Distinct Networks Research Articles

A2GCN: Graph Convolutional Networks with Adaptive Frequency and Arbitrary Order

Graph Neural Networks (GNNs) gain remarkable success in various graph learning tasks under homophily graph assumption. This assumption is extremely fragile since real-world graphs with heterophily are ubiquitous. Under this circumstance, existing GNNs attempt to design or learn graph spectral filters for observed graphs. The representation ability of them, however, is limited due to: (1) Constant frequency response is incapable of simulating complicated filters that real-world applications require. (2) Fixed polynomial order fails to effectively uncover the node label patterns concealing different order neighborhoods. To this end, we propose a novel Graph Convolutional Networks with Adaptive Frequency and Arbitrary Order (A2GCN) to learn various graph spectral filters suitable for distinct networks. Specifically, a simple but elegant filter with adaptive frequency response is designed to span across multiple layers for capturing different frequency components hiding in varying orders, producing A2GCN filter bases. Afterward, the coefficients of the A2GCN basis for each node are learned to achieve A2GCN filters with arbitrary polynomial order. The resulting A2GCN filters possess flexible frequency response that can automatically adapt to the node label pattern, as such, it empowers A2GCN with stronger expressiveness and naturally alleviates the over-smoothing problem. Theoretical analysis is provided to show the superiority of the proposed A2GCN. Additionally, extensive experiments on both node-level and graph-level tasks validate that the proposed A2GCN accomplishes highly competitive performance and improves classification accuracy. Codes are available at https://github.com/AIG22/A2GCN.

Spectral reconstruction using neural networks in filter-array-based chip-size spectrometers

Abstract Spectral reconstruction in filter-based miniature spectrometers remains challenging due to the ill-posed nature of identifying stable solutions. Even minor deviations in sensor data can cause misleading reconstruction outcomes, particularly in the absence of proper regularization techniques. While previous research has attempted to mitigate this instability by incorporating neural networks into the reconstruction pipeline to denoise the data before reconstruction or correct it after reconstruction, these approaches have not fully resolved the underlying issue. This work functions as a proof-of-concept for data-driven reconstruction that relies exclusively on neural networks, thereby circumventing the need to address the ill-posed inverse problem. We curate a dataset holding transmission spectra from various colored foils, commonly used in theatrical, and train five distinct neural networks optimized for spectral reconstruction. Subsequently, we benchmark these networks against each other and compare their reconstruction capabilities with a linear reconstruction model to show the applicability of cognitive sensors to the problem of spectral reconstruction. In our testing, we discovered that (i) spectral reconstruction can be achieved using neural networks with an end-to-end approach, and (ii) while a classic linear model can perform equal to neural networks under optimal conditions, the latter can be considered more robust against data deviations.

The complex network patterns of human migration at different geographical scales: network science meets regression analysis

Migration’s influence in shaping population dynamics in times of impending climate and population crises exposes its crucial role in upholding societal cohesion. As migration impacts virtually all aspects of life, it continues to require attention across scientific disciplines. This study aims to bridge the gap between theoretical understanding and practical application by integrating network analysis and regression methodologies within Migration Studies. In the study we employ network analysis to elucidate migration patterns at various geographical scales-city, country, and global. Additionally, regression analysis is discussed on an exploratory level, where we focus on the underlying factors driving migration, and identifying the key independent variables to enhance predictive accuracy. The study exposes distinct migration network structure and its features, and the consequences these have on conventional regression analysis applications. We conclude on the importance of methodological coherence and disciplinary integration, and highlight the avenues for enhancing the predictive power of migration models.

