Miracle-dz/UniFAR-FTUs huggingface dataset:
Genomic Selection for Grain Yield in the CIMMYT Wheat Breeding Program—Status and Perspectives

Genomic breeding technologies offer new opportunities for grain yield (GY) improvement in common wheat. In this study, we have evaluated the potential of genomic selection (GS) in breeding for GY in wheat by modeling a large dataset of 48,562 GY observations from the International Maize and Wheat Improvement Center (CIMMYT), including 36 yield trials evaluated between 2012 and 2019 in Obregón, Sonora, Mexico. Our key objective was to determine the value that GS can add to the current three-stage yield testing strategy at CIMMYT, and we draw inferences from predictive modeling of GY using 420 different populations, environments, cycles, and model combinations. First, we evaluated the potential of genomic predictions for minimizing the number of replications and lines tested within a site and year and obtained mean prediction accuracies (PAs) of 0.56, 0.5, and 0.42 in Stages 1, 2, and 3 of yield testing, respectively. However, these PAs were similar to the mean pedigree-based PAs indicating that genomic relationships added no value to pedigree relationships in the yield testing stages, characterized by small family-sizes. Second, we evaluated genomic predictions for minimizing GY testing across stages/years in Obregón and observed mean PAs of 0.41, 0.31, and 0.37, respectively when GY in the full irrigation bed planting (FI BP), drought stress (DS), and late-sown heat stress environments were predicted across years using genotype × environment (G × E) interaction models. Third, we evaluated genomic predictions for minimizing the number of yield testing environments and observed that in Stage 2, the FI BP, full irrigation flat planting and early-sown heat stress environments (mean PA of 0.37 ± 0.12) and the reduced irrigation and DS environments (mean PA of 0.45 ± 0.07) had moderate predictabilities among them. However, in both predictions across years and environments, the PAs were inconsistent across years and the G × E models had no advantage over the baseline model with environment and line effects. Overall, our results provide excellent insights into the predictability of a quantitative trait like GY and will have important implications on the future design of GS for GY in wheat breeding programs globally.
From Pest to Profit—The Potential of Shipworms for Sustainable Aquaculture

We face a food crisis. Suboptimal diet is the biggest cause of death worldwide, food production the biggest greenhouse gas emitting sector, and by 2050 an extra 2.5 billion people need affordable nutrition. Current farming systems will fail to tackle this crisis, and there is an urgent need to diversify global food production and find effective solutions in currently underexploited food sectors. Shipworms, or shell-less Teredo clams, could prove a highly valuable component of such solutions. Historically viewed as a marine pest, they have unique physiological characteristics which make them an ideal food source, including exceptionally fast growth rates, the ability to feed on waste wood or sustainable microalgae, and a high protein and omega 3 content. Today only a select few traditional cultures in the Philippines consume shipworms, but there is considerable opportunity to develop mechanisms to farm shipworms and provide a sustainable, nutrient rich, affordable food source. This will require significant challenges to be overcome, ranging from fundamental research to industry development to food processing and marketing. Leveraging new innovations in breeding, aquaculture feeds, growth systems, food processing methodologies and consumer engagement can however offer powerful solutions, and could help turn what was once a maritime villain into a nutritional savior.
In vivo Imaging With 18F-FDG- and 18F-Florbetaben-PET/MRI Detects Pathological Changes in the Brain of the Commonly Used 5XFAD Mouse Model of Alzheimer's Disease

