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This study employs a simple liquid-phase deposition method to grow TiO2 onto nanoscale silver wires, with a diameter of 90 nm and a length of 15∼20 μm. This core-shell structure possesses a high specific surface area, which enhances the gas reaction surface. Measurements can be conducted at room temperature. The main mechanism relies on the conductive properties of silver to transmit electrons, while Schottky barriers between Ag and TiO2 further accelerate oxygen ionization and surface redox reactions. This structure may contribute to enhancing the response value, response time, and recovery time of gas sensors.

Abstract Amorphous indium tin zinc oxide (a-ITZO)/Bi2Se3 nanoplatelets (NPs) were fabricated using a two-step procedure. First, Bi2Se3 NPs were synthesized through thermal chemical vapor deposition at 600 °C on a glass substrate, and then a-ITZO was deposited on the surface of the Bi2Se3 NPs via magnetron sputtering at room-temperature. The crystal structures of the a-ITZO/Bi2Se3 NPs were determined via X-ray diffraction spectroscopy and high-resolution transmission electron microscopy. The elemental vibration modes and binding energies were measured using Raman spectroscopy and X-ray photoelectron spectroscopy. The morphologies were examined using field-emission scanning electron microscopy. The electrical properties of the a-ITZO/Bi2Se3 NPs were evaluated using Hall effect measurements. The bulk carrier concentration of a-ITZO was not affected by the heterostructure with Bi2Se3. In the case of the Bi2Se3 heterostructure, the carrier mobility and conductivity of a-ITZO were increased by 263.6% and 281.4%, respectively, whereas the resistivity of a-ITZO was reduced by 73.57%. This indicates that Bi2Se3 significantly improves the electrical properties of a-ITZO through its heterostructure, expanding its potential applications in electronic and thermoelectric devices.

Abstract The gut microbiome and its metabolites are increasingly implicated in several cardiovascular diseases, but their role in human myocardial infarction (MI) injury responses have yet to be established. To address this, we examined stool samples from 77 ST-elevation MI (STEMI) patients using 16 S V3-V4 next-generation sequencing, metagenomics and machine learning. Our analysis identified an enriched population of butyrate-producing bacteria. These findings were then validated using a controlled ischemia/reperfusion model using eight nonhuman primates. To elucidate mechanisms, we inoculated gnotobiotic mice with these bacteria and found that they can produce beta-hydroxybutyrate, supporting cardiac function post-MI. This was further confirmed using HMGCS2-deficient mice which lack endogenous ketogenesis and have poor outcomes after MI. Inoculation increased plasma ketone levels and provided significant improvements in cardiac function post-MI. Together, this demonstrates a previously unknown role of gut butyrate-producers in the post-MI response.

Sediment concentration (SC) monitoring has always been a pressing issue in water resource management, as many existing instruments still face challenges in accurately measuring due to environmental factors and instrument limitations. A robust technology is worth presenting to apply in the field site. This study firstly uses mean-absolute-error (MAE), root-mean-square error (RMSE), correlation coefficient (CC), and Nash–Sutcliffe efficiency coefficient (NSE) to describe the performance of the proposed convolutional neural network (CNN). Moreover, adapting the ensemble learning concept to compare the multiple machine learning (ML) approaches, the CNN presents the highest predicted accuracy, 91%, better than SVM (79%), VGG19 (63%) and ResNet50 (35%). As a result, the proposed CNN framework can appropriately apply the monitoring needs. The primary purpose is to develop a simple, accurate, and stable SC monitoring technology. Instead of some complex architectures, a simple and small neural network is adopted to implement real-time application (RTA). Via our design, such a traditional but critical issue can be improved to a new state. For example, by incorporating the concept of the Internet of Things (IoT) with our design, the distributed computing system for large-scale environmental monitoring can be realized quickly and easily. HIGHLIGHTS Safety: Contactless technology.; Simple: Improving the computation efficiency for real-time prediction.; Accuracy: Presenting satisfied accuracy by comparing it with the current technology.; Stability: Overcoming outlier trouble in the prediction process to show sufficient stability.; Development: Incorporating the IoT to develop the large-scale environmental monitoring platform quickly and easily.;

In recent years, with the increasing demand for high-quality images in various fields, more and more attention has been focused on noise removal techniques for image processing. The effective elimination of unwanted noise plays a crucial role in improving image quality. To meet this challenge, many noise removal methods have been proposed, among which the diffusion model has become one of the focuses of many researchers. In order to make the restored image closer to the real image and retain more features of the image, this paper proposes a DIR-SDE method with reference to the diffusion models of IR-SDE and IDM, which improve the feature retention of the image in the de-raining process, and then improve the realism of the image for the image de-raining task. In this study, IR-SDE was used as the base structure of the diffusion model, IR-SDE was improved, and DINO-ViT was combined to enhance the image features. During the diffusion process, the image features were extracted using DINO-ViT, and these features were fused with the original images to enhance the learning effect of the model. The model was also trained and validated with the Rain100H dataset. Compared with the IR-SDE method, it improved 0.003 in the SSIM, 0.003 in the LPIPS, and 1.23 in the FID. The experimental results show that the diffusion model proposed in this study can effectively improve the image restoration performance.

