Paradoxical experiences of healthcare workers during COVID-19: a qualitative analysis of anonymous, web-based, audio narratives.

PURPOSE: To gain a deeper understanding of healthcare workers experiences during COVID-19 using an anonymous, web-based, audio narrative platform. METHODS: Data were collected from healthcare workers in the midwestern United States using a web-enabled audio diary approach. Participant recordings were analysed using a narrative coding and conceptualization process derived from grounded theory coding techniques. RESULTS: Fifteen healthcare workers, in direct patient care or non-patient care roles, submitted 18 audio narratives. Two paradoxical themes emerged: 1) A paradox of distress and meaningfulness, where a harsh work environment resulted in psychological distress while simultaneously resulting in new rewarding experiences, sense of purpose and positive outlooks. 2) A paradox of social isolation and connection, where despite extreme isolation, healthcare workers formed intense and meaningful interpersonal connections with patients and colleagues in new ways. CONCLUSIONS: A web-enabled audio diary approach provided an opportunity for healthcare workers to reflect deeper on their experiences without investigator influence, which led to some unique findings. Paradoxically, amid social isolation and extreme distress, a sense of value, meaning and rewarding human connections emerged. These findings suggest that interventions addressing healthcare worker burnout and distress might be enhanced by leveraging naturally occurring positive experiences as much as mitigating negative ones.

Risk factors associated with glycated hemoglobin A1c trajectories progressing to type 2 diabetes.

BACKGROUND AND OBJECTIVE: The notion of prediabetes, defined by the ADA as glycated hemoglobin A1c (HbA1c) of 5.7-6.4%, implies increased vascular inflammatory and immunologic processes and higher risk for developing diabetes mellitus and major cardiovascular events. We aimed to determine the risk factors associated with rapid progression of normal and prediabetes patients to type 2 diabetes mellitus (T2DM). METHODS: Retrospective cohort study in a single 8-hospital health system in southeast Michigan, between 2006 and 2020. All patients with HbA1c <6.5% at baseline and at least 2 other HbA1c measurements were clustered in five trajectories encompassing more than 95% of the study population. Multivariate linear regression analysis was performed to examine the association of demographic and comorbidities with HbA1c trajectories progressing to diabetes. RESULTS: A total of 5,347 prediabetic patients were clustered based on their HbA1c progression (C1: 4,853, C2: 253, C66: 102, C12: 85, C68: 54). The largest cluster (C1) had a baseline median HbA1c value of 6.0% and exhibited stable HbA1c levels in prediabetic range across all subsequent years. The smallest cluster (C68) had the lowest median baseline HbA1c value and also remained stable across subsequent years. The proportion of normal HbA1c in each of the pre-diabetic trajectories ranged from 0 to 12.7%, whereas 81.5% of the reference cluster (C68) were normal HbA1c at baseline. The C2 (steady rising) trajectory was significantly associated with BMI (adj OR 1.10, 95%CI 1.03-1.17), and family history of DM (adj OR 2.75, 95%CI 1.32-5.74). With respect to the late rising trajectories, baseline BMI was significantly associated with both C66 and C12 trajectory (adj OR 1.10, 95%CI 1.03-1.18) and (adj OR 1.13, 95%CI 1.05-1.23) respectively, whereas, the C12 trajectory was also significantly associated with age (adj OR 1.62, 95%CI 1.04-2.53) and history of MACE (adj OR 3.20, 95%CI 1.14-8.93). CONCLUSIONS: We suggest that perhaps a more aggressive preventative approach should be considered in patients with a family history of T2DM who have high BMI and year-to-year increase in HbA1c, whether they have normal hemoglobin A1c or they have prediabetes.KEY MESSAGESProgression to diabetes from normal or prediabetic hemoglobin A1c within four years is associated with baseline BMI.A steady rise in HbA1c during a four-year period is associated with age and family history of T2DM, whereas age and personal history of MACE are associated with a rapid rise in HbA1c.A more aggressive preventative approach should be considered in patients with a family history of T2DM who have high BMI and year-to-year increase in HbA1c.

Nanoscale inhomogeneity of charge density waves dynamics in La2-xSrxNiO4.

