Results Of Linear Discriminant Analysis Research Articles

Primary Controlling Factors of Apatite Trace Element Composition and Implications for Exploration in Orogenic Gold Deposits

AbstractSignificant and readily accessible orogenic gold deposits have been previously recognized, exploited, and progressively depleted. Innovative approaches are required to discover new and deeply buried deposits. Recently, trace element variations in apatite have been used to distinguish fertile and barren environments as reliable mineral exploration tools. In this study, machine learning models using Random Forest and Deep Neutral Network are utilized to assess the fertility of quartz veins and altered zones in the orogenic gold systems. The two models have been trained using trace element data of apatite, and the performance of both models yield good classification accuracy (∼90% on average) with low false positive rates. Feature importance analysis (Gini decrease and hidden layer weights) suggest that Pb, As, U, Sr, Eu, Mn, and Fe are the important parameters. Arsenic, U, Eu, Mn, and Fe are redox‐sensitive elements, with their concentrations responding to changes in fluid redox conditions. Strontium primarily originates from the breakdown of plagioclase, which is more likely to occur under oxidizing fluid conditions. Therefore, we infer that the main controlling factor of the models is the redox conditions. The two distinct models consistently highlight the most significant contribution of Pb to this differentiation, even though Pb is not a redox‐sensitive element and can only substitute for Ca2+ in apatite as Pb2+. We infer that the high contribution of Pb may be attributed to the potential transportation of Au in the form of a Pb‐(Bi)‐Au melt, and the Pb content in apatite is influenced by the Pb content in the melt, fluid oxygen, and sulfur fugacity. We also propose a novel discriminant plot using Linear Discriminant Analysis (LDA) to assess the mineralization potential in quartz veins and alteration zones based on apatite trace element data. The machine learning and LDA results suggest that apatite trace elements could be used effectively in the future orogenic gold deposit exploration.

Altered Gut Microbiota in Dementia and Mild Cognitive Impairment in Community‐Dwelling Older Adults in Taiwan

AbstractBackgroundThe microbiota‐gut‐brain axis has been implicated in the pathogenesis of dementia. However, research about gut microbiota changes in cognitive impairment remains scarce, and results vary with methodological differences. This study examines biodiversity and the differential abundance of gut flora associated with cognitive impairment in a community‐based cohort of older adults.MethodThe study sample of 298 older adults was derived from a community‐based prospective cohort study, the Epidemiology of Mild Cognitive Impairment in Taiwan (EMCIT). Among them, 82 were ascertained as MCI or dementia. Fecal samples were analyzed by Illumina shotgun metagenomic sequencing. The Metagenomic Phylogenetic Analysis (MetaPhlAn2) pipeline was applied to generate a relative abundance of gut microbiota.ResultIndividuals with dementia or MCI were older (76.6 ± 7.2 years vs. 67.8 ± 6.6 years) and had less education (9.3 ± 4.5 years vs.12.0 ± 3.9 years). There is no significant difference in alpha‐diversity between MCI/dementia and cognitively normal adults after adjusting for age, gender, education, and BMI. However, the PERMANOVA test revealed significant differences in beta‐diversity. The results of linear discriminant analysis (LDA) effect size (LEfSe) showed a differential abundance of taxonomy between the two groups. Increased abundance of several species, including Bacteroides thetaiotaomicron and Ruminococcus torques, and reduced abundance of Prevotella, Eubacterium, and Bifidobacterium were associated with MCI/dementia (Figure 1).ConclusionThe gut microbiota of older adults with MCI/dementia significantly altered in overall diversity and compositions. The mechanism of how altered gut microbiota contributes to the development of dementia needs to be further investigated.

Intestinal Microbiota Community and Blood Fatty Acid Profiles of Albas Cashmere Goats Fed with Flaxseed Oil and Whole Flaxseed.