BackgroundStudies have confirmed that osteoporosis has been considered as one of the complications of diabetes, and the health hazards to patients are more obvious. This study is mainly based on the Taiwan National Health Insurance Database (TNHID). Through the analysis of TNHID, it is shown that the combined treatment of traditional Chinese medicine (TCM) medicine in patients of diabetes with osteoporosis (T2DOP) with lower related risks.MethodsAccording to the study design, 3131 patients selected from TNHID who received TCM treatment were matched by 1-fold propensity score according to gender, age, and inclusion date as the control group. Cox proportional hazards analyzes were performed to compare fracture surgery, hospitalization, and all-cause mortality during a mean follow-up from 2000 to 2015.ResultsA total of 1055/1469/715 subjects (16.85%/23.46%/11.42%) had fracture surgery/inpatient/all-cause mortality of which 433/624/318 (13.83%/19.93%/10.16%) were in the TCM group) and 622/845/397 (19.87%/26.99%/12.68%) in the control group. Cox proportional hazards regression analysis showed that subjects in the TCM group had lower rates of fracture surgery, inpatient and all-cause mortality (adjusted HR = 0.467; 95% CI = 0.225-0.680, PConclusionThis study provides longitudinal evidence through a cohort study of the value of integrated TCM for T2DOP. More research is needed to fully understand the clinical significance of these results.

The purpose of this study is to examine the viability, precision, and consistency of a computer-based optokinetic nystagmus analyzer (nystagmus meter) for diagnosing eyesight in preschoolers. A total of 59 subjects who could pass the log of minimum angle of resolution (LogMAR) visual acuity chart were divided into three groups by age, 4-, 5-, and 6-year-old groups, and their visual acuity was tested with nystagmus meter. The percentage of children in each age group that could be detected by nystagmus was recorded along with the differences between these groups. The correlation between the test results from the two methods was found for each age group using the correlation coefficient method. Repeated measurements were used to assess the two visual acuity values of the measured nystagmus, and the repeatability of the two measurement techniques for different age groups was compared. The overall measurability of the visual acuity detected by nystagmus was 93.22%, and the measurability of the 4-, 5-, and 6-year-old groups was 90%, 95%, and 94.74%, respectively. There was no statistically significant difference in the measurability of subjects among all age groups (P = 1.0). The outcomes of the LogMAR visual acuity chart had a negative correlation with the visual acuity measured by the nystagmus meter. The overall correlation coefficient R value was -0.80, and the correlation coefficient R value of the 4-, 5-, and 6-year-old groups was -0.79, -0.76, and -0.87, respectively. The nystagmus meter has good feasibility, accuracy, and stability in visual acuity testing and can be used for visual acuity testing in children.

Predicting the length of hospital stay for myasthenia gravis (MG) patients is challenging due to the complex pathogenesis, high clinical variability, and non-linear relationships between variables. Considering the management of MG during hospitalization, it is important to conduct a risk assessment to predict the length of hospital stay. The present study aimed to successfully predict the length of hospital stay for MG based on an expandable data mining technique, multivariate adaptive regression splines (MARS). Data from 196 MG patients' hospitalization were analyzed, and the MARS model was compared with classical multiple linear regression (MLR) and three other machine learning (ML) algorithms. The average hospital stay duration was 12.3 days. The MARS model, leveraging its ability to capture non-linearity, identified four significant factors: disease duration, age at admission, MGFA clinical classification, and daily prednisolone dose. Cut-off points and correlation curves were determined for these risk factors. The MARS model outperformed the MLR and the other ML methods (including least absolute shrinkage and selection operator MLR, classification and regression tree, and random forest) in assessing hospital stay length. This is the first study to utilize data mining methods to explore factors influencing hospital stay in patients with MG. The results highlight the effectiveness of the MARS model in identifying the cut-off points and correlation for risk factors associated with MG hospitalization. Furthermore, a MARS-based formula was developed as a practical tool to assist in the measurement of hospital stay, which can be feasibly supported as an extension of clinical risk assessment.

Abstract ZnO/carbon-black heterostructures were synthesized using a sol–gel method and crystallized by annealing at 500 °C under 2 × 10−2 Torr for 10 min. The crystal structures and binding vibration modes were determined by XRD, HRTEM, and Raman spectrometry. Their surface morphologies were observed by FESEM. The Moiré pattern that is observed in the HRTEM images confirms that the carbon-black nanoparticles were covered by the ZnO crystals. Measurements of optical absorptance revealed that the optical band gap of the ZnO/carbon-black heterostructures increased from 2.33 to 2.98 eV as the carbon-black nanoparticle content increases from 0 to 8.33 × 10−3 mol owing to the Burstein–Moss effect. The photoluminescence intensities at the near-band edge and of the violet, and blue light were increased by factors about 68.3, 62.8, and 56.8, respectively, when the carbon-black contents is of the 2.03 × 10−3 mol. This work reveals that the proper carbon-black nanoparticle content involved increases the PL intensities of the ZnO crystals in the short wavelength regime, supporting their potential application in the light-emitting devices.