While stripe phases with broken rotational symmetry of charge density are known to emerge in doped strongly correlated perovskites, the dynamics and heterogeneity of spatial ordering remain elusive. Here we shed light on the temperature dependent lattice motion and the spatial nanoscale phase separation of charge density wave order in the archetypal striped phase in La2-xSrxNiO4+y (LSNO) perovskite using X-ray photon correlation spectroscopy (XPCS) joint with scanning micro X-ray diffraction (SµXRD). While it is known that the CDW in 1/8 doped cuprates shows a remarkable stability we report the CDW motion dynamics by XPCS in nickelates with an anomalous quantum glass regime at low temperature, T < 65 K, and the expected thermal melting at higher temperature 65 < T < 120 K. The nanoscale CDW puddles with a shorter correlation length are more mobile than CDW puddles with a longer correlation length. The direct imaging of nanoscale spatial inhomogeneity of CDW by scanning micro X-ray diffraction (SµXRD) shows a nanoscale landscape of percolating short range dynamic CDW puddles competing with large quasi-static CDW puddles giving rise to a novel form of nanoscale phase separation of the incommensurate stripes order landscape.

Imaging mesoscopic antiferromagnetic spin textures in the dilute limit from single-geometry resonant coherent x-ray diffraction.

The detection and manipulation of antiferromagnetic domains and topological antiferromagnetic textures are of central interest to solid-state physics. A fundamental step is identifying tools to probe the mesoscopic texture of an antiferromagnetic order parameter. In this work, we demonstrate that Bragg coherent diffractive imaging can be extended to study the mesoscopic texture of an antiferromagnetic order parameter using resonant magnetic x-ray scattering. We study the onset of the antiferromagnet transition in PrNiO3, focusing on a temperature regime in which the antiferromagnetic domains are dilute in the beam spot and the coherent diffraction pattern modulating the antiferromagnetic peak is greatly simplified. We demonstrate that it is possible to extract the arrangements and sizes of these domains from single diffraction patterns and show that the approach could be extended to a time-structured light source to study the motion of dilute domains or the motion of topological defects in an antiferromagnetic spin texture.

Measurement of Spin Dynamics in a Layered Nickelate Using X-Ray Photon Correlation Spectroscopy: Evidence for Intrinsic Destabilization of Incommensurate Stripes at Low Temperatures.

We study the temporal stability of stripe-type spin order in a layered nickelate with x-ray photon correlation spectroscopy and observe fluctuations on timescales of tens of minutes over a wide temperature range. These fluctuations show an anomalous temperature dependence: they slow down at intermediate temperatures and speed up on both heating and cooling. This behavior appears to be directly connected with spatial correlations: stripes fluctuate slowly when stripe correlation lengths are large and become faster when spatial correlations decrease. A low-temperature decay of nickelate stripe correlations, reminiscent of what occurs in cuprates as a result of a competition between stripes and superconductivity, hence occurs via loss of both spatial and temporal correlations.

Simultaneous ambient pressure x-ray photoelectron spectroscopy and grazing incidence x-ray scattering in gas environments.

We have developed an experimental system to simultaneously measure surface structure, morphology, composition, chemical state, and chemical activity for samples in gas phase environments. This is accomplished by simultaneously measuring x-ray photoelectron spectroscopy (XPS) and grazing incidence x-ray scattering in gas pressures as high as the multi-Torr regime while also recording mass spectrometry. Scattering patterns reflect near-surface sample structures from the nano-scale to the meso-scale, and the grazing incidence geometry provides tunable depth sensitivity of structural measurements. Scattered x rays are detected across a broad range of angles using a newly designed pivoting-UHV-manipulator for detector positioning. At the same time, XPS and mass spectrometry can be measured, all from the same sample spot and under ambient conditions. To demonstrate the capabilities of this system, we measured the chemical state, composition, and structure of Ag-behenate on a Si(001) wafer in vacuum and in O2 atmosphere at various temperatures. These simultaneous structural, chemical, and gas phase product probes enable detailed insights into the interplay between the structure and chemical state for samples in gas phase environments. The compact size of our pivoting-UHV-manipulator makes it possible to retrofit this technique into existing spectroscopic instruments installed at synchrotron beamlines. Because many synchrotron facilities are planning or undergoing upgrades to diffraction limited storage rings with transversely coherent beams, a newly emerging set of coherent x-ray scattering experiments can greatly benefit from the concepts we present here.

Photon-counting MCP/Timepix detectors for soft X-ray imaging and spectroscopic applications.

