Exploring single-cell data with deep multitasking neural networks.

It is currently challenging to analyze single-cell data consisting of many cells and samples, and to address variations arising from batch effects and different sample preparations. For this purpose, we present SAUCIE, a deep neural network that combines parallelization and scalability offered by neural networks, with the deep representation of data that can be learned by them to perform many single-cell data analysis tasks. Our regularizations (penalties) render features learned in hidden layers of the neural network interpretable. On large, multi-patient datasets, SAUCIE's various hidden layers contain denoised and batch-corrected data, a low-dimensional visualization and unsupervised clustering, as well as other information that can be used to explore the data. We analyze a 180-sample dataset consisting of 11 million T cells from dengue patients in India, measured with mass cytometry. SAUCIE can batch correct and identify cluster-based signatures of acute dengue infection and create a patient manifold, stratifying immune response to dengue.

GRAM: A GeneRAlized Model to predict the molecular effect of a non-coding variant in a cell-type specific manner.

There has been much effort to prioritize genomic variants with respect to their impact on "function". However, function is often not precisely defined: sometimes it is the disease association of a variant; on other occasions, it reflects a molecular effect on transcription or epigenetics. Here, we coupled multiple genomic predictors to build GRAM, a GeneRAlized Model, to predict a well-defined experimental target: the expression-modulating effect of a non-coding variant on its associated gene, in a transferable, cell-specific manner. Firstly, we performed feature engineering: using LASSO, a regularized linear model, we found transcription factor (TF) binding most predictive, especially for TFs that are hubs in the regulatory network; in contrast, evolutionary conservation, a popular feature in many other variant-impact predictors, has almost no contribution. Moreover, TF binding inferred from in vitro SELEX is as effective as that from in vivo ChIP-Seq. Second, we implemented GRAM integrating only SELEX features and expression profiles; thus, the program combines a universal regulatory score with an easily obtainable modifier reflecting the particular cell type. We benchmarked GRAM on large-scale MPRA datasets, achieving AUROC scores of 0.72 in GM12878 and 0.66 in a multi-cell line dataset. We then evaluated the performance of GRAM on targeted regions using luciferase assays in the MCF7 and K562 cell lines. We noted that changing the insertion position of the construct relative to the reporter gene gave very different results, highlighting the importance of carefully defining the exact prediction target of the model. Finally, we illustrated the utility of GRAM in fine-mapping causal variants and developed a practical software pipeline to carry this out. In particular, we demonstrated in specific examples how the pipeline could pinpoint variants that directly modulate gene expression within a larger linkage-disequilibrium block associated with a phenotype of interest (e.g., for an eQTL).

Race and Genetics: Somber History, Troubled Present.

Following the completion of the Human Genome Project (HGP) in 2003, advances in DNA sequencing technologies further popularized the field of genomics and brought its social ramifications to the fore. Scholars across disciplines recently voiced serious concerns about the re-emergence of genomic research that might be used to justify racism. In this piece, I trace the history of attempts to biologize the concept of race and its diffused presence in today's genomic research. I then include a brief analysis inspired by concepts from the field of Science and Technology Studies (STS) to suggest selected ways to produce better scientific knowledge. The text highlights historic landmarks of interest to science practitioners curious about the ways science of the past co-shapes science of the present. I then argue that science has never been isolated from the socio-political climate it is produced in; instead, it has been morphed by its surroundings and historically used as a potent tool to justify systemic oppression.

Solid Lipid Nanoparticles (SLNs): Advancements in Modification Strategies Toward Drug Delivery Vehicle.

Solid lipid nanoparticles are at the cornerstone of the swiftly growing area of medical nanotechnology, having several potential functions in drug delivery, research, clinical care, and a variety of other fields. They provide the opportunity of developing novel therapies due to their unique properties, such as small particle size and being prepared from physiological biodegradable lipids. The loading of bioactive molecules into nanocarriers is a novel drug delivery prototype employed for various drug targeting levels. Hence, SLNs hold a great promise for achieving the aim of targeted and controlled drug delivery. For this reason, they have attracted the extensive attention of scientists and researchers. This review is based on recent studies and research, and here we present advantages, disadvantages, and preparation methods, several advanced modifications, targeting strategies, and recent applications of solid lipid nanocarriers in drug delivery systems.

Cancer Relevance of Human Genes.

BACKGROUND: We hypothesize that genes that directly or indirectly interact with core cancer genes (CCGs) in a comprehensive gene-gene interaction network may have functional importance in cancer. METHODS: We categorized 12 767 human genes into CCGs (n = 468), 1 (n = 5467), 2 (n = 5573), 3 (n = 915), and more than 3 steps (n = 416) removed from the nearest CCG in the Search Tool for the Retrieval of Interacting Genes/Proteins network. We estimated cancer-relevant functional importance in these neighborhood categories using 1) gene dependency score, which reflects the effect of a gene on cell viability after knockdown; 2) somatic mutation frequency in The Cancer Genome Atlas; 3) effect size that estimates to what extent a mutation in a gene enhances cell survival; and 4) negative selection pressure of germline protein-truncating variants in healthy populations. RESULTS: Cancer biology-related functional importance of genes decreases as their distance from the CCGs increases. Genes closer to cancer genes show greater connectedness in the network, have greater importance in maintaining cancer cell viability, are under greater negative germline selection pressure, and have higher somatic mutation frequency in cancer. Based on these 4 metrics, we provide cancer relevance annotation to known human genes. CONCLUSIONS: A large number of human genes are connected to CCGs and could influence cancer biology to various extent when dysregulated; any given mutation may be functionally important in one but not in another individual depending on genomic context.

