Tunable superconductivity in electron- and hole-doped Bernal bilayer graphene.

Graphene-based, high-quality, two-dimensional electronic systems have emerged as a highly tunable platform for studying superconductivity1-21. Specifically, superconductivity has been observed in both electron- and hole-doped twisted graphene moiré systems1-17, whereas in crystalline graphene systems, superconductivity has so far been observed only in hole-doped rhombohedral trilayer graphene (RTG)18 and hole-doped Bernal bilayer graphene (BBG)19-21. Recently, enhanced superconductivity has been demonstrated20,21 in BBG because of the proximity to a monolayer WSe2. Here we report the observation of superconductivity and a series of flavour-symmetry-breaking phases in electron- and hole-doped BBG/WSe2 devices by electrostatic doping. The strength of the observed superconductivity is tunable by applied vertical electric fields. The maximum Berezinskii-Kosterlitz-Thouless transition temperature for the electron- and hole-doped superconductivity is about 210 mK and 400 mK, respectively. Superconductivities emerge only when the applied electric fields drive the BBG electron or hole wavefunctions towards the WSe2 layer, underscoring the importance of the WSe2 layer in the observed superconductivity. The hole-doped superconductivity violates the Pauli paramagnetic limit, consistent with an Ising-like superconductor. By contrast, the electron-doped superconductivity obeys the Pauli limit, although the proximity-induced Ising spin-orbit coupling is also notable in the conduction band. Our findings highlight the rich physics associated with the conduction band in BBG, paving the way for further studies into the superconducting mechanisms of crystalline graphene and the development of superconductor devices based on BBG.

Recognition of Occluded Goods under Prior Inference Based on Generative Adversarial Network.

Aiming at the recognition of intelligent retail dynamic visual container goods, two problems that lead to low recognition accuracy must be addressed; one is the lack of goods features caused by the occlusion of the hand, and the other is the high similarity of goods. Therefore, this study proposes an approach for occluding goods recognition based on a generative adversarial network combined with prior inference to address the two abovementioned problems. With DarkNet53 as the backbone network, semantic segmentation is used to locate the occluded part in the feature extraction network, and simultaneously, the YOLOX decoupling head is used to obtain the detection frame. Subsequently, a generative adversarial network under prior inference is used to restore and expand the features of the occluded parts, and a multi-scale spatial attention and effective channel attention weighted attention mechanism module is proposed to select fine-grained features of goods. Finally, a metric learning method based on von Mises-Fisher distribution is proposed to increase the class spacing of features to achieve the effect of feature distinction, whilst the distinguished features are utilized to recognize goods at a fine-grained level. The experimental data used in this study were all obtained from the self-made smart retail container dataset, which contains a total of 12 types of goods used for recognition and includes four couples of similar goods. Experimental results reveal that the peak signal-to-noise ratio and structural similarity under improved prior inference are 0.7743 and 0.0183 higher than those of the other models, respectively. Compared with other optimal models, mAP improves the recognition accuracy by 1.2% and the recognition accuracy by 2.82%. This study solves two problems: one is the occlusion caused by hands, and the other is the high similarity of goods, thus meeting the requirements of commodity recognition accuracy in the field of intelligent retail and exhibiting good application prospects.

Preparation of Saccharomyces boulardii powder by spray drying: thermoprotectants optimization and stability evaluation.

Saccharomyces boulardii as the probiotic yeast was widely used in the pharmaceutical, feed and food industries. The influence of skim milk, gelatin, and carbohydrates on the heat resistance of S. boulardii is explored in the article. Response surface methodology was effectively applied to optimize the thermoprotectant composition for S. boulardii during spray-drying. The accelerated test is applied to evaluate its the subsequent storage stability. The results show that the thermoprotectants composition was comprehensively optimized such as: 15.12% skim milk, 1.81% gelatin, and 9.73% trehalose. The highest viability was 17.77%, which was basically the same as the predicted value of 18.21%. The inactivation rate constant of spray-dried powder was k-18 = 1.04 × 10-5 h-1, the quantity of viable cells stored at this temperature for 1 and 10 years was 8.25 × 108 CFU/g and 1.25 × 108 CFU/g, separately. This work provides a thermoprotectants formula for the S. boulardii during the spray drying process.

