Thermophotovoltaic System Efficiency Simulation

Thermophotovoltaic (TPV) power systems, which convert heat into electricity using a photovoltaic diode to collect thermal radiation, have attracted increasing attention in recent work. It has recently been proposed that new optical structures such as photonic crystals can significantly improve the efficiency of these devices in two ways. First, the electronic bandgap of the TPV diode should match the photonic bandgap of the emitter, in order to ensure that the majority of emitted photons can be converted. Second, a photonic crystal short-pass optical filter can be added to the front of the TPV diode to send long wavelength photons back to the hot emitter, which is known as photon recycling. This filter can consist of a quarter wave stack of two materials, or many materials blended together into a so-called rugate filter. Here we present a tool, freely available through nanoHUB.org that allows one to simulate and optimize TPV performance when using these components at a system level. A graphical user interface (GUI) was developed using the Rappture toolkit that allows one to specify the materials and the geometric structure of the selective emitter, filter, and TPV diode. This information is subsequently supplied to two simulations: a finite difference time-domain simulation, known as MEEP, which yields the thermal emission spectrum of the photonic structure; and a Fourier modal method simulation, known as S4, which outputs the filter spectrum. Finally, we explored a constrained range of design parameters to find optimal values that warrant further theoretical and experimental investigation

CAS9 is a genome mutator by directly disrupting DNA-PK dependent DNA repair pathway.

With its high efficiency for site-specific genome editing and easy manipulation, the clustered regularly interspaced short palindromic repeats (CRISPR)/ CRISPR associated protein 9 (CAS9) system has become the most widely used gene editing technology in biomedical research. In addition, significant progress has been made for the clinical development of CRISPR/CAS9 based gene therapies of human diseases, several of which are entering clinical trials. Here we report that CAS9 protein can function as a genome mutator independent of any exogenous guide RNA (gRNA) in human cells, promoting genomic DNA double-stranded break (DSB) damage and genomic instability. CAS9 interacts with the KU86 subunit of the DNA-dependent protein kinase (DNA-PK) complex and disrupts the interaction between KU86 and its kinase subunit, leading to defective DNA-PK-dependent repair of DNA DSB damage via non-homologous end-joining (NHEJ) pathway. XCAS9 is a CAS9 variant with potentially higher fidelity and broader compatibility, and dCAS9 is a CAS9 variant without nuclease activity. We show that XCAS9 and dCAS9 also interact with KU86 and disrupt DNA DSB repair. Considering the critical roles of DNA-PK in maintaining genomic stability and the pleiotropic impact of DNA DSB damage responses on cellular proliferation and survival, our findings caution the interpretation of data involving CRISPR/CAS9-based gene editing and raise serious safety concerns of CRISPR/CAS9 system in clinical application

CNN Based Touch Interaction Detection for Infant Speech Development

In this paper, we investigate the detection of interaction in videos between two people, namely, a caregiver and an infant. We are interested in a particular type of human interaction known as touch, as touch is a key social and emotional signal used by caregivers when interacting with their children. We propose an automatic touch event recognition method to determine the potential time interval when the caregiver touches the infant. In addition to label the touch events, we also classify them into six touch types based on which body part of infant has been touched. CNN based human pose estimation and person segmentation are used to analyze the spatial relationship between the caregivers hands and the infants. We demonstrate promising results for touch detection and show great potential of reducing human effort in manually generating precise touch annotations

Touch Event Recognition For Human Interaction

This paper investigates the interaction between two people, namely, a caregiver and an infant. A particular type of action in human interaction known as “touch” is described. We propose a method to detect “touch event” that uses color and motion features to track the hand positions of the caregiver. Our approach addresses the problem of hand occlusions during tracking. We propose an event recognition method to determine the time when the caregiver touches the infant and label it as a “touch event” by analyzing the merging contours of the caregiver’s hands and the infant’s contour. The proposed method shows promising results compared to human annotated dat

MTR4 drives liver tumorigenesis by promoting cancer metabolic switch through alternative splicing.

