Arable land and water footprints for food consumption in China: From the perspective of urban and rural dietary change.

Since the adoption of the open-door policy, the Chinese dietary pattern has changed greatly. Based on the dietary changes, this study analyzed the arable land and water footprints (WFs) for the food consumption of urban and rural residents in China. The results showed that the arable land demand and WFs for meat, vegetable oil, soybeans and liquor exceeded those for other foods, and the per capita arable land and WFs for food consumption of urban residents were higher than those of rural residents. The total arable land and WFs for the food consumption of residents increased by 16.9 million ha (from 91.1 to 108 million ha) and 214.5 billion m3 (from 457.9 to 672.4 billion m3), respectively, from 1983 to 2017. Specifically, the total arable land and WFs for the food consumption of urban residents increased by 45.9 million hm2 (from 22.6 to 68.5 million hm2) and 318.3 billion m3 (from 113.2 to 431.5 billion m3), respectively. Additionally, those of rural residents decreased by 29.7 million hm2 (from 69.2 to 39.5 million hm2) and 103.9 billion m3 (344.8 to 240.9 billion m3), respectively, mainly due to the migration of the rural population to cities and the reductions in per capita arable land and WFs due to increased crop yields. The arable land and blue WFs required for food consumption will reach 127.7 million hm2 and 221.1 billion m3, respectively, in 2030. However, these values will be reduced by approximately 23% and 20%, respectively, to 98.9 million hm2 and 177.8 billion m3 under a balanced dietary pattern. Measures such as improving the investment in agricultural research and development, advocating a balanced diet, and increasing the import of resource-intensive foods could alleviate the pressure on land and water resources.

Learning Spatial-Spectral-Temporal EEG Representations with Deep Attentive-Recurrent-Convolutional Neural Networks for Pain Intensity Assessment.

Electroencephalogram (EEG)-based quantitative pain measurement is valuable in the field of clinical pain treatment, providing objective pain intensity assessment especially for nonverbal patients who are unable to self-report. At present, a key challenge in modeling pain events from EEG is to find invariant representations for intra- and inter-subject variations, where current methods based on hand-crafted features cannot provide satisfactory results. Hence, we propose a novel method based on deep learning to learn such invariant representations from multi-channel EEG signals and demonstrate its great advantages in EEG-based pain classification tasks. To begin, instead of using typical EEG analysis techniques that ignore spatial information of EEG, we convert raw EEG signals into a sequence of multi-spectral topography maps (topology-preserving EEG images). Next, inspired by various deep learning techniques applied in neuroimaging domain, a deep Attentive-Recurrent-Convolutional Neural Network (ARCNN) is proposed here to learn spatial-spectral-temporal representations from EEG images. The proposed method aims to jointly preserve the spatial-spectral-temporal structures of EEG, for learning representations with high robustness against intra-subject and inter-subject variations, making it more conducive to multi-class and subject-independent scenarios. Empirical evaluation on 4-level pain intensity assessment within the subject-independent scenario demonstrated significant improvement over baseline and state-of-the-art methods in this field. Our approach applies deep neural networks (DNNs) to pain intensity assessment for the first time and demonstrates its potential advantages in modeling pain events from EEG.

An aggregate analysis of many predicted structures to reduce errors in protein structure comparison caused by conformational flexibility.

BACKGROUND: Conformational flexibility creates errors in the comparison of protein structures. Even small changes in backbone or sidechain conformation can radically alter the shape of ligand binding cavities. These changes can cause structure comparison programs to overlook functionally related proteins with remote evolutionary similarities, and cause others to incorrectly conclude that closely related proteins have different binding preferences, when their specificities are actually similar. Towards the latter effort, this paper applies protein structure prediction algorithms to enhance the classification of homologous proteins according to their binding preferences, despite radical conformational differences. METHODS: Specifically, structure prediction algorithms can be used to "remodel" existing structures against the same template. This process can return proteins in very different conformations to similar, objectively comparable states. Operating on close homologs exploits the accuracy of structure predictions on closely related proteins, but structure prediction is often a nondeterministic process. Identical inputs can generate subtly different models with very different binding cavities that make structure comparison difficult. We present a first method to mitigate such errors, called "medial remodeling", that examines a large number of predicted structures to eliminate extreme models of the same binding cavity. RESULTS: Our results, on the enolase and tyrosine kinase superfamilies, demonstrate that remodeling can enable proteins in very different conformations to be returned to states that can be objectively compared. Structures that would have been erroneously classified as having different binding preferences were often correctly classified after remodeling, while structures that would have been correctly classified as having different binding preferences almost always remained distinct. The enolase superfamily, which exhibited less sequential diversity than the tyrosine kinase superfamily, was classified more accurately after remodeling than the tyrosine kinases. Medial remodeling reduced errors from models with unusual perturbations that distort the shape of the binding site, enhancing classification accuracy. CONCLUSIONS: This paper demonstrates that protein structure prediction can compensate for conformational variety in the comparison of protein-ligand binding sites. While protein structure prediction introduces new uncertainties into the structure comparison problem, our results indicate that unusual models can be ignored through an analysis of many models, using techniques like medial remodeling. These results point to applications of protein structure comparison that extend beyond existing crystal structures.

Isolating influential regions of electrostatic focusing in protein and DNA structure.

Electrostatic focusing is a general phenomenon that occurs in cavities and grooves on the molecular surface of biomolecules. Narrow surface features can partially shield charged atoms from the high-dielectric solvent, enhancing electrostatic potentials inside the cavity and projecting electric field lines outward into the solvent. This effect has been observed in many instances and is widely considered in the human examination of molecular structure, but it is rarely integrated into the digital representations used in protein structure comparison software. To create a computational representation of electrostatic focusing, that is compatible with structure comparison algorithms, this paper presents an approach that generates three-dimensional solids that approximate regions where focusing occurs. We verify the accuracy of this representation against instances of focusing in proteins and DNA. Noting that this representation also identifies thin focusing regions on the molecular surface that are unlikely to affect binding, we describe a second algorithm that conservatively isolates larger focusing regions. The resulting 3D solids can be compared with Boolean set operations, permitting a new range of analyses on the regions where electrostatic focusing occurs. They also represent a novel integration of molecular shape and electrostatic focusing into the same structure comparison framework.