Liver esterase polymorphisms in sepat Siam (Trichogaster pectoralis)

Esterase D and general esterases (which use a- or J3-naphthyl acetate as substrates) were investigated electrophoretically in the paddy field fish, Trichogaster pectoralis. Variants were observed for these enzymes. It is hypothesized that esterase D phenotypes are due to two codominant alleles at an autosomal locus, and that four loci are involved in the control of the general esterases

Hexokinase, Malate Dehydrogenase, Fluorescent Esterase and Malic Enzyme Polymorphisms in the Cocoa Pod Borer, Conopomorpha cramerella (Snellen)

Cocoa pod borers collected in the field from Tawau, Sabah and from Sua Betong, Negeri Sembilan and rambutan fruit borers collected from Puchong and the campus of Universiti Pertanian Malaysia (UPM) in Serdang, Selangor, Malaysia were analysed by polyacrylamide gel electrophoresis. Hexokinase was found to be polymorphic in the UPM population, malate dehydrogenase in the Tawau, Sua Betong and UPM populations and fluorescent esterase and malic enzyme were polymorphic in all four populations

Pixel machine learning with clonal selection algorithm for lung nodules visualization

The early detection of lung nodules is critical to provide a better chance of survival from lung cancer. Since benign/malignant lung cancer may be caused by the growth of lung nodules, the diagnosis of an early detection of lung nodules is important. With rapidly development of advanced technology, detection of lung nodules becomes efficient by utilizing computer-aided detection (CAD) systems that can automatically detect and localize the nodules from computed tomography (CT) scans. CAD is fundamentally based on pattern recognition by extensive use of machine learning approaches which is highly interrelated to mathematical algorithms. In this study, a pixel machine learning algorithm which is developed by artificial immune system (AIS) based algorithm – Clonal Section Algorithm (CSA) is proposed for lung nodules visualization. By using pixel machine learning algorithm, several pre-processing procedures can be avoided to prevent the loss of information from image intensities. It is found that the proposed classification algorithm using original intensity values from CT scans is able to provide reasonable visualization results for lung nodules detection

Biochemical polymorphisms in the Malaysian water buffaloes

Ten enzymes and proteins: transferrin, amylase, haemoglobin, esterase D, red cell acid pho.phatau, superoxide dismutase, phosphoglycolate phosphatase, glucose-6-phosphate dehydrogenase, 6-phosphogluconate dehydrogenase and soluble glutamate oxaloace.tate transaminase, from the serum and red blood cell of 88 water buffaloes, Bubalus bubahs, have been investigated by starch gel or polyacrylamide gel electrophoresis. Four of these: transferrin, amylase, haemoglobin and esterase D show electrophoretic variation at polymorphic proportions

Peptidase Polymorphism in Natural Populations of the Cocoa Pest, Helopeltis theobromae Miller

Helopeltis theobromae caught off cocoa plants in Kuala Selangor and off Acalypha plants in Serdang, Selangor, Malaysia were investigated electrophoretically for nine biochemical markers: peptidase, a -glycerophosphate dehydrogenase, 6-phosphogluconate dehydrogenase, xanthine dehydrogenase, aldehyde oxidase, phosphoglucose isomerase, isocitrate dehydrogenase, glutamate transaminase and adenylate kinase. Only peptidase-2 polymorphism could be easily interpreted

An Electrophoretic Study of Natural Populations of the Cocoa Pod Borer, Canopomorpha cramerella (Snellen) from Malaysia

Cocoa pod borers from Tawau, Sabah and Sua Betong, Negeri Sembilan and rambutan fruit borers from Serdang and Puchong, Selangor and Kuala Kangsar, Perak, Malaysia were subjected to electrophoretic analysis in an effort to find diagnostic electromorphs between these two bio types of Conopomorpha cramerella. Thirty enzymes and general proteins were successfully demonstrated on zymograms but none of them could serve as diagnostic markers between cocoa pod borers and rambutan fruit borers. The allelic frequencies for 8 polymorphic enzymes are presented

Prognostic significance of minichromosome maintenance proteins in breast cancer.

A role for the minichromosome maintenance (MCM) proteins in cancer initiation and progression is slowly emerging. Functioning as a complex to ensure a single chromosomal replication per cell cycle, the six family members have been implicated in several neoplastic disease states, including breast cancer. Our study aim to investigate the prognostic significance of these proteins in breast cancer. We studied the expression of MCMs in various datasets and the associations of the expression with clinicopathological parameters. When considered alone, high level MCM4 overexpression was only weakly associated with shorter survival in the combined breast cancer patient cohort (n = 1441, Hazard Ratio = 1.31; 95% Confidence Interval = 1.11-1.55; p = 0.001). On the other hand, when we studied all six components of the MCM complex, we found that overexpression of all MCMs was strongly associated with shorter survival in the same cohort (n = 1441, Hazard Ratio = 1.75; 95% Confidence Interval = 1.31-2.34; p < 0.001), suggesting these MCM proteins may cooperate to promote breast cancer progression. Indeed, their expressions were significantly correlated with each other in these cohorts. In addition, we found that increasing number of overexpressed MCMs was associated with negative ER status as well as treatment response. Together, our findings are reproducible in seven independent breast cancer cohorts, with 1441 patients, and suggest that MCM profiling could potentially be used to predict response to treatment and prognosis in breast cancer patients.published_or_final_versio

Optimum symbol-by-symbol detection of uncoded digital data over the Gaussian channel with unknown carrier phase

10.1109/26.310614IEEE Transactions on Communications4282543-2552IECM

Deep learning models for predicting RNA degradation via dual crowdsourcing

Medicines based on messenger RNA (mRNA) hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition (‘Stanford OpenVaccine’) on Kaggle, involving single-nucleotide resolution measurements on 6,043 diverse 102–130-nucleotide RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504–1,588 nucleotides) with improved accuracy compared with previously published models. These results indicate that such models can represent in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for dataset creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales

Plato's Cave Algorithm: Inferring Functional Signaling Networks from Early Gene Expression Shadows

Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes) show a linear accumulation of product in the first hour after the change in the cell's state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Plato's Cave algorithm; PLACA) to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are feasible for any signaling network to predict the functional topology of the network and to identify novel relationships