Diagnostic markers for myasthenia gravis (MG) are limited; thus, innovative approaches are required for supportive diagnosis and personalized care. Gut microbes are associated with MG pathogenesis; however, few studies have adopted machine learning (ML) to identify the associations among MG, gut microbiota, and metabolites. In this study, we developed an explainable ML model to predict biomarkers for MG diagnosis. We enrolled 19 MG patients and 10 non-MG individuals. Stool samples were collected and microbiome assessment was performed using 16S rRNA sequencing. Untargeted metabolic profiling was conducted to identify fecal amplicon significant variants (ASVs) and metabolites. We developed an explainable ML model in which the top ASVs and metabolites are combined to identify the best predictive performance. This model uses the SHapley Additive exPlanations method to generate both global and personalized explanations. Fecal microbe–metabolite composition differed significantly between groups. The key bacterial families were Lachnospiraceae and Ruminococcaceae, and the top three features were Lachnospiraceae, inosine, and methylhistidine. An ML model trained with the top 1 % ASVs and top 15 % metabolites combined outperformed all other models. Personalized explanations revealed different patterns of microbe–metabolite contributions in patients with MG. The integration of the microbiota-metabolite features and the development of an explainable ML framework can accurately identify MG and provide personalized explanations, revealing the associations between gut microbiota, metabolites, and MG. An online calculator employing this algorithm was developed that provides a streamlined interface for MG diagnosis screening and conducting personalized evaluations.

Abstract Background Diabetes with co-existing bone fragility or osteoporosis is common in elderly patients, whereas is frequently underestimated. Methods We conducted dual-energy x-ray absorptiometry (DXA) with 7-site skinfold (SF) and dominant hand grip strength measurements among patients with type 2 diabetes (T2DM) to assess their gender-specific associations. A total of 103 patients with T2DM (60 females and 43 males), aged between 50 and 80 years (median 68.0 years) were enrolled and 45 non-DM females were also included to compare with T2DM females. Results Our results revealed osteoporosis was negatively correlated with grip strength in both genders, negatively correlated with lean mass solely in males and negatively correlated with fat mass (particular the gynoid fat mass and thigh SF thickness) in females. Via performing multivariable stepwise logistic regression, we identified grip strength in both genders and thigh SF thickness in females as predictors for osteoporosis. Receiver operating characteristic curve analysis further disclosed 20.5 mm female thigh skinfold thickness, 18.1 kg female grip strength and 29.0 kg male grip strength as reasonable cutoff levels for predicting osteoporosis in the Taiwanese patients with T2DM. Conclusions Patients with T2DM presented gender-specific associations between osteoporosis, body composition and grip strength. Grip strength and thigh SF thickness might serve as predictors for detection of osteoporosis in patients with T2DM.

Myasthenia gravis (MG) is a neuromuscular junction disease with a complex pathophysiology and clinical variation for which no clear biomarker has been discovered. We hypothesized that because changes in gut microbiome composition often occur in autoimmune diseases, the gut microbiome structures of patients with MG would differ from those without, and supervised machine learning (ML) analysis strategy could be trained using data from gut microbiota for diagnostic screening of MG. Genomic DNA from the stool samples of MG and those without were collected and established a sequencing library by constructing amplicon sequence variants (ASVs) and completing taxonomic classification of each representative DNA sequence. Four ML methods, namely least absolute shrinkage and selection operator, extreme gradient boosting (XGBoost), random forest, and classification and regression trees with nested leave-one-out cross-validation were trained using ASV taxon–based data and full ASV–based data to identify key ASVs in each data set. The results revealed XGBoost to have the best predicted performance. Overlapping key features extracted when XGBoost was trained using the full ASV–based and ASV taxon–based data were identified, and 31 high-importance ASVs (HIASVs) were obtained, assigned importance scores, and ranked. The most significant difference observed was in the abundance of bacteria in the Lachnospiraceae and Ruminococcaceae families. The 31 HIASVs were used to train the XGBoost algorithm to differentiate individuals with and without MG. The model had high diagnostic classification power and could accurately predict and identify patients with MG. In addition, the abundance of Lachnospiraceae was associated with limb weakness severity. In this study, we discovered that the composition of gut microbiomes differed between MG and non-MG subjects. In addition, the proposed XGBoost model trained using 31 HIASVs had the most favorable performance with respect to analyzing gut microbiomes. These HIASVs selected by the ML model may serve as biomarkers for clinical use and mechanistic study in the future. Our proposed ML model can identify several taxonomic markers and effectively discriminate patients with MG from those without with a high accuracy, the ML strategy can be applied as a benchmark to conduct noninvasive screening of MG.

Lumbar disc bulging or herniation (LDBH) is one of the major causes of spinal stenosis and related nerve compression, and its severity is the major determinant for spine surgery. MRI of the spine is the most important diagnostic tool for evaluating the need for surgical intervention in patients with LDBH. However, MRI utilization is limited by its low accessibility. Spinal X-rays can rapidly provide information on the bony structure of the patient. Our study aimed to identify the factors associated with LDBH, including disc height, and establish a clinical diagnostic tool to support its diagnosis based on lumbar X-ray findings. In this study, a total of 458 patients were used for analysis and 13 clinical and imaging variables were collected. Five machine-learning (ML) methods, including LASSO regression, MARS, decision tree, random forest, and extreme gradient boosting, were applied and integrated to identify important variables for predicting LDBH from lumbar spine X-rays. The results showed L4-5 posterior disc height, age, and L1-2 anterior disc height to be the top predictors, and a decision tree algorithm was constructed to support clinical decision-making. Our study highlights the potential of ML-based decision tools for surgeons and emphasizes the importance of L1-2 disc height in relation to LDBH. Future research will expand on these findings to develop a more comprehensive decision-supporting model.

