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Progressive accumulation of misfolded SNCA/α-synuclein is key to the pathology of Parkinson’s disease (PD). Drugs aiming at degrading SNCA may be an efficient therapeutic strategy for PD. Our previous study showed that mesencephalic astrocyte-derived neurotrophic factor (MANF) facilitated the removal of misfolded SNCA and rescued dopaminergic (DA) neurons, but the underlying mechanisms remain unknown. In this study, we showed that AAV8-MANF relieved Parkinsonian behavior in rotenone-induced PD model and reduced SNCA accumulation in the substantia nigra. By establishing wildtype (WT) SNCA overexpression cellular model, we found that chaperone-mediated-autophagy (CMA) and macroautophagy were both participated in MANF-mediated degradation of SNCAWT. Nuclear factor erythroid 2-related factor (Nrf2) was activated to stimulating macroautophagy activity when CMA pathway was impaired. Using A53T mutant SNCA overexpression cellular model to mimic CMA dysfunction situation, we concluded that macroautophagy rather than CMA was responsible to the degradation of SNCAA53T, and this degradation was mediated by Nrf2 activation. Hence, our findings suggested that MANF has potential therapeutic value for PD. Nrf2 and its role in MANF-mediated degradation may provide new sights that target degradation pathways to counteract SNCA pathology in PD.

T lymphocytes are involved in the pathogenesis of Parkinson’s disease (PD), while the heterogeneity of T-cell subpopulations remains elusive. In this study, we analyzed up to 22 subpopulations of T lymphocytes in 115 PD patients and 60 matched healthy controls (HC) using flow cytometry. We found that PD patients exhibited decreased naïve CD8+ T cells (CD3+ CD8+ CD45RA+ CD45RO−) and increased late-differentiated CD4+ T cells (CD3+ CD4+ CD28− CD27−), compared to HC, which were not affected by anti-parkinsonism medication administration. The proportion of naïve CD8+ T cells in PD patients was positively correlated with their severity of autonomic dysfunction and psychiatric complications, but negatively associated with the severity of rapid eye movement and sleep behavior disorder. The proportion of late-differentiated CD4+ T cells was negatively correlated with the onset age of the disease. We further developed individualized PD risk prediction models with high reliability and accuracy on the base of the T lymphocyte subpopulations. These data suggest that peripheral cellular immunity is disturbed in PD patients, and changes in CD8+ T cells and late-differentiated CD4+ T cells are representative and significant. Therefore, we recommend naïve CD8 + and late-differentiated CD4+ T cells as candidates for multicentric clinical study and pathomechanism study of PD.

Nanoparticles are characterized by their large surface area per unit and high dispersion, with excellent affinity and adhesion to the tissue, which help them to contact drugs with tissues. However, the relationship between nuclear-targeted nanoparticles and PI3K/Akt/mTOR pathway, as well as their roles in cervical spondylosis of vertebral artery type (CSA) remain unclear. bEnd.3 cells were in this study exposed to nuclear-targeted nanoparticles, followed by determination of cell biological processes. The role of nuclear-targeted nanoparticles in CSA in relation to PI3K/Akt/mTOR pathway was then analyzed through detection of autophagy-related proteins pathway-related proteins. Nuclear-targeted nanoparticles led to reduced bEnd.3 cell proliferation with IC50 at indicated time points shown as (12.8±0.67), (8.8±0.43), and (4.6±0.42) μmol/L, respectively. Nuclear-targeted nanoparticles blocked bEnd.3 cells in G2/M phase, and induced apoptosis. In addition, nuclear-targeted nanoparticles inhibited the PI3K/Akt/mTOR pathway in the bEnd.3 cells, as evidenced by reduced PI3K, Akt and mTOR levels. Nuclear-targeted nanoparticles decreased the expression of Beclin-1, LC3, p62, Cathepsin D, and ATG5, and increased expression of GSK-3 and Bcl-2. Our present study demonstrated that the nucleartargeted nanoparticles could regulate the growth of bEnd.3 cells in CSA and promote autophagy of cells through blockage of the PI3K/Akt/mTOR signaling pathway.