Network propagation-based prioritization of long tail genes in 17 cancer types.

BACKGROUND: The diversity of genomic alterations in cancer poses challenges to fully understanding the etiologies of the disease. Recent interest in infrequent mutations, in genes that reside in the "long tail" of the mutational distribution, uncovered new genes with significant implications in cancer development. The study of cancer-relevant genes often requires integrative approaches pooling together multiple types of biological data. Network propagation methods demonstrate high efficacy in achieving this integration. Yet, the majority of these methods focus their assessment on detecting known cancer genes or identifying altered subnetworks. In this paper, we introduce a network propagation approach that entirely focuses on prioritizing long tail genes with potential functional impact on cancer development. RESULTS: We identify sets of often overlooked, rarely to moderately mutated genes whose biological interactions significantly propel their mutation-frequency-based rank upwards during propagation in 17 cancer types. We call these sets "upward mobility genes" and hypothesize that their significant rank improvement indicates functional importance. We report new cancer-pathway associations based on upward mobility genes that are not previously identified using driver genes alone, validate their role in cancer cell survival in vitro using extensive genome-wide RNAi and CRISPR data repositories, and further conduct in vitro functional screenings resulting in the validation of 18 previously unreported genes. CONCLUSION: Our analysis extends the spectrum of cancer-relevant genes and identifies novel potential therapeutic targets.

Germline variant burden in cancer genes correlates with age at diagnosis and somatic mutation burden.

Cancers harbor many somatic mutations and germline variants, we hypothesized that the combined effect of germline variants that alter the structure, expression, or function of protein-coding regions of cancer-biology related genes (gHFI) determines which and how many somatic mutations (sM) must occur for malignant transformation. We show that gHFI and sM affect overlapping genes and the average number of gHFI in cancer hallmark genes is higher in patients who develop cancer at a younger age (r = -0.77, P = 0.0051), while the average number of sM increases in increasing age groups (r = 0.92, P = 0.000073). A strong negative correlation exists between average gHFI and average sM burden in increasing age groups (r = -0.70, P = 0.017). In early-onset cancers, the larger gHFI burden in cancer genes suggests a greater contribution of germline alterations to the transformation process while late-onset cancers are more driven by somatic mutations.

Palatoscopy and odontometrics' potential role in sex determination among an adult Egyptian population sample: A pilot study.

Background: Sex determination is the first step in personal identification in the forensic field. This study aimed to evaluate sexual dimorphism by using palatal rugae pattern and odontometrics, construct an Egyptian equation for sex determination using the discriminant function analysis. Participants and methods: The study sample included 200 participants of both sexes. Maxillary arch impressions were taken with alginate impression material using stainless steel impression tray. Identification and measurements were done to study the palatal rugae pattern, dental arch dimensions, and maxillary canine. Results: There was a highly statistically significant difference between males and females with higher male values regarding the total palatal rugae number, wavy rugae number, primary rugae number, arch length, width, maxillary canine right width, maxillary canine left width, intercanine distance, and maxillary canine right index while there was a highly statistically significant difference with higher female value regarding the straight rugae numbers. Four Egyptian equations were constructed using discriminant function analysis to determine the sex of an unidentified person. Conclusions: Palatal rugae pattern, dental arch dimensions, and maxillary canine index can be considered as useful tools for sex determination in the forensic field.

Genomics and data science: an application within an umbrella.

Data science allows the extraction of practical insights from large-scale data. Here, we contextualize it as an umbrella term, encompassing several disparate subdomains. We focus on how genomics fits as a specific application subdomain, in terms of well-known 3 V data and 4 M process frameworks (volume-velocity-variety and measurement-mining-modeling-manipulation, respectively). We further analyze the technical and cultural "exports" and "imports" between genomics and other data-science subdomains (e.g., astronomy). Finally, we discuss how data value, privacy, and ownership are pressing issues for data science applications, in general, and are especially relevant to genomics, due to the persistent nature of DNA.

Clinical Behaviour and Marginal Sealing of Bulk-Fill Resin Composite Restorations Using Light Amplified High-Intensity LEDs Curing: A Randomized Controlled Clinical Trial.

BACKGROUND: Delivering sufficient intensity output of curing lights is mandatory to ensure optimum cure and clinical success of bulk-fill resin composite restorations and to avoid undesirable clinical outcomes. AIM: To evaluate the effectiveness of using light amplified high intensity LED curing on the clinical performance and marginal sealing of posterior bulk-fill resin composite restorations. MATERIAL AND METHODS: This study was designed as a randomised, controlled, double-blind, Unicenter, parallel, two arms, superiority trial with 1:1 allocation ratio. Adult patients who required posterior tooth-coloured restorations were asked to participate in this trial. All participants signed written informed consent after being completely aware of the settings of the study. The participants who fulfilled the eligibility criteria were divided into two groups according to the type of light curing mode used. Adhesive compound proximal cavities were prepared. All restorative materials were applied according to the respective manufacturer's instructions. Assessments of the restorations were done at baseline (one week after placement of the restoration), after 6 months and after 12 months using the modified US Public Health Service (USPHS) criteria. For quantitative assessment of the marginal sealing, resin replicas were analysed using scanning electron microscopy. Statistical analysis was done using Chi-square, Mann Whitney, independent t-test and dependent t-tests. RESULTS: There were no statistical differences between the two groups for the tested clinical parameters along the study periods. For marginal analysis, there were no statistical differences between the intervention and control group at baseline and six months (p-value = 0.347 and 0.516) respectively. At 12 months the control group showed statistically significant higher percentages (p-value = 0.031). CONCLUSION: Light amplified high-intensity curing units have clinical performance comparable with the conventional LED.