Crystal structure of 4,4'-(diazenediyl)dipyridinium nitrate perchlorate.

The title compound, C10H10N4 2+·NO3 -·ClO4 -, was obtained unexpectedly by the reaction of Co(ClO4)2·6H2O and cytidine-5'-monophosphate with 4,4'-azo-pyridine in an aqueous solution of nitric acid. The mol-ecular structure comprises two planar 4,4'-diazenediyldipyridinium dications lying on inversion centres and perchlorate and nitrate anions in general positions. In the crystal, N-H⋯O hydrogen bonds between dications and anions lead to the formation of [232] chains.

Objective Assessments of Mental Fatigue During a Continuous Long-Term Stress Condition.

Prolonged periods of cognitive workload will cause mental fatigue, but objective, quantitative, and sensitive measurements that reflect long-term, stress-induced mental fatigue have yet to be elucidated. This study aims to apply a potential marker of Rényi entropy to investigate the mental fatigue changes in a long-term, high-level stress condition and compare three different instruments for assessment of mental fatigue: EEG, the oddball task, and self-scoring. We recruited nine individuals who participated in a 5-day intellectually challenging competition. The participants were assessed for mental fatigue each day of the competition using prefrontal cortex electroencephalogram (EEG). Reaction time in an oddball task and self-rated scoring were used comparatively to evaluate the performance of the EEG. Repeated measures ANOVA was utilized to analyze the differences among score, reaction time, and wavelet Rényi entropy. The results demonstrated that both wavelet Rényi entropy extracted from EEG and self-rated scoring revealed significant increases in mental fatigue during the 5 days of competition (P < 0.001). The reaction time of the oddball task did not show significant changes during the five-day competition (P = 0.066). Moreover, the wavelet Rényi entropy analysis of EEG showed greater sensitivity than the self-rated scoring and reaction time of the oddball task for measuring mental fatigue changes. In conclusion, this study shows that mental fatigue accumulates during long-term, high-level stress situations. The study also indicates that EEG wavelet Rényi entropy is an efficient metric to reflect the change of mental fatigue under a long-term stress condition and that EEG is a better method to assess long-term mental fatigue.

Toward practical driving fatigue detection using three frontal EEG channels: a proof-of-concept study.

Objective. Although various driving fatigue detection strategies have been introduced, the limited practicability is still an obstacle for the real application of these technologies. This study is based on the newly proposed non-hair-bearing (NHB) method to achieve practical driving fatigue detection with fewer channels from NHB areas and more efficient electroencephalogram (EEG) features.Approach. EEG data were recorded from 20 healthy subjects (15 males, age = 22.2 ± 3.2 years) in a 90 min simulated driving task using a remote wireless cap. Behaviorally, subjects demonstrated a salient fatigue effect, as reflected by a monotonic increase in reaction time. Using a sliding-window approach, we determined the vigilant and fatigued states at individual level to reduce the inter-subject differences in behavioral impairment and brain activity. Multiple EEG features, including power-spectrum density (PSD), functional connectivity (FC), and entropy, were estimated in a pairwise manner, which were set as input for fatigue classification.Main results. Intriguingly, this data-driven approach showed that the best classification performance was achieved using three EEG channel pairs located in the NHB area. The mixed features of the frontal NHB area lead to the high within-subject detection rate of driving fatigue (92.7% ± 0.92%) with satisfactory generalizability for fatigue classification across different subjects (77.13% ± 0.85%). Moreover, we found the most prominent contributing features were PSD of different frequency bands within the frontal NHB area and FC within the frontal NHB area and between frontal and parietal areas.Significance. In summary, the current work provided objective evidence to support the effectiveness of the NHB method and further improved the performance, thereby moving a step forward towards practical driving fatigue detection in real-world scenarios.

Mid-Task Physical Exercise Keeps Your Mind Vigilant: Evidences From Behavioral Performance and EEG Functional Connectivity.

Accumulating efforts have been made to discover effective solutions for fatigue recovery with the ultimate aim of reducing adverse consequences of mental fatigue in real life. The previously-reported behavioral benefits of physical exercise on mental fatigue recovery prompted us to investigate the restorative effect and reveal the underlying neural mechanisms. Specifically, we introduced an empirical method to investigate the beneficial effect of physical exercise on the reorganization of EEG functional connectivity (FC) in a two-session experiment where one session including a successive 30-min psychomotor vigilance task (PVT) (No-intervention session) compared to an insertion of a mid-task 15-min cycling exercise (Intervention session). EEG FC was obtained from 21 participants and quantitatively assessed via graph theoretical analysis and a classification framework. The findings demonstrated the effectiveness of exercise intervention on behavioral performance as shown in improved reaction time and response accuracy. Although we found significantly altered network alterations towards the end of experiment in both sessions, no significant differences between the two sessions and no interaction between session and time were found in EEG network topology. Further interrogation of functional connectivity through classification analysis showed decreased FC in distributed brain areas, which may lead to the significant reduction of network efficiency in both sessions. Moreover, we showed distinct patterns of FC alterations between the two sessions, indicating different information processing strategies adopted in the intervention session. In sum, these results provide some of the first quantitative insights into the complex neural mechanism of exercise intervention for fatigue recovery and lead a new direction for further application research in real-world situations.

