Decreased intrinsic neural timescales in obsessive compulsive disorder and two distinct subtypes revealed by heterogeneity through discriminative analysis.

BACKGROUND: OCD is featured as the destruction of information storage and processing. The cognition of neurobiological and clinical heterogeneity is in suspense and poorly studied. METHODS: Ninety-nine patients and matched HCs(n = 104) were recruited and underwent resting-state functional MRI scans. We applied INT to evaluate altered local neural dynamics representing the ability of information integration. Moreover, considering OCD was a highly heterogeneous disorder, we investigated putative OCD subtypes from INT using a novel semi-supervised machine learning, named HYDRA. RESULTS: Compared with HCs, patients with OCD showed decreased INTs in extensive brain regions, including bilateral cerebellum and precuneus, STG/MTG and PCC, hippocampus in DMN; right IFG/MFG/SFG, SPL and bilateral angular gyrus in CEN and insula, SMA in SN. Moreover, many other regions involved in visual processing also had disrupted dynamics of local neural organization, consisting of bilateral CUN, LING and fusiform gyrus and occipital lobe. HYDRA divided patients into two distinct neuroanatomical subtypes from INT. Subtype 1 showed decreased INTs in distributed networks, while subtype 2 presented increased in several common regions which were also found to be decreased in subtype 1, such as STG, IPL, postcentral gyrus and left insula, supramarginal gyrus. CONCLUSION: This study showed distinct abnormalities from the perspective of dynamics of local neural organization in OCD. Such alteration and dimensional approach may provide a new insight into the prior traditional cognition of this disorder and to some extent do favor of more precise diagnosis and treatment response in the future.

Corrigendum: Discriminative analysis of schizophrenia patients using graph convolutional networks: a combined multimodal MRI and connectomics analysis.

[This corrects the article DOI: 10.3389/fnins.2023.1140801.].

Discriminative analysis of schizophrenia patients using graph convolutional networks: A combined multimodal MRI and connectomics analysis.

Introduction: Recent studies in human brain connectomics with multimodal magnetic resonance imaging (MRI) data have widely reported abnormalities in brain structure, function and connectivity associated with schizophrenia (SZ). However, most previous discriminative studies of SZ patients were based on MRI features of brain regions, ignoring the complex relationships within brain networks. Methods: We applied a graph convolutional network (GCN) to discriminating SZ patients using the features of brain region and connectivity derived from a combined multimodal MRI and connectomics analysis. Structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 140 SZ patients and 205 normal controls. Eighteen types of brain graphs were constructed for each subject using 3 types of node features, 3 types of edge features, and 2 brain atlases. We investigated the performance of 18 brain graphs and used the TopK pooling layers to highlight salient brain regions (nodes in the graph). Results: The GCN model, which used functional connectivity as edge features and multimodal features (sMRI + fMRI) of brain regions as node features, obtained the highest average accuracy of 95.8%, and outperformed other existing classification studies in SZ patients. In the explainability analysis, we reported that the top 10 salient brain regions, predominantly distributed in the prefrontal and occipital cortices, were mainly involved in the systems of emotion and visual processing. Discussion: Our findings demonstrated that GCN with a combined multimodal MRI and connectomics analysis can effectively improve the classification of SZ at an individual level, indicating a promising direction for the diagnosis of SZ patients. The code is available at https://github.com/CXY-scut/GCN-SZ.git.

Deep Learning with Discriminative Margin Loss for Cross-Domain Consumer-to-Shop Clothes Retrieval.