Detectors with microchannel plates (MCPs) provide unique capabilities to detect single photons with high spatial (<10 µm) and timing (<25 ps) resolution. Although this detection technology was originally developed for applications with low event rates, recent progress in readout electronics has enabled their operation at substantially higher rates by simultaneous detection of multiple particles. In this study, the potential use of MCP detectors with Timepix readout for soft X-ray imaging and spectroscopic applications where the position and time of each photon needs to be recorded is investigated. The proof-of-principle experiments conducted at the Advanced Light Source demonstrate the capabilities of MCP/Timepix detectors to operate at relatively high input counting rates, paving the way for the application of these detectors in resonance inelastic X-ray scattering and X-ray photon correlation spectroscopy (XPCS) applications. Local count rate saturation was investigated for the MCP/Timepix detector, which requires optimization of acquisition parameters for a specific scattering pattern. A single photon cluster analysis algorithm was developed to eliminate the charge spreading effects in the detector and increase the spatial resolution to subpixel values. Results of these experiments will guide the ongoing development of future MCP devices optimized for soft X-ray photon-counting applications, which should enable XPCS dynamics measurements down to sub-microsecond timescales.

Switchable X-Ray Orbital Angular Momentum from an Artificial Spin Ice.

Artificial spin ices (ASI) have been widely investigated as magnetic metamaterials with exotic properties governed by their geometries. In parallel, interest in x-ray photon orbital angular momentum (OAM) has been rapidly growing. Here we show that a square ASI with a patterned topological defect, a double edge dislocation, imparts OAM to scattered x rays. Unlike single dislocations, a double dislocation does not introduce magnetic frustration, and the ASI equilibrates to its antiferromagnetic (AFM) ground state. The topological charge of the defect differs with respect to the structural and magnetic order; thus, x-ray diffraction from the ASI produces photons with even and odd OAM quantum numbers at the structural and AFM Bragg conditions, respectively. The magnetic transitions of the ASI allow the AFM OAM beams to be switched on and off by modest variations of temperature and applied magnetic field. These results demonstrate ASIs can serve as metasurfaces for reconfigurable x-ray optics that could enable selective probes of electronic and magnetic properties.

Chiral Spin Textures in Amorphous Iron-Germanium Thick Films.

Topological solitary fields, such as magnetic and polar skyrmions, are envisioned to revolutionize microelectronics. These configurations have been stabilized in solid-state materials with a global inversion symmetry breaking, which translates in magnetic materials into a vector spin exchange known as the Dzyaloshinskii-Moriya interaction (DMI), as well as spin chirality selection and isotropic solitons. This work reports experimental evidence of 3D chiral spin textures, such as helical spins and skyrmions with different chirality and topological charge, stabilized in amorphous Fe-Ge thick films. These results demonstrate that structurally and chemically disordered materials with a random DMI can resemble inversion symmetry broken systems with similar magnetic properties, moments, and states. Disordered systems are distinguished from systems with global inversion symmetry breaking by their degenerate spin chirality that allows for forming isotropic and anisotropic topological spin textures at remanence, while offering greater flexibility in materials synthesis, voltage, and strain manipulation.

Determining child drowning mortality in the Sundarbans, India: applying the community knowledge approach.

BACKGROUND: The Sundarbans in India is a rural, forested region where children are exposed to a high risk of drowning due to its waterlogged geography. Current data collection systems capture few drowning deaths in this region. METHODS: A community-based survey was conducted in the Sundarbans to determine the drowning mortality rate for children aged 1 to 4 years and 5 to 9 years. A community knowledge approach was used. Meetings were held with community residents and key informants to identify drowning deaths in the population. Identified deaths were verified by the child's household through a structured survey, inquiring on the circumstances around the drowning death. RESULTS: The drowning mortality rate for children aged 1 to 4 years was 243.8 per 100 000 children and for 5 to 9 years was 38.8 per 100 000 children. 58.0% of deaths were among children aged 1 to 2 years. No differences in rates between boys and girls were found. Most children drowned in ponds within 50 metres of their homes. Children were usually unaccompanied with their primary caretaker engaged in household work. A minority of children were treated by formal health providers. CONCLUSIONS: Drowning is a major cause of death among children in the Sundarbans, particularly those aged 1 to 4 years. Interventions keeping children in safe spaces away from water are urgently required. The results illustrate how routine data collection systems grossly underestimate drowning deaths, emphasising the importance of community-based surveys in capturing these deaths in rural low- and middle-income country contexts. The community knowledge approach provides a low-resource, validated methodology for this purpose.

Interventions for Child Drowning Reduction in the Indian Sundarbans: Perspectives from the Ground.

