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BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.

BackgroundResting state Functional Magnetic Resonance Imaging fMRI (rs-fMRI) has been used extensively to study brain function in psychiatric disorders, yielding insights into brain organization. However, the high dimensionality of the rs-fMRI data presents significant challenges for data analysis. Variational autoencoders (VAEs), a type of neural network, have been instrumental in extracting low-dimensional latent representations of resting state functional connectivity (rsFC) patterns, thereby addressing the complex nonlinear structure of rs-fMRI data. Despite these advances, interpreting these latent representations remains a challenge. This paper aims to address this gap by developing explainable VAE models and testing their utility using rs-fMRI data in autism spectrum disorder (ASD).MethodsOne-thousand one hundred and fifty participants (601 healthy controls [HC] and 549 patients with ASD) were included in the analysis. RsFC correlation matrices were extracted from the preprocessed rs-fMRI data using the Power atlas, which includes 264 regions of interest (ROIs). Then VAEs were trained in an unsupervised manner. Lastly, we introduce our latent contribution scores to explain the relationship between estimated representations and the original rs-fMRI brain measures.ResultsWe quantified the latent contribution scores for both the ASD and HC groups at the network level. We found that both ASD and HC groups share the top network connectivitives contributing to all estimated latent components. For example, latent 0 was driven by rsFC within ventral attention network (VAN) in both the ASD and HC. However, we found significant differences in the latent contribution scores between the ASD and HC groups within the VAN for latent 0 and the sensory/somatomotor network for latent 2.ConclusionThis study introduced latent contribution scores to interpret nonlinear patterns identified by VAEs. These scores effectively capture changes in each observed rsFC feature as the estimated latent representation changes, enabling an explainable deep learning model that better understands the underlying neural mechanisms of ASD.

In this article, we consider the role of ocean literacy in coastal communities as an approach that fosters relevant, community-based learning. We also propose solutions to challenges facing human-ocean relationships by cultivating common understanding and collective action. We present four examples of community-based ocean literacy in Mi'kma'ki/Atlantic Canada demonstrate how intersectional approaches to ocean literacy that are context-specific and responsive to community priorities can foster healthier human-ocean relationships: (1) Oceans Week Halifax's organization of community events to strengthen human-ocean relationships; (2) the Apoqnmatulti'k (Mi'kmaw: we help each other) project's partnerships between Mi'kmaq and local knowledge holders and academia; (3) Fishing For Success's (Newfoundland and Labrador) inclusive approaches to connecting marginalized communities to the ocean; and (4) the Co-Existing With North Atlantic Right Whale Project's protection of whales without jeopardizing coastal community livelihoods. Without denying there are barriers to bridging community learning with formal education, we focus on opportunities for collaborations and the importance of ocean optimism in guiding these urgently needed efforts to benefit future community-based, ocean-focused, and solutions-orientated initiatives.

This review delves into the molecular complexities underpinning the epithelial-to-mesenchymal transition (EMT) induced by cigarette smoke (CS) in human bronchial epithelial cells (HBECs). The complex interplay of pathways, including those related to WNT//β-catenin, TGF-β/SMAD, hypoxia, oxidative stress, PI3K/Akt, and NF-κB, plays a central role in mediating this transition. While these findings significantly broaden our understanding of CS-induced EMT, the research reviewed herein leans heavily on 2D cell cultures, highlighting a research gap. Furthermore, the review identifies a stark omission of genetic and epigenetic factors in recent studies. Despite these shortcomings, the findings furnish a consolidated foundation not only for the academic community but also for the broader scientific and industrial sectors, including large tobacco companies and manufacturers of related products, both highlighting areas of current understanding and identifying areas for deeper exploration. The synthesis herein aims to propel further research, hoping to unravel the complexities of the EMT in the context of CS exposure. This review not only expands our understanding of CS-induced EMT but also reveals critical limitations in current methodologies, primarily the reliance on 2D cell cultures, which may not adequately simulate more complex biological interactions. Additionally, it highlights a significant gap in the literature concerning the genetic and epigenetic factors involved in CS-induced EMT, suggesting an urgent need for comprehensive studies that incorporate these types of experiments.

Werner syndrome (WS) is an autosomal recessive disease caused by loss of function of WRN. WS is a segmental progeroid disease and shows early onset or increased frequency of many characteristics of normal aging. WRN possesses helicase, annealing, strand exchange, and exonuclease activities and acts on a variety of DNA substrates, even complex replication and recombination intermediates. Here, we review the genetics, biochemistry, and probably physiological functions of the WRN protein. Although its precise role is unclear, evidence suggests WRN plays a role in pathways that respond to replication stress and maintain genome stability particularly in telomeric regions.

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.