Imaging biomarkers of Alzheimer's disease (AD) that are able to detect molecular changes in vivo and transgenic animal models mimicking AD pathologies are essential for the evaluation of new therapeutic strategies. Positron-emission tomography (PET) using either 18F-Fluorodeoxyglucose (18F-FDG) or amyloid-tracers is a well-established, non-invasive tool in the clinical diagnostics of AD assessing two major pathological hallmarks. 18F-FDG-PET is able to detect early changes in cerebral glucose metabolism and amyloid-PET shows cerebral amyloid load. However, the suitability of 18F-FDG- and amyloid-PET in the widely used 5XFAD mouse model of AD is unclear as only a few studies on the use of PET biomarkers are available showing some conflicting results. The aim of this study was the evaluation of 18F-FDG-PET and amyloid-PET in 5XFAD mice in comparison to neurological deficits and neuropathological changes. Seven- and 12-month-old male 5XFAD mice showed a significant reduction in brain glucose metabolism in 18F-FDG-PET and amyloid-PET with 18F-Florbetaben demonstrated an increased cerebral amyloid deposition (n = 4–6 per group). Deficits in spatial reference memory were detected in 12-month-old 5XFAD mice in the Morris Water Maze (n = 10–12 per group). Furthermore, an increased plaque load and gliosis could be proven immunohistochemically in 5XFAD mice (n = 4–6 per group). PET biomarkers 18F-FDG and 18F-Florbetaben detected cerebral hypometabolism and increased plaque load even before the onset of severe memory deficits. Therefore, the 5XFAD mouse model of AD is well-suited for in vivo monitoring of AD pathologies and longitudinal testing of new therapeutic approaches.
Systematic Identification of Hub Genes in Placenta Accreta Spectrum Based on Integrated Transcriptomic and Proteomic Analysis

Placenta accreta spectrum (PAS) is a pathological condition of the placenta with abnormal adhesion or invasion of the placental villi to the uterine wall, which is associated with a variety of adverse maternal and fetal outcomes. Although some PAS-related molecules have been reported, the underlying regulatory mechanism is still unclear. Compared with the study of single gene or pathway, omics study, using advanced sequencing technology and bioinformatics methods, can increase our systematic understanding of diseases. In this study, placenta tissues from 5 patients with PAS and 5 healthy pregnant women were collected for transcriptomic and proteomic sequencing and integrated analysis. A total of 728 messenger RNAs and 439 proteins were found to be significantly different between PAS group and non-PAS group, in which 23 hub genes were differentially expressed in both transcriptome and proteome. Functional enrichment analysis showed that the differentially expressed genes were mainly related to cell proliferation, migration and vascular development. Totally 18 long non-coding RNA were found that might regulate the expression of hub genes. Many kinds of single nucleotide polymorphism, alternative splicing and gene fusion of hub genes were detected. This is the first time to systematically explore the hub genes and gene structure variations of PAS through integrated omics analysis, which provided a genetic basis for further in-depth study on the underlying regulatory mechanism of PAS.
Argo Data 1999–2019: Two Million Temperature-Salinity Profiles and Subsurface Velocity Observations From a Global Array of Profiling Floats

In the past two decades, the Argo Program has collected, processed, and distributed over two million vertical profiles of temperature and salinity from the upper two kilometers of the global ocean. A similar number of subsurface velocity observations near 1,000 dbar have also been collected. This paper recounts the history of the global Argo Program, from its aspiration arising out of the World Ocean Circulation Experiment, to the development and implementation of its instrumentation and telecommunication systems, and the various technical problems encountered. We describe the Argo data system and its quality control procedures, and the gradual changes in the vertical resolution and spatial coverage of Argo data from 1999 to 2019. The accuracies of the float data have been assessed by comparison with high-quality shipboard measurements, and are concluded to be 0.002°C for temperature, 2.4 dbar for pressure, and 0.01 PSS-78 for salinity, after delayed-mode adjustments. Finally, the challenges faced by the vision of an expanding Argo Program beyond 2020 are discussed.
Clinical and Prognostic Relevance of B7-H3 and Indicators of Glucose Metabolism in Colorectal Cancer

