Batches Of Samples Research Articles

A comprehensive quality evaluation for Huangqi Guizhi Wuwu decoction by integrating UPLC-DAD/MS chemical profile and pharmacodynamics combined with chemometric analysis

Ethnopharmacological relevanceHuangqi Guizhi Wuwu Decoction (HGWD), a classical Chinese formula originally recorded in Jin Kui Yao Lue, was used for the treatment of human “blood impediment” (a type of “Bi” syndrome). In clinical practice, HGWD has been applied to treat rheumatoid arthritis (RA). Aim of the studyThe characterization of chemical markers reflecting both efficacy and chemical characteristics is of great significance for TCM quality control. With the anti-RA effects of HGWD as an example, the aim of this study was to develop a comprehensive strategy combining the overall chemical profile and biological activity data to identify chemical markers. Materials and methodsFirst, an ultra-performance liquid chromatography-diode array detector (UPLC-DAD) fingerprint was established and validated to evaluate the holistic quality of HGWD of different origins. Characteristic markers associated with HGWD from different geographical origins were screened by a combination of UPLC-DAD fingerprint and chemometrics methods. Second, the chemical profiles of the 15 batches of HGWD samples were characterized by UPLC coupled tohybrid linear ion trap-Orbitrap mass spectrometry (UPLC-HRMS). The in vitro anti-RA activities of the 15 HGWD samples were then evaluated. Third, spectrum-effect relationship analysis was performed to identify bioactive compounds that could potentially be used as quality markers. Finally, a UPLC-triple quadrupole tandem mass spectrometry approach was optimized and established for quantitative analysis of the characteristic and quality markers in 15 batches of HGWD. ResultsIn total, 30 common peaks were assigned in the UPLC-DAD fingerprint. Nine peaks were recognized and considered characteristic markers: protocatechuic acid, coumarin, cinnamic acid, oxypaeoniflorin, paeoniflorin, calycosin, formononetin, catechin, and albiflorin. Furthermore, ninety-five common compounds were identified in the UPLC-HRMS chemical profile. The pharmacological analysis indicated that the anti-RA activities of the 15 HGWD samples were vastly different. The spectrum-effect relationship analysis revealed 30 potential bioactive constituents positively correlated with anti-RA activity. Among them, five compounds with relative amounts >1%, paeoniflorin, astragaloside IV, hexahydrocurcumin, formononetin and calycosin-7-glucoside, were selected as quality markers, and their activity was verified in LPS-induced RAW264.7 macrophages. Finally, the above 12 representative components were simultaneously quantified in the 15 batches of HGWD samples. ConclusionCombining a holistic chemical profile with representative component evaluation, this systematic strategy could be a reliable and effective method to improve quality evaluations of HGWD.

Comparative evaluation of the effectiveness of various solvents on dissolving efficacy of gutta percha- An in vitro- study

This study's objective was to compare how well xylene, chloroform, turpentine oil, halothane, and orange oil dissolved materials at three different immersion time intervals.For the investigation. An ISO no. 40 cone and gutta-percha cones with a 0.06 taper were gathered. In each of the tested solvents, five batches of samples were made and submerged for 6, 12, and 18 minutes. To ascertain the process of gutta-percha dissolution in the solvents, the weight before immersion and the weight after immersion in the solvents were measured on a digital analytical scale. Analysis of Variance (ANOVA) and multiple comparisons with Scheffe's test (p 0.05) were used to statistically analyse the data. With xylene, the best solvency capacity was attained. Halothane, orange oil, and chloroform all had comparable dissolving abilities.

