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Obstructive sleep apnea (OSA) results from upper airway remodeling, which has been suggested to alter sensory and motor neuron function due to hypoxia or snore vibration. This study investigated whether OSA was associated with the risk of flavor disorder (FD).Seven thousand and eight hundred sixty-five patients with OSA and 7865 propensity score-matched controls without OSA were enrolled between 1999 and 2013 through a nationwide cohort study. The propensity score matching was based on age, sex, comorbidities including hypertension, hyperlipidemia, chronic obstructive pulmonary disease (COPD), asthma, ankylosing spondylitis, and Charlson comorbidity index, and co-medications during the study period, including statins and angiotensin-converting enzyme (ACE) inhibitors. The adjusted hazard ratio (aHR) of incident FD following OSA was derived using a Cox proportional hazard model. A log-rank test was used to evaluate the time-dependent effect of OSA on FD. Age, sex, comorbidities, and co-medications were stratified to identify subgroups susceptible to OSA-associated FD.Patients with OSA were at a significantly great risk of FD (aHR = 1.91, 95% CI = 1.08-3.38), which was time-dependent (log-rank test p = 0.013). Likewise, patients with hyperlipidemia were at a significant great risk of FD (aHR = 2.99, 95% CI = 1.33-6.69). Subgroup analysis revealed that female patients with OSA were at higher risks of FD (aHR = 2.39, 95%CI = 1.05-5.47).Patients with OSA were at significantly great risk of incident FD during the 15-year follow-up period, especially in female patients with OSA.Timely interventions for OSA may prevent OSA-associated FD.

Visual recognition is currently one of the most important and active research areas in computer vision, pattern recognition, and even the general field of artificial intelligence. It has great fundamental importance and strong industrial needs, particularly the modern deep neural networks (DNNs) and some brain-inspired methodologies, have largely boosted the recognition performance on many concrete tasks, with the help of large amounts of training data and new powerful computation resources. Although recognition accuracy is usually the first concern for new progresses, efficiency is actually rather important and sometimes critical for both academic research and industrial applications. Moreover, insightful views on the opportunities and challenges of efficiency are also highly required for the entire community. While general surveys on the efficiency issue have been done from various perspectives, as far as we are aware, scarcely any of them focused on visual recognition systematically, and thus it is unclear which progresses are applicable to it and what else should be concerned. In this survey, we present the review of recent advances with our suggestions on the new possible directions towards improving the efficiency of DNN-related and brain-inspired visual recognition approaches, including efficient network compression and dynamic brain-inspired networks. We investigate not only from the model but also from the data point of view (which is not the case in existing surveys) and focus on four typical data types (images, video, points, and events). This survey attempts to provide a systematic summary via a comprehensive survey that can serve as a valuable reference and inspire both researchers and practitioners working on visual recognition problems.

High precision and large measurement range are the target of any one three-dimensional scanner. For a line structure light vision sensor, measurement precision depends on its calibration results, i.e., determining mathematical expression of the light plane in camera coordinate system. However, as calibration results are locally optimal solutions, high precise measurement in a large range is difficult. In this paper, we give a precise measurement method and the corresponding calibration procedure for a line structure light vision sensor with a large measurement range. A motorized linear translation stages with a travel range of 150 mm and a planar target which is a surface plate with a machining precision of 0.05 mm are utilized. With the help of the linear translation stage and the planar target, functions which gives the relationship between center point of the laser stripe and the perpendicular/ horizontal distance are obtained. Once image of light stripe is captured, we can get a precise measurement result from the normalized feature points. Compared with a traditional measurement method, distortion compensation is not necessary and precision of measurement is improved significantly. Experiments show that root mean square error of measurement results according to our proposed method is reduced by 64.67% related to the traditional method.