Objective This study aimed to investigate the clinical and prognostic relevance of B7-H3 expression and indicators of glucose metabolism in patients with colorectal cancer (CRC). Methods Using immunohistochemistry, the expression of B7-H3 was detected in a total of 213 formalin-fixed paraffin-embedded CRC tissue specimens. Furthermore, levels of fasting blood glucose (FBG), lactic dehydrogenase (LDH), and fructosamine (FMN) as indicators of glucose metabolism were analyzed in CRC patients and stratified into high or low expression sub-groups based on Youden Index. The relationship between B7-H3, FBG, LDH, FMN expression, and clinicopathological characteristics were also evaluated to establish their prognostic significance in patients with CRC. Results B7-H3 was highly expressed in CRC tissue. The positive rates of B7-H3 expression was 63.8% (136/213). We found a linear correlation between B7-H3 and FBG in depth of tumor invasion (T3/4) (p = 0.037, r = 0.259), lymph node metastasis (N0) (p = 0.004, r = 0.259), and TNM stage (I/II) (p = 0.009, r = 0.242). High expression of FBG, LDH, FMN [hazard ratio (HR) = 1.916, 95% CI: 1.223–3.00, p = 0.005; HR = 1.801, 95% CI: 1.153–2.813, p = 0.010; HR = 2.154, 95% CI: 1.336–3.472, p = 0.002], respectively, was identified as a significant independent predictor of poor overall survival (OS). Although B7-H3 expression did not affect OS, CRC patients expressing both high B7-H3 and high FMN contributed to a significant decrease in OS (HR = 1.881, 95%CI: 1.059–3.339, p = 0.031). Moreover, with low expression of B7-H3, high expression of FBG, LDH and FMN were also recognized as predictors of inferior OS (HR = 3.393, 95% CI: 1.493-7.709, p = 0.004; HR = 7.107, 95% CI: 2.785–18.138, p = 0.000; HR = 2.800, 95% CI: 1.184–6.625, p = 0.019). Conclusion B7-H3 combined with FBG, LDH, or FMN, could reflect the clinical outcomes of patients with CRC.
Riboflavin Biosynthesis and Overproduction by a Derivative of the Human Gut Commensal Bifidobacterium longum subsp. infantis ATCC 15697

Riboflavin or vitamin B2 is the precursor of the essential coenzymes flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD). Despite increased interest in microbial synthesis of this water-soluble vitamin, the metabolic pathway for riboflavin biosynthesis has been characterized in just a handful of bacteria. Here, comparative genome analysis identified the genes involved in the de novo biosynthetic pathway of riboflavin in certain bifidobacterial species, including the human gut commensal Bifidobacterium longum subsp. infantis (B. infantis) ATCC 15697. Using comparative genomics and phylogenomic analysis, we investigated the evolutionary acquisition route of the riboflavin biosynthesis or rib gene cluster in Bifidobacterium and the distribution of riboflavin biosynthesis-associated genes across the genus. Using B. infantis ATCC 15697 as model organism for this pathway, we isolated spontaneous riboflavin overproducers, which had lost transcriptional regulation of the genes required for riboflavin biosynthesis. Among them, one mutant was shown to allow riboflavin release into the medium to a concentration of 60.8 ng mL–1. This mutant increased vitamin B2 concentration in a fecal fermentation system, thus providing promising data for application of this isolate as a functional food ingredient.
Information Linkage between Carbon and Energy Markets: Multiplex Recurrence Network Approach

In this paper, a multilayer recurrence network is introduced to examine the information linkage between carbon and energy markets. We first construct a multilayer recurrence network of energy and carbon markets, and we define the information linkage coefficient to measure the linkage relationship between the network layers based on the network microstructure. To measure the mutual leading relationship between carbon and energy markets, we construct a time-delay multilayer recurrence network and introduce the time-delay information linkage coefficient to measure the intersystem interaction. The carbon and energy prices, including West Texas Intermediate crude oil, coal, natural gas, and gasoline, from February 22, 2011, to April 1, 2019, are selected as sample data for empirical analysis. The results show that the linkage relationship between oil, coal, natural gas, and carbon prices presents a U-shaped trend in the second, transitional, and third phases of the European Union carbon market, while the linkage trend of gasoline and carbon prices continues to rise. The mutual leading relationship between energy and carbon prices changes in different stages, and carbon price plays a leading role at the present stage.
The Effects of High-intensity Functional Training (HIFT) on Spatial Learning, Visual Pattern Separation and Attention Span in Adolescents

