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Background The functional alternation of distinct brain networks contribute to motor impairment in Parkinson’s disease (PD) remains unclear. Identifying a whole-brain connectome-based predictive model (CPM) in drug-naïve patients and verifying its predictability among drug-managed patients would be helpful to detect generalizable brain-behavior association and reflect intrinsic functional underpinning of motor impairment. Methods Resting-state functional data of 47 drug-naïve patients were enrolled to construct a predictive model by using the CPM approach, which was subsequently validated in 115 drug-managed patients. The severity of motor impairment was assessed by calculating Unified Parkinson’s Disease Rating Scale part III (UPDRS III) scores. Predictive performance was evaluated with the correlation coefficient(rtrue) and the mean squared error (MSE) between observed and predicted scores. Results A CPM for predicting individual motor impairment in drug-naïve PD was identified with significant performance (rtrue=0.845, p

BackgroundBrain invasion in meningioma has independent associations with increased risks of tumor progression, lesion recurrence, and poor prognosis. Therefore, this study aimed to construct a model for predicting brain invasion in WHO grade II meningioma by using preoperative MRI.MethodsOne hundred seventy-three patients with brain invasion and 111 patients without brain invasion were included. Three mainstream features, namely, traditional semantic features and radiomics features from tumor and tumor-to-brain interface regions, were acquired. Predictive models correspondingly constructed on each feature set or joint feature set were constructed.ResultsTraditional semantic findings, e.g., peritumoral edema and other four features, had comparable performance in predicting brain invasion with each radiomics feature set. By taking advantage of semantic features and radiomics features from tumoral and tumor-to-brain interface regions, an integrated nomogram that quantifies the risk factor of each selected feature was constructed and had the best performance in predicting brain invasion (area under the curve values were 0.905 in the training set and 0.895 in the test set).ConclusionsThis study provided a clinically available and promising approach to predict brain invasion in WHO grade II meningiomas by using preoperative MRI.

Many animal experiments and clinical trials showed that probiotics are effective for the treatment of alcoholic liver disease. Alcohol disrupts the composition of intestinal flora; probiotics modulate the gut microbiota and reverse alcohol-associated intestinal barrier dysfunction by decreasing intestinal mucosal permeability and preventing intestinal bacteria from translocating. Probiotics enhance immune responses and reduce the levels of alcohol-induced inflammatory cytokines and reactive oxygen species (ROS) production in the liver and intestine. Probiotics also increase fatty acid β-oxidation and reduce lipogenesis, combating alcohol-induced hepatic steatosis. In this review, we summarize the current knowledge regarding the mechanism of action of probiotics for reducing the effects of alcoholic liver disease.

Depression, as one of the largest influence disorders, needs more attention on its growing patients. Medicine treatment has been seeing as the major treatment for depression. As one of the most commonly used medicine, Selective Serotonin Reuptake Inhibitors (SSRIS) increases serotonin levels to treat depression effectively, and the data from clinical data prove its value. Furthermore, Serotonin and Norepinephrine Reuptake Inhibitors (SNRIs) and Monoamine Oxidase Inhibitors (MAOIs) treat depression by affecting at least one neurotransmitter, generally is chemical in the brain and digestive system. Other therapies were mostly as an auxiliary, such as psychological therapy, Electroconvulsive Therapy (ECT), Transcranial Magnetic Stimulation (TMS), and Vagus Nerve Stimulation (VNS). These therapies often work effectively by stimulating neurotransmitters’ activity and increasing the activeness of the reward system. After new study and technology production, new development like ketamine can satisfy Individual Differences by more detailed inspection. Optogenetics in auxiliary treatment can be another way to increase the exactness of ECT and VNS after solving problems such as actual circuits in the whole network. In this review, we first focus mostly on drug treatments. We then summarize current information for other treatments and discuss the mechanisms that lead to in effect. Ultimately, we predict future development in the drug and optogenetics area. The previous study on depression treatment leads us a way to discover a future solution.

In cloud service systems, customers will report the service issues they have encountered to cloud service providers. Despite many issues can be handled by the support team, sometimes the customer issues can not be easily solved, thus raising customer incidents. Quick troubleshooting of a customer incident is critical. To this end, a customer incident should be assigned to its responsible team accurately in a timely manner. Our industrial experiences show that linking customer incidents with detected system incidents can help the customer incident triage. In particular, our empirical study on 7 real cloud service systems shows that with the additional information about the system incidents (i.e., incident reports generated by system monitors), the triage time of customer incidents can be accelerated 13.1× on average. Based on this observation, in this paper, we propose LinkCM, a learning based approach to automatically link customer incidents to monitor reported system incidents. LinkCM incorporates a novel learning-based model that effectively extracts related information from two resources, and a transfer learning strategy is proposed to help LinkCM achieve better performance without huge amount of data. The experimental results indicate that LinkCM is able to achieve accurate link prediction. Furthermore, case studies are presented to demonstrate how LinkCM can help the customer incident triage procedure in real production cloud service systems.

In recent years, with the increasing investment in scientific research, the number of papers has proliferated, and the development of scientific research has also received widespread attention from scholars. Paper citations as an indicator to measure academic influence have essential reference value for future research directions. Aiming at the future paper citation number prediction problem, we propose a citation number prediction model based on the recurrent neural network method with gated recurrent unit(GRU-CPM). In this paper, we first extract features from real data sets that are useful for predicting the number of citations in papers, and then the features were input into GRU-CPM for prediction. Finally, the prediction results are compared with other regression models. Experimental results demonstrate that the GRU-CPM has higher prediction accuracy and faster convergence speed. The time series prediction of citation count is better than existing methods.

