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Survival prediction is highly valued in end-of-life care clinical practice, and patient performance status evaluation stands as a predominant component in survival prognostication. While current performance status evaluation tools are limited to their subjective nature, the advent of wearable technology enables continual recordings of patients' activity and has the potential to measure performance status objectively. We hypothesize that wristband actigraphy monitoring devices can predict in-hospital death of end-stage cancer patients during the time of their hospital admissions. The objective of this study was to train and validate a long short-term memory (LSTM) deep-learning prediction model based on activity data of wearable actigraphy devices. The study recruited 60 end-stage cancer patients in a hospice care unit, with 28 deaths and 32 discharged in stable condition at the end of their hospital stay. The standard Karnofsky Performance Status score had an overall prognostic accuracy of 0.83. The LSTM prediction model based on patients' continual actigraphy monitoring had an overall prognostic accuracy of 0.83. Furthermore, the model performance improved with longer input data length up to 48 h. In conclusion, our research suggests the potential feasibility of wristband actigraphy to predict end-of-life admission outcomes in palliative care for end-stage cancer patients.Clinical Trial Registration: The study protocol was registered on ClinicalTrials.gov (ID: NCT04883879).

Fashion is very much a social and cultural statement. It documents the tastes and values of an era, and can even reflect the political orientation and economic development of a nation. While a fashion style can be defined by combinations of basic elements, fashion itself can take on indefinite forms. It is shaped by group acceptance, social forces important in an era, people's desire to relate to specific lifestyles, and it evolves over time. Fashion being so tightly wrapped up in our daily lives, our fashion appearance becomes one of the most direct ways to express ourselves. Understanding fashion computationally would allow better personalization, help the industry analyze its enterprise, and bring us closer towards building socially intelligent agents. Unfortunately, current computer vision systems for fashion are lacking in multiple ways. They fail to consider the synergy between multiple garments that compose an outfit, the garments' relation to the human wearer, and the underlying social factors that influence our fashion choices. The goal of this thesis is to devise computational models that understand fashion in the full context of outfits, humans and society. Our solution is to develop models that learn to utilize "in-the-wild" fashion images with their associated meta-data (e.g., geotags, timestamps, user tags), and capture human's aesthetics and rationales for fashion in an unsupervised manner. These freely available Internet images come at large-scale and are natural examples of what people choose to wear, allowing models with easy adaptation to new definitions of fashion as it evolves. The thesis begins by introducing our method that accounts for the synergy between multiple garments in an outfit. Specifically, we devise a topic-model-based method that automatically analyzes the interplay between garments, and discovers underlying combinations that form fashion styles. As outfits are eventually worn by humans, the thesis moves on to introduce new fashion recommendation systems that are aware of an individual's body shape and give suggestions on dressing in a more fashionable way. A 3D mesh of a human body is recovered and mapped into a visual embedding to identify flattering garments for the given body shape. In order to communicate with users how to dress fashionably, our model learns to score fashionability of outfits by seeing tens of thousands of natural examples from Internet images, and renders the optimized look of an outfit via an image generation neural network. Finally, the thesis zooms out to consider fashion in the history of human culture, where we conduct our study on millions of news articles and fashion images spanning from the beginning of the twentieth century to nowadays. We first extend our style topic model to adapt to the fluid and ever-evolving nature of fashion styles, and then devise a multi-modal statistical model to identify the links from world events to fashion evolution. All together, I hope my research lays down foundations for human-aware, society-aware, and evolving fashion understanding.

Aim To investigate the association between specific dental therapy for periodontal disease and the risk of ischaemic stroke. Materials and methods We conducted a population-based cohort study that used data from the Taiwan National Health Insurance Research Database 2005 for the period of 2000-2013. Our observations focused on patients with the diagnoses of gingivitis or periodontitis with and without specific treatment and subsequent incidence of ischaemic stroke. Dental care services include dental scaling, intensive treatment (subgingival curettage and root planing) and tooth extraction. Multivariate Cox regression analysis was used to estimate the hazard ratios and corresponding 95% confidence intervals (95% CI). Results Compared with those in the gingivitis cohort, patients with periodontitis have a higher risk of ischaemic stroke and a lower survival rate of stroke over the 10-year follow-up period. After integrative dental care, both dental scaling and intensive treatment, the risk was reduced, especially in patients with periodontitis, while patients with periodontal disease may have an increased risk of stroke after tooth extraction therapy. Conclusions Our study showed that periodontitis is a risk factor for ischaemic stroke. Both dental scaling and intensive treatment for periodontal disease are associated with a lower risk of further ischaemic stroke events.

