Analysis of Weight-Directed Functional Brain Networks in the Deception State Based on EEG Signal

Although analyzing the brain's functional and structural network has revealed that numerous brain networks are necessary to collaborate during deception, the directionality of these functional networks is still unknown. This study investigated the effective connectivity of the brain networks during deception and uncovers the information-interaction patterns of lying neural oscillations. The electroencephalography (EEG) data of 40 lying persons and 40 honest persons were used to create the weight- directed functional brain networks (WDFBN). Specifically, the connecting edge weight was defined based on the normalized phase transfer entropy (dPTE) between each electrode pair, where the network nodes involved 30 electrode channels. Additionally, the signal connectivity matrices were constructed in four frequency bands: delta, theta, alpha, and beta and were subjected to a difference analysis of entropy values between the groups. Statistical analysis of the classification results revealed that all frequency bands correctly detect deception and innocence with an accuracy of 92.83%, 94.17%, 85.93%, and 92.25%, respectively. Therefore, dPTE can be considered a valuable feature for identifying lying. According to WDFBN analysis, deception has stronger information flow in the frontoparietal, frontotemporal and temporoparietal networks compare to honest people. Furthermore, the prefrontal cortex was also found to be activated in all frequency ranges. This study examined the critical pathways of brain information interaction during deception, providing new insights into the underlying neural mechanisms. Our analysis offers significant evidence for the development of brain networks that could potentially be used for lie detection.

A Joint Extraction System Based on Conditional Layer Normalization for Health Monitoring

Natural language processing (NLP) technology has played a pivotal role in health monitoring as an important artificial intelligence method. As a key technology in NLP, relation triplet extraction is closely related to the performance of health monitoring. In this paper, a novel model is proposed for joint extraction of entities and relations, combining conditional layer normalization with the talking-head attention mechanism to strengthen the interaction between entity recognition and relation extraction. In addition, the proposed model utilizes position information to enhance the extraction accuracy of overlapping triplets. Experiments on the Baidu2019 and CHIP2020 datasets demonstrate that the proposed model can effectively extract overlapping triplets, which leads to significant performance improvements compared with baselines.

The secure judgment of graphic similarity against malicious adversaries and its applications

With the advent of the era of big data, privacy computing analyzes and calculates data on the premise of protecting data privacy, to achieve data 'available and invisible'. As an important branch of secure multi-party computation, the geometric problem can solve practical problems in the military, national defense, finance, life, and other fields, and has important research significance. In this paper, we study the similarity problem of geometric graphics. First, this paper proposes the adjacency matrix vector coding method of isomorphic graphics, and use the Paillier variant encryption cryptography to solve the problem of isomorphic graphics confidentiality under the semi-honest model. Using cryptography tools such as elliptic curve cryptosystem, zero-knowledge proof, and cut-choose method, this paper designs a graphic similarity security decision protocol that can resist malicious adversary attacks. The analysis shows that the protocol has high computational efficiency and has wide application value in terrain matching, mechanical parts, biomolecules, face recognition, and other fields.

An Intelligent Epileptic Prediction System Based on Synchrosqueezed Wavelet Transform and Multi-Level Feature CNN for Smart Healthcare IoT

Epilepsy is a common neurological disease worldwide, characterized by recurrent seizures. There is currently no cure for epilepsy. However, seizures can be controlled by drugs and surgeries in about 70% of epileptic patients. A timely and accurate prediction of seizures can prevent injuries during seizures and improve the patients' quality of life. In this paper, we proposed an intelligent epileptic prediction system based on Synchrosqueezed Wavelet Transform (SWT) and Multi-Level Feature Convolutional Neural Network (MLF-CNN) for smart healthcare IoT network. In this system, we used SWT to map EEG signals to the frequency domain, which was able to measure the energy changes in EEG signals caused by seizures within a well-defined Time-Frequency (TF) plane. MLF-CNN was then applied to extract multi-level features from the processed EEG signals and classify the different seizure segments. The performance of our proposed system was evaluated with the publicly available CHB-MIT dataset and our private ZJU4H dataset. The system achieved an accuracy of 96.99% and 94.25%, a sensitivity of 96.48% and 97.76%, a specificity of 97.46% and 94.07% and a false prediction rate (FPR/h) of 0.031 and 0.049 FPR/h on the CHB-MIT dataset and the ZJU4H dataset, respectively.

