Genome-wide investigation and expression analysis of OSCA gene family in response to abiotic stress in alfalfa

Alfalfa is an excellent leguminous forage crop that is widely cultivated worldwide, but its yield and quality are often affected by drought and soil salinization. Hyperosmolality-gated calcium-permeable channel (OSCA) proteins are hyperosmotic calcium ion (Ca2+) receptors that play an essential role in regulating plant growth, development, and abiotic stress responses. However, no systematic analysis of the OSCA gene family has been conducted in alfalfa. In this study, a total of 14 OSCA genes were identified from the alfalfa genome and classified into three groups based on their sequence composition and phylogenetic relationships. Gene structure, conserved motifs and functional domain prediction showed that all MsOSCA genes had the same functional domain DUF221. Cis-acting element analysis showed that MsOSCA genes had many cis-regulatory elements in response to abiotic or biotic stresses and hormones. Tissue expression pattern analysis demonstrated that the MsOSCA genes had tissue-specific expression; for example, MsOSCA12 was only expressed in roots and leaves but not in stem and petiole tissues. Furthermore, RT–qPCR results indicated that the expression of MsOSCA genes was induced by abiotic stress (drought and salt) and hormones (JA, SA, and ABA). In particular, the expression levels of MsOSCA3, MsOSCA5, MsOSCA12 and MsOSCA13 were significantly increased under drought and salt stress, and MsOSCA7, MsOSCA10, MsOSCA12 and MsOSCA13 genes exhibited significant upregulation under plant hormone treatments, indicating that these genes play a positive role in drought, salt and hormone responses. Subcellular localization results showed that the MsOSCA3 protein was localized on the plasma membrane. This study provides a basis for understanding the biological information and further functional analysis of the MsOSCA gene family and provides candidate genes for stress resistance breeding in alfalfa

Calibrating Multimodal Learning

Multimodal machine learning has achieved remarkable progress in a wide range of scenarios. However, the reliability of multimodal learning remains largely unexplored. In this paper, through extensive empirical studies, we identify current multimodal classification methods suffer from unreliable predictive confidence that tend to rely on partial modalities when estimating confidence. Specifically, we find that the confidence estimated by current models could even increase when some modalities are corrupted. To address the issue, we introduce an intuitive principle for multimodal learning, i.e., the confidence should not increase when one modality is removed. Accordingly, we propose a novel regularization technique, i.e., Calibrating Multimodal Learning (CML) regularization, to calibrate the predictive confidence of previous methods. This technique could be flexibly equipped by existing models and improve the performance in terms of confidence calibration, classification accuracy, and model robustness

Fairness-guided Few-shot Prompting for Large Language Models

Large language models have demonstrated surprising ability to perform in-context learning, i.e., these models can be directly applied to solve numerous downstream tasks by conditioning on a prompt constructed by a few input-output examples. However, prior research has shown that in-context learning can suffer from high instability due to variations in training examples, example order, and prompt formats. Therefore, the construction of an appropriate prompt is essential for improving the performance of in-context learning. In this paper, we revisit this problem from the view of predictive bias. Specifically, we introduce a metric to evaluate the predictive bias of a fixed prompt against labels or a given attributes. Then we empirically show that prompts with higher bias always lead to unsatisfactory predictive quality. Based on this observation, we propose a novel search strategy based on the greedy search to identify the near-optimal prompt for improving the performance of in-context learning. We perform comprehensive experiments with state-of-the-art mainstream models such as GPT-3 on various downstream tasks. Our results indicate that our method can enhance the model's in-context learning performance in an effective and interpretable manner

An improved neural network model for battery smarter state-of-charge estimation of energy-transportation system

The safety and reliability of battery storage systems are critical to the mass roll-out of electrified transportation and new energy generation. To achieve safe management and optimal control of batteries, the state of charge (SOC) is one of the important parameters. The machine-learning based SOC estimation methods of lithium-ion batteries have attracted substantial interests in recent years. However, a common problem with these models is that their estimation performances are not always stable, which makes them difficult to use in practical applications. To address this problem, an optimized radial basis function neural network (RBF-NN) that combines the concepts of Golden Section Method (GSM) and Sparrow Search Algorithm (SSA) is proposed in this paper. Specifically, GSM is used to determine the optimum number of neurons in hidden layer of the RBF-NN model, and its parameters such as radial base center, connection weights and so on are optimized by SSA, which greatly improve the performance of RBF-NN in SOC estimation. In the experiments, data collected from different working conditions are used to demonstrate the accuracy and generalization ability of the proposed model, and the results of the experiment indicate that the maximum error of the proposed model is less than 2%

New method to evaluate fracture toughness of case-hardened steel

A new method is proposed for the evaluation of the fracture toughness of case-hardened steel. The proposed method uses the concept of the elastic unloading compliance method. The principle of this method is that cracks with different lengths will show different elastic unloading compliance values, and vice versa. Lastly, the correctness of the proposed method is verified by measuring the elastic unloading compliance value using a series of specimens with given crack lengths

