Mental disorders, especially schizophrenia, still pose a great challenge for diagnosis in early stages. Recently, computer-aided diagnosis techniques based on resting-state functional magnetic ...resonance imaging (Rs-fMRI) have been developed to tackle this challenge. In this work, we investigate different decision-level and feature-level fusion schemes for discriminating between schizophrenic and normal subjects. Four types of fMRI features are investigated, namely the regional homogeneity, voxel-mirrored homotopic connectivity, fractional amplitude of low-frequency fluctuations and amplitude of low-frequency fluctuations. Data denoising and preprocessing were first applied, followed by the feature extraction module. Four different feature selection algorithms were applied, and the best discriminative features were selected using the algorithm of feature selection via concave minimization (FSV). Support vector machine classifiers were trained and tested on the COBRE dataset formed of 70 schizophrenic subjects and 70 healthy subjects. The decision-level fusion method outperformed the single-feature-type approaches and achieved a 97.85% accuracy, a 98.33% sensitivity, a 96.83% specificity. Moreover, feature-fusion scheme resulted in a 98.57% accuracy, a 99.71% sensitivity, a 97.66% specificity, and an area under the ROC curve of 0.9984. In general, decision-level and feature-level fusion schemes boosted the performance of schizophrenia detectors based on fMRI features.
Selection and use of genetically diverse genotypes are key factors in any crop breeding program to develop cultivars with a broad genetic base. Molecular markers play a major role in selecting ...diverse genotypes. In the present study, a reference set representing a wide range of sorghum genetic diversity was screened with 40 EST-SSR markers to validate both the use of these markers for genetic structure analyses and the population structure of this set. Grouping of accessions is identical in distance-based and model-based clustering methods. Genotypes were grouped primarily based on race within the geographic origins. Accessions derived from the African continent contributed 88.6 % of alleles confirming the African origin of sorghum. In total, 360 alleles were detected in the reference set with an average of 9 alleles per marker. The average PIC value was 0.5230 with a range of 0.1379–0.9483. Sub-race, guinea margaritiferum (Gma) from West Africa formed a separate cluster in close proximity to wild accessions suggesting that the Gma group represents an independent domestication event. Guineas from India and Western Africa formed two distinct clusters. Accessions belongs to the kafir race formed the most homogeneous group as observed in earlier studies. This analysis suggests that the EST-SSR markers used in the present study have greater discriminating power than the genomic SSRs. Genetic variance within the subpopulations was very high (71.7 %) suggesting that the germplasm lines included in the set are more diverse. Thus, this reference set representing the global germplasm is an ideal material for the breeding community, serving as a community resource for trait-specific allele mining as well as genome-wide association mapping.
The layer-by-layer (LbL) technique has been widely used to produce nanofilms for biomedical applications. Naturally occurring polymers such as ECM macromolecules are attractive candidates for LbL ...film preparation. In this study, we assessed the build-up of type I collagen (Col1)/chondroitin sulfate (CS) or Col1/Heparin (HN) on polydimethylsiloxane (PDMS) substrates. The build-up was assessed by quartz crystal microbalance with dissipation (QCM-D) and atomic force microscopy (AFM). Integrin-mediated cell adhesion was assessed by studying the cytoskeletal organization of mammalian primary cells (chondrocytes) seeded on different end layers and number of layers. Data generated from the QCM-D observations showed a consistent build-up of films with more adsorption in the case of Col1/HN. Col1/CS films were stable in media, whereas Col1/HN films were not. AFM analysis showed that the layers were fibrillar in structure for both systems and between 20 and 30 nm thick. The films promoted cell adhesion when compared with tissue culture plastic in serum-free media with cycloheximide. Crosslinking of the films resulted in constrained cell spreading and a ruffled morphology. Finally, beta1 integrin blocking antibodies prevented cell spreading, suggesting that cell adhesion and spreading were mediated mainly by interaction with the collagen fibrils. The ability to construct stable ECM-based films on PDMS has particular relevance in mechanobiology, microfluidics, and other biomedical applications.
