Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of sarcopenia on prognosis of patients with pancreatic cancer: A systematic review and meta-analysis

Objective To summarize recent findings on the association of low skeletal muscle mass and muscle quality with overall survival and recurrence-free survival in patients with pancreatic cancer. Methods A systematic search was conducted using Medline (via PubMed), Embase and Scopus databases for observational studies reporting on the overall survival and recurrence-free survival. Pooled effect sizes were reported as hazards ratio along with 95% confidence intervals. Results A total of 34 studies were included. Low skeletal muscle index (indicating muscle mass) was associated with poor overall survival (hazards ratio: 1.50; 95% confidence interval: 1.34, 1.67) and lower recurrence-free survival (hazards ratio: 1.28, 95% confidence interval: 1.15, 1.43). Low skeletal muscle attenuation (indicating muscle quality) was associated with poor overall survival (hazards ratio: 1.32; 95% confidence interval: 1.05, 1.66). Recurrence-free survival was similar in patients with low and normal/high skeletal muscle attenuation (hazards ratio: 1.12, 95% confidence interval: 0.89, 1.40). Conclusion Both low skeletal muscle mass and poor muscle quality are associated with poor long-term survival. Low skeletal muscle index, but not low skeletal muscle attenuation, are associated with poor recurrence-free survival.
Classification of semiurban landscapes from very high-resolution satellite images using a regionalized multiscale segmentation approach

Abstract. In object-based image analysis, obtaining representative image objects is an important prerequisite for a successful image classification. The major threat is the issue of scale selection due to the complex spatial structure of landscapes portrayed as an image. This study proposes a two-stage approach to conduct regionalized multiscale segmentation. In the first stage, an initial high-level segmentation is applied through a “broadscale,” and a set of image objects characterizing natural borders of the landscape features are extracted. Contiguous objects are then merged to create regions by considering their normalized difference vegetation index resemblance. In the second stage, optimal scale values are estimated for the extracted regions, and multiresolution segmentation is applied with these settings. Two satellite images with different spatial and spectral resolutions were utilized to test the effectiveness of the proposed approach and its transferability to different geographical sites. Results were compared to those of image-based single-scale segmentation and it was found that the proposed approach outperformed the single-scale segmentations. Using the proposed methodology, significant improvement in terms of segmentation quality and classification accuracy (up to 5%) was achieved. In addition, the highest classification accuracies were produced using fine-scale values.
Bi-Nuclear Tensor Schatten-p Norm Minimization for Multi-View Subspace Clustering

Multi-view subspace clustering aims to integrate the complementary information contained in different views to facilitate data representation. Currently, low-rank representation (LRR) serves as a benchmark method. However, we observe that these LRR-based methods would suffer from two issues: <italic>limited clustering performance</italic> and <italic>high computational cost</italic> since (1) they usually adopt the nuclear norm with biased estimation to explore the low-rank structures; (2) the singular value decomposition of large-scale matrices is inevitably involved. Moreover, LRR may not achieve low-rank properties in both intra-views and inter-views simultaneously. To address the above issues, this paper proposes the <italic>Bi-nuclear tensor Schatten-</italic> <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> <italic>norm minimization for multi-view</italic> subspace clustering (BTMSC). Specifically, BTMSC constructs a third-order tensor from the view dimension to explore the high-order correlation and the subspace structures of multi-view features. The Bi-Nuclear Quasi-Norm (BiN) factorization form of the Schatten-<inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> norm is utilized to factorize the third-order tensor as the product of two small-scale third-order tensors, which not only captures the low-rank property of the third-order tensor but also improves the computational efficiency. Finally, an efficient alternating optimization algorithm is designed to solve the BTMSC model. Extensive experiments with ten datasets of texts and images illustrate the performance superiority of the proposed BTMSC method over state-of-the-art methods.
African swine fever virus protein p17 promotes mitophagy by facilitating the interaction of SQSTM1 with TOMM70

