ABSTRACT
Two-dimensional (2D) Fe3Sn2, which is a room-temperature ferromagnetic kagome metal, has potential applications in spintronic devices. However, the systematic synthesis and magnetic study of 2D Fe3Sn2 single crystals have rarely been reported. Here we have synthesized 2D hexagonal and triangular Fe3Sn2 nanosheets by controlling the amount of FeCl2 precursors in the chemical vapor deposition (CVD) method. It is found that the hexagonal Fe3Sn2 nanosheets exist with Fe vacancy defects and show no obvious coercivity. While the triangular Fe3Sn2 nanosheet has obvious hysteresis loops at room temperature, its coercivity first increases and then remains stable with an increase in temperature, which should result from the competition of the thermal activation mechanism and spin direction rotation mechanism. A first-principles calculation study shows that the Fe vacancy defects in Fe3Sn2 can increase the distances between Fe atoms and weaken the ferromagnetism of Fe3Sn2. The resulting 2D Fe3Sn2 nanosheets provide a new choice for spintronic devices.
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BACKGROUND: With the development of artificial intelligence, the application of machine learning to develop predictive models for sepsis-associated acute kidney injury has made potential breakthroughs in early identification, grading, diagnosis, and prognosis determination. METHODS: Here, we conducted a systematic search of the PubMed, Cochrane Library, Embase (Ovid), Web of Science, and Scopus databases on April 28, 2023, and screened relevant literature. Then, we comprehensively extracted relevant data related to machine learning algorithms, predictors, and predicted objectives. We subsequently performed a critical evaluation of research quality, data aggregation, and analyses. RESULTS: We screened 25 studies on predictive models for sepsis-associated acute kidney injury from a total of originally identified 2898 studies. The most commonly used machine learning algorithm is traditional logistic regression, followed by eXtreme gradient boosting. We categorized these predictive models into early identification models (60%), prognostic prediction models (32%), and subtype identification models (8%) according to their predictive purpose. The five most commonly used predictors were serum creatinine levels, lactate levels, age, blood urea nitrogen concentration, and diabetes mellitus. In addition, a single data source, insufficient assessment of clinical utility, lack of model bias assessment, and hyperparameter adjustment may be the main reasons for the low quality of the current research. CONCLUSIONS: However, studies on the nondeath prognostic outcomes, the long-term clinical outcomes, and the subtype identification models are insufficient. Additionally, the poor quality of the research and the insufficient practicality of the model are problems that need to be addressed urgently.
Subject(s)
Acute Kidney Injury , Machine Learning , Sepsis , Humans , Acute Kidney Injury/diagnosis , Acute Kidney Injury/etiology , Acute Kidney Injury/blood , Sepsis/complications , Sepsis/diagnosis , Prognosis , Creatinine/blood , Blood Urea NitrogenABSTRACT
2D materials with mixed crystal phase will lead to the nonuniformity of performance and go against the practical application. Therefore, it is of great significance to develop a valid method to synthesize 2D materials with typical stoichiometry. Here, 2D palladium sulfides with centimeter scale and uniform stoichiometric ratio are synthesized via controlling the sulfurization temperature of palladium thin films. The relationship between sulfurization temperature and products is investigated in depth. Besides, the high-quality 2D PdS2 films are synthesized via sulfurization at the temperature of 450-550 °C, which would be compatible with back-end-of-line processes in semiconductor industry with considering of process temperature. The PdS2 films show an n-type semiconducting behavior with high mobility of 10.4 cm2 V-1 s-1 . The PdS2 photodetector presents a broadband photoresponse from 450 to 1550 nm. These findings provide a reliable way to synthesizing high-quality and large-area 2D materials with uniform crystal phase. The result suggests that 2D PdS2 has significant potential in future nanoelectronics and optoelectronic applications.