Staircase modulation is a switching technique ubiquitous in multilevel inverters utilizing large number of output voltage levels. With tens of levels, the output of a multilevel inverter employing ...staircase modulation approaches a sinusoid without requiring switching harmonics filters. Out of various multilevel inverter topologies, the modular multilevel converter (MMC) became prominent due to its modularity, scalability, and efficiency. However, balancing the submodule (SM) capacitor voltages poses a significant challenge in MMC operation. In this work, a staircase matrix modulation (SMM) strategy, which achieves sensor-less capacitor voltage balancing, is proposed for the switched - capacitor MMC (SCMMC), an MMC topology with a very small arm inductor. The proposed SMM utilizes a full rank, symmetric switching matrix, where specific switching patterns are assigned for each voltage level. The structure of the proposed matrix, its unique features, and the process of populating its entries for any converter voltage level are described. Theoretical analysis on the operation of the proposed SMM, simulations for an 11-level SCMMC, and experimental results on a single-phase, 2 kW, 425 V, 4-level SCMMC prototype are presented to illustrate the voltage balancing capability of the proposed SMM. The resulting switching frequency of the SCMMC under SMM is also analyzed.
Sorghum has shown the adaptability necessary to sustain its improvement during time and geographical extension despite a genetic foundation constricted by domestication bottlenecks. Initially ...domesticated in the northeastern part of sub-Saharan Africa several millenia ago, sorghum quickly spread throughout Africa, and to Asia. We performed phylogeographic analysis of sequence diversity for six candidate genes for grain quality (Shrunken2, Brittle2, Soluble starch synthaseI, Waxy, Amylose extender1, and Opaque2) in a representative sample of sorghum cultivars. Haplotypes along 1-kb segments appeared little affected by recombination. Sequence similarity enabled clustering of closely related alleles and discrimination of two or three distantly related groups depending on the gene. This scheme indicated that sorghum domestication involved structured founder populations, while confirming a specific status for the guinea margaritiferum subrace. Allele rooted genealogy revealed derivation relationships by mutation or, less frequently, by recombination. Comparison of germplasm compartments revealed contrasts between genes. Sh2, Bt2, and SssI displayed a loss of diversity outside the area of origin of sorghum, whereas O2 and, to some extent, Wx and Ae1 displayed novel variation, derived from postdomestication mutations. These are likely to have been conserved under the effect of human selection, thus releasing valuable neodiversity whose extent will influence germplasm management strategies.
•103 photoperiod-insensitive sorghum accessions were evaluated for stem composition and agronomic traits in South of France.•This genetic panel showed a great variation for key parameters of stem ...composition (lignin content, fibre digestibility…).•Our study indicated that variability for stem composition was highly structured according to the genetic origin.•Correlation analyses revealed that both biomass yield and quality can be improved simultaneously.•We identified genetic clusters and accessions of specific interest to use sorghum stems for energy and fodder purposes.
Over the last decade, the merits of sorghum as a dedicated feedstock have been highlighted. The relevance of sorghum biomass as an energy or biomaterial source relies on the possibility that its composition meets the requirement of the different production processes. In order to better assess the phenotypic variation existing in sorghum for stem composition, 103 accessions representing a broad genetic panel of insensitive and moderate photoperiod-sensitive landraces were evaluated during two consecutive years at Montpellier, South of France. Our data confirmed the existence of a great range of variation for several key parameters of stem composition, in particular for crude protein, lignin content and in vitro organic matter degradation and at a less extent for fibre digestibility. Sugar content in stems estimated by Brix was positively correlated with cycle duration and dry stem yield. Lignin content and fibre digestibility, two key traits linked to the potential utilization of sorghum biomass for fodder and 2nd-generation energy source, were strongly (positive and negative, respectively) correlated with plant height but not linked with dry stem yield. These results indicated that both biomass yield and quality can be improved simultaneously. Variability of stem composition was highly structured according to the genetic origins of the accessions. Durra and kafir groups emerged as good sources of high fibre digestibility. The same two groups and the photoperiod-insensitive caudatum from Africa comprised the best accessions for high sugar content in stems. Several Chinese caudatum and bicolor-caudatum accessions included in this panel stand out for high lignin content. This information will be mobilized to broaden the genetic diversity relevant for on-going breeding programs of sweet and biomass sorghum for temperate areas.
Even after achieving tremendous advances in diagnosis and treatment of rheumatoid arthritis (RA), many of the patients undergo delays in diagnosis and initiation of treatment, which leads to ...worsening of the condition and poor prognosis.
The objective of this study was to perform a literature review to quantify the lag times in diagnosis and treatment of RA and study the reported factors associated with it.
The authors searched literature published until September 2016 in electronic full-text and abstract databases and hand-searched the suitable articles.