Cont-ID: detection of sample cross-contamination in viral metagenomic data

BackgroundHigh-throughput sequencing (HTS) technologies completed by the bioinformatic analysis of the generated data are becoming an important detection technique for virus diagnostics. They have the potential to replace or complement the current PCR-based methods thanks to their improved inclusivity and analytical sensitivity, as well as their overall good repeatability and reproducibility. Cross-contamination is a well-known phenomenon in molecular diagnostics and corresponds to the exchange of genetic material between samples. Cross-contamination management was a key drawback during the development of PCR-based detection and is now adequately monitored in routine diagnostics. HTS technologies are facing similar difficulties due to their very high analytical sensitivity. As a single viral read could be detected in millions of sequencing reads, it is mandatory to fix a detection threshold that will be informed by estimated cross-contamination. Cross-contamination monitoring should therefore be a priority when detecting viruses by HTS technologies.ResultsWe present Cont-ID, a bioinformatic tool designed to check for cross-contamination by analysing the relative abundance of virus sequencing reads identified in sequence metagenomic datasets and their duplication between samples. It can be applied when the samples in a sequencing batch have been processed in parallel in the laboratory and with at least one specific external control called Alien control. Using 273 real datasets, including 68 virus species from different hosts (fruit tree, plant, human) and several library preparation protocols (Ribodepleted total RNA, small RNA and double-stranded RNA), we demonstrated that Cont-ID classifies with high accuracy (91%) viral species detection into (true) infection or (cross) contamination. This classification raises confidence in the detection and facilitates the downstream interpretation and confirmation of the results by prioritising the virus detections that should be confirmed.ConclusionsCross-contamination between samples when detecting viruses using HTS (Illumina technology) can be monitored and highlighted by Cont-ID (provided an alien control is present). Cont-ID is based on a flexible methodology relying on the output of bioinformatics analyses of the sequencing reads and considering the contamination pattern specific to each batch of samples. The Cont-ID method is adaptable so that each laboratory can optimise it before its validation and routine use.

Life Cycle Assessment of Production of Hydrochar via Hydrothermal Carbonization of Date Palm Fronds Biomass.

This study presents novel life cycle assessment (LCA) findings on hydrochar production from Saudi-Arabia-based date palm fronds biomass waste using hydrothermal carbonization (HTC). The LCA procedure incorporated normalization, weighting, and improvement assessment. The system boundary encompassed water consumption and energy requirements within a lab setting representing a gate-to-gate process. The OpenLCA 1.11.0 software with the European Life Cycle Database 3.2 (ELCD 3.2) was utilized for the study and we employed the ReCiPe Midpoint (H) 2016 and Environmental Footprint 3.0 (EF 3.0) impact assessment methods. The results indicated that fossil fuel usage represented the most significant impact category with the HTC and drying processes identified as major contributors. It was also observed that the HTC process exerted far greater detrimental impacts on the environment than the biomass grinding process. The overwhelming impact of fossil fuel resources could be mitigated by optimizing the batches of biomass or hydrochar samples in each operation, which could alleviate fossil fuel consumption by up to 94%. The findings emphasize the need for targeted interventions to mitigate the environmental burden and contribute to sustainable hydrochar production.

Development and validation of a two-step assay for differentiation of Penis et testis cervi from cervi nippon Temmink and cervi elaphus Linnaeus based on specific-species polymerase chain reaction-restriction fragment length polymorphism patterns

Penis et testis cerviis one of the precious traditional Chinese medicines (TCMs) in China, which, whenauthentic, is sourcedfrom species ofCervi nippon Temmink(C. Nippon)andCervi elaphus Linnaeus(C.elaphus L).Authentication of species ofPenis et testis cerviremainstechnically challenging. This study attempted to modify the species-specific polymerase chain reaction (PCR) and PCR-restriction fragment length polymorphism (PCR-RFLP) method using restriction enzymeHpy188III for the identification ofPenis et testis cervispecies.First, thecytochrome b (Cytb)and cytochrome c oxidase subunit I (COI) genes in mitochondrial DNAfrom two authentic species and their counterfeitsor adulterantswere downloaded and analyzed with bioinformatics tools to establish the phylogenetic tree, then a series of primers were designed, and the restriction enzymesites were confirmed. Genomic DNA was extracted fromall samplesby the improved SDS method. Species-specific PCR reactions were optimizedto differentiate authentic Sika deer and ReddeerPenis et testis cervifrom their counterfeits or adulterants. PCR-RFLP using single restriction enzyme Hpy188III was performed inCytb regionsto identify the species of Sika deer and Reddeer. Simultaneously, recombination plasmid carrying thecytb fragmentcould be cloned, sequenced, and blasted.The assay specificity and sensitivity were evaluated, and30 batches of commercial samples werevalidated. Thephylogenetic treeshowed that the physico-chemical properties of Sika deer and Reddeer variants were similar.Sufficient DNA extracts were extracted from all samples used inthe improved SDS method.Establishment and optimization of a species-specific PCR reactionsand PCR-RFLPsystem were undertaken to identify deer species inPenis et testis cervisamples. Aspecific PCR-RFLP pattern with three distinct products of sizes 155, 113, and 204 bp was exhibited in Sika deerspecies,and two distinct products of 204 and 268 bp in size were evidentin Reddeerspecies,which were significantly different from the pattern of thecounterfeitsor adulterants.The distinctive PCR-RFLP fingerprint pattern using the restriction enzyme Hpy188III is a rapid and reliable assay for identification of thePenis et testis cervispecies.