Fashion is intertwined with external cultural factors, but identifying these links remains a manual process limited to only the most salient phenomena. We propose a data-driven approach to identify specific cultural factors affecting the clothes people wear. Using large-scale datasets of news articles and vintage photos spanning a century, we present a multi-modal statistical model to detect influence relationships between happenings in the world and people's choice of clothing. Furthermore, on two image datasets we apply our model to improve the concrete vision tasks of visual style forecasting and photo timestamping. Our work is a first step towards a computational, scalable, and easily refreshable approach to link culture to clothing., Comment: Accepted to ICCV 2021

Smart photography carries the promise of quality improvement and functionality extension in making aesthetically appealing pictures. In this article, we focus on self-portrait photographs and introduce new methods that guide a user in how to best pose while taking a selfie. While most of the current solutions use a post processing procedure to beautify a picture, the developed tool enables a novel function of recommending a good look before the photo is captured. Given an input face image, the tool automatically estimates the pose-based aesthetic score, finds the most attractive angle of the face, and suggests how the pose should be adjusted. The recommendation results are determined adaptively to the appearance and initial pose of the input face. We apply a data mining approach to find distinctive, frequent itemsets and association rules from online profile pictures, upon which the aesthetic estimation and pose recommendation methods are developed. A simulated and a real image set are used for experimental evaluation. The results show the proposed aesthetic estimation method can effectively select user-favorable photos. Moreover, the recommendation performance for the vertical adjustment is moderately related to the degree of conformity among the professional photographers’ recommendations. This study echoes the trend of instant photo sharing, in which a user takes a picture and then immediately shares it on a social network without engaging in tedious editing.

The extracellular matrix (ECM) plays a critical role in the development and invasion of primary breast tumors. Lysyl oxidase (LOX), which is an ECM remodeling enzyme, appears to play roles in promoting cancer cell motility and invasion. To ascertain whether LOX overexpression in breast tumor tissues from Asian patients is associated with decreases in metastasis-free and overall survival in breast cancer patients, the mRNA levels of LOX were examined in paired tumor/normal tissue samples using real-time RT-PCR analysis (n = 246 pair-matched samples). To test whether specifically targeting LOX by inhibiting its activity (using beta-aminopropionitrile (β-APN), a LOX inhibitor), mRNA expression (using siRNA), or protein expression (using 25 μM magnolol) attenuates the invasion of MDA-MB-231 breast cancer cells, a cancer cell migration assay was performed. Interestingly, only 78.5% (n = 193) of the breast cancer tumors displayed detectable LOX expression. Nearly 60% (n = 120) of the cases fell into Group 1 (tumor > normal, T > N); in this group, the mean LOX expression in the tumor cells was 20.2-fold greater than in normal cells. However, in Group 2 (normal > tumor, N > T), the LOX expression level in most of the normal tissues examined (80%, 59/73) was less than fivefold greater than in the tumor tissues. The increased level of active LOX in the invasive breast cancer cell line MDA-MB-231 was accompanied by the increased phosphorylation of focal adhesion kinase at Tyr-576 and of paxillin at Tyr-118. We also found that the addition of β-APN (300 μM) and magnolol (25 μM), synergistically inhibited the migration and invasion of MDA-MB-231 cells. In this article, we describe, for the first time, higher expression of a LOX protein in breast tumors compared with normal tissues from Asian patients. Moreover, the results indicate that the inhibition of LOX using magnolol may represent a more desirable strategy for breast cancer therapy than the use of β-APN.

The growth rate and the growth mechanism are investigated by examining the results of scanning electron microscope (SEM) and X-ray photoelectron spectroscopy (XPS) on TiO2 nanorods fabricated in the second anodization process through the nanopores of anodic aluminum oxide. Due to the strong electric field of 1.86 GV/m to 1.33 GV/m, the average ultra-fast growth rate of 250 nm/s is observed, and the abrupt increase of height reaches to 85.2% of its destined height in the early growth stage of 0.6 s. Then the growth rate decreases when the electric field decays with the increase of height, and it is interrupted by the dielectric breakdown under an electric field of 0.65 GV/m. According to the XPS analyzes, the TiO2 species on the outer shell of nanorods are observed, and the sub-oxides, Ti2O3 and TiO, are exhibited after Ar+ ions sputtering. It is proposed that the bottom growth mechanism is identified by the analyzes upon SEM and XPS results. © 2012 The Electrochemical Society. [DOI: 10.1149/2.034209jes] All rights reserved.

