Explore the vast range of titles available.

Corn starch/polyvinyl alcohol (PVA)/glycerol composite films incorporated with ε-polylysine were prepared, and their properties were investigated. The Fourier-transform infrared (FTIR) spectroscopy indicated that the interactions happened between the amino group of ε-polylysine and hydroxyl group starch/PVA composite films. X-ray diffraction (XRD) analysis showed that the addition of ε-polylysine decreased the intensity of all crystal peaks. Thermogravimetric (TGA) analysis suggested that ε-polylysine improved the thermal stability of composite films. Scanning electron microscopic (SEM) analysis showed that the upper surface of composite films incorporated with ε-polylysine presented more compact and flat surface. The antimicrobial activity of the composite film progressively increased with the increasing of ε-polylysine concentration ( < 0.05). The tensile strength, elongation at break and water absorption significantly increased, whereas water solubility decreased with the increasing of ε-polylysine concentration ( < 0.05). Therefore, the corn starch/PVA/glycerol composite films incorporated with ε-polylysine had good mechanical, physical and antimicrobial properties and could have potential application as a novel antimicrobial packaging material.

The k-nearest neighbor algorithm is a powerful classification method. However, its classification performance will be affected in small-size samples with existing outliers. To address this issue, a pre-averaged pseudo nearest neighbor classifier (PAPNN) is proposed to improve classification performance. In the PAPNN rule, the pre-averaged categorical vectors are calculated by taking the average of any two points of the training sets in each class. Then, k-pseudo nearest neighbors are chosen from the preprocessed vectors of every class to determine the category of a query point. The pre-averaged vectors can reduce the negative impact of outliers to some degree. Extensive experiments are conducted on nineteen numerical real data sets and three high dimensional real data sets by comparing PAPNN to other twelve classification methods. The experimental results demonstrate that the proposed PAPNN rule is effective for classification tasks in the case of small-size samples with existing outliers.

Crop yield prediction before the harvest is crucial for food security, grain trade, and policy making. Previously, several machine learning methods have been applied to predict crop yield using different types of variables. In this study, we propose using the Geographically Weighted Random Forest Regression (GWRFR) approach to improve crop yield prediction at the county level in the US Corn Belt. We trained the GWRFR and five other popular machine learning algorithms (Multiple Linear Regression (MLR), Partial Least Square Regression (PLSR), Support Vector Regression (SVR), Decision Tree Regression (DTR), and Random Forest Regression (RFR)) with the following different sets of features: (1) full length features; (2) vegetation indices; (3) gross primary production (GPP); (4) climate data; and (5) soil data. We compared the results of the GWRFR with those of the other five models. The results show that the GWRFR with full length features (R2 = 0.90 and RMSE = 0.764 MT/ha) outperforms other machine learning algorithms. For individual categories of features such as GPP, vegetation indices, climate, and soil features, the GWRFR also outperforms other models. The Moran’s I value of the residuals generated by GWRFR is smaller than that of other models, which shows that GWRFR can better address the spatial non-stationarity issue. The proposed method in this article can also be potentially used to improve yield prediction for other types of crops in other regions.

Animals respond to environmental threats, e.g. looming visual stimuli, with innate defensive behaviors such as escape and freezing. The key neural circuits that participate in the generation of such dimorphic defensive behaviors remain unclear. Here we show that the dimorphic behavioral patterns triggered by looming visual stimuli are mediated by parvalbumin-positive (PV + ) projection neurons in mouse superior colliculus (SC). Two distinct groups of SC PV + neurons form divergent pathways to transmit threat-relevant visual signals to neurons in the parabigeminal nucleus (PBGN) and lateral posterior thalamic nucleus (LPTN). Activations of PV + SC-PBGN and SC-LPTN pathways mimic the dimorphic defensive behaviors. The PBGN and LPTN neurons are co-activated by looming visual stimuli. Bilateral inactivation of either nucleus results in the defensive behavior dominated by the other nucleus. Together, these data suggest that the SC orchestrates dimorphic defensive behaviors through two separate tectofugal pathways that may have interactions. In response to environmental threats, such as visual looming stimuli, mice either freeze or escape. Here the authors demonstrate that these two behaviors are mediated by separate tectofugal pathways formed by parvalbumin-positive neurons in the superior colliculus.

Background The origin of sex chromosomes requires the establishment of recombination suppression between the proto-sex chromosomes. In many fish species, the sex chromosome pair is homomorphic with a recent origin, providing species for studying how and why recombination suppression evolved in the initial stages of sex chromosome differentiation, but this requires accurate sequence assembly of the X and Y (or Z and W) chromosomes, which may be difficult if they are recently diverged. Results Here we produce a haplotype-resolved genome assembly of zig-zag eel ( Mastacembelus armatus ), an aquaculture fish, at the chromosomal scale. The diploid assembly is nearly gap-free, and in most chromosomes, we resolve the centromeric and subtelomeric heterochromatic sequences. In particular, the Y chromosome, including its highly repetitive short arm, has zero gaps. Using resequencing data, we identify a ~7 Mb fully sex-linked region (SLR), spanning the sex chromosome centromere and almost entirely embedded in the pericentromeric heterochromatin. The SLRs on the X and Y chromosomes are almost identical in sequence and gene content, but both are repetitive and heterochromatic, consistent with zero or low recombination. We further identify an HMG-domain containing gene HMGN6 in the SLR as a candidate sex-determining gene that is expressed at the onset of testis development. Conclusions Our study supports the idea that preexisting regions of low recombination, such as pericentromeric regions, can give rise to SLR in the absence of structural variations between the proto-sex chromosomes.