The present study investigated the effects of flaxseed oil or flaxseed grain on the intestinal microbiota and blood fatty acid profiles of Albas cashmere goats. Sixty kid goats were allocated to three treatments and fed for 90 days with a control treatment, comprising a basal diet (CON, total-mixed ration with flaxseed meal), or experimental treatments, comprising a basal diet with added flaxseed oil (LNO) and a basal diet with added heated flaxseed grain (HLS). On day 90, two goats were randomly selected from each pen (eight goats per treatment) for euthanizing; then, five of the eight goats were randomly selected, and we collected their intestinal (duodenum, jejunum, ileum, cecum, and colon) digesta for analysis of the bacteria community. The results indicated that Firmicutes are the most predominant phylum in different segments of the intestinal digesta. Compared with the CON group, the relative abundance of duodenal Firmicutes, jejunal Saccharibacteria, and Verrucomicrobia significantly decreased in the LNO and HLS groups (p < 0.05), but there was no significant difference between the LNO and HLS groups. Compared with the CON and HLS groups, the RA of duodenal and jejunal Proteobacteria remarkably increased in the LNO group (p < 0.05), and there was no significant difference between the CON and HLS groups. Compared with the CON and LNO groups, the RA of Actinobacteria remarkably increased in the small intestine of the HLS group (p < 0.05), but there was no significant difference between the CON and LNO groups in the duodenum and ileum. The results of linear discriminant analysis (LDA) effect size (LEfSe) analysis showed that the HLS group was characterized by a higher RA of the [Eubacterium]_coprostanoligenes_group in the small intestine and the LNO group was represented by a higher RA of the Lachnospiraceae_NK3A20_group in the cecum and colon, while the CON group was represented by a higher RA of Solobacterium, Pseudoramibacter, and Acetitomaculum in the small intestine and a higher RA of norank_o__Bradymonadales, the Prevotellaceae_Ga6A1_group, and Ruminiclostridium_1 in the cecum and colon. In conclusion, the addition of flaxseed oil and grain rich in c18:3n3 to the diet could reduce the microbial diversity of the small intestinal segments and the microbial diversity and richness of the cecum and colon in Albas cashmere goats. And flaxseed grain is more efficient than flaxseed oil in protecting intestinal health and promoting the absorption of c18:3n3.

Integrative species delimitation of the Selaginella sanguinolenta (Selaginellaceae) group with description of a new species, S. baodongii

AbstractSelaginella sanguinolenta group is widely distributed from the Himalayas to Siberia and the Russian Far East, including four to nine taxa. Despite progress in recent phylogenetic studies of the S. sanguinolenta group, the problem of species delimitation in this group remains. We revise the taxonomy of the S. sanguinolenta group based on an integrative approach considering morphological, molecular, and geographical evidence. The sampling for molecular analysis based on rbcL, psbA, and 26S nrDNA sequence data involved 71 individuals, covering the geographical range of this group. A total of 21 morphological traits from 103 specimens were explored using principal component analysis and linear discriminant analysis (LDA). The LDA results indicate that the combination of ventral leaf cilium length and ventral/dorsal leaf carinate characteristics enable a success rate of more than 90% in distinguishing between S. aitchisonii (western Himalayas to northern China), S. jacquemontii (western Himalayas to southwest China), and S. sanguinolenta (Russian Far East regions to northeast China). The phylogenetic analysis results of nuclear and plastid DNA markers further disclose the clear molecular differentiation. Selaginella nummularifolia, S. rossii, and S. sajanensis are well‐defined species based on morphological and molecular evidence with narrow distribution ranges respectively. Based on molecular and morphological evidence, in combination with distribution information, we finally recognize seven species in the S. sanguinolenta group, including a new species, S. baodongii sp. nov. from the bordering regions of Russia and China in northeast Asia.

Effects of the Transformation from Natural Alpine Grassland to Mixed Artificial Grassland on the Characteristics of Soil Microbial Community

This study aimed to analyze the effects of the transformation from natural alpine grassland (NAG) to mixed artificial grassland (MAG) on the characteristics of soil microbial community. We used Illumina Miseq high-throughput sequencing technology to investigate the soil microbial community of natural grassland and mixed artificial grassland. The results showed that plant diversity and the content of soil organic matter decreased significantly from NAG to MAG. In total, 29 and 11 phyla bacteria and fungi were detected, respectively. Compared with that in NAG, the Shannon indexes of the bacterial community increased significantly in MAG (from 9.51 to 9.89), whereas these differences were not significant between the NAG and MAG fungal community. The structure and composition of the soil microbial community showed significant differences between NAG and MAG. In addition, Mantel test results suggested that soil total organic matter, total nitrogen, and soil moisture were significantly correlated with variations in the bacterial community, and soil total organic matter and soil moisture were significantly correlated with variations in fungal community. The results of linear discriminant analysis (LEfSe) indicated that Atribacteria and Ascomycota microorganisms could be considered as the indicator groups for NAG, whereas Gemmata and Trichocomaceae microorganisms could be considered as the indicator groups for MAG. Tax4Fun2 results showed that the transition from NAG to MAG affected the utilization of different carbon sources by bacteria.