Unveiling soil microbial community dynamics in desertification: A case study from the tianshan mountains, xinjiang

Microorganisms constitute the primary ecological group in desertified soil, driving soil biogeochemical cycles. Understanding their structure and diversity is crucial for the ecological restoration of desertification areas. This study focuses on the typical desert slopes in the eastern section of the northern foothills of the Tianshan Mountain in Xinjiang, China. It investigates changes in the main prokaryotic microbial community structure, including bacteria and archaea, and their relationship with environmental factors. Results show generally low soil water content, increasing with depth, and a weak alkaline nature with no significant vertical variations. While microbial communities do not distinctly respond to soil water content, there are noticeable variations in microbial diversity with clear stratification, negatively correlated with total organic carbon. Molecular ecological network analyses of five bare soil profiles in Xinjiang’s Hami Tianshan Desert reveal positive interactions among soil microorganisms in vertical layers. Surprisingly, the topsoil, despite having complex networks, shows low diversity and weak interconnectivity. Intriguingly, subsurface soils exhibit distinct molecular ecological networks similar to those found in ecological transition zones (These zones are characterized by rapid environmental shifts from one type to another), further highlighting significant ecological disparities between surface and subsurface soils. By analyzing the structure of soil microbial communities and their relationship with environmental factors on typical desert slopes in the Tianshan Mountains of Xinjiang, significant microbial ecological differences are found between surface and subsurface soils. Notably, in subsurface soils, microbial networks similar to those in ecological transition zones are discovered. These areas have complex microbial community structures and diverse ecological functions, providing new insights into the mechanisms of ecological restoration in desertified soils. Consequently, this study suggests that the vertical distribution patterns of microbial communities in bare desert soil areas differ from those in vegetated areas such as forests and grasslands. The interactions among species, the unique distribution of soil organic matter, and various physical factors are likely pivotal in the distribution patterns of microbial communities in bare desert soils. This research provides foundational theories and empirical support for the ecological restoration of bare desert soils.

Shared and distinctive brain networks underlying trait and state rumination

Although trait and state rumination play a central role in the exacerbation of negative affect, evidence suggests that they are weakly correlated and exert distinct influences on emotional reactivity to stressors. Whether trait and state rumination share a common or exhibit distinct neural substrate remains unclear. In this study, we utilized functional near-infrared spectroscopy (fNIRS) combined with connectome-based predictive modeling (CPM) to identify neural fingerprints associated with trait and state rumination. CPM identified distinctive functional connectivity (FC) profiles that contribute to the prediction of trait rumination, primarily involving FC within the default mode network (DMN) and the dorsal attention network (DAN) as well as FC between the DMN, control network (CN), DAN, and salience network (SN). Conversely, state rumination was predominantly associated with FC between the DMN and CN. Furthermore, the predictive features of trait rumination can be robustly generalized to predict state rumination, and vice versa. In conclusion, this study illuminates the importance of both DMN and non-DMN systems in the emergence and persistence of rumination. While trait rumination was associated with stronger and broader FC than state rumination, the generalizability of the predictive features underscores the presence of shared neural mechanisms between the two forms of rumination. These identified connectivity fingerprints may hold promise as targets for innovative therapeutic interventions aimed at mitigating rumination-related negative affect.

Intelligent computing for the electro-osmotically modulated peristaltic pumping of blood-based nanofluid

The study delves into a novel application of artificial neural networks within the biomedical realm, specifically focusing on the peristaltic pumping of ionized blood-infused nanofluid using the Casson model. The investigation takes into account the impacts of electrokinetic forces, chemical reaction, thermal radiation and variable thermal conductivity. The endorsed nanofluid model encompasses both thermophoretic and Brownian diffusions. The Nernst-Planck and Poisson equations are used to characterize electroosmotic process. The mathematical model is then numerically solved using NDSolve in Mathematica. The variation in relevant hemodynamic characteristics concerning key flow parameters is explored through numerous graphical representations. Two distinct artificial neural networks have been developed to estimate values for rates of mass and heat transfer. The Levenberg-Marquardt algorithm is used as the training algorithm in network models with multi-layer perceptron architecture. Graphical inspection of the results demonstrates a decrease in blood flow with an elevated Helmholtz-Smoluchowski velocity. A lower pressure gradient is recorded with a higher electroosmotic parameter. Furthermore, an augmentation in the thermal conductivity parameter leads to a reduction in heat transfer rate at the wall. The concentration profile demonstrates an increase with a rise in the chemical reaction parameter. The results obtained from the neural network models exhibit an ideal harmony with the target values. The developed neural network models demonstrated the ability to predict rates of heat and mass transfer values with average deviations of −0.01% and 0.004%, respectively.