Background Cancer is the leading cause of death in Taiwan. Medical expenditures related to cancer accounted for 44.8% of all major illness insurance claims in Taiwan. Prior research has indicated that the dual presence of cancer and mental disorder in patients led to increased medical burden. Furthermore, patients with cancer and concomitant mental disorder could incur as much as 50% more annual costs than those without. Although previous studies have investigated the utilization of patients with both diseases, the effects of morbidity sequence order on patient costs are, however, uncertain. This study explored medical expenditures linked with the comorbidity of cancer and mental disorder, with a focus on the impact of diagnosis sequence order. Methods This population-based retrospective matched cohort study retrieved patients with cancer and mental disorder (aged ≥ 20 years) from the Ministry of Health and Welfare Data Science Center 2005–2015 database. 321,045 patients were divided based on having one or both diseases, as well as on the sequence of mental disorder and cancer diagnosis. Study subjects were paired with comparison counterparts free of both diseases using Propensity Score Matching at a 1:1 ratio. Annual Cost per Patient Linear Model (with a log-link function and gamma distribution) was used to assess the average annual cost, covarying for socio-demographic and clinical factors. Binomial Logistic Regression was used to evaluate factors associated with the risk of high-utilization. Results The “Cancer only” group had higher adjusted mean annual costs (NT$126,198), more than 5-times that of the reference group (e^β: 5.45, p p Conclusion Presence of mental disorder prior to cancer had a diminishing effect on medical utilization in patients, possibly indicating low medical compliance or adherence in patients with mental disorder on initial treatments after cancer diagnosis. Patients with post-cancer mental disorder had the highest average annual cost. Similar results were found in the odds of reaching high-utilizer status. The follow-up of cancer treatment for patients with pre-existing mental disorders warrants more emphasis in an attempt to effectively allocate medical resources.

Objective Due to inconsistency in neoadjuvant chemotherapy (NACT) response in advanced gastric cancer (GC), the indications remain the source of controversy. This study focused on identifying factors related to NACT chemosensitivity and providing the best treatment for GC cases. Methods Clinical data in 867 GC cases treated with neoadjuvant chemotherapy were downloaded from two medical centers between January 2014 and December 2020, and analyzed by logistic regression and the least absolute shrinkage and selection operator (LASSO) for identifying potential factors that predicted NACT response and might be incorporated in constructing the prediction nomogram. Results After the inclusion and exclusion criteria were applied, totally 460 cases were enrolled, among which, 307 were males (66.74%) whereas 153 were females (33.26%), with the age of 24–77 (average, 59.37 ± 10.60) years. Consistent with RECIST standard, 242 patients were classified into effective group (PR or CR) while 218 were into ineffective group (PD or SD), with the effective rate of 52.61%. In training set, LASSO and logistic regression analysis showed that five risk factors were significantly associated with NACT effectiveness, including tumor location, Smoking history, T and N stages, and differentiation. In terms of our prediction model, its C-index was 0.842. Moreover, calibration curve showed that the model-predicted results were in good consistence with actual results. Validation based on internal and external validation sets exhibited consistency between training set results and ours. Conclusions This study identified five risk factors which were significantly associated with NACT response, including smoking history, clinical T stage, clinical N stage, tumor location and differentiation. The prediction model that exhibited satisfying ability to predict NACT effectiveness was constructed, which may be adopted for identifying the best therapeutic strategy for advanced GC by gastrointestinal surgeons.

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/

Despite decades of intense theoretical, experimental and computational effort, a microscopic theory of high-temperature superconductivity is not yet established. Eight researchers share their contributions to the search for a better understanding of unconventional superconductivity and their hopes for the future of the field.

Wire arc additive manufacturing (WAAM) is a novel manufacturing technique by which high strength metal components can be fabricated layer by layer using an electric arc as the heat source and metal wire as feedstock, and offers the potential to produce large dimensional structures at much higher build rate and minimum waste of raw material. In the present work, a cold metal transfer (CMT) based additive manufacturing was carried out and the effect of deposition rate on the microstructure and mechanical properties of WAAM Ti-6Al-4V components was investigated. The microstructure of WAAM components showed similar microstructural morphology in all deposition conditions. When the deposition rate increased from 1.63 to 2.23 kg/h, the ultimate tensile strength (UTS) decreased from 984.6 MPa to 899.2 MPa and the micro-hardness showed a scattered but clear decline trend.