Consumer-to-shop clothes retrieval refers to the problem of matching photos taken by customers with their counterparts in the shop. Due to some problems, such as a large number of clothing categories, different appearances of clothing items due to different camera angles and shooting conditions, different background environments, and different body postures, the retrieval accuracy of traditional consumer-to-shop models is always low. With advances in convolutional neural networks (CNNs), the accuracy of garment retrieval has been significantly improved. Most approaches addressing this problem use single CNNs in conjunction with a softmax loss function to extract discriminative features. In the fashion domain, negative pairs can have small or large visual differences that make it difficult to minimize intraclass variance and maximize interclass variance with softmax. Margin-based softmax losses such as Additive Margin-Softmax (aka CosFace) improve the discriminative power of the original softmax loss, but since they consider the same margin for the positive and negative pairs, they are not suitable for cross-domain fashion search. In this work, we introduce the cross-domain discriminative margin loss (DML) to deal with the large variability of negative pairs in fashion. DML learns two different margins for positive and negative pairs such that the negative margin is larger than the positive margin, which provides stronger intraclass reduction for negative pairs. The experiments conducted on publicly available fashion datasets DARN and two benchmarks of the DeepFashion dataset-(1) Consumer-to-Shop Clothes Retrieval and (2) InShop Clothes Retrieval-confirm that the proposed loss function not only outperforms the existing loss functions but also achieves the best performance.

Discriminative and quantitative analysis of norepinephrine and epinephrine by surface-enhanced Raman spectroscopy with gold nanoparticle suspensions.

Surface-enhanced Raman spectroscopy (SERS) is a powerful analytical technique capable of increasing the Raman signal of an analyte using specific nanostructures. The close contact between those nanostructures, usually a suspension of nanoparticles, and the molecule of interest produces an important exaltation of the intensity of the Raman signal. Even if the exaltation leads to an improvement of Raman spectroscopy sensitivity, the complexity of the SERS signal and the numbers of parameters to be controlled allow the use of SERS for detection rather than quantification. The aim of this study was to develop a robust discriminative and quantitative analysis in accordance with pharmaceutical standards. In this present work, we develop a discriminative and quantitative analysis based on the previous optimized parameters obtained by the design of experiments fixed for norepinephrine (NOR) and extended to epinephrine (EPI) which are two neurotransmitters with very similar structures. Studying the short evolution of the Raman signal intensity over time coupled with chemometric tools allowed the identification of outliers and their removal from the data set. The discriminant analysis showed an excellent separation of EPI and NOR. The comparative analysis of the data showed the superiority of the multivariate analysis after logarithmic transformation. The quantitative analysis allowed the development of robust quantification models from several gold nanoparticle batches with limits of quantification of 32 µg/mL for NOR and below 20 µg/mL for EPI even though no Raman signal is observable for such concentrations. This study improves SERS analysis over ultrasensitive detection for discrimination and quantification using a handheld Raman spectrometer.

Substitution-rearrangement-cyclization strategy to construct fluorescent probe for multicolor discriminative analysis biothiols in cells and zebrafish.

Discriminative detection of biothiols (Cysteine, homocysteine and glutathione) is of great significance to clarificate their complex physiological processes, the occurrence and development of related diseases. However, similar structure and reactivity among such species pose huge challenges in developing fluorescent probes to distinguish among of them. In this work, a dual-site probe CTT reacted with the analytes to regulate molecular conjugation through substitution-rearrangement-cyclization strategy, utilizing a multi-channel signal combination mode to realize the distinguishing detection of the three biothiols. Cell and zebrafish imaging experiments sufficiently demonstrated that CTT could semiquantify biothiols, which will provide valuable chemical tool for elucidating the complex biological functions of biothiols.

Bile acid-receptor TGR5 deficiency worsens liver injury in alcohol-fed mice by inducing intestinal microbiota dysbiosis.