The weighted average of median lag time from symptom onset to therapy was 11.79 months (12 studies, 5,512 patients, range 3.6-24.0 months). Lag1 was 3.14 months (onset of symptoms to first physician consultant; 12 studies, 6,055 patients, range 0-5.7 months); lag2 was 2.13 months (physician visit to RA specialist referral; 13 studies, 34,767 patients, range 0.5-6.6 months); lag3 was 2.91 months (consultation with rheumatologist to diagnosis; 3 studies, 563 patients, range 0-5 months), lag4 was 2.14 months (diagnosis to initiation of disease-modifying antirheumatic drug therapy; 5 studies, 30,685 patients, range 0-2.2 months). Numerous patient-and physician-related factors like gender, ethnicity, primary care physician knowledge of the condition, availability of diagnostics, and so on were responsible for the delays.
This review estimated the delay times and identified the main factors for delay in RA patients in diagnosis and initiation of treatment. A most plausible solution to this is coordinated effort by the rheumatology and primary care physicians.
Gallstone disease is a common condition affecting a substantial number of individuals globally. The risk factors for gallstones include obesity, rapid weight loss, diabetes, and genetic ...predisposition. Gallstones can lead to serious complications such as calculous cholecystitis, cholangitis, biliary pancreatitis, and an increased risk for gallbladder (GB) cancer. Abdominal ultrasound (US) is the primary diagnostic method due to its affordability and high sensitivity, while computed tomography (CT) and magnetic resonance cholangiopancreatography (MRCP) offer higher sensitivity and specificity. This review assesses the diagnostic accuracy of machine learning (ML) technologies in detecting gallstones. This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for reporting systematic reviews and meta-analyses. An electronic search was conducted in PubMed, Cochrane Library, Scopus, and Embase, covering literature up to April 2024, focusing on human studies, and including all relevant keywords. Various Boolean operators and Medical Subject Heading (MeSH) terms were used. Additionally, reference lists were manually screened. The review included all study designs and performance indicators but excluded studies not involving artificial intelligence (AI)/ML algorithms, non-imaging diagnostic modalities, microscopic images, other diseases, editorials, commentaries, reviews, and studies with incomplete data. Data extraction covered study characteristics, imaging modalities, ML architectures, training/testing/validation, performance metrics, reference standards, and reported advantages and drawbacks of the diagnostic models. The electronic search yielded 1,002 records, of which 34 underwent full-text screening, resulting in the inclusion of seven studies. An additional study identified through citation searching brought the total to eight articles. Most studies employed a retrospective cross-sectional design, except for one prospective study. Imaging modalities included ultrasonography (four studies), computed tomography (three studies), and magnetic resonance cholangiopancreatography (one study). Patient numbers ranged from 60 to 2,386, and image numbers ranged from 60 to 17,560 images included in the training, validation, and testing of the diagnostic models. All studies utilized neural networks, predominantly convolutional neural networks (CNNs). Expert radiologists served as the reference standard for image labelling, and model performances were compared against human doctors or other algorithms. Performance indicators such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were commonly used. In conclusion, while the reviewed machine learning models show promising performance in diagnosing gallstones, significant work remains to be done to ensure their reliability and generalizability across diverse clinical settings. The potential for these models to improve diagnostic accuracy and efficiency is evident, but the careful consideration of their limitations and rigorous validation are essential steps toward their successful integration into clinical practice.Gallstone disease is a common condition affecting a substantial number of individuals globally. The risk factors for gallstones include obesity, rapid weight loss, diabetes, and genetic predisposition. Gallstones can lead to serious complications such as calculous cholecystitis, cholangitis, biliary pancreatitis, and an increased risk for gallbladder (GB) cancer. Abdominal ultrasound (US) is the primary diagnostic method due to its affordability and high sensitivity, while computed tomography (CT) and magnetic resonance cholangiopancreatography (MRCP) offer higher sensitivity and specificity. This review assesses the diagnostic accuracy of machine learning (ML) technologies in detecting gallstones. This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for reporting systematic reviews and meta-analyses. An electronic search was conducted in PubMed, Cochrane Library, Scopus, and Embase, covering literature up to April 2024, focusing on human studies, and including all relevant keywords. Various Boolean operators and Medical Subject Heading (MeSH) terms were used. Additionally, reference lists were manually screened. The review included all study designs and performance