The metabolic switch from oxidative phosphorylation to glycolysis is required for tumorigenesis in order to provide cancer cells with energy and substrates of biosynthesis. Therefore, it is important to elucidate mechanisms controlling the cancer metabolic switch. MTR4 is a RNA helicase associated with a nuclear exosome that plays key roles in RNA processing and surveillance. We demonstrate that MTR4 is frequently overexpressed in hepatocellular carcinoma (HCC) and is an independent diagnostic marker predicting the poor prognosis of HCC patients. MTR4 drives cancer metabolism by ensuring correct alternative splicing of pre-mRNAs of critical glycolytic genes such as GLUT1 and PKM2. c-Myc binds to the promoter of the MTR4 gene and is important for MTR4 expression in HCC cells, indicating that MTR4 is a mediator of the functions of c-Myc in cancer metabolism. These findings reveal important roles of MTR4 in the cancer metabolic switch and present MTR4 as a promising therapeutic target for treating HCC

Mushroom-derived bioactive components with definite structures in alleviating the pathogenesis of Alzheimer’s disease

Alzheimer’s disease (AD) is a complicated neurodegenerative condition with two forms: familial and sporadic. The familial presentation is marked by autosomal dominance, typically occurring early in individuals under 65 years of age, while the sporadic presentation is late-onset, occurring in individuals over the age of 65. The majority of AD cases are characterized by late-onset and sporadic. Despite extensive research conducted over several decades, there is a scarcity of effective therapies and strategies. Considering the lack of a cure for AD, it is essential to explore alternative natural substances with higher efficacy and fewer side effects for AD treatment. Bioactive compounds derived from mushrooms have demonstrated significant potential in AD prevention and treatment by different mechanisms such as targeting amyloid formation, tau, cholinesterase dysfunction, oxidative stress, neuroinflammation, neuronal apoptosis, neurotrophic factors, ER stress, excitotoxicity, and mitochondrial dysfunction. These compounds have garnered considerable interest from the academic community owing to their advantages of multi-channel, multi-target, high safety and low toxicity. This review focuses on the various mechanisms involved in the development and progression of AD, presents the regulatory effects of bioactive components with definite structure from mushroom on AD in recent years, highlights the possible intervention pathways of mushroom bioactive components targeting different mechanisms, and discusses the clinical studies, limitations, and future perspectives of mushroom bioactive components in AD prevention and treatment

Touch Event Detection and Texture Analysis for Video Compression

Touch event detection investigates the interaction between two people from video recordings. We are interested in a particular type of interaction which occurs between a caregiver and an infant, as touch is a key social and emotional signal used by caregivers when interacting with their children. We propose an automatic touch event detection and recognition method to determine the potential timing when the caregiver touches the infant, and classify the event into six touch types based on which body part of the infant has been touched. We leverage deep learning based human pose estimation and person segmentation to analyze the spatial relationship between the caregivers’ hands and the infant. We demonstrate promising performance on touch event detection and classification, showing great potential for reducing human effort when generating groundtruth annotation. Recently, artificial intelligence powered techniques have shown great potential to increase the efficiency of video compression. In this thesis, we describe a texture analysis pre-processing method that leverages deep learning based scene understanding to extract semantic areas for the improvement of subsequent video coder. Our proposed method generates a pixel-level texture mask by combining the semantic segmentation with simple postprocessing strategy. Our approach is integrated into a switchable texture-based video coding method. We demonstrate that for many standard and user generated test sequences, the proposed method achieves significant data rate reduction without noticeable visual artifacts

Novel model based on ultrasound predict axillary lymph node metastasis in breast cancer