Evaluation of NMR-based strategies to differentiate fresh from frozen-thawed fish supported by multivariate data analysis

The differentiation of fresh and frozen-thawed fish is a relevant authenticity aspect as in the European Union fish holds a high statistical risk of being adulterated. Here, nuclear magnetic resonance spectroscopy (NMR) in combination with principal components analysis followed by linear discriminant analysis (PCA-LDA) was used for a non-targeted based differentiation of fresh from frozen-thawed fish. To identify the most promising NMR approach(es), six different approaches were applied to 96 fish samples (mackerel, trout, cod). These approaches included different sample preparation procedures and different NMR methods to investigate both the lipid fraction and the polar fraction of the fish samples. After cross-validation embedded in a Monte Carlo resampling design, six independent classification models were obtained. Evaluation of the multivariate data analysis revealed that the most promising approaches were the 1H NMR analysis of the lipid fraction (correct prediction of about 90.0%) and the 1H NMR based screening of minor components of the lipid fraction with a correct prediction of about 91.9%. 1H NMR analysis of the water extract of the fish samples showed a correct prediction of about 82.6%. Hence, a general differentiation of fresh from frozen-thawed fish via non-targeted NMR is feasible, even though the underlying sample batch contained different fish species. Additional fish samples need to be analyzed with the three most promising NMR approaches to further improve the developed classification models.

A feasible molecular diagnostic strategy for rare genetic disorders within resource-constrained environments

Timely and accurate diagnosis of rare genetic disorders is critical, as it enables improved patient management and prognosis. In a resource-constrained environment such as the South African State healthcare system, the challenge is to design appropriate and cost-effective assays that will enable accurate genetic diagnostic services in patients of African ancestry across a broad disease spectrum. Next-generation sequencing (NGS) has transformed testing approaches for many Mendelian disorders, but this technology is still relatively new in our setting and requires cost-effective ways to implement. As a proof of concept, we describe a feasible diagnostic strategy for genetic disorders frequently seen in our genetics clinics (RASopathies, Cornelia de Lange syndrome, Treacher Collins syndrome, and CHARGE syndrome). The custom-designed targeted NGS gene panel enabled concurrent variant screening for these disorders. Samples were batched during sequencing and analyzed selectively based on the clinical phenotype. The strategy employed in the current study was cost-effective, with sequencing and analysis done at USD849.68 per sample and achieving an overall detection rate of 54.5%. The strategy employed is cost-effective as it allows batching of samples from patients with different diseases in a single run, an approach that can be utilized with rare and less frequently ordered molecular diagnostic tests. The subsequent selective analysis pipeline allowed for timeous reporting back of patients results. This is feasible with a reasonable yield and can be employed for the molecular diagnosis of a wide range of rare monogenic disorders in a resource-constrained environment.

Open acid dissolution-Ammonia solution extraction-ICP OES rapid determination of 7 trace metal elements in soil.

To overcome the corrosion of hydrofluoric acid on the ICP OES injection system in the acid dissolution system, this paper makes some improvements based on the traditional open digestion. The improved method does not require the complete removal of hydrofluoric acid. After appropriate digestion of the sample with a mixed acid, the solution can be transferred to a colorimetric tube containing ammonium hydroxide solution to give the final volume for analysis. In this paper, two-point standard curves are plotted using soil standards and process blanks, which is not only convenient but also overcomes the interference of the matrix effect. Through continuous experiments, the preferred ratio of mixed acid is 3 mL nitric acid + 5 mL hydrofluoric acid, and the concentration of ammonia solution is 0.5%. The spectral lines of the measured elements V (292.4), Cr (283.5), Co (228.6), Ni (231.6), Cu (324.7), Zn (213.8) and Pb (220.3) were determined. The method quantification limits of the seven measured elements V, Cr, Co, Ni, Cu, Zn and Pb were 0.909, 4.32, 0.269, 0.261, 0.968, 3.69 and 2.64 μg g-1, respectively, and the precision was 3.5%, 5.2%, 4.8%, 2.4%, 6.1% and 4.5%, respectively. After processing six national standard materials according to the experimental method, the measured values of each measured element were basically in agreement with the certified values, indicating that this method is fully feasible for the measurement of V, Cr, Co, Ni, Cu, Zn and Pb in soil. This method greatly improves the efficiency of pretreatment and is particularly suitable for analysing large batches of samples.

