Neural network architecture optimization using automated machine learning for borehole resistivity measurements

Deep neural networks (DNNs) offer a real-time solution for the inversion of borehole resistivity measurements to approximate forward and inverse operators. Using extremely large DNNs to approximate the operators is possible, but it demands considerable training time. Moreover, evaluating the network after training also requires a significant amount of memory and processing power. In addition, we may overfit the model. In this work, we propose a scoring function that accounts for the accuracy and size of the DNNs compared to a reference DNNs that provides good approximations for the operators. Using this scoring function, we use DNN architecture search algorithms to obtain a quasi-optimal DNN smaller than the reference network; hence, it requires less computational effort during training and evaluation. The quasi-optimal DNN delivers comparable accuracy to the original large DNN.PDC2021-121093-I00 IA4TES RYC2021-032853-

Interleukin-1[beta] drives prostate cancer cell cooperation in the bone metastatic niche

Advanced prostate cancer frequently leads to bone metastasis, a major cause of morbidity and mortality. In this study, we reveal a novel phenomenon in which interleukin-1[beta] derived from one population of disseminated prostate cancer cells conditions the bone microenvironment to enable metastatic colonization by a diverse cohort of prostate cancer cells. We propose that this cooperation among cancer cells demonstrates a functional role for phenotypic heterogeneity in human bone metastasis, which may be clinically exploited for the benefit of patients with metastatic prostate adenocarcinoma.Ph.D., Pharmacology and Physiology -- Drexel University, 201

Identification of Turnip mosaic virus isolated from canola in northeast area of Iran

During March and April of 2011, 436 samples showing viral disease symptoms were collected from canola fields in the Khorasan Razavi province. The samples were tested by double-antibody sandwich (DAS)-enzyme linked immunosorbent assay (ELISA) for the presence of Turnip mosaic virus (TuMV). Among the 436 samples, 117 samples were found to be infected with TuMV. One of the infected samples from Govareshk region (TuMV-IRN GSK) was selected for biological purification. Total RNA of this isolate were extracted and reverse transcriptase (RT)-PCR was performed with specific primers according to the coat protein gene. PCR products (986 bp) was first purified and then directly sequenced. Phylogenetic analyses based on ClustalW multiple alignments with previously reported 33 isolates indicated 88 to 98% similarity in nucleotide and 94 to 99% in amino acid levels among isolates. TuMV-IRN GSK represented the highest identity to another Iranian isolate (IRN TRa6). Phylogenetic tree clustered all sequences into four groups and IRN GSK fell into the basal-B group. Nucleotide and amino acid distances between IRN GSK and other isolates in the basal-B group showed that this isolate was closely related to another Iranian isolate IRN TRa6, and distinct from other isolates in the basal-B group. These results indicate that TuMV is a common pathogen of canola crops in the Khorasan Razavi province.Key words: Turnip mosaic virus (TuMV), canola, reverse-transcription polymerase chain reaction (RT-PCR), coat protein gene, sequence analysis

A numerical 1.5D method for the rapid simulation of geophysical resistivity measurements

In some geological formations, borehole resistivity measurements can be simulated using a sequence of 1D models. By considering a 1D layered media, we can reduce the dimensionality of the problem from 3D to 1.5D via a Hankel transform. The resulting formulation is often solved via a semi-analytic method, mainly due to its high performance. However, semi-analytic methods have important limitations such as, for example, their inability to model piecewise linear variations on the resistivity. Herein, we develop a multi-scale finite element method (FEM) to solve the secondary field formulation. This numerical scheme overcomes the limitations of semi-analytic methods while still delivering high performance. We illustrate the performance of the method with numerical synthetic examples based on two symmetric logging-while-drilling (LWD) induction devices operating at 2 MHz and 500 KHz, respectively

Characterizing the degradation of alginate hydrogel for use in multilumen scaffolds for spinal cord repair

Alginate was studied as a degradable nerve guidance scaffold material in vitro and in vivo. In vitro degradation rates were determined using rheology to measure the change in shear modulus vs time. The shear modulus decreased from 155 kPa to 5 kPa within 2 days; however, alginate samples maintained their superficial geometry for over 28 days. The degradation behavior was supported by materials characterization data showing alginate consisted of high internal surface area (400 m2/g), which likely facilitated the release of cross‐linking cations resulting in the rapid decrease in shear modulus. To assess the degradation rate in vivo, multilumen scaffolds were fabricated using a fiber templating technique. The scaffolds were implanted in a 2‐mm‐long T3 full transection rodent spinal cord lesion model for 14 days. Although there was some evidence of axon guidance, in general, alginate scaffolds degraded before axons could grow over the 2‐mm‐long lesion. Enabling alginate‐based scaffolds for nerve repair will likely require approaches to slow its degradation. © 2015 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 104A: 611–619, 2016.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137597/1/jbma35600.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137597/2/jbma35600_am.pd

