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Microcontact printing (μCP) techniques have sparked a surge of interests in microfabrication since they help produce arrays on a wide range of target substrates in a facile and efficient manner. Polydimethylsiloxane (PDMS), as a well-established material for stamps, has constraints resulting from its hydrophobicity and softness, and the replication of PDMS stamps usually requires rigid masters or processes using a photoresist. Herein, a novel μCP stamp based on cyclo-olefin polymer (COP) is produced through vacuum ultraviolet (VUV) lithography. 2,4,6,8-Tetramethylcyclotetrasiloxane is selectively deposited at the affinity-patterns on the COP surface, and these patterned siloxane films are converted into SiOx meanwhile protecting the COP beneath them from the VUV photoetching. By this means, a patterned relief is fabricated on the COP plates, resulting in a hydrophilic SiOx/COP μCP stamp with punch heights of ∼180 nm. The novelty arises from the simplicity of the master- and photoresist-free microstructuring, and the higher stiffness of SiOx/COP stamps prevents the deformation during pressing. Finally, an example μCP is given to transfer titania precursor gel and produce TiO2 micropatterns on flexible polymer substrates. The SiOx/COP stamps and the μCP of TiO2 provide simple and cost-effective patterning techniques, which should contribute to the future design and creation of flexible devices.

In this report, micropatterns of (3-aminopropyl)trimethoxysilane (APTMS) were developed on hydrophilic and hydrophobic surfaces after patterning using 172 nm vacuum ultraviolet (VUV) photolithography. Self-assembled monolayers (SAMs) formed on Si substrates through UV hydrosilylation of 1-hexadecene (HD) and 10-undecenoic acid (UDA) were used as hydrophilic and hydrophobic surfaces, respectively. For templating the HD- and UDA-SAMs, the VUV light was exposed to HD- and UDA-SAMs from the slits of photomasks in atmospheric and evacuated environments, respectively. Various oxygenated groups were generated at the exposed domains of HD-SAM, while the COOH groups were trimmed from the irradiated domains of UDA-SAM. The APTMS molecules were immobilized on the domains that were terminated by oxygenated groups after chemical vapor deposition (CVD). The thicknesses of the developed APTMS micropatterns increased significantly by raising the CVD temperature and in the presence of ambient air in the CVD Teflon container as well. The increase in thicknesses was ascribed to the formation of APTMS multilayers, which were mediated by H3N+ ions. Also, the developed APTMS micropatterns on the UDA-SAM patterned by VUV light irradiation in a high-vacuum environment (HV-VUV) were thicker than those on the VUV/(O) patterned HD-SAM due to the presence of inactive oxygenated groups at the surface of VUV/(O)-terminated domains of HD-SAM such as COO-C and C-O-C groups. The presence of water or ambient air facilitated the silane coupling between the silyl groups with the oxygenated and amino groups The combination of VUV photolithography and the CVD method with control of the conditions would enable us to control the thicknesses and shapes of the developed APTMS micropatterns. These findings illustrate the applicability of VUV photolithography for templating hydrophobic and hydrophobic surfaces toward the development of organosilane architectures, which can be feasible for several applications.