Study of altered gut microbial dynamics and their association with gestational diabetes mellitus

The human gastrointestinal system is home to billions of different microbial species that coexist peacefully with the host. Age, diet, and environment affect gut microbial structure and diversity. Bacteroidetes and Firmicutes are the main gut microbial groups in humans. Human physiology is greatly affected by gut microbial metabolism and host–microbe interactions. Gestational diabetes mellitus (GDM) women have a different gut microbial makeup. However, whether these microbial changes in the stomach may be detected before the diagnosis of GDM remains unknown. This study aims to study the dynamics of gut microbe in healthy and GDM women and analyze the potential biomarkers for early detection of GDM in pregnant women. In this study, gut microbial variations have been characterized and their relationship is evaluated with GDM development. A large multi-taxon microbial shift between GDM and normal cases was evident, indicating that few microbial disruptions from early to mid-pregnancy were related to the later development of GDM. Results of linear discriminant analysis (LDA) show the variable relative abundance of 13 taxonomic groups (at the family level) between the GDM and normal groups. Moreover, 8 taxonomic groups (at the family level) were found to be present in the GDM group only. The results contribute to the gut microbiota’s potential relevance as a biomarker for the early detection of GDM.

A Comparative Investigation of Automatic Speech Recognition Platforms for Aphasia Assessment Batteries.

The rehabilitation of aphasics is fundamentally based on the assessment of speech impairment. Developing methods for assessing speech impairment automatically is important due to the growing number of stroke cases each year. Traditionally, aphasia is assessed manually using one of the well-known assessment batteries, such as the Western Aphasia Battery (WAB), the Chinese Rehabilitation Research Center Aphasia Examination (CRRCAE), and the Boston Diagnostic Aphasia Examination (BDAE). In aphasia testing, a speech-language pathologist (SLP) administers multiple subtests to assess people with aphasia (PWA). The traditional assessment is a resource-intensive process that requires the presence of an SLP. Thus, automating the assessment of aphasia is essential. This paper evaluated and compared custom machine learning (ML) speech recognition algorithms against off-the-shelf platforms using healthy and aphasic speech datasets on the naming and repetition subtests of the aphasia battery. Convolutional neural networks (CNN) and linear discriminant analysis (LDA) are the customized ML algorithms, while Microsoft Azure and Google speech recognition are off-the-shelf platforms. The results of this study demonstrated that CNN-based speech recognition algorithms outperform LDA and off-the-shelf platforms. The ResNet-50 architecture of CNN yielded an accuracy of 99.64 ± 0.26% on the healthy dataset. Even though Microsoft Azure was not trained on the same healthy dataset, it still generated comparable results to the LDA and superior results to Google's speech recognition platform.

The Gut Microbiota in Mild Cognitive Impairment in Community‐Dwelling Older Adults in Taiwan

AbstractBackgroundRecent microbial studies of Alzheimer’s disease and mild cognitive impairment (MCI) have focused on the alterations of gut microbiota in cognitively impaired patients. However, related research remains limited and varies with analytical pipelines. This study aims to examine biodiversity and the differential abundance of gut flora in MCI.MethodThe study sample of 96 older adults was derived from a community‐based prospective cohort study, the Epidemiology of Mild Cognitive Impairment in Taiwan (EMCIT). Among them, 31 were ascertained as MCIs, matched with 65 cognitively normal individuals on age, gender and community. Fecal samples were analyzed by Illumina sequencing of the V3‐V4 regions of the 16S rRNA gene. The divisive amplicon denoising algorithm 2 (DADA2) pipeline was applied to the production of the amplicon sequence variant (ASV) table, and the SILVA database 138.1 was used to assign taxonomies.ResultThere is no significant difference in alpha‐ and beta‐ diversity between MCI and cognitively normal adults after adjusting for age, gender, community, education and hypertension. (Fig. 1). However, the results of linear discriminant analysis (LDA) effect size (LEfSe) showed a differential abundance of ASVs between two groups. Increased abundance of several genera within Firmicutes phylum and reduced abundance of Ruminococcus, Butyricimonas, Oxalobacter are associated with MCI. A ROC curve using these microbial genera of interest can distinguish MCI patients from controls with AUC = 84%. (Fig. 2)ConclusionCompared with cognitively normal older adults, the gut microbiota of older adults with MCI identified from the community was significantly altered, particularly manifesting in enriched genera from the Firmicutes phylum and decreased abundance of genera Ruminococcus, Butyricimonas, Oxalobacter.