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BACKGROUND: Our epidemiological study showed that the intestinal flora of Uygur T2DM patients differed from that of normal glucose-tolerant people. However, whether the Uygur T2DM fecal microbiota transplantation could reproduce the glucose metabolism disorder and the mechanism behind has not been reported. This study was designed to explore whether Uygur T2DM fecal microbiota transplantation could reproduce the glucose metabolism disorder and its mechanism. METHODS: The normal diet and high fat diet group consisted of C57BL/6 mice orally administered 0.2 mL sterile normal saline. For the MT (microbiota transplantation) intervention groups, C57BL/6 mice received oral 0.2 mL faecal microorganisms from Uygur T2DM. All mice were treated daily for 8 weeks and Blood glucose levels of mice were detected. Mice faecal DNA samples were sequenced and quantified using 16S rDNA gene sequencing. Then we detected the ability of the intestinal flora to metabolize bile acids (BAs) through co-culture of fecal bacteria and BAs. BA levels in plasma were determined by UPLC-MS. Further BA receptors and glucagon-like peptide-1 (GLP-1) expression levels were determined with RT-q PCR and western blotting. RESULTS: MT impaired insulin and oral glucose tolerance. Deoxycholic acid increased and tauro-ß-muricholic acid and the non-12-OH BA:12-OH BA ratio decreased in plasma. MT improved the ability of intestinal flora to produce deoxycholic acid. Besides, the vitamin D receptor in the liver and ileum and GLP-1 in the ileum decreased significantly. CONCLUSIONS: Uygur T2DM fecal microbiota transplantation disrupts glucose metabolism by changing the ability of intestinal flora to metabolize BAs and the BAs/GLP-1 pathway.
Subject(s)
Diabetes Mellitus, Type 2 , Gastrointestinal Microbiome , Animals , Bile Acids and Salts , Blood Glucose/metabolism , Chromatography, Liquid , DNA, Ribosomal , Deoxycholic Acid , Fecal Microbiota Transplantation , Glucagon-Like Peptide 1 , Humans , Insulin , Mice , Mice, Inbred C57BL , Receptors, Calcitriol , Saline Solution , Tandem Mass SpectrometryABSTRACT
Musculoskeletal and neurological disorders are the most common causes of walking problems among older people, and they often lead to diminished quality of life. Analyzing walking motion data manually requires trained professionals and the evaluations may not always be objective. To facilitate early diagnosis, recent deep learning-based methods have shown promising results for automated analysis, which can discover patterns that have not been found in traditional machine learning methods. We observe that existing work mostly applies deep learning on individual joint features such as the time series of joint positions. Due to the challenge of discovering inter-joint features such as the distance between feet (i.e. the stride width) from generally smaller-scale medical datasets, these methods usually perform sub-optimally. As a result, we propose a solution that explicitly takes both individual joint features and inter-joint features as input, relieving the system from the need of discovering more complicated features from small data. Due to the distinctive nature of the two types of features, we introduce a two-stream framework, with one stream learning from the time series of joint position and the other from the time series of relative joint displacement. We further develop a mid-layer fusion module to combine the discovered patterns in these two streams for diagnosis, which results in a complementary representation of the data for better prediction performance. We validate our system with a benchmark dataset of 3D skeleton motion that involves 45 patients with musculoskeletal and neurological disorders, and achieve a prediction accuracy of 95.56%, outperforming state-of-the-art methods.