Background Fatty liver disease (FLD) arises from the accumulation of fat in the liver and may cause liver inflammation, which, if not well controlled, may develop into liver fibrosis, cirrhosis, or even hepatocellular carcinoma. Objective We describe the construction of machine-learning models for current-visit prediction (CVP), which can help physicians obtain more information for accurate diagnosis, and next-visit prediction (NVP), which can help physicians provide potential high-risk patients with advice to effectively prevent FLD. Methods The large-scale and high-dimensional dataset used in this study comes from Taipei MJ Health Research Foundation in Taiwan. We used one-pass ranking and sequential forward selection (SFS) for feature selection in FLD prediction. For CVP, we explored multiple models, including k-nearest-neighbor classifier (KNNC), Adaboost, support vector machine (SVM), logistic regression (LR), random forest (RF), Gaussian naïve Bayes (GNB), decision trees C4.5 (C4.5), and classification and regression trees (CART). For NVP, we used long short-term memory (LSTM) and several of its variants as sequence classifiers that use various input sets for prediction. Model performance was evaluated based on two criteria: the accuracy of the test set and the intersection over union/coverage between the features selected by one-pass ranking/SFS and by domain experts. The accuracy, precision, recall, F-measure, and area under the receiver operating characteristic curve were calculated for both CVP and NVP for males and females, respectively. Results After data cleaning, the dataset included 34,856 and 31,394 unique visits respectively for males and females for the period 2009-2016. The test accuracy of CVP using KNNC, Adaboost, SVM, LR, RF, GNB, C4.5, and CART was respectively 84.28%, 83.84%, 82.22%, 82.21%, 76.03%, 75.78%, and 75.53%. The test accuracy of NVP using LSTM, bidirectional LSTM (biLSTM), Stack-LSTM, Stack-biLSTM, and Attention-LSTM was respectively 76.54%, 76.66%, 77.23%, 76.84%, and 77.31% for fixed-interval features, and was 79.29%, 79.12%, 79.32%, 79.29%, and 78.36%, respectively, for variable-interval features. Conclusions This study explored a large-scale FLD dataset with high dimensionality. We developed FLD prediction models for CVP and NVP. We also implemented efficient feature selection schemes for current- and next-visit prediction to compare the automatically selected features with expert-selected features. In particular, NVP emerged as more valuable from the viewpoint of preventive medicine. For NVP, we propose use of feature set 2 (with variable intervals), which is more compact and flexible. We have also tested several variants of LSTM in combination with two feature sets to identify the best match for male and female FLD prediction. More specifically, the best model for males was Stack-LSTM using feature set 2 (with 79.32% accuracy), whereas the best model for females was LSTM using feature set 1 (with 81.90% accuracy).

BACKGROUND Fatty liver disease (FLD) arises from the accumulation of fat in the liver and may cause liver inflammation, which, if not well controlled, may develop into liver fibrosis, cirrhosis, or even hepatocellular carcinoma. OBJECTIVE We describe the construction of machine-learning models for current-visit prediction (CVP), which can help physicians obtain more information for accurate diagnosis, and next-visit prediction (NVP), which can help physicians provide potential high-risk patients with advice to effectively prevent FLD. METHODS The large-scale and high-dimensional dataset used in this study comes from Taipei MJ Health Research Foundation in Taiwan. We used one-pass ranking and sequential forward selection (SFS) for feature selection in FLD prediction. For CVP, we explored multiple models, including k-nearest-neighbor classifier (KNNC), Adaboost, support vector machine (SVM), logistic regression (LR), random forest (RF), Gaussian naïve Bayes (GNB), decision trees C4.5 (C4.5), and classification and regression trees (CART). For NVP, we used long short-term memory (LSTM) and several of its variants as sequence classifiers that use various input sets for prediction. Model performance was evaluated based on two criteria: the accuracy of the test set and the intersection over union/coverage between the features selected by one-pass ranking/SFS and by domain experts. The accuracy, precision, recall, F-measure, and area under the receiver operating characteristic curve were calculated for both CVP and NVP for males and females, respectively. RESULTS After data cleaning, the dataset included 34,856 and 31,394 unique visits respectively for males and females for the period 2009-2016. The test accuracy of CVP using KNNC, Adaboost, SVM, LR, RF, GNB, C4.5, and CART was respectively 84.28%, 83.84%, 82.22%, 82.21%, 76.03%, 75.78%, and 75.53%. The test accuracy of NVP using LSTM, bidirectional LSTM (biLSTM), Stack-LSTM, Stack-biLSTM, and Attention-LSTM was respectively 76.54%, 76.66%, 77.23%, 76.84%, and 77.31% for fixed-interval features, and was 79.29%, 79.12%, 79.32%, 79.29%, and 78.36%, respectively, for variable-interval features. CONCLUSIONS This study explored a large-scale FLD dataset with high dimensionality. We developed FLD prediction models for CVP and NVP. We also implemented efficient feature selection schemes for current- and next-visit prediction to compare the automatically selected features with expert-selected features. In particular, NVP emerged as more valuable from the viewpoint of preventive medicine. For NVP, we propose use of feature set 2 (with variable intervals), which is more compact and flexible. We have also tested several variants of LSTM in combination with two feature sets to identify the best match for male and female FLD prediction. More specifically, the best model for males was Stack-LSTM using feature set 2 (with 79.32% accuracy), whereas the best model for females was LSTM using feature set 1 (with 81.90% accuracy).

