Coupling machine learning and theoretical models to compare key properties of biochar in adsorption kinetics rate and maximum adsorption capacity for emerging contaminants.

Insights into key properties of biochar with a fast adsorption rate and high adsorption capacity are urgent to design biochar as an adsorbent in pollution emergency treatment. Machine learning (ML) incorporating classical theoretical adsorption models was applied to build prediction models for adsorption kinetics rate (i.e.

Elucidation of the evolutionary history of in China using comparative transcriptomic analysis.

Phylogenetic analysis provides crucial insights into the evolutionary relationships and diversification patterns within specific taxonomic groups. In this study, we aimed to identify the phylogenetic relationships and explore the evolutionary history of using transcriptomic data. Samples of 12 species were collected from the Qinghai-Tibet Plateau and Mongolian Plateau, where they are widely distributed, and transcriptome sequencing was performed using their fresh spikelet tissues.

Middle Meningeal Artery Embolization Combined With Endoscopic Treatment for Chronic Subdural Hematoma.

Objective: To explore the clinical feasibility of middle meningeal artery (MMA) embolization combined with endoscopic treatment for new or recurrent chronic subdural hematoma (CSDH).

Methods: Twenty patients with CSDH treated in the Binzhou Medical University Hospital from June 2020 to October 2022 were analyzed retrospectively. The clinical information, prognosis, imaging results, and surgical results of the patients were collected and analyzed.

Vitamin E Exerts Neuroprotective Effects in Pentylenetetrazole Kindling Epilepsy via Suppression of Ferroptosis.

Epilepsy is one of the most common chronic neurological diseases. There is increasing evidence for ferroptosis playing an important role in the occurrence and development of epilepsy. Vitamin E is a common fat-soluble antioxidant that can regulate ferroptosis.

First Report of Fusarium fujikuroi Causing Leaf spot of Dalbergia odorifera T.Chen in China.

Dalbergia odorifera T. Chen is a national second-grade protected and one of the four famous trees in China, with high medicinal and economic value. Leaf spot disease in this plant can cause the leaves to dry up, perforate or even fall off, which affects the growth and development, and also has a great influence on its products.

Ibuprofen Exerts Antiepileptic and Neuroprotective Effects in the Rat Model of Pentylenetetrazol-Induced Epilepsy via the COX-2/NLRP3/IL-18 Pathway.

Epilepsy is one of the most common diseases of the central nervous system. Recent studies have shown that a variety of inflammatory mediators play a key role in the pathogenesis of the disease. Ibuprofen (IBP) is a well-known anti-inflammatory agent that reduces the neuroinflammatory response and neuronal damage.

The transcriptional reprograming and functional identification of WRKY family members in pepper's response to Phytophthora capsici infection.

Background: Plant transcription factors (TFs) are key transcriptional regulators to manipulate the regulatory network of host immunity. However, the globally transcriptional reprogramming of plant TF families in response to pathogens, especially between the resistant and susceptible host plants, remains largely unknown.

Results: Here, we performed time-series RNA-seq from a resistant pepper line CM334 and a susceptible pepper line EC01 upon challenged with Phytophthora capsici, and enrichment analysis indicated that WRKY family most significantly enriched in both CM334 and EC01.

Genome and Transcriptome Analyses Provide Insight Into the Omega-3 Long-Chain Polyunsaturated Fatty Acids Biosynthesis of SR21.

sp. is the best natural resource for omega-3 long-chain polyunsaturated fatty acids. We report a high-quality genome sequence of SR21, which has a 63 Mb genome size, with a contig N50 of 2.

Maximum Likelihood Estimation-Based Joint Sparse Representation for the Classification of Hyperspectral Remote Sensing Images.

A joint sparse representation (JSR) method has shown superior performance for the classification of hyperspectral images (HSIs). However, it is prone to be affected by outliers in the HSI spatial neighborhood. In order to improve the robustness of JSR, we propose a maximum likelihood estimation (MLE)-based JSR (MLEJSR) model, which replaces the traditional quadratic loss function with an MLE-like estimator for measuring the joint approximation error.

Correlation and clinical significance of LC3, CD68+ microglia, CD4+ T lymphocytes, and CD8+ T lymphocytes in gliomas.

Objective: To investigate the relationship between the expression of microtubule-associated protein LC3 and the numbers of CD68 + microglia, CD4 + T lymphocytes and CD8 + T lymphocytes, as well as the clinical significance of those factors in gliomas.

Patients And Methods: The study group consisted of 127 patients with gliomas who were operated to our hospital, we examined the expression of LC3 by Immunohistochemistry and Western blot, and we assessed the numbers of CD68 + microglia, CD4 + T lymphocytes and CD8 + T lymphocytes by Immunohistochemistry, we analyze the relationship between all the factors and explore the significance.

Results: Immunohistochemistry and Western blotting showed that the expression of LC3 in normal brain tissue, low-grade gliomas, and high-grade gliomas are elevated to varying degrees (P < 0.

Maximum likelihood estimation based regression for multivariate calibration.

