Comparative genomic and phylogenetic analyses of mitochondrial genomes of hawthorn (Crataegus spp.) in Northeast China.

Hawthorn (Crataegus spp.) plants are major sources of health food and medicines. Twenty species and seven variations of Crataegus are present in China. A variety of unique Crataegus species was found in their natural distribution in northeast China. In the present study, we assembled and annotated the mitochondrial genomes of five Crataegus species from northeastern China. The sizes of the newly sequenced mitochondrial genomes ranged from 245,907 bp to 410,837 bp. A total of 45-55 genes, including 12-19 transfer RNA genes, three ribosomal RNA genes, and 29-33 protein-coding genes (PCGs) were encoded by these mitochondrial genomes. Seven divergent hotspot regions were identified by comparative analyses: atp6, nad3, ccmFN, matR, nad1, nad5, and rps1. The most conserved genes among the Crataegus species, according to the whole-genome correlation analysis, were nad1, matR, nad5, ccmFN, cox1, nad4, trnQ-TTG, trnK-TTT, trnE-TTC, and trnM-CAT. Horizontal gene transfer between organellar genomes was common in Crataegus plants. Based on the phylogenetic trees of mitochondrial PCGs, C. maximowiczii, C. maximowiczii var. ninganensis, and C. bretschneideri shared similar maternal relationships. This study improves Crataegus mitochondrial genome resources and offers important insights into the taxonomy and species identification of this genus.

SecureNet: Proactive intellectual property protection and model security defense for DNNs based on backdoor learning.

With the widespread application of deep neural networks (DNNs), the risk of privacy breaches against DNN models is constantly on the rise, resulting in an increasing need for intellectual property (IP) protection for such models. Although neural network watermarking techniques are widely used to safeguard the IP of DNNs, they can only achieve passive protection and cannot actively prevent unauthorized users from illicit use or embezzlement of the trained DNN models. Therefore, the development of proactive protection techniques to prevent IP infringement is imperative. To this end, we propose SecureNet, a key-based access license framework for DNN models. The proposed approach involves injecting license keys into the model through backdoor learning, enabling correct model functionality only when the appropriate license key is included in the input. To ensure the reusability of DNN models, we also propose a license key replacement algorithm. In addition, based on SecureNet, we designed defense mechanisms against adversarial attacks and backdoor attacks, respectively. Furthermore, we introduce a fine-grained authorization method that enables flexible granting of model permissions to different users. We have designed four license-key schemes with different privileges, tailored to various scenarios. We evaluated SecureNet on five benchmark datasets including MNIST, Cifar10, Cifar100, FaceScrub, and CelebA, and assessed its performance on six classic DNN models: LeNet-5, VGG16, ResNet18, ResNet101, NFNet-F5, and MobileNetV3. The results demonstrate that our approach outperforms the state-of-the-art model parameter encryption methods by at least 95% in terms of computational efficiency. Additionally, it provides effective defense against adversarial attacks and backdoor attacks without compromising the model's overall performance.

Zhuifeng Tougu capsules in the treatment of knee osteoarthritis (cold dampness obstruction syndrome): a randomized, double blind, multicenter clinical study.

