Sheep (Ovis aries) are a major source of meat, milk and fiber in the form of wool, and represent a distinct class of animals that have a specialized digestive organ, the rumen, which carries out the initial digestion of plant material. We have developed and analyzed a high quality reference sheep genome and transcriptomes from 40 different tissues. We identified highly expressed genes encoding keratin cross-linking proteins associated with rumen evolution. We also identified genes involved in lipid metabolism that had been amplified and/or had altered tissue expression patterns. This may be in response to changes in the barrier lipids of the skin, an interaction between lipid metabolism and wool synthesis, and an increased role of volatile fatty acids in ruminants, compared to non-ruminant animals.

Searching for new chemically durable and radiation-resistant absorbent materials for actinides and their fission products generated in the nuclear fuel cycle remain highly desirable, for both waste management and contamination remediation. Here we present a rare case of 3D uranyl organic framework material built through polycatenating of three sets of graphene-like layers, which exhibits significant umbellate distortions in the uranyl equatorial planes studied thoroughly by linear transit calculations. This unique structural arrangement leads to high β and γ radiation-resistance and chemical stability in aqueous solutions within a wide pH range from 3 to 12. Being equipped with the highest surface area among all actinide compounds known to date and completely exchangeable [(CH3)2NH2](+) cations in the structure, this material is able to selectively remove cesium from aqueous solutions while retaining the polycatenated framework structure.

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The molecular weight of a conjugated polymer is one of the key factors determining the electrical, morphological, and mechanical properties as well as its solubility in organic solvents and miscibility with other polymers. In this study, a series of semicrystalline poly[(2,5-bis(2-hexyldecyloxy)phenylene)-alt-(5,6-difluoro-4,7-di(thiophen-2-yl)benzo[c][1,2,5]thiadiazole)] (PPDT2FBT) polymers with different number-average molecular weights (M(n)'s) (PPDT2FBT(L), M(n) = 12 kg/mol; PPDT2FBT(M), M(n) = 24 kg/mol; PPDT2FBT(H), M(n) = 40 kg/mol) were synthesized, and their photovoltaic properties as electron donors for all-polymer solar cells (all-PSCs) with poly[[N,N'-bis(2-octyldodecyl)-napthalene-1,4,5,8-bis(dicarboximide)-2,6-diyl]-alt-5,5'-(2,2'-bithiophene)] (P(NDI2OD-T2)) acceptor were studied. The M(n) effect of PPDT2FBT on the structural, morphological, electrical, and photovoltaic properties was systematically investigated. In particular, tuning the M(n) induced dramatic effects on the aggregation behaviors of the polymers and their bulk heterojunction morphology of all-PSCs, which was thoroughly examined by grazing incident X-ray scattering, resonant soft X-ray scattering, and other microscopy measurements. High M(n) PPDT2FBT(H) promoted a strong "face-on" geometry in the blend film, suppressed the formation of an excessively large crystalline domain, and facilitated molecularly intermixed phases with P(NDI2OD-T2). Therefore, the optimized all-PSCs based on PPDT2FBT(H)/P(NDI2OD-T2) showed substantially higher hole and electron mobilities than those of PPDT2FBT(L)/P(NDI2OD-T2), leading to a power conversion efficiency exceeding 5%, which is one of the highest values for all-PSCs reported thus far.

IMPORTANCE COVID-19 has disproportionately affected racial and ethnic minority groups, and race and ethnicity have been associated with disease severity. However, the association of socioeconomic determinants with racial disparities in COVID-19 outcomes remains unclear.OBJECTIVE To evaluate the association of race and ethnicity with COVID-19 outcomes and to examine the association between race, ethnicity, COVID-19 outcomes, and socioeconomic determinants.

Despite the significant progresses made in all-polymer solar cells (all-PSCs) recently, the relatively low short-circuit current density (J sc ) and large energy loss are still quite difficult to overcome for further development. To address these challenges, we developed a new class of narrow-bandgap polymer acceptors incorporating a benzotriazole (BTz)-core fused-ring segment, named the PZT series. Compared to the commonly used benzothiadiazole (BT)-containing polymer PYT, the less electron-deficient BTz renders PZT derivatives with significantly red-shifted optical absorption and up-shifted energy levels, leading to simultaneously improved J sc and open-circuit voltage in the resultant all-PSCs. More importantly, a regioregular PZT (PZT-γ) has been developed to achieve higher regiospecificity for avoiding the formation of isomers during polymerization. Benefiting from the more extended absorption, better backbone ordering, and more optimal blend morphology with donor component, PZT-γ-based all-PSCs exhibit a record-high power conversion efficiency of 15.8% with a greatly enhanced J sc of 24.7 mA/cm 2 and a low energy loss of 0.51 eV.

Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four common types of apple leaf diseases. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This paper proposes an accurate identifying approach for apple leaf diseases based on deep convolutional neural networks. It includes generating sufficient pathological images and designing a novel architecture of a deep convolutional neural network based on AlexNet to detect apple leaf diseases. Using a dataset of 13,689 images of diseased apple leaves, the proposed deep convolutional neural network model is trained to identify the four common apple leaf diseases. Under the hold-out test set, the experimental results show that the proposed disease identification approach based on the convolutional neural network achieves an overall accuracy of 97.62%, the model parameters are reduced by 51,206,928 compared with those in the standard AlexNet model, and the accuracy of the proposed model with generated pathological images obtains an improvement of 10.83%. This research indicates that the proposed deep learning model provides a better solution in disease control for apple leaf diseases with high accuracy and a faster convergence rate, and that the image generation technique proposed in this paper can enhance the robustness of the convolutional neural network model.

The Trudinger-Moser inequality states that for functions u ∈ H 1,n 0 (Ω) (Ω ⊂ R n a bounded domain) with Ω |∇u| n dx ≤ 1 one has Ω (e αn|u| n n−1 − 1)dx ≤ c |Ω|, with c independent of u. Recently, the second author has shown that for n = 2 the bound c |Ω| may be replaced by a uniform constant d independent of Ω if the Dirichlet norm is replaced by the Sobolev norm, i.e. requiring Ω (|∇u| n + |u| n )dx ≤ 1. We extend here this result to arbitrary dimensions n > 2. Also, we prove that for Ω = R n the supremum of R n (e αn|u| n n−1 − 1)dx over all such functions is attained. The proof is based on a blow-up procedure.