The apolipoprotein ε4 allele (APOE4) is the strongest genetic risk factor for Alzheimer's disease (AD). APOE4 carriers develop systemic metabolic dysfunction decades before showing AD symptoms. ...Accumulating evidence shows that the metabolic dysfunction accelerates AD development, including exacerbated amyloid-beta (Aβ) retention, neuroinflammation and cognitive decline. Therefore, preserving metabolic function early on may be critical to reducing the risk for AD. Here, we show that inulin increases beneficial microbiota and decreases harmful microbiota in the feces of young, asymptomatic APOE4 transgenic (E4FAD) mice and enhances metabolism in the cecum, periphery and brain, as demonstrated by increases in the levels of SCFAs, tryptophan-derived metabolites, bile acids, glycolytic metabolites and scyllo-inositol. We show that inulin also reduces inflammatory gene expression in the hippocampus. This knowledge can be utilized to design early precision nutrition intervention strategies that use a prebiotic diet to enhance systemic metabolism and may be useful for reducing AD risk in asymptomatic APOE4 carriers.
Parent-child interactions are a critical pathway to emotion socialization, with disruption to these processes associated with risk for childhood behavior problems. Using computational linguistics ...methods, we tested whether (1) play context influenced parent-child socioemotional language, and (2) child conduct problems or callous-unemotional traits were associated with patterns of socioemotional or nonsocial language across contexts. Seventy-nine parent-child dyads (children, 5-6 years old) played a socioemotional skills ("social context") or math ("nonsocial context") game at home. We transcribed and analyzed game play, which had been audio recorded by participants. The social context elicited more socioemotional and cognitive words, while the nonsocial context elicited more mathematical words. The use of socioemotional language by parents and children was more strongly correlated in the social context, but context did not moderate the degree of correlation in cognitive or mathematical word use between parents and children. Children with more conduct problems used fewer socioemotional words in the social context, while children with higher callous-unemotional traits used fewer cognitive words in both contexts. We highlight the role of context in supporting socioemotionally rich parent-child language interactions and provide preliminary evidence for the existence of linguistic markers of child behavior problems. Our results also inform naturalistic assessments of parent-child interactions and home-based interventions for parents and children facing socioemotional or behavioral challenges.
In the past decade, wired and wireless computer networks have substantially evolved because of the rapid development of technologies such as the Internet of Things (IoT), wireless handled devices, ...vehicular networks, 4G and 5G, cyber-physical systems, etc. These technologies exchange large amount of data, and as a result, they are prone to several malicious actions, attacks and security threats that can compromise the availability and integrity of information or services. Therefore, the security and protection of the various communication infrastructures using an intrusion detection system (IDS) is of critical importance. In this research, we propose a Feed-Forward Deep Neural Network (FFDNN) wireless IDS system using a Wrapper Based Feature Extraction Unit (WFEU). The extraction method of the WFEU uses the Extra Trees algorithm in order to generate a reduced optimal feature vector. The effectiveness and efficiency of the WFEU-FFDNN is studied based on the UNSW-NB15 and the AWID intrusion detection datasets. Furthermore, the WFEU-FFDNN is compared to standard machine learning (ML) algorithms that include Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT) and k-Nearest Neighbor (kNN). The experimental studies include binary and multiclass types of attacks. The results suggested that the proposed WFEU-FFDNN has greater detection accuracy than other approaches. In the instance of the UNSW-NB15, the WFEU generated an optimal feature vector consisting of 22 attributes. Using this input vector; our approach achieved overall accuracies of 87.10% and 77.16% for the binary and multiclass classification schemes, respectively. In the instance of the AWID, a reduced input vector of 26 attributes was generated by the WFEU, and the experiments demonstrated that our method obtained overall accuracies of 99.66% and 99.77% for the binary and the multiclass classification configurations, respectively.