PerAE: An Effective Personalized AutoEncoder for ECG-based Biometric in Augmented Reality System

With the development of the Augmented and Virtual Reality (AR/VR) technologies, massive biometric data are collected by different organizations. These data have great significance but also worsen the privacy risks. Electro-CardioGram (ECG)-based Identity Recognition (EIR) is a popular Biometric technology. An ECG record is an internal Biology feature of a person and has time continuity. Thus, compared with traditional Biometric methods like face recognition, EIR may be less vulnerable to attack. We propose an Autoencoder-based EIR system, called Personalized AutoEncoder (PerAE). PerAE maintains a small autoencoder model (called Attention-MemAE) for each registered user of a system. The Attention-MemAE enhances the autoencoder by using a memory module and two attention mechanisms. A users Attention-MemAE classifies the hearbeats of other users as anomalies. An Attention-MemAE can be updated when the distribution of the users ECG data is changed. By using personalized autoencoder, PerAE can improve the time efficiency and reduce the memory overhead. It improves the adaptability, scalability, and maintainability of EIR systems. Experiment results show that to train an Attention-MemAE with 90% identification accuracy for a user, we can just take five minutes to collect the users ECG data (around 500 heartbeat samples).

Design and analysis of statistical probability distribution and non-parametric trend analysis for reference evapotranspiration

Accurate estimates of reference evapotranspiration are critical for water-resource management strategies such as irrigation scheduling and operation. Therefore, knowledge of events such as spatial and temporal reference evapotranspiration (ET_o) and their related principle of statistical probability theory plays a vital role in amplifying sustainable irrigation planning. Spatiotemporal statistical probability distribution and its associated trends have not yet has explored in Pakistan. In this study, we have two objectives: (1) to determine the most appropriate statistical probability distribution that better describes ET_o on mean monthly and seasons wise estimates for the design of irrigation system in Khyber Pakhtunkhwa, and (2) to check the trends in ET_o on a monthly, seasonal, and annual basis. To check the ET_o trends, we used the modified version of the Mann-Kendall and Sen Slope. We used Bayesian Kriging for spatial interpolation and propose a practical approach to the design and study of statistical probability distributions for the irrigation system and water supplies management. Also, the scheme preeminent explains ET_o, on a monthly and seasonal basis. The statistical distribution that showed the best fit ET_o result occupying 58.33% and 25% performance for the design of irrigation scheme in the entire study region on the monthly level was Johnson SB and Generalized Pareto, respectively. However, according to the Anderson-Darling (AD) and Kolmogorov–Smirnov (KS) goodness of fit measure, seasonal ET_o estimates were preferably suited to the Burr, Johnson SB & Generalized Extreme Value. More research work must be conduct to assess the significance of this study to other fields. In conclusion, these findings might be helpful for water resource management and policymaker in future operations.

Tobacco smoking confers risk for severe COVID-19 unexplainable by pulmonary imaging

BACKGROUND

COVID-19 is a new pneumonia. It has been hypothesized that tobacco smoking history may increase severity of this disease in the patients once infected by the underlying coronavirus SARS-CoV-2 because smoking and COVID-19 both cause lung damage. However, this hypothesis has not been tested.

OBJECTIVE

Current study was designed to focus on smoking history in patients with COVID-19 and test this hypothesis that tobacco smoking history increases risk for severe COVID-19 by damaging the lungs.

