Search

Gut microbiota is closely related to the Parkinson's disease (PD) pathogenesis. Additionally, aggregation of α-synuclein (α-syn) is central to PD pathogenesis. Here we identified the further mechanisms of gut microbiota in PD. A mouse model with PD was established via injection of MPTP. Normal or MPTP-induced PD like animals were treated with FMT from healthy normal mice. Pole test and traction test were performed to examine the effects of FMT on motor function of PD mice. Fecal SCFAs were assessed by gas chromatography-mass spectrometry. The α-syn level in the substantia nigra pars compacta (SN) of mice was measured using western blot. Dopaminergic neurons and microglial activation in the SN were analyzed by immunohistochemistry (IHC) and immunofluorescence (IF) staining. FMT alleviated physical impairment, decreased fecal SCFAs in a mouse model of PD. Additionally, FMT decreased the expression of α-syn, as well as inhibited the activation of microglia in the SN, and blocked the TLR4/PI3K/AKT/NF-κB signaling in the SN and striatum. FMT could protect mice against PD via suppressing α-syn expression and inactivating the TLR4/PI3K/AKT/NF-κB signaling.

To investigate the effectiveness of simultaneous multislice (SMS) accelerated readout-segmented echo planar imaging (RESOLVE) DWI for assessing rectal cancer in the clinic.Sixty consecutive histologically proven rectal cancer patients were enrolled. They all received MRI examinations, including both SMS-RESOLVE and RESOLVE sequences. Two readers visually assessed the overall image quality, distinction of anatomical structures, lesion conspicuity, and artifacts of two sequences by using a qualitative 4-point Likert scale. The quantitative ADC value, lesion contrast, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and temporal SNR (tSNR) were independently calculated in rectal cancer on the largest slice of the tumor.The scan time was shortened from 3 min and 50 s to 1 min and 47 s. The interobserver agreement of visual and quantitative assessments between the two readers was good overall. There were no differences in overall image quality, lesion conspicuity or artifact scores between the two sequences in both readers (all p 0.05). The lesion contrast (p = 0.013) was significantly higher in SMS-RESOLVE, and the CNR was similar in the two DWIs. The scores of distinctions of anatomical structures in SMS-RESOLVE were lower (all p 0.05) in both readers. The SNR of SMS-RESOLVE was lower than that of RESOLVE (p = 0.004), and the tSNR of SMS-RESOLVE was significantly higher (p 0.001). The ADC value of the tumor was lower in SMS-RESOLVE (p = 0.001), but the ADC values of the normal rectal wall showed no difference between the two DWIs.SMS-RESOLVE allowed a substantial reduction in acquisition time while maintaining overall image quality and lesion conspicuity in rectal cancer. It also had a higher contrast of the lesion and a higher temporal SNR.

Background Accurate assessment of tumor response in rectal cancer could help individualize treatment. Purpose To evaluate the role of diffusion-weighted imaging (DWI) based on readout-segmented echo-planar imaging (rs-EPI) in assessing tumor response after neoadjuvant chemoradiotherapy (CRT) in locally advanced rectal cancer (LARC). Material and Methods Sixty-three patients with LARC who received neoadjuvant CRT and surgery were enrolled retrospectively. They all underwent pre- and post-CRT magnetic resonance examinations, including DWI using rs-EPI. According to pathological results, patients were grouped as pathological complete responder (pCR, n = 16) and non-pCR (n = 47). Visual assessment of residual tumor and whole-tumor histogram analysis of pre- and post-CRT apparent diffusion coefficient (ADC) map was performed by two radiologists; tumor volume on ADC map was also recorded. Results Overall inter-observer agreement was good for histogram analysis (ICC = 0.543–0.999). Tumor volume reduction rate on ADC map showed no significant difference between the two groups ( P = 0.468). Post-CRT mean, quantile values, and their percentage changes were higher in the pCR group (all P −3 mm2/s, yielding a sensitivity of 83%, specificity of 81.3%. Post-CRT 95% quantile value had the highest AUC (AUC = 0.868) among quantile values, and a higher specificity (87.5% vs. 81.3%) than mean value with comparable overall diagnostic performance ( P = 0.563). Visual assessment showed a sensitivity of 85.1%, specificity of 68.8% in selecting pCR. Conclusion Quantitative ADC value of rs-EPI DWI could reliably evaluate tumor response in patients with LARC. Post-CRT 95% quantile ADC value could help mean value to more accurately identify pCR.