Enhancing classification performance through multi-source online transfer learning algorithm with oversampling

Multi-source online transfer learning uses the tagged data from multiple source domains to enhance the classification performance of the target domain. For unbalanced data sets, a multi-source online transfer learning algorithm that can oversample in the feature spaces of the source domain and the target domain is proposed. The algorithm consists of two parts: oversampling multiple source domains and oversampling online target domains. In the oversampling phase of the source domain, oversampling is performed in the feature space of the support vector machine (SVM) to generate minority samples. New samples are obtained by amplifying the original Gram matrix through neighborhood information in the source domain feature space. In the oversampling phase of the online target domain, minority samples from the current batch search for k-nearest neighbors in the feature space from multiple batches that have already arrived, and use the generated new samples and the original samples in the current batch to train the target domain function together. The samples from the source domain and the target domain are mapped to the same feature space through the kernel function for oversampling, and the corresponding decision function is trained using the data from the source domain and the target domain with relatively balanced class distribution, so as to improve the overall performance of the algorithm. Comprehensive experiments were conducted on four real datasets, and compared to other baseline algorithms on the Office Home dataset, the accuracy improved by 0.0311 and the G-mean value improved by 0.0702.

Network pharmacology integrated with experimental validation revealed the mechanism of Fengshi Gutong Capsule in the treatment of osteoarthritis

Ethnopharmacological relevanceFengshi Gutong Capsule (FSGTC) is a commonly used Chinese medicine for the treatment of joint pain caused by osteoarthritis (OA). However, the mechanism of action of FSGTC for OA remains unclear. Aim of the studyThis study aimed to explore the alleviating effects and potential mechanisms of action of FSGTC for OA through data mining, network pharmacology, and in vitro experiments. Materials and methodsHigh-performance liquid chromatography (HPLC) was performed to establish the fingerprints of FSGTC and detect the components of FSGTC absorbed in the blood. The effects of FSGTC on inflammation, immunity, and liver and kidney functions in patients with OA were evaluated by mining clinical data. The potential targets and pathways of FSGTC were screened using network pharmacology. Subsequently, CCK-8 assay, flow cytometry, western blotting, RT-qPCR, ELISA, and immunofluorescence were performed in IL-1β-stimulated chondrocytes for further validation. ResultsEighty-seven common peaks and 10 components were identified using the HPLC fingerprints of 12 batches of samples, and the similarity was in the range of 0.973–0.998. Retrospective clinical analysis demonstrated a significant reduction in inflammatory response levels among patients with OA who received FSGTC treatment. Network pharmacology analysis revealed that FSGTC potentially targeted processes related to inflammation, oxidative stress, and apoptosis. The phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT), The nuclear factor-κB (NF-κB), and janus kinase 2 (JAK2)/signal transducer and activator of transcription 3 (STAT3) signaling pathways were predicted to be the main pathways involved in the therapeutic effects of FSGTC in OA. In vitro, FSGTC-containing serum aided the proliferation of chondrocytes stimulated by IL-1β, while concurrently mitigating apoptosis, suppressing the expression of inflammatory cytokines and oxidative molecules, and inhibiting the degradation of the chondrocyte extracellular matrix (ECM). ConclusionsFSGTC alleviates the inflammatory response in patients with OA. This therapeutic effect was attributed to its anti-inflammatory and antioxidant properties, and its ability to promote IL-1β-induced chondrocyte proliferation, inhibit apoptosis, and prevent the degradation of extracellular matrix. These favorable results were associated with the inhibition of the PI3K/AKT, NF-κB, and JAK2/STAT3 signaling pathways.