Drowning is a leading cause of child death in the coastal Sundarbans region of India due to the presence of open water, lack of supervision and poor infrastructure, but no prevention programs are currently implemented. The World Health Organization has identified interventions that may prevent child drowning in rural low-and middle-income country contexts, including the provision of home-based barriers, supervised childcare, swim and rescue training and first responder training. Child health programs should consider the local context and identify barriers for implementation. To ensure the sustainability of any drowning prevention programs implemented, we conducted a qualitative study to identify the considerations for the implementation of these interventions, and to understand how existing government programs could be leveraged. We also identified key stakeholders for involvement. We found that contextual factors such as geography, cultural beliefs around drowning, as well as skillsets of local people, would influence program delivery. Government programs such as accredited social health activists (ASHAs) and self-help groups could be leveraged for program implementation, while Anganwadi centres would require additional support due to poor resourcing. Gaining government permissions to change Anganwadi processes to provide childcare services may be challenging. The results showed that adapting drowning programs to the Sundarbans context presents unique challenges and program customisation.

Coordinated regulation of Rel expression by MAP3K4, CBP, and HDAC6 controls phenotypic switching.

Coordinated gene expression is required for phenotypic switching between epithelial and mesenchymal phenotypes during normal development and in disease states. Trophoblast stem (TS) cells undergo epithelial-mesenchymal transition (EMT) during implantation and placentation. Mechanisms coordinating gene expression during these processes are poorly understood. We have previously demonstrated that MAP3K4-regulated chromatin modifiers CBP and HDAC6 each regulate thousands of genes during EMT in TS cells. Here we show that CBP and HDAC6 coordinate expression of only 183 genes predicted to be critical regulators of phenotypic switching. The highest-ranking co-regulated gene is the NF-κB family member Rel. Although NF-κB is primarily regulated post-transcriptionally, CBP and HDAC6 control Rel transcript levels by binding Rel regulatory regions and controlling histone acetylation. REL re-expression in mesenchymal-like TS cells induces a mesenchymal-epithelial transition. Importantly, REL forms a feedback loop, blocking HDAC6 expression and nuclear localization. Together, our work defines a developmental program coordinating phenotypic switching.

Evaluation of Sirtuin-3 probe quality and co-expressed genes using literature cohesion.

BACKGROUND: Gene co-expression studies can provide important insights into molecular and cellular signaling pathways. The GeneNetwork database is a unique resource for co-expression analysis using data from a variety of tissues across genetically distinct inbred mice. However, extraction of biologically meaningful co-expressed gene sets is challenging due to variability in microarray platforms, probe quality, normalization methods, and confounding biological factors. In this study, we tested whether literature derived functional cohesion could be used as an objective metric in lieu of 'ground truth' to evaluate the quality of probes and microarray datasets. RESULTS: We examined Sirtuin-3 (Sirt3) co-expressed gene sets extracted from either liver or brain tissues of BXD recombinant inbred mice in the GeneNetwork database. Depending on the microarray platform, there were as many as 26 probes that targeted different regions of Sirt3 primary transcript. Co-expressed gene sets (ranging from 100-1000 genes) associated with each Sirt3 probe were evaluated using the previously developed literature-derived cohesion p-value (LPv) and benchmarked against 'gold standards' derived from proteomic studies or Gene Ontology classifications. We found that the maximal F-measure was obtained at an average window size of 535 genes. Using set size of 500 genes, the Pearson correlations between LPv and F-measure as well as between LPv and mitochondrial gene enrichment p-values were 0.90 and 0.93, respectively. Importantly, we found that the LPv approach can distinguish high quality Sirt3 probes. Analysis of the most functionally cohesive Sirt3 co-expressed gene set revealed core metabolic pathways that were shared between hippocampus and liver as well as distinct pathways which were unique to each tissue. These results are consistent with other studies that suggest Sirt3 is a key metabolic regulator and has distinct functions in energy-producing vs. energy-demanding tissues. CONCLUSIONS: Our results provide proof-of-concept that literature cohesion analysis is useful for evaluating the quality of probes and microarray datasets, particularly when experimentally derived gold standards are unavailable. Our approach would enable researchers to rapidly identify biologically meaningful co-expressed gene sets and facilitate discovery from high throughput genomic data.

Landmark Free Face Attribute Prediction.