Deep learning and data-driven approaches have shown great potential in scientific domains. The promise of data-driven techniques relies on the availability of a large volume of high-quality training datasets. Due to the high cost of obtaining data through expensive physical experiments, instruments, and simulations, data augmentation techniques for scientific applications have emerged as a new direction for obtaining scientific data recently. However, existing data augmentation techniques originating from computer vision, yield physically unacceptable data samples that are not helpful for the domain problems that we are interested in. In this paper, we develop new data augmentation techniques based on convolutional neural networks. Specifically, our generative models leverage different physics knowledge (such as governing equations, observable perception, and physics phenomena) to improve the quality of the synthetic data. To validate the effectiveness of our data augmentation techniques, we apply them to solve a subsurface seismic full-waveform inversion using simulated CO$_2$ leakage data. Our interest is to invert for subsurface velocity models associated with very small CO$_2$ leakage. We validate the performance of our methods using comprehensive numerical tests. Via comparison and analysis, we show that data-driven seismic imaging can be significantly enhanced by using our data augmentation techniques. Particularly, the imaging quality has been improved by 15% in test scenarios of general-sized leakage and 17% in small-sized leakage when using an augmented training set obtained with our techniques., Comment: 15 pages, 15 figures, accepted by IEEE Transactions on Geoscience and Remote Sensing, available as early access

BackgroundCarotid body tumor surgery is associated with various complications. However, intraoperative cardiac arrest is very rare and no more than 10 cases have been reported.Case descriptionA 58-year-old woman diagnosed with bilateral carotid body tumors underwent right carotid body tumor surgery. Sudden cardiac arrest occurred during the resection and was attributed to carotid sinus hypersensitivity. The patient recovered after prompt treatment and the tumor was removed completely with no complications.ConclusionCardiac arrest attributed to carotid sinus hypersensitivity during carotid body tumor resection is very rare. Proper treatments can reverse intraoperative cardiac arrest. If carotid sinus hypersensitivity is detected preoperatively, prophylactic temporary pacemaker implantation may be appropriate.

Leaf spot (LS) caused by Cercosporidium personatum is one of the most harmful peanut diseases in the late growth stage and severely affects the yield of peanuts. Hyperspectral disease detection technology is efficient, objective, and accurate and is suitable for large-scale crop management practices. To establish a multi-scale spectral index (SI) with high accuracy and stability for the detection of peanut LS disease, the spectral reflectance of different disease severity levels at leaf, plant, and field scales was collected, and the difference in wavelength caused by disease severity was analyzed using the mean, variance, and dispersion matrix of hyperspectral reflectance. Meanwhile, the feature weights at different scales were obtained using Relief-F, and the average feature weights identified 540, 660, and 770 nm as multi-scale sensitive wavelengths. Three new SIs were constructed by combining single, ratiometric, and normalized wavelengths. The new SIs were compared and analyzed with 35 commonly used SIs by correlation analysis and M-statistic values, and 6 SIs were significantly correlated with disease severity levels and had good separability. Finally, k-nearest neighbor (KNN) and multinomial logistic regression (MLR) were used to evaluate the ability of the above SIs to detect LS severity. The results showed that the leaf spot multi-scale spectral index (LS-MSSI) constructed in this study was superior to the other SIs and obtained high accuracy at different scales simultaneously. At the leaf and plant scales, the MLR obtained high accuracy, with the overall accuracy (OA) reaching 93.77% and 92.50% and Kappa reaching 91.59% and 89.97%, respectively. At the field scale, the KNN obtained high accuracy, with the OA and Kappa reaching 90.29% and 87.04%, respectively. The LS-MSSI proposed in this study has high accuracy, stability, and robustness in the detection of LS severity at multiple scales, providing a technical basis and scientific guidance for the detection and precise management of peanuts.