In this paper, we propose a maximum likelihood estimation based regression (MLER) model for multivariate calibration. The proposed MLER method seeks for the maximum likelihood estimation (MLE) solution of the least-squares problem, and it is much more robust to noise or outliers and accurate than the traditional least-squares method. An efficient iteratively reweighted least squares technique is proposed to solve the MLER model.

Near and Mid-Infrared Emission Characteristics of Er³⁺/Tm³⁺/Ho³⁺-Doped LiYF₄ Single Crystals Excited by Laser Diode.

Yttrium lithium fluoride (LiYF₄) single crystals triply doped with Er³⁺/Tm³⁺/Ho³⁺ are synthesized by a vertical Bridgman method. Absorption spectra, emission spectra, and decay curves are measured to investigate the luminescent properties of the crystals. Compared with Er³⁺ singly doped and Er³⁺/Tm³⁺ and Er³⁺/Ho³⁺ doubly doped LiYF₄ crystals, an intense emission around 2.

Extreme learning machine for ranking: generalization analysis and applications.

The extreme learning machine (ELM) has attracted increasing attention recently with its successful applications in classification and regression. In this paper, we investigate the generalization performance of ELM-based ranking. A new regularized ranking algorithm is proposed based on the combinations of activation functions in ELM.

[FTIR spectral fitting algorithm based on continuous wavelet transform].

An FTIR spectrum fitting algorithm based on continuous wavelet transform is proposed. In calculating the factor of difference spectrum, the algorithm takes into account both the original spectrum and its continuous wavelet transformed spectra, which effectively overcomes the problem of reference peak selection and manual factor selection in most commercial software. The detailed discussions on wavelet scale, order and basis are included.

An improved ensemble partial least squares for analysis of near-infrared spectra.

Traditional ensemble regression algorithms such as BAgging Partial Least Squares (BAPLS) and BOosting Partial Least Squares (BOPLS) do not perform very well in the data set that is relatively small or contaminated by random noise. To make the method robust and improve its prediction ability, inspired from bias-variance-covariance decomposition, we propose an improved ensemble partial least squares method based on the diversity. The new method is applied to quantitative analysis of Near InfraRed (NIR) data sets.

Partial least squares and random sample consensus in outlier detection.

A novel outlier detection method in partial least squares based on random sample consensus is proposed. The proposed algorithm repeatedly generates partial least squares solutions estimated from random samples and then tests each solution for the support from the complete dataset for consistency. A comparative study of the proposed method and leave-one-out cross validation in outlier detection on simulated data and near-infrared data of pharmaceutical tablets is presented.

Baseline correction combined partial least squares algorithm and its application in on-line Fourier transform infrared quantitative analysis.

In order to eliminate the lower order polynomial interferences, a new quantitative calibration algorithm "Baseline Correction Combined Partial Least Squares (BCC-PLS)", which combines baseline correction and conventional PLS, is proposed. By embedding baseline correction constraints into PLS weights selection, the proposed calibration algorithm overcomes the uncertainty in baseline correction and can meet the requirement of on-line attenuated total reflectance Fourier transform infrared (ATR-FTIR) quantitative analysis. The effectiveness of the algorithm is evaluated by the analysis of glucose and marzipan ATR-FTIR spectra.

Near-infrared calibration transfer based on spectral regression.

A calibration transfer method for near-infrared (NIR) spectra based on spectral regression is proposed. Spectral regression method can reveal low dimensional manifold structure in high dimensional spectroscopic data and is suitable to transfer the NIR spectra of different instruments. A comparative study of the proposed method and piecewise direct standardization (PDS) for standardization on two benchmark NIR data sets is presented.

Asymmetric least squares for multiple spectra baseline correction.

In this paper, based on asymmetric least squares smoothing, a new algorithm for multiple spectra baseline correction is proposed. By means of the similarity among the multiple spectra, the algorithm estimates the baselines by penalizing the differences in the baseline corrected signals, which makes the algorithm possible to eliminate scatter effects on the spectra. In addition, a relaxation factor which measures the similarity of the baseline corrected spectra is incorporated into the optimization model and an alternate iteration strategy is used to solve the optimization problem.

Semi-supervised learning based on high density region estimation.

In this paper, we consider local regression problems on high density regions. We propose a semi-supervised local empirical risk minimization algorithm and bound its generalization error. The theoretical analysis shows that our method can utilize unlabeled data effectively and achieve fast learning rate.

[Analysis of liquor flavor spectra and pattern recognition computation].

Chinese liquor is a complex mixture and contains a large amount of microconstituents, which affects the quality and flavor of liquor. In order to discriminate liquor flavors rapidly, the spectra of liquors were obtained by FTIR and employed as the input patterns of pattern classification algorithms, then liquor flavor discrimination models were built. This paper introduces liquor flavor pattern recognition algorithms comprehensively and systematically for the first time, and the algorithms contain statistical classifications (linear discriminant function, quadratic discriminant function, regularized discriminant analysis, and K nearest neighbor), prototype learning algorithm (learning vector quantization), support vector machine and adaboost algorithm.