participants and intervention： This study was a randomised, controlled and single-blinded trial, involving 44 healthy women（mean age：（26.20 ± 4.71） years）. The subjects were randomised to one of three arms： RMT with aromatic oil（RA）, RMT without aromatic oil（RM） or standardised sham massage（SM）. In the study the subjects were exposed to a standardised stress situation followed by one of the study techniques and Holter electrocardiograms（ECGs） were recorded for 24 h.Main outcome measures： HRV parameters were calculated from linear（time and frequency domain） and nonlinear dynamics（symbolic dynamics, Poincare plot analysis） of the 24-h Holter ECG records.Results： Short-and long-term effects of massage on autonomic regulation differed significantly among the three groups. Immediately after an RMT session, stimulation of HRV was found in the groups RA and RM. The use of an aromatic oil produced greater short-term measurable changes in HRV compared with rhythmic massage alone, but after 24 h the effect was no longer distinguishable from the RM group.The lowest stimulation of HRV parameters was measured in the SM group.Conclusion： RMT causes specific and marked stimulation of the autonomic nervous system. Use of a medicinal aromatic oil had only a temporary effect on HRV, indicating that the RM causes the most relevant long-term effect. The effect is relatively specific, as the physiological effects seen in the group of subjects who received only SM were considerably less pronounced.
基金the National High Technology Research and Development Programme of China(2006AA01Z452)
文摘Building attack scenario is one of the most important aspects in network security.This paper pro-posed a system which collects intrusion alerts,clusters them as sub-attacks using alerts abstraction,ag-gregates the similar sub-attacks,and then correlates and generates correlation graphs.The scenarios wererepresented by alert classes instead of alerts themselves so as to reduce the required rules and have the a-bility of detecting new variations of attacks.The proposed system is capable of passing some of the missedattacks.To evaluate system effectiveness,it was tested with different datasets which contain multi-stepattacks.Compressed and easily understandable Correlation graphs which reflect attack scenarios were gen-erated.The proposed system can correlate related alerts,uncover the attack strategies,and detect newvariations of attacks.
文摘Background: The rapid diagnosis of mycobacterial infections is essential to implement the adequate antimicrobial therapy. This study evaluates the performance of the BacT/ALERT 3D system for isolates and identification of mycobacteria from clinical samples. Methods: 1011 clinical specimens from nonsterile and sterile body sites were studied from August 2010 to December 2012 at the National Reference Laboratory of Tuberculosis, IPK, Cuba. The results obtained were compared with respect to time detection of mycobacteria and contamination rates, and performance indicators of BacT/ALERT 3D were calculated. Results: The time detection of growth (TDG) for Mycobacterium tuberculosis (Mtb) and nontuberculous mycobacteria (NTM) by BacT/ALERT 3D was 16,435 and 10,956, respectively;by LJ the TDG was 33.577 for Mtb and 35.952 for NTM. By culture method used the TDG for LJ was 33,577 and 6.435 by BacT/ALERT 3D, this difference being statistically significant. The overall contamination rate (CR) for BacT/ALERT 3D was 4.6% and 7.8% for LJ. Conclusions: BacT/ALERT 3D were a suitable method for recovering mycobacteria from clinical samples. It demonstrated a shorter time to detection of mycobacteria growth;it was very useful to provide faster treatment and a better prognosis in patients AFB smear negative with HIV. The use of LJ culture and BacT/ALERT 3D System was useful to assure a total mycobacterial recovery.
文摘Incessant fire-outbreak in urban settlements has remained intractable especially in developing countries like Nigeria. This is often characterized by grave socio-economic aftermath effects. Urban fire outbreak in Nigerian cities has been on increase in recent times. The major problem faced by fire fighters in Nigerian urban centres is that there are no mechanisms to detect fire outbreaks early enough to save lives and properties. They often rely on calls made by neighbours or occupants when an outbreak occurs and this accounts for the delay in fighting fire outbreaks. This work uses Artificial Neural Networks (ANN) with backpropagation method to detect the occurrence of urban fires. The method uses smoke density, room temperature and cooking gas concentration as inputs. The work was implemented using Java programming language and results showed that it detected the occurrence of urban fires with reasonable accuracy. The work is recommended for use to minimize the effect of urban fire outbreak.
基金supported by the National Natural Science Foundation of China(Grant Nos.:22173065 and 21575094).
文摘Accurate prediction of drug-induced adverse drug reactions(ADRs)is crucial for drug safety evaluation,as it directly impacts public health and safety.While various models have shown promising results in predicting ADRs,their accuracy still needs improvement.Additionally,many existing models often lack interpretability when linking molecular structures to specific ADRs and frequently rely on manually selected molecular fingerprints,which can introduce bias.To address these challenges,we propose ToxBERT,an efficient transformer encoder model that leverages attention and masking mechanisms for simplified molecular input line entry system(SMILES)representations.Our results demonstrate that ToxBERT achieved area under the receiver operating characteristic curve(AUROC)scores of 0.839,0.759,and 0.664 for predicting drug-induced QT prolongation(DIQT),rhabdomyolysis,and liver injury,respectively,outperforming previous studies.Furthermore,ToxBERT can identify drug substructures that are closely associated with specific ADRs.These findings indicate that ToxBERT is not only a valuable tool for understanding the mechanisms underlying specific drug-induced ADRs but also for mitigating potential ADRs in the drug discovery pipeline.
文摘Objective To report the development,validation,and findings of the Multi-dimensional Attention Rating Scale(MARS),a self-report tool crafted to evaluate six-dimension attention levels.Methods The MARS was developed based on Classical Test Theory(CTT).Totally 202 highly educated healthy adult participants were recruited for reliability and validity tests.Reliability was measured using Cronbach's alpha and test-retest reliability.Structural validity was explored using principal component analysis.Criterion validity was analyzed by correlating MARS scores with the Toronto Hospital Alertness Test(THAT),the Attentional Control Scale(ACS),and the Attention Network Test(ANT).Results The MARS comprises 12 items spanning six distinct dimensions of attention:focused attention,sustained attention,shifting attention,selective attention,divided attention,and response inhibition.As assessed by six experts,the content validation index(CVI)was 0.95,the Cronbach's alpha for the MARS was 0.78,and the test-retest reliability was 0.81.Four factors were identified(cumulative variance contribution rate 68.79%).The total score of MARS was correlated positively with THAT(r=0.60,P<0.01)and ACS(r=0.78,P<0.01)and negatively with ANT's reaction time for alerting(r=−0.31,P=0.049).Conclusion The MARS can reliably and validly assess six-dimension attention levels in real-world settings and is expected to be a new tool for assessing multi-dimensional attention impairments in different mental disorders.