BACKGROUND: In Traditional Chinese Medicine (TCM) theory, cold dampness obstruction is one of the common syndromes of osteoarthritis. Therefore, in clinical practice, the main treatment methods are to dispel wind, remove dampness, and dissipate cold, used to treat knee osteoarthritis (KOA). This report describes a mulitercenter clinical study to assess Zhuifeng Tougu Capsule's efficacy and safety in the treatment of patients who are cold dampness obstruction syndrome in KOA, and to provide evidence-based medical for the rational use of Zhuifeng Tougu Capsules in clinical practice. METHODS: This randomized, parallel group controlled, double-blind, double dummy trial will include a total of 215 KOA patients who meet the study criteria. 215 patients underwent 1:1 randomisation, with 107 cases assigned the experimental group (Zhuifeng Tougu Capsules + Glucosamine Sulfate Capsules Simulator) and 108 assigned the control group (Glucosamine Sulfate Capsules + Zhuifeng Tougu Capsules Simulator). After enrolment, patients received 12 weeks of treatment. The main efficacy measure is the Western Ontario and McMaster University Osteoarthritis Index (WOMAC) pain score. Visual analogue scale (VAS) pain score, Self-condition assessment VAS score, WOMAC KOA score, TCM syndrome score and TCM syndrome efficacy, ESR level, CRP level, suprapatellar bursa effusion depth, use of rescue drugs, and safety indicators are secondary efficacy indicators. RESULTS: Compared with before treatment, WOMAC pain score, VAS pain score, Self-condition assessment VAS score, WOMAC KOA score, and TCM syndrome score decreased significantly in both groups (P < 0.01). Also, the experimental group showed significant differences in the above indicators compared to control (P < 0.01). However, after treatment, no significant differences were showed in the ESR level, CRP level, and suprapatellar bursa effusion depth between the two groups (P > 0.05). No any serious adverse effects showed in the experimental group and control group. CONCLUSIONS: Zhuifeng Tougu Capsules can effectively improve knee joint function and significantly alleviate the pain of KOA. TRIAL REGISTRATION: Clinical trial registration was completed with the China Clinical Trial Registration Center for this research protocol (No. ChiCTR2000028750) on January 2, 2020.

GaitSG: Gait Recognition with SMPLs in Graph Structure.

Gait recognition aims to identify a person based on his unique walking pattern. Compared with silhouettes and skeletons, skinned multi-person linear (SMPL) models can simultaneously provide human pose and shape information and are robust to viewpoint and clothing variances. However, previous approaches have only considered SMPL parameters as a whole and are yet to explore their potential for gait recognition thoroughly. To address this problem, we concentrate on SMPL representations and propose a novel SMPL-based method named GaitSG for gait recognition, which takes SMPL parameters in the graph structure as input. Specifically, we represent the SMPL model as graph nodes and employ graph convolution techniques to effectively model the human model topology and generate discriminative gait features. Further, we utilize prior knowledge of the human body and elaborately design a novel part graph pooling block, PGPB, to encode viewpoint information explicitly. The PGPB also alleviates the physical distance-unaware limitation of the graph structure. Comprehensive experiments on public gait recognition datasets, Gait3D and CASIA-B, demonstrate that GaitSG can achieve better performance and faster convergence than existing model-based approaches. Specifically, compared with the baseline SMPLGait (3D only), our model achieves approximately twice the Rank-1 accuracy and requires three times fewer training iterations on Gait3D.

Surface hydroxide promotes CO2 electrolysis to ethylene in acidic conditions.

Performing CO2 reduction in acidic conditions enables high single-pass CO2 conversion efficiency. However, a faster kinetics of the hydrogen evolution reaction compared to CO2 reduction limits the selectivity toward multicarbon products. Prior studies have shown that adsorbed hydroxide on the Cu surface promotes CO2 reduction in neutral and alkaline conditions. We posited that limited adsorbed hydroxide species in acidic CO2 reduction could contribute to a low selectivity to multicarbon products. Here we report an electrodeposited Cu catalyst that suppresses hydrogen formation and promotes selective CO2 reduction in acidic conditions. Using in situ time-resolved Raman spectroscopy, we show that a high concentration of CO and OH on the catalyst surface promotes C-C coupling, a finding that we correlate with evidence of increased CO residence time. The optimized electrodeposited Cu catalyst achieves a 60% faradaic efficiency for ethylene and 90% for multicarbon products. When deployed in a slim flow cell, the catalyst attains a 20% energy efficiency to ethylene, and 30% to multicarbon products.

Integrative metabolome and transcriptome analyses reveals the black fruit coloring mechanism of Crataegus maximowiczii C. K. Schneid.

