Proteomic analysis shows decreased type I fibers and ectopic fat accumulation in skeletal muscle from women with PCOS.

Background: Polycystic ovary syndrome's (PCOS) main feature is hyperandrogenism, which is linked to a higher risk of metabolic disorders. Gene expression analyses in adipose tissue and skeletal muscle reveal dysregulated metabolic pathways in women with PCOS, but these differences do not necessarily lead to changes in protein levels and biological function. Methods: To advance our understanding of the molecular alterations in PCOS, we performed global proteomic and phosphorylation site analysis using tandem mass spectrometry, and analyzed gene expression and methylation. Adipose tissue and skeletal muscle were collected at baseline from 10 women with and without PCOS, and in women with PCOS after 5 weeks of treatment with electrical stimulation. Results: Perilipin-1, a protein that typically coats the surface of lipid droplets in adipocytes, was increased whereas proteins involved in muscle contraction and type I muscle fiber function were downregulated in PCOS muscle. Proteins in the thick and thin filaments had many altered phosphorylation sites, indicating differences in protein activity and function. A mouse model was used to corroborate that androgen exposure leads to a shift in muscle fiber type in controls but not in skeletal muscle-specific androgen receptor knockout mice. The upregulated proteins in muscle post treatment were enriched in pathways involved in extracellular matrix organization and wound healing, which may reflect a protective adaptation to repeated contractions and tissue damage due to needling. A similar, albeit less pronounced, upregulation in extracellular matrix organization pathways was also seen in adipose tissue. Conclusions: Our results suggest that hyperandrogenic women with PCOS have higher levels of extra-myocellular lipids and fewer oxidative insulin-sensitive type I muscle fibers. These could be key factors leading to insulin resistance in PCOS muscle while electric stimulation-induced tissue remodeling may be protective. Funding: Swedish Research Council (2020-02485, 2022-00550, 2020-01463), Novo Nordisk Foundation (NNF22OC0072904), and IngaBritt and Arne Lundberg Foundation. Clinical trial number NTC01457209.

Fingerprint Systems: Sensors, Image Acquisition, Interoperability and Challenges.

The fingerprint is a widely adopted biometric trait in forensic and civil applications. Fingerprint biometric systems have been investigated using contact prints and latent and contactless images which range from low to high resolution. While the imaging techniques are advancing with sensor variations, the input fingerprint images also vary. A general fingerprint recognition pipeline consists of a sensor module to acquire images, followed by feature representation, matching and decision modules. In the sensor module, the image quality of the biometric traits significantly affects the biometric system's accuracy and performance. Imaging modality, such as contact and contactless, plays a key role in poor image quality, and therefore, paying attention to imaging modality is important to obtain better performance. Further, underlying physical principles and the working of the sensor can lead to their own forms of distortions during acquisition. There are certain challenges in each module of the fingerprint recognition pipeline, particularly sensors, image acquisition and feature representation. Present reviews in fingerprint systems only analyze the imaging techniques in fingerprint sensing that have existed for a decade. However, the latest emerging trends and recent advances in fingerprint sensing, image acquisition and their challenges have been left behind. Since the present reviews are either obsolete or restricted to a particular subset of the fingerprint systems, this work comprehensively analyzes the state of the art in the field of contact-based, contactless 2D and 3D fingerprint systems and their challenges in the aspects of sensors, image acquisition and interoperability. It outlines the open issues and challenges encountered in fingerprint systems, such as fingerprint performance, environmental factors, acceptability and interoperability, and alternate directions are proposed for a better fingerprint system.

Classification of HEp-2 Staining Pattern Images Using Adapted Multilayer Perceptron Neural Network-Based Intra-Class Variation of Cell Shape.

There exists a growing interest from the clinical practice research communities in the development of methods to automate HEp-2 stained cells classification procedure from histopathological images. Challenges faced by these methods include variations in cell densities and cell patterns, overfitting of features, large-scale data volume and stained cells. In this paper, a multi-class multilayer perceptron technique is adapted by adding a new hidden layer to calculate the variation in the mean, scale, kurtosis and skewness of higher order spectra features of the cell shape information. The adapted technique is then jointly trained and the probability of classification calculated using a Softmax activation function. This method is proposed to address overfitting, stained and large-scale data volume problems, and classify HEp-2 staining cells into six classes. An extensive experimental analysis is studied to verify the results of the proposed method. The technique has been trained and tested on the dataset from ICPR-2014 and ICPR-2016 competitions using the Task-1. The experimental results have shown that the proposed model achieved higher accuracy of 90.3% (with data augmentation) than of 87.5% (with no data augmentation). In addition, the proposed framework is compared with existing methods, as well as, the results of methods using in ICPR2014 and ICPR2016 competitions.The results demonstrate that our proposed method effectively outperforms recent methods.

Acetyl-CoA metabolism drives epigenome change and contributes to carcinogenesis risk in fatty liver disease.

