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Fast radio bursts (FRBs) are millisecond-duration radio transients 1 , 2 of unknown origin. Two possible mechanisms that could generate extremely coherent emission from FRBs invoke neutron star magnetospheres 3 – 5 or relativistic shocks far from the central energy source 6 – 8 . Detailed polarization observations may help us to understand the emission mechanism. However, the available FRB polarization data have been perplexing, because they show a host of polarimetric properties, including either a constant polarization angle during each burst for some repeaters 9 , 10 or variable polarization angles in some other apparently one-off events 11 , 12 . Here we report observations of 15 bursts from FRB 180301 and find various polarization angle swings in seven of them. The diversity of the polarization angle features of these bursts is consistent with a magnetospheric origin of the radio emission, and disfavours the radiation models invoking relativistic shocks. Polarization observations of the fast radio burst FRB 180301 with the FAST radio telescope show diverse polarization angle swings, consistent with a magnetospheric origin of the emission.

Short chain fatty acids (SCFAs) are the main products of indigestible carbohydrates that are fermented by microbiota in the hindgut. This study was designed to investigate the effects of oral SCFAs administration on the lipid metabolism of weaned pigs. A total of 21 barrows were randomly allocated into three groups, including control group (orally infused with 200 mL physiological saline per day), low dose SCFAs group (orally infused with 200 mL SCFAs containing acetic acid 20.04 mM, propionic acid 7.71 mM and butyric acid 4.89 mM per day), and high dose SCFAs group (orally infused with 200 mL SCFAs containing acetic acid 40.08 mM, propionic acid 15.42 mM and butyric acid 9.78 mM per day). The results showed that the average daily feed intake of SCFAs groups were lower than that of control group (P<0.05). Oral administration of SCFAs decreased the concentrations of triglyceride (TG), total cholesterol (TC), high density lipoprotein-cholesterol and insulin (P<0.05), and increased the leptin concentration in serum (P<0.05). The total fat, as well as TC and TG levels in liver, was decreased by oral SCFAs administration (P<0.05). In addition, SCFAs down-regulated the mRNA expressions of fatty acid synthase (FAS) and sterol regulatory element binding protein 1c (P<0.05), and enhanced the mRNA expression of carnitine palmitoyltransferase-1α (CPT-1α) in liver (P<0.05). SCFAs also decreased FAS, acetyl-CoA carboxylase (ACC) and peroxisome proliferator activated receptor σ mRNA expressions in longissimus dorsi (P<0.05). And in abdominal fat, SCFAs reduced FAS and ACC mRNA expressions (P<0.05), and increased CPT-1α mRNA expression (P<0.05). These results suggested that oral administration of SCFAs could attenuate fat deposition in weaned pigs via reducing lipogenesis and enhancing lipolysis of different tissues.

This paper describes the mean ocean circulation and the tropical variability simulated by the Max Planck Institute for Meteorology (MPI-M) coupled atmosphere–ocean general circulation model (AOGCM). Results are presented from a version of the coupled model that served as a prototype for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) simulations. The model does not require flux adjustment to maintain a stable climate. A control simulation with present-day greenhouse gases is analyzed, and the simulation of key oceanic features, such as sea surface temperatures (SSTs), large-scale circulation, meridional heat and freshwater transports, and sea ice are compared with observations. A parameterization that accounts for the effect of ocean currents on surface wind stress is implemented in the model. The largest impact of this parameterization is in the tropical Pacific, where the mean state is significantly improved: the strength of the trade winds and the associated equatorial upwelling weaken, and there is a reduction of the model’s equatorial cold SST bias by more than 1 K. Equatorial SST variability also becomes more realistic. The strength of the variability is reduced by about 30% in the eastern equatorial Pacific and the extension of SST variability into the warm pool is significantly reduced. The dominant El Niño–Southern Oscillation (ENSO) period shifts from 3 to 4 yr. Without the parameterization an unrealistically strong westward propagation of SST anomalies is simulated. The reasons for the changes in variability are linked to changes in both the mean state and to a reduction in atmospheric sensitivity to SST changes and oceanic sensitivity to wind anomalies.

The overall skill of ENSO prediction in retrospective forecasts made with ten different coupled GCMs is investigated. The coupled GCM datasets of the APCC/CliPAS and DEMETER projects are used for four seasons in the common 22 years from 1980 to 2001. As a baseline, a dynamic-statistical SST forecast and persistence are compared. Our study focuses on the tropical Pacific SST, especially by analyzing the NINO34 index. In coupled models, the accuracy of the simulated variability is related to the accuracy of the simulated mean state. Almost all models have problems in simulating the mean and mean annual cycle of SST, in spite of the positive influence of realistic initial conditions. As a result, the simulation of the interannual SST variability is also far from perfect in most coupled models. With increasing lead time, this discrepancy gets worse. As one measure of forecast skill, the tier-1 multi-model ensemble (MME) forecasts of NINO3.4 SST have an anomaly correlation coefficient of 0.86 at the month 6. This is higher than that of any individual model as well as both forecasts based on persistence and those made with the dynamic-statistical model. The forecast skill of individual models and the MME depends strongly on season, ENSO phase, and ENSO intensity. A stronger El Niño is better predicted. The growth phases of both the warm and cold events are better predicted than the corresponding decaying phases. ENSO-neutral periods are far worse predicted than warm or cold events. The skill of forecasts that start in February or May drops faster than that of forecasts that start in August or November. This behavior, often termed the spring predictability barrier, is in part because predictions starting from February or May contain more events in the decaying phase of ENSO.

We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980-2004) retrospective forecasts performed by 14 climate model systems (7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models' MME for the period of 1981-2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial-temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80-90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120°E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Niño 3.4 SST variation. The forecasts for 2 m air temperature are better in El Niño years than in La Niña years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the one-tier climate model's slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework.