Crataegus is an economically important plant due to its medicinal and health-promoting properties. Flavonoids are the main functional components of Crataegus fruit. Fruits of naturally pollinated Crataegus maximowiczii possess an extraordinary black skin and are rich in anthocyanins and other flavonoids. However, the composition of anthocyanins and the overall molecular mechanism of anthocyanin biosynthesis in C. maximowiczii fruits have not been fully elucidated. In this study, the metabolome and transcriptome of C. maximowiczii fruits with black and red skin were analyzed. The results revealed that the differential metabolites and genes were enriched in the anthocyanin biosynthesis pathways in C. maximowiczii fruits. In total, 52 differentially accumulated flavonoid metabolites, 12 differentially accumulated anthocyanins and 22 differentially expressed genes were identified. After weighted gene coexpression network analysis, two modules were found to be highly interrelated with the accumulation of anthocyanin components. The coexpression networks of these two modules were used to identify key candidate transcription factors associated with anthocyanin biosynthesis, such as MYB5, MYB113, bHLH60, ERF105, bZIP44, NAC082, and WRKY11. The results revealed that cyanidin-based anthocyanins were the main pigments responsible for the black coloration of C. maximowiczii fruits. Based on these differentially accumulated anthocyanins and key genes, genetic and metabolic regulatory networks of anthocyanin biosynthesis were also proposed. Overall, this study elucidates the molecular basis of the formation of black color in C. maximowiczii fruits, and provides an intensive study on anthocyanin biosynthesis in C. maximowiczii for comprehensive utilization.

[Mechanism of Tianhe Zhuifeng Ointment in treating rheumatoid arthritis with syndrome of internal obstruction and cold-dampness and compatibility principles based on "disease-syndrome-formula" association network].

This study aims to explore the mechanism of Tianhe Zhuifeng Ointment in treating rheumatoid arthritis(RA) with syndrome of internal obstruction and cold-dampness and the compatibility characteristics based on the "disease-syndrome-formula" association network. A gene set associated with the clinical symptoms of RA was collected from Integrative Pharmacology-based Research Platform of Traditional Chinese Medicine v2.0(TCMIP v2.0). The different expression gene set of RA with syndrome of internal obstruction and cold-dampness was screened out by transcriptomic expression profile detection and bioinformatics data mining of the comparison of RA patients with syndrome of internal obstruction and cold-dampness and healthy volunteers. The chemical composition information of 35 Chinese medicines from Tianhe Zhuifeng Ointment was collected from TCMIP v2.0 and Traditional Chinese Medicine Bank(TCMBank). The candidate targets were predicted based on the similarity principle of compounds structure. The interactive network of "related gene of RA with syndrome of internal obstruction and cold-dampness-candidate target of Tianhe Zhuifeng Ointment" was constructed. The core network targets were screened out by topological characteristics of calculating network, and the functional exploration was carried out based on Kyoto Encyclopedia of Genes and Genomes(KEGG) and Reactome Pathway Database. The compatibility mechanisms of various efficacy groups of Tianhe Zhuifeng Ointment were further explored. The results showed that the candidate targets of Tianhe Zhuifeng Ointment were mainly involved into the modules of "immune-inflammation" regulation, nervous system function, cell function, and substance and energy metabolism, etc. The mechanisms of various efficacy groups emphasized on different aspects. The group of dispelling wind and removing dampness-dredging channels and activating collaterals, the group of extinguishing wind and stopping convulsions, and the group of pungent analgesia regulated "immune-inflammation" system by warming meridians and dissipating cold. The group of activating blood and resolving stasis and the group of strengthening sinews and bones regulated "immune-inflammation" system by activating blood and dredging channels. The group of dispelling wind and removing dampness-dredging channels and activating collaterals, the group of extinguishing wind and stopping convulsions, the group of activating blood and resolving stasis, the group of strengthening sinews and bones, and the group of clearing heat and draining water affected the nervous system by invigorating Qi-blood and benefiting spirit. The group of dispelling wind and removing dampness-dredging channels and activating collaterals and the group of extinguishing wind and stopping convulsions regulated cell function and substance and energy metabolism by dispelling wind and eliminating cold-dampness. The group of activating blood and resolving stasis and the group of strengthening sinews and bones regulated the cell function and substance and energy metabolism by activating blood and strengthening sinews and bones. The results showed that Tianhe Zhuifeng Ointment exerted the comprehensive efficacy of dispelling wind, removing dampness, activating blood, removing stasis, warming meridians, dredging channels, and strengthening sinews and bones through adjusting the imbalance of "immune-inflammation", regulating nervous system, cell function, and interfering with substance and energy metabolism, thus improving the syndrome of internal obstruction and cold-dampness. The findings of this study laid foundations for clarifying the therapeutic characteristics and clinical orientation of Tianhe Zhuifeng Ointment.