Subject(s)
Nervous System Diseases , Quality of Life , Aged , Early Diagnosis , Humans , Machine Learning , Nervous System Diseases/diagnosisABSTRACT
CONTEXT: Faecal microbiota transplantation (FMT) from Kazak individuals with normal glucose tolerance (KNGT) significantly reduces plasma glycolipid levels in type 2 diabetes mellitus db/db mice. However, the mechanism behind this effect has not been reported. OBJECTIVE: To study the mechanism of improved glycolipid disorders in db/db mice by FMT from a KNGT donor. MATERIALS AND METHODS: The normal diet group consisted of db/m mice orally administered 0.2 mL phosphate buffer saline (PBS) (db/m + PBS). For the db/db + PBS (Vehicle) and db/db + KNGT (FMT intervention group) groups, db/db mice received oral 0.2 mL PBS or faecal microorganisms from a KNGT donor, respectively. All mice were treated daily for 0, 6 or 10 weeks. Faecal DNA samples were sequenced and quantified using 16S rRNA gene sequencing and RT-qPCR, respectively. Short-chain fatty acid (SCFA) levels in the mouse faeces were determined by gas chromatography. G protein-coupled receptor 43 (GPR43) and glucagon-like peptide-1 (GLP-1) expression levels were determined. RESULTS: FMT intervention significantly increased the relative abundance of Bacteroides uniformis (0.038%, p < 0.05). Clostridium levels (LogSQ) were increased (p < 0.01), while Mucispirillum schaedleri levels (LogSQ) were decreased (p < 0.01). Acetate and butyrate levels in the faeces were significantly increased (acetate; butyrate: 22.68 ± 1.82 mmol/L; 4.13 ± 1.09 mmol/L, p < 0.05). GPR43 mRNA expression and GLP-1 protein expression increased in colon tissue (p < 0.05). DISCUSSION AND CONCLUSIONS: Mechanistically, FMT-KNGT could improve glycolipid disorders by changing the bacterial composition responsible for producing SCFAs and activating the GPR43/GLP-1 pathway.
Subject(s)
Diabetes Mellitus, Experimental/therapy , Diabetes Mellitus, Type 2/therapy , Fatty Acids, Volatile/metabolism , Fecal Microbiota Transplantation/methods , Animals , Bacteroides/isolation & purification , Diabetes Mellitus, Experimental/physiopathology , Diabetes Mellitus, Type 2/physiopathology , Gastrointestinal Microbiome , Glucagon-Like Peptide 1/metabolism , Glucose Tolerance Test , Glycolipids/metabolism , Humans , Mice , RNA, Ribosomal, 16S , Receptors, G-Protein-Coupled/metabolismABSTRACT
Cell viability requires the maintenance of intracellular homeostasis through the unfolded protein response mediated by receptors localized on the endoplasmic reticulum (ER) membrane. The receptor IRE1 mediates not only various adaptive outputs but also programmed cell death (PCD) under varying stress levels. However, little is known about the mechanism by which the same receptors trigger different responses in plants. Arabidopsis Golgi anti-apoptotic protein 1 (GAAP1) and GAAP3 resist PCD upon ER stress and negatively modulate the adaptive response of the IRE1-bZIP60 pathway through IRE1 association. To elucidate the mechanism underlying the anti-PCD activity of GAAPs, we attempted to isolate interactors of GAAPs by yeast two-hybrid screening. Membrane-associated progesterone receptor 3 (MAPR3) was isolated as one of the factors interacting with GAAP. Mutations in GAAP1/GAAP3 and/or MAPR3 enhanced the sensitivity of seedlings to ER stress. Whole-transcriptome analysis combined with quantitative reverse transcription-PCR and cellular analysis showed that regulated IRE1-dependent decay (RIDD) and autophagy were impaired in mutants mapr3, gaap1mapr3, and gaap3mapr3. MAPR3, GAAP1, and GAAP3 interacted with IRE1B as determined by protein interaction assays. These data suggest that the interaction of GAAP1/GAAP3 with MAPR3 mitigates ER stress to some extent through regulating IRE10-mediated RIDD and autophagy.