Computer networks intrusion detection systems (IDSs) and intrusion prevention systems (IPSs) are critical aspects that contribute to the success of an organization. Over the past years, IDSs and IPSs ...using different approaches have been developed and implemented to ensure that computer networks within enterprises are secure, reliable and available. In this paper, we focus on IDSs that are built using machine learning (ML) techniques. IDSs based on ML methods are effective and accurate in detecting networks attacks. However, the performance of these systems decreases for high dimensional data spaces. Therefore, it is crucial to implement an appropriate feature extraction method that can prune some of the features that do not possess a great impact in the classification process. Moreover, many of the ML based IDSs suffer from an increase in false positive rate and a low detection accuracy when the models are trained on highly imbalanced datasets. In this paper, we present an analysis the UNSW-NB15 intrusion detection dataset that will be used for training and testing our models. Moreover, we apply a filter-based feature reduction technique using the XGBoost algorithm. We then implement the following ML approaches using the reduced feature space: Support Vector Machine (SVM), k-Nearest-Neighbour (kNN), Logistic Regression (LR), Artificial Neural Network (ANN) and Decision Tree (DT). In our experiments, we considered both the binary and multiclass classification configurations. The results demonstrated that the XGBoost-based feature selection method allows for methods such as the DT to increase its test accuracy from 88.13 to 90.85% for the binary classification scheme.
In recent years, the increased use of wireless networks for the transmission of large volumes of information has generated a myriad of security threats and privacy concerns; consequently, there has ...been the development of a number of preventive and protective measures including intrusion detection systems (IDS). Intrusion detection mechanisms play a pivotal role in securing computer and network systems; however, for various IDS, the performance remains a major issue. Moreover, the accuracy of existing methodologies for IDS using machine learning is heavily affected when the feature space grows. In this paper, we propose a IDS based on deep learning using feed forward deep neural networks (FFDNNs) coupled with a filter-based feature selection algorithm. The FFDNN-IDS is evaluated using the well-known NSL-knowledge discovery and data mining (NSL-KDD) dataset and it is compared to the following existing machine learning methods: support vectors machines, decision tree, K-Nearest Neighbor, and Naïve Bayes. The experimental results prove that the FFDNN-IDS achieves an increase in accuracy in comparison to other methods.
Abstract
Ultra-compact wireless implantable medical devices are in great demand for healthcare applications, in particular for neural recording and stimulation. Current implantable technologies based ...on miniaturized micro-coils suffer from low wireless power transfer efficiency (PTE) and are not always compliant with the specific absorption rate imposed by the Federal Communications Commission. Moreover, current implantable devices are reliant on differential recording of voltage or current across space and require direct contact between electrode and tissue. Here, we show an ultra-compact dual-band smart nanoelectromechanical systems magnetoelectric (ME) antenna with a size of 250 × 174 µm
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that can efficiently perform wireless energy harvesting and sense ultra-small magnetic fields. The proposed ME antenna has a wireless PTE 1–2 orders of magnitude higher than any other reported miniaturized micro-coil, allowing the wireless IMDs to be compliant with the SAR limit. Furthermore, the antenna’s magnetic field detectivity of 300–500 pT allows the IMDs to record neural magnetic fields.
IMPORTANCE: Interictal epileptiform discharges (IEDs) in electroencephalograms (EEGs) are a biomarker of epilepsy, seizure risk, and clinical decline. However, there is a scarcity of experts ...qualified to interpret EEG results. Prior attempts to automate IED detection have been limited by small samples and have not demonstrated expert-level performance. There is a need for a validated automated method to detect IEDs with expert-level reliability. OBJECTIVE: To develop and validate a computer algorithm with the ability to identify IEDs as reliably as experts and classify an EEG recording as containing IEDs vs no IEDs. DESIGN, SETTING, AND PARTICIPANTS: A total of 9571 scalp EEG records with and without IEDs were used to train a deep neural network (SpikeNet) to perform IED detection. Independent training and testing data sets were generated from 13 262 IED candidates, independently annotated by 8 fellowship-trained clinical neurophysiologists, and 8520 EEG records containing no IEDs based on clinical EEG reports. Using the estimated spike probability, a classifier designating the whole EEG recording as positive or negative was also built. MAIN OUTCOMES AND MEASURES: SpikeNet accuracy, sensitivity, and specificity compared with fellowship-trained neurophysiology experts for identifying IEDs and classifying EEGs as positive or negative or negative for IEDs. Statistical performance was assessed via calibration error and area under the receiver operating characteristic curve (AUC). All performance statistics were estimated using 10-fold cross-validation. RESULTS: SpikeNet surpassed both expert interpretation and an industry standard commercial IED detector, based on calibration error (SpikeNet, 0.041; 95% CI, 0.033-0.049; vs industry standard, 0.066; 95% CI, 0.060-0.078; vs experts, mean, 0.183; range, 0.081-0.364) and binary classification performance based on AUC (SpikeNet, 0.980; 95% CI, 0.977-0.984; vs industry standard, 0.882; 95% CI, 0.872-0.893). Whole EEG classification had a mean calibration error of 0.126 (range, 0.109-0.1444) vs experts (mean, 0.197; range, 0.099-0.372) and AUC of 0.847 (95% CI, 0.830-0.865). CONCLUSIONS AND RELEVANCE: In this study, SpikeNet automatically detected IEDs and classified whole EEGs as IED-positive or IED-negative. This may be the first time an algorithm has been shown to exceed expert performance for IED detection in a representative sample of EEGs and may thus be a valuable tool for expedited review of EEGs.