Biofluid markers of blood-brain barrier disruption and neurodegeneration in Lewy body spectrum diseases: A systematic review and meta-analysis.

BACKGROUND: Mixed evidence supports blood-brain barrier (BBB) dysfunction in Lewy body spectrum diseases. METHODS: We compare biofluid markers in people with idiopathic Parkinson's disease (PD) and people with PD dementia (PDD) and/or dementia with Lewy bodies (DLB), compared with healthy controls (HC). Seven databases were searched up to May 10, 2021. Outcomes included cerebrospinal fluid to blood albumin ratio (Qalb), and concentrations of 7 blood protein markers that also reflect BBB disruption and/or neurodegenerative co-pathology. We further explore differences between PD patients with and without evidence of dementia. Random-effects models were used to obtain standardized mean differences (SMD) with 95% confidence interval. RESULTS: Of 13,949 unique records, 51 studies were meta-analyzed. Compared to HC, Qalb was higher in PD (NPD/NHC = 224/563; SMD = 0.960 [0.227-1.694], p = 0.010; I2 = 92.2%) and in PDD/DLB (NPDD/DLB/NHC = 265/670; SMD = 1.126 [0.358-1.893], p < 0.001; I2 = 78.2%). Blood neurofilament light chain (NfL) was higher in PD (NPD/NHC = 1848/1130; SMD = 0.747 [0.442-1.052], p < 0.001; I2 = 91.9%) and PDD/DLB (NPDD/DLB/NHC = 183/469; SMD = 1.051 [0.678-1.423], p = 0.004; I2 = 92.7%) than in HC. p-tau 181 (NPD/NHC = 276/164; SMD = 0.698 [0.149-1.247], p = 0.013; I2 = 82.7%) was also higher in PD compared to HC. In exploratory analyses, blood NfL was higher in PD without dementia (NPDND/NHC = 1005/740; SMD = 0.252 [0.042-0.462], p = 0.018; I2 = 71.8%) and higher in PDD (NPDD/NHC = 100/111; SMD = 0.780 [0.347-1.214], p < 0.001; I2 = 46.7%) compared to HC. Qalb (NPDD/NPDND = 63/191; SMD = 0.482 [0.189-0.774], p = 0.010; I2<0.001%) and NfL (NPDD/NPDND = 100/223; SMD = 0.595 [0.346-0.844], p < 0.001; I2 = 3.4%) were higher in PDD than in PD without dementia. CONCLUSIONS: Biofluid markers suggest BBB disruption and neurodegenerative co-pathology involvement in common Lewy body diseases. Greater evidence of BBB breakdown was seen in Lewy body disease with cognitive impairment.

Heterogeneous overtaking and learning styles with varied EEG patterns in a reinforced driving task.

