PubMed QUEST: The PubMed Query Search Tool. An informatics tool to aid cancer centers and cancer investigators in searching the PubMed databases

Searching PubMed for citations related to a specific cancer center or group of authors can be labor-intensive. We have created a tool, PubMed QUEST, to aid in the rapid searching of PubMed for publications of interest. It was designed by taking into account the needs of entire cancer centers as well as individual investigators. The experience of using the tool by our institution’s cancer center administration and investigators has been favorable and we believe it could easily be adapted to other institutions. Use of the tool has identified limitations of automated searches for publications based on an author’s name, especially for common names. These limitations could likely be solved if the PubMed database assigned a unique identifier to each author

Differential proteomic alterations between localised and metastatic prostate cancer

Molecular alterations in the prostate cancer proteome mediate the functional and phenotypic transformation from clinically localised to metastatic cancer, a transition that drives patient's mortality and challenges therapeutic intervention. A first approximation of differential proteomic alterations stratified by disease stage has yielded repertoires of potential diagnostic and prognostic markers, multiplex signatures of predictive value, and yield fundamental insight into molecular commonalities in cancer progression. Deciphering these causative proteomic alterations from the molecular noise will continue to mature our understanding of tumour biology and drive new computational and integrative approaches to model a system's view that accommodates the heterogeneity of prostate cancer progression

Oculus: faster sequence alignment by streaming read compression

Abstract Background Despite significant advancement in alignment algorithms, the exponential growth of nucleotide sequencing throughput threatens to outpace bioinformatic analysis. Computation may become the bottleneck of genome analysis if growing alignment costs are not mitigated by further improvement in algorithms. Much gain has been gleaned from indexing and compressing alignment databases, but many widely used alignment tools process input reads sequentially and are oblivious to any underlying redundancy in the reads themselves. Results Here we present Oculus, a software package that attaches to standard aligners and exploits read redundancy by performing streaming compression, alignment, and decompression of input sequences. This nearly lossless process (> 99.9%) led to alignment speedups of up to 270% across a variety of data sets, while requiring a modest amount of memory. We expect that streaming read compressors such as Oculus could become a standard addition to existing RNA-Seq and ChIP-Seq alignment pipelines, and potentially other applications in the future as throughput increases. Conclusions Oculus efficiently condenses redundant input reads and wraps existing aligners to provide nearly identical SAM output in a fraction of the aligner runtime. It includes a number of useful features, such as tunable performance and fidelity options, compatibility with FASTA or FASTQ files, and adherence to the SAM format. The platform-independent C++ source code is freely available online, at http://code.google.com/p/oculus-bio .http://deepblue.lib.umich.edu/bitstream/2027.42/112673/1/12859_2012_Article_5548.pd

Deep Learning based CNN Model for Classification and Detection of Individuals Wearing Face Mask

In response to the global COVID-19 pandemic, there has been a critical demand for protective measures, with face masks emerging as a primary safeguard. The approach involves a two-fold strategy: first, recognizing the presence of a face by detecting faces, and second, identifying masks on those faces. This project utilizes deep learning to create a model that can detect face masks in real-time streaming video as well as images. Face detection, a facet of object detection, finds applications in diverse fields such as security, biometrics, and law enforcement. Various detector systems worldwide have been developed and implemented, with convolutional neural networks chosen for their superior performance accuracy and speed in object detection. Experimental results attest to the model's excellent accuracy on test data. The primary focus of this research is to enhance security, particularly in sensitive areas. The research paper proposes a rapid image pre-processing method with masks centred on faces. Employing feature extraction and Convolutional Neural Network, the system classifies and detects individuals wearing masks. The research unfolds in three stages: image pre-processing, image cropping, and image classification, collectively contributing to the identification of masked faces. Continuous surveillance through webcams or CCTV cameras ensures constant monitoring, triggering a security alert if a person is detected without a mask.Comment: 8 Pages , 6 figures , 1 Tabl

Subclinical coronary atherosclerosis identified by coronary computed tomographic angiography in asymptomatic morbidly obese patients

Obesity is a common public health problem and obese individuals in particular have a disproportionate incidence of acute coronary events. This study was undertaken to identify coronary artery lesions as well as associated clinical features, risk factors and demographics in patients with a body mass index (BMI) >40 kg/m2 without known coronary artery disease (CAD). Morbidly obese subjects were prospectively recruited to undergo coronary computed tomographic angiography (CCTA) using a dual-source computed tomography (CT) system. CAD was defined as the presence of any atherosclerotic lesion in any one coronary artery segment. The presence, location, and severity of atherosclerosis were related to patient characteristics. Forty-one patients (28 women, mean age, 50.4±10.0 years, mean BMI, 43.8±4.8 kg/m2) served as the study population. Of these, 25 patients (61%) had at least one coronary stenosis. All but 2 patients within the CAD cohort had coronary artery calcium (CAC) scores >0, and most plaques identified (75.4%) were non-calcified. There was a predilection of calcified and non-calcified atherosclerosis involving the left anterior descending (LAD) coronary artery compared with other coronary segments. Univariate predictors of CAD included older age, dyslipidemia, and diabetes. In this preliminary study of young morbidly obese patients, CCTA detected a high prevalence of calcified and non-calcified CAD, although the later predominated