BACKGROUND & AIMS: Bile-acid metabolism and the intestinal microbiota are impaired in alcohol-related liver disease. Activation of the bile-acid receptor TGR5 (or GPBAR1) controls both biliary homeostasis and inflammatory processes. We examined the role of TGR5 in alcohol-induced liver injury in mice. METHODS: We used TGR5-deficient (TGR5-KO) and wild-type (WT) female mice, fed alcohol or not, to study the involvement of liver macrophages, the intestinal microbiota (16S sequencing), and bile-acid profiles (high-performance liquid chromatography coupled to tandem mass spectrometry). Hepatic triglyceride accumulation and inflammatory processes were assessed in parallel. RESULTS: TGR5 deficiency worsened liver injury, as shown by greater steatosis and inflammation than in WT mice. Isolation of liver macrophages from WT and TGR5-KO alcohol-fed mice showed that TGR5 deficiency did not increase the pro-inflammatory phenotype of liver macrophages but increased their recruitment to the liver. TGR5 deficiency induced dysbiosis, independently of alcohol intake, and transplantation of the TGR5-KO intestinal microbiota to WT mice was sufficient to worsen alcohol-induced liver inflammation. Secondary bile-acid levels were markedly lower in alcohol-fed TGR5-KO than normally fed WT and TGR5-KO mice. Consistent with these results, predictive analysis showed the abundance of bacterial genes involved in bile-acid transformation to be lower in alcohol-fed TGR5-KO than WT mice. This altered bile-acid profile may explain, in particular, why bile-acid synthesis was not repressed and inflammatory processes were exacerbated. CONCLUSIONS: A lack of TGR5 was associated with worsening of alcohol-induced liver injury, a phenotype mainly related to intestinal microbiota dysbiosis and an altered bile-acid profile, following the consumption of alcohol. LAY SUMMARY: Excessive chronic alcohol intake can induce liver disease. Bile acids are molecules produced by the liver and can modulate disease severity. We addressed the specific role of TGR5, a bile-acid receptor. We found that TGR5 deficiency worsened alcohol-induced liver injury and induced both intestinal microbiota dysbiosis and bile-acid pool remodelling. Our data suggest that both the intestinal microbiota and TGR5 may be targeted in the context of human alcohol-induced liver injury.

Discriminative Analysis of Schizophrenia Patients Using Topological Properties of Structural and Functional Brain Networks: A Multimodal Magnetic Resonance Imaging Study.

Interest in the application of machine learning (ML) techniques to multimodal magnetic resonance imaging (MRI) data for the diagnosis of schizophrenia (SZ) at the individual level is growing. However, a few studies have applied the features of structural and functional brain networks derived from multimodal MRI data to the discriminative analysis of SZ patients at different clinical stages. In this study, 205 normal controls (NCs), 61 first-episode drug-naive SZ (FESZ) patients, and 79 chronic SZ (CSZ) patients were recruited. We acquired their structural MRI, diffusion tensor imaging, and resting-state functional MRI data and constructed brain networks for each participant, including the gray matter network (GMN), white matter network (WMN), and functional brain network (FBN). We then calculated 3 nodal properties for each brain network, including degree centrality, nodal efficiency, and betweenness centrality. Two classifications (SZ vs. NC and FESZ vs. CSZ) were performed using five ML algorithms. We found that the SVM classifier with the input features of the combination of nodal properties of both the GMN and FBN achieved the best performance to discriminate SZ patients from NCs [accuracy, 81.2%; area under the receiver operating characteristic curve (AUC), 85.2%; p < 0.05]. Moreover, the SVM classifier with the input features of the combination of the nodal properties of both the GMN and WMN achieved the best performance to discriminate FESZ from CSZ patients (accuracy, 86.2%; AUC, 92.3%; p < 0.05). Furthermore, the brain areas in the subcortical/cerebellum network and the frontoparietal network showed significant importance in both classifications. Together, our findings provide new insights to understand the neuropathology of SZ and further highlight the potential advantages of multimodal network properties for identifying SZ patients at different clinical stages.

Profiling of cured residue of solvent-based adhesives by attenuated total reflectance-Fourier transform infrared spectroscopy with chemometrics for forensic investigation.

Abuse of solvent-based adhesives jeopardizes world population, especially the young generation. Adhesive-related exhibits encountered in forensic cases might need to be determined if they could have come from a particular source or to establish link between cases or persons. This study was aimed to discriminate solvent-based adhesives, especially to aid forensic investigation of glue sniffing activities. In this study, thirteen brands with three samples each, totaling at 39 adhesive samples, were analyzed using attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy followed by chemometric methods. Experimental output showed that adhesive samples utilized in this study were less likely to change in their ATR-FTIR profiles over time, at least up to 2 months. No interference from plastic materials was noticed based on ATR-FTIR profile comparison. Physical examination could differentiate the samples into two groups, namely contact adhesives and cement adhesives. A principal component analysis-score linear discriminative analysis (PC-score LDA) model resulted in 100% and 98.6% correct classification in discriminating the two groups of adhesive samples, forming seven discriminative clusters. Test set with adhesive samples applied glass slide and plastic substrates also demonstrated a 100% correct classification into their respective groups. As a conclusion, the method allowed for discrimination of adhesive samples based on the spectral features, displaying relationship among samples. It is hoped that this comparative information is beneficial to trace the possible source of solvent-based adhesives, whenever they are recovered from a crime scene, for forensic investigation.