Subject(s)
Arabidopsis Proteins/metabolism , Arabidopsis/metabolism , Endoplasmic Reticulum Stress , Signal Transduction , Arabidopsis Proteins/genetics , Autophagy/genetics , Endoplasmic Reticulum/metabolism , Gene Expression Regulation, Plant , Mutation/genetics , Protein Binding , RNA Stability/geneticsABSTRACT
OBJECTIVE: To find anti-phytopathogen compounds from endophytic fungi associated with the endangered species Heptacodium miconioides. METHODS: Fungi from H. miconioides with antifungal activities were isolated according to the plate growth inhibition method. The fungus with preferable antifungal activities was identified by morphological identification and 5. 8S rRNA sequence analysis. The bioactive metabolites were isolated and purified by chromatographic methods; the structures were determined by spectroscopic analysis. RESULTS: Alternaria solani QZH 10 showed better antifungal activity against Rhizoctorzia solani and Valsa mali with the inhibition rates of 89.1% and 67.9%, respectively. The ethyl acetate crude extract of QZH 10 had strong antifungal activity against Magnaporthe oryzae with the rate of 100. 0% under the concentration of 100 µg/mL. Two antifungal metabolites altersolanol A and 6-O-methylalaternin were isolated and determined from QZH 10. Altersolanol A possessed strong activity against M. oryzae with the inhibition rate of more than 85%, 6-O-methylalaternin had the mightily activity against V. mali with the inhibition rate of 100.0% under the concentration of 100 µg/mL. CONCLUSION: Altersolanol A and 6-O-methylalaternin are potential fungicides originated from microorganisms.
Subject(s)
Antifungal Agents/metabolism , Antifungal Agents/pharmacology , Caprifoliaceae/microbiology , Endophytes/isolation & purification , Endophytes/metabolism , Fungi/isolation & purification , Plant Diseases/microbiology , Rhizoctonia/drug effects , Antifungal Agents/chemistry , Endophytes/classification , Endophytes/genetics , Fungi/classification , Fungi/genetics , Fungi/metabolism , Molecular Sequence Data , Molecular Structure , Phylogeny , Rhizoctonia/physiology , Secondary MetabolismABSTRACT
Lactic acid bacteria (LAB) fermentation is frequently employed to improve the nutritional, functional, and sensory characteristics of foods. Our study explored the effects of co-fermentation with Lacticaseibacillus paracasei ZH8 and Lactococcus lactis subsp. lactis YM313 on the physicochemical properties, antioxidant activity, and metabolomic profiles of wolfberry-longan juice (WLJ). Fermentation was carried out at 35 °C for 15 h. The results suggest that WLJ is a favorable substrate for LAB growth, reaching a total viable count exceeding 8 log CFU/mL after fermentation. LAB fermentation increased acidity, reduced the sugar content, and significantly impacted the juice color. The total phenolic and flavonoid contents of the WLJ and the antioxidant capacities based on 2,2-diphenyl-1-picrylhydrazyl (DPPH), ABTS radical scavenging abilities and FRAP were significantly improved by LAB fermentation. Nontargeted metabolomics analysis suggested that the contents of small molecule substances in WLJ were considerably affected by LAB fermentation. A total of 374 differential metabolites were identified in the juice before and after fermentation, with 193 significantly upregulated metabolites and 181 siginificantly downregulated metabolites. The regulation of metabolites is important for improving the flavor and functions of juices, such as L-eucylproline, Isovitexin, Netivudine, 3-Phenyllactic acid, vanillin, and ethyl maltol, ect. This study provides a theoretical foundation for developing plant-based foods fermented with LAB.
Subject(s)
Lactobacillales , Lycium , Lactobacillales/metabolism , Antioxidants/analysis , Fermentation , Fruit and Vegetable Juices/analysisABSTRACT
The effect of adding native or germinated quinoa flour to wheat flour on gluten structure, starch digestibility and quality properties in pasta was evaluated. The free sulfhydryl contents in wheat/quinoa dough (1.41-3.16 µmol/g) were higher than the wheat dough content (0.764 µmol/g). The gluten network was gradually disrupted as additions of quinoa increased, resulting in improved starch digestibility. Further, germinated quinoa showed greater disruption and starch digestibility effects than native quinoa. Although the cooking quality of pasta decreased with additions of quinoa, cooking losses were below 7 %, which is acceptable. Adding excess germinated quinoa (30 %) had negative impacts on the textural properties. The sensory quality of pasta with 10-20 % native or germinated quinoa (24 h) flour (QF24) was acceptable. These findings suggested that a recipe of 20 % QF24 in wheat flour is recommended to develop wheat/germinated quinoa pasta with improved digestibility and acceptable changes in qualities.