Privacy-Preserving Federated Recurrent Neural Networks

We present RHODE, a novel system that enables privacy-preserving training of and prediction on Recurrent Neural Networks (RNNs) in a cross-silo federated learning setting by relying on multiparty homomorphic encryption. RHODE preserves the confidentiality of the training data, the model, and the prediction data; and it mitigates federated learning attacks that target the gradients under a passive-adversary threat model. We propose a packing scheme, multi-dimensional packing, for a better utilization of Single Instruction, Multiple Data (SIMD) operations under encryption. With multi-dimensional packing, RHODE enables the efficient processing, in parallel, of a batch of samples. To avoid the exploding gradients problem, RHODE provides several clipping approximations for performing gradient clipping under encryption. We experimentally show that the model performance with RHODE remains similar to non-secure solutions both for homogeneous and heterogeneous data distributions among the data holders. Our experimental evaluation shows that RHODE scales linearly with the number of data holders and the number of timesteps, sub-linearly and sub-quadratically with the number of features and the number of hidden units of RNNs, respectively. To the best of our knowledge, RHODE is the first system that provides the building blocks for the training of RNNs and its variants, under encryption in a federated learning setting.

Ultrastructural studies on implants failure with immediate or late loading

Objectives. The purpose of the present study was to evaluate by scanning electron microscope (SEM) and energy dispersive spectrophotometry (EDS), the degree of bone mineralization of the tissues on the surface of failure dental implants with immediate or late loading. Materials and method. In the study, 8 dental explants from 8 clinically healthy, non-smoking patients were taken. All implants were inserted by the same dental surgeon, 4 of them were immediate loaded and 4 were late prosthetically loaded by the same dentist. The ablation of the implants was performed as atraumatically as possible and they were collected in sterile containers and sent to the BIOMAT Research Center, where they were subjected to SEM and EDS analyses. The ratios between the chemical elements calcium and nitrogen (Ca/N), phosphorus and nitrogen (P/N), respectively calcium and phosphorus (Ca/P) were calculated for each of the 8 samples and were statistically analyzed. Results. If we compare the degree of coverage of the implants with bone tissue in different phases of mineralization, we find that the bone structures occupy a larger surface area of the implants in the cases with immediate loading compared to the cases with late loading. Thus, out of 4 immediately loaded implants, 2 were completely covered, one showed very little exposed areas, and the fourth showed alternating covered and uncovered areas. Regarding the late loaded implants, three showed alternation between covered and uncovered areas and only one was completely covered with bone. Conclusions. The degree of coverage of the explants with bone tissue was better represented for the immediate loading cases. The degree of mineralization of the bone tissue covering the explants was higher for cases with immediate loading. To confirm the obtained results, it is necessary to expand the study on larger batches of samples.

Quality testing of mifepristone and misoprostol in 11 countries.

Previous studies have demonstrated quality concerns with misoprostol. Mifepristone, however, has not been extensively assessed for quality. Between 2020 and 2021, Concept Foundation and the International Planned Parenthood Federation conducted a study to determine the quality of these medical abortion drugs in low- and middle-income countries (LMIC). The collection of batch samples of misoprostol and mifepristone was carried out by trained sampling agents in selected LMIC. Single drug packs and combipacks were sampled. A World Health Organization prequalified laboratory conducted testing method verifications and subsequent sample analysis. Tests included identification, assay, related substances, and content uniformity for misoprostol, and identification, assay, related substances, and dissolution for mifepristone. Samples were collected from Burkina Faso, Cambodia, Democratic Republic of Congo, India, Kyrgyzstan, Moldova, Nepal, Nigeria, Pakistan, Uganda and Vietnam. Sixty-four pooled batch samples were tested, consisting of 31 combipacks, 26 misoprostol-only and seven mifepristone-only products. Overall, 54.7% of samples were non-compliant with one or more of the specifications, representing 51.6% of combipack products, 57.1% of misoprostol tablets analyzed and 23.7% of mifepristone tablets. One falsified misoprostol-only product was found. The present study confirms that a significant problem still exists in relation to the quality of medical abortion drugs in LMIC. For misoprostol, our findings suggest that historical concerns around primary packaging may have been largely resolved but that manufacturing processes for both finished product and active pharmaceutical ingredient need to be improved. The present study also provides evidence of mifepristone quality issues.