Overtaking maneuvers occur when vehicle drivers pursue higher driving speeds or comfort scenarios through back-to-back lane-changing behaviors, which require active participation of mental resources and certain self-learning practices. However, few studies have investigated how brain activities change during overtaking. Moreover, the learning process, which indicates the heterogeneity of drivers from a process-based perspective, has been neglected. In this work, we studied varied overtaking and learning styles using electroencephalogram (EEG) signals collected from drivers during a simulated driving task with a possible learning process. The average speed, standard deviation of speed, steering wheel angle and lateral movement distance of overtaking behaviors are analyzed in these reinforced tasks to evaluate overtaking performance. Four types of overtaking styles (i.e., low-speed type, low-speed & strong-oscillation type, high-speed & strong-steering type, and high-speed & close-distance type) and three types of learning styles (i.e., stable, adaptive and changeful) are discovered, not only from eventual overtaking behaviors but also from behavioral changes in a certain learning process. EEG features, such as the power spectral density (PSD) in the θ, α, ß and γ bands, are extracted to characterize driver mental states and to correlate with heterogeneous learning styles. The obtained results show that fatigue and fatigue confrontation are more likely with a stable learning style, and the mental workload is reduced with an adaptive learning style, whereas no significant changes in brain activity are apparent with a changeful learning style. Understanding and recognizing heterogeneous overtaking and learning styles with varying EEG patterns will be extremely useful in the future for deep integration of advanced driving assistance systems (ADASs) and brain computer interface (BCI) systems.

Toward COVID-19 Contact Tracing though Wi-Fi Probes.

COVID-19 is currently the biggest threat that challenges all of humankind's health and property. One promising and effective way to control the rapid spreading of this infection is searching for primary close contacts of the confirmed cases. In response, we propose COVID-19 Tracer, a low-cost passive searching system to find COVID-19 patients' close contacts. The main idea is utilizing ubiquitous WiFi probe requests to describe the location similarity, which is then achieved by two designed range-free judgment indicators: location similarity coefficient and close contact distance. We have carried out extensive experiments in a school office building, and the experimental results show an average accuracy of more than 98%, demonstrating our system's effectiveness in judging close contacts. Last but not least, we have developed a prototype system for a school building to find potential close contacts.

[Biological connotation of four traditional Chinese medicine syndromes of rheumatoid arthritis based on "disease-syndrome-symptom" association network].

The present study explored the biological connotation of traditional Chinese medicine(TCM) syndromes of rheumatoid arthritis(RA) from the "disease-syndrome-symptom" association network. RA patients with four TCM syndromes(dampness-heat obstruction, phlegm-stasis obstruction, Qi-blood deficiency, and liver and kidney deficiency), three for each type, were assigned as the RA TCM syndrome group, and three healthy volunteers as the normal control group. The differential gene sets of four syndromes were screened out through transcriptome expression profiling and bioinformatics mining. The relevant gene sets of syndrome-related clinical symptoms were collected from TCMIP v2.0(http://www.tcmip.cn/). The "disease-syndrome-symptom" association networks of four RA syndromes were established by using the intersection genes of syndrome-related differential genes and symptom-related genes, and the key network target genes of each syndrome were screened out and the corresponding biological functions were mined through topological feature calculation and enrichment analysis. The genes associated with clinical symptoms such as vasculitis, joint pain, and fever in the damp-heat obstruction syndrome ranked the top, and the key network target genes of this syndrome were most significantly enriched in the pathways related to material and energy metabolism and thermal reaction biological processes. The clinical symptom-related genes of the phlegm-stasis obstruction syndrome were most significantly enriched in the pathways related to "immunity-inflammation", nervous system regulation, and sensory response. The clinical symptoms such as hypoglycemia, hypotension, weight loss, palpitation, and arrhythmia in Qi-blood deficiency syndrome were predominant, and its key network target genes were most significantly enriched in the pathways related to the nervous system and "immunity-inflammation" response. The abnormal symptoms in the liver and kidney in the liver and kidney deficiency syndrome were commonly seen, and its key network target genes were most significantly enriched in the "immunity-inflammation" regulatory pathways, and liver and kidney development and metabolic response. In conclusion, the differences and connections of the biological basis between different TCM syndromes of RA are in line with the theoretical interpretation of TCM on the etiology and pathogenesis of RA. This study summarized the objective essence of syndromes to a certain extent from the "disease-syndrome-symptom" association network and is expected to provide a theoretical basis for the discovery of serum biomarkers of RA syndromes.

