DistPred: A Distribution-Free Probabilistic Inference Method for Regression and Forecasting

Traditional regression and prediction tasks often only provide deterministic point estimates. To estimate the uncertainty or distribution information of the response variable, methods such as Bayesian inference, model ensembling, or MC Dropout are typically used. These methods either assume that the posterior distribution of samples follows a Gaussian process or require thousands of forward passes for sample generation. We propose a novel approach called DistPred for regression and forecasting tasks, which overcomes the limitations of existing methods while remaining simple and powerful. Specifically, we transform proper scoring rules that measure the discrepancy between the predicted distribution and the target distribution into a differentiable discrete form and use it as a loss function to train the model end-to-end. This allows the model to sample numerous samples in a single forward pass to estimate the potential distribution of the response variable. We have compared our method with several existing approaches on multiple datasets and achieved state-of-the-art performance. Additionally, our method significantly improves computational efficiency. For example, compared to state-of-the-art models, DistPred has a 90x faster inference speed. Experimental results can be reproduced through https://github.com/Anoise/DistPred

In silico comparative analysis of EST-SSRs in three cotton genomes

In this study, expressed sequence tags- simple sequence repeat (EST-SSRs) were surveyed in three cotton genomes (Gossypium arboreum, Ga; Gossypium raimondii, Gr and Gossypium hirsutum, Gh). The frequency of EST-SSRs was highest in Gr, and motif type for hexanucleotide was obviously abundant in Gr. Trinucleotide repeats were the most abundant motif; AT and AG, AAG and ATC were the most frequent motifs for dinucleotide and trinucleotide, respectively. The repeat number was greatly diverse between the three genomes with the highest variation in Gh. AG and AAG had a high frequency both in homologue groups (HGs) with and without repeat number change between genomes. The range of repeat number change in each HG was wider in Gr-Gh. The annotation of the SSR-ESTs showed that more Gene Ontology (GO) items targeted by SSR-ESTs of Ga and Gr than those of Gh. This study gave us new insights into the difference between the three cotton genomes, which will be more helpful to understand the differentiation and evolution of the three genomes.Key words: Cotton, simple sequence repeat, expressed sequence tags, motif, gene ontology

Minusformer: Improving Time Series Forecasting by Progressively Learning Residuals

In this paper, we find that ubiquitous time series (TS) forecasting models are prone to severe overfitting. To cope with this problem, we embrace a de-redundancy approach to progressively reinstate the intrinsic values of TS for future intervals. Specifically, we introduce a dual-stream and subtraction mechanism, which is a deep Boosting ensemble learning method. And the vanilla Transformer is renovated by reorienting the information aggregation mechanism from addition to subtraction. Then, we incorporate an auxiliary output branch into each block of the original model to construct a highway leading to the ultimate prediction. The output of subsequent modules in this branch will subtract the previously learned results, enabling the model to learn the residuals of the supervision signal, layer by layer. This designing facilitates the learning-driven implicit progressive decomposition of the input and output streams, empowering the model with heightened versatility, interpretability, and resilience against overfitting. Since all aggregations in the model are minus signs, which is called Minusformer. Extensive experiments demonstrate the proposed method outperform existing state-of-the-art methods, yielding an average performance improvement of 11.9% across various datasets.The code has been released at https://github.com/Anoise/Minusformer

Electrografting of amino-TEMPO on graphene oxide and electrochemically reduced graphene oxide for electrocatalytic applications.

4-Amino-2,2,6,6-tetramethyl-1-piperridine N-oxyl (4-amino-TEMPO), an electroactive nitroxide radical, was attached to the surface of graphene oxide (GO) and electrochemically reduced graphene oxide (ERGO) modified glassy carbon electrode by a simple, rapid and green electrografting method. The electroactive interfaces were analyzed by X-ray photoelectron spectroscopy (XPS) and cyclic voltammetry (CV). The calculated surface coverage for 4-amino-TEMPO is up to 1.55 × 10− 9 mol·cm− 2. The modified electroactive interface exhibited excellent electrocatalytic activity towards the electro-oxidation of reduced glutathione (GSH) and hydrogen peroxide (H2O2)

Glassy carbon electrode modified with 7,7,8,8-tetracyanoquinodimethane and graphene oxide triggered a synergistic effect: low-potential amperometric detection of reduced glutathione.

A sensitive electrochemical sensor based on the synergistic effect of 7,7,8,8-tetracyanoquinodimethane (TCNQ) and graphene oxide (GO) for low-potential amperometric detection of reduced glutathione (GSH) in pH 7.2 phosphate buffer solution (PBS) has been reported. This is the first time that the combination of GO and TCNQ have been successfully employed to construct an electrochemical sensor for the detection of glutathione. The surface of the glassy carbon electrode (GCE) was modified by a drop casting using TCNQ and GO. Cyclic voltammetric measurements showed that TCNQ and GO triggered a synergistic effect and exhibited an unexpected electrocatalytic activity towards GSH oxidation, compared to GCE modified with only GO, TCNQ or TCNQ/electrochemically reduced GO. Three oxidation waves for GSH were found at −0.05, 0.1 and 0.5 V, respectively. Amperometric techniques were employed to detect GSH sensitively using a GCE modified with TCNQ/GO at −0.05 V. The electrochemical sensor showed a wide linear range from 0.25 to 124.3 μM and 124.3 μM to 1.67 mM with a limit of detection of 0.15 μM. The electroanalytical sensor was successfully applied towards the detection of GSH in an eye drop solution

Facile synthesis of a nickel sulfide (NiS) hierarchical flower for the electrochemical oxidation of H2O2 and the methanol oxidation reaction (MOR).