Separation of Crocin/Betanin Using Aqueous Two-phase Systems Containing Ionic Liquid and Sorbitol

Betanin and crocin, two food additives with attractive colors, are bioactive compounds of plants that are widely used in food, pharmaceutical, and medical industries. These bioactive pigments are sensitive to light, heat, organic solvents, and pH. It seems that a benign economic method is needed to extract these biomolecules, especially for industrial applications. The aqueous two-phase system (ATPS) is a liquid-liquid extraction technique that has shown its potential in recent years to extract and separate biomolecules. In this study, an ATPS consisting of carbohydrate (sorbitol) and ionic liquid (tetrabutyl phosphonium bromide) has been proposed as a new separation system with unique properties to study the partition coefficient of crocin and betanin. The results indicated that crocin and betanin had more tendency to the ionic liquid (IL)-rich phase and carbohydrate-rich phase, respectively. The influence of the concentration of IL and sorbitol on the partition coefficient was studied. The results showed that an increase in the tie-line length (concentrations) increased the partition coefficient of crocin and betanin. Enhancement in temperature increased the partition coefficient of crocin. The highest values of crocin recovery (97.55 %) and partition coefficient (39.85) at 308 K show that our proposed ATPS can be considered for crocin separation in one step

Modeling extra-deep electromagnetic logs using a deep neural network

Modern geosteering is heavily dependent on real-time interpretation of deep electromagnetic (EM) measurements. We have developed a methodology to construct a deep neural network (DNN) model trained to reproduce a full set of extra-deep EM logs consisting of 22 measurements per logging position. The model is trained in a 1D layered environment consisting of up to seven layers with different resistivity values. A commercial simulator provided by a tool vendor is used to generate a training data set. The data set size is limited because the simulator provided by the vendor is optimized for sequential execution. Therefore, we design a training data set that embraces the geologic rules and geosteering specifics supported by the forward model. We use this data set to produce an EM simulator based on a DNN without access to the proprietary information about the EM tool configuration or the original simulator source code. Despite using a relatively small training set size, the resulting DNN forward model is quite accurate for the considered examples: a multilayer synthetic case and a section of a published historical operation from the Goliat field. The observed average evaluation time of 0.15 ms per logging position makes it also suitable for future use as part of evaluation-hungry statistical and/or Monte Carlo inversion algorithms within geosteering workflows.POCTEFA 2014-2020 PIXIL (EFA362/19) MTM2016-76329-

Extreme capsule is a bottleneck for ventral pathway

As neuroscience literature suggests, extreme capsule is considered a whiter matter tract. Nevertheless, it is not clear whether extreme capsule itself is an association fiber pathway or only a bottleneck for other association fibers to pass. Via our review, investigating anatomical position, connectivity and cognitive role of the bundles in extreme capsule, and by analyzing data from the dissection, it can be argued that extreme capsule is probably a bottleneck for the passage of uncinated fasciculus (UF) and inferior fronto-occipital fasciculus (IFOF), and these fasciculi are responsible for the respective roles in language processing. © 202

Enhancement of Vanillin Partitioning and Recovery in Nanoparticle-based Aqueous Two-phase System Containing PEG and Dextran Polymers

Vanillin, widely utilized in the food, medicinal, and pharmaceutical industries, requires an improved extraction process that is cost-effective and environmentally friendly to meet the growing industrial demand. To tackle this challenge, we conducted an investigation on a nanoparticle-based aqueous two-phase system (ATPS), incorporating polyethylene glycol (PEG) and dextran (DEX). The primary objective was to develop an ATPS that is non-alkaline, operates under mild environmental conditions, and is both non-toxic and cost-effective. The study focused on identifying a suitable nanoparticle that could improve the partitioning of vanillin in ATPS and facilitate economically favorable separation processes. Various nanoparticles were evaluated as additives to enhance vanillin partitioning. The study explores the influence of parameters, such as polymer weight percentages and DEX molecular weight on vanillin partitioning and recovery percentage. Additionally, the impact of incorporating different nanoparticles was assessed in the optimized system composed of 6.5 wt% PEG6000 and 7.8 wt% DEX15000. Results indicate that the addition of only 0.001 g of silver nanoparticles to the optimal system improved the partition coefficient by 42 % and the vanillin recovery percentage by approximately 8 % compared to the nanoparticle-free ATPS