A Multi-Sensor Wearable System for the Quantitative Assessment of Parkinson's Disease.

The quantitative characterization of movement disorders and their related neurophysiological signals is important for the management of Parkinson's disease (PD). The aim of this study is to develop a novel wearable system enabling the simultaneous measurement of both motion and other neurophysiological signals in PD patients. We designed a wearable system that consists of five motion sensors and three electrophysiology sensors to measure the motion signals of the body, electroencephalogram, electrocardiogram, and electromyography, respectively. The data captured by the sensors are transferred wirelessly in real time, and the outcomes are analyzed and uploaded to the cloud-based server automatically. We completed pilot studies to (1) test its validity by comparing outcomes to the commercialized systems, and (2) evaluate the deep brain stimulation (DBS) treatment effects in seven PD patients. Our results showed: (1) the motion and neurophysiological signals measured by this wearable system were strongly correlated with those measured by the commercialized systems (r > 0.94, p < 0.001); and (2) by completing the clinical supination and pronation frequency test, the frequency of motion as measured by this system increased when DBS was turned on. The results demonstrated that this multi-sensor wearable system can be utilized to quantitatively characterize and monitor motion and neurophysiological PD.

A Real-Time Fire Detection Method from Video with Multifeature Fusion.

The threat to people's lives and property posed by fires has become increasingly serious. To address the problem of a high false alarm rate in traditional fire detection, an innovative detection method based on multifeature fusion of flame is proposed. First, we combined the motion detection and color detection of the flame as the fire preprocessing stage. This method saves a lot of computation time in screening the fire candidate pixels. Second, although the flame is irregular, it has a certain similarity in the sequence of the image. According to this feature, a novel algorithm of flame centroid stabilization based on spatiotemporal relation is proposed, and we calculated the centroid of the flame region of each frame of the image and added the temporal information to obtain the spatiotemporal information of the flame centroid. Then, we extracted features including spatial variability, shape variability, and area variability of the flame to improve the accuracy of recognition. Finally, we used support vector machine for training, completed the analysis of candidate fire images, and achieved automatic fire monitoring. Experimental results showed that the proposed method could improve the accuracy and reduce the false alarm rate compared with a state-of-the-art technique. The method can be applied to real-time camera monitoring systems, such as home security, forest fire alarms, and commercial monitoring.

A Literature Review: Geometric Methods and Their Applications in Human-Related Analysis.

Geometric features, such as the topological and manifold properties, are utilized to extract geometric properties. Geometric methods that exploit the applications of geometrics, e.g., geometric features, are widely used in computer graphics and computer vision problems. This review presents a literature review on geometric concepts, geometric methods, and their applications in human-related analysis, e.g., human shape analysis, human pose analysis, and human action analysis. This review proposes to categorize geometric methods based on the scope of the geometric properties that are extracted: object-oriented geometric methods, feature-oriented geometric methods, and routine-based geometric methods. Considering the broad applications of deep learning methods, this review also studies geometric deep learning, which has recently become a popular topic of research. Validation datasets are collected, and method performances are collected and compared. Finally, research trends and possible research topics are discussed.

Analysis of changes in intestinal flora and intravascular inflammation and coronary heart disease in obese patients.

Changes in intestinal flora in obese patients and intravascular C-reactive protein (CRP), interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) and coronary heart disease (CHD) were analyzed. A total of 75 cases of obese patients were divided into obesity (OB) alone (n=40) and OB with CHD group (n=35). There was no statistically significant difference in age, sex, pre-existing basic diabetes, history of hypertension, and body mass index (P>0.05). Results showed that total bacterial load of CHD was obviously higher than that of OB group. The uric acid decomposed by intestinal flora (IFUA) and blood uric acid levels in CHD were higher than those in OB group, but the fecal uric acid level was lower than that of OB group (P<0.05). Levels of inflammatory factors in CHD, were significantly higher than those in OB group (P<0.05). Correlation analyses showed that the intestinal flora total load and CRP were positively correlated (r=0.793, P<0.001). Intestinal flora and Gensini score were also positively related to total load (r=0.893, P=0.893). Furthermore, CRP and Gensini score were positively related (r=0.796, P<0.796). IFUA and Gensini score were positively related to (r=0.647, P<0.001). Over-reaction in the flammation system in obese patients may lead to intestinal flora disorder, disturbance and also increased levels of IFUA and inflammatory factors.