From long-term to short-term: Distinct neural networks underlying semantic knowledge and its recruitment in working memory

Although numerous studies suggest that working memory (WM) and semantic long-term knowledge interact, the nature and underlying neural mechanisms of this intervention remain poorly understood. Using functional magnetic resonance imaging (fMRI), this study investigated the extent to which neural markers of semantic knowledge in long-term memory (LTM) are activated during the WM maintenance stage in 32 young adults. First, the multivariate neural patterns associated with four semantic categories were determined via an implicit semantic activation task. Next, the participants maintained words – the names of the four semantic categories implicitly activated in the first task – in a verbal WM task. Multi-voxel pattern analyses showed reliable neural decoding of the four semantic categories in the implicit semantic activation and the verbal WM tasks. Critically, however, no between-task classification of semantic categories was observed. Searchlight analyses showed that for the WM task, semantic category information could be decoded in anterior temporal areas associated with abstract semantic category knowledge. In the implicit semantic activation task, semantic category information was decoded in superior temporal, occipital and frontal cortices associated with domain-specific semantic feature representations. These results indicate that item-level semantic activation during verbal WM involves shallow rather than deep semantic information.

Lineage-dependence of the neuroblastoma surfaceome defines tumor cell state-dependent and independent immunotherapeutic targets.

Neuroblastoma is a heterogeneous disease with adrenergic (ADRN)- and therapy resistant mesenchymal (MES)-like cells driven by distinct transcription factor networks. Here, we investigate the expression of immunotherapeutic targets in each neuroblastoma subtype and propose pan-neuroblastoma and cell state specific targetable cell-surface proteins. We characterized cell lines, patient-derived xenografts, and patient samples as ADRN-dominant or MES- dominant to define subtype-specific and pan-neuroblastoma gene sets. Targets were validated with ChIP- sequencing, immunoblotting, and flow cytometry in neuroblastoma cell lines and isogenic ADRN-to-MES transition cell line models. Finally, we evaluated the activity of MES-specific agents in vivo and in vitro . Most immunotherapeutic targets being developed for neuroblastoma showed significantly higher expression in the ADRN subtype with limited expression in MES-like tumor cells. In contrast, CD276 (B7-H3) and L1CAM maintained expression across both ADRN and MES states. We identified several receptor tyrosine kinases (RTKs) enriched in MES-dominant samples and showed that AXL targeting with ADCT-601 was potently cytotoxic in MES-dominant cell lines and showed specific anti-tumor activity in a MES cell line-derived xenograft. Immunotherapeutic strategies for neuroblastoma must address the potential of epigenetic downregulation of antigen density as a mechanism for immune evasion. We identified several RTKs as candidate MES-specific immunotherapeutic target proteins for the elimination of therapy-resistant cells. We hypothesize that the phenomena of immune escape will be less likely when targeting pan-neuroblastoma cell surface proteins such as B7-H3 and L1CAM, and/or dual targeting strategies that consider both the ADRN- and MES-cell states. Cellular plasticity influences the abundance of immunotherapeutic targets.Subtype-specific targets may be susceptible to epigenetically-mediated downregulation.Immunotherapeutic targets in development, B7-H3 and L1CAM, show "pan-subtype" expression. Neuroblastoma is a lethal childhood malignancy that shows cellular plasticity in response to anti-cancer therapies. Several plasma membrane proteins are being developed as immunotherapeutic targets in this disease. Here we define which cell surface proteins are susceptible to epigenetically regulated downregulation during an adrenergic to mesenchymal cell state switch and propose immunotherapeutic strategies to anticipate and circumvent acquired immunotherapeutic resistance.