To explore if there is a correlation between T2WI histogram features of the primary tumor and the existence of regional lymph node (LN) metastasis in rectal cancer. Eighty-eight patients with pathologically proven rectal adenocarcinoma, who received direct surgical resection and underwent preoperative rectal MRIs, were enrolled retrospectively. Based on pathological analysis of surgical specimen, patients were classified into negative LN (LN−) and positive LN (LN+) groups. The degree of differentiation and pathological T stage were recorded. Clinical T stage, tumor location, and maximum diameter of tumor were evaluated of each patient. Whole-tumor texture analysis was independently performed by two radiologists on axial T2WI, including skewness, kurtosis, energy, and entropy. The interobserver agreement was overall good for texture analysis between two radiologists, with intraclass correlation coefficients (ICCs) ranging from 0.626 to 0.826. The LN− group had a significantly higher skewness (p

The purpose of this study is to determine the performance of the apparent diffusion coefficient (ADC) value calculated from high-resolution DWI using readout-segmented echo-planar imaging (rs-EPI) and to assess the texture parameters of T2-weighted MR images in identifying pathologic complete response (pCR) after patients with locally advanced rectal cancer (LARC) undergo preoperative chemoradiotherapy (CRT).A total of 76 patients with LARC who underwent preoperative CRT and subsequent surgery were enrolled in the study retrospectively. All patients underwent post-CRT MRI, which included acquisition of a DWI sequence with use of the rs-EPI technique. The histopathologic tumor regression grade was the reference standard. Patients were subdivided into pCR and non-pCR groups. Two radiologists independently drew whole-tumor ROIs on DW images and T2-weighted MR images to calculate the mean ADC value and first-order texture parameters.Interobserver agreement was good to excellent (intraclass correlation coefficient [ICC], 0.79-0.993) for imaging analysis. Calculated from high-resolution DWI, the mean post-CRT ADC value was significantly higher in the pCR group (p0.001). The pCR group also showed lower uniformity (p0.001) of the T2-weighted image. The mean ADC value and uniformity were significantly correlated with the tumor regression grade. The mean ADC value was a good indicator for differentiating pCR from absence of pCR (ROC AUC value, 0.912). Uniformity (ROC AUC value, 0.776) showed a moderate ability to identify pCR. Combining the mean ADC value and uniformity yielded an ROC AUC value comparable to that of the mean ADC value (p = 0.125).Mean post-CRT ADC values calculated from high-resolution DWI using rs-EPI could effectively select for patients with LARC who have a pCR after preoperative CRT. First-order texture parameters of T2-weighted MR images could also identify patients with pCR by reflecting tumor heterogeneity, even though they could not significantly improve the diagnostic performance.

A novel label-free point-of-care quartz crystal microbalance (QCM) aptasensing platform based on metal-organic frameworks (MOFs) and gold nanoparticles (AuNPs) was fabricated for tetracycline (TC) detection. MOFs (HKUST-1) and AuNPs were modified onto the sensing interface of QCM sensor to enhance the sensing performance of the QCM aptasensor. TC aptamer with sulfhydryl group was fixed through Au-S bond. The recognition performance of the aptasensor was predicted and verified by the computer simulation. At the optimal conditions, the frequency change of the sensor was adopted for quantitative detection of TC. The prepared QCM aptasensor exhibited a wide linear range from 1 × 10

The efficiency of human-computer interaction is greatly hindered by the small size of the touchscreens on mobile devices, such as smart phones and watches. This has prompted widespread interest in handwriting recognition systems, which can be divided into active and passive systems. Active systems require additional hardware devices to perceive movements of handwriting or the tracking accuracy is not adequate for hand-writing recognition. Passive methods use the acoustic signal of pen rubbing and are susceptible to environmental noise (above 60dB). This paper presents a novel handwriting recognition system based on vibration signals detected by the built-in accelerometer of smart phones. VibWriter is highly resistant to interference since the normal environmental noise will not cause the vibration of the accelerometer. Extensive experiments demonstrated the efficacy of the system in terms of accuracy in letter recognition (76.15%) and word recognition (88.14%) when dealing with words of various lengths written by various users in a variety of writing positions under a variety of environmental conditions.