Isotopic and proteomic evidence for communal stability at Pre-Pottery Neolithic Jericho in the Southern Levant

As one of the key, long-term occupied sites in the Southern Levant, Jericho was one of the most important early Neolithic centres to witness social and economic changes associated with the domestication of plants and animals. This study applies strontium (87Sr/86Sr), oxygen (δ18O) and carbon (δ13C) isotope analyses to the enamel of 52 human teeth from Pre-Pottery Neolithic (PPN) layers of Jericho to directly study human diet and mobility and investigate the degree of consolidation and the flexibility of social organization of Jericho society in the PPN period. The results indicate only two non-local individuals out of the 44 sampled inhabitants identified by strontium isotope analysis and are consistent with the presence of a largely sedentary community at PPN Jericho with no evidence for large-scale migration. We also construct strontium spatial baselines (87Sr/86Sr map) with local 87Sr/86Sr signatures for the sites across the Southern Levant based on systematic compilation and analysis of available data. In addition, we apply proteomic analysis of sex-specific amelogenin peptides in tooth enamel for sex estimation of the sampled individuals (n = 44), the results of which showed a sex-biased ratio (more male than female detected in this sample pool) in Jericho society during the PPN period, which may be due to the limited sample size or selective ritual practices like particular burial zones used for specific groups. We also pretreated a batch of human bone samples recovered from PPNB Jericho for stable carbon and nitrogen isotope analyses for dietary investigations. However, the extracted collagen showed poor preservation and no valid δ13C or δ15N data were obtained.

Centrifugal Extraction-Assisted Fiber-Enhanced Raman Spectroscopy for Online Detection of Trace Furfural in Oil Power Equipment.

Oil-paper insulated equipment is integral in power conversion and supports low-loss electricity transport. As a characteristic byproduct of the oil-paper insulation system, the realization of efficient detection of furfural in oil is crucial to the safe operation of the power grid. We proposed a novel approach using dual-enhanced Raman spectroscopy for sensing trace liquid components. This method employs a centrifugal extractor to separate and enrich the targeted components, achieving selective enhancement. The optimal phase ratio was determined to be 30:1. A liquid-core fiber was used to optimize the laser transmission efficiency and Raman signal collection efficiency, resulting in a nonselective signal enhancement of 44.86. It also investigated the impact of intermolecular interactions on the shift of Raman spectra, identifying the reasons for the differences in Raman signals between pure furfural, furfural in oil, and furfural in water. A batch of samples with furfural dissolved in insulation oil was measured using this system and achieved a limit of detection of 0.091 mg/L. The stability of the dual-enhanced Raman platform was experimentally verified with a spectral intensity fluctuation of 0.68%. This method is fast, stable, adaptable, and suitable for the detection of a wide range of liquid ingredients.

Stereolithography for manufacturing of advanced porous solids

The aim of this paper is focused on benefits of stereolithography (SLA) technology for the fabrication of the lightweight lattice structures with potential for load-bearing function and high absorption of impact energy. SLA is an additive manufacturing technology employing the principle of liquid resins curing moderated by radiation of a wavelength from ultra-violet band where resulting material parameters are tunable by setting of the curing process. The batches of samples manufactured using three different resins were subjected to quasi-static uni-axial tensile and compression tests. Acquired data were processed to derive deformation behaviour expressed as stress-strain diagrams and fundamental material properties. Based on the knowledge obtained from the mechanical tests, the setup of the fabrication parameters, the most suitable resin for manufacturing of the lattice structures and the overall suitability of SLA technology for the fabrication of advanced porous materials, were determined.

ADFSNet: An Adaptive Domain Feature Separation Network for the Classification of Wheat Seed Using Hyperspectral Images.