Strain engineering of electronic structure and thermoelectric properties of quasi-hexagonal fullerene monolayer

As a newly synthesized two-dimensional (2D) carbon material, monolayer quasi-hexagonal phase fullerene (qHP C60) has an excellent electronic structure and low thermal conductivity. qHP C60 attracted significant attention from scientists because it has potential applications in thermoelectric materials. Thermoelectric properties of 2D materials significantly depend on the transport of carriers (such as electrons and phonons), and strain engineering is an essential method for modulating the transport of electrons and phonons in 2D materials. However, the strain engineering method for the modulation of the thermoelectric properties of monolayer qHP C60 has not been reported yet. In the present paper, the first-principles combined with the non-equilibrium Green's function method are used to investigate the ballistic transport properties of electrons and phonons in monolayer qHP C60. The effects of temperature, chemical potential, and biaxial tensile strain on the thermoelectric transport parameters (including conductivity, Seebeck coefficient, power factor, and thermal conductivity) as well as the figure of merit (ZT) of monolayer qHP C60 are presented, compared, discussed, and analyzed. We found that monolayer qHP C60 exhibits anisotropic characteristics in electron and phonon transport properties, showcasing outstanding thermoelectric properties. The distinctive quasi-hexagonal phase fullerene network structure offers a novel platform for exploring innovative 2D thermoelectric materials in research. This study provides crucial theoretical insights to guide the designing and implementation of 2D thermoelectric materials based on fullerenes.

FTMMR: Fusion Transformer for Integrating Multiple Molecular Representations.

Molecular property prediction has gained substantial attention due to its potential for various bio-chemical applications. Numerous attempts have been made to enhance the performance by combining multiple molecular representations (1D, 2D, and 3D). However, most prior works only merged a limited number of representations or tried to embed multiple representations through a single network without using representation-specific networks. Furthermore, the heterogeneous characteristics of each representation made the fusion more challenging. Addressing these challenges, we introduce the Fusion Transformer for Multiple Molecular Representations (FTMMR) framework. Our strategy employs three distinct representation-specific networks and integrates information from each network using a fusion transformer architecture to generate fused representations. Additionally, we use self-supervised learning methods to align heterogeneous representations and to effectively utilize the limited chemical data available. In particular, we adopt a combinatorial loss function to leverage the contrastive loss for all three representations. We evaluate the performance of FTMMR using seven benchmark datasets, demonstrating that our framework outperforms existing fusion and self-supervised methods.

The neural network of sensory attenuation: A neuroimaging meta-analysis.

Sensory attenuation refers to the reduction in sensory intensity resulting from self-initiated actions compared to stimuli initiated externally. A classic example is scratching oneself without feeling itchy. This phenomenon extends across various sensory modalities, including visual, auditory, somatosensory, and nociceptive stimuli. The internal forward model proposes that during voluntary actions, an efferent copy of the action command is sent out to predict sensory feedback. This predicted sensory feedback is then compared with the actual sensory feedback, leading to the suppression or reduction of sensory stimuli originating from self-initiated actions. To further elucidate the neural mechanisms underlying sensory attenuation effect, we conducted an extensive meta-analysis of functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) studies. Utilizing activation likelihood estimation (ALE) analysis, our results revealed significant activations in a prominent cluster encompassing the right superior temporal gyrus (rSTG), right middle temporal gyrus (rMTG), and right insula when comparing external-generated with self-generated conditions. Additionally, significant activation was observed in the right anterior cerebellum when comparing self-generated to external-generated conditions. Further analysis using meta-analytic connectivity modeling (MACM) unveiled distinct brain networks co-activated with the rMTG and right cerebellum, respectively. Based on these findings, we propose that sensory attenuation arises from the suppression of reflexive inputs elicited by self-initiated actions through the internal forward modeling of a cerebellum-centered action prediction network, enabling the "sensory conflict detection" regions to effectively discriminate between inputs resulting from self-induced actions and those originating externally.