ADFAC: Automatic detection of facial articulatory features.

Using computer-vision and image processing techniques, we aim to identify specific visual cues as induced by facial movements made during monosyllabic speech production. The method is named ADFAC: Automatic Detection of Facial Articulatory Cues. Four facial points of interest were detected automatically to represent head, eyebrow and lip movements: nose tip (proxy for head movement), medial point of left eyebrow, and midpoints of the upper and lower lips. The detected points were then automatically tracked in the subsequent video frames. Critical features such as the distance, velocity, and acceleration describing local facial movements with respect to the resting face of each speaker were extracted from the positional profiles of each tracked point. In this work, a variant of random forest is proposed to determine which facial features are significant in classifying speech sound categories. The method takes in both video and audio as input and extracts features from any video with a plain or simple background. The method is implemented in MATLAB and scripts are made available on GitHub for easy access.•Using innovative computer-vision and image processing techniques to automatically detect and track keypoints on the face during speech production in videos, thus allowing more natural articulation than previous sensor-based approaches.•Measuring multi-dimensional and dynamic facial movements by extracting time-related, distance-related and kinematics-related features in speech production.•Adopting the novel random forest classification approach to determine and rank the significance of facial features toward accurate speech sound categorization.

Front-face fluorescence spectroscopy combined with chemometrics to detect high proteinaceous matter in milk and whey ultrafiltration permeate.

Proteinaceous matter can leak into the permeate stream during ultrafiltration (UF) of milk and whey and lead to financial losses. Although manufacturers can measure protein content in the finished permeate powders, there is currently no rapid monitoring tool during UF to identify protein leak. This study applied front-face fluorescence spectroscopy (FFFS) and chemometrics to identify the fluorophore of interest associated with the protein leak, develop predictive models to quantify true protein content, and classify the types of protein leak in permeate streams. Crude protein (CP), nonprotein nitrogen (NPN), true protein (TP), tryptone-equivalent peptide (TEP), α-lactalbumin (α-LA), and ß-lactoglobulin (ß-LG) contents were measured for 37 lots of whey permeate and 29 lots of milk permeate from commercial manufacturers. Whey permeate contained more TEP than did milk permeate, whereas milk permeate contained more α-LA and ß-LG than did whey permeate. The types of protein leak were thus identified for predictive model development. Based on excitation-emission matrix (EEM) of high- and low-TP permeates, tryptophan excitation spectra were collected for predictive model development, measuring TP content in permeate. With external validation, a useful model for quality control purposes was developed, with a root mean square error of prediction of 0.22% (dry basis) and a residual prediction deviation of 2.8. Moreover, classification models were developed using partial least square discriminant analysis. These classification methods can detect high TP level, high TEP level, and presence of α-LA or ß-LG with 83.3%, 84.8%, and 98.5% cross-validated accuracy, respectively. This method showed that FFFS and chemometrics can rapidly detect protein leaks and identify the types of protein leak in UF permeate. Implementation of this method in UF processing plants can reduce financial loss from protein leaks and maintain high-quality permeate production.

Qualitative and quantitative analysis of therapeutic solutions using Raman and infrared spectroscopy.