Subject(s)
Chenopodium quinoa , Flour , Chenopodium quinoa/chemistry , Cooking/methods , Flour/analysis , Glutens/chemistry , Starch/chemistry , Triticum/chemistryABSTRACT
The ancient traditional Chinese drink Bian-Que Triple-Bean Soup made by fermentation (FTBS) of Lactococcus lactis subsp. lactis YM313 and Lacticaseibacillus casei YQ336 is a potential functional drink. The effect of fermentation on the flavor and biological activity of FTBS was evaluated by analyzing its chemical composition. Five volatile flavors were detected in modified FTBS. Fermentation decreased the proportion of nonanal (beany flavor substances) but significantly increased the total flavone contents, phenol contents and many bioactive small molecule substances in FTBS. The changes of these substances led to the significant improvement of FTBS sensory evaluation, antioxidant activity and prebiotic potential. This research provides a theoretical basis for the application of Lactic acid bacteria (LAB) in the fermentation of edible plant-based foods and transformation from traditional food to industrial production.
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Introduction: Tartary buckwheat and adzuki bean, which are classified as coarse grain, has attracted increasing attention as potential functional ingredient or food source because of their high levels of bioactive components and various health benefits. Methods: This work investigated the effect of two different extrusion modes including individual extrusion and mixing extrusion on the phytochemical compositions, physicochemical properties and in vitro starch digestibility of instant powder which consists mainly of Tartary buckwheat and adzuki bean flour. Results: Compared to mixing extrusion, instant powder obtained with individual extrusion retained higher levels of protein, resistant starch, polyphenols, flavonoids and lower gelatinization degree and estimated glycemic index. The α-glucosidase inhibitory activity (35.45%) of the instant powder obtained with individual extrusion was stronger than that obtained with mixing extrusion (26.58%). Lower levels of digestibility (39.65%) and slower digestion rate coefficient (0.25 min-1) were observed in the instant powder obtained with individual extrusion than in mixing extrusion (50.40%, 0.40 min-1) by logarithm-of-slope analysis. Moreover, two extrusion modes had no significant impact on the sensory quality of instant powder. Correlation analysis showed that the flavonoids were significantly correlated with physicochemical properties and starch digestibility of the instant powder. Discussion: These findings suggest that the instant powder obtained with individual extrusion could be used as an ideal functional food resource with anti-diabetic potential.
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Most of the current methods for the synthesis of two-dimensional materials (2DMs) require temperatures not compatible with traditional back-end-of-line (BEOL) processes in semiconductor industry (450 °C). Here, we report a general BiOCl-assisted chemical vapor deposition (CVD) approach for the low-temperature synthesis of 27 ultrathin 2DMs. In particular, by mixing BiOCl with selected metal powders to produce volatile intermediates, we show that ultrathin 2DMs can be produced at 280-500 °C, which are ~200-300 °C lower than the temperatures required for salt-assisted CVD processes. In-depth characterizations and theoretical calculations reveal the low-temperature processes promoting 2D growth and the oxygen-inhibited synthetic mechanism ensuring the formation of ultrathin nonlayered 2DMs. We demonstrate that the resulting 2DMs exhibit electrical, magnetic and optoelectronic properties comparable to those of 2DMs grown at much higher temperatures. The general low-temperature preparation of ultrathin 2DMs defines a rich material platform for exploring exotic physics and facile BEOL integration in semiconductor industry.