Discrimination of three varieties of Codonopsis Radix based on fingerprint profiles of oligosaccharides by high performance liquid chromatography- evaporative light scattering detector combined with multivariate analysis

Codonopsis Radix (CR) is a plant that is important in the practice of traditional Chinese medicine (TCM). The Chinese Pharmacopoeia 2020 records dried roots prepared from three varieties of Campanulaceae plants under the designation CR (“Dang-shen” in Chinese), including Codonopsis pilosula (Franch.) Nannf (C. pilosula), Codonopsis pilosula Nannf.var.modesta (Nannf.) L81. T. Shen (C. pilosula var. modesta) and Codonopsis tangshen Oliv (C. tangshen). As major constituents of CR, oligosaccharides might contribute to its clinical efficacy except for other known active compounds, yet the differences in the oligosaccharide profiles of these three varieties of CR remain incompletely understood. In the present study, 135 samples from these different CR varieties were harvested, and oligosaccharide fingerprints for these samples were characterized via HPLC-ELSD, with 19 common peaks being matched. Oligosaccharides were further identified through the combination of electrospray ionization MS/MS (ESI-MS/MS) with nuclear magnetic resonance (NMR). Principal component analysis and linear discriminant analysis (LDA) approaches were then used to compare the oligosaccharide profiles of these three CR varieties. These analyses ultimately revealed that CR was compared with principal component analysis (PCA) and linear discriminant (LDA) methods. The analyses ultimately revealed that these CR samples contained high levels of inulin- and levan-type fructooligosaccharides (FOS), with variations in the relative levels of these FOS compounds among the three analyzed CR varieties. Through the combined analysis of oligosaccharide fingerprints and LDA results, it was possible to differentiate among these CR varieties, with an accurate classification rate of 96.3% and a cross-validation rate of 95.6%. Together, these results highlight a valuable approach to the classification and identification of different CR varieties.

Leaf Volatiles and Relevant Gene Expression as the Specific Characteristics in Citrus depressa Accession Discrimination

Citrus depressa Hayata is the native and widespread citrus species in Taiwan. The notable character is that C. depressa has a distinct aroma different from local citrus. The ex situ germplasm of scions from different collection regions has variant leaf shapes and different odor characteristics. Establishing volatile biomarkers for classifying the local C. depressa is beneficial to commercial development. Volatile organic compounds (VOCs) of fresh leaves from seven C. depressa accessions which were collected from different locations in Taiwan were extracted by headspace solid-phase microextraction and analyzed by GC-MS. The volatile compositions from each season showed the diversity, and linalool, of which the average relative content is 52.7%, was the most volatile component in any season. The other main VOCs of leaves of C. depressa were γ-terpinene, limonene, β-ocimene, and α-terpineol. The result of linear discriminant analysis by VOC markers shows that there are two main different types which are (1) accessions from the central and the east of Taiwan and (2) accessions which are closer to C. depressa in Okinawa, Japan. Five major VOC-related synthase genes were selected and the gene expression was used to classify the varieties. The clustering result is the same with VOC-based discrimination. Our results reveal leaf volatile profiling is capable of being the discrimination markers, and the possibility for constructing molecular markers is directly related to characteristics from secondary metabolites phenotyping.

Application of Supervised and Unsupervised Learning Approaches for Mapping Storage Conditions of Biopharmaceutical Product-A Case Study of Human Serum Albumin.

The stability of biopharmaceutical therapeutics over the storage period/shelf life has been a challenging concern for manufacturers. A noble strategy for mapping best and suitable storage conditions for recombinant human serum albumin (rHSA) in laboratory mixture was optimized using chromatographic data as per principal component analysis (PCA), and similarity was defined using hierarchical cluster analysis. In contrast, separability was defined using linear discriminant analysis (LDA) models. The quantitation was performed for rHSA peak (analyte of interest) and its degraded products, i.e., dimer, trimer, agglomerates and other degradation products. The chromatographic variables were calculated using validated stability-indicating assay method. The chromatographic data mapping was done for the above-mentioned peaks over three months at different temperatures, i.e., 20°C, 5-8°C and at room temperature (25°C). The PCA had figured out the ungrouped variable, whereas supervised mapping was done using LDA. As an outcome result of LDA, about 60% of data were correctly classified with the highest sensitivity for 25°C (Aq), 25°C and 5-8°C (Aq with 5% glucose as a stabilizer), whereas the highest specificity was observed for samples stored at 5-8°C (Aq with 5% glucose as a stabilizer).