Texting-while-driving (T&D) is one of the top dangerous behaviors for drivers. Many interesting systems and mobile phone applications have been designed to help to detect or combat T&D. However, for a T&D detection system to be practical, a key property is its capability to distinguish driver's mobile phone from passengers’. Existing solutions to this problem generally rely on the user's manual input, or utilize specific localization devices to determine whether a mobile phone is at the driver's location. In this paper, we propose a method which is able to detect T&D automatically without using any extra devices. The idea is very simple: when a user is composing messages, the smartphone embedded sensors (i.e., gyroscopes, accelerometers, and GPS) collect the associated information such as touchstrokes, holding orientation and vehicle speed. This information will then be analyzed to see whether there exists some specific T&D patterns. Extensive experiments have been conducted by different persons and in different driving scenarios. The results show that our approach can achieve a good detection accuracy with low false positive rate. Besides being infrastructure-free and with high accuracy, the method does not access the content of messages and therefore is privacy-preserving.

Non-invasive human sensing based on radio signals has attracted numerous research interests in recent years. Previous work mainly focused on detecting the presence of a person or identifying human gestures and activities. In this paper, we show that with off-the-shelf WiFi devices, fine-grained respiration information of a person under different sleeping positions can be extracted successfully. We do this by introducing a breath monitoring system based on WiFi signals. This system adopts off-the-shelf WiFi devices to continuously collect the fine-grained wireless channel state information (CSI) around a person. From the CSI, the rhythmic patterns associated with respiration and abrupt changes due to the body movement are identified. Compared to existing respiration monitoring systems that usually require special devices attached to human body, this system is completely contactless. In addition, different from many vision-based sleep monitoring systems, it is robust to low-light environments and does not raise privacy concerns. Preliminary testing results show that our system can reliably track a person's respiration reliably in different sleeping postures.

Event-triggered wake-up, in which sensor nodes wake up to work in the presence of some pre-defined events, has been widely used in wireless sensor networks (WSNs) to save energy while still completing the tasks required. However, some recently emerged domain-specific WSN applications such as structural health monitoring (SHM) and volcano seismic tomography, have different requirements with regard to wake-up as compared to conventional WSN applications. In these domain-specific applications, the wake-up should be network-wide and nodes to be woken up are not limited to those close to event locations. In addition, the wake-up should be fast to capture enough information during generally short events and be reliable to avoid costly false-positive wake-ups. This problem has not been addressed in the literature. In this paper, we designed two types of wake-up units, based on which we propose a new chain-reaction wake-up mechanism to address this challenge. In this mechanism, we carefully select some nodes used to initiate the wake-up process, such that the wake-up delay is minimized under the false alarm constraint. We propose two greedy algorithms and a randomized one that leverages the solution to the classic Knapsack problem. The performance of the proposed wake-up mechanism is demonstrated through both simulation and experiments.

The security issue of public WiFi is gaining more and more concern. By listening to probe requests, an adversary can obtain the SSID list of the APs to which a smartphone previously connected, and utilizes this information to trick the smartphone into associating to it. However, with the enhancement of security level, most smartphones now do not proactively disclose their SSID lists, making these attacks obsolete. In this paper, we propose City-Hunter, an attacker that can lure nearby smartphones without knowing their SSID information. City-Hunter establishes and maintains an SSID database by integrating both offline and online information. Meanwhile, it smartly chooses some SSIDs to hit a smartphone according to the past record and freshness. We evaluate the performance of City-Hunter in different public places. The results demonstrate that City-Hunter is able to successfully hit 12% ∼ 18% smartphones without knowing their SSID information, which is about 4 ∼ 8 times improvement compared to the similar attacks like KARMA and MANA.

Locating smartphone users will enable numerous potential applications such as monitoring customers in shopping malls. However, conventional received signal strength (RSS)-based room-level localization methods are not likely to distinguish neighboring zones accurately due to similar RSS fingerprints. We solve this problem by proposing a system called feature-based room-level localization (FRL). FRL is based on an observation that different rooms vary in internal structures and human activities which can be reflected by RSS fluctuation ranges and user dwell time respectively. These two features combing with RSS can be exploited to improve the localization accuracy. To enable localization of unmodified smartphones, FRL utilizes probe requests, which are periodically broadcast by smartphones to discover nearby access points (APs). Experiments indicate that FRL can reliably locate users in neighboring zones and achieve a 10 % accuracy gain, compared with conventional methods like the histogram method.

Is it possible to leverage WiFi signals collected in bedrooms to monitor a person's sleep? In this paper, we show that with off-the-shelf WiFi devices, fine-grained sleep information like a person's respiration, sleeping postures and rollovers can be successfully extracted. We do this by introducing Wi-Sleep, the first sleep monitoring system based on WiFi signals. Wi-Sleep adopts off-the-shelf WiFi devices to continuously collect the fine-grained wireless channel state information (CSI) around a person. From the CSI, Wi-Sleep extracts rhythmic patterns associated with respiration and abrupt changes due to the body movement. Compared to existing sleep monitoring systems that usually require special devices attached to human body (i.e. Probes, head belt, and wrist band), Wi-Sleep is completely contact less. In addition, different from many vision-based sleep monitoring systems, Wi-Sleep is robust to low-light environments and does not raise privacy concerns. Preliminary testing results show that the Wi-Sleep can reliably track a person's respiration and sleeping postures in different conditions.