Characteristics of pain empathic networks in healthy and primary dysmenorrhea women: an fMRI study.

Pain empathy enables us to understand and share how others feel pain. Few studies have investigated pain empathy-related functional interactions at the whole-brain level across all networks. Additionally, women with primary dysmenorrhea (PDM) have abnormal pain empathy, and the association among the whole-brain functional network, pain, and pain empathy remain unclear. Using resting-state functional magnetic resonance imaging (fMRI) and machine learning analysis, we identified the brain functional network connectivity (FNC)-based features that are associated with pain empathy in two studies. Specifically, Study 1 examined 41 healthy controls (HCs), while Study 2 investigated 45 women with PDM. Additionally, in Study 3, a classification analysis was performed to examine the differences in FNC between HCs and women with PDM. Pain empathy was evaluated using a visual stimuli experiment, and trait and state of menstrual pain were recorded. In Study 1, the results showed that pain empathy in HCs relied on dynamic interactions across whole-brain networks and was not concentrated in a single or two brain networks, suggesting the dynamic cooperation of networks for pain empathy in HCs. In Study 2, PDM exhibited a distinctive network for pain empathy. The features associated with pain empathy were concentrated in the sensorimotor network (SMN). In Study 3, the SMN-related dynamic FNC could accurately distinguish women with PDM from HCs and exhibited a significant association with trait menstrual pain. This study may deepen our understanding of the neural mechanisms underpinning pain empathy and suggest that menstrual pain may affect pain empathy through maladaptive dynamic interaction between brain networks.

Ant social network structure is highly conserved across species.

The ecological success of social insects makes their colony organization fascinating to scientists studying collective systems. In recent years, the combination of automated behavioural tracking and social network analysis has deepened our understanding of many aspects of colony organization. However, because studies have typically worked with single species, we know little about interspecific variation in network structure. Here, we conduct a comparative network analysis across five ant species from five subfamilies, separated by more than 100 Myr of evolution. We find that social network structure is highly conserved across subfamilies. All species studied form modular networks, with two social communities, a similar distribution of individuals between the two communities, and equivalent mapping of task performance onto the communities. Against this backdrop of organizational similarity, queens of the different species occupied qualitatively distinct network positions. The deep conservation of the two community structure implies that the most fundamental behavioural division of labour in social insects is between workers that stay in the nest to rear brood, and those that leave the nest to forage. This division has parallels across the animal kingdom in systems of biparental care and probably represents the most readily evolvable form of behavioural division of labour.

Distinct visual processing networks for foveal and peripheral visual fields.

This study investigates the systematic differences between foveal and peripheral vision, two crucial components of visual processing essential for navigating our surroundings. By simultaneously recording from a large number of neurons in the visual attentional neural network, we revealed substantial variations in the proportion and functional characteristics of foveal and peripheral units across different brain regions. We uncovered differential modulation of functional connectivity by attention and visual selectivity, elucidated the intricate interplay between foveal and peripheral processing in spatial attention and saccade selection, and linked neural responses to search behavior. Overall, our study contributes to a deeper understanding of how the brain processes and integrates visual information for active visual behaviors.

Task functional networks predict individual differences in the speed of emotional facial discrimination

Every individual experiences negative emotions, such as fear and anger, significantly influencing how external information is perceived and processed. With the gradual rise in brain-behavior relationship studies, analyses investigating individual differences in negative emotion processing and a more objective measure such as the response time (RT) remain unexplored. This study aims to address this gap by establishing that the individual differences in the speed of negative facial emotion discrimination can be predicted from whole-brain functional connectivity when participants were performing a face discrimination task. Employing the connectome predictive modeling (CPM) framework, we demonstrated this in the young healthy adult group from the Human Connectome Project-Young Adults (HCP-YA) dataset and the healthy group of the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) dataset. We identified distinct network contributions in the adult and adolescent predictive models. The highest represented brain networks involved in the adult model predictions included representations from the motor, visual association, salience, and medial frontal networks. Conversely, the adolescent predictive models showed substantial contributions from the cerebellum-frontoparietal network interactions. Finally, we observed that despite the successful within-dataset prediction in healthy adults and adolescents, the predictive models failed in the cross-dataset generalization. In conclusion, our study shows that individual differences in the speed of emotional facial discrimination can be predicted in healthy adults and adolescent samples using their functional connectivity during negative facial emotion processing. Future research is needed in the derivation of more generalizable models.