Detection and Classification of Finer-Grained Human Activities Based on Stepped-Frequency Continuous-Wave Through-Wall Radar.

The through-wall detection and classification of human activities are critical for anti-terrorism, security, and disaster rescue operations. An effective through-wall detection and classification technology is proposed for finer-grained human activities such as piaffe, picking up an object, waving, jumping, standing with random micro-shakes, and breathing while sitting. A stepped-frequency continuous wave (SFCW) bio-radar sensor is first used to conduct through-wall detection of finer-grained human activities; Then, a comprehensive range accumulation time-frequency transform (CRATFR) based on inverse weight coefficients is proposed, which aims to strengthen the micro-Doppler features of finer activity signals. Finally, in combination with the effective eigenvalues extracted from the CRATFR spectrum, an optimal self-adaption support vector machine (OS-SVM) based on prior human position information is introduced to classify different finer-grained activities. At a fixed position (3 m) behind a wall, the classification accuracies of six activities performed by eight individuals were 98.78% and 93.23%, respectively, for the two scenarios defined in this paper. In the position-changing experiment, an average classification accuracy of 86.67% was obtained for five finer-grained activities (excluding breathing) of eight individuals within 6 m behind the wall for the most practical scenario, a significant improvement over the 79% accuracy of the current method.

Searching for Survivors through Random Human-Body Movement Outdoors by Continuous-Wave Radar Array.

It is a major challenge to search for survivors after chemical or nuclear leakage or explosions. At present, biological radar can be used to achieve this goal by detecting the survivor's respiration signal. However, owing to the random posture of an injured person at a rescue site, the radar wave may directly irradiate the person's head or feet, in which it is difficult to detect the respiration signal. This paper describes a multichannel-based antenna array technology, which forms an omnidirectional detection system via 24-GHz Doppler biological radar, to address the random positioning relative to the antenna of an object to be detected. Furthermore, since the survivors often have random body movement such as struggling and twitching, the slight movements of the body caused by breathing are obscured by these movements. Therefore, a method is proposed to identify random human-body movement by utilizing multichannel information to calculate the background variance of the environment in combination with a constant-false-alarm-rate detector. The conducted outdoor experiments indicate that the system can realize the omnidirectional detection of random human-body movement and distinguish body movement from environmental interference such as movement of leaves and grass. The methods proposed in this paper will be a promising way to search for survivors outdoors.

[Development of Non-Contact Monitoring Device for Breathing and Heartbeat].

Physiological monitoring devices in modern clinical area are basically used electrodes or sensors directly touching the surface of human subject body, which will increase physiological and psychological load of the subjects. In order to realize non-contact monitoring of respiration and heartbeat, firstly, the micro bioradar was used to detect human body motion signal. Then, the respiration signal and heartbeat signal was extracted from the body-motion signal by using signal and conditioning circuits, digital filter and signal processing. Finally, the results of heart rate and breathing rate was wirelessly transmitted. The experimental results showed that the device for non-contact monitoring of respiration and heartbeat waveforms has advantages of small volume, low power consumption, which can realize the monitoring of physiological parameters in real time.

A Method for Remotely Sensing Vital Signs of Human Subjects Outdoors.

After chemical or nuclear leakage or explosions, finding survivors is a huge challenge. Although human bodies can be found by smart vehicles and drones equipped with cameras, it is difficult to verify if the person is alive or dead this way. This paper describes a continuous wave radar sensor for remotely sensing the vital signs of human subjects. Firstly, a compact and portable 24 GHz Doppler radar system is designed to conduct non-contact detection of respiration signal. Secondly, in order to improve the quality of the respiration signals, the self-correlation and adaptive line enhancer (ALE) methods are proposed to minimize the interferences of any moving objects around the human subject. Finally, the detection capabilities of the radar system and the signal processing method are verified through experiments which show that human respiration signals can be extracted when the subject is 7 m away outdoors. The method provided in this paper will be a promising way to search for human subjects outdoors.

A Novel Method for Speech Acquisition and Enhancement by 94 GHz Millimeter-Wave Sensor.