Background: Pacemaker implantation combined with atrioventricular node ablation (AVNA) could be a practical choice for atrial fibrillation (AF) patients with heart failure (HF). Left bundle branch area pacing (LBBaP) has been widely reported. Objectives: To explore the safety and efficacy of LBBaP combined with AVNA in AF patients with HF. Methods and results: Fifty-six AF patients with HF attempted LBBaP and AVNA from January 2019 to December 2020. Standard LBBaP was achieved in forty-six patients, and another ten received left ventricular septal pacing (LVSP). The cardiac function indexes and pacemaker parameters were evaluated at baseline, and we conducted a 1-month and 1-year follow-up. Result: At the time of implantation and 1-month and 1-year follow-up, QRS duration of LVSP group was longer than that of LBBaP group. The pacemaker parameters remained stable in both the LBBaP and LVSP groups. At 1-month and 1-year follow-up after LBBaP and AVNA, left ventricular ejection fraction, left ventricular end-diastolic diameter, and NYHA classification continued to improve. Baseline left ventricular ejection fraction and QRS duration change at implantation can predict the magnitude of improvement of left ventricular ejection fraction at 1-year after LBBaP. Baseline right atrial left-right diameter, the degree of tricuspid regurgitation, and interventricular septum thickness may be the factors affecting the success of LBBaP. Conclusion: LBBaP combined with AVNA is safe and effective for patients with AF and HF. Baseline right atrial left-right diameter, the degree of tricuspid regurgitation, and interventricular septum thickness may be the factors affecting the success of LBBaP.

BackgroundAxial disturbances are the most disabling symptoms of Parkinson’s disease (PD). Kinect-based objective measures could extract motion characteristics with high reliability and validity.PurposeThe present research aimed to quantify the therapy–response of axial motor symptoms to daily medication regimen and to explore the correlates of the improvement rate (IR) of axial motor symptoms based on a Kinect camera.Materials and methodsWe enrolled 44 patients with PD and 21 healthy controls. All 65 participants performed the Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale part III and the Kinect-based kinematic evaluation to assess arising from a chair, gait, posture, and postural stability before and after medication. Spearman’s correlation analysis and multiple linear regression model were performed to explore the relationships between motor feature IR and clinical data.ResultsAll the features arising from a chair (P = 0.001), stride length (P = 0.001), velocity (P < 0.001), the height of foot lift (P < 0.001), and turning time (P = 0.001) improved significantly after a daily drug regimen in patients with PD. In addition, the anterior trunk flexion (lumbar level) exhibited significant improvement (P = 0.004). The IR of the axial motor symptoms score was significantly correlated with the IRs of kinematic features for gait velocity, stride length, foot lift height, and sitting speed (rs = 0.345, P = 0.022; rs = 0.382, P = 0.010; rs = 0.314, P = 0.038; rs = 0.518, P < 0.001, respectively). A multivariable regression analysis showed that the improvement in axial motor symptoms was associated with the IR of gait velocity only (β = 0.593, 95% CI = 0.023–1.164, P = 0.042).ConclusionAxial symptoms were not completely drug-resistant, and some kinematic features can be improved after the daily medication regimen of patients with PD.

Postural abnormalities are common disabling motor complications affecting patients with Parkinson’s disease (PD). We proposed a summary index for postural abnormalities (IPA) based on Kinect depth camera and explored the clinical value of this indicator. Seventy individuals with PD and thirty age-matched healthy controls (HCs) were enrolled. All participants were tested using a Kinect-based system with IPA automatically obtained by algorithms. Significant correlations were detected between IPA and the Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) total score (rs = 0.369, p = 0.002), MDS-UPDRS-III total score (rs = 0.431, p rs = 0.573, p rs = 0.311, p = 0.010), the 39-item Parkinson’s Disease Questionnaire (PDQ-39) (rs = 0.272, p = 0.0027) and the Berg Balance Scale (BBS) score (rs = −0.350, p = 0.006). The optimal cut-off value of IPA for distinguishing PD from HCs was 12.96 with a sensitivity of 97.14%, specificity of 100.00%, area under the curve (AUC) of 0.999 (0.997–1.002, p p p p