Unraveling the Shared Genetic Elements Among COVID-19 and Epilepsy: A Computational and Bioinformatics Analysis

The outbreak of COVID-19 linked to the SARS-CoV-2 virus has raised worries about its potential to worsen pre-existing health issues such as autoimmune conditions. One common autoimmune condition seen across all individuals is epilepsy. Our detailed computational study examined the complex connection between COVID-19 and epilepsy, with a focus on their possible impact on neurodegenerative disorders. Leveraging transcriptome analysis, we aimed to interpret shared pathways and molecular biomarkers between COVID-19 and epilepsy using RNA-seq datasets. Cutting-edge bioinformatics tools facilitated Network, Enrichment, and route analysis, along with the identification of pivotal gene signatures, notably through comprehensive exploration of machine learning methods and protein-protein interactions. This work identified 1040 common differentially expressed genes (DEGs), forming the basis for unraveling shared pathways and potential drug targets. By employing statistical methods and diverse network analyses, we meticulously identified genetic elements shared between COVID-19 and epilepsy. The analysis reveals that 10 key genes, including CATIP, CDC25C, GPR132, NTS, PDE8B, PLK1, SLC12A9, SPC25, TUBAIA, and TYMS, are crucial in bridging transcriptomic alterations across various scenarios. A subset of these genes exhibited existence within distinct regulatory networks, signifying their significant role in the shared disease mechanisms of COVID-19 and epilepsy. Notably, CDC25C, PLK1, and TYMS emerged as prominent genes within the COVID-19 and epilepsy networks, strongly suggesting their vital roles in connecting regulatory mechanisms across these conditions. Further validation through molecular docking studies will confirm the significance of CDC25C, PLK1, and TYMS, potentially shedding light on new opportunities for targeted therapeutic interventions aimed at reducing the risk of seizures in individuals affected by COVID-19 and epilepsy.

Lung Cancer Detection using Ensemble Techniques

This paper implements a system for enhancing the detection of lung cancer through an ensemble approach, which amalgamates the predictive outputs generated by three distinct convolutional neural networks (CNNs): ResNet50, EfficientNet, and InceptionNet. Leveraging the diverse architectural features and learning capabilities of these CNNs, the ensemble method aims to synergistically fuse their individual predictions to achieve heightened accuracy and robustness in identifying potential lung cancer manifestations.

Overlap between individual differences in cognition and symptoms of schizophrenia

BackgroundNeurocognitive impairment is a core feature of schizophrenia spectrum disorders (SSDs), and the relationship between cognition and symptoms in SSDs has been widely researched. Negative symptoms are related to a wide range of cognitive impairments; however, the aspects of negative symptoms that underpin this relationship have yet to be specified. Study designWe used iterative Constrained Principal Component Analysis (iCPCA) to explore the relationship between 18 cognitive measures (including processing speed, attention, working, spatial and verbal memory and executive functions) and 46 symptoms in schizophrenia at the individual item level while minimizing the risk of Type I errors. ICPCA was conducted on a sample of SSD patients in the early stages of psychiatric treatment (n = 121) to determine the components of cognition overlapping with symptoms measured by the Scale for the Assessment of Negative Symptoms (SANS) and the Scale for the Assessment of Positive Symptoms (SAPS). ResultsWe found that a verbal memory component was associated with items from SANS and SAPS related to impoverished and disorganized emotional communication, language, and thought. In contrast, a working memory component was associated with SANS items related to motor system impoverishment. ConclusionsThe iCPCA allowed us to explore the associations between individual items, optimized to understand the overlap between symptoms and cognition. The specific symptoms linked to verbal and working memory impairments imply distinct brain networks, which further investigation may lead to our deeper understanding of the illness and the development of treatment methods.