METHODS AND RESULTS

This was a single-site, retrospective case series study of clinical associations, between epidemiological findings and clinical manifestations, radiographical or laboratory results. In our well-characterized cohort of 954 patients including 56 with tobacco smoking history, smoking history increased the risk for severe COVID-19 with an odds ratio (OR) of 5.5 (95% CI: 3.1-9.9; P = 7.3 × 10^-8 ). Meta-analysis of ten cohorts for 2891 patients together obtained an OR of 2.5 (95% CI: 1.9-3.3; P < 0.00001). Semi-quantitative analysis of lung images for each of five lobes revealed a significant difference in neither lung damage at first examination nor dynamics of the lung damage at different time-points of examinations between the smoking and nonsmoking groups. No significant differences were found either in laboratory results including D-dimer and C-reactive protein levels except different covariances for density of the immune cells lymphocyte (P = 3.8 × 10^-64 ) and neutrophil (P = 3.9 × 10^-46 ).

CONCLUSION

Tobacco smoking history increases the risk for great severity of COVID-19 but this risk is achieved unlikely by affecting the lungs.

Limited Sampling Strategy for Estimating Mycophenolic Acid Exposure on Day 7 Post-Transplant for Two Mycophenolate Mofetil Formulations Derived From 20 Chinese Renal Transplant Recipients

PURPOSE

To assess the pharmacokinetic properties of mycophenolate mofetil (MMF) dispersible tablets and capsules by the enzyme multiplied immunoassay technique (EMIT) in Chinese kidney transplant recipients in the early post-transplantation phase and to develop the equations to predict mycophenolic acid (MPA) area under the 12-hour concentration-time curve (AUC_0-12h) using a limited sampling strategy (LSS).

METHODS

Forty patients who underwent renal transplantation from brain-dead donors were randomly divided into dispersible tablets (Sai KE Ping; Hangzhou Zhongmei Huadong Pharma) and capsules (Cellcept; Roche Pharma, Why, NSW, Australia) groups, and treated with MMF combined with combination tacrolimus and prednisone as a basic immunosuppressive regimen. Blood samples were collected before treatment (0) and at 0.5,1, 1.5, 2, 4, 6, 8, 10, and 12 hours post-treatment and 7 days after renal transplantation. Plasma MPA concentrations were measured using EMIT. LSS equations were identified using multiple stepwise linear regression analysis.

RESULTS

The peak concentration (C_max) in the MMF dispersible tablets (MMFdt) group (7.0 ± 2.8) mg/L was reduced compared with that in the MMF capsules (MMFc) group (10.8 ± 6.2 mg/L; P = .012); time to peak concentration in the MMFdt group was 3.2 ± 2.3 hours, which was nonsignificantly elevated compared with that of the MMFc group (2.2 ± 1.7 hours). Three-point estimation formulas were generated by multiple linear regression for both groups: MPA-AUC_MMFdt = 3.542 + 3.332C_0.5h + 1.117C_1.5h + 3.946C_4h (adjusted r² = 0.90, P < .001); MPA-AUC_MMFc = 8.149 + 1.442C_2h + 1.056C_4h + 7.133C_6h (adjusted r² = 0.88, P < .001). Both predicted and measured AUCs showed good consistency.

CONCLUSIONS

After treatment with MMF dispersible tables or MMF capsules, the C_max of MPA for the MMFdt group was significantly lower than that of the MMFc group; there was no significant difference in other pharmacokinetic parameters. Three-time point equations can be used as a predictable measure of the AUC_0-12h of MPA.

Relationship between methylation status of RASSF2A gene promoter and endometriosis-associated ovarian cancer