Wheat seed classification is a critical task for ensuring crop quality and yield. However, the characteristics of wheat seeds can vary due to variations in climate, soil, and other environmental factors across different years. Consequently, the present classification model is no longer adequate for accurately classifying novel samples. To tackle this issue, this paper proposes an adaptive domain feature separation (ADFS) network that utilizes hyperspectral imaging techniques for cross-year classification of wheat seed varieties. The primary objective is to improve the generalization ability of the model at a minimum cost. ADFS leverages deep learning techniques to acquire domain-irrelevant features from hyperspectral data, thus effectively addressing the issue of domain shifts across datasets. The feature spaces are divided into three parts using different modules. One shared module aligns feature distributions between the source and target datasets from different years, thereby enhancing the model's generalization and robustness. Additionally, two private modules extract class-specific features and domain-specific features. The transfer mechanism does not learn domain-specific features to reduce negative transfer and improve classification accuracy. Extensive experiments conducted on a two-year dataset comprising four wheat seed varieties demonstrate the effectiveness of ADFS in wheat seed classification. Compared with three typical transfer learning networks, ADFS can achieve the best accuracy of wheat seed classification with small batch samples updated, thereby addressing new seasonal variability.

Data-driven safe control via finite-time Koopman identifier

In this paper, a novel data-driven invariant-based safe control scheme for control of a nonlinear vehicle is developed. First, a novel incremental finite-time Koopman representation-based identifier is introduced to learn approximated lifted-states linear system model of system from a limited budget of incrementally sampled data points which not only minimises the instantaneous Koopman representation's identification errors but also the identification errors for a batch of past samples collected in a history stack. A great advantage of the presented identifier is that the uncertainty of the identified system is also quantified and updated over time on a slower time scale. Using set-theoretic tools, then, the quantified uncertainty will be leveraged in conjunction with the learned model in order to impose robust contractivity on the safe set, which guarantees its safety and stability. The proposed approach significantly reduces conservativeness through quantifying and updating the uncertainty level of the learned model over time.

Qualitative identification and quantitative comparison of Physochlainae Radix from different regions based on chemometric methods.

Physochlainae Radix (PR) is an essential herbal medicine that has been generally applied for treating cough and asthma. In this study, a comprehensive strategy for quality evaluation of PR from different origins was established by integrating qualitative identification, quantitative analysis, and chemometric methods. A total of 58 chemical components were identified by ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UHPLC-Q-TOF-MS/MS), and a sensitive and rapid UHPLC-QqQ-MS/MS method was established for the simultaneous determination of 12 compounds. In addition, multivariate statistical analysis was applied for discriminant analysis to compare the differences among 30 batches of PR samples. The results showed that the 30 batches of PR collected from four provinces could be clustered into three categories, in which scoparone, protocatechuic acid, tropic acid, and scopolin were important components to distinguish the primary and non-primary producing areas, as well as superior and inferior products of PR. Chemometric results were consistent and validated each other, and systematically explained the intrinsic quality characteristics of PR. This study first demonstrated that LC-MS combined with multivariate statistical analysis, provided a comprehensive and effective means for quality evaluation of PR.

MSFC: a new feature construction method for accurate diagnosis of mass spectrometry data

Mass spectrometry technology can realize dynamic detection of many complex matrix samples in a simple, rapid, compassionate, precise, and high-throughput manner and has become an indispensable tool in accurate diagnosis. The mass spectrometry data analysis is mainly to analyze all metabolites in the organism quantitatively and to find the relative relationship between metabolites and physiological and pathological changes. A feature construction of mass spectrometry data (MSFS) method is proposed to construct the features of the original mass spectrometry data, so as to reduce the noise in the mass spectrometry data, reduce the redundancy of the original data and improve the information content of the data. Chi-square test is used to select the optimal non-redundant feature subset from high-dimensional features. And the optimal feature subset is visually analyzed and corresponds to the original mass spectrum interval. Training in 10 kinds of supervised learning models, and evaluating the classification effect of the models through various evaluation indexes. Taking two public mass spectrometry datasets as examples, the feasibility of the method proposed in this paper is verified. In the coronary heart disease dataset, during the identification process of mixed batch samples, the classification accuracy on the test set reached 1.000; During the recognition process, the classification accuracy on the test set advanced to 0.979. On the colorectal liver metastases data set, the classification accuracy on the test set reached 1.000. This paper attempts to use a new raw mass spectrometry data preprocessing method to realize the alignment operation of the raw mass spectrometry data, which significantly improves the classification accuracy and provides another new idea for mass spectrometry data analysis. Compared with MetaboAnalyst software and existing experimental results, the method proposed in this paper has obtained better classification results.