Anticancer drugs are prescribed and administrated to an increasing number of patients on a daily basis. As a consequence, a number of concerns have been raised about the patient health and safety in the case that the drugs administered are not at the required concentration or even worse not the correct ones. Quality control of therapeutic solutions has therefore been extensively implemented in hospital environments, in order to avoid any failure in the intense workflow faced by administering pharmacists. In the present study, infrared (IR) and Raman spectroscopy have been employed for the analysis of 3 commercially available therapeutic solutions TEVA®, MYLAN®, CERUBIDINE®, respectively containing doxorubicin, epirubicin and daunorubicin. They perfectly illustrate the analytical difficulties encountered, as these 3 chemotherapeutic drugs are isomers, hardly distinguishable with conventional approaches such as UV/VIS spectrometry. Any analytical failure to identify these molecules can lead to delays in patient treatment. While Partial Least Squares Regression analysis demonstrates that both Raman and IR can deliver satisfactory quantitative analysis in the clinical range, with respective Root Mean Square Error of Cross Validation (RMSECV) between 0.0127 - 0.0220 g·L-1 and 0.0573 - 0.0759 g·L-1, the identification rate between the 2 techniques differs substantially. Indeed, Principal Component Analysis - Factorial Discriminant Analysis (PCA-FDA) highlights that, depending on the data preprocessing applied to Raman spectra, the discrimination between the 3 drugs is decreased, with in some cases specificity and sensitivity below 50%. However, IR analysis displays encouraging results with an overall specificity and sensitivity between 99 and 100%, suggesting that reliable validation of the therapeutic solution for administration to patients can be achieved. IR and Raman spectroscopy could assist and support quality control of chemotherapeutic solutions prepared in personalised concentrations for each patient. The effective and reliable characterisation of therapeutic solutions could have a lot to offer to improve current practices in a near future.

Eyes Closed Elevates Brain Intrinsic Activity of Sensory Dominance Networks: A Classifier Discrimination Analysis.

Brain neocortex is usually dominated by visual input (with eyes open [EO]), whereas this visual predominance could be reduced by closing eyes. Cutting off visual input from the eyes (with eyes closed [EC]) would also benefit other sensory performance; however, the neural basis underlying the state-switching remains unclear. In this study, we investigated the brain intrinsic activity of either the EO or EC states by using the resting-state functional magnetic resonance imaging data from 22 healthy participants. The 10 resting-state networks (RSNs) of these participants were explored by the independent component analysis method. Within each RSN, various network parameters (i.e., the amplitude of low-frequency fluctuation, the voxel-wise weighted degree centrality, and the RSN-wise functional connectivity) were measured to depict the brain intrinsic activity properties underlying the EO and EC states. Taking these brain intrinsic activity properties as discriminative features in a linear classifier, we found that the EO and EC states could be effectively classified using the intrinsic properties of the sensory dominance networks and the salience network (SN). Further analysis showed that the brain intrinsic activity within the sensory dominance networks was constantly overwhelmed during the EC state relative to that in the EO state. The SN might play a key role as a switcher between state-switching. Therefore, this study indicated that the brain intrinsic activity in the sensory dominance networks would be enhanced with EC, which might improve other sensory-relative task performance.

[The optimization of sex identification based on the craniometric information in the population of the Caucasus for the purpose of medical criminalistics personality identification]. / Optimizatsiia kraniometricheskoi diagnostiki pola u naseleniia Kavkaza pri mediko-kriminalisticheskoi identifikatsii lichnosti.

The authors describe the newly developed diagnostic methods for sex identification based on the step-by-step variant of the Fisher unidimensional (UDA) and multidimentional (MDA) discriminative analysis. The study is based on the materials including the craniometric data (absolute measurements n=87, indicators n=63) characteristic of the main anthropological features of the Russian Caucasian population (691 men and 376 women). Sex diagnostics using the ODA technique performed separately for each trait yielded significant, probable, and uncertain estimates in about 78, 15 and 7% cases of the total, respectively. It was shown that the results obtained with the use of the MDA method allow for the practically significant conclusions in 35.50-67.33% of the cases, probable and uncertain conclusions in 16.47-30.10 and 15.87-34.59% of the cases respectively. The probable conclusions were found to be associated with the erroneous classification in 3.30 to 7.58% of the cases. The application of the ODA method in the combination with MDA guarantees the significant solution of the problem in 85.8 to 92.7% of the cases and practically excludes erroneous diagnostics. The results of verification of the proposed methods based on the results of the measurement of 13 skulls are in excellent agreement with the calculated data.

Searching for best lower dimensional visualization angles for high dimensional RNA-Seq data.