ABSTRACT Viruses have developed different strategies to hijack mitophagy to facilitate their replication. However, whether and how African swine fever virus (ASFV) regulates mitophagy are largely unknown. Here, we found that the ASFV-encoded p17 induced mitophagy. Coimmunoprecipitation/mass spectrometry assays identified translocase of outer mitochondrial membrane 70 (TOMM70) as the protein that interacted with p17. The binding of TOMM70 to p17 promoted the binding of the mitophagy receptor SQSTM1 to TOMM70, led to engulfment of mitochondria by autophagosomes, and consequently decreased the number of mitochondria. Consistently, the levels of TOMM70 and TOMM20 decreased substantially after p17 expression or ASFV infection. Furthermore, p17-mediated mitophagy resulted in the degradation of mitochondrial antiviral signalling proteins and inhibited the production of IFN-α, IL-6 and TNFα. Overall, our findings suggest that ASFV p17 regulates innate immunity by inducing mitophagy via the interaction of SQSTM1 with TOMM70.
Was Unstable Medicaid Coverage Among Older Medicare Beneficiaries Associated With Worse Clinical Outcomes? Evidence From the Delivery of Breast Cancer Care

Background: Medicare and Medicaid dually eligible beneficiaries (duals) could experience Medicaid coverage changes without losing Medicaid. It is unknown whether health care use and clinical outcomes among elderly duals with coverage changes would be like those among duals without coverage changes or duals ever lost Medicaid and whether various types of unstable coverage due to income/asset changes are associated with worse clinical outcomes. Objectives: Examine the associations of unstable Medicaid coverage with clinical outcomes among older Medicare beneficiaries. Research Design: Population-based cohort study. Subjects: A total of 131,202 women newly diagnosed with breast cancer at 65 years and older between 2007 and 2015 were identified from the Surveillance, Epidemiology, and End Results-Medicare linked database. Measures: We examined 2 types of unstable Medicaid coverage: (1) those who had changes in the types of Medicaid support they received and (2) those who ever lost Medicaid. We examined outcomes that predict better cancer survival and involve the use of inpatient and outpatient services and prescription drugs: early diagnosis, receiving surgery, receiving radiation, hormonal therapy adherence, and discontinuation. We used logistic regressions to estimate the predicted probabilities of outcomes for dual groups. Results: Duals had poorer outcomes than those who were “never dual. ” Women with the 2 types of unstable Medicaid coverage had similarly worse outcomes than those with stable coverage. Those with stable coverage had similar outcomes regardless of the generosity of Medicaid support. Conclusions: These patterns are concerning and, in the context of well-defined clinical guidelines for beneficial treatments that extend survival, point to the importance of stable insurance coverage and income.
Anomaly Detection in Streaming Nonstationary Temporal Data

Abstract This article proposes a framework that provides early detection of anomalous series within a large collection of nonstationary streaming time-series data. We define an anomaly as an observation, that is, very unlikely given the recent distribution of a given system. The proposed framework first calculates a boundary for the system’s typical behavior using extreme value theory. Then a sliding window is used to test for anomalous series within a newly arrived collection of series. The model uses time series features as inputs, and a density-based comparison to detect any significant changes in the distribution of the features. Using various synthetic and real world datasets, we demonstrate the wide applicability and usefulness of our proposed framework. We show that the proposed algorithm can work well in the presence of noisy nonstationarity data within multiple classes of time series. This framework is implemented in the open source R package oddstream. R code and data are available in the online supplementary materials.
Effects of multigrain rice and white rice on periodontitis: an analysis using data from the Korea National Health and Nutrition Examination Survey 2012-2015