Deep learning improves the ability of sgRNA off-target propensity prediction.

BACKGROUND: CRISPR/Cas9 system, as the third-generation genome editing technology, has been widely applied in target gene repair and gene expression regulation. Selection of appropriate sgRNA can improve the on-target knockout efficacy of CRISPR/Cas9 system with high sensitivity and specificity. However, when CRISPR/Cas9 system is operating, unexpected cleavage may occur at some sites, known as off-target. Presently, a number of prediction methods have been developed to predict the off-target propensity of sgRNA at specific DNA fragments. Most of them use artificial feature extraction operations and machine learning techniques to obtain off-target scores. With the rapid expansion of off-target data and the rapid development of deep learning theory, the existing prediction methods can no longer satisfy the prediction accuracy at the clinical level. RESULTS: Here, we propose a prediction method named CnnCrispr to predict the off-target propensity of sgRNA at specific DNA fragments. CnnCrispr automatically trains the sequence features of sgRNA-DNA pairs with GloVe model, and embeds the trained word vector matrix into the deep learning model including biLSTM and CNN with five hidden layers. We conducted performance verification on the data set provided by DeepCrispr, and found that the auROC and auPRC in the "leave-one-sgRNA-out" cross validation could reach 0.957 and 0.429 respectively (the Pearson value and spearman value could reach 0.495 and 0.151 respectively under the same settings). CONCLUSION: Our results show that CnnCrispr has better classification and regression performance than the existing states-of-art models. The code for CnnCrispr can be freely downloaded from https://github.com/LQYoLH/CnnCrispr.

Liquid-Liquid phase separation in bacteria.

Cells are the essential building blocks of living organisms, responsible for carrying out various biochemical reactions and performing specific functions. In eukaryotic cells, numerous membrane organelles have evolved to facilitate these processes by providing specific spatial locations. In recent years, it has also been discovered that membraneless organelles play a crucial role in the subcellular organization of bacteria, which are single-celled prokaryotic microorganisms characterized by their simple structure and small size. These membraneless organelles in bacteria have been found to undergo Liquid-Liquid phase separation (LLPS), a molecular mechanism that allows for their assembly. Through extensive research, the occurrence of LLPS and its role in the spatial organization of bacteria have been better understood. Various biomacromolecules have been identified to exhibit LLPS properties in different bacterial species. LLPS which is introduced into synthetic biology applies to bacteria has important implications, and three recent research reports have shed light on its potential applications in this field. Overall, this review investigates the molecular mechanisms of LLPS occurrence and its significance in bacteria while also considering the future prospects of implementing LLPS in synthetic biology.

Proposal Distribution Calibration for Few-Shot Object Detection.

Adapting object detectors learned with sufficient supervision to novel classes under low data regimes is charming yet challenging. In few-shot object detection (FSOD), the two-step training paradigm is widely adopted to mitigate the severe sample imbalance, i.e., holistic pre-training on base classes, then partial fine-tuning in a balanced setting with all classes. Since unlabeled instances are suppressed as backgrounds in the base training phase, the learned region proposal network (RPN) is prone to produce biased proposals for novel instances, resulting in dramatic performance degradation. Unfortunately, the extreme data scarcity aggravates the proposal distribution bias, hindering the region of interest (RoI) head from evolving toward novel classes. In this brief, we introduce a simple yet effective proposal distribution calibration (PDC) approach to neatly enhance the localization and classification abilities of the RoI head by recycling its localization ability endowed in base training and enriching high-quality positive samples for semantic fine-tuning. Specifically, we sample proposals based on the base proposal statistics to calibrate the distribution bias and impose additional localization and classification losses upon the sampled proposals for fast expanding the base detector to novel classes. Experiments on the commonly used Pascal VOC and MS COCO datasets with explicit state-of-the-art performances justify the efficacy of our PDC for FSOD. Code is available at github.com/Bohao-Lee/PDC.