Relationship between the methylation status of the RASSF2A gene promoter and endometriosis-associated ovarian cancer (EAOC) was explored. Between January 2013 and January 2016, tissue samples were collected from 30 patients diagnosed with ovarian endometriosis cyst (EC group), 30 patients diagnosed with ovarian endometrial adenocarcinoma (OEA group) and 30 patients diagnosed with ovarian clear cell carcinoma (OCC group). Additionally, 30 cases of normal endometrium tissues were collected for the control group. The methylation status of the RASSF2A promoter was evaluated by combined bisulfite restriction enzyme analysis (COBRA). RT-PCR was used to detect the expression level of RASSF2A mRNA in tissues. Relationship between methylation status and RASSF2A mRNA expression level and the patient age, tumor clinical stage, tumor grading and pathological type were analyzed. Results showed that in the OEA and OCC groups, the methylation degrees of the RASSF2A promoter were obviously higher than that of the other two groups. The expression level of RASSF2A mRNA in the OEA and OCC groups was lower than that of the other two groups. The methylation degree of the RASSF2A promoter was related to clinical staging and grading. No relationship between the methylation degree of the RASSF2A promoter and patient’s age and the pathological type of the tissue was detected. We concluded that the methylation status of the RASSF2A gene promoter could be considered an excellent indicator for early detection of ovarian cancers.

Spatial-Temporal Data Collection with Compressive Sensing in Mobile Sensor Networks

Compressive sensing (CS) provides an energy-efficient paradigm for data gathering in wireless sensor networks (WSNs). However, the existing work on spatial-temporal data gathering using compressive sensing only considers either multi-hop relaying based or multiple random walks based approaches. In this paper, we exploit the mobility pattern for spatial-temporal data collection and propose a novel mobile data gathering scheme by employing the Metropolis-Hastings algorithm with delayed acceptance, an improved random walk algorithm for a mobile collector to collect data from a sensing field. The proposed scheme exploits Kronecker compressive sensing (KCS) for spatial-temporal correlation of sensory data by allowing the mobile collector to gather temporal compressive measurements from a small subset of randomly selected nodes along a random routing path. More importantly, from the theoretical perspective we prove that the equivalent sensing matrix constructed from the proposed scheme for spatial-temporal compressible signal can satisfy the property of KCS models. The simulation results demonstrate that the proposed scheme can not only significantly reduce communication cost but also improve recovery accuracy for mobile data gathering compared to the other existing schemes. In particular, we also show that the proposed scheme is robust in unreliable wireless environment under various packet losses. All this indicates that the proposed scheme can be an efficient alternative for data gathering application in WSNs .

Non-typhoidal Salmonella encephalopathy involving lipopolysaccharide in cattle

This study assessed the involvement of lipopolysaccharide (LPS) in the non-typhoidal Salmonella encephalopathy (NTSE) caused by a unique isolate of Salmonella enterica serovar Saint-paul (SstpNPG). NTSE was prevented by genetic (deletion of murE) or pharmacologic (polymyxin) disruption of LPS on SstpNPG although the disruption of LPS did not deter brain penetration of the strain. This is the first study to demonstrate that LPS is involved in the manifestations of NTSE.

Potential autophagy enhancers attenuate rotenone-induced toxicity in SH-SY5Y

Recent studies have shown that autophagy upregulation may be a tractable therapeutic intervention for clearing the disease-causing proteins, including α-synuclein, ubiquitin, and other misfolded or aggregated proteins in Parkinson's disease (PD). In this study, we explored a novel pharmacotherapeutic approach to treating PD by utilizing potential autophagy enhancers valproic acid (VPA) and carbamazepine (CBZ). Pretreatment with VPA (3 mM) and CBZ (50 μM) along with positive control rapamycin (Rap, 0.2 μM) or lithium (LiCl, 10 mM) significantly enhanced cell viability, decreased rotenone-induced nuclear fragmentation and apoptosis, ameliorated the decrease in mitochondrial membrane potential, reduced reactive oxygen species generation in the human neuroblastoma SH-SY5Y cells. Specifically, the numbers of lysosomes and autophagic vacuolar organelles were increased and the microtubule-associated protein 1 light chain 3-II (LC3-II) expression was up-regulated by VPA, CBZ, Rap, and LiCl (53%, 31%, 72%, and 63%), suggesting that these agents activated autophagic pathways. Moreover, pretreatment with the autophagy inhibitor chloroquine (Chl, 10 μM) remarkably strengthened rotenone toxicity in these cells. Our results suggest that VPA and CBZ, the most commonly used anti-epilepsy and mood-stabilizing medications with low-risk and easy administration might be potential therapeutics for PD.