OBJECTIVES Numerous studies have investigated the efficacy of whole grains; however, research on multigrain remains limited. Grains exhibit combined positive effects against various diseases. The purpose of this study was to examine the impact of multigrain and white rice consumption on periodontitis. METHODS We analyzed data from the Korea National Health and Nutrition Examination Survey V-3 and VI, collected between 2012 and 2015, which included 12,450 patients (4,859 male and 7,591 female) aged 19-64 years. The World Health Organization’s Community Periodontal Index (CPI) was utilized to assess the presence of periodontitis, with periodontitis defined as a CPI index score of ≥3. Multivariable logistic regression analysis was performed after adjusting for potential confounding variables. RESULTS The group that consumed only multigrain rice was less likely to have periodontitis than the group that consumed only white rice (odds ratio [OR], 0.80; 95% confidence interval [CI], 0.69 to 0.93). When stratified by sex, the risk of periodontitis demonstrated a 24% decrease in female who consumed only multigrain rice (OR, 0.76; 95% CI, 0.62 to 0.93). A similar result was observed in the age group of 40-64 years (OR, 0.84; 95% CI, 0.71 to 0.99). In the diabetes stratification model, the normal group that consumed only multigrain rice exhibited a 25% decrease in the odds of periodontitis (OR, 0.75; 95% CI, 0.62 to 0.91). CONCLUSIONS Our findings suggest that the prevalence of periodontitis may vary depending on the type of rice consumed.
Design the finite-time H∞ resilient filter of a class of switched systems with uncertain parameters

This paper is concerned with the problem of finite-time H∞ resilient filtering for a class of switch systems. The filtering error dynamics is constructed based on the H∞ resilient filter. The objective is to design a filter such that the finite-time H∞ gain from the unknown input to an estimation error is minimized or guaranteed to be less than or equal to a prescribed value. By selecting the proper multiple Lyapunov function and using the average dwell-time approach, sufficient conditions are obtained for the existence of the desired H∞ resilient filter, which also guarantee the finite-time boundedness of the filtering error dynamic systems. The design criteria are proposed in the form of linear matrix inequalities and then described as an optimization algorithm. Finally, a numerical example is employed to illustrate the effectiveness of the developed techniques.
Phenolic secondary metabolites from Acorus calamus (Acorales: Acoraceae) rhizomes: the feeding deterrents for Spodoptera litura (Lepidoptera: Noctuidae)

Spodoptera litura Fabricius (Lepidoptera: Noctuidae) is one of the most destructive pests of various crops cultivated in Thailand. Spodoptera litura larvae, at early stages, attack the leaves and feed on every part of infested crops in later stages. Acorus calamus essential oil contains toxic asarones, which are generalistic cytotoxic compounds. However, the present study is the first attempt to look at safer metabolites from the rhizomes that could deter insect feeding. The objective was to use such compounds as safer residues on crops that would prevent the feeding of herbivorous lepidopterans. Accordingly, phenolic metabolites were isolated and evaluated to establish the feeding deterrence against polyphagous S. litura larvae. Methanol extract of A. calamus, chrysin, and 4-hydroxy acetophenone compounds were the most effective feeding deterrents with FD50 of 87.18, 10.33, and 70.77 µg/cm2, respectively, after 4 h of feeding on treated kale leaves in a no-choice leaf disc assay. Chrysin also reduced carboxylesterase activities (1.37-fold), whereas A. calamus methanol extract reduced glutathione-S-transferase activities (1.44-fold). Some larvae were also seen dead if they consumed the treated kale leaves. Feeding deterrent activity in the methanol extract of A. calamus was due to chrysin and 4-hydroxy acetophenone. The large-scale utilization of such compounds could help develop feeding deterrent strategies in the integrated pest management of lepidopterans. Graphical Abstract
Online Regression with Model Selection

Online learning algorithms have a wide variety of applications in large scale machine learning problems due to their low computational and memory requirements. However, standard online learning methods still suffer some issues such as lower convergence rates and limited capability to select features or to recover the true features. In this paper, we present a novel framework for online learning based on running averages and introduce a series of online versions of some popular existing offline algorithms such as Adaptive Lasso, Elastic Net and Feature Selection with Annealing. We prove the equivalence between our online methods and their offline counterparts and give theoretical feature selection and convergence guarantees for some of them. In contrast to the existing online methods, the proposed methods can extract model with any desired sparsity level at any time. Numerical experiments indicate that our new methods enjoy high feature selection accuracy and a fast convergence rate, compared with standard stochastic algorithms and offline learning algorithms. We also present some applications to large datasets where again the proposed framework shows competitive results compared to popular online and offline algorithms.
Landscape of Psychological Profiles in Patients With Esophageal Achalasia