BACKGROUND: The incidence of non-alcoholic fatty liver disease (NAFLD)-associated hepatocellular carcinoma (HCC) is increasing worldwide, but the steps in precancerous hepatocytes which lead to HCC driver mutations are not well understood. Here we provide evidence that metabolically driven histone hyperacetylation in steatotic hepatocytes can increase DNA damage to initiate carcinogenesis. METHODS: Global epigenetic state was assessed in liver samples from high-fat diet or high-fructose diet rodent models, as well as in cultured immortalized human hepatocytes (IHH cells). The mechanisms linking steatosis, histone acetylation and DNA damage were investigated by computational metabolic modelling as well as through manipulation of IHH cells with metabolic and epigenetic inhibitors. Chromatin immunoprecipitation and next-generation sequencing (ChIP-seq) and transcriptome (RNA-seq) analyses were performed on IHH cells. Mutation locations and patterns were compared between the IHH cell model and genome sequence data from preneoplastic fatty liver samples from patients with alcohol-related liver disease and NAFLD. RESULTS: Genome-wide histone acetylation was increased in steatotic livers of rodents fed high-fructose or high-fat diet. In vitro, steatosis relaxed chromatin and increased DNA damage marker γH2AX, which was reversed by inhibiting acetyl-CoA production. Steatosis-associated acetylation and γH2AX were enriched at gene clusters in telomere-proximal regions which contained HCC tumour suppressors in hepatocytes and human fatty livers. Regions of metabolically driven epigenetic change also had increased levels of DNA mutation in non-cancerous tissue from NAFLD and alcohol-related liver disease patients. Finally, genome-scale network modelling indicated that redox balance could be a key contributor to this mechanism. CONCLUSIONS: Abnormal histone hyperacetylation facilitates DNA damage in steatotic hepatocytes and is a potential initiating event in hepatocellular carcinogenesis.

A culture of care: How Lotus House Women's Shelter heals program participants through genuineness, space, high expectations, dignity, individualized attention, and community.

The present study was designed to examine perceptions of Lotus House Women's Shelter from the perspective of former program participants, for the purpose of informing shelter programming and policies. Our qualitative research followed a community-based participatory research framework. Fifty diverse women graduates of Lotus House Women's Shelter participated in eight focus groups to discuss their experiences with Lotus House and other shelters. Findings from this study highlight the elements that create a "culture of care" within an organization. Participants described Lotus House shelter culture as genuine, defined by dignity and respect, having high expectations for guest independence and accountability, giving space to rest and recuperate, recognizing and accommodating individual needs and experiences, and fostering a sense of community. Creating an organizational "culture of care" is an avenue by which any shelter or related organization can enhance the experience of program participants.

Gender and Racial Identity Moderate the Effects of Online and Offline Discrimination on Mental Health: Dismantling Systems of Racism and Oppression during Adolescence.

The present study highlights the growing need to examine Black youths' exposure to racial discrimination in online and offline contexts. Using a sample of 353 Black college students, findings indicate that high public regard moderates the positive association between online and offline racial discrimination and psychological consequences (i.e., depression, anxiety, and psychological well-being) among Black women. Additionally, racial centrality moderated the positive association between online and offline racial discrimination and mental health consequences regardless of gender. The findings highlight the importance of considering context, gender, and racial identity when examining the links between Black emerging adults' experiences of discrimination and mental health.

Segmentation of White Blood Cell, Nucleus and Cytoplasm in Digital Haematology Microscope Images: A Review-Challenges, Current and Future Potential Techniques.

Segmentation of white blood cells in digital haematology microscope images represents one of the major tools in the diagnosis and evaluation of blood disorders. Pathological examinations are being the gold standard in many haematology and histophathology, and also play a key role in the diagnosis of diseases. In clinical diagnosis, white blood cells are analysed by pathologists from peripheral blood smears samples of patients. This analysis is mainly based on morphological features and characteristics of the white blood cells and their nuclei and cytoplasm, including, shapes, sizes, colours, textures, maturity stages and staining processes. Recently, Computer Aided Diagnosis techniques have been rapidly growing in the digital haematology area related to white blood cells, and their nuclei and cytoplasm detection, as well as their segmentation and classification techniques. In digital haematology image analysis, these techniques have played and will continue to play, a vital role for providing traceable clinical information, consolidating pertinent second opinions, and minimizing human intervention. This study outlines, discusses, and introduces the major trends from a particular review of detection and segmentation methods for white blood cells and their nuclei and cytoplasm from digital haematology microscope images. Performance of existing methods have been comprehensively compared, taking into account databases used, number of images and limitations. This study can also help us to identify the challenges that remain, in achieving a robust analysis of white blood cell microscope images, which could support the diagnosis of blood disorders and assist researchers and pathologists in the future. The impact of this work is to enhance the accuracy of pathologists' decisions and their efficiency, and overall benefit the patients for faster and more accurate diagnosis. The significant of the paper on intelligent system is that provides future potential techniques for solving overlapping white blood cell identification and other problems microscopic images. The accurate segmentation and detection of white blood cells can increase the accuracy of cell counting system for diagnosing diseases in the future.