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This work investigated the phytochemical properties and health benefits of Tartary buckwheat flour obtained with different extrusion conditions including high, medium, and low temperature. Extrusion significantly decreased the fat content and changed the original color of Tartary buckwheat flour. The contents of protein, total flavonoids, and D-chiro-inositol were affected by the extrusion temperature and moisture. Extrusion significantly decreased the total flavonoids and flavonoid glycosides contents, while it significantly increased aglycones. Compared to native Tartary buckwheat flour and pregelatinization Tartary buckwheat flour obtained with traditional extrusion processing technology, the pregelatinization Tartary buckwheat flour obtained with improved extrusion processing technology contained higher aglycones and lower flavonoid glycosides, which had stronger antioxidant capacity, α-glucosidase inhibitory activity and relatively mild α-amylase inhibitory activity. Correlation analysis proved that the aglycone content was positively correlated with antioxidant and α-glucosidase inhibitory activities. These findings indicate that the pregelatinization Tartary buckwheat flour obtained with improved extrusion processing technology could be used as an ideal functional food resource with antioxidant and anti-diabetic potential.
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The unfolded protein response (UPR) is activated to sustain cell survival by reducing misfolded protein accumulation in the endoplasmic reticulum (ER). The UPR also promotes cell death when the ER stress is severe. However, the underlying molecular mechanisms of UPR activity regulation and cell death transition are less understood in plants. Arabidopsis GAAP1 and GAAP3 are involved in the regulation of UPR and cell death. Five GAAP gene members are found in Arabidopsis. Here, we analyzed the function of GAAP2 in addition to GAAP1 and GAAP3 in ER stress response using single, double, and triple mutants. Results showed that single or double or triple mutants reduced plant survival and enhanced cell death under ER stress. And the sensitivity increased with the number of mutation genes increase. Quantitative real-time polymerase chain reaction analysis showed that mutation in triple genes promoted UPR signaling when confronted with mild ER stress, advanced SA target genes upregulation when confronted with severe stress. Moreover, Quantitative detection by UPLC-ESI-MS/MS showed that ER stress upregulated salicylic acid (SA) content in plants. These data suggest that GAAP1 to GAAP3 played redundant roles in cell death resistance and fine tuning UPR activation. And the anti-cell death function of GAAPs might be achieved by impairing the up-regulation of the SA pathway under ER stress.
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Clubroot disease caused by Plasmodiophora brassicae can lead to serious yield losses in crucifers such as Brassica napus. In this study, 323 bacterial strains were isolated from the rhizosphere of severely diseased B. napus in Dangyang county, Hubei province, China. Antagonistic strains were first identified based on dual culture inhibition zones with Fusarium oxysporum and Magnaporthe oryzae. These were then further screened in germination inhibition and viability assays of resting spores of P. brassicae. Finally, eight of the antagonistic strains were found to significantly reduce the disease severity of clubroot by more than 40% under greenhouse conditions, and two strains, F85 and T113, were found to have efficacy of more than 80%. Root hair infection experiments showed that F85 and T113 can inhibit early infection of root hairs, reduce the differentiation of primary plasmodia of P. brassicae, and inhibit formation of secondary zoosporangia. Based on sequence analysis of 16S rDNA gene, gyrA gene and 22 housekeeping genes as well as carbon source utilization analysis, the F85 was identified as Bacillus velezensis and T113 as Bacillus amyloliquefaciens. Genome analysis, PCR and RT-PCR detection revealed that both F85 and T113 harbor various antibiotic biosynthesis gene clusters required to form peptides with antimicrobial activity. To our knowledge, this is the first report of B. velezensis as a biocontrol agent against clubroot disease.