The synthesis of a novel hierarchical flower-like NiS via a solvothermal method for the electrochemcial oxidation of H2O2 on a carbon paste electrode with high catalytic activity for the (MOR) in an alkaline medium has been reported. Novel nickel sulfide (NiS) hierarchical flower-like structures were characterized by X-ray diffraction, scanning electron microscope, and transmission electron microscopy. A carbon paste electrode was modified with the as-prepared hierarchical flower-like NiS, resulting in a high electrocatalytic activity toward the oxidation of H2O2. The NiS-modified electrode was used for H2O2 sensing, which was achieved over a wide linear range from 0.5 μMto1.37mM(I/μA =-0.19025 + 0.06094 C/mM) with a low limit of detection (LOD) of 0.3 μM and a limit of quantitation (LOQ) of 0.8 μM. The hierarchical flower-like NiS also exhibited a high electrocatalytic activity for the methanol oxidation reaction (MOR) in an alkaline medium with a high tolerance toward the catalyst-poisoning species generated during the MOR. The MOR proceeded via the direct electrooxidation of methanol on the oxidized NiS surface layer because the oxidation peak potential of the MOR was more positive than that of the oxidation of NiS

Lignin metabolism has a central role in the resistance of cotton to the wilt fungus Verticillium dahliae as revealed by RNA-Seq-dependent transcriptional analysis and histochemistry

The incompatible pathosystem between resistant cotton (Gossypium barbadense cv. 7124) and Verticillium dahliae strain V991 was used to study the cotton transcriptome changes after pathogen inoculation by RNA-Seq. Of 32 774 genes detected by mapping the tags to assembly cotton contigs, 3442 defence-responsive genes were identified. Gene cluster analyses and functional assignments of differentially expressed genes indicated a significant transcriptional complexity. Quantitative real-time PCR (qPCR) was performed on selected genes with different expression levels and functional assignments to demonstrate the utility of RNA-Seq for gene expression profiles during the cotton defence response. Detailed elucidation of responses of leucine-rich repeat receptor-like kinases (LRR-RLKs), phytohormone signalling-related genes, and transcription factors described the interplay of signals that allowed the plant to fine-tune defence responses. On the basis of global gene regulation of phenylpropanoid metabolism-related genes, phenylpropanoid metabolism was deduced to be involved in the cotton defence response. A closer look at the expression of these genes, enzyme activity, and lignin levels revealed differences between resistant and susceptible cotton plants. Both types of plants showed an increased level of expression of lignin synthesis-related genes and increased phenylalanine-ammonia lyase (PAL) and peroxidase (POD) enzyme activity after inoculation with V. dahliae, but the increase was greater and faster in the resistant line. Histochemical analysis of lignin revealed that the resistant cotton not only retains its vascular structure, but also accumulates high levels of lignin. Furthermore, quantitative analysis demonstrated increased lignification and cross-linking of lignin in resistant cotton stems. Overall, a critical role for lignin was believed to contribute to the resistance of cotton to disease

Dietary Supplementation of Astragalus Polysaccharides Enhanced Immune Components and Growth Factors EGF and IGF-1 in Sow Colostrum

Colostrum is the main external resource providing piglets with nutrients and maternal immune molecules. Astragalus polysaccharides (APS) have been used as immunopotentiators in vitro and several animal models. This study aimed to determine the effects of APS on immune factors in sow colostrum and milk. The sow diet was supplemented with APS one week before the expected delivery date. Colostrum and milk were collected and designated as 0 h- (onset of parturition), 12 h-, and 24 h-colostrum and 36 h-milk postpartum. Samples were measured using porcine immunoglobulin (Ig) G, IgM, classical swine fever virus antibody (CSFV Ab), epidermal growth factor (EGF), and insulin-like growth factor- (IGF-) 1 ELISA Quantitation Kits. Dietary supplementation of APS significantly enhanced the presence of IgG, IgM, EGF, and IGF-1 in 0 h-colostrum (P<0.001). The blocking rates of CSFV Ab were increased in samples from APS-supplemented sow when compared to those from the matched samples without APS treatment. The results indicate that supplement of APS could improve the immune components in sow colostrum and/or milk; and status of some specific vaccination could be determined through using colostrum or early milk in sow

Gene Prioritization of Resistant Rice Gene against<i>Xanthomas oryzae pv. oryzae</i>by Using Text Mining Technologies

To effectively assess the possibility of the unknown rice protein resistant toXanthomonas oryzae pv. oryzae, a hybrid strategy is proposed to enhance gene prioritization by combining text mining technologies with a sequence-based approach. The text mining technique of term frequency inverse document frequency is used to measure the importance of distinguished terms which reflect biomedical activity in rice before candidate genes are screened and vital terms are produced. Afterwards, a built-in classifier under the chaos games representation algorithm is used to sieve the best possible candidate gene. Our experiment results show that the combination of these two methods achieves enhanced gene prioritization.</jats:p

Gene prioritization of resistant rice gene against Xanthomas oryzae pv. oryzae by using text mining technologies

To effectively assess the possibility of the unknown rice protein resistant to Xanthomonas oryzae pv. oryzae, a hybrid strategy is proposed to enhance gene prioritization by combining text mining technologies with a sequence-based approach. The text mining technique of term frequency inverse document frequency is used to measure the importance of distinguished terms which reflect biomedical activity in rice before candidate genes are screened and vital terms are produced. Afterwards, a built-in classifier under the chaos games representation algorithm is used to sieve the best possible candidate gene. Our experiment results show that the combination of these two methods achieves enhanced gene prioritization