Microcontact printing (μCP) techniques have sparked a surge of interests in microfabrication since they help produce arrays on a wide range of target substrates in a facile and efficient manner. Polydimethylsiloxane (PDMS), as a well-established material for stamps, has constraints resulting from its hydrophobicity and softness, and the replication of PDMS stamps usually requires rigid masters or processes using a photoresist. Herein, a novel μCP stamp based on cyclo-olefin polymer (COP) is produced through vacuum ultraviolet (VUV) lithography. 2,4,6,8-Tetramethylcyclotetrasiloxane is selectively deposited at the affinity-patterns on the COP surface, and these patterned siloxane films are converted into SiO

Formation of precise and high-resolution silica micropatterns on polymer substrates is of importance in surface structuring for flexible device fabrication of optics, microelectronic, and biotechnology. To achieve that, substrates modified with affinity-patterns serve as a strategy for site-selective deposition. In the present paper, vacuum ultraviolet (VUV) treatment is utilized to achieve spatially-controlled surface functionalization on a cyclo-olefin polymer (COP) substrate. An organosilane, 2, 4, 6, 8-tetramethylcyclotetrasiloxane (TMCTS), preferentially deposits on the functionalized regions. Well-defined patterns of TMCTS are formed with a minimum feature of ∼500 nm. The secondary VUV/(O)-treatment converts TMCTS into SiOx, meanwhile etches the bare COP surface, forming patterned SiOx/COP microstructures with an average height of ∼150 nm. The resulting SiOx patterns retain a good copy of TMCTS patterns, which are also consistent with the patterns of photomask used in polymer affinity-patterning. The high quality SiOx patterns are of interests in microdevice fabrication, and the hydrophilicity contrast and adjustable heights reveal their potential application as a “stamp” for microcontact printing (μCP) techniques.

This research investigated the risks involved in bicycle riding while using various sensory modalities to deliver training information. To understand the risks associated with using bike computers, this study evaluated hazard perception performance through lab-based simulations of authentic riding conditions. Analysing hazard sensitivity (d') of signal detection theory, the rider's response time, and eye glances provided insights into the risks of using bike computers. In this study, 30 participants were tested with eight hazard perception tasks while they maintained a cadence of 60 ± 5 RPM and used bike computers with different sensory displays, namely visual, auditory, and tactile feedback signals. The results indicated that synchronously using different sense organs to receive cadence feedback significantly affects hazard perception performance; direct visual information leads to the worst rider distraction, with a mean sensitivity to hazards (d') of −1.03. For systems with multiple interacting sensory aids, auditory aids were found to result in the greatest reduction in sensitivity to hazards (d' mean = −0.57), whereas tactile sensory aids reduced the degree of rider distraction (d' mean = −0.23). Our work complements existing work in this domain by advancing the understanding of how to design devices that deliver information subtly, thereby preventing disruption of a rider's perception of road hazards.

Target—Most of the Cardiac Arrest (CA) cases are preventable because the CA patients usually had abnormal clinical signs or symptoms before their suffering from CA. In general, the appropriate steps of Cardiopulmonary resuscitation (CPR) for CA patients will increase the patients’ survival rate and reduce the consequent medical expenses. Accordingly, we propose a system and the related methods for detecting CA before the CPR event occurred earlier and it is not only assisting physicians to early diagnose of CA and immediately warning but also improving the medical quality.Methods—In this study, the raw dataset is collected from the electronic health records (EHRs) of the adult patients (age ≧20 years) who visited emergency department (ED) and stayed in the emergency detention area for more than 6 hours during January 2014 to December 2015, and it is provided by National Taiwan University Hospital (NTUH). We perform the data preprocessing and cleaning for the dataset using a resampling technique to balance the data amount of CPR and Non-CPR patients of the dataset, and then we construct a sliding window and apply several classifiers for model training and reducing the possible overfitting problem. Additionally, we use the measures such as the Area Under the Receiver Operating Characteristic Curve (AUROC) and the Area Under the Precision-Recall Curve (AUPRC) to comparative evaluate the performance of our models built.Results—Our approach avoids the problems of dataset imbalance and possible overfitting effectively. The performance among classifiers selected show that the best one is random forest (RF) when CPR event happened, but the better ones are Logistic Regression and LSTM than the remaining classifiers and close to that of RF during 1 to 4 hours before the CPR time.Conclusion—We have the contribution in predicting CA and CPR event before it occurred around 3 to 3.5 hours in advance, and it is similar to that of the state-of-the-art such Early Warning Score (EWS) with around 3.5 hours. In addition, avoiding the problem of dataset imbalanced may effectively improve the accuracy of predicting CA as well. Accordingly, it helps to assist the emergency clinical physicians to achieve the hospital’s quality management including the clinical or medical resources allocation.