INTRODUCTION: Esophageal achalasia (EA) is a chronic esophageal dysmotility disease, of which psychological distress was poorly understood. This study aims to assess the status of psychosocial characteristics in EA and to determine the relationship between psychological distress and EA. METHODS: Seventy pairs of age and gender-matched patients with EA and healthy control individuals were prospectively enrolled from December 2019 to April 2020 at our hospital. Demographic, psychosocial, and clinical data were obtained. Psychosocial assessments contained psychological distress (Symptom Checklist-90 Revised), perceived stress (Perceived Stress Scale-14), and stressful life events (Life Events Scale). Comparison for psychological parameters was made between patients with EA and controls as well as for EA before/after per oral endoscopic myotomy (POEM). Spearman rank correlation coefficients were used to testify the association between psychological distress and achalasia symptoms. RESULTS: The mean course and Eckardt score of patients with EA were 4.26 ± 5.11 years and 6.63 ± 2.21, respectively. There was a significant difference between patients with EA and healthy individuals in Global Severity Index (P = 0.039) and Positive Symptoms Total (P = 0.041) for Symptom Checklist-90 Revised as well as positive intensity (P = 0.011) for the Life Events Scale. Somatization (P < 0.001), anxiety (P = 0.021), anger-hostility (P = 0.009), and others (appetite and sleep, P = 0.010) accounted for the most difference. Somatization was positively associated with chest pain (P = 0.045). Two patients with EA developed recurrence and showed no relationship with psychological status. Psychological status was significantly improved after POEM. DISCUSSION: Psychological distress, especially somatization, was more prevalent in patients with EA than healthy controls. POEM seemed able to improve psychological distress.
Developing feasible healthy diets for Ethiopian women of reproductive age: a linear goal programming approach

Abstract Objective: To develop a healthy diet for Ethiopian women closely resembling their current diet and taking fasting periods into account while tracking the cost difference. Design: Linear goal programming models were built for three scenarios (non-fasting, continuous fasting and intermittent fasting). Each model minimised a function of deviations from nutrient reference values for eleven nutrients (protein, Ca, Fe, Zn, folate, and the vitamins A, B1, B2, B3, B6, and B12). The energy intake in optimised diets could only deviate 5 % from the current diet. Settings: Five regions are included in the urban and rural areas of Ethiopia. Participants: Two non-consecutive 24-h dietary recalls (24HDR) were collected from 494 Ethiopian women of reproductive age from November to December 2019. Results: Women’s mean energy intake was well above 2000 kcal across all socio-demographic subgroups. Compared to the current diet, the estimated intake of several food groups was considerably higher in the optimised modelled diets, that is, milk and dairy foods (396 v. 30 g/d), nuts and seeds (20 v. 1 g/d) and fruits (200 v. 7 g/d). Except for Ca and vitamin B12 intake in the continuous fasting diet, the proposed diets provide an adequate intake of the targeted micronutrients. The proposed diets had a maximum cost of 120 Ethiopian birrs ($3·5) per d, twice the current diet’s cost. Conclusion: The modelled diets may be feasible for women of reproductive age as they are close to their current diets and fulfil their energy and nutrient demands. However, the costs may be a barrier to implementation.
A Knowledge Graph-Based Survey on Distributed Ledger Technology for IoT Verticals