Face attribute prediction in the wild is important for many facial analysis applications yet it is very challenging due to ubiquitous face variations. In this paper, we address face attribute prediction in the wild by proposing a novel method, lAndmark Free Face AttrIbute pRediction (AFFAIR). Unlike traditional face attribute prediction methods that require facial landmark detection and face alignment, AFFAIR uses an endto- end learning pipeline to jointly learn a hierarchy of spatial transformations that optimize facial attribute prediction with no reliance on landmark annotations or pre-trained landmark detectors. AFFAIR achieves this through simultaneously 1) learning a global transformation which effectively alleviates negative effect of global face variation for the following attribute prediction tailored for each face, 2) locating the most relevant facial part for attribute prediction and 3) aggregating the global and local features for robust attribute prediction. Within AFFAIR, a new competitive learning strategy is developed that effectively enhances global transformation learning for better attribute prediction. We show that with zero information about landmarks, AFFAIR achieves state-of-the-art performance on three face attribute prediction benchmarks, which simultaneously learns the face-level transformation and attribute-level localization within a unified framework.

Navigating the Functional Landscape of Transcription Factors via Non-Negative Tensor Factorization Analysis of MEDLINE Abstracts.

In this study, we developed and evaluated a novel text-mining approach, using non-negative tensor factorization (NTF), to simultaneously extract and functionally annotate transcriptional modules consisting of sets of genes, transcription factors (TFs), and terms from MEDLINE abstracts. A sparse 3-mode term × gene × TF tensor was constructed that contained weighted frequencies of 106,895 terms in 26,781 abstracts shared among 7,695 genes and 994 TFs. The tensor was decomposed into sub-tensors using non-negative tensor factorization (NTF) across 16 different approximation ranks. Dominant entries of each of 2,861 sub-tensors were extracted to form term-gene-TF annotated transcriptional modules (ATMs). More than 94% of the ATMs were found to be enriched in at least one KEGG pathway or GO category, suggesting that the ATMs are functionally relevant. One advantage of this method is that it can discover potentially new gene-TF associations from the literature. Using a set of microarray and ChIP-Seq datasets as gold standard, we show that the precision of our method for predicting gene-TF associations is significantly higher than chance. In addition, we demonstrate that the terms in each ATM can be used to suggest new GO classifications to genes and TFs. Taken together, our results indicate that NTF is useful for simultaneous extraction and functional annotation of transcriptional regulatory networks from unstructured text, as well as for literature based discovery. A web tool called Transcriptional Regulatory Modules Extracted from Literature (TREMEL), available at http://binf1.memphis.edu/tremel, was built to enable browsing and searching of ATMs.

Loss of C-5 Sterol Desaturase Activity Results in Increased Resistance to Azole and Echinocandin Antifungals in a Clinical Isolate of Candida parapsilosis.

Among emerging non-albicans Candida species, Candida parapsilosis is of particular concern as a cause of nosocomial bloodstream infections in neonatal and intensive care unit patients. While fluconazole and echinocandins are considered effective treatments for such infections, recent reports of fluconazole and echinocandin resistance in C. parapsilosis indicate a growing problem. The present study describes a novel mechanism of antifungal resistance in this organism affecting susceptibility to azole and echinocandin antifungals in a clinical isolate obtained from a patient with prosthetic valve endocarditis. Transcriptome analysis indicated differential expression of several genes in the resistant isolate, including upregulation of ergosterol biosynthesis pathway genes ERG2, ERG5, ERG6, ERG11, ERG24, ERG25, and UPC2 Whole-genome sequencing revealed that the resistant isolate possessed an ERG3 mutation resulting in a G111R amino acid substitution. Sterol profiles indicated a reduction in sterol desaturase activity as a result of this mutation. Replacement of both mutant alleles in the resistant isolate with the susceptible isolate's allele restored wild-type susceptibility to all azoles and echinocandins tested. Disruption of ERG3 in the susceptible and resistant isolates resulted in a loss of sterol desaturase activity, high-level azole resistance, and an echinocandin-intermediate to -resistant phenotype. While disruption of ERG3 in C. albicans resulted in azole resistance, echinocandin MICs, while elevated, remained within the susceptible range. This work demonstrates that the G111R substitution in Erg3 is wholly responsible for the altered azole and echinocandin susceptibilities observed in this C. parapsilosis isolate and is the first report of an ERG3 mutation influencing susceptibility to the echinocandins.

MAP3K4 Controls the Chromatin Modifier HDAC6 during Trophoblast Stem Cell Epithelial-to-Mesenchymal Transition.

