A Variational Recurrent Neural Network for Session-Based Recommendations using Bayesian Personalized Ranking

This work introduces VRNN-BPR, a novel deep learning model, which is utilized in session-based Recommender systems tackling the data sparsity problem. The proposed model combines a Recurrent Neural Network with an amortized variational inference setup (AVI) and a Bayesian Personalized Ranking in order to produce predictions on sequence-based data and generate recommendations. The model is assessed using a large real-world dataset and the results demonstrate its superiority over current state-of-the-art techniques

Intrafamilial Phenotype Variability in Two Male Siblings, With X-linked Juvenile Retinoschisis and Dorzolamide Treatment Effect in the Natural History of the Disease

To investigate how genotype is related to phenotype and document correlations of genotype-phenotype with response of topical administration of dorzolamide in siblings affected with X-linked juvenile retinoschisis (XLRS). We performed a retrospective study on two male siblings (four eyes) with XLRS, who were treated with topical installation of dorzolamide. Clinical diagnosis was supported with familial genetic analysis with bi-directional Sanger sequencing of RS1 pathogenic variant. Optical coherence tomography (OCT), fundus fluorescein angiography (FFA), ultrasound scan (U/S) and electroretinogram (ERG) were used in the evaluation. Central macular thickness (CMT) and best corrected visual acuity (BCVA) were recorded monthly for eighteen months. We performed genetic analysis in their family for mutations in the gene that encodes the protein retinoschisin, responsible for retinoschisis (RS1).  It was proved that phenotype variability might be related to the same pathogenic variant. While there was an improvement in BCVA and OCT central macular thickness in the patient with the mild form of disease, the visual acuity and the OCT scans of the patient with severe form of disease did not improve. Intrafamilial phenotypic variability between individuals sharing identical pathogenic variant was documented. Both our patients had a pathogenic variant in a hemizygous state at a genomic location in exon 6 of the RS1 gene; Frameshift mutation that is likely to cause protein truncation was identified which is suggested to result in greater clinical severity. Consequently, it was found that response to dorzolamide is correlated to phenotypic severity

Intrafamilial Phenotype Variability in Two Male Siblings, With X-linked Juvenile Retinoschisis and Dorzolamide Treatment Effect in the Natural History of the Disease

To investigate how genotype is related to phenotype and document correlations of genotype-phenotype with response of topical administration of dorzolamide in siblings affected with X-linked juvenile retinoschisis (XLRS). We performed a retrospective study on two male siblings (four eyes) with XLRS, who were treated with topical installation of dorzolamide. Clinical diagnosis was supported with familial genetic analysis with bi-directional Sanger sequencing of RS1 pathogenic variant. Optical coherence tomography (OCT), fundus fluorescein angiography (FFA), ultrasound scan (U/S) and electroretinogram (ERG) were used in the evaluation. Central macular thickness (CMT) and best corrected visual acuity (BCVA) were recorded monthly for eighteen months. We performed genetic analysis in their family for mutations in the gene that encodes the protein retinoschisin, responsible for retinoschisis (RS1).  It was proved that phenotype variability might be related to the same pathogenic variant. While there was an improvement in BCVA and OCT central macular thickness in the patient with the mild form of disease, the visual acuity and the OCT scans of the patient with severe form of disease did not improve. Intrafamilial phenotypic variability between individuals sharing identical pathogenic variant was documented. Both our patients had a pathogenic variant in a hemizygous state at a genomic location in exon 6 of the RS1 gene; Frameshift mutation that is likely to cause protein truncation was identified which is suggested to result in greater clinical severity. Consequently, it was found that response to dorzolamide is correlated to phenotypic severity

RNA deep sequencing reveals differential MicroRNA expression during development of sea urchin and sea star

microRNAs (miRNAs) are small (20-23 nt), non-coding single stranded RNA molecules that act as post-transcriptional regulators of mRNA gene expression. They have been implicated in regulation of developmental processes in diverse organisms. The echinoderms, Strongylocentrotus purpuratus (sea urchin) and Patiria miniata (sea star) are excellent model organisms for studying development with well-characterized transcriptional networks. However, to date, nothing is known about the role of miRNAs during development in these organisms, except that the genes that are involved in the miRNA biogenesis pathway are expressed during their developmental stages. In this paper, we used Illumina Genome Analyzer (Illumina, Inc.) to sequence small RNA libraries in mixed stage population of embryos from one to three days after fertilization of sea urchin and sea star (total of 22,670,000 reads). Analysis of these data revealed the miRNA populations in these two species. We found that 47 and 38 known miRNAs are expressed in sea urchin and sea star, respectively, during early development (32 in common). We also found 13 potentially novel miRNAs in the sea urchin embryonic library. miRNA expression is generally conserved between the two species during development, but 7 miRNAs are highly expressed in only one species. We expect that our two datasets will be a valuable resource for everyone working in the field of developmental biology and the regulatory networks that affect it. The computational pipeline to analyze Illumina reads is available at http://www.benoslab.pitt.edu/services.html. © 2011 Kadri et al

Global disparities in surgeons’ workloads, academic engagement and rest periods: the on-calL shIft fOr geNEral SurgeonS (LIONESS) study

: The workload of general surgeons is multifaceted, encompassing not only surgical procedures but also a myriad of other responsibilities. From April to May 2023, we conducted a CHERRIES-compliant internet-based survey analyzing clinical practice, academic engagement, and post-on-call rest. The questionnaire featured six sections with 35 questions. Statistical analysis used Chi-square tests, ANOVA, and logistic regression (SPSS® v. 28). The survey received a total of 1.046 responses (65.4%). Over 78.0% of responders came from Europe, 65.1% came from a general surgery unit; 92.8% of European and 87.5% of North American respondents were involved in research, compared to 71.7% in Africa. Europe led in publishing research studies (6.6 ± 8.6 yearly). Teaching involvement was high in North America (100%) and Africa (91.7%). Surgeons reported an average of 6.7 ± 4.9 on-call shifts per month, with European and North American surgeons experiencing 6.5 ± 4.9 and 7.8 ± 4.1 on-calls monthly, respectively. African surgeons had the highest on-call frequency (8.7 ± 6.1). Post-on-call, only 35.1% of respondents received a day off. Europeans were most likely (40%) to have a day off, while African surgeons were least likely (6.7%). On the adjusted multivariable analysis HDI (Human Development Index) (aOR 1.993) hospital capacity > 400 beds (aOR 2.423), working in a specialty surgery unit (aOR 2.087), and making the on-call in-house (aOR 5.446), significantly predicted the likelihood of having a day off after an on-call shift. Our study revealed critical insights into the disparities in workload, access to research, and professional opportunities for surgeons across different continents, underscored by the HDI

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Designing and evaluating intelligent context-aware recommender systems: methodologies and applications

This research introduces new concepts and methodologies for Recommender Systems aiming to enhance the user experience and at the same time to improve the system’s accuracy by dealing with the challenges of RS. The thesis and the corresponding research is structured in three main parts. The first part of this thesis concentrates more on the development of new Multi-criteria RS to improve the accuracy and performance of RS. Our study examines solutions on how to deal with data sparsity, scalability issues and the cold-start problem by utilizing various techniques. The second part deals with the classification prediction problem. We propose a new methodology for developing hybrid models to improve the accuracy of classification models and thus provide better recommendations. The final part introduces a Recurrent Latent Variable framework based on a variational Recurrent Neural Network that deals with data sparsity and uncertainty met on session-based recommendations and sequence-based data. Experimentation was performed in all three parts mentioned and the results demonstrated the validity of the proposed methodologies when compared with state-of-the-art methods.Complete