Feature Selection and Molecular Classification of Cancer Using Genetic Programming

AbstractDespite important advances in microarray-based molecular classification of tumors, its application in clinical settings remains formidable. This is in part due to the limitation of current analysis programs in discovering robust biomarkers and developing classifiers with a practical set of genes. Genetic programming (GP) is a type of machine learning technique that uses evolutionary algorithm to simulate natural selection as well as population dynamics, hence leading to simple and comprehensible classifiers. Here we applied GP to cancer expression profiling data to select feature genes and build molecular classifiers by mathematical integration of these genes. Analysis of thousands of GP classifiers generated for a prostate cancer data set revealed repetitive use of a set of highly discriminative feature genes, many of which are known to be disease associated. GP classifiers often comprise five or less genes and successfully predict cancer types and subtypes. More importantly, GP classifiers generated in one study are able to predict samples from an independent study, which may have used different microarray platforms. In addition, GP yielded classification accuracy better than or similar to conventional classification methods. Furthermore, the mathematical expression of GP classifiers provides insights into relationships between classifier genes. Taken together, our results demonstrate that GP may be valuable for generating effective classifiers containing a practical set of genes for diagnostic/ prognostic cancer classification

Internet-based profiler system as integrative framework to support translational research

BACKGROUND: Translational research requires taking basic science observations and developing them into clinically useful tests and therapeutics. We have developed a process to develop molecular biomarkers for diagnosis and prognosis by integrating tissue microarray (TMA) technology and an internet-database tool, Profiler. TMA technology allows investigators to study hundreds of patient samples on a single glass slide resulting in the conservation of tissue and the reduction in inter-experimental variability. The Profiler system allows investigator to reliably track, store, and evaluate TMA experiments. Here within we describe the process that has evolved through an empirical basis over the past 5 years at two academic institutions. RESULTS: The generic design of this system makes it compatible with multiple organ system (e.g., prostate, breast, lung, renal, and hematopoietic system,). Studies and folders are restricted to authorized users as required. Over the past 5 years, investigators at 2 academic institutions have scanned 656 TMA experiments and collected 63,311 digital images of these tissue samples. 68 pathologists from 12 major user groups have accessed the system. Two groups directly link clinical data from over 500 patients for immediate access and the remaining groups choose to maintain clinical and pathology data on separate systems. Profiler currently has 170 K data points such as staining intensity, tumor grade, and nuclear size. Due to the relational database structure, analysis can be easily performed on single or multiple TMA experimental results. The TMA module of Profiler can maintain images acquired from multiple systems. CONCLUSION: We have developed a robust process to develop molecular biomarkers using TMA technology and an internet-based database system to track all steps of this process. This system is extendable to other types of molecular data as separate modules and is freely available to academic institutions for licensing

Molecular profiling of human prostate tissues: insights into gene expression patterns of prostate development during puberty

Testosterone production surges during puberty and orchestrates massive growth and reorganization of the prostate gland, and this glandular architecture is maintained thereafter throughout adulthood. Benign prostatic hyperplasia (BPH) and prostate adenocarcinoma (PCA) are common diseases in adulthood that do not develop in the absence of androgens. Our objective was to gain insight into gene expression changes of the prostate gland at puberty, a crucial juncture in prostate development that is androgen dependent. Understanding the role played by androgens in normal prostate development may provide greater insight into androgen involvement in prostatic diseases. Benign prostate tissues obtained from pubertal and adult age group cadaveric organ donors were harvested and profiled using 20,000 element cDNA microarrays. Statistical analysis of the microarray data identified 375 genes that were differentially expressed in pubertal prostates relative to adult prostates including genes such as Nkx3.1, TMEPAI, TGFBR3, FASN, ANKH, TGFBR2, FAAH, S100P, HoxB13, fibronectin, and TSC2 among others. Comparisons of pubertal and BPH expression profiles revealed a subset of genes that shared the expression pattern between the two groups. In addition, we observed that several genes from this list were previously demonstrated to be regulated by androgen and hence could also be potential in vivo targets of androgen action in the pubertal human prostate. Promoter searches revealed the presence of androgen response elements in a cohort of genes including tumor necrosis factor‐α induced adipose related protein, which was found to be induced by androgen. In summary, this is the first report that provides a comprehensive view of the molecular events that occur during puberty in the human prostate and provides a cohort of genes that could be potential in vivo targets of androgenic action during puberty.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154303/1/fsb2fj042415fje.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154303/2/fsb2fj042415fje-sup-0001.pd