Abstract Background Whether there is axillary lymph node metastasis is crucial for formulating the treatment plan for breast cancer. Currently, invasive methods are still used for preoperative evaluation of lymph nodes. If non-invasive preoperative evaluation can be achieved, it will effectively improve the treatment plan. Objective Constructed a predict model based on ultrasound examination, which forest axillary lymph node metastasis in breast cancer, and validated this model. Method Patients admitted to Xiamen First Hospital from April 2018 to August 2021 with complete case data were included in this study. Patients who had undergone breast cancer resection and axillary lymph node dissection or sentinel lymph node biopsy were divided into a training and validation cohort in a 7:3 ratio. In the training cohort, patients were divided into metastatic and non-metastatic groups based on whether axillary lymph nodes had metastasis. The parameters of the two groups were compared, and statistically significant parameters were included in multivariate analysis. Then, a Nomogram model was constructed, named Lymph metastasis predict model (LMPM). Calibration curves, receiver operating curve (ROC), and decision curve analysis (DCA) were plotted between the training and validation cohort, calculate the risk score of each patient, identify the optimal cutoff value, and test the predictive efficacy of LMPM. Result Two hundred seventy-three patients were enrolled in final study, the average age 49.7 ± 8.7, training cohort included 191 patients, the diameter of breast cancer, the lymph node peak systolic flow velocity (LNPS) and the cortex area hilum ratio (CH) of lymph node were exist significant difference in metastatic and non-metastatic group. Multivariate analysis showed cancer diameter, LNPS and CH included in LMPM, the cutoff value was 95, the calibration curve, ROC, DCA in training and validation cohort show satisfactory result. Conclusion The predict model-LMPM, can predict axillary lymph node metastasis in breast cancer, which is useful for developing personalized treatment plans. However, further validation of the model is required by incorporating a larger number of patients

A machine learning model for predicting noise limits of motor vehicles in UNECE R51 regulations

It is vital to greatly reduce traffic noises emitted by motor vehicles during accelerating through determining limit values of noises and further improve technical specifications and comforts of these automobiles for automotive manufacturers. The United Nations Economic Commission for Europe (UNECE) R51 regulations define the noise limits for all vehicle categories, which are kept updating, and these noise limits are implemented by governments all over the world; however, the automobile manufactures need to estimate future values of noise limits for developing their next-generation vehicles. In this study, a machine learning model using the back-propagation neural network (BPNN) approach is developed to determine noise limits of a vehicle during accelerating by using historic data and predict its noise limits for future revisions of the UNECE R51 regulations. The proposed prediction model adopts the Levenberg-Marquardt algorithm which can automatically adapt its learning rate to train the model with input data, and at the same time randomly select the validation data and test data to verify the correlation and determine the accuracy of the prediction results. To showcase the proposed prediction model, acceleration noise limits from six historic data are used for training the model, and the noise limits at the seventh version can be predicted and validated. As the results achieve a required accuracy, vehicle noise limits in the next revision as the future eighth version can be predicted based on these data. It can be found that the obtained prediction results are much close to those noise limits defined in current regulations and negative error ratios are reduced significantly, compared to those values obtained using a quadratic regression model. As a result, the proposed BPNN model can predict future noise limits for the next revision of the UNECE R51automotive noise limit regulations

Clinical and imaging characteristics of growing skull fractures in children

Abstract Growing skull fracture (GSF) is an uncommon form of head trauma among young children. In prior research, the majority of GSFs were typically classified based on pathophysiological mechanisms or the duration following injury. However, considering the varying severity of initial trauma and the disparities in the time elapsed between injury and hospital admission among patients, our objective was to devise a clinically useful classification system for GSFs among children, grounded in both clinical presentations and imaging findings, in order to guide clinical diagnosis and treatment decisions. The clinical and imaging data of 23 patients less than 12 years who underwent GSF were retrospectively collected and classified into four types. The clinical and imaging characteristics of the different types were reviewed in detail and statistically analyzed. In all 23 patients, 5 in type I, 7 in type II, 8 in type III, and 3 in type IV. 21/23 (91.3%) were younger than 3 years. Age ≤ 3 years and subscalp fluctuating mass were common in type I–III (P = 0.026, P = 0.005). Fracture width ≥ 4 mm was more common in type II–IV (P = 0.003), while neurological dysfunction mostly occurred in type III and IV (P < 0.001).Skull “crater-like” changes were existed in all type IV. 10/12 (83.3%) patients with neurological dysfunction had improved in motor or linguistic function. There was not improved in patients with type IV. GCS in different stage has its unique clinical and imaging characteristics. This classification could help early diagnosis and treatment for GCS, also could improve the prognosis significantly