In-hospital cardiac arrest (IHCA) diminish the survival rate of patients, despite most of the IHCA cases are preventable. More than 54% IHCA patient had abnormal clinical manifestation before they suffered a cardiac arrest. If appropriate steps were taken, patients’ survival rate would be higher and medical expense would be decreased. This paper proposes a novel approach to detect IHCA before the event occurred. We construct two types of shifting windows (corresponding to two tasks) that allow machine learning to be applied for our dataset which is severely imbalanced. The results show that our approach can effectively handle the imbalanced dataset for detecting cardiac arrest. As the selection of performance index, we used the area under the receiver operating characteristic curve (AUROC) and the area under the precision–recall curve (AUPRC). In our experiments, the best classifier is random forest for task 1, with AUROC of 0.88. LSTM is the best for task 2, with AUPRC of 0.71 for the second task.

This paper describes a 172-nm vacuum ultraviolet (VUV) light treatment in ambient air towards the formation of nanocrystalline zinc oxide (ZnO) micropatterns. Thin layers of zinc acetate (Zn(OAc)2) as a precursor are spun on substrates, and subsequently treated by the VUV irradiation. The VUV treatments lead to an organic-free ZnO films with wurtzite crystalline structures, determined by Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy and transmission electron microscopy. The nanocrystalline ZnO patterns with a fine feature of 1 μm are fabricated on cycloolefin polymer (COP) substrates via the precise VUV lithography. This one-step ZnO crystallizing and patterning process is suited for polymer substrates because the potentially damaging procedures involving acidic etchants and thermal treatments are no longer needed. The present method proves to be a simple, effective, controllable patterning approach, which might be recommended for the future fabrication of flexible devices.

Target-In this paper, we focus on using medicine for patients who have cardiac arrest then must have to do Cardiopulmonary Resuscitation (CPR). We want to know the medicine influence in predicting state of an illness deterioration. Therefore, we proposes a Medication for Cardiac Arrest Early Warning System (MCAEWS). It's not only assist physicians to early diagnose of an illness and immediately warning, but also increase sensitivity, decrease false positive rate and mortality rate. The most important role is greatly improve medical quality. Methods-In this study, the data is from the emergency department of National Taiwan University Hospital (NTUH). It is from January 2014 to December 2015. The patients who stayed in the emergency detention area for more than six hours during this two years. The patients were included in the retrospective cohort study. To comparative measures for the machine learning models, we used such as the Area Under the Receiver Operating Characteristic Curve (AUROC) and the Area under the Precision-Recall Curve (AUPRC). Results-The data were analyzed for CPR and non-CPR groups respectively. Furthermore, we evaluated sensitivity and specificity. The Random Forest Algorithm (AUC: 0.98; AUP: 0.23) compare with others such as Logistic Regression Algorithm (AUC: 0.94; AUP: 0.13), Decision Tree (AUC: 0.97; AUP: 0.05), and Extreme Random Tree (AUC: 0.91; AUP: 0.08), it was significantly high performance. Conclusion-Increasing the drug factors in vital signs, that it effectively improved the accuracy of predicting cardiac arrest. The results of this study, it's help for emergency clinical Physicians and hospital quality management will validly solve clinical medical resource allocation issues and improve medical quality through decision support systems.

AdLocus is an APP developed by HyXen Company for mobile advertisements. This advertising software can push the message to the target users within specified locations. Based on the real big data provided by AdLocus, we design a dynamic advertisements recommendation system to increase the advertising efficiency. The proposed method uses the regression models and the click probability to recommend the amount of mobile advertisements for every 30 minutes. The results show that our recommendation can efficiently raise the successful clicking rate to satisfy the required clicks number. Moreover, the proposed method avoids the redundant advertisements to reduce the system cost.