Ozone reduces the fruit decay of postharvest winter jujube by altering the microbial community structure on fruit surface

Microbial community structure on fruit surface plays an important role in fruit decay during postharvest storage, although the underlying mechanism has not been fully elucidated. Winter jujube (Ziziphus jujuba Miller cv. Dongzao) is a unique fruit resource with high edible and commercial value in China, while postharvest decay has always been a severe problem leading to short shelf life and poor quality of fruit. Ozone treatment is regarded as one of the most effective means to control decay and extend shelf life because of its cost-effective and eco-friendly properties. In the present study, three concentrations of ozone (2.5, 5 and 10 μL L−1) were found to reduce significantly postharvest decay of winter jujube on days 10 and 15, which were produced from Huanghua City, Hebei, China. High-throughput sequencing revealed significant changes in the bacterial and fungal communities in response to the application of ozone treatment, while Didymella, Rhizopus, Alternaria, Phialemoniopsis and Mycosphaerella were found to be the most abundant in fungi, and Methylobacterium, Pseudomonas, Pantoea, Sphingomonas and Gluconobacter being the most abundant in bacteria. Results of linear discriminant analysis (LDA) effect size (LEfSe) indicated that ozone treatments considerably reduced the abundance of Rhizopus and Gluconobacter on the surface of winter jujube fruit. Furthermore, Pearson correlation analysis showed that Rhizopus was positively correlated with Gluconobacter (r = 0.97) while negatively correlated with Didymella (r = −0.96). By predicting the metabolic function, ozone may inhibit metabolic pathways including nucleoside and nucleotide biosynthesis, amino acid biosynthesis, fatty acid and lipid degradation, respiration, and electron transfer, thereby reducing the incidence of fruit decay and maintaining the firmness of winter jujube fruit.

Bacterial diversity in the intestinal mucosa of heart failure rats treated with Sini Decoction

BackgroundSini Decoction (SND), a classic Chinese medicine prescription, has been proved to have a good effect on heart failure (HF), whereas its underlying mechanism is still unclear. In order to explore the therapeutic mechanism of SND, we combined with 16S rRNA gene sequencing to analyze the composition of gut microflora in rats with HF.Material and methodsTwenty Sprague–Dawley (SD) rats were divided into four groups (n = 5): normal group, model group, SND treatment group (SNT group), and metoprolol (Met) treatment group (Meto group). All the rats except the normal group were intraperitoneally injected with doxorubicin (concentration 2 mg/mL, dose 0.15 mL/100 g) once a week to induce HF. After successfully modeling, SND and Met were gavaged to rats, respectively. After the treatment period, blood was collected for hematological analyses, myocardial tissue and colon tissues were collected for Hematoxylin–Eosin (H&E) staining, and mucosal scrapings were collected for Illumina Miseq high-throughput sequencing.ResultsEchocardiographic results suggested that both left ventricular ejection fraction (LVEF) and left ventricular fraction shortening (LVFS) in Model rats decreased compared with normal rats. The results of H&E staining showed that compared with the model group, the structures of myocardial tissue and colon tissue in the SNT group and Meto group showed a recovery trend. Alpha results showed that the model group had higher species diversity and richness compared with the normal group. After treatment, the richness and diversity of intestinal bacteria in the SNT group were significantly restored, and Met also showed the effect of adjusting bacterial diversity, but its effect on bacterial richness was not ideal. At the Family level, we found that the number of several bacteria associated with HF in the model group increased significantly. Excitingly, SND and Met had shown positive effects in restoring these HF-associated bacteria. Similarly, the results of Linear discriminant analysis (LDA) showed that both SND and Met could reduce the accumulation of bacteria in the model group caused by HF.ConclusionCollectively, SND can improve HF by regulating the intestinal flora. This will provide new ideas for the clinical treatment of patients with HF.