Automatic Detection of Epileptiform EEG Discharges based on the Semi-Classical Signal Analysis (SCSA) method.

In this paper we utilize a signal processing tool, which can help physicians and clinical researchers to automate the process of EEG epileptiform spike detection. The semi-classical signal analysis method (SCSA) is a data-driven signal decomposition method developed for pulse-shaped signal characterization. We present an algorithm framework to process and extract features from the patient's EEG recording by deriving the mathematical motivation behind SCSA and quantifying existing spike diagnosis criterion with it. The proposed method can help reduce the amount of data to manually analyse. We have tested our proposed algorithm framework with real data, which guarantees the method's statistical reliability and robustness.

Effect of integrated Traditional Chinese and Western Medicine on gout.

OBJECTIVE: To evaluate the curative effect of integrated Traditional Chinese and Western Medicine on gout, and to investigate the therapy timing and exact treatment options of integrated medicine. METHODS: Totally 860 patients were enrolled, including 460 patients with intermittent gout, 200 patients with active Traditional Chinese Medicine (TCM) syndrome (TCM syndrome score ≥ 6) and 200 patients with stable TCM syndrome (score < 6). They were randomly divided into intervention and control groups. The control group was treated according to Western Medicine guidelines. The intervention group was treated with integrated Traditional Chinese and Western Medicine. The efficacy of TCM syndrome, joint pain score, joint swelling score, ESR, C-reactive protein, serum uric acid, liver and kidney function, and the duration of remission of TCM syndrome were compared between the two groups before and after treatments. RESULTS: For the patients with stable TCM syndrome, there was no significant difference in the effective rate and inefficiency between the intervention group and the control group. For the active type, the effective rate of the intervention group is better than the control group significantly. For the stable type, there was no significant difference between the intervention group and the control group in improving the scores of joint pain and swelling, reducing the level of ESR, C-reactive protein, serum uric acid and improving liver and kidney function. For the active type, the differences between the two groups were significant. The stable stage of gout in the intervention group was longer than the control group. CONCLUSION: For the gout patients with stable TCM syndrome in the acute stage of gout, we can use TCM treatment or Western Medicine alternatively; for the patients with active TCM syndrome, the scheme of combination of Traditional Chinese and Western Medicine can be applied, with the better curative effect than any medicine alone.

Efficacy of Acupuncture-Related Therapy in the Treatment of Knee Osteoarthritis: A Network Meta-Analysis of Randomized Controlled Trials.

OBJECTIVE: Knee osteoarthritis (KOA) is prevalent in middle-aged and elderly people. This condition negatively affects the quality of life of patients. Although non-steroidal anti-inflammatory drugs (NSAIDs) are often used to relieve symptoms associated with KOA, it is associated with many side effects. Acupuncture and moxibustion therapies have been applied in the treatment of KOA. However, the efficacy of various acupuncture and moxibustion treatments has not been compared. METHODS: Randomized controlled trials (RCTs) on the application of acupuncture and moxibustion in the treatment of KOA were searched in English databases and Chinese databases. Data were retrieved from establishment of the database to September 2020. Data analysis was performed using Stata14.0 and GeMTC 0.14.3 softwares. RESULTS: A total of 40 RCTs involving 3215 patients with KOA were retrieved. Network meta-analysis revealed that the fire needle was superior to western medicine, electro-acupuncture, conventional acupuncture, warm needle and sham acupuncture; warm needle was better than conventional acupuncture and western medicine whereas electro-acupuncture was better than conventional acupuncture in improving pain scores in the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Moreover, we found that fire needle and warm needle more effectively improved WOMAC stiffness scores than western medicine and sham moxibustion, whereas electro-acupuncture was superior to western medicine and sham moxibustion in improving WOMAC stiffness scores. Further analysis revealed that fire needle, warm needle and electro-acupuncture were more effective in improving WOMAC joint function scores than conventional acupuncture and western medicine. The fire needle was superior to conventional acupuncture and sham acupuncture, whereas electro-acupuncture was better than western medicine, conventional acupuncture and sham acupuncture in improving visual analogue scale scores. CONCLUSION: This study shows that fire needle is superior to warm needle and electro-acupuncture, whereas warm needle and electro-acupuncture are better than conventional acupuncture, western medicine, sham moxibustion and sham acupuncture.