BackgroundExercise plays an essential role in improving motor symptoms in Parkinson’s disease (PD), but the underlying mechanism in the central nervous system remains unclear.MethodsMotor ability was observed after 12-week treadmill exercise on a 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced mouse model of PD. RNA-sequencing on four brain regions (cerebellum, cortex, substantia nigra (SN), and striatum) from control animals, MPTP-induced PD, and MPTP-induced PD model treated with exercise for 12 weeks were performed. Transcriptional networks on the four regions were further identified by an integrative network biology approach.ResultsThe 12-week treadmill exercise significantly improved the motor ability of an MPTP-induced mouse model of PD. RNA-seq analysis showed SN and striatum were remarkably different among individual region’s response to exercise in the PD model. Especially, synaptic regulation pathways about axon guidance, synapse assembly, neurogenesis, synaptogenesis, transmitter transport-related pathway, and synaptic regulation genes, including Neurod2, Rtn4rl2, and Cd5, were upregulated in SN and striatum. Lastly, immunofluorescence staining revealed that exercise rescued the loss of TH+ synapses in the striatal region in PD mice, which validates the key role of synaptic regulation pathways in exercise-induced protective effects in vivo.ConclusionSN and striatum are important brain regions in which critical transcriptional changes, such as in synaptic regulation pathways, occur after the exercise intervention on the PD model.

Quantum computing is a new technology that is expected to revolutionize the computation paradigm in the next few years. Qubits exploit the quantum physics proprieties to increase the parallelism and speed of computation. Unfortunately, besides being intrinsically noisy, qubits have also been shown to be highly susceptible to external sources of faults, such as ionizing radiation. The latest discoveries highlight a much higher radiation sensitivity of qubits than traditional transistors and identify a much more complex fault model than bit-flip. We propose a framework to identify the quantum circuits sensitivity to radiation-induced faults and the probability for a fault in a qubit to propagate to the output. Based on the latest studies and radiation experiments performed on real quantum machines, we model the transient faults in a qubit as a phase shift with a parametrized magnitude. Additionally, our framework can inject multiple qubit faults, tuning the phase shift magnitude based on the proximity of the qubit to the particle strike location. As we show in the paper, the proposed fault injector is highly flexible, and it can be used on both quantum circuit simulators and real quantum machines. We report the finding of more than 285M injections on the Qiskit simulator and 53K injections on real IBM machines. We consider three quantum algorithms and identify the faults and qubits that are more likely to impact the output. We also consider the fault propagation dependence on the circuit scale, showing that the reliability profile for some quantum algorithms is scale-dependent, with increased impact from radiation-induced faults as we increase the number of qubits. Finally, we also consider multi qubits faults, showing that they are much more critical than single faults. The fault injector and the data presented in this paper are available in a public repository to allow further analysis., 13 pages, 11 figures. To be published in the 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN'22)

Leaf spot disease is a dangerous disease that affects peanut growth, and its severity can significantly impact peanut yield. Hyperspectral-based disease detection technology is a popular non-destructive technique due to its high efficiency, objectivity, and accuracy. In this study, peanut leaf spectra at different levels of severity of leaf spot disease were collected in Liaoning Province, China, in mid-August. This study analyzed the differences in wavelengths using mean spectral reflectance and sensitivity. Using improved principal component analysis loading (I-PCA loading) based on the contribution weight assignment approach, we identified three feature wavelengths of 570 nm, 671 nm, and 750 nm. We evaluated the ability of these feature wavelengths to detect the severity of leaf spot disease using k-nearest neighbor (KNN), support vector machine (SVM), and back-propagation (BP) neural network classifiers. Our experimental results showed that our improved PCA loading method achieved higher classification accuracy with fewer wavelengths than the seven commonly used feature selection methods. Among these classifiers, the SVM achieved the highest accuracy, with an overall accuracy (OA) of 96.88% and a Kappa of 95.81%. Therefore, our proposed method can accurately detect the severity of peanut leaf spot disease and provide scientific and technical support for accurately managing peanut crops.