The accumulation of RNA sequencing (RNA-Seq) gene expression data in recent years has resulted in large and complex data sets of high dimensions. Exploratory analysis, including data mining and visualization, reveals hidden patterns and potential outliers in such data, but is often challenged by the high dimensional nature of the data. The scatterplot matrix is a commonly used tool for visualizing multivariate data, and allows us to view multiple bivariate relationships simultaneously. However, the scatterplot matrix becomes less effective for high dimensional data because the number of bivariate displays increases quadratically with data dimensionality. In this study, we introduce a selection criterion for each bivariate scatterplot and design/implement an algorithm that automatically scan and rank all possible scatterplots, with the goal of identifying the plots in which separation between two pre-defined groups is maximized. By applying our method to a multi-experiment Arabidopsis RNA-Seq data set, we were able to successfully pinpoint the visualization angles where genes from two biological pathways are the most separated, as well as identify potential outliers.

Medical image classification based on multi-scale non-negative sparse coding.

With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance. To solve this problem, we propose a multi-scale non-negative sparse coding based medical image classification algorithm. Firstly, Medical images are decomposed into multiple scale layers, thus diverse visual details can be extracted from different scale layers. Secondly, for each scale layer, the non-negative sparse coding model with fisher discriminative analysis is constructed to obtain the discriminative sparse representation of medical images. Then, the obtained multi-scale non-negative sparse coding features are combined to form a multi-scale feature histogram as the final representation for a medical image. Finally, SVM classifier is combined to conduct medical image classification. The experimental results demonstrate that our proposed algorithm can effectively utilize multi-scale and contextual spatial information of medical images, reduce the semantic gap in a large degree and improve medical image classification performance.

Discriminant Analysis of Platform Parameters of Rhesus at Different Stages of SIV/SAIDS / 广州中医药大学学报

Objective To predict the disease progression risks of healthy rhesus ( normal) and rhesus infected with simian immunodeficiency virus ( SIV) in the stages of long-term nonprogressor ( LTNP) , normal progressor ( NP) , rapid progressor ( RP) by discriminant analysis. Methods Five-year observation was carried out in SIV infected rhesus model without any intervention. The SIV infected rhesus model at the stages of LTNP, NP, RP were selected, 10 in each group, and T lymphocyte subsets and serum parameters for spleen-deficiency syndrome and kidney-deficiency syndrome in SIV infected rhesus were compared with 5 healthy monkey having the same survival time. The influence factors of different types of disease progression were screened from T cell subsets and Chinese medical syndrome indexes, and then the discriminant equation was established to predict the risks. Results White blood cell ( WBC) count and lymphocyte ( LYM) ratio were enrolled into the discriminant equation before infection, and T4 level and Log10RNA of set point were enrolled into the discriminant equation in the platform period. The test results for the uniform rate of the established discriminant function showed that the total coincidence rate of theoretic distinguish to the actual data was 57.1% , 91.2%respectively before infection and in the platform period. Conclusion The pre-infection WBC count and LYM ratio can be used as a reference for the evaluation of different types of disease progresson, and Log10RNA and T4 level at platform phase can be used as the predicting factors of different types of disease progression risk prediction.

Discriminative Analysis of TCM Syndrome of Chronic Fatigue Syndrome / 中国中医药信息杂志

Objective To analyse the TCM syndrome of chronic fatigue syndrome (CFS) patients by discriminative analysis. Methods 237 CFS patients were collected to made a stepwise discriminative analysis of symptom variables, basing on their TCM syndromes. Results 10 variables were obtained from symptoms by the way of stepwise discriminative analysis (P

Identification of Yangti (crispate rhubarb) by near-infrared diffuse reflectance spectroscopy / 中草药

Object To establish a new method for the identification of Yangti (Chinese herb from Rumex L., Polygonaceae) by near infrared diffuse reflectance spectrometry. Methods Cluster analysis and discriminative analysis were adopted for their identification. Results The method can identify crude Yangti to a certain degree with results coincident with that of the traditional phytotoxnomy. Conclusion This method can be used for the rapid and accurate differentiation of crude drug of Rumex L..