Localization with Transfer Learning Based on Fine-Grained Subcarrier Information for Dynamic Indoor Environments.

Indoor localization provides robust solutions in many applications, and Wi-Fi-based methods are considered some of the most promising means for optimizing indoor fingerprinting localization accuracy. However, Wi-Fi signals are vulnerable to environmental variations, resulting in data across different times being subjected to different distributions. To solve this problem, this paper proposes an across-time indoor localization solution based on channel state information (CSI) fingerprinting via multi-domain representations and transfer component analysis (TCA). We represent the format of CSI readings in multiple domains, extending the characterization of fine-grained information. TCA, a domain adaptation method in transfer learning, is applied to shorten the distribution distances among several CSI readings, which overcomes various CSI distribution problems at different time periods. Finally, we present a modified Bayesian model averaging approach to integrate the multi-domain outcomes and give the estimated positions. We conducted test-bed experiments in three scenarios on both personal computer (PC) and smartphone platforms in which the source and target fingerprinting data were collected across different days. The experimental results showed that our method outperforms state-of-the-art methods in localization accuracy.

Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis.

This study aims at analyzing the separability of acute cerebral infarction lesions which were invisible in CT. 38 patients, who were diagnosed with acute cerebral infarction and performed both CT and MRI, and 18 patients, who had no positive finding in either CT or MRI, were enrolled. Comparative studies were performed on lesion and symmetrical regions, normal brain and symmetrical regions, lesion, and normal brain regions. MRI was reconstructed and affine transformed to obtain accurate lesion position of CT. Radiomic features and information gain were introduced to capture efficient features. Finally, 10 classifiers were established with selected features to evaluate the effectiveness of analysis. 1301 radiomic features were extracted from candidate regions after registration. For lesion and their symmetrical regions, there were 280 features with information gain greater than 0.1 and 2 features with information gain greater than 0.3. The average classification accuracy was 0.6467, and the best classification accuracy was 0.7748. For normal brain and their symmetrical regions, there were 176 features with information gain greater than 0.1, 1 feature with information gain greater than 0.2. The average classification accuracy was 0.5414, and the best classification accuracy was 0.6782. For normal brain and lesions, there were 501 features with information gain greater than 0.1 and 1 feature with information gain greater than 0.5. The average classification accuracy was 0.7480, and the best classification accuracy was 0.8694. In conclusion, the study captured significant features correlated with acute cerebral infarction and confirmed the separability of acute lesions in CT, which established foundation for further artificial intelligence-assisted CT diagnosis.

Schrödinger Spectrum Based PPG Features for the Estimation of the Arterial Blood Pressure.

In this paper, photoplethysmogram (PPG) features are combined with supervised machine learning algorithms to estimate arterial blood pressure (ABP). Three algorithms for the estimation of cuffless ABP using PPG signals are compared. Since PPG signals are measured non-invasively, this method guarantees an individuals comfort while not omitting important ABP information. The proposed framework predicts the ABP values by processing PPG signals with semi-classical signal analysis (SCSA) method, extracting several categories of features, which reflect the PPG signal morphology variations. Then, regression algorithms are selected for the ABP estimation. The proposed method is evaluated based on a virtual dataset with more than four thousand subjects and MIMIC II database with over eight thousand subjects for model training and testing. Mean average error (MAE) and standard deviation (STD) are evaluated for different machine learning algorithms during the prediction and estimation process. Multiple linear regression (MLR) meets the AAMI standard in terms of estimation accuracy, which proves that the ABP can be accurately estimated in a nonintrusive fashion. Given the easy implementation of the ABP estimation method, we regard that the proposed features and machine learning algorithms for the cuffless estimation of the ABP can potentially provide the means for mobile healthcare equipment to monitor the ABP continuously.