The Internet of Things (IoT) and distributed ledger technology (DLT) have significantly changed our daily lives. Due to their distributed operational environment and naturally decentralized applications, the convergence of these two technologies indicates a more lavish arrangement for the future. This article develops a comprehensive survey to investigate and illustrate state-of-the-art DLT for various IoT use cases, from smart homes to autonomous vehicles and smart cities. We develop a novel framework for conducting a systematic and comprehensive review of DLT over the IoT by extending the knowledge graph approach. With relevant insights from this review, we extract innovative and pragmatic techniques to DLT design that enable high-performance, sustainable, and highly scalable IoT systems. Our findings support designing an end-to-end IoT-native DLT architecture for the future that fully coordinates network-assisted functionalities.
Updating Human Pose Estimation using Event-based Camera to Improve Its Accuracy

Ippei Otake Kazuya Kitano Takahiro Kushida otake.ippei. oj2@is.naist. jp kitano.kazuya@is.naist. jp kushida.takahiro. kh3@is.naist. jp NAIST Japan Hiroyuki Kubo hkubo@chiba-u.jp Chiba University Japan Akinobu Maejima akinobu.maejima@olm.co.jp OLM Digital, Inc. IMAGICA GROUP, Inc. Japan Yuki Fujimura Takuya Funatomi Yasuhiro Mukaigawa fujimura.yuki@is.naist. jp funatomi@is.naist. jp mukaigawa@is.naist. jp NAIST Japan
Query-by-Example Spoken Term Detection for Zero-Resource Languages Using Heuristic Search

Query-by-Example spoken content retrieval is a demanding and challenging task when a large volume of spoken content is piled up in the repositories without annotation. In the absence of annotation, spoken content retrieval is achieved by capturing the similarities between the query and spoken terms from the acoustic feature representation itself. Dynamic Time Warping (DTW) centric techniques identify the optimal alignment between the acoustic feature representations and capture the similarities between query and spoken terms. Despite feasibility, the DTW-centric techniques produce a lot of false alarms due to the variabilities that exist in natural speech and degrade the performance. In the proposed approach, the variability challenges are addressed in two stages. At first, the speaker-independent acoustic feature representation was obtained from the deep convolutional neural networks that reduce the speaker variabilities. In the second stage, the similarities between the query and spoken term were captured using the heuristic search method. The proposed approach was compared with other state-of-the-art methods using Microsoft Low-Resource Language speech corpus. A 3% improvement and 32% reduction in the hit and false alarm ratio were achieved across languages.
Prioritizing the Risk and Management of Introduced Species in a Landscape with High Indigenous Biodiversity

Abstract. Risk analysis protocols for prioritizing the management of non-native species are numerous, yet few incorporate risk and management in the same analysis or accommodate a broad diversity of taxa outside of a specific geographic area. We adapted a protocol that accounts for these factors to address non-native animal species in the Southern California/Northern Baja California Coast Ecoregion near the international border in San Diego County, an area with high indigenous biodiversity and high numbers of species of conservation concern. This stepwise, semi-quantitative protocol is applicable to any animal group in any predefined geographic area, relies on consensus-building among taxonomic experts, and has been vetted through previous use and in peer-reviewed literature. Our results show that the final prioritization was driven mainly by management feasibility, with top-ranked species having multitrophic effects that favor other non-native invaders over native residents. Conditions within the assessment area required some modification to the protocol as it was originally designed, namely a shift in emphasis from eradication to control, given that eradication is implausible for most non-native species in the assessment area. We call attention to taxon-specific issues that surfaced during the analysis, identify areas for improvement in this first-ever risk assessment for invasive animal species in the Natural Communities Conservation Plan/Habitat Conservation Plan (NCCP/HCP) reserve system of San Diego County, and provide suggestions for further refinement of the protocol. This study builds on the effort to standardize risk analysis for invasive species globally, given that many of the same invaders present threats to indigenous biodiversity worldwide.
Single Smog Image Dehazing Method

Images in fog or smog degrade dreadfully, although there were quite a few image dehazing methods, which were not very effective to smog images. This paper proposed a novel dehazing method based on propagating deconvolution and dark-channel prior. Propagating deconvolution aimed at recovering smog image to get rid of the smog in front of the scene, it could transit the "smog image" to "fog image". While dark channel prior could enhance the details and improve the contrast. The results shows that this method performances better in smog image recovery.