This paper proposes a solution to mitigate the trade-off between dark and photocurrents in the indium gallium zinc oxide (IGZO) thin-film transistor (TFT) applications, such as ultraviolet photodetectors, by using a stacked Pt/NiO dual capping layer (CL). The Pt CL forms a Schottky contact with the IGZO channel, which maximizes the depletion width on the channel layer and allows the use of a thicker channel to suppress both dark current and channel resistance. On the other hand, the NiO CL forms a pn heterojunction with the IGZO channel, which provides additional space for generating electron–hole pairs and is forward biased by the photovoltaic voltage under UV irradiation, resulting in a further negative shift in the threshold voltage and a significant increase in photocurrent. Experimental results show that the proposed CL scheme exhibits excellent photoresponsivity, photosensitivity, and specific detectivity in a 40 nm-thick IGZO TFT, with the values of 1888 A/W, 3.37 × 108 A/A, and 3.99 × 1016 Jones, respectively, which are about 55%, 83%, and 68% higher than a traditional 30 nm-thick IGZO TFT using only NiO CL under 275 nm UV irradiation.

In recent years, with the increasing demand for high-quality images in various fields, more and more attention has been focused on noise removal techniques for image processing. The effective elimination of unwanted noise plays a crucial role in improving image quality. To meet this challenge, many noise removal methods have been proposed, among which the diffusion model has become one of the focuses of many researchers. In order to make the restored image closer to the real image and retain more features of the image, this paper proposes a DIR-SDE method with reference to the diffusion models of IR-SDE and IDM, which improve the feature retention of the image in the de-raining process, and then improve the realism of the image for the image de-raining task. In this study, IR-SDE was used as the base structure of the diffusion model, IR-SDE was improved, and DINO-ViT was combined to enhance the image features. During the diffusion process, the image features were extracted using DINO-ViT, and these features were fused with the original images to enhance the learning effect of the model. The model was also trained and validated with the Rain100H dataset. Compared with the IR-SDE method, it improved 0.003 in the SSIM, 0.003 in the LPIPS, and 1.23 in the FID. The experimental results show that the diffusion model proposed in this study can effectively improve the image restoration performance. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have shown promise in accurately diagnosing coronavirus disease 2019 (COVID-19) and bacterial pneumonia using chest X-ray images. However, determining the optimal feature extraction approach is challenging. This study investigates the use of fusion-extracted features by deep networks to improve the accuracy of COVID-19 and bacterial pneumonia classification with chest X-ray radiography. A Fusion CNN method was developed using five different deep learning models after transferred learning to extract image features (Fusion CNN). The combined features were used to build a support vector machine (SVM) classifier with a RBF kernel. The performance of the model was evaluated using accuracy, Kappa values, recall rate, and precision scores. The Fusion CNN model achieved an accuracy and Kappa value of 0.994 and 0.991, with precision scores for normal, COVID-19, and bacterial groups of 0.991, 0.998, and 0.994, respectively. The results indicate that the Fusion CNN models with the SVM classifier provided reliable and accurate classification performance, with Kappa values no less than 0.990. Using a Fusion CNN approach could be a possible solution to enhance accuracy further. Therefore, the study demonstrates the potential of deep learning and fusion-extracted features for accurate COVID-19 and bacterial pneumonia classification with chest X-ray radiography.

Summary: In this study, we establish a population-based human induced pluripotent stem cell (hiPSC) drug screening platform for toxicity assessment. After recruiting 1,000 healthy donors and screening for high-frequency human leukocyte antigen (HLA) haplotypes, we identify 13 HLA-homozygous “super donors” to represent the population. These “super donors” are also expected to represent at least 477,611,135 of the global population. By differentiating these representative hiPSCs into cardiomyocytes and neurons we show their utility in a high-throughput toxicity screen. To validate hit compounds, we demonstrate dose-dependent toxicity of the hit compounds and assess functional modulation. We also show reproducible in vivo drug toxicity results using mouse models with select hit compounds. This study shows the feasibility of using a population-based hiPSC drug screening platform to assess cytotoxicity, which can be used as an innovative tool to study inter-population differences in drug toxicity and adverse drug reactions in drug discovery applications.

It is not practical to assume that all vehicles must have the communication ability for the purpose of realizing the IoV-based collision avoidance architecture. Hence, we need some extra designs to make up for the deficiency. In the literature, some researches use a camera mounted on the infrastructure at an intersection to implement the collision detection. Particularly, they utilize the real-time object detection and dynamic prediction to predict the future positions of vehicles for the collision avoidance. In this paper, we propose a new method to predict the future positions of vehicles as well, which is based on a well-known real-time object detection project, YOLOv4. By incorporating the concept of vehicle dynamics with our machine learning architecture, our design can estimate the further future vehicular position more accurately and stably. Lastly, the experimental results show the performance of our algorithm for predicting the future vehicular positions and realizing the collision avoidance architecture.

