OrchidBase 5.0: updates of the orchid genome knowledgebase.

Containing the largest number of species, the orchid family provides not only materials for studying plant evolution and environmental adaptation, but economically and culturally important ornamental plants for human society. Previously, we collected genome and transcriptome information of Dendrobium catenatum, Phalaenopsis equestris, and Apostasia shenzhenica which belong to two different subfamilies of Orchidaceae, and developed user-friendly tools to explore the orchid genetic sequences in the OrchidBase 4.0. The OrchidBase 4.0 offers the opportunity for plant science community to compare orchid genomes and transcriptomes and retrieve orchid sequences for further study.In the year 2022, two whole-genome sequences of Orchidoideae species, Platanthera zijinensis and Platanthera guangdongensis, were de novo sequenced, assembled and analyzed. In addition, systemic transcriptomes from these two species were also established. Therefore, we included these datasets to develop the new version of OrchidBase 5.0. In addition, three new functions including synteny, gene order, and miRNA information were also developed for orchid genome comparisons and miRNA characterization.OrchidBase 5.0 extended the genetic information to three orchid subfamilies (including five orchid species) and provided new tools for orchid researchers to analyze orchid genomes and transcriptomes. The online resources can be accessed at https://cosbi.ee.ncku.edu.tw/orchidbase5/.

OrchidBase 4.0: a database for orchid genomics and molecular biology.

BACKGROUND: The Orchid family is the largest families of the monocotyledons and an economically important ornamental plant worldwide. Given the pivotal role of this plant to humans, botanical researchers and breeding communities should have access to valuable genomic and transcriptomic information of this plant. Previously, we established OrchidBase, which contains expressed sequence tags (ESTs) from different tissues and developmental stages of Phalaenopsis as well as biotic and abiotic stress-treated Phalaenopsis. The database includes floral transcriptomic sequences from 10 orchid species across all the five subfamilies of Orchidaceae. DESCRIPTION: Recently, the whole-genome sequences of Apostasia shenzhenica, Dendrobium catenatum, and Phalaenopsis equestris were de novo assembled and analyzed. These datasets were used to develop OrchidBase 4.0, including genomic and transcriptomic data for these three orchid species. OrchidBase 4.0 offers information for gene annotation, gene expression with fragments per kilobase of transcript per millions mapped reads (FPKM), KEGG pathways and BLAST search. In addition, assembled genome sequences and location of genes and miRNAs could be visualized by the genome browser. The online resources in OrchidBase 4.0 can be accessed by browsing or using BLAST. Users can also download the assembled scaffold sequences and the predicted gene and protein sequences of these three orchid species. CONCLUSIONS: OrchidBase 4.0 is the first database that contain the whole-genome sequences and annotations of multiple orchid species. OrchidBase 4.0 is available at http://orchidbase.itps.ncku.edu.tw/.

A cluster validity measure with outlier detection for support vector clustering.

This paper focuses on the development of an effective cluster validity measure with outlier detection and cluster merging algorithms for support vector clustering (SVC). Since SVC is a kernel-based clustering approach, the parameter of kernel functions and the soft-margin constants in Lagrangian functions play a crucial role in the clustering results. The major contribution of this paper is that our proposed validity measure and algorithms are capable of identifying ideal parameters for SVC to reveal a suitable cluster configuration for a given data set. A validity measure, which is based on a ratio of cluster compactness to separation with outlier detection and a cluster-merging mechanism, has been developed to automatically determine ideal parameters for the kernel functions and soft-margin constants as well. With these parameters, the SVC algorithm is capable of identifying the optimal number of clusters with compact and smooth arbitrary-shaped cluster contours for the given data set and increasing robustness to outliers and noise. Several simulations, including artificial and benchmark data sets, have been conducted to demonstrate the effectiveness of the proposed cluster validity measure for the SVC algorithm.

Sleep disturbances at the time of a new diagnosis: a comparative study of human immunodeficiency virus patients, cancer patients, and general population controls.

OBJECTIVES: Sleep disturbances are a prevalent and troubling symptom of patients with highly stressful illnesses, such as human immunodeficiency virus (HIV) and cancer. The aim of this study was to compare the prevalence and incidence of sleep disturbances among persons with HIV, those with cancer, and the general population of Taiwan. METHODS: A matched cohort study design was used to compare the risk of sleep disturbances among three groups using reimbursement claims recorded in Taiwan's National Health Insurance Research Database (NHIRD). A total of 14,531 HIV-infected persons were compared with 1493 cancer patients and 1373 general population controls matched by gender and age. Cox proportional hazard regression models were used to test the hazard risk of sleep disturbances among the groups. RESULTS: The mean durations between the date of the initial HIV/cancer diagnosis and onset of sleep disturbances of HIV-infected persons, cancer patients, and controls were 1.7, 2.3, and 1.8 years, respectively. The risk of developing sleep disturbances was significantly higher in HIV-infected persons (adjusted hazard ratio [AHR] = 3.74, p < 0.001) and cancer patients (AHR = 2.72, p < 0.001) than in controls. HIV-infected persons had a 20% higher risk of sleep disturbances than cancer patients (AHR = 1.20, p < 0.001). CONCLUSIONS: HIV-infected persons exhibited a higher risk of developing sleep disturbances than cancer patients and general population controls. With efficacious treatments for sleep disturbances, we should focus on training and research programs for health care providers to intervene and treat earlier for the present and future health of cancer patients and HIV-infected persons.