Various characteristics of mobile applications (apps) and associated in-app services have been used reveal potentially-sensitive user information; however, privacy concerns have prompted third-party apps to rigorously restrict access to data related to mobile app usage. This paper outlines a novel approach to the extraction of detailed app usage information based on analysis of the electromagnetic (EM) signals emitted from mobile devices when executing app-related tasks. Note that this type of EM leakage becomes high-complex when multiple apps are used simultaneously and is subject to interference from geomagnetic signals generated by device movement. This paper proposes a deep learning-based multi-label classification system to identify apps and in-app services based on magnetometer readings. The proposed MAGTHIEF system uses accelerometer and gyroscope data to cancel out the offset in geomagnetic signals followed by an elaborate deep region convolution neural network (DRCNN) to differentiate among multiple apps and the corresponding inapp services. Experiments on 50 apps demonstrated the efficacy of MAGTHIEF in identifying multiple apps and in-app services, achieving high average macro F1 scores of 0.87 and 0.95, respectively. MAGTHIEF also achieved time duration accuracy of 89.5% in recognizing app trajectory in the real-world scene.

Quick response (QR) codes have been widely used in mobile applications due to its convenience and the pervasive built-in cameras on smartphones. Recently, however, attacks against QR codes have been reported that attackers can capture a QR code of the victim and replay it to achieve a fraudulent transaction or intercept private information, just before the original QR code is scanned. In this study, we enhance the security of a QR code by identifying its authenticity. We propose SCREENID, which embeds a QR code with information of the screen which displays it, thereby the QR code can reveal whether it is reproduced by an adversary or not. In SCREENID, PWM frequency of screens is exploited as the unique screen fingerprint. To improve the estimation accuracy of PWM frequency, SCREENID incorporates a model for the interaction between the camera and screen in the temporal and spatial domains. Extensive experiments demonstrate that SCREENID can differentiate screens of different models, types, and manufacturers, thus improve the security of QR codes.

Background: To develop a radiomics model for bounding box-based region of interest (ROI) annotation for the preoperative diagnosis of occult peritoneal metastasis (OPM) in advanced gastric cancer (AGC) patients. Methods: 599 AGC patients from 3 centers were retrospectively enrolled and were divided into training, validation, and testing cohorts. The ROIs for the largest tumor area, the minimum circumscribed rectangle of the ROIs (BBOX), the nonoverlapping area between the ROIs and BBOX (peritumoral area; PERI) and the smallest rectangle that could completely contain the lesion determined by a radiologist (M_BBOX) were used as inputs to extract radiomic features. Multivariate logistic regression was used to construct a radiomics model to estimate the preoperative probability of OPM in GC patients. Findings: The BBOX radiomics model had the highest area under the receiver operating characteristic curve (AUC) of 0.873 [0.820-0.940], outperforming the other radiomics models. The M_BBOX model was not significantly different from BBOX in the validation cohort (AUC: M_BBOX model 0.871 [0.814-0.940] vs. BBOX model 0.873 [0.820-0.940]; p = 0.937). M_BBOX was selected as the final radiomics model because of its extremely low annotation cost and superior OPM discrimination performance (sensitivity of 85.7% and specificity of 82.8%) over the clinical model, and this radiomics model showed comparable diagnostic efficacy in the testing cohort. Interpretation: The radiomics model based on bounding box annotation achieved higher OPM detection accuracy in AGC patients than the clinical model and had potential generalizability. Funding Information: This work was supported by the 1*3*5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (No. ZY2017304). Declaration of Interests: The authors have no relevant conflicts of interest to disclose. Ethics Approval Statement: This multicenter retrospective study was approved by the institutional review board of each center, and the informed consent requirement was waived.