Novel concepts are essential for design innovation and can be generated with the aid of data stimuli and computers. However, current generative design algorithms focus on diagrammatic or spatial concepts that are either too abstract to understand or too detailed for early phase design exploration. This paper explores the uses of generative pre-trained transformers (GPT) for natural language design concept generation. Our experiments involve the use of GPT-2 and GPT-3 for different creative reasonings in design tasks. Both show reasonably good performance for verbal design concept generation.

Rice is the staple food and rice production consumes about 50% of the fresh water resources in China. In addition, drought is one of the most important constraints in rice resulting in large yield losses and limiting the average yield increase of the country. There is an urgent need to enhance water-saving (W) capacity or drought resistance (DR) of rice. WDR varieties can be developed through introgressing the water-saving and drought resistance capacity mainly from the traditional upland to the commercialized paddy rice cultivars. The breeding target is a high yield potential under irrigation, an acceptable grain quality, and water consumption reduced by about 50% compared with paddy rice. In a water-limited environment, a higher level of drought resistance and reduced yield loss by drought stress are required. In recent years, the field drought-resistance screening facility was established and the evaluation standard was developed. Some DR rice varieties were identified and used in both molecular mapping and breeding programmes. Several WDR varieties were developed and released to farmers. This article describes our initial achievement towards this goal and provides some details on the rationale and the specific steps and methods used.

Balling defect of the additively manufactured titanium lattice implants easily leads to muscle tissue rejection, which might cause failure of implantation. Electropolishing is widely used in surface polishing of complex components and has potential to deal with the balling defect. However, a clad layer could be formed on the surface of titanium alloy after electropolishing, which may affect the biocompatibility of the metal implants. To manufacture lattice structured β-type Ti-Ni-Ta-Zr (TNTZ) for bio-medical applications, it is necessary to investigate the impact of electropolishing on material biocompatibility. In this study, animal experiments were conducted to investigate the in vivo biocompatibility of the as-printed TNTZ alloy with or without electropolishing; and proteomics technology was used to elaborate the results. The following conclusions were drawn: (a) a 30% oxalic acid electropolishing treatment was effective in solving balling defects, and ~21 nm amorphous clad layer would be formed on the surface of the material after polishing; (b) the electropolished TNTZ suggested decreased cell cytotoxicity and improved blood biocompatibility as compared to as-printed TNTZ; (c) the amorphous clad layer could make a barrier to prevent Ta and Zr ions from penetrating into the muscle tissue, and could form a good tissue regeneration at the implantation site during 4 weeks, indicating that the electropolished TNTZ has the potential as implants; and (d) the cells attached to the electropolished TNTZ showed higher antioxidant capacity but less proliferation than attached to as-printed TNTZ.

Increasing evidence indicates that long noncoding RNAs (lncRNAs) have important roles in various physiological processes and dysfunction of lncRNAs could be a prevalent cause in human diseases. Here we functionally characterized the nuclear-enriched lncRNA SNHG1 , which is highly expressed in multiple types of cancer. We also provide evidence that SNHG1 promotes cancer cell growth by regulating gene expression both in cis and in trans . SNHG1 was involved in the AKT signaling pathway as it promotes the neighboring transcription of the protein-coding gene SLC3A2 in cis by binding the Mediator complex to facilitate the establishment of enhancer–promoter interaction. In trans , SNHG1 directly interacted with central domain of FUBP1 and antagonize the binding of FBP-interacting repressor to FUBP1, thereby coordinating the expression of the oncogene MYC . Collectively, our findings demonstrate that lncRNA SNHG1 can function both in cis and in trans with distinct mechanisms to regulate transcription, promoting tumorigenesis and cancer progression.

Given observed initial conditions, how well do coupled atmosphere-ocean models predict precipitation climatology with 1-month lead forecast? And how do the models' biases in climatology in turn affect prediction of seasonal anomalies? We address these questions based on analysis of 1-month lead retrospective predictions for 21 years of 1981-2001 made by 13 state-of-the-art coupled climate models and their multi-model ensemble (MME). The evaluation of the precipitation climatology is based on a newly designed metrics that consists of the annual mean, the solstitial mode and equinoctial asymmetric mode of the annual cycle, and the rainy season characteristics. We find that the 1-month lead seasonal prediction made by the 13-model ensemble has skills that are much higher than those in individual model ensemble predictions and approached to those in the ERA-40 and NCEP-2 reanalysis in terms of both the precipitation climatology and seasonal anomalies. We also demonstrate that the skill for individual coupled models in predicting seasonal precipitation anomalies is positively correlated with its performances on prediction of the annual mean and annual cycle of precipitation. In addition, the seasonal prediction skill for the tropical SST anomalies, which are the major predictability source of monsoon precipitation in the current coupled models, is closely link to the models' ability in simulating the SST mean state. Correction of the inherent bias in the mean state is critical for improving the long-lead seasonal prediction. Most individual coupled models reproduce realistically the long-term annual mean precipitation and the first annual cycle (solstitial mode), but they have difficulty in capturing the second annual (equinoctial asymmetric) mode faithfully, especially over the Indian Ocean (IO) and Western North Pacific (WNP) where the seasonal cycle in SST has significant biases. The coupled models replicate the monsoon rain domains very well except in the East Asian subtropical monsoon and the tropical WNP summer monsoon regions. The models also capture the gross features of the seasonal march of the rainy season including onset and withdraw of the Asian-Australian monsoon system over four major sub-domains, but striking deficiencies in the coupled model predictions are observed over the South China Sea and WNP region, where considerable biases exist in both the amplitude and phase of the annual cycle and the summer precipitation amount and its interannual variability are underestimated.