Two distinct superconducting states controlled by orientation of local wrinkles in LiFeAs

We observe two types of superconducting states controlled by orientations of local wrinkles on the surface of LiFeAs. Using scanning tunneling microscopy/spectroscopy, we find type-I wrinkles enlarge the superconducting gaps and enhance the transition temperature, whereas type-II wrinkles significantly suppress the superconducting gaps. The vortices on wrinkles show a C2 symmetry, indicating the strain effects on the wrinkles. A discontinuous switch of superconductivity occurs at the border between two different wrinkles. Our results demonstrate that the local strain effect could affect superconducting order parameter of LiFeAs with a possible Lifshitz transition, by alternating crystal structure in different directions.Comment: 21 pages, 9 figure

The Alleviation of Heat Damage to Photosystem II and Enzymatic Antioxidants by Exogenous Spermidine in Tall Fescue

Tall fescue (Festuca arundinacea Schreb) is a typical cool-season grass that is widely used in turf and pasture. However, high temperature as an abiotic stress seriously affects its utilization. The objective of this study was to explore the effect of spermidine (Spd) on heat stress response of tall fescue. The samples were exposed to 22°C (normal condition) or 44°C (heat stress) for 4 h. The results showed that exogenous Spd partially improved the quality of tall fescue leaves under normal temperature conditions. Nevertheless, after heat stress treatment, exogenous Spd significantly decreased the electrolyte leakage of tall fescue leaves. Spd also profoundly reduced the H2O2 and O2⋅- content and increased antioxidant enzymes activities. In addition, PAs can also regulate antioxidant enzymes activities including SOD, POD, and APX which could help to scavenge ROS. Moreover, application of Spd could also remarkably increase the chlorophyll content and had a positive effect on the chlorophyll α fluorescence transients under high temperature. The Spd reagent enhanced the performance of photosystem II (PSII) as observed by the JIP-test. Under heat stress, the Spd profoundly improved the partial potentials at the steps of energy bifurcations (PIABS and PItotal) and the quantum yields and efficiencies (φP0, δR0, φR0, and γRC). Exogenous Spd could also reduce the specific energy fluxes per QA- reducing PSII reaction center (RC) (TP0/RC and ET0/RC). Additionally, exogenous Spd improved the expression level of psbA and psbB, which encoded the proteins of PSII core reaction center complex. We infer that PAs can stabilize the structure of nucleic acids and protect RNA from the degradation of ribonuclease. In brief, our study indicates that exogenous Spd enhances the heat tolerance of tall fescue by maintaining cell membrane stability, increasing antioxidant enzymes activities, improving PSII, and relevant gene expression

Exogenous Calcium Enhances the Photosystem II Photochemistry Response in Salt Stressed Tall Fescue

Calcium enhances turfgrass response to salt stress. However, little is known about PSII photochemical changes when exogenous calcium was applied in salinity-stressed turfgrass. Here, we probe into the rearrangements of PSII electron transport and endogenous ion accumulation in tall fescue (Festuca arundinacea Schreber) treated with exogenous calcium under salt stress. Three-month-old seedlings of genotype “TF133” were subjected to the control (CK), salinity (S), salinity + calcium nitrate (SC), and salinity + ethylene glycol tetraacetic acid (SE). Calcium nitrate and ethylene glycol tetraacetic acid was used as exogenous calcium donor and calcium chelating agent respectively. At the end of a 5-day duration treatment, samples in SC regime had better photochemistry performance on several parameters than salinity only. Such as the Area (equal to the plastoquinone pool size), N (number of QA- redox turnovers until Fm is reached), ψE0, or δRo (Efficiencdy/probability with which a PSII trapped electron is transferred from QA to QB or PSI acceptors), ABS/RC (Absorbed photon flux per RC). All the above suggested that calcium enhanced the electron transfer of PSII (especially beyond QA-) and prevented reaction centers from inactivation in salt-stressed tall fescue. Furthermore, both grass shoot and root tissues generally accumulated more C, N, Ca2+, and K+ in the SC regime than S regime. Interrelated analysis indicated that ψE0, δRo, ABS/RC, C, and N content in shoots was highly correlated to each other and significantly positively related to Ca2+ and K+ content in roots. Besides, high salt increased ATP6E and CAMK2 transcription level in shoot at 1 and 5 day, respectively while exogenous calcium relieved it. In root, CAMK2 level was reduced by Salinity at 5 day and exogenous calcium recovered it. These observations involved in electron transport capacity and ion accumulation assist in understanding better the protective role of exogenous calcium in tall fescue under salt stress