To evaluate the role of IVIM and diffusion kurtosis imaging (DKI) in identifying pathologic complete response (pCR) and T stages after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC).Forty-two patients with biopsy-proven rectal adenocarcinoma, who underwent both pre-and post-CRT MRI with IVIM and DKI sequences on a 3 T scanner, were enrolled prospectively. According to the pathologic ypTNM stages and tumor regression grade (TRG), patients were grouped into pCR (TRG0) and non-pCR (TRG1-3) groups and low T stage (ypT0-2) and high T stage (ypT3-4) groups. IVIM parameters (the slow diffusion coefficient [D], fast diffusion coefficient [D*], perfusion fraction [f]), DKI parameters (mean diffusivity [MD] and mean kurtosis [MK]), and mono-exponential ADC were calculated and analyzed between groups.The pCR group had significantly higher post-CRT ADC, D*, f, and MD values than non-pCR group, and higher percent changes in the ADC, f, and MD values (all P 0.05). The post-CRT MD values yielded the highest AUC (0.788) with higher sensitivity than post-ADC values (82.9 % vs. 77.1 %, respectively). Post-CRT ADC and MD values and the percent changes in the ADC and MD values were also negatively correlated with TRG (all P 0.05). Besides, negative correlations were found among the pre-CRT MD, post-CRT ADC, D, f, and MD values and the ypT stages (all P 0.05).Both IVIM and DKI parameters could provide more information when evaluating pCR and T stages after nCRT. In particular, the diagnostic performance of the MD values was more valuable than ADC values in being able to determine pCR.

Quick response (QR) codes have been widely used in mobile applications, due to its convenience and the pervasive built-in cameras on smartphones. Recently, however, QR codes have been reported suffering attacks for being sniffed just before the QR code is scanned, which lead to financial loss. In this study, we propose ScreenID, for enhancing the QR code security by identifying its authenticity, which embeds a QR code with information of unique screen fingerprint - PWM frequency. PWM frequencies are adjusted to different values by screen manufacturers, therefore can successfully differentiate screens. To improve the estimation accuracy of PWM frequency, ScreenID incorporates a model for the interaction between the camera and screen in the temporal and spatial domains. Extensive experiments demonstrate that ScreenID can differentiate screens of different models, types and manufacturers and thus improve the security of QR codes.

Recognizing the working appliances is of great importance for smart environment to provide services including energy conservation, user activity recognition, fire hazard prevention, etc. There have been many methods proposed to recognize appliances by analyzing the power voltage, current, electromagnetic emissions, vibration, light, and sound from appliances. Among these methods, measuring the power voltage and current requires installing intrusive sensors to each appliance. Measuring the electromagnetic emissions and vibration requires sensors to be attached or close (e.g., cm) to the appliances. Methods relying on light are not universally applicable since only part of appliances generate light. Similarly, methods using sound relying on the sound from motor vibration or mechanical collision so are not applicable for many appliances. As a result, existing methods for appliance fingerprinting are intrusive, have high deployment cost, or only work for part of appliances. In this work, we proposed to use the inaudible high-frequency sound generated by the switching-mode power supply (SMPS) of the appliances as fingerprints to recognize appliances. Since SMPS is widely adopted in home appliances, the proposed method can work for most appliances. Our preliminary experiments on 18 household appliances (where 10 are of the same models) showed that the recognition accuracy achieves 97.6%.

Understanding the nature of user-device interactions (e.g., who is using the device and what he/she is doing with it) is critical to many applications including time management, user profiles, and privacy protection. However, in scenarios where mobile devices are shared among family members or multiple employees in a company, conventional account-based statistics are not meaningful. This poses an even bigger problem when dealing with sensitive data. Moreover, fingerprint readers and front-facing cameras were not designed to continuously identify users. In this study, we developed MagPrint, a novel approach to fingerprint users based on unique patterns in the electromagnetic (EM) signals associated with the specific use patterns of users. Initial experiments showed that time-varying EM patterns are unique to individual users. They are also temporally and spatially consistent, which makes them suitable for fingerprinting. MagPrint has a number of advantages over existing schemes: i) Non-intrusive fingerprinting, ii) implementation using a small and easy-to-deploy device, and iii) high accuracy thanks to the proposed classification algorithm. In experiments involving 30 users, MagPrint achieves 94.3% accuracy in classifying users from these traces, which represents an 10.9% improvement over the state-of-the-art classification method.