Using machine learning algorithms to multidimensional analysis of subjective thermal comfort in a library

The thermal comfort in public buildings is affected by multiple psychological and physical factors. A deep understanding of these factors is necessary for intelligent ventilation control and architectural design. In this study, it was quantified the physiological and psychological parameters of the occupants in a library situated in Changsha using a questionnaire. Then, with the use of Principal Component Analysis (PCA), the dimensionality of the database was reduced. We found that the Zero-mean and unit variance projection is better than the Zero-mean projection, and 43-dimensional data can replace more than 90% of the original 61-dimensional data. Machine learning algorithms were used to analyze the results after PCA, which showed that the effect of thermal comfort on emotions is greater than that of emotions on thermal comfort. In addition, the performance of six machine learning algorithms (Linear Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Gaussian Naive Bayes (NB), and Support Vector Machine (SVM)) were compared. It is found that the predicted results of LDA were more accurate, and other algorithms showed different performances in different cases. These findings can contribute to the study of the subjective and objective feelings of indoor thermal comfort in public buildings, thereby guiding architectural design, intelligent control of ventilation systems, and realizing human-building interaction interfaces.

Predicting bitcoin price movements using sentiment analysis: a machine learning approach

PurposeCryptocurrencies such as Bitcoin (BTC) attracted a lot of attention in recent months due to their unprecedented price fluctuations. This paper aims to propose a new method for predicting the direction of BTC price using linear discriminant analysis (LDA) together with sentiment analysis.Design/methodology/approachConcretely, the authors train an LDA-based classifier that uses the current BTC price information and BTC news announcements headlines to forecast the next-day direction of BTC prices. The authors compare the results with a Support Vector Machine (SVM) model and random guess approach. The use of BTC price information and news announcements related to crypto enables us to value the importance of these different sources and types of information.FindingsRelative to the LDA results, the SVM model was more accurate in predicting BTC next day’s price movement. All models yielded better forecasts of an increase in tomorrow’s BTC price compared to forecasting a decrease in the crypto price. The inclusion of news sentiment resulted in the highest forecast accuracy of 0.585 on the test data, which is superior to a random guess. The LDA (SVM) model with asset specific (news sentiment and asset specific) input features ranked first within their respective model classifiers, suggesting both BTC news sentiment and asset specific are prized factors in predicting tomorrow’s price direction.Originality/valueTo the best of the authors’ knowledge, this is the first study to analyze the potential effect of crypto-related sentiment and BTC specific news on BTC’s price using LDA and sentiment analysis.

A Smartphone-Based Fluorescent Electronic Tongue for Tracing Dopaminergic Agents in Human Urine.

The importance of tracing dopaminergic agents in the progression assessment of Parkinson's disease has boosted the demand for fast, sensitive, and real-time multi-analyte detection. Herein, visual and fingerprint fluorimetric patterns have been created by an optical sensor array to simultaneously detect and discriminate among levodopa, carbidopa, benserazide, and entacapone, as important dopaminergic agents. A dual emissive nanoprobe consisting of red quantum dots and blue carbon dots with an overall pink emission has been fabricated to provide unique emission patterns in the presence of the target analytes. The sensor elements in the array come from it's differential response in the absence and presence of cetyltrimethylammonium bromide under alkaline conditions. A smartphone camera was used to take photos from the solutions in the wells. Distinct changes in the spectral profiles along with vivid and concentration-dependent color variations led to visual discrimination of dopaminergic agents in a broad concentration range. The results of linear discriminant analysis revealed great discrimination accuracies. Different concentrations of the target analytes were excellently recognized in human urine. The high sensitivity of the array, which is a bonus to rapid, on-site, and visual discrimination of dopaminergic agents, holds great promise for routine analysis of real-world clinical samples.

The chemical-microbial release and transformation of arsenic induced by citric acid in paddy soil

Citric acid (CA) is the major exudate of rice roots, yet the effects of CA on arsenic (As) transformation and microbial community in flooded paddy soil have not been clearly elucidated. In this study, microcosms were established by amending CA to As contaminated paddy soils, mimicking the rhizosphere environment. Results showed that 0.5% CA addition significantly enhanced As mobilization after one-hour incubation, increased total As in porewater by about 20-fold. CA addition induced arsenate release into porewater, and subsequently formed ternary complex of As, iron and organic matters, inhibiting further As transformation (including arsenate reduction and arsenite methylation). Furthermore, the results of linear discriminant analysis (LDA) effect size (LEfSe) and network analysis revealed that CA addition significantly enriched bacteria associated with arsenic and iron reductions, such as Clostridium (up to 35-fold) and Desulfitobacterium (up to 4-fold). Our results suggest that CA exhibits robust ability to mobilize As through both chemical and microbial processes, increasing the risk of As accumulation by rice. This study sheds light on our understanding of As mobilization and transformation in rhizosphere soil, potentially providing effective strategies to restrict As accumulation in food crops by screening or cultivating varieties with low CA exuding.