Multi-Attention Module for Dynamic Facial Emotion Recognition

Video-based dynamic facial emotion recognition (FER) is a challenging task, as one must capture and distinguish tiny facial movements representing emotional changes while ignoring the facial differences of different objects. Recent state-of-the-art studies have usually adopted more complex methods to solve this task, such as large-scale deep learning models or multimodal analysis with reference to multiple sub-models. According to the characteristics of the FER task and the shortcomings of existing methods, in this paper we propose a lightweight method and design three attention modules that can be flexibly inserted into the backbone network. The key information for the three dimensions of space, channel, and time is extracted by means of convolution layer, pooling layer, multi-layer perception (MLP), and other approaches, and attention weights are generated. By sharing parameters at the same level, the three modules do not add too many network parameters while enhancing the focus on specific areas of the face, effective feature information of static images, and key frames. The experimental results on CK+ and eNTERFACE’05 datasets show that this method can achieve higher accuracy

Multi-Attention Module for Dynamic Facial Emotion Recognition

Video-based dynamic facial emotion recognition (FER) is a challenging task, as one must capture and distinguish tiny facial movements representing emotional changes while ignoring the facial differences of different objects. Recent state-of-the-art studies have usually adopted more complex methods to solve this task, such as large-scale deep learning models or multimodal analysis with reference to multiple sub-models. According to the characteristics of the FER task and the shortcomings of existing methods, in this paper we propose a lightweight method and design three attention modules that can be flexibly inserted into the backbone network. The key information for the three dimensions of space, channel, and time is extracted by means of convolution layer, pooling layer, multi-layer perception (MLP), and other approaches, and attention weights are generated. By sharing parameters at the same level, the three modules do not add too many network parameters while enhancing the focus on specific areas of the face, effective feature information of static images, and key frames. The experimental results on CK+ and eNTERFACE’05 datasets show that this method can achieve higher accuracy

Classification of cow behavior patterns using inertial measurement units and a fully convolutional network model

ABSTRACT: In this study, we aimed to classify 7 cow behavior patterns automatically with an inertial measurement unit (IMU) using a fully convolutional network (FCN) algorithm. Behavioral data of 12 cows were collected by attaching an IMU in a waterproof box on the neck behind the head of each cow. Seven behavior patterns were considered: rub scratching (leg), ruminating-lying, lying, feeding, self-licking, rub scratching (neck), and social licking. To simplify the data and compare classification performance with or without magnetometer data, the 9-axis IMU data were reduced using the square root of the sum of squares to develop 2 datasets. Comparing the classification accuracy of the 3 models using a window size of 64 with 6-axis data and a window size of 128 with both 6-axis and 9-axis data, the best overall accuracy (83.75%) was achieved using the FCN model with a window size of 128 (12.8 s) using all IMU data. This model achieved classification accuracies of 83.2, 96.5, 92.8, 98.1, 82.9, 87.2, and 45.2% for ruminating-lying, lying, feeding, rub scratching (leg), self-licking, rub scratching (neck), and social licking, respectively. As a sequence of varied and intensive movement, the classification accuracy of behavior patterns related to skin disease was lower; better classification of these behavior patterns could be achieved with full IMU data and a larger window size. In the future, additional data will take into account different data types, such as audio and video data, to further enhance performance. In addition, an adaptive sliding window size will be used to improve model performance