Gas sensors are operated based on a variety of mechanisms. Basically, they are classified into two types: chemical types operated by gas adsorption and physical types managed by gas ionization. Chemical types mainly detect the electrical response of the gas molecules adsorbed by an active layer in the gas sensor to identify the concentration and composition of gases. However, they usually require a high working temperature, a long pre-heating time and a large surface area. They also encounter high power consumption problem and are not able to detect inert gases. 1‐5 Physical types usually use ionization

Aims and objectives. The purposes of this study were to explore the lived experiences of spiritual suffering and the change mechanism in healing processes among Taiwanese patients with terminal cancer. Methods. The approach to this study was phenomenological-hermeneutic. Twenty-one patients with terminal cancer were invited to participate in a semi-structured interview that dealt with their experiences of spiritual suffering and the healing process. This study was conducted in the inpatient unit of the oncology department in two general hospitals. The interviews were recorded, transcribed and later analysed using the approach of narrative analysis. Results. According to the results of case narration, the causes of spiritual suffering included cancer, known as a life-threatening illness, physical pain, treatment complications, uncertain illness progression, disability problems and lack of support. Patients turned to internal resources (including regarding the suffering as a life challenge, volunteering to help other cancer patients and searching for life wisdoms) and external resources (including peer support groups and family support) as they endured spiritual suffering. Taiwanese patients turned to Eastern and Western philosophies of Taoism, Confucianism, Buddhism and Christianity as methods to interpret their spiritual suffering. Conclusion. Patients’ positive views of misfortune because of cancer and sufficient social supports were the key elements of the healing process to alleviate spiritual suffering. Relevance to clinical practice. Nurses who learn to participate in suffering assessment are better able to understand spiritual needs of cancer patients. Cancer patients’ views on the change mechanism in healing processes could provide essential information for nurses in developing an effective intervention programme. If nurses consider cultural factors that shape patients’ experiences of spiritual suffering and the healing process, they could learn how to meet the needs of patients better from different cultural backgrounds.

Selfies are in vogue because smartphones and social media websites have helped popularize the phenomenon whereby people can easily take and share self-portrait photographs. However, taking good selfies is not always a trivial task for end users. In this demonstration we present a tool for guiding users on which angles to take selfies from. Dissimilar to existing techniques that enhance the aesthetic quality of selfies through a post-processing procedure, the proposed approach focuses on the creation of a good look before the photo is captured. A few distinctive patterns automatically discovered from a collection of online profile pictures are used to score a selfie in angle. The technique should enable new features such as "virtual portraitist" for current consumer cameras and smartphones, providing an interactive approach for capturing selfies.

The Geophysical Database Management System (GDMS) is an integrated and web-based open data service which has been developed by the Central Weather Bureau (CWB), Taiwan, ROC since 2005. This service went online on August 1, 2008. The GDMS provides six types of geophysical data acquired from the Short-period Seismographic System, Broadband Seismographic System, Free-field Strong-motion Station, Strong-motion Building Array, Global Positioning System, and Groundwater Observation System. When utilizing the GDMS website, users can download seismic event data and continuous geophysical data. At present, many researchers have accessed this public platform to obtain geophysical data. Clearly, the establishment of GDMS is a significant improvement in data sorting for interested researchers.

The growth mechanism of TiO2 nanorods fabricated by the anodization of Ti films using a mask of anodic aluminum oxide is investigated by examining the growth rate and morphology of the fabricated nanorods. It is found that the growth rate is 250 nm/s within the first 0.1 s at 117 V and decreases abruptly to 0.79 nm/s after 3 s. In addition, the diameter of the nanorods is larger near the top than near the root of the nanorods. A growth mechanism is proposed to explain these interesting phenomena. © 2010 The Electrochemical Society. DOI: 10.1149/1.3496404 All rights reserved.

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With the recent advances in equipment and software, ultrasonography (US) has been widely used in the diagnosis of muscle injury. We report a case of hamstring muscle tear initially diagnosed by US and subsequently confirmed by magnetic resonance imaging (MRI). The patient received conservative treatment and rehabilitation, after which the symptoms gradually subsided. US was used as the initial tool to identify the muscle injury and to assess the severity of the tear, as this technique has been proved to be equal in sensitivity to MRI by previous researchers. We conclude that US is a reliable and cost-effective tool in the detection of muscle injuries.