Background Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique that has been closely examined as a possible treatment for Parkinson’s disease (PD). Owing to various rTMS protocols and results, the optimal mode and suitable PD symptoms have yet to be established. Objectives This study intends to systematically evaluate the efficacy of rTMS intervention and identify optimal stimulation protocol of rTMS for specific motor symptoms. Methods PubMed and web of Science databases were searched before January 2022. Eligible studies included sham-controlled and randomized clinical trials of rTMS intervention for motor dysfunction in patients with PD. Standard mean difference (SMD) was calculated with random-effects models. The effects of rTMS on motor symptoms were mainly estimated by the UPDRS-III. Results A total of 1172 articles were identified, of which 32 articles met the inclusion criteria for meta-analysis. The pooled evidence suggested that rTMS relieves motor symptoms of patients with PD (SMD 0.64, 95%CI [0.47, 0.80]). High frequency stimulation on M1 is the most effective mode of intervention (SMD 0.79, 95%CI [0.52, 1.07]). HF rTMS has significant therapeutic effects on limbs motor function (SMD 1.93, 95%CI [0.73, 3.12] for upper limb function and SMD 0.88, 95%CI [0.43, 1.33] for lower limb function), akinesia (SMD 1.17, 95%CI [0.43, 1.92), rigidity (SMD 1.02, 95%CI [0.12, 1.92]) and tremor(SMD 0.91, 95%CI [0.15, 1.67]). Conclusion rTMS therapy is an effective treatment for motor symptoms of PD and the individualized stimulation protocols for different symptoms would further improve its clinical efficacy.

Atherosclerosis is a key pathogenic factor of cardiovascular diseases. However, the role of protein tyrosine phosphatase 1B (PTP1B) in oxidized low-density lipoprotein (ox-LDL)-treated vascular endothelial cells remains unclear. The aim of the present study was to explore the possible physiological roles and mechanism of PTP1B in atherosclerosis using HUVECs as an

Gastroesophageal reflux disease (GERD) is a common digestive disorder that usually has symptoms including reflux, heartburn, pain when swallowing, etc. Evolving from traditional needle acupuncture and electroacupuncture (EA), transcutaneous electrical acustimulation (TEA) becomes a popular method for treating GERD with its non-invasive intervention feature. Recently, an even more effective method synchronized with respiration in TEA is emerging. However, the current procedure for conducting synchronized TEA (STEA) treatment is mostly based on patients’ manual synchronization, which can generate a big delay or error in the synchronization, significantly compromising the effectiveness of this method. To solve this issue, this research presents a novel STEA device that can automatically detect the user’s respiration wave and synchronize with the breath to conduct TEA. With this automated synchronization device, the patients can inhale and exhale with an uninterrupted and normal respiration pace while receiving the TEA treatment, largely simplifying the treatment procedures and enhancing the effectiveness of the method. The system of the STEA device consists of a chest strip respiration sensing element, a stimulation point identifier, and a stimulation current generator. Experiments were conducted to verify human respiration detection, electrical current generation and synchronization. The results demonstrated the feasibility, effectiveness and reliability of the automated device system.

Progressive accumulation of misfolded SNCA/

Background: Pancreatic cancer (PC) is a leading cause of cancer-related deaths worldwide. Human leukocyte antigen complex P5 (HCP5), a member of long noncoding RNAs (lncRNAs), was reported to be associated with the poor prognosis of PC. However, the mechanism of HCP5 in regulating the progression of PC remains poorly defined. Materials and Methods: Quantitative real-time polymerase chain reaction was performed to detect the expression levels of HCP5, microRNA (miR)-140-5p, and cyclin-dependent kinase 8 (CDK8) in PC tissues and cells. Cell counting kit-8 (CCK-8) assay was utilized to check cell proliferation. Transwell assay was employed to evaluate the abilities of cell migration and invasion. Xenograft tumor model was established to investigate the biological role of HCP5 in PC in vivo. The interaction between miR-140-5p and HCP5 or CDK8 was predicted by starBase or TargetScan, respectively. The dual-luciferase reporter assay was conducted to corroborate the interaction. The protein level of CDK8 was measured by Western blot. Results: HCP5 and CDK8 were significantly upregulated in PC tissues and cells, opposite to the expression of miR-140-5p. High expression of HCP5 contributed to the low survival rate and HCP5 silencing inhibited proliferation, migration, and invasion of PC cells in vitro. Simultaneously, in vivo experiments indicated that downregulation of HCP5 suppressed tumor growth. In addition, miR-140-5p was a target of HCP5 and bound to the 3'-untranslated region (3'UTR) of CDK8. Further studies revealed that overexpression of CDK8 reversed the miR-140-5p-mediated inhibitory effect on PC progression. Moreover, downregulation of miR-140-5p or upregulation of CDK8 inverted the silencing-mediated repressive impact of HCP5 on PC progression. Conclusion: Downregulation of HCP5 impeded PC progression by downregulating CDK8 via sponging miR-140-5p.