indicators but excluded studies not involving artificial intelligence (AI)/ML algorithms, non-imaging diagnostic modalities, microscopic images, other diseases, editorials, commentaries, reviews, and studies with incomplete data. Data extraction covered study characteristics, imaging modalities, ML architectures, training/testing/validation, performance metrics, reference standards, and reported advantages and drawbacks of the diagnostic models. The electronic search yielded 1,002 records, of which 34 underwent full-text screening, resulting in the inclusion of seven studies. An additional study identified through citation searching brought the total to eight articles. Most studies employed a retrospective cross-sectional design, except for one prospective study. Imaging modalities included ultrasonography (four studies), computed tomography (three studies), and magnetic resonance cholangiopancreatography (one study). Patient numbers ranged from 60 to 2,386, and image numbers ranged from 60 to 17,560 images included in the training, validation, and testing of the diagnostic models. All studies utilized neural networks, predominantly convolutional neural networks (CNNs). Expert radiologists served as the reference standard for image labelling, and model performances were compared against human doctors or other algorithms. Performance indicators such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were commonly used. In conclusion, while the reviewed machine learning models show promising performance in diagnosing gallstones, significant work remains to be done to ensure their reliability and generalizability across diverse clinical settings. The potential for these models to improve diagnostic accuracy and efficiency is evident, but the careful consideration of their limitations and rigorous validation are essential steps toward their successful integration into clinical practice.
The complex, tetraploid genome structure of peanut (Arachis hypogaea) has obstructed advances in genetics and genomics in the species. The aim of this study is to understand the genome structure of ...Arachis by developing a high-density integrated consensus map. Three recombinant inbred line populations derived from crosses between the A genome diploid species, Arachis duranensis and Arachis stenosperma; the B genome diploid species, Arachis ipaënsis and Arachis magna; and between the AB genome tetraploids, A. hypogaea and an artificial amphidiploid (A. ipaënsis × A. duranensis)(4×), were used to construct genetic linkage maps: 10 linkage groups (LGs) of 544 cM with 597 loci for the A genome; 10 LGs of 461 cM with 798 loci for the B genome; and 20 LGs of 1442 cM with 1469 loci for the AB genome. The resultant maps plus 13 published maps were integrated into a consensus map covering 2651 cM with 3693 marker loci which was anchored to 20 consensus LGs corresponding to the A and B genomes. The comparative genomics with genome sequences of Cajanus cajan, Glycine max, Lotus japonicus, and Medicago truncatula revealed that the Arachis genome has segmented synteny relationship to the other legumes. The comparative maps in legumes, integrated tetraploid consensus maps, and genome-specific diploid maps will increase the genetic and genomic understanding of Arachis and should facilitate molecular breeding.
To ensure food security in Africa and Asia, developing sorghum varieties with grain quality that matches consumer demand is a major breeding objective that requires a better understanding of the ...genetic control of grain quality traits. The objective of this targeted association study was to assess whether the polymorphism detected in six genes involved in synthesis pathways of starch (
Sh2
,
Bt2
,
SssI
,
Ae1
, and
Wx
) or grain storage proteins (
O2
) could explain the phenotypic variability of six grain quality traits amylose content (AM), protein content (PR), lipid content (LI), hardness (HD), endosperm texture (ET), peak gelatinization temperature (PGT), two yield component traits thousand grain weight (TGW) and number of grains per panicle (NBG), and yield itself (YLD). We used a core collection of 195 accessions which had been previously phenotyped and for which polymorphic sites had been identified in sequenced segments of the six genes. The associations between gene polymorphism and phenotypic traits were analyzed with Tassel. The percentages of admixture of each accession, estimated using 60 RFLP probes, were used as cofactors in the analyses, decreasing the proportion of false-positive tests (70%) due to population structure. The significant associations observed matched generally well the role of the enzymes encoded by the genes known to determine starch amount or type.
Sh2
,
Bt2
,
Ae1
, and
Wx
were associated with TGW.
SssI
and
Ae1
were associated with PGT, a trait influenced by amylopectin amount.
Sh2
was associated with AM while
Wx
was not, possibly because of the absence of waxy accessions in our collection.
O2
and
Wx
were associated with HD and ET. No association was found between
O2
and PR. These results were consistent with QTL or association data in sorghum and in orthologous zones of maize. This study represents the first targeted association mapping study for grain quality in sorghum and paves the way for marker-aided selection.
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