A Novel Mine Cage Safety Monitoring Algorithm Utilizing Visible Light.

The mine cage has an important role in the production of coal mines. It has many safety problems in the transportation of people, such as overloading of personnel and illegal outreach of human limbs. However, the harsh mine environment makes it very difficult to monitor personnel overload and limb extension. To solve these two problems, we propose a novel safety monitoring algorithm of the mine cage based on visible light. With visible light technology, our algorithm cleverly utilizes the existing underground lighting equipment (i.e., miner's headlamp and the miner's lamp deployed on the mine cage) as the transmitter to broadcast the light beacons representing unique identity information through visible light frequency modulation. Next, cheap photodiodes deployed in the mine cage are used as the receiver to perceive the modulated optical signals. Then we use the frequency matching method for personnel counting and the frequency power comparison method for illegal limb extension monitoring. Moreover, a novel method of monitoring the delineated safe area of the mine cage is also proposed to ensure that all the miners are in the delineated safe area. Finally, we conducted extensive experiments with a simulated mine cage model. Results show that our algorithm has superior performance. With the photodiode SD5421-002, the accuracy of personnel overload judgment and safe area monitoring of our algorithm can reach 99%, and the accuracy of limb extension monitoring is more than 96%.

A Novel Gesture Recognition System Based on CSI Extracted from a Smartphone with Nexmon Firmware.

In this paper, we propose a novel gesture recognition system based on a smartphone. Due to the limitation of Channel State Information (CSI) extraction equipment, existing WiFi-based gesture recognition is limited to the microcomputer terminal equipped with Intel 5300 or Atheros 9580 network cards. Therefore, accurate gesture recognition can only be performed in an area relatively fixed to the transceiver link. The new gesture recognition system proposed by us breaks this limitation. First, we use nexmon firmware to obtain 256 CSI subcarriers from the bottom layer of the smartphone in IEEE 802.11ac mode on 80 MHz bandwidth to realize the gesture recognition system's mobility. Second, we adopt the cross-correlation method to integrate the extracted CSI features in the time and frequency domain to reduce the influence of changes in the smartphone location. Third, we use a new improved DTW algorithm to classify and recognize gestures. We implemented vast experiments to verify the system's recognition accuracy at different distances in different directions and environments. The results show that the system can effectively improve the recognition accuracy.

Multidimensional Integrated Chalcogenides Nanoarchitecture Achieves Highly Stable and Ultrafast Potassium-Ion Storage.

Potassium-ion batteries (KIBs) have come into the spotlight in large-scale energy storage systems because of cost-effective and abundant potassium resources. However, the poor rate performance and problematic cycle life of existing electrode materials are the main bottlenecks to future potential applications. Here, the first example of preparing 3D hierarchical nanoboxes multidimensionally assembled from interlayer-expanded nano-2D MoS2 @dot-like Co9 S8 embedded into a nitrogen and sulfur codoped porous carbon matrix (Co9 S8 /NSC@MoS2 @NSC) for greatly boosting the electrochemical properties of KIBs in terms of reversible capacity, rate capability, and cycling lifespan, is reported. Benefiting from the synergistic effects, Co9 S8 /NSC@MoS2 @NSC manifest a very high reversible capacity of 403 mAh g-1 at 100 mA g-1 after 100 cycles, an unprecedented rate capability of 141 mAh g-1 at 3000 mA g-1 over 800 cycles, and a negligible capacity decay of 0.02% cycle-1 , boosting promising applications in high-performance KIBs. Density functional theory calculations demonstrate that Co9 S8 /NSC@MoS2 @NSC nanoboxes have large adsorption energy and low diffusion barriers during K-ion storage reactions, implying fast K-ion diffusion capability. This work may enlighten the design and construction of advanced electrode materials combined with strong chemical bonding and integrated functional advantages for future large-scale stationary energy storage.