Wireless networks have evolved over the years and they have become some of the most prominent communication media. These networks generally transmit large volumes of information at any given time. ...This has engendered a number of security threats and privacy concerns. This paper presents a Deep Long Short-Term Memory (DLSTM) based classifier for wireless intrusion detection system (IDS). Using the NSL-KDD dataset, we compare the DLSTM IDS to existing methods such as Deep Feed Forward Neural Networks, Support Vector Machines, k-Nearest Neighbors, Random Forests and Naïve Bayes. The experimental results suggest that the DLSTM IDS outperformed existing approaches.
'Smart' bandages based on multimodal wearable devices could enable real-time physiological monitoring and active intervention to promote healing of chronic wounds. However, there has been limited ...development in incorporation of both sensors and stimulators for the current smart bandage technologies. Additionally, while adhesive electrodes are essential for robust signal transduction, detachment of existing adhesive dressings can lead to secondary damage to delicate wound tissues without switchable adhesion. Here we overcome these issues by developing a flexible bioelectronic system consisting of wirelessly powered, closed-loop sensing and stimulation circuits with skin-interfacing hydrogel electrodes capable of on-demand adhesion and detachment. In mice, we demonstrate that our wound care system can continuously monitor skin impedance and temperature and deliver electrical stimulation in response to the wound environment. Across preclinical wound models, the treatment group healed ~25% more rapidly and with ~50% enhancement in dermal remodeling compared with control. Further, we observed activation of proregenerative genes in monocyte and macrophage cell populations, which may enhance tissue regeneration, neovascularization and dermal recovery.
Abstract Study Objectives Recent studies suggest that sleepy patients with obstructive sleep apnea (OSA) are at higher risk for incident cardiovascular disease. This study assessed cardiac autonomic ...function in sleepy versus non-sleepy patients with OSA using heart rate variability (HRV) analysis. We hypothesized that HRV profiles of sleepy patients would indicate higher cardiovascular risk. Methods Electrocardiograms (ECG) derived from polysomnograms (PSG) collected by the Sydney Sleep Biobank were used to study HRV in groups of sleepy (ESS ≥ 10) and non-sleepy OSA patients (ESS < 10). HRV parameters were averaged across available ECG signals during N2 sleep. Results A total of 421 patients were evaluated, with a mean age of 54 (14) years, body mass index of 33 (9) kg/m2, apnea–hypopnea index of 21 (28) events/h, and 66% male. The sleepy group consisted of 119 patients and the non-sleepy group 302 patients. Sleepy patients exhibited lower HRV values for: root mean square successive difference (RMSSD, p = 0.028), total power (TP, p = 0.031), absolute low frequency (LF, p = 0.045), and high-frequency (HF, p = 0.010) power compared to non-sleepy patients. Sleepy patients with moderate-to-severe OSA exhibited lower HRV values for: (RMSSD, p = 0.045; TP, p = 0.052), absolute LF (p = 0.051), and HF power (p = 0.025). There were no differences in other time and frequency domain HRV markers. Conclusions This study shows a trend toward parasympathetic withdrawal in sleepy OSA patients, particularly in moderate-to-severe cases, lending mechanistic support to the link between the sleepy phenotype and CVD risk in OSA.
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