In order to improve the speech acquisition ability of a non-contact method, a 94 GHz millimeter wave (MMW) radar sensor was employed to detect speech signals. This novel non-contact speech acquisition method was shown to have high directional sensitivity, and to be immune to strong acoustical disturbance. However, MMW radar speech is often degraded by combined sources of noise, which mainly include harmonic, electrical circuit and channel noise. In this paper, an algorithm combining empirical mode decomposition (EMD) and mutual information entropy (MIE) was proposed for enhancing the perceptibility and intelligibility of radar speech. Firstly, the radar speech signal was adaptively decomposed into oscillatory components called intrinsic mode functions (IMFs) by EMD. Secondly, MIE was used to determine the number of reconstructive components, and then an adaptive threshold was employed to remove the noise from the radar speech. The experimental results show that human speech can be effectively acquired by a 94 GHz MMW radar sensor when the detection distance is 20 m. Moreover, the noise of the radar speech is greatly suppressed and the speech sounds become more pleasant to human listeners after being enhanced by the proposed algorithm, suggesting that this novel speech acquisition and enhancement method will provide a promising alternative for various applications associated with speech detection.

[An ambispective cohort study of the natural history of HIV infection among former unsafe commercial blood and plasma donors].

OBJECTIVE: Discussing the natural history and the influencing factors of HIV infection among former commercial blood and plasma donors engaged in unsafe blood donation practices in China. METHODS: Using ambispective cohort study, with data obtained from ten counties (districts) from six provinces in the National AIDS Control Demonstration Area. HIV/AIDS cases were found and confirmed prior to July 24, 2006 being former commercial blood. Plasma donors were selected and data regarding infection, incidence, death, and influencing factors was collected. Analysis was performed using SPSS 12.0 statistical analysis software. RESULTS: (1) In 7551 cases of HIV infection, there were 6533 typical progressors (86.52%, 4757 cases of AIDS), 108 rapid progressors (1.43%), 910 long-term non-progressors (12.05%) with 4865 cases progressed to AIDS (64.43%). The median incubation period for HIV progression to AIDS was nine years (95% CI:8.96-9.04). (2) According to data, from a total of 1157 AIDS cases without ARV therapy (23.78% of total AIDS cases), there were 283 confirmed AIDS-related deaths, of which the median survival time was 6 months (95% CI:4-7) and the two and three year fatality rates were 95% and 99%, respectively. (3) The duration of HIV incubation period was irrespective to gender and age at the time of HIV infection (P > 0.05). Length of survival for untreated AIDS showed correlation to gender (P < 0.05) but no correlation with culture, marital status or age at the time of diagnosis of AIDS (P > 0.05). CONCLUSION: Compared with the UNAIDS theory regarding slow disease progressors among adults, our study showed a longer AIDS incubation period and shorter outlook for untreated survival, but a similar incubation period for other routes of HIV infection.

[Epidemiology of pediatric HIV infection in six provinces of China].

OBJECTIVE: To investigate the epidemiology of pediatric human immunodeficiency virus (HIV) infection in six provinces of China. METHODS: A cross-sectional study was conducted in six provinces with the highest HIV prevalence. Surveys on demographics and HIV-related questions (transmission modes, time of diagnosis, clinical stage, laboratory test) were distributed to clinicians in these provinces. Descriptive and bivariate analyses were performed on the completed surveys. RESULTS: Survey results of 650 children [405 males and 245 females; average age: (7.9 +/- 3.2) years] were eligible for analysis. The interval between possible transmission and diagnosis was (7.1 +/- 3.2) years. The location distribution was as follows: 570 cases (87.7%) in Henan Province, 23 cases (3.5%) in Guangxi Province, 21 cases (3.2%) in Yunnan Province, 19 cases (2.9%) in Hubei Province, 10 cases (1.5%) in Anhui Province, and 7 cases (1.1%) in Shanxi Province. Transmission routes included mother-to-child transmission (75.1%), blood transfusion/ plasma donation (15.7 %), and injecting drug use (IDU, 0.5%). Former plasma donation (FPD) was the main transmission route in some provinces (Henan, Shanxi, Hubei, and Anhui), while IDU was the main transmission route in other provinces (Guangxi and Yunnan). The average age in the FPD provinces was significantly higher than that in IDU provinces [(8.1 +/- 3.2) vs. (5.4 +/- 2.2) years, P <0.001]. Among 178 patients in all six provinces who required treatment (on the basis of CD4 count or WHO staging), 133 (74.7%) did not receive treatment and 45 (25.3%) received antiretroviral therapy. CONCLUSION: Mother-to-child transmission is the main transmission mode in pediatric patients. Efforts should be made to strengthen the diagnosis and treatment of pediatric HIV/AIDS patients.