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The largest obstacle that suppresses the increase of wind power penetration within the power grid is uncertainties and fluctuations in wind speeds. Therefore, accurate wind power forecasting is a challenging task, which can significantly impact the effective operation of power systems. Wind power forecasting is also vital for planning unit commitment, maintenance scheduling and profit maximisation of power traders. The current development of cost-effective operation and maintenance methods for modern wind turbines benefits from the advancement of effective and accurate wind power forecasting approaches. This paper systematically reviewed the state-of-the-art approaches of wind power forecasting with regard to physical, statistical (time series and artificial neural networks) and hybrid methods, including factors that affect accuracy and computational time in the predictive modelling efforts. Besides, this study provided a guideline for wind power forecasting process screening, allowing the wind turbine/farm operators to identify the most appropriate predictive methods based on time horizons, input features, computational time, error measurements, etc. More specifically, further recommendations for the research community of wind power forecasting were proposed based on reviewed literature.

False positives in a Genome-Wide Association Study (GWAS) can be effectively controlled by a fixed effect and random effect Mixed Linear Model (MLM) that incorporates population structure and kinship among individuals to adjust association tests on markers; however, the adjustment also compromises true positives. The modified MLM method, Multiple Loci Linear Mixed Model (MLMM), incorporates multiple markers simultaneously as covariates in a stepwise MLM to partially remove the confounding between testing markers and kinship. To completely eliminate the confounding, we divided MLMM into two parts: Fixed Effect Model (FEM) and a Random Effect Model (REM) and use them iteratively. FEM contains testing markers, one at a time, and multiple associated markers as covariates to control false positives. To avoid model over-fitting problem in FEM, the associated markers are estimated in REM by using them to define kinship. The P values of testing markers and the associated markers are unified at each iteration. We named the new method as Fixed and random model Circulating Probability Unification (FarmCPU). Both real and simulated data analyses demonstrated that FarmCPU improves statistical power compared to current methods. Additional benefits include an efficient computing time that is linear to both number of individuals and number of markers. Now, a dataset with half million individuals and half million markers can be analyzed within three days.

Clearance of dying cells by efferocytosis is necessary for cardiac repair after myocardial infarction (MI). Recent reports have suggested a protective role for vascular endothelial growth factor C (VEGFC) during acute cardiac lymphangiogenesis after MI. Here, we report that defective efferocytosis by macrophages after experimental MI led to a reduction in cardiac lymphangiogenesis and Vegfc expression. Cell-intrinsic evidence for efferocytic induction of Vegfc was revealed after adding apoptotic cells to cultured primary macrophages, which subsequently triggered Vegfc transcription and VEGFC secretion. Similarly, cardiac macrophages elevated Vegfc expression levels after MI, and mice deficient for myeloid Vegfc exhibited impaired ventricular contractility, adverse tissue remodeling, and reduced lymphangiogenesis. These results were observed in mouse models of permanent coronary occlusion and clinically relevant ischemia and reperfusion. Interestingly, myeloid Vegfc deficiency also led to increases in acute infarct size, prior to the amplitude of the acute cardiac lymphangiogenesis response. RNA-Seq and cardiac flow cytometry revealed that myeloid Vegfc deficiency was also characterized by a defective inflammatory response, and macrophage-produced VEGFC was directly effective at suppressing proinflammatory macrophage activation. Taken together, our findings indicate that cardiac macrophages promote healing through the promotion of myocardial lymphangiogenesis and the suppression of inflammatory cytokines.

Ocean mining activities have been ongoing for nearly 70 years, making great contributions to industrialization. Given the increasing demand for energy, along with the restructuring of the energy supply catalyzed by efforts to achieve a low-carbon economy, deep seabed mining will play an important role in addressing energy- and resource-related problems in the future. However, deep seabed mining remains in the exploratory stage, with many challenges presented by the high-pressure, low-temperature, and complex geologic and hydrodynamic environments in deep-sea mining areas, which are inaccessible to human activities. Thus, considerable efforts are required to ensure sustainable, economic, reliable, and safe deep seabed mining. This study reviews the latest advances in marine engineering geology and the environment related to deep-sea mining activities, presents a bibliometric analysis of the development of ocean mineral resources since the 1950s, summarizes the development, theory, and issues related to techniques for the three stages of ocean mining (i.e., exploration, extraction, and closure), and discusses the engineering geology environment, geological disasters, in-situ monitoring techniques, environmental protection requirements, and environmental effects in detail. Finally, this paper gives some key conclusions and future perspectives to provide insights for subsequent studies and commercial mining operations.

Adaptive changes in plant phenology are often considered to be a feature of the so-called ‘domestication syndrome’ that distinguishes modern crops from their wild progenitors, but little detailed evidence supports this idea. In soybean, a major legume crop, flowering time variation is well characterized within domesticated germplasm and is critical for modern production, but its importance during domestication is unclear. Here, we identify sequential contributions of two homeologous pseudo-response-regulator genes, Tof12 and Tof11 , to ancient flowering time adaptation, and demonstrate that they act via LHY homologs to promote expression of the legume-specific E1 gene and delay flowering under long photoperiods. We show that Tof12 -dependent acceleration of maturity accompanied a reduction in dormancy and seed dispersal during soybean domestication, possibly predisposing the incipient crop to latitudinal expansion. Better understanding of this early phase of crop evolution will help to identify functional variation lost during domestication and exploit its potential for future crop improvement. Whole-genome resequencing and association analyses in 424 soybean accessions identify two homeologous genes that contributed to flowering time adaptation during soybean domestication.