Abstract B-cell translocation gene 3 (BTG3) is a member of the antiproliferative BTG gene family and is a downstream target of p53. Here, we show that senescence triggered by BTG3 depletion was accompanied by a secretome enriched with cytokines, growth factors, and matrix-remodeling enzymes, which could promote angiogenesis and cell scattering in vitro. We present evidence that at least part of these activities can be explained by elevated HIF-1α activity. Mechanistically, the BTG3 C-terminal domain competes with the coactivator p300 for binding the HIF-1α transactivation domain. The angiogenic promoting effect of BTG3 knockdown was largely diminished upon co-depletion of HIF-1α, indicating that HIF-1α is a major downstream target of BTG3 in the control of angiogenesis. In vivo, ectopic expression of BTG3 suppresses angiogenesis in xenograft tumors; and syngenic tumor growth and metastasis were enhanced in Btg3-null mice. Moreover, analysis of clinical datasets revealed that a higher BTG3/VEGFA expression ratio correlates with improved patient survival in a number of cancer types. Taken together, our findings highlight the non-autonomous regulation of tumor microenvironment by BTG3 while suppressing tumor progression.

Advanced Driver Assistance Systems (ADAS) are only applied to relatively simple scenarios, such as highways. If there is an emergency while driving, the driver should take control of the car to deal properly with the situation at any time. Obviously, this incurs the uncertainty of safety. Recently, in the literature, several studies have been proposed for the above-mentioned issue via Artificial Intelligence (AI). The achievement is exactly the aim that we look forward to, i.e., the autonomous vehicle. In this paper, we realize the autonomous driving control via Deep Reinforcement Learning (DRL) based on the CARLA (Car Learning to Act) simulator. Specifically, we use the ordinary Red-Green-Blue (RGB) camera and semantic segmentation camera to observe the view in front of the vehicle while driving. Then, the captured information is utilized as the input for different DRL models so as to evaluate the performance, where the DRL models include DDPG (Deep Deterministic Policy Gradient) and RDPG (Recurrent Deterministic Policy Gradient). Moreover, we also design an appropriate reward mechanism for these DRL models to realize efficient autonomous driving control. According to the results, only the RDPG strategies can finish the driving mission with the scenario that does not appear/include in the training scenario, and with the help of the semantic segmentation camera, the RDPG control strategy can further improve its efficiency.

This research incorporated an auction mechanism into the vehicle routing problem with occasional drivers and produced simulations in an agent-based environment. Auctions were used to match online orders with potential occasional drivers. While a centralized system optimizes system performance under global objectives, the novel decentralized approach presented here illustrates emergent phenomena resulting from the interaction of individual entities in highly dynamic cases. In the simulations, the auctions were executed after a fixed time interval called a rolling time horizon. Our results suggest that the appropriate rolling time horizon produces a lower average unit compensation cost because better matches can be found when the accumulation of online orders and occasional drivers is maintained at a certain level. The simulation results also indicate that the use of an auction mechanism instead of simple nonauction rules can improve the average unit compensation cost by up to 25.1%., Author(s): Chung-Wei Shen (corresponding author) [1,2]; Che-Cheng Hsu [1]; Kuan-Hua Tseng [1] 1. Introduction E-retail, with its revolutionary business model, is radically changing the way people shop. One of the [...]

Abstract Background The Taiwan Human Disease iPSC Service Consortium was established to accelerate Taiwan’s growing stem cell research initiatives and provide a platform for researchers interested in utilizing induced pluripotent stem cell (iPSC) technology. The consortium has generated and characterized 83 iPSC lines: 11 normal and 72 disease iPSC lines covering 21 different diseases, several of which are of high incidence in Taiwan. Whether there are any reprogramming-induced recurrent copy number variant (CNV) hotspots in iPSCs is still largely unknown. Methods We performed genome-wide copy number variant screening of 83 Han Taiwanese iPSC lines and compared them with 1093 control subjects using an Affymetrix genome-wide human SNP array. Results In the iPSCs, we identified ten specific CNV loci and seven “polymorphic” CNV regions that are associated with the reprogramming process. Additionally, we established several differentiation protocols for our iPSC lines. We demonstrated that our iPSC-derived cardiomyocytes respond to pharmacological agents and were successfully engrafted into the mouse myocardium demonstrating their potential application in cell therapy. Conclusions The CNV hotspots induced by cell reprogramming have successfully been identified in the current study. This finding may be used as a reference index for evaluating iPSC quality for future clinical applications. Our aim was to establish a national iPSC resource center generating iPSCs, made available to researchers, to benefit the stem cell community in Taiwan and throughout the world.

This research incorporated an auction mechanism into the vehicle routing problem with occasional drivers and produced simulations in an agent-based environment. Auctions were used to match online orders with potential occasional drivers. While a centralized system optimizes system performance under global objectives, the novel decentralized approach presented here illustrates emergent phenomena resulting from the interaction of individual entities in highly dynamic cases. In the simulations, the auctions were executed after a fixed time interval called a rolling time horizon. Our results suggest that the appropriate rolling time horizon produces a lower average unit compensation cost because better matches can be found when the accumulation of online orders and occasional drivers is maintained at a certain level. The simulation results also indicate that the use of an auction mechanism instead of simple nonauction rules can improve the average unit compensation cost by up to 25.1%.