Augmenting Telepostpartum Care With Vision-Based Detection of Breastfeeding-Related Conditions: Algorithm Development and Validation.

Breastfeeding benefits both the mother and infant and is a topic of attention in public health. After childbirth, untreated medical conditions or lack of support lead many mothers to discontinue breastfeeding. For instance, nipple damage and mastitis affect 80% and 20% of US mothers, respectively. Lactation consultants (LCs) help mothers with breastfeeding, providing in-person, remote, and hybrid lactation support. LCs guide, encourage, and find ways for mothers to have a better experience breastfeeding. Current telehealth services help mothers seek LCs for breastfeeding support, where images help them identify and address many issues. Due to the disproportional ratio of LCs and mothers in need, these professionals are often overloaded and burned out. This study aims to investigate the effectiveness of 5 distinct convolutional neural networks in detecting healthy lactating breasts and 6 breastfeeding-related issues by only using red, green, and blue images. Our goal was to assess the applicability of this algorithm as an auxiliary resource for LCs to identify painful breast conditions quickly, better manage their patients through triage, respond promptly to patient needs, and enhance the overall experience and care for breastfeeding mothers. We evaluated the potential for 5 classification models to detect breastfeeding-related conditions using 1078 breast and nipple images gathered from web-based and physical educational resources. We used the convolutional neural networks Resnet50, Visual Geometry Group model with 16 layers (VGG16), InceptionV3, EfficientNetV2, and DenseNet169 to classify the images across 7 classes: healthy, abscess, mastitis, nipple blebs, dermatosis, engorgement, and nipple damage by improper feeding or misuse of breast pumps. We also evaluated the models' ability to distinguish between healthy and unhealthy images. We present an analysis of the classification challenges, identifying image traits that may confound the detection model. The best model achieves an average area under the receiver operating characteristic curve of 0.93 for all conditions after data augmentation for multiclass classification. For binary classification, we achieved, with the best model, an average area under the curve of 0.96 for all conditions after data augmentation. Several factors contributed to the misclassification of images, including similar visual features in the conditions that precede other conditions (such as the mastitis spectrum disorder), partially covered breasts or nipples, and images depicting multiple conditions in the same breast. This vision-based automated detection technique offers an opportunity to enhance postpartum care for mothers and can potentially help alleviate the workload of LCs by expediting decision-making processes.

Three autism subtypes based on single-subject gray matter network revealed by semi-supervised machine learning.

Autism spectrum disorder (ASD) is a heterogeneous, early-onset neurodevelopmental condition characterized by persistent impairments in social interaction and communication. This study aims to delineate ASD subtypes based on individual gray matter brain networks and provide new insights from a graph theory perspective. In this study, we extracted and normalized single-subject gray matter networks and calculated each network's topological properties. The heterogeneity through discriminative analysis (HYDRA) method was utilized to subtype all patients based on network properties. Next, we explored the differences among ASD subtypes in terms of network properties and clinical measures. Our investigation identified three distinct ASD subtypes. In the case-control study, these subtypes exhibited significant differences, particularly in the precentral gyrus, lingual gyrus, and middle frontal gyrus. In the case analysis, significant differences in global and nodal properties were observed between any two subtypes. Clinically, subtype 1 showed lower VIQ and PIQ compared to subtype 3, but exhibited higher scores in ADOS-Communication and ADOS-Total compared to subtype 2. The results highlight the distinct brain network properties and behaviors among different subtypes of male patients with ASD, providing valuable insights into the neural mechanisms underlying ASD heterogeneity.