Recent Advances in Reliable Deep Graph Learning: Adversarial Attack, Inherent Noise, and Distribution Shift

Deep graph learning (DGL) has achieved remarkable progress in both business and scientific areas ranging from finance and e-commerce to drug and advanced material discovery. Despite the progress, applying DGL to real-world applications faces a series of reliability threats including adversarial attacks, inherent noise, and distribution shift. This survey aims to provide a comprehensive review of recent advances for improving the reliability of DGL algorithms against the above threats. In contrast to prior related surveys which mainly focus on adversarial attacks and defense, our survey covers more reliability-related aspects of DGL, i.e., inherent noise and distribution shift. Additionally, we discuss the relationships among above aspects and highlight some important issues to be explored in future research

Exogenous Proline Improves Salt Tolerance of Alfalfa through Modulation of Antioxidant Capacity, Ion Homeostasis, and Proline Metabolism

Alfalfa (Medicago sativa L.) is an important forage crop, and its productivity is severely affected by salt stress. Although proline is a compatible osmolyte that plays an important role in regulating plant abiotic stress resistance, the basic mechanism of proline requires further clarification regarding the effect of proline in mitigating the harmful effects of salinity. Here, we investigate the protective effects and regulatory mechanisms of proline on salt tolerance of alfalfa. The results show that exogenous proline obviously promotes seed germination and seedling growth of salt-stressed alfalfa. Salt stress results in stunted plant growth, while proline application alleviates this phenomenon by increasing photosynthetic capacity and antioxidant enzyme activities and decreasing cell membrane damage and reactive oxygen species (ROS) accumulation. Plants with proline treatment maintain a better K+/Na+ ratio by reducing Na+ accumulation and increasing K+ content under salt stress. Additionally, proline induces the expression of genes related to antioxidant biosynthesis (Cu/Zn-SOD and APX) and ion homeostasis (SOS1, HKT1, and NHX1) under salt stress conditions. Proline metabolism is mainly regulated by ornithine-δ-aminotransferase (OAT) and proline dehydrogenase (ProDH) activities and their transcription levels, with the proline-treated plants displaying an increase in proline content under salt stress. In addition, OAT activity in the ornithine (Orn) pathway rather than Δ1-pyrroline-5-carboxylate synthetase (P5CS) activity in the glutamate (Glu) pathway is strongly increased under salt stress, made evident by the sharp increase in the expression level of the OAT gene compared to P5CS1 and P5CS2. Our study provides new insight into how exogenous proline improves salt tolerance in plants and that it might be used as a significant practical strategy for cultivating salt-tolerant alfalfa

Identification of Competing Endogenous RNAs (ceRNAs) Network Associated with Drought Tolerance in Medicago truncatula with Rhizobium Symbiosis

Drought, bringing the risks of agricultural production losses, is becoming a globally environmental stress. Previous results suggested that legumes with nodules exhibited superior drought tolerance compared with the non-nodule group. To investigate the molecular mechanism of rhizobium symbiosis impacting drought tolerance, transcriptome and sRNAome sequencing were performed to identify the potential mRNA–miRNA–ncRNA dynamic network. Our results revealed that seedlings with active nodules exhibited enhanced drought tolerance by reserving energy, synthesizing N-glycans, and medicating systemic acquired resistance due to the early effects of symbiotic nitrogen fixation (SNF) triggered in contrast to the drought susceptible with inactive nodules. The improved drought tolerance might be involved in the decreased expression levels of miRNA such as mtr_miR169l-5p, mtr_miR398b, and mtr_miR398c and its target genes in seedlings with active nodules. Based on the negative expression pattern between miRNA and its target genes, we constructed an mRNA–miR169l–ncRNA ceRNA network. During severe drought stress, the lncRNA alternative splicings TCONS_00049507 and TCONS_00049510 competitively interacted with mtr_miR169l-5p, which upregulated the expression of NUCLEAR FACTOR-Y (NF-Y) transcription factor subfamily NF-YA genes MtNF-YA2 and MtNF-YA3 to regulate their downstream drought-response genes. Our results emphasized the importance of SNF plants affecting drought tolerance. In conclusion, our work provides insight into ceRNA involvement in rhizobium symbiosis contributing to drought tolerance and provides molecular evidence for future study

Additional file 1 of Genome-wide identification of B-box zinc finger (BBX) gene family in Medicago sativa and their roles in abiotic stress responses

Supplementary Material 1: Table S1. Paralogous gene pairs in segmental duplication events of alfalfa MsBBX genes. Table S2. Collinear genes of MsBBXs between alfalfa and Arabidopsis, alfalfa and O. sativa, alfalfa and M. truncatula. Table S3. The functions of Cis-regulatory elements in MsBBX gene promoters. Table S4. The primers used in this stud