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Background The spread of antibiotic resistance has become one of the most urgent threats to global health, which is estimated to cause 700,000 deaths each year globally. Its surrogates, antibiotic resistance genes (ARGs), are highly transmittable between food, water, animal, and human to mitigate the efficacy of antibiotics. Accurately identifying ARGs is thus an indispensable step to understanding the ecology, and transmission of ARGs between environmental and human-associated reservoirs. Unfortunately, the previous computational methods for identifying ARGs are mostly based on sequence alignment, which cannot identify novel ARGs, and their applications are limited by currently incomplete knowledge about ARGs. Results Here, we propose an end-to-end Hierarchical Multi-task Deep learning framework for ARG annotation (HMD-ARG). Taking raw sequence encoding as input, HMD-ARG can identify, without querying against existing sequence databases, multiple ARG properties simultaneously, including if the input protein sequence is an ARG, and if so, what antibiotic family it is resistant to, what resistant mechanism the ARG takes, and if the ARG is an intrinsic one or acquired one. In addition, if the predicted antibiotic family is beta-lactamase, HMD-ARG further predicts the subclass of beta-lactamase that the ARG is resistant to. Comprehensive experiments, including cross-fold validation, third-party dataset validation in human gut microbiota, wet-experimental functional validation, and structural investigation of predicted conserved sites, demonstrate not only the superior performance of our method over the state-of-art methods, but also the effectiveness and robustness of the proposed method. Conclusions We propose a hierarchical multi-task method, HMD-ARG, which is based on deep learning and can provide detailed annotations of ARGs from three important aspects: resistant antibiotic class, resistant mechanism, and gene mobility. We believe that HMD-ARG can serve as a powerful tool to identify antibiotic resistance genes and, therefore mitigate their global threat. Our method and the constructed database are available at http://www.cbrc.kaust.edu.sa/HMDARG/.

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Double network hydrogels (DN gels), consisting of two networks with strongly asymmetric network structures and properties, are one of most investigated high strength hydrogels. In most cases, the first network of DN gels is rigid, brittle and tightly crosslinked, while the second network is soft, ductile and loosely crosslinked. Because of the tunable and diverse network structures, DN gels with controlled shape deformation have attracted great attention in recent years. The shape deformation of DN gels can be controlled by first network, second network, or both networks. In this mini review, the shape deformation of DN gels via different networks will be summarized, and the application and future perspectives also are discussed. REVIEW usually soft, ductile, and loosely crosslinked which acts as "hidden length" to protect the integrity of DN gel after the first network fractured. Generally, the molar concentration of second network is 20-30 times higher than that of first network, and the two networks are strongly entangled with each other. The first network is also stiffer and more brittle than second network. "Stiffer" means the first network has higher elastic modulus than second network, while "more brittle" means the first network is often fracture earlier than second network. In this way, the first network bears stress firstly and is fractured to dissipate energy. Gong and co-workers 42 developed a two-step sequential free-radical polymerization method to synthesize the first fully chemically crosslinked poly(2-acrylamido-2methylpropanesulfonic acid)/polyacrylamide (PAMPS/PAAm) DN hydrogels (named as chemical DN gels, i.e., both first and second network were crosslinked by covalent bonds), which can achieve fracture toughness of 10 2 -10 3 J m −2 , fracture tensile stress of 1-10 MPa, and fracture tensile strain of 1000-2000%. Since then, various chemical DN gels have been prepared, including microgel-reinforced gels, 48 "molecular stent" DN gels, 49 void-DN gels, 50 inverse DN gels, 51 and nanocomposite DN (NC-DN) gels. 52-56 However, chemical DN gels often suffer from severe softening phenomenon and are lack of fatigue resistance. If one of the networks is crosslinked by physical interactions (such as ionic interactions, hydrogen bonds, hydrophobic associations, coordination interactions, etc.), while the other network is covalently crosslinked, the DN gels are named hybrid DN gels. If both the networks are crosslinked by physical interactions, the DN gels are called fully physical DN gels (i.e., physical DN gels). In recent years, hybrid DN gels and physical DN gels have been successfully developed. Representatively, Suo et al. 57 reported Ca 2+ -alginate/polyacrylamide (Caalginate/PAAm) hybrid gels, which exhibited extremely high toughness of 10 4 J/m 2 . Our group also developed a robust

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Compact polarimetric (CP) synthetic aperture radar (SAR) has proven its potential in distinguishing oil slicks and look-alikes. Polarimetric information can be retrieved directly from scattering vector or from reconstructed pseudo-Quad-Pol covariance matrix of CP SAR data. In this paper, we analysed features from Circular Transmit and Linear Receive (CTLR) CP SAR data that are derived by taking both of these two methods.K-means clustering followed by accuracy assessment was also implemented for performance evaluation. Through experiments that were conducted based on L-band UAVSAR fully polarimetric data, it was found that optimum extraction methods varied for different features. The histogram analysis and segmentation results also demonstrated the comparable performance of CP SAR features in distinguishing different damping properties within oil slicks. This study proposed a framework of statistically analyzing polarimetric SAR (Pol-SAR) features and provided guidelines for determining optimum feature extraction methods from CP SAR data and for marine oil-spills detection and classification.

Core Ideas Alpine desert had higher soil δ13C and δ15N than alpine meadow and steppe. Soil δ13C and δ15N increased with depth in the alpine grasslands. Land degradation enriched the soil δ13C in the alpine steppe. Land degradation enhanced the soil δ15N in alpine desert. Plant and soil features were key for the dynamics of the soil δ13C and δ15N. Estimation of natural isotopic abundances can integrate across biogeochemical processes affecting the carbon and nitrogen dynamics in an ecosystem. Here, we investigated the natural isotopic abundances (δ13C and δ15N) of the soil in healthy and degraded alpine ecosystems, including alpine meadow, alpine steppe, and alpine desert on the Qinghai‐Tibetan Plateau (QTP). We also examined the effects of plant factors and soil chemical and physical factors on the soil δ13C and δ15N in these alpine ecosystems of the QTP. The results indicated that the soil δ13C and δ15N varied significantly with the grassland type, the land degradation, and soil depth. The C4‐plant dominated alpine desert was much higher in the soil δ13C and δ15N than the C3‐plant dominated alpine meadow and alpine steppe. Along the soil depth of 0 to 30 cm, the δ13C and δ15N were enriched in all types of the alpine ecosystems. The land degradation lowered the plant cover, aboveground plant biomass, soil organic carbon (SOC) and soil total nitrogen (TN) in all alpine ecosystems. Land degradation enriched the soil δ13C in the alpine steppe and the soil δ15N in alpine desert by changing their interactions with the plant and soil features.

This study followed up Wang, Shu, Zhang, Liu, and Zhang [(2013). J. Acoust. Soc. Am. 34(1), EL91–EL97] to investigate factors influencing older listeners' Mandarin speech recognition in quiet vs single-talker interference. Listening condition significantly interacted with F0 contours but not with semantic context, revealing that natural F0 contours provided benefit in the interference condition whereas semantic context contributed similarly to both conditions. Furthermore, the significant interaction between semantic context and F0 contours demonstrated the importance of semantic context when F0 was flattened. Together, findings from the two studies indicate that aging differentially affects tonal language speakers' dependence on F0 contours and semantic context for speech perception in suboptimal conditions.