This review delves into the molecular complexities underpinning the epithelial-to-mesenchymal transition (EMT) induced by cigarette smoke (CS) in human bronchial epithelial cells (HBECs). The complex interplay of pathways, including those related to WNT//β-catenin, TGF-β/SMAD, hypoxia, oxidative stress, PI3K/Akt, and NF-κB, plays a central role in mediating this transition. While these findings significantly broaden our understanding of CS-induced EMT, the research reviewed herein leans heavily on 2D cell cultures, highlighting a research gap. Furthermore, the review identifies a stark omission of genetic and epigenetic factors in recent studies. Despite these shortcomings, the findings furnish a consolidated foundation not only for the academic community but also for the broader scientific and industrial sectors, including large tobacco companies and manufacturers of related products, both highlighting areas of current understanding and identifying areas for deeper exploration. The synthesis herein aims to propel further research, hoping to unravel the complexities of the EMT in the context of CS exposure. This review not only expands our understanding of CS-induced EMT but also reveals critical limitations in current methodologies, primarily the reliance on 2D cell cultures, which may not adequately simulate more complex biological interactions. Additionally, it highlights a significant gap in the literature concerning the genetic and epigenetic factors involved in CS-induced EMT, suggesting an urgent need for comprehensive studies that incorporate these types of experiments. [ABSTRACT FROM AUTHOR]

Werner syndrome (WS) is an autosomal recessive disease caused by loss of function of WRN. WS is a segmental progeroid disease and shows early onset or increased frequency of many characteristics of normal aging. WRN possesses helicase, annealing, strand exchange, and exonuclease activities and acts on a variety of DNA substrates, even complex replication and recombination intermediates. Here, we review the genetics, biochemistry, and probably physiological functions of the WRN protein. Although its precise role is unclear, evidence suggests WRN plays a role in pathways that respond to replication stress and maintain genome stability particularly in telomeric regions. [ABSTRACT FROM AUTHOR]

This study explores the experiences of homosexual teenagers who are members of the Israeli lesbian, gay, bisexual, transgender, and queer youth organization called Israel Gay Youth. Through an interview-based study with members and counselors, this study reveals how although various therapeutic practices take place during the weekly meetings, all members seem to place an emphasis on Israel Gay Youth being a bona fide youth movement. Israel Gay Youth's adoption of the Israeli youth movement model allows members to position the organization and subsequently themselves, in a place of greater equality to the rest of society, thus shirking off any array of morbid associations related both to support groups and homosexual tendencies.

Emulsion-templated foams have displayed promise as injectable bone grafts; however, the use of a surfactant as an emulsifier resulted in relatively small pores and impedes cell attachment. Hydroxyapatite nanoparticles were explored as an alternative stabilizer to address these limitations. To this end, hydroxyapatite nanoparticles were first modified with myristic acid to generate the appropriate balance of hydrophobicity to stabilize a water-in-oil emulsion of neopentyl glycol diacrylate and 1,4-butanedithiol. In situ surface modification of the resulting foam with hydroxyapatite was confirmed with elemental mapping and transmission electron microscopy. Nanoparticle-stabilized foams displayed improved human mesenchymal stem cell viability (91 ± 5%) over surfactant-stabilized foams (23 ± 11%). Although the pore size was appropriate for bone grafting applications (115 ± 71 μm), the foams lacked the interconnected architecture necessary for cell infiltration. We hypothesized that a co-stabilization approach with both surfactant and nanoparticles could be used to achieve interconnected pores while maintaining improved cell attachment and larger pore sizes. A range of hydroxyapatite nanoparticle and surfactant concentrations were investigated to determine the effects on microarchitecture and cell behavior. By balancing these interactions, a co-stabilized foam was identified that possessed large, interconnected pores (108 ± 67 μm) and improved cell viability and attachment. The co-stabilized foam was then evaluated as an injectable bone graft including network formation, microscale integration with bone, push out strength, and compressive properties. Overall, this work demonstrated that in situ surface modification with nHA improved cell attachment while retaining desirable bone grafting features and injectability. [ABSTRACT FROM AUTHOR]

Appalachian Kentucky (App KY) leads the nation in lung cancer incidence and mortality. Trace elements, such as As, have been associated with lung cancers in other regions of the country and we hypothesized that a population-based study would reveal higher trace element concentrations in App KY individuals with cancer compared to controls. Using toenail and drinking water trace element concentrations, this study investigated a possible association between lung cancer incidence and trace-element exposure in residents of this region. This population-based case-control study had 520 subjects, and 367 subjects provided toenail samples. Additionally, we explored the relationship between toenail and fingernail trace-element concentrations to determine if fingernails could be used as a surrogate for toenails when patients are unable to provide toenail samples. We found that, contrary to our initial hypothesis, trace element concentrations (Al, As, Cr, Mn, Co, Fe, Ni, Cu, Se, and Pb) were not higher in cancer cases than controls with the exception of Zn where concentrations were slightly higher in cases. In fact, univariate logistic regression models showed that individuals with lower concentrations of several elements (Al, Mn, Cr, and Se) were more likely to have lung cancer, although only Mn was significant in multivariate models which controlled for confounding factors. While drinking water concentrations of Al, Cr and Co were positively related to cancer incidence in univariate models, only Co remained significant in multivariate models. However, since the drinking water concentrations were extremely low and not reflected in the toenail concentrations, the significance of this finding is unclear. We also found that fingernail concentrations were not consistently predictive of toenail concentrations, indicating that fingernails should not be used as surrogates for toenails in future studies.