As a practical reflection of the opportunity window of Madden‐Julian Oscillation (MJO), there are intermittent periods of relatively high forecasting skills, namely the forecast skill windows. Robust forecast skill windows are identified based on the subseasonal‐seasonal reforecast database, during which the majority of models show high forecast skills. A total of 15 MJO forecast skill windows during 1993–2020 have been identified. Most of the forecast skill windows are closely associated with active MJO events with high amplitude. Whether a high‐skill forecast window appears significantly depends on the magnitude of MJO intensity during the same period. The maintenance of active strong MJO events is potentially related with the warmer surface sea temperature anomalies in the western Pacific. Further research into such processes may unveil the MJO development mechanism and improve the MJO forecast skill. Plain Language Summary With the continuous development of the economy and society, there is an increasing demand for subseasonal‐seasonal forecasts. The forecast skills of Madden‐Julian Oscillation (MJO) show significant high and low variabilities, associated with specific conditions. MJO forecast skill windows are confirmed based on subseasonal‐seasonal (S2S) forecast products in this study. These windows represent the periods when most models in the S2S database can accurately capture the MJO signal, indicating the MJO forecast skill experiences intermittent enhancements. When the MJO intensity is high in the same period, the high‐skill forecast window is more likely to appear. One potential reason for the maintenance of strong MJO is the anomalously warmer sea surface temperature in the western Pacific, along with other potential factors, providing the opportunity to forecast MJO with high skill. When the opportunity arises, the forecast skill of MJO improves, highlighting the significance of exploring the processes leading to the occurrence of these skill windows. This provides a pathway for more accurate MJO forecasting and improves the predictability of S2S. Key Points Robust forecast skill windows of Madden‐Julian Oscillation (MJO) are identified based on the subseasonal‐seasonal reforecast database The forecast skill windows are mostly related to strong MJO amplitude Warmer sea surface temperature anomalies in the western Pacific potentially favors the maintenance of active strong MJO events

Background: As the situation of cognitive aging is getting worse, preventing or treating cognitive decline through effective strategies is highly important. This systematic review aims to investigate whether mind-body exercise is an effective approach for treating cognition decline. Methods: Searches for the potential studies were performed on the eight electronic databases (MEDLINE, Scopus, Web of Science, SPORTDiscus, CINAHL, PsycArtilces, CNKI, and Wanfang). Randomized controlled trials (RCTs) examining the effect of mind-body exercise on cognitive performance in older adults were included. Data were extracted and effect sizes were pooled with 95% confidence intervals (95% CI) using random-effects models. The Physiotherapy Evidence Database Scale was employed to examine the study quality. Results: Nineteen RCTs including 2539 elders (67.3% female) with fair to good study quality were identified. Mind-body exercise, relative to control intervention, showed significant benefits on cognitive performance, global cognition (Hedges’g = 0.23), executive functions (Hedges’g = 0.25 to 0.65), learning and memory (Hedges’g = 0.37 to 0.49), and language (Hedges’g = 0.35). In addition, no significant adverse events were reported. Conclusion: Mind-body exercise may be a safe and effective intervention for enhancing cognitive function among people aged 60 years or older. Further research evidence is still needed to make a more conclusive statement.

Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis . This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell–cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M .  fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs. A large-scale single-cell transcriptomic atlas of the non-human primate Macaca fascicularis encompasses over 1 million cells from 45 adult tissues.

Advances in high-throughput sequencing technologies have reduced the cost of genotyping dramatically and led to genomic prediction being widely used in animal and plant breeding, and increasingly in human genetics. Inspired by the efficient computing of linear mixed model and the accurate prediction of Bayesian methods, we propose a machine learning-based method incorporating cross-validation, multiple regression, grid search, and bisection algorithms named KAML that aims to combine the advantages of prediction accuracy with computing efficiency. KAML exhibits higher prediction accuracy than existing methods, and it is available at https://github.com/YinLiLin/KAML .

Ambient particulate matter pollution is one of the leading causes of global disease burden. Epidemiological studies have revealed the connections between particulate exposure and cardiovascular and respiratory diseases. However, until now, the real species of ambient ultrafine particles (UFPs) in humans are still scarcely known. Here we report the discovery and characterization of exogenous nanoparticles (NPs) in human serum and pleural effusion (PE) samples collected from non-occupational subjects in a typical polluted region. We show the wide presence of NPs in human serum and PE samples with extreme diversity in chemical species, concentration, and morphology. Through chemical multi-fingerprinting (including elemental fingerprints, high-resolution structural fingerprints, and stable iron isotopic fingerprints) of NPs, we identify the sources of the NPs to be abiogenic, particularly, combustion-derived particulate emission. Our results provide evidence for the translocation of ambient UFPs into the human circulatory system, and also provide information for understanding their systemic health effects. Exposure to ambient particulate matter is one of the leading global health risks. Here, the authors reveal, by means of chemical multi-fingerprinting, the presence of exogenous ultrafine particles with diverse species and morphology in non-occupational human serum and pleural effusion.