Evaluation and benchmarking of level set-based three forces via geometric active contours for segmentation of white blood cell nuclei shape.

The segmentation of white blood cells and their nuclei is still difficult and challenging for many reasons, including the differences in their colour, shape, background and staining techniques, the overlapping of cells, and changing cell topologies. This paper shows how these challenges can be addressed by using level set forces via edge-based geometric active contours. In this work, three level set forces-based (curvature, normal direction, and vector field) are comprehensively studied in the context of the problem of segmenting white blood cell nuclei based on geometric flows. Cell images are first pre-processed, using contrast stretching and morphological opening and closing in order to standardise the image colour intensity, to create an initial estimate of the cell foreground and to remove the narrow links between lobes and cell bulges. Next, segmentation is conducted to prune out the white blood cell nucleus region from the cell wall and cytoplasm by combining the theory of curve evolution using curvature, normal direction, and vector field-based level set forces and edge-based geometric active contours. The overall performance of the proposed segmentation method is compared and benchmarked against existing techniques for nucleus shape detection, using the same databases. The three level set forces studied here (curvature, normal direction, and vector field) via edge-based geometric active contours achieve F-index values of 92.09%, 91.13%, and 90.76%, respectively, and the proposed segmentation method results in better performance than all other techniques for all indices, including Jaccard distance, boundary displacement error, and Rand index.

SwarmPS: rapid, semi-automated single particle selection software.

Single particle analysis (SPA) coupled with high-resolution electron cryo-microscopy is emerging as a powerful technique for the structure determination of membrane protein complexes and soluble macromolecular assemblies. Current estimates suggest that approximately 10(4)-10(5) particle projections are required to attain a 3A resolution 3D reconstruction (symmetry dependent). Selecting this number of molecular projections differing in size, shape and symmetry is a rate-limiting step for the automation of 3D image reconstruction. Here, we present Swarm(PS), a feature rich GUI based software package to manage large scale, semi-automated particle picking projects. The software provides cross-correlation and edge-detection algorithms. Algorithm-specific parameters are transparently and automatically determined through user interaction with the image, rather than by trial and error. Other features include multiple image handling (approximately 10(2)), local and global particle selection options, interactive image freezing, automatic particle centering, and full manual override to correct false positives and negatives. Swarm(PS) is user friendly, flexible, extensible, fast, and capable of exporting boxed out projection images, or particle coordinates, compatible with downstream image processing suites.

Virtual reality exposure therapy: 150-degree screen to desktop PC.

Virtual reality exposure therapy (VRET) developed using immersive or semi-immersive virtual environments present a usability problem for practitioners. To meet practitioner requirements for lower cost and portability VRET programs must often be ported onto desktop environments such as the personal computer (PC). However, success of VRET has been shown to be linked to presence, and the environment's ability to evoke the same reactions and emotions as a real experience. It is generally accepted that high-end virtual environments (VEs) are more immersive than desktop PCs, but level of immersion does not always predict level of presence. This paper reports on the impact on presence of porting a therapeutic VR application for schizophrenia from the initial research environment of a semi-immersive curved screen to PC. Presence in these two environments is measured both introspectively and across a number of causal factors thought to underlie the experience of presence. Results show that the VR exposure program successfully made users feel they were "present" in both platforms. While the desktop PC achieved higher scores on presence across causal factors participants reported they felt more present in the curved screen environment. While comparison of the two groups was statistically significant for the PQ but not for the IPQ, subjective reports of experiences in the environments should be considered in future research as the success of VRET relies heavily on the emotional response of patients to the therapeutic program.

The discriminative bilateral filter: an enhanced denoising filter for electron microscopy data.

Advances in three-dimensional (3D) electron microscopy (EM) and image processing are providing considerable improvements in the resolution of subcellular volumes, macromolecular assemblies and individual proteins. However, the recovery of high-frequency information from biological samples is hindered by specimen sensitivity to beam damage. Low dose electron cryo-microscopy conditions afford reduced beam damage but typically yield images with reduced contrast and low signal-to-noise ratios (SNRs). Here, we describe the properties of a new discriminative bilateral (DBL) filter that is based upon the bilateral filter implementation of Jiang et al. (Jiang, W., Baker, M.L., Wu, Q., Bajaj, C., Chiu, W., 2003. Applications of a bilateral denoising filter in biological electron microscopy. J. Struc. Biol. 128, 82-97.). In contrast to the latter, the DBL filter can distinguish between object edges and high-frequency noise pixels through the use of an additional photometric exclusion function. As a result, high frequency noise pixels are smoothed, yet object edge detail is preserved. In the present study, we show that the DBL filter effectively reduces noise in low SNR single particle data as well as cellular tomograms of stained plastic sections. The properties of the DBL filter are discussed in terms of its usefulness for single particle analysis and for pre-processing cellular tomograms ahead of image segmentation.