On-line identification of silkworm pupae gender by short-wavelength near infrared spectroscopy and pattern recognition technology

The gender identification of silkworm pupae is a critical step in the sericulture industry's breeding process. In this study, a low cost, short-wavelength (815-1075 nm) near infrared (NIR) spectrometer combined with multivariate spectra evaluation methods was used to establish calibration models for the on-line identification of female and male pupae of eight silkworm varieties. The diffuse reflection short-wavelength spectra were recorded, and then principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares discriminant analysis (PLSDA) were tested for calibration model development. The PCA and LDA results showed, that spectral differences between the female and male silkworm pupae existed, however, the two evaluation techniques could not separate the female and male silkworm pupae with the required accuracy. The PLSDA calibration models, on the other hand, could separate the pupae according to their gender with the necessary prediction accuracy of &gt;98%. Thus, it has been proved, that a low-cost, short-wavelength range NIR spectrometer in combination with a PLSDA calibration routine can be successfully applied for the reliable on-line identification of female and male silkworm pupae.

Discrimination of goat, buffalo, and yak milk from different livestock, regions, and lactation using microelement contents

This study aims at investigating whether the livestock, lactation stage, or geographical origin of specialty milk in China could be classified by the elemental contents. Samples of goat, buffalo, and yak milk were collected from main production provinces in China and the contents of nine elements in milk from different livestock, regions, and lactation stages were determined. Statistical analysis indicated that the contents of Mn, Cu, As, Se, Cs, and Mo in mature milk were significantly different between the three livestock (p<0.05). The trends of Mn and Cs contents of the goat milk and buffalo milk increased first and then decreased gradually during lactation. The contents of Se, Cs, Ba, and Mo in mature milk of three livestock were significantly different between the three regions (p<0.05). The result of linear discriminant analysis (LDA) and partial least squares discrimination analysis (PLS-DA) showed that the total correct classification rates in different livestock for mature milk reached 86.7 and 90%. For goat milk, the total correct classification rates of different lactations and regions reached 88.9 and 77.8% for LDA (100 and 87.5% for PLS-DA), and followed by yak milk of different regions which achieved 88.9% for LDA (88.9% for PLS-DA). In summary, the microelements in milk showed a significant correlation with livestock, region, and lactation of goat, buffalo, and yak milk. The elemental fingerprints combined with the LDA could be potential for classifying the livestock, lactation stage, and geographical origin of milk. PRACTICAL APPLICATION: According to study the contents of miroelements in milk from different livestock, regions, and lactation stages, provide evidence and support for classifying the livestock, lactation stage, and geographical origin of milk.

Classification of aflatoxin B1 naturally contaminated peanut using visible and near-infrared hyperspectral imaging by integrating spectral and texture features

The aim of this study is to carry out a non-destructive, hyperspectral imaging-based method to discriminate between normal and naturally aflatoxin B1 (AFB1) contaminated peanuts. Two varieties of peanut were imaged under a hyperspectral imaging system in the spectral range from 400 to 1000 nm. The reference AFB1 levels were measured by enzyme linked immunosorbentassay(ELISA) method, then the peanuts were divided into two categories according to the threshold of 20 ppb. The spectral, color and texture features were extracted and integrated to discriminate AFB1 contaminated from normal peanuts. Different pretreatment methods were conducted on the full spectra, the linear discriminant analysis (LDA) results indicated that firstly Savitzky-Golay smoothing (SGS) then standard normal transformation (SNV) could achieve the best discrimination with an accuracy of 90% and 92% for calibration and validation sets respectively. The LDA results of feature integration proved that the optimized spectral features combined with the texture features realized the best classification, with an accuracy of 91% and 94% for calibration and validation sets respectively. Finally, the performance of partial least squares discrimination analysis (PLS-DA) and support vector machine (SVM) was compared with the LDA, the SVM with RBF kernel revealed the best results with an accuracy of 93% and 94% for calibration and validation sets respectively. This study presented the potential of hyperspectral imaging in direct AFB1 contamination classification of peanut, and demonstrated that the combination of texture and spectra features could improve the modelling results.