The Effects of an Activity Promotion System on active living in overweight subjects with metabolic abnormalities.

OBJECTIVES: The aim of this study is to examine the effects of Activity Promotion System (APs) on promoting physical activity (PA) for overweight subjects with metabolic abnormalities. METHODS: We designed a six-month randomised controlled trial with a cross-over design, and recruited 53 subjects. Subjects in group A used APs with a wearable device measuring whole-day PA, including sleep time, sedentary, light, moderate and vigorous PA and a web-based feedback system in the first three months and followed by usual care with traditional health education in the next three months. Subjects in group B received the above programs in a reverse order. PA and metabolic profiles were measured prior to the intervention (T1), three months after the first intervention (T2), and six months after the other intervention (T3), respectively. An independent t test was used to test the differences between periods with and without Aps. RESULTS: This study found that the APs had short-term effects on decreasing sedentary time and increasing mild PA, total PA, daily step counts, and calories burnt. With regard to the secondary outcome measures of metabolic abnormalities, the results showed that APs had had no effect on metabolic abnormalities, except a borderline decreasing of waist circumference. CONCLUSION: Using this APs might be an effective approach to decrease sedentary time and increase PA for an overweight non-elderly adult population with only metabolic abnormalities. However, long-term studies with APs are needed to further confirm the effectiveness of this innovative Activity Promotion System.

Gait and balance analysis for patients with Alzheimer's disease using an inertial-sensor-based wearable instrument.

Despite patients with Alzheimer's disease (AD) were reported of revealing gait disorders and balance problems, there is still lack of objective quantitative measurement of gait patterns and balance capability of AD patients. Based on an inertial-sensor-based wearable device, this paper develops gait and balance analyzing algorithms to obtain quantitative measurements and explores the essential indicators from the measurements for AD diagnosis. The gait analyzing algorithm is composed of stride detection followed by gait cycle decomposition so that gait parameters are developed from the decomposed gait details. On the other hand, the balance is measured by the sway speed in anterior-posterior (AP) and medial-lateral (ML) directions of the projection path of body's center of mass (COM). These devised gait and balance parameters were explored on twenty-one AD patients and fifty healthy controls (HCs). Special evaluation procedure including single-task and dual-task walking experiments for observing the cognitive function and attention is also devised for the comparison of AD and HC groups. Experimental results show that the wearable instrument with the designed gait and balance analyzing system is a promising tool for automatically analyzing gait information and balance ability, serving as assistant indicators for early diagnosis of AD.

A wearable sensor module with a neural-network-based activity classification algorithm for daily energy expenditure estimation.

This paper presents a wearable module and neural-network-based activity classification algorithm for energy expenditure estimation. The purpose of our design is first to categorize physical activities with similar intensity levels, and then to construct energy expenditure regression (EER) models using neural networks in order to optimize the estimation performance. The classification of physical activities for EER model construction is based on the acceleration and ECG signal data collected by wearable sensor modules developed by our research lab. The proposed algorithm consists of procedures for data collection, data preprocessing, activity classification, feature selection, and construction of EER models using neural networks. In order to reduce the computational load and achieve satisfactory estimation performance, we employed sequential forward and backward search strategies for feature selection. Two representative neural networks, a radial basis function network (RBFN) and a generalized regression neural network (GRNN), were employed as EER models for performance comparisons. Our experimental results have successfully validated the effectiveness of our wearable sensor module and its neural-network-based activity classification algorithm for energy expenditure estimation. In addition, our results demonstrate the superior performance of GRNN as compared to RBFN.

Walking pattern classification and walking distance estimation algorithms using gait phase information.

This paper presents a walking pattern classification and a walking distance estimation algorithm using gait phase information. A gait phase information retrieval algorithm was developed to analyze the duration of the phases in a gait cycle (i.e., stance, push-off, swing, and heel-strike phases). Based on the gait phase information, a decision tree based on the relations between gait phases was constructed for classifying three different walking patterns (level walking, walking upstairs, and walking downstairs). Gait phase information was also used for developing a walking distance estimation algorithm. The walking distance estimation algorithm consists of the processes of step count and step length estimation. The proposed walking pattern classification and walking distance estimation algorithm have been validated by a series of experiments. The accuracy of the proposed walking pattern classification was 98.87%, 95.45%, and 95.00% for level walking, walking upstairs, and walking downstairs, respectively. The accuracy of the proposed walking distance estimation algorithm was 96.42% over a walking distance.