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BACKGROUND: Microarray-based tumor classification is characterized by a very large number of features (genes) and small number of samples. In such cases, statistical techniques cannot determine which genes are correlated to each tumor type. A popular solution is the use of a subset of pre-specified genes. However, molecular variations are generally correlated to a large number of genes. A gene that is not correlated to some disease may, by combination with other genes, express itself. RESULTS: In this paper, we propose a new classiification strategy that can reduce the effect of over-fitting without the need to pre-select a small subset of genes. Our solution works by taking advantage of the information embedded in the testing samples. We note that a well-defined classification algorithm works best when the data is properly labeled. Hence, our classification algorithm will discriminate all samples best when the testing sample is assumed to belong to the correct class. We compare our solution with several well-known alternatives for tumor classification on a variety of publicly available data-sets. Our approach consistently leads to better classification results. CONCLUSION: Studies indicate that thousands of samples may be required to extract useful statistical information from microarray data. Herein, it is shown that this problem can be circumvented by using the information embedded in the testing samples.
Subject(s)
Gene Expression Profiling , Neoplasms/genetics , Oligonucleotide Array Sequence Analysis/methods , Algorithms , Gene Expression Regulation, Neoplastic , HumansABSTRACT
The success of linear discriminant analysis (LDA) is due in part to the simplicity of its formulation, which reduces to a simultaneous diagonalization of two symmetric matrices A and B;. However, a fundamental drawback of this approach is that it cannot be efficiently applied wherever the matrix A is singular or when some of the smallest variances in A are due to noise. In this paper, we present a factorization of A(-1) and a correlation-based criterion that can be readily employed to solve these problems. We provide detailed derivations for the linear and nonlinear classification problems. The usefulness of the proposed approach is demonstrated thoroughly using a large variety of databases.
Subject(s)
Discriminant Analysis , Neural Networks, Computer , Noise , Pattern Recognition, Automated/methods , Algorithms , Humans , Information Storage and Retrieval , Linear ModelsABSTRACT
The function of human Golgi antiapoptotic proteins (GAAPs) resembles that of BAX inhibitor-1, with apoptosis inhibition triggered by intrinsic and extrinsic stimuli. However, little is known about the function of GAAP-related proteins in plants. Here, we studied Arabidopsis GAAP1 and GAAP3 and found that they were localized on the cellular membrane, including the endoplasmic reticulum (ER) membrane. The function of GAAP1/GAAP3 in ER-stress response was tested, and results showed that single or double mutation in GAAP1 and GAAP3 reduced plant survival and enhanced cell death under ER stress. The expression of both genes was induced by various abiotic stress signals. Quantitative real-time polymerase chain reaction analysis showed that GAAP1/GAAP3 level affected the expression pattern of the unfolded-protein response (UPR) signaling pathway genes upon prolonged ER stress. The mutation in both GAAP1 and GAAP3 genes promoted and enhanced UPR signaling when confronted with mild ER stress. Moreover, GAAP1/GAAP3 inhibited cell death caused by ER stress and promoted plant-growth recovery by turning down inositol-requiring enzyme 1 (IRE1) signaling after ER stress had been relieved. Co-immunoprecipitation (Co-Ip) and BiFC assays showed that GAAP1/GAAP3 interacted with IRE1. These data suggested that GAAP1/GAAP3 played dual roles in the negative regulation of IRE1 activity and anti-programmed cell death.
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Over the years, many Discriminant Analysis (DA) algorithms have been proposed for the study of high-dimensional data in a large variety of problems. Each of these algorithms is tuned to a specific type of data distribution (that which best models the problem at hand). Unfortunately, in most problems the form of each class pdf is a priori unknown, and the selection of the DA algorithm that best fits our data is done over trial-and-error. Ideally, one would like to have a single formulation which can be used for most distribution types. This can be achieved by approximating the underlying distribution of each class with a mixture of Gaussians. In this approach, the major problem to be addressed is that of determining the optimal number of Gaussians per class, i.e., the number of subclasses. In this paper, two criteria able to find the most convenient division of each class into a set of subclasses are derived. Extensive experimental results are shown using five databases. Comparisons are given against Linear Discriminant Analysis (LDA), Direct LDA (DLDA), Heteroscedastic LDA (HLDA), Nonparametric DA (NDA), and Kernel-Based LDA (K-LDA). We show that our method is always the best or comparable to the best.