How people make choices among alternatives are of interest in different areas of psychological research. One paradigm to answer this question is by applying Likert Scale (LS) to compare the agreements to different alternatives, and the respective LS scores are then transferred into rank order of preference. However, using LS to infer choices is somewhat debatable because the measurement format of LS was not designed for revealing psychological preference. In this article, we examined to what extent it is appropriate to use quantitative indicators derived from LS to infer choices, with which we used the Comparative Judgment (CJ) procedure to represent a direct measurement of choice decision to compare. A total of 929 adolescents reported their effort and ability attributions for academic failure and success using both LS and CJ. We found that while using LS is generally accurate in predicting results obtained via CJ, the percentage of people revealing different choices inferred from LS versus CJ was 14.7% and 12.1% for the success and failure scenarios, respectively, suggesting that inferring psychological preference from LS is not without risk, at least for this sample of adolescents from a culturally-Chinese society. Furthermore, the majority of participants displaying incongruent decisions of achievement attribution via LS and CJ showed equivalent LS scores between effort and ability attributions. A goodness-of-fit test was conducted (on a model motivated by the beta-binomial distribution) and successfully eliminated the possibility that the tied LS scores in effort and ability attributions actually represent participants’ true psychological state.

Aggressive tracing of contacts of confirmed cases is crucial to Taiwan's successful control of the early spread of COVID-19. As the pandemic lingers, an epidemiological investigation that can be conducted efficiently in a timely manner can help decrease the burden on the health personnel and increase the usefulness of such information in decision making. To develop a new tool that can improve the current practice of epidemiological investigation by incorporating new technologies in digital platform and knowledge graphs. To meet the various needs of the epidemiological investigation, we decided to develop an e-Outbreak Platform that provides a semi-structured, multifaceted, computer-aided questionnaire for outbreak investigation. There are three major parts of the platform: (1) a graphic portal that allows users to have an at-glance grasp of the functions provided by the platform and then choose the one they need; (2) disease-specific questionnaires that can accommodate different formats of the information, including text typing, button selection, and pull-down menu; and (3) functions to utilize the stored information, including report generation, statistical analyses, and knowledge graphs displaying contact tracing. When the number of outbreak investigation increases, the knowledge graphs can be extended to encompass other persons appearing in the same location at the same time, i.e., constituting a potential contact cluster. The information extracted can also be used to display the tracing on a map in animation. Overall, this system can provide a basis for further refinement that can be generalized to a variety of outbreak investigations.

Abstract The introduction of induced pluripotent stem cells (iPSCs) has opened up the potential for personalized cell therapies and ushered in new opportunities for regenerative medicine, disease modeling, iPSC-based drug discovery and toxicity assessment. Over the past 10 years, several initiatives have been established that aim to collect and generate a large amount of human iPSCs for scientific research purposes. In this review, we compare the construction and operation strategy of some iPSC banks as well as their ongoing development. We also introduce the technical challenges and offer future perspectives pertaining to the establishment and management of iPSC banks.

While many monocular depth estimation methods have been proposed, determining depth variations in outdoor scenes remains challenging. Accordingly, this paper proposes an image segmentation-based monocular depth estimation model with attention mechanisms that can address outdoor scene variations. The segmentation model segments images into foreground and background regions and individually predicts depth maps. Moreover, attention mechanisms are also adopted to extract meaningful features from complex scenes to improve foreground and background depth map prediction via a multi-scale decoding scheme. From our experimental results, we observed that our proposed model outperformed previous methods by 27.5% on the KITTI dataset.

Given virtual reality (VR)’s popularity, VR already impacts various applications such as education and tourism. In the above applications, users usually need to follow a specific target, such as a teacher or a tour guide, to explore and learn from the environment. However, because of the constraint with visual senses and unintuitive locomotion, following a target in VR is not as easy as doing it in real life. As a result, the user may not concentrate on the audio narration or the surroundings. Therefore, finding a following mechanism that can help the user concentrate and learn in VR is important. In this paper, we focus on the following techniques in VR. We propose four types of following mechanisms: limited (which restricts the user’s ability to move), semi-limited (which constrains the user’s range of activity), improved semi-limited (with visual assistance), and not limited (user’s movement is not constrained). This study consisted of 29 participants divided into four groups and aimed to evaluate how the following mechanisms affect user experience and make users more concentrated in the virtual world. The experiment shows that the semi-limited following mechanism with visual assistance is superior to the other three types in the performance of the touring experience and helping users pay attention to the tour content.

An ultra-wideband (UWB) positioning system consists of at least three anchors and a tag for the positioning procedure. Via the UWB transceivers mounted on all devices in the system, we can obtain the distance information between each pair of devices and further realize the tag localization. However, the uncertain measurement in the real world may introduce incorrect measurement information, e.g., time, distance, positioning, and so on. Therefore, we intend to incorporate the technique of ensemble learning with UWB positioning to improve its performance. In this paper, we present two methods. The experimental results show that our ideas can be applied to different scenarios and work well. Of note, compared with the existing research in the literature, our first algorithm was more accurate and stable. Further, our second algorithm possessed even better performance than the first. Moreover, we also provide a comprehensive discussion for an ill-advised point, which is often used to evaluate the positioning efficiency in the literature.