CNN-XG: A Hybrid Framework for sgRNA On-Target Prediction.

As the third generation gene editing technology, Crispr/Cas9 has a wide range of applications. The success of Crispr depends on the editing of the target gene via a functional complex of sgRNA and Cas9 proteins. Therefore, highly specific and high on-target cleavage efficiency sgRNA can make this process more accurate and efficient. Although there are already many sophisticated machine learning or deep learning models to predict the on-target cleavage efficiency of sgRNA, prediction accuracy remains to be improved. XGBoost is good at classification as the ensemble model could overcome the deficiency of a single classifier to classify, and we would like to improve the prediction efficiency for sgRNA on-target activity by introducing XGBoost into the model. We present a novel machine learning framework which combines a convolutional neural network (CNN) and XGBoost to predict sgRNA on-target knockout efficacy. Our framework, called CNN-XG, is mainly composed of two parts: a feature extractor CNN is used to automatically extract features from sequences and predictor XGBoost is applied to predict features extracted after convolution. Experiments on commonly used datasets show that CNN-XG performed significantly better than other existing frameworks in the predicted classification mode.

Part-Based Semantic Transform for Few-Shot Semantic Segmentation.

Few-shot semantic segmentation remains an open problem for the lack of an effective method to handle the semantic misalignment between objects. In this article, we propose part-based semantic transform (PST) and target at aligning object semantics in support images with those in query images by semantic decomposition-and-match. The semantic decomposition process is implemented with prototype mixture models (PMMs), which use an expectation-maximization (EM) algorithm to decompose object semantics into multiple prototypes corresponding to object parts. The semantic match between prototypes is performed with a min-cost flow module, which encourages correct correspondence while depressing mismatches between object parts. With semantic decomposition-and-match, PST enforces the network's tolerance to objects' appearance and/or pose variation and facilities channelwise and spatial semantic activation of objects in query images. Extensive experiments on Pascal VOC and MS-COCO datasets show that PST significantly improves upon state-of-the-arts. In particular, on MS-COCO, it improves the performance of five-shot semantic segmentation by up to 7.79% with a moderate cost of inference speed and model size. Code for PST is released at https://github.com/Yang-Bob/PST.

Nano-EMB-SP improves the solubility, foliar affinity, photostability and bioactivity of emamectin benzoate.

BACKGROUND: Emamectin benzoate (EMB), a frequently used biopesticide, is poorly soluble in water, making it difficult to wet the leaf surface, is prone to degrade in sunlight and readily loses its bioactivity. Traditional methods such as organic solvent application, pH adjustment and addition of photoprotectants either increase the economic and environmental costs or barely achieve the desired goal. We hypothesized that nanotechnology could improve the solubility, foliar affinity, photostability and bioactivity of EMB. This research set out to prepare a nano-EMB solid powder (nano-EMB-SP) and test this hypothesis. RESULTS: Nano-EMB-SP was prepared using a self-emulsifying method combined with carrier solidification. The mean particle size and Polydispersity index (PDI) of nano-EMB-SP were 14.64 nm and 0.24, respectively. A scanning electron microscopy image showed that EMB nanoparticles were mainly spherical or ellipsoidal in shape. Without organic solvent, the aqueous solubility of EMB in nano-EMB-SP was 4500 mg L-1 , at least 14-fold that of the EMB soluble granule (EMB-SG), which is solubilized by pH adjustment. Excellent foliar affinity of EMB was achieved by nano-EMB-SP, which completely wet and penetrated the superhydrophobic surface of cabbage (Brassica oleracea L.) leaf. Without photoprotectants, up to 82% of EMB content can be protected from ultraviolet (UV) damage in nano-EMB-SP. The combined effects of excellent photostability and foliar affinity of nano-EMB-SP led to the bioactivity of EMB being almost unchanged before and after UV radiation. CONCLUSION: Nano-EMB-SP is an eco-friendly and efficient way to improve the solubility, foliar affinity, photostability and bioactivity of EMB. This research provides a good approach to improving the efficacy of poorly soluble and UV-sensitive pesticides. © 2022 Society of Chemical Industry.