Yttrium aluminum garnet is abbreviated as YAG, which has many excellent high temperature characteristics. YAG structural materials and functional materials are promising candidates. In this paper, YAG porous ceramic materials with different mechanical properties are prepared by adjusting the parameters of the sintering process to provide reference for the preparation of high-performance porous ceramics. From the experimental results, the following conclusions can be drawn: when the sintering temperature is 1450°C and the holding time is 2h, the YAG gradient porous ceramics have no deformation and shrinkage, have a regular good shape, and have good strength after sintering. The sintering rate was 8 °C/min and carbon emission temperature is 800°C, the structure of the YAG gradient porous material is good, the pores are uniform, and the strength of the sintered sample is high, The porosity is 25.7%, the compressive strength is 8.12MPa.

Dye-sensitized solar cells have better development prospects than silicon cells, and their main structural composition of nanoporous semiconductor films is particularly important. It is the tin dioxide film, and the effect of preparing tin oxide film on dye-sensitized solar cells under different conditions is studied. In this paper, the SnO2 powder was prepared by hydrothermal method, and the experiment was studied by controlled variable method. The properties of the obtained tin dioxide powder were characterized by SEM and three-dimensional ultra-depth microscope and XRD. The XRD diffraction peak is shown as tetragonal phase rutile type SnO2, With the increase of the concentration of tin tetrachloride solution, the coarser the particle size of the tin oxide crystal in the film, the more complicated the surface morphology, so that the specific surface area of the film is larger. At 0.1 mol/L, the surface of the film is a porous structure in the form of a loose sheet is presented. After analyzing the surface microstructure and flatness of the tin dioxide film, it can be concluded that the effect of salt concentration on the specific surface area of the film is extremely large. When the salt concentration is higher or lower, the obtained two the surface of the tin oxide film is relatively flat and has poor performance. When the salt concentration is 0.1mol/L, the surface of the obtained film has a large undulation and a large specific surface area, and the ability to adsorb the dye molecules can be predicted to be the greatest, and the photoelectric conversion efficiency is optimal in the photocatalytic process.

Leaf blast is a disease of rice leaves caused by the Pyricularia oryzae. It is considered a significant disease is affecting rice yield and quality and causing economic losses to food worldwide. Early detection of rice leaf blast is essential for early intervention and limiting the spread of the disease. To quickly and non-destructively classify rice leaf blast levels for accurate leaf blast detection and timely control. This study used hyperspectral imaging technology to obtain hyperspectral image data of rice leaves. The descending dimension methods got rice leaf disease characteristics of different disease classes, and the disease characteristics obtained by screening were used as model inputs to construct a model for early detection of leaf blast disease. First, three methods, ElasticNet, principal component analysis loadings (PCA loadings), and successive projections algorithm (SPA), were used to select the wavelengths of spectral features associated with leaf blast, respectively. Next, the texture features of the images were extracted using a gray level co-occurrence matrix (GLCM), and the texture features with high correlation were screened by the Pearson correlation analysis. Finally, an adaptive-weight immune particle swarm optimization extreme learning machine (AIPSO-ELM) based disease level classification method is proposed to further improve the model classification accuracy. It was also compared and analyzed with a support vector machine (SVM) and extreme learning machine (ELM). The results show that the disease level classification model constructed using a combination of spectral characteristic wavelengths and texture features is significantly better than a single disease feature in terms of classification accuracy. Among them, the model built with ElasticNet + TFs has the highest classification accuracy, with OA and Kappa greater than 90 and 87%, respectively. Meanwhile, the AIPSO-ELM proposed in this study has higher classification accuracy for leaf blast level classification than SVM and ELM classification models. In particular, the AIPSO-ELM model constructed with ElasticNet+TFs as features obtained the best classification performance, with OA and Kappa of 97.62 and 96.82%, respectively. In summary, the combination of spectral characteristic wavelength and texture features can significantly improve disease classification accuracy. At the same time, the AIPSO-ELM classification model proposed in this study has sure accuracy and stability, which can provide a reference for rice leaf blast disease detection.