In this paper, linearity improved Doherty power amplifier (DPA) is proposed for linearity improvement by modifying the input capacitor value of peaking amplifier. The non-foster circuits are substituted for the conventional λ/4 transmission line in front of peaking amplifier, which the 90° phase shifter and impedance matching can be performed by the proposed structure. Furthermore, the input capacitor value of peaking amplifier FET can be modified through modifying bias voltage and match resistance of non-foster circuits. Therefore, the AM-PM distortion of peak amplifier is suppressed, and the linearity of total DPA is improved. Compared to a conventional DPA, the third-order inter-modulation distortion of proposed DPA is reduced by 12 dB without cutting efficiency.

Exploring high-efficiency and low-cost electrocatalysts with outstanding durableness for oxygen evolution reaction (OER) is the key to practical production of eco-friendly hydrogen energy by water electrolysis technology, which remains challenging. In this paper, the tremella-like MnO2/(Fe,Ni)3S4 hybrid nanostructures are supported on nickel foam matrix by a facile and governable method, with predominant OER properties in 1.0 M KOH solution. Profiting from the synergistic effect between MnO2 and (Fe,Ni)3S4, this optimal MnO2/(Fe,Ni)3S4 catalyst only requires 220 and 325 mV to fulfill current densities of 10 and 500 mA cm−2, respectively. Through the inheritance and optimization of MnO2 and (Fe,Ni)3S4 nanostructures, this hybrid catalyst possesses a large specific surface area, appropriate exposure active site and favorable electrical conductivity. Besides, the self-supported MnO2/(Fe,Ni)3S4 electrode exhibits eminent stability at 10 and 100 mA cm−2 during the 20 h OER process.

Quick response (QR) codes are becoming pervasive due to their rapid readability and the popularity of smartphones with built-in cameras. QR codes are also gaining importance in the retail sector as a convenient mobile payment method. However, researchers have concerns regarding the security of QR codes, which leave users susceptible to financial loss or private information leakage. In this study, we address this issue by developing a novel QR code (called mQR code), which exploits patterns presenting a specific spatial frequency as a form of camouflage. When the targeted receiver holds a camera in a designated position (e.g., directly in front at a distance of 30 cm from the camouflaged QR code), the original QR code is revealed in form of a Moire pattern. From any other position, only the camouflaged QR code can be seen. In experiments, the decryption rate of mQR codes is $> 98%$. The decryption rate for cameras positioned $20\degree$ off axis or $> 10cm$ from the designated location drops to $0%$, indicating that any attackers will be unable to steal a usable image.

Quick response (QR) codes are becoming pervasive due to their rapid readability and the popularity of smartphones with built-in cameras. QR codes are also gaining importance in the retail sector as a convenient mobile payment method. However, researchers have concerns regarding the security of QR codes, which leave users susceptible to financial loss or private information leakage. In this study, we addressed this issue by developing a novel QR code (called mQRCode), which exploits patterns presenting a specific spatial frequency as a form of camouflage. When the targeted receiver holds a camera in a designated position (e.g., directly in front at a distance of 30 cm from the camouflaged QR code), the original QR code is revealed in form of a Moire pattern. From any other position, only the camouflaged QR code can be seen. In experiments, the decryption rate of mQRCode was > 98.6% within 10.2 frames via a multi-frame decryption method. The decryption rate for cameras positioned 20° off axis or > 10cm away from the designated location dropped to 0%, indicating that mQRCode is robust against attacks.

With the development of intelligent technology, more and more self-driving, cooperating cars appear on the road, which will impact on traffic flow greatly. In this paper, we used the cellular automata algorithm to simulate the impact of the proportion of the number of self-driving cars to non-self-driving cars on traffic flow in the case of different lanes, confluence and divergence, traffic peak and dedicated lanes for self-driving car. And we got the conclusion by analyzing the simulation results: On the whole, Traffic flow increased with the increase of the proportion of the self-driving cars to non-self-driving cars. And in different cases, the proportion was different when the traffic flow reached the maximum, that is, the best proportion was different in different cases.