Aerobic, anaerobic, and strength exercises are known to improve various cognitive functions, such as executive functions, pattern separation, and working memory. High-intensity functional training (HIFT) is a form of physical activity that can be modified to any fitness level and elicits greater muscle recruitment than repetitive aerobic exercises, thereby improving cardiovascular endurance, strength, and flexibility. HIFT emphasizes functional, multi-joint movements via high-intensity interval training (HIIT) and muscle-strengthening exercises. It is yet unknown, however, whether HIFT affects cognitive functions in adolescents. To address this question, we subjected adolescents to 3 × 20 min training sessions/week of HIFT for 3 months. The effects of HIFT were tested on performance in: (1) virtual reality (VR)-based spatial learning task; (2) computerized visual pattern separation; and (3) attention span. The control group performed a typical physical class three times per week. The effects on cognition were tested at baseline and following 3 months of HIFT. Three months into the intervention, the HIFT group achieved higher scores in the spatial learning task, pattern separation task, and in the attention span test, compared with controls. These data suggest that HIFT can potentially translate into improving school performance in adolescents.
Water Extract of Potentilla discolor Bunge Improves Hepatic Glucose Homeostasis by Regulating Gluconeogenesis and Glycogen Synthesis in High-Fat Diet and Streptozotocin-Induced Type 2 Diabetic Mice

Potentilla discolor Bunge, as a traditional Chinese medicine, exhibits many phytochemical activities. The aim of the present study was to investigate the effects of Potentilla discolor Bunge water extract (PDBW) and its underlying mechanisms on gluconeogenesis and glycogen synthesis in high-fat diet/streptozotocin (HFD/STZ)-induced type 2 diabetic mice. LC-MS/MS analyses of PDBW identified 6 major compounds including apigenin-7-O-β-D-glucoside, epicatechin, quercetin 3-O-β-D-glucuronide, kaempferol-3-O-β-D-glucopyranoside, scutellarin, and quercitrin. In the study, a mouse model of type 2 diabetes was induced by 4-week HFD combined with STZ (40 mg/kg body weight) for 5 days. After oral administration of PDBW at 400 mg/kg body weight daily for 8 weeks, the mice with type 2 diabetes showed significant decrease in the levels of fasting blood glucose and glycated hemoglobin A1c (HbA1c), and increase in the insulin level. PDBW improved the glucose tolerance, insulin sensitivity and lipid profiles. Furthermore, PDBW inhibited the mRNA levels of key gluconeogenic enzymes [phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphatase (G6Pase)] in liver. PDBW also promoted glycogen synthesis by raising the liver glycogen content, decreasing the phosphorylation of glycogen synthase (GS) and increasing the phosphorylation of glycogen synthase kinase3β (GSK3β). Besides, PDBW induced the activation of protein kinase B (Akt) and AMP-activated protein kinase (AMPK), which might explain changes in the phosphorylation of above enzymes. In summary, PDBW supplementation ameliorates metabolic disorders in a HFD/STZ diabetic mouse model, suggesting the potential application of PDBW in prevention and amelioration of type 2 diabetes.
A Community-Based Lifestyle-Integrated Physical Activity Intervention to Enhance Physical Activity, Positive Family Communication, and Perceived Health in Deprived Families: A Cluster Randomized Controlled Trial

Background: Zero-time exercise (ZTEx) is an approach integrating simple strength- and stamina-enhancing physical activity into daily life. The study evaluated the effectiveness of a community-based lifestyle-integrated physical activity intervention using ZTEx to enhance participants' physical activity, family communication, perceived health and happiness, and family harmony. Methods: A parallel group, cluster randomized controlled trial was conducted in a sample of 673 participants from eight Integrated Family Service Centers in Hong Kong. The experimental group (n = 316) received a physical activity intervention. The control group (n = 357) received information on healthy eating. Both groups received three face-to-face intervention sessions (totalling 6 h and 30 min) and 16 text messages. The primary outcome was the change in days spent engaged in ZTEx. Secondary outcomes included changes in sitting time, days engaged in moderate or vigorous physical activities, family communication (encouraging and engaging family members in ZTEx), dietary habits, perceived health and happiness, and family harmony. Self-administered questionnaires were used at baseline and at 3, 6, and 12 months. Mixed effects models with intention-to-treat analysis was used. Results: Compared with the control group at 3 months, the experimental group showed significantly greater increases of 1.3 days spent doing ZTEx (Cohen's d: 0.60), 0.3 days spent doing moderate physical activity (Cohen's d: 0.08), 0.3 days encouraging family members to do ZTEx (Cohen's d: 0.16), and 0.7 days doing ZTEx with family members (Cohen's d: 0.39) during the 7 days prior. At 3 months, the experimental group also showed a significantly greater improvement in perceived health, by a score of 0.2 (Cohen's d: 0.14). The effect sizes ranged from small to medium, with similar intervention effects at the 6-month and 1-year assessments. Compared with the experimental group, the control group showed a significantly greater reduction of 0.4 days on which sweetened beverages were consumed (95% CI: 0.01, 0.9, p < 0.05, Cohen's d: 0.28). The qualitative results supported the quantitative findings. Conclusions: Our findings show that a community-based lifestyle-integrated physical activity (PA) intervention can enhance physical activity, family communication, and perceived health in deprived families in Hong Kong. Trial registration: The research protocol was retrospectively registered at the National Institutes of Health (identifier number: NCT02601534) on November 10, 2015.
A Noncommunicative Memory-Pushing Fuzzy Control Strategy for Sensorless Multirobot Systems