The sound-evoked electrical compound potential known as auditory brainstem response (ABR) represents the firing of a heterogenous population of auditory neurons in response to sound stimuli, and is often used for clinical diagnosis based on wave amplitude and latency. However, recent ABR applications to detect human cochlear synaptopathy have led to inconsistent results, mainly due to the high variability of ABR wave-1 amplitude. Here, rather than focusing on the amplitude of ABR wave 1, we evaluated the use of ABR wave curvature to detect cochlear synaptic loss. We first compared four curvature quantification methods using simulated ABR waves, and identified that the cubic spline method using five data points produced the most accurate quantification. We next evaluated this quantification method with ABR data from an established mouse model with cochlear synaptopathy. The data clearly demonstrated that curvature measurement is more sensitive and consistent in identifying cochlear synaptic loss in mice compared to the amplitude and latency measurements. We further tested this curvature method in a different mouse model presenting with otitis media. The change in curvature profile due to middle ear infection in otitis media is different from the profile of mice with cochlear synaptopathy. Thus, our study suggests that curvature quantification can be used to address the current ABR variability issue, and may lead to additional applications in the clinic diagnosis of hearing disorders.

Objective To quantify bradykinesia in Parkinson's disease (PD) with a Kinect depth camera-based motion analysis system and to compare PD and healthy control (HC) subjects. Methods Fifty PD patients and twenty-five HCs were recruited. The Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) was used to evaluate the motor symptoms of PD. Kinematic features of five bradykinesia-related motor tasks were collected using Kinect depth camera. Then, kinematic features were correlated with the clinical scales and compared between groups. Results Significant correlations were found between kinematic features and clinical scales ( P Conclusion The Kinect-based motion analysis system can be applied to evaluate bradykinesia in PD. Kinematic features can be used to differentiate PD patients from HCs and combining kinematic features from different motor tasks can significantly improve the diagnostic value.

State change comparison of multiple data items is often necessary in multiple application domains, such as medical science, financial engineering, sociology, biological science, etc. Slope graphs and grouped bar charts have been widely used to show a "before-and-after" story of different data states and indicate their changes. However, they visualize state changes as either slope or difference of bars, which has been proved less effective for quantitative comparison. Also, both visual designs suffer from visual clutter issues with an increasing number of data items. In this paper, we propose Intercept Graph, a novel visual design to facilitate effective interactive comparison of state changes. Specifically, a radial design is proposed to visualize the starting and ending states of each data item and the line segment length explicitly encodes the "state change". By interactively adjusting the radius of the inner circular axis, Intercept Graph can smoothly filter the large state changes and magnify the difference between similar state changes, mitigating the visual clutter issues and enhancing the effective comparison of state changes. We conducted a case study through comparing Intercept Graph with slope graphs and grouped bar charts on real datasets to demonstrate the effectiveness of Intercept Graph.

Tilapia is an economically important fish worldwide, but its quality is affected by storage practices. To improve the quality of tilapia fillets during frozen storage, we examined the effect of pretreatment with various combinations of different concentrations of trehalose, potassium bicarbonate, and chitosan. Following pretreatment, we analyzed the tilapia fillets using quality indicators, including soaking weight gain, coating weight gain, water-holding capacity, thawing loss, pH, Ca