Recently, many artificial intelligence applications in smart cars have been utilized in real life. Making an unmanned ground vehicle (UGV) capable of moving autonomously has become a critical topic. Hence, in this work, a novel method for a UGV to realize path planning and obstacle avoidance is presented using a deep deterministic policy gradient approach (DDPG). More specifically, the lidar sensor mounted on the vehicle is utilized to measure the distance between the vehicle and the surrounding obstacles, and the odometer measures the mileage of the vehicle for the purpose of estimating the current location. Then, the above sensed data are treated as the training data for the DDPG training procedure, and several experiments are performed in different settings utilizing the robot operating system (ROS) and the Gazebo simulator with a real robot module, TurtleBot3, to present a comprehensive discussion. The simulation results show that using the presented design and reward architecture, the DDPG method is better than the classic deep Q-network (DQN) method, e.g., taking fewer steps to reach the goal, less training time to find the smallest number of steps for reaching the goal, and so on.

Since December 2019, the novel coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has infected ~435 million people and caused ~6 million related deaths as of March 2022. To combat COVID-19, there have been many attempts to repurpose FDA-approved drugs or revive old drugs. However, many of the current treatment options have been known to cause adverse drug reactions. We employed a population-based drug screening platform using 13 human leukocyte antigen (HLA) homozygous human induced pluripotent cell (iPSC) lines to assess the cardiotoxicity and neurotoxicity of the first line of anti-COVID-19 drugs. We also infected iPSC-derived cells to understand the viral infection of cardiomyocytes and neurons. We found that iPSC-derived cardiomyocytes express the ACE2 receptor which correlated with a higher infection of the SARS-CoV-2 virus (r = 0.86). However, we were unable to detect ACE2 expression in neurons which correlated with a low infection rate. We then assessed the toxicity of anti-COVID-19 drugs and identified two cardiotoxic compounds (remdesivir and arbidol) and four neurotoxic compounds (arbidol, remdesivir, hydroxychloroquine, and chloroquine). These data show that this platform can quickly and easily be employed to further our understanding of cell-specific infection and identify drug toxicity of potential treatment options helping clinicians better decide on treatment options.

Dermatophytes are the group of keratinophilic fungi that cause superficial cutaneous infection, which traditionally belong to the genera Trichophyton, Microsporum, and Epidermophyton. Dermatophyte infection is not only a threat to the health of small animals, but also an important zoonotic and public health issue because of the potential transmission from animals to humans. Rabbit dermatophytosis is often clinically identified; however, limited information was found in Asia. The aims of this study are to investigate the prevalence and to evaluate the risk factors of dermatophytosis in pet rabbits in Northern Taiwan. Between March 2016 and October 2018, dander samples of pet rabbits were collected for fungal infection examination by Wood’s lamp, microscopic examination (KOH preparation), fungal culture, and PCR assay (molecular identification). Z test and Fisher’s exact test were performed to evaluate the potential risk factors, and logistic regression analysis was then performed to build the model of risk factors related to dermatophyte infection. Of the collected 250 dander samples of pet rabbits, 29 (11.6%) samples were positive for dermatophytes by molecular identification. In those samples, 28 samples were identified as the T. mentagrophytes complex and 1 sample was identified as M. canis. Based on the results of the Firth’s bias reduction logistic analyses, animal source (rabbits purchased from pet shops) and number of rearing rabbits (three rabbits or more) were shown as the main risks for dermatophyte infection in the pet rabbits in Taiwan. The results of the present study elucidate the prevalence of rabbit dermatophyte infection, pathogens, and risk factors in Taiwan, and provide an important reference for the prevention and control of rabbit dermatophytosis.

The advent of induced pluripotent stem cells (iPSCs) has advanced our understanding of the molecular mechanisms of human disease, drug discovery, and regenerative medicine. As such, the use of iPSCs in drug development and validation has shown a sharp increase in the past 15 years. Furthermore, many labs have been successful in reproducing many disease phenotypes, often difficult or impossible to capture, in commonly used cell lines or animal models. However, there still remain limitations such as the variability between iPSC lines as well as their maturity. Here, we aim to discuss the strategies in generating iPSC-derived cardiomyocytes and neurons for use in disease modeling, drug development and their use in cell therapy.

Foreign body embolization can cause intracranial artery occlusion with ischemic stroke. Reported etiologies include post cerebrovascular interventions, migration of esophageal foreign body and neck trauma. We reported a case with punctured wound at left neck, X-ray and computed tomography revealed a foreign body located in the carotid region. The patient eventually developed stroke symptoms in the next day after operation. Non-contrast brain Computer tomography at that time revealed that porcelain fragment located at the suprasellar area, and infarction of the left anterior basal ganglion. Our patient is the first reported case having an embolic stroke secondary to distal migration of a foreign body from the carotid artery after neck trauma. We call attention to this rare neurologic complication of neck trauma with foreign body retention. Appropriate and prompt identification of concurrent vascular injuries with retention of foreign body is strongly advised in neck trauma patients.