Cell-based therapeutic neovascularization is a promising method for treating ischemic disorders. In this work, human umbilical vein endothelial cells (HUVECs) were thoroughly premixed with cord-blood mesenchymal stem cells (cbMSCs) and cultivated to form three-dimensional (3D) cell aggregates for cellular cardiomyoplasty. In the in vitro study, tubular networks were formed at day 1 after the co-culturing of dissociated HUVECs and cbMSCs on Matrigel; however, as time progressed, the grown tubular networks regressed severely. Conversely, when 3D cell aggregates were grown on Matrigel, mature and stable tubular networks were observed over time, under the influence of their intensive cell-extracellular matrix (ECM) interactions and cell-cell contacts. 3D cell aggregates were transplanted into the peri-infarct zones of rats with myocardial infarction (MI) via direct intramyocardial injection. Based on our pinhole single photon emission computed tomography (SPECT) myocardial-perfusion observations, echocardiographic heart-function examinations and histological analyses, the engrafted 3D cell aggregates considerably enhanced the vascular densities and the blood flow recovery in the ischemic myocardium over those of their dissociated counterparts, thereby reducing the size of perfusion defects and restoring cardiac function. These results demonstrate that the intramuscular delivery of 3D cell aggregates of HUVECs/cbMSCs can be a valuable cell-based regenerative therapeutic strategy against MI.

The proximity of cells in three-dimensional (3D) organization maximizes the cell-cell communication and signaling that are critical for cell function. In this study, 3D cell aggregates composed of human umbilical vein endothelial cells (HUVECs) and cord-blood mesenchymal stem cells (cbMSCs) were used for therapeutic neovascularization to rescue tissues from critical limb ischemia. Within the cell aggregates, homogeneously mixed HUVECs and cbMSCs had direct cell-cell contact with expressions of endogenous extracellular matrices and adhesion molecules. Although dissociated HUVECs/cbMSCs initially formed tubular structures on Matrigel, the grown tubular network substantially regressed over time. Conversely, 3D HUVEC/cbMSC aggregates seeded on Matrigel exhibited an extensive tubular network that continued to expand without regression. Immunostaining experiments show that, by differentiating into smooth muscle cell (SMC) lineages, the cbMSCs stabilize the HUVEC-derived tubular network. The real-time PCR analysis results suggest that, through myocardin, TGF-β signaling regulates the differentiation of cbMSCs into SMCs. Transplantation of 3D HUVEC/cbMSC aggregates recovered blood perfusion in a mouse model of hindlimb ischemia more effectively compared to their dissociated counterparts. The experimental results confirm that the transplanted 3D HUVEC/cbMSC aggregates enhanced functional vessel formation within the ischemic limb and protected it from degeneration. The 3D HUVEC/cbMSC aggregates can therefore facilitate the cell-based therapeutic strategies for modulating postnatal neovascularization.

Myocardial infarction is often associated with abnormalities in electrical function due to a massive loss of functioning cardiomyocytes. This work develops a mesh, consisting of aligned composite nanofibers of polyaniline (PANI) and poly(lactic-co-glycolic acid) (PLGA), as an electrically active scaffold for coordinating the beatings of the cultured cardiomyocytes synchronously. Following doping by HCl, the electrospun fibers could be transformed into a conductive form carrying positive charges, which could then attract negatively charged adhesive proteins (i.e. fibronectin and laminin) and enhance cell adhesion. During incubation, the adhered cardiomyocytes became associated with each other and formed isolated cell clusters; the cells within each cluster elongated and aligned their morphology along the major axis of the fibrous mesh. After culture, expression of the gap-junction protein connexin 43 was clearly observed intercellularly in isolated clusters. All of the cardiomyocytes within each cluster beat synchronously, implying that the coupling between the cells was fully developed. Additionally, the beating rates among these isolated cell clusters could be synchronized via an electrical stimulation designed to imitate that generated in a native heart. Importantly, improving the impaired heart function depends on electrical coupling between the engrafted cells and the host myocardium to ensure their synchronized beating.