Considering the characteristics of equipment on underground fully mechanized coal mining face, a multirobot system, which takes heavy-duty mobile support robot (HMSR) as the pushing robot and middle trough (MT) as the manipulated object, is established. To overcome the problem of unstable communication and potential pressure loss, a memory-pushing fuzzy control strategy is proposed to achieve better practical performance without human-guided operations. The pushing dynamics without communication is derived to proof the convergence of the dynamic system, and the time-based memory-pushing fuzzy model is built for compensating the potential pressure loss. Finally, the proposed control strategy is simulated in virtual environment, which integrates our pushing dynamics, and an industrial experiment is demonstrated as well. Both the simulation and industrial experiments show the efficiency and feasibility of the proposed method.
A New Method for Interindustry Linkage Analysis Based on Demand-Driven and Multisector Input-Output Model and Its Application in China's Manufacturing and Producer Services

Interindustry linkage analysis is an important interdisciplinary research field of technical economic and complex systems, and the results can be used as critical bases for making strategies and policies of economic development. This study reviews the previous methods for measuring interindustry linkages and their disadvantages and puts forward a new method for interindustry linkage analysis in a complex economic system on the basis of demand-driven and multisector input-output model. Firstly, it makes a further decomposition of the Leontief inverse matrix in the economic sense and decomposes the gross output of one industrial sector or its sub-industries into three components. Then, it analyzes the structural features of output and measures the interindustry linkages between two industrial sectors with three indices: interindustry linkage effect, interindustry linkage contribution, and interindustry linkage coefficient. Compared with the previous measurements, the method in this study has three obvious advantages: it integrates the sectoral internal effect and external linkage effect at the same time; it can not only measure the interindustry linkage effects between two given industrial sectors but also clearly describe the composition ratio of the direct and indirect interindustry linkage effects; and it adopts, respectively, the absolute flow value, relative flow value, and unit relative value to measure the linkages comprehensively. Finally, this study takes China’s input and output in 2017 as an application case to analyze the structural features of output of its manufacturing and producer services and measure the interindustry linkages between them.
Synthetic Training for Accurate 3D Human Pose and Shape Estimation in the Wild

This paper addresses the problem of monocular 3D human shape and pose estimation from an RGB image. Despite great progress in this field in terms of pose prediction accuracy, state-of-the-art methods often predict inaccurate body shapes. We suggest that this is primarily due to the scarcity of in-the-wild training data with diverse and accurate body shape labels. Thus, we propose STRAPS (Synthetic Training for Real Accurate Pose and Shape), a system that utilises proxy representations, such as silhouettes and 2D joints, as inputs to a shape and pose regression neural network, which is trained with synthetic training data (generated on-the-fly during training using the SMPL statistical body model) to overcome data scarcity. We bridge the gap between synthetic training inputs and noisy real inputs, which are predicted by keypoint detection and segmentation CNNs at test-time, by using data augmentation and corruption during training. In order to evaluate our approach, we curate and provide a challenging evaluation dataset for monocular human shape estimation, Sports Shape and Pose 3D (SSP-3D). It consists of RGB images of tightly-clothed sports-persons with a variety of body shapes and corresponding pseudo-ground-truth SMPL shape and pose parameters, obtained via multi-frame optimisation. We show that STRAPS outperforms other state-of-the-art methods on SSP-3D in terms of shape prediction accuracy, while remaining competitive with the state-of-the-art on pose-centric datasets and metrics.