A positioning system in a specific space is for the purpose of determining the location of an object. The Global Positioning System (GPS) is the most popular and valuable development for classical navigation. However, it may not always be precise and available due to the effects of multi-path propagation and signal attenuation. Thus, we need some additional skills to keep its stability and accuracy or improve its performance. Based on the technique of neural networks, the positioning information from multiple GPS receivers is composed to obtain a better version which is more accurate and stable and thus can be applied to advanced applications. Particularly, the concepts of shallow and asymmetric neural networks are used in this work. Our design possesses fewer hidden layers via the former property and further reduces the connections of classical Fully Connected Neural Networks (FCNNs) via the latter property. Hence, it takes very little time to realize the training and predicting procedures. Also, it will help promote several works in practice, such as the implementation of embedded systems. Finally, a practical test, called the vehicular road test, is utilized to guarantee the level of confidence in the improvement of our algorithm. [ABSTRACT FROM AUTHOR]

Lumbar disc bulging or herniation (LDBH) is one of the major causes of spinal stenosis and related nerve compression, and its severity is the major determinant for spine surgery. MRI of the spine is the most important diagnostic tool for evaluating the need for surgical intervention in patients with LDBH. However, MRI utilization is limited by its low accessibility. Spinal X-rays can rapidly provide information on the bony structure of the patient. Our study aimed to identify the factors associated with LDBH, including disc height, and establish a clinical diagnostic tool to support its diagnosis based on lumbar X-ray findings. In this study, a total of 458 patients were used for analysis and 13 clinical and imaging variables were collected. Five machine-learning (ML) methods, including LASSO regression, MARS, decision tree, random forest, and extreme gradient boosting, were applied and integrated to identify important variables for predicting LDBH from lumbar spine X-rays. The results showed L4-5 posterior disc height, age, and L1-2 anterior disc height to be the top predictors, and a decision tree algorithm was constructed to support clinical decision-making. Our study highlights the potential of ML-based decision tools for surgeons and emphasizes the importance of L1-2 disc height in relation to LDBH. Future research will expand on these findings to develop a more comprehensive decision-supporting model. [ABSTRACT FROM AUTHOR]

As N-alkyl-norlitebamines have been demonstrated to possess antiacetylcholinesterase activity, this study aimed to prepare related analogs to verify their structure-activity relationship. The compounds with the chemical skeletons of 2a-alkylnorlitebamines (6b-6e) and (7a,8)-didehydroisohomoaporphines (8b-8e, 9a, and 10b-10e) were designed and synthesized. In silico molecular docking of the 13 compounds on the acetylcholinesterase template indicated that 8a and 10b showed the highest docking scores (ΔG) (-20.56 Kcal/mol and -19.76 Kcal/mol, respectively). The reaction of peracylsecolaurolitsines under the modified Bischler-Napieralski procedure and subsequent NaBH4 reduction yielded 7a-alkyl-Bhomoaurolitsines 8a-8e, possessing a new skeleton, besides the expected 2a-alkyl-norlitebamines 10b-10e. Among these compounds, the two with the highest docking scores [Δ7a (8)-Bhomoaurolitsine (8a) and 2a-methyl-norlitebamine (10b)] showed better inhibitory activities against acetylcholin-esterase (eel) with IC50 value of 24.71 µM and 26.13 µM, respectively. Therefore, compounds 8a and 10b could serve as leads for exploring potential acetylcholinesterase inhibitors for the treatment of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Background: Myocardial infarction (MI) is a life-threatening disease, often leading to heart failure. Defining therapeutic targets at an early time point is important to prevent heart failure. Methods: MicroRNA screening was performed at early time points after MI using paired samples isolated from the infarcted and remote myocardium of pigs. We also examined the microRNA expression in plasma of MI patients and pigs. For mechanistic studies, AAV9-mediated microRNA knockdown and overexpression were administrated in mice undergoing MI. Findings: MicroRNAs let-7a and let-7f were significantly downregulated in the infarct area within 24 h post-MI in pigs. We also observed a reduction of let-7a and let-7f in plasma of MI patients and pigs. Inhibition of let-7 exacerbated cardiomyocyte apoptosis, induced a cardiac hypertrophic phenotype, and resulted in worsened left ventricular ejection fraction. In contrast, ectopic let-7 overexpression significantly reduced those phenotypes and improved heart function. We then identified TGFBR3 as a target of let-7, and found that induction of Tgfbr3 in cardiomyocytes caused apoptosis, likely through p38 MAPK activation. Finally, we showed that the plasma TGFBR3 level was elevated after MI in plasma of MI patients and pigs. Interpretation: Together, we conclude that the let-7-Tgfbr3-p38 MAPK signalling plays an important role in cardiomyocyte apoptosis after MI. Furthermore, microRNA let-7 and Tgfbr3 may serve as therapeutic targets and biomarkers for myocardial damage. Fund: Ministry of Science and Technology, National Health Research Institutes, Academia Sinica Program for Translational Innovation of Biopharmaceutical Development-Technology Supporting Platform Axis, Thematic Research Program and the Summit Research Program, Taiwan. Keywords: MicroRNA let-7, TGFBR3, Gene therapy, Biomarkers