The fear responses to environmental threats play a fundamental role in survival. Little is known about the neural circuits specifically processing threat-relevant sensory information in the mammalian brain. We identified parvalbumin-positive (PV+) excitatory projection neurons in mouse superior colliculus (SC) as a key neuronal subtype for detecting looming objects and triggering fear responses. These neurons, distributed predominantly in the superficial SC, divergently projected to different brain areas, including the parabigeminal nucleus (PBGN), an intermediate station leading to the amygdala. Activation of the PV+ SC-PBGN pathway triggered fear responses, induced conditioned aversion, and caused depression-related behaviors. Approximately 20% of mice subjected to the fear-conditioning paradigm developed a generalized fear memory.

Quantification of the cardiac function is vital for diagnosing and curing the cardiovascular diseases. Left ventricular function measurement is the most commonly used measure to evaluate the function of cardiac in clinical practice, how to improve the accuracy of left ventricular quantitative assessment results has always been the subject of research by medical researchers. Although considerable efforts have been put forward to measure the left ventricle (LV) automatically using deep learning methods, the accurate quantification is yet a challenge work as a result of the changeable anatomy structure of heart in the systolic diastolic cycle. Besides, most methods used direct regression method which lacks of visual based analysis. In this work, a deep learning segmentation and regression task-unified network with transformer and spatial–temporal convolution is proposed to segment and quantify the LV simultaneously. The segmentation module leverages a U-Net like 3D Transformer model to predict the contour of three anatomy structures, while the regression module learns spatial–temporal representations from the original images and the reconstruct feature map from segmentation path to estimate the finally desired quantification metrics. Furthermore, we employ a joint task loss function to train the two module networks. Our framework is evaluated on the MICCAI 2017 Left Ventricle Full Quantification Challenge dataset. The results of experiments demonstrate the effectiveness of our framework, which achieves competitive cardiac quantification metric results and at the same time produces visualized segmentation results that are conducive to later analysis.

Background Adoption of thermal processing of the diet drives human evolution and gut microbiota diversity changes in a dietary habit-dependent manner. However, whether thermal processing of food triggers gut microbial variation remains unknown. Herein, we compared the microbiota of non-thermally processed and thermally processed food (NF and TF) and investigated gut microbiota associated with NF and TF in catfish Silurus meridionalis and C57BL/6 mice to assess effects of thermal processing of food on gut microbiota and to further identify the differences in host responses. Results We found no differences in overall microbial composition and structure in the pairwise NF and TF, but identified differential microbial communities between food and gut. Both fish and mice fed TF had significantly lower gut microbial diversity than those fed NF. Moreover, thermal processing of food triggered the changes in their microbial communities. Comparative host studies further indicated host species determined gut microbial assemblies, even if fed with the same food. Fusobacteria was the most abundant phylum in the fish, and Bacteroidetes and Firmicutes dominated in the mice. Besides the consistent reduction of Bacteroidetes and the balanced Protebacteria , the response of other dominated gut microbiota in the fish and mice to TF was taxonomically opposite at the phylum level, and those further found at the genus level. Conclusions Our results reveal that thermal processing of food strongly contributes to the reduction of gut microbial diversity and differentially drives microbial alterations in a host-dependent manner, suggesting specific adaptations of host-gut microbiota in vertebrates responding to thermal processing of food. These findings open a window of opportunity to understand the decline in gut microbial diversity and the community variation in human evolution and provide new insights into the host-specific microbial assemblages associated with the use of processing techniques in food preparation in humans and domesticated animals.

Self-supervised monocular depth estimation can exhibit excellent performance in static environments due to the multi-view consistency assumption during the training process. However, it is hard to maintain depth consistency in dynamic scenes when considering the occlusion problem caused by moving objects. For this reason, we propose a method of self-supervised self-distillation for monocular depth estimation (SS-MDE) in dynamic scenes, where a deep network with a multi-scale decoder and a lightweight pose network are designed to predict depth in a self-supervised manner via the disparity, motion information, and the association between two adjacent frames in the image sequence. Meanwhile, in order to improve the depth estimation accuracy of static areas, the pseudo-depth images generated by the LeReS network are used to provide the pseudo-supervision information, enhancing the effect of depth refinement in static areas. Furthermore, a forgetting factor is leveraged to alleviate the dependency on the pseudo-supervision. In addition, a teacher model is introduced to generate depth prior information, and a multi-view mask filter module is designed to implement feature extraction and noise filtering. This can enable the student model to better learn the deep structure of dynamic scenes, enhancing the generalization and robustness of the entire model in a self-distillation manner. Finally, on four public data datasets, the performance of the proposed SS-MDE method outperformed several state-of-the-art monocular depth estimation techniques, achieving an accuracy (δ1) of 89% while minimizing the error (AbsRel) by 0.102 in NYU-Depth V2 and achieving an accuracy (δ1) of 87% while minimizing the error (AbsRel) by 0.111 in KITTI.

The East Australian Current (EAC) southern extension is one of the ocean warming hotspots under greenhouse warming, yet its underlying mechanism remains debated. Here we investigate ocean warming in the EAC southern extension based on simulations from a state‐of‐the‐art high‐resolution global climate model. In the changing climate, the EAC heat transports intensify near the coasts. However, ocean warming is substantially greater offshore than in coastal regions. Heat budget analysis reveals that this pattern is driven by oceanic eddies, which redistribute the inshore EAC heat fluxes off the coast, suppressing coastal warming while enhancing offshore warming. Under global warming, eddies strengthen the cross‐shore warming structure at a rate of 1.6°C/100 km per year per century. Our result highlights the dominance of eddies in influencing the spatial structure of ocean warming in the EAC southern extension, with important implications for marine ecosystems such as coral community and coastal fishery.