Glyceraldehyde-3-phosphate dehydrogenase: activity inhibition and protein overexpression in rotenone models for Parkinson's disease

Rotenone, a widely used pesticide and an environmental risk factor for Parkinson's disease (PD), induces nigrostriatal injury, Lewy body-like inclusions, and Parkinsonian symptoms in rat models for PD. Our previous data indicated that glyceraldehyde-3-phosphate dehydrogenase (GAPDH) overexpression and glycolytic inhibition were co-current in rotenone-induced PC12 (rat adrenal pheochromocytoma cells) cell death. However, whether GAPDH overexpression plays any role in dopaminergic neurodegeneration in vivo remains unknown. In this study, we have found that GAPDH overexpression and GAPDH-positive Lewy body-like aggregates in nigral dopaminergic neurons while nigral GAPDH glycolytic activity decreases in rotenone-based PD animal models. Furthermore, GAPDH knockdown reduces rotenone toxicity significantly in PC12. These in vitro and in vivo data suggest that GAPDH contributes to the pathogenesis of Parkinson's disease, possibly representing a new molecular target for neuroprotective strategies and alternative therapies for PD.

Platelet immunoregulatory factors

A number of soluble and membrane-associated proteins are known to mediate platelet:leukocyte interactions. Platelet-derived factors that have attracted the most attention to date include transforming growth factor beta, interleukin 1 and platelet factor 4. Recently, we have uncovered another protein within platelets that has leukocyte modulatory activity. It was previously characterized as an endometrial glycoprotein named placental protein 14 (PP14) with suppressive effects upon lymphocyte proliferation, pro-inflammatory cytokine production and natural killer cell function. The "hematopoietic" PP14 derived from cells of the megakaryocytic lineage shares this immunosuppressive property, as evaluated by two-way mixed lymphocyte cultures. Interestingly, two alternatively spliced hematopoietic PP14 mRNAs have been cloned which differ in their encoded proteins. Cell-free translation and transfection analyses have verified the translatability of both PP14 mRNA species and allowed for the analysis of their glycosylation properties. PP14, a member of the lipocalin structural superfamily of proteins, now offers an intriguing new link between the coagulation and immune systems.

Hematopoietic placental protein 14. An immunosuppressive factor in cells of the megakaryocytic lineage

Placental protein 14 (PP14), an immunosuppressive molecule previously known to be expressed in the female and male reproductive tracts only, was shown to be expressed by hematopoietic cells of the megakaryocytic lineage. Northern blot analysis confirmed the induction specificity of PP14 mRNA in phorbol ester-treated K562 cells. Potent immunosuppressive activity in conditioned medium from phorbol ester-treated K562 cells was attributed to hematopoietic PP14 by anti-PP14 antibody blocking. Immunoprecipitation with anti-PP14 antibodies from conditioned medium revealed two distinct PP14 protein isoforms, designated PP14.1 and PP14.2. Polymerase chain reaction cloning and analysis demonstrated the presence of distinct mRNA counterparts to PP14.1 and PP14.2 that had not been resolved by Northern blot analyses. Hematopoietic PP14.1 mRNA corresponds in size to endometrial PP14 mRNA, whereas the smaller hematopoietic PP14.2 mRNA displays an internal in-frame 66-nucleotide deletion that can be explained by alternative splicing and predicts a 22-amino-acid deletion in the encoded gene product. Both PP14 mRNA isoforms were additionally detected by reverse transcriptase polymerase chain reaction analyses in two human megakaryocytic cell lines and in normal human megakaryocytes and platelets. PP14 mRNA was not detected by reverse transcriptase polymerase chain reaction in a panel of nonhematopoietic, nonendometrial tissues examined. The finding of hematopoietic PP14 within the megakaryocytic lineage provides an additional regulatory link between the coagulation and immune systems in normal and pathological settings.