The first epithelial-to-mesenchymal transition (EMT) occurs in trophoblast stem (TS) cells during implantation. Inactivation of the serine/threonine kinase MAP3K4 in TS cells (TSKI4 cells) induces an intermediate state of EMT, where cells retain stemness, lose epithelial markers, and gain mesenchymal characteristics. Investigation of relationships among MAP3K4 activity, stemness, and EMT in TS cells may reveal key regulators of EMT. Here, we show that MAP3K4 activity controls EMT through the ubiquitination and degradation of HDAC6. Loss of MAP3K4 activity in TSKI4 cells results in elevated HDAC6 expression and the deacetylation of cytoplasmic and nuclear targets. In the nucleus, HDAC6 deacetylates the promoters of tight junction genes, promoting the dissolution of tight junctions. Importantly, HDAC6 knockdown in TSKI4 cells restores epithelial features, including cell-cell adhesion and barrier formation. These data define a role for HDAC6 in regulating gene expression during transitions between epithelial and mesenchymal phenotypes.

Prioritization, clustering and functional annotation of MicroRNAs using latent semantic indexing of MEDLINE abstracts.

BACKGROUND: The amount of scientific information about MicroRNAs (miRNAs) is growing exponentially, making it difficult for researchers to interpret experimental results. In this study, we present an automated text mining approach using Latent Semantic Indexing (LSI) for prioritization, clustering and functional annotation of miRNAs. RESULTS: For approximately 900 human miRNAs indexed in miRBase, text documents were created by concatenating titles and abstracts of MEDLINE citations which refer to the miRNAs. The documents were parsed and a weighted term-by-miRNA frequency matrix was created, which was subsequently factorized via singular value decomposition to extract pair-wise cosine values between the term (keyword) and miRNA vectors in reduced rank semantic space. LSI enables derivation of both explicit and implicit associations between entities based on word usage patterns. Using miR2Disease as a gold standard, we found that LSI identified keyword-to-miRNA relationships with high accuracy. In addition, we demonstrate that pair-wise associations between miRNAs can be used to group them into categories which are functionally aligned. Finally, term ranking by querying the LSI space with a group of miRNAs enabled annotation of the clusters with functionally related terms. CONCLUSIONS: LSI modeling of MEDLINE abstracts provides a robust and automated method for miRNA related knowledge discovery. The latest collection of miRNA abstracts and LSI model can be accessed through the web tool miRNA Literature Network (miRLiN) at http://bioinfo.memphis.edu/mirlin .

Understanding Deep Representations Learned in Modeling Users Likes.

Automatically understanding and discriminating different users' liking for an image is a challenging problem. This is because the relationship between image features (even semantic ones extracted by existing tools, viz., faces, objects, and so on) and users' likes is non-linear, influenced by several subtle factors. This paper presents a deep bi-modal knowledge representation of images based on their visual content and associated tags (text). A mapping step between the different levels of visual and textual representations allows for the transfer of semantic knowledge between the two modalities. Feature selection is applied before learning deep representation to identify the important features for a user to like an image. The proposed representation is shown to be effective in discriminating users based on images they like and also in recommending images that a given user likes, outperforming the state-of-the-art feature representations by  âˆ¼ 15 %-20%. Beyond this test-set performance, an attempt is made to qualitatively understand the representations learned by the deep architecture used to model user likes.

A Bayesian approach for inducing sparsity in generalized linear models with multi-category response.

BACKGROUND: The dimension and complexity of high-throughput gene expression data create many challenges for downstream analysis. Several approaches exist to reduce the number of variables with respect to small sample sizes. In this study, we utilized the Generalized Double Pareto (GDP) prior to induce sparsity in a Bayesian Generalized Linear Model (GLM) setting. The approach was evaluated using a publicly available microarray dataset containing 99 samples corresponding to four different prostate cancer subtypes. RESULTS: A hierarchical Sparse Bayesian GLM using GDP prior (SBGG) was developed to take into account the progressive nature of the response variable. We obtained an average overall classification accuracy between 82.5% and 94%, which was higher than Support Vector Machine, Random Forest or a Sparse Bayesian GLM using double exponential priors. Additionally, SBGG outperforms the other 3 methods in correctly identifying pre-metastatic stages of cancer progression, which can prove extremely valuable for therapeutic and diagnostic purposes. Importantly, using Geneset Cohesion Analysis Tool, we found that the top 100 genes produced by SBGG had an average functional cohesion p-value of 2.0E-4 compared to 0.007 to 0.131 produced by the other methods. CONCLUSIONS: Using GDP in a Bayesian GLM model applied to cancer progression data results in better subclass prediction. In particular, the method identifies pre-metastatic stages of prostate cancer with substantially better accuracy and produces more functionally relevant gene sets.