NEURAL NETWORK-BASED APPROACH TO RESISTIVITY LOGS EXPRESS SIMULATION IN REALISTIC MODELS OF COMPLEX TERRIGENOUS SEDIMENTS

The article proposes a new algorithmic approach to resistivity logs simulation based on convolutional neural networks wich allows constructing algorithms for solving forward problems for specific logging tools in detailed models of near-wellbore space with thin layers, accounting for radial resistivity changes, borehole wall irregularities and drilling fluid displacement by the logging tool. Experimental algorithms for expressmodeling for three common Russian galvanic and induсtion logging methods in two-dimensional models of the near-wellbore space have been implemented based on the proposed approach. Logs simulation using the developed neural network algorithms is multi pletimes faster than using numerical solvers. The proposed solutions open up possibilities to use more sophisticated basic geoelectric models of the near-wellbore space. The use of models adequate in complexity to the actual target geological objects will increase the reliability of interpretation results of resistivity logs measured in complex geological conditions.

Introducing Depth Information Into Generative Target Tracking

Common visual features used in target tracking, including colour and grayscale, are prone to failure in a confusingly similar-looking background. As the technology of three-dimensional visual information acquisition has gradually gained ground in recent years, the conditions for the wide use of depth information in target tracking has been made available. This study focuses on discussing the possible ways to introduce depth information into the generative target tracking methods based on a kernel density estimation as well as the performance of different methods of introduction, thereby providing a reference for the use of depth information in actual target tracking systems. First, an analysis of the mean-shift technical framework, a typical algorithm used for generative target tracking, is described, and four methods of introducing the depth information are proposed, i.e., the thresholding of the data source, thresholding of the density distribution of the dataset applied, weighting of the data source, and weighting of the density distribution of the dataset. Details of an experimental study conducted to evaluate the validity, characteristics, and advantages of each method are then described. The experimental results showed that the four methods can improve the validity of the basic method to a certain extent and meet the requirements of real-time target tracking in a confusingly similar background. The method of weighting the density distribution of the dataset, into which depth information is introduced, is the prime choice in engineering practise because it delivers an excellent comprehensive performance and the highest level of accuracy, whereas methods such as the thresholding of both the data sources and the density distribution of the dataset are less time-consuming. The performance in comparison with that of a state-of-the-art tracker further verifies the practicality of the proposed approach. Finally, the research results also provide a reference for improvements in other target tracking methods in which depth information can be introduced.

Shadow Detection and Removal Technique using CNN

The Shadow detection and removal Technique is used in many real-world applications, such as surveillance systems, computer vision applications and indoor outdoor system. The shape and orientation of an object, as well as the light source, can be revealed by shadows in an image. In a traffic surveillance system, the shadow can misclassify the actual target, lowering the system’s accuracy. Numerous algorithms and techniques have been developed by researchers to aid in the detection and removal of shadows in images. This paper aims to provide an overview of different shadow detection and removal techniques, their advantages and drawbacks. Also implementation of Convolutional Neural Network for shadow detection and OpenCV features to remove shadows by re-designing the output and analysing different loss functions to train the network. Keywords: Shadow Detection and Removal Techniques, Shadow Image Processing.

Fine mapping of candidate regions associated with fat deposition in thin and fat tail sheep breeds suggests new insights into molecular aspects of fat tail selection

Background Fatness related traits are economically very important in sheep production and are associated with serious diseases in humans. The fat tail is a phenotype that divides domesticated sheep into two major groups. The objective of the present study is to refine the map location of candidate regions associated with fat deposition, obtained via two separate whole genome scans contrasting thin and fat tail breeds, and to determine the nature of the selection occurring in these regions using hitchhiking approach. Results Zel (thin tail) and Lori-Bakhtiari (fat tail) breed samples that had previously been run on the Illumina Ovine 50k BeadChip, were genotyped with a denser set of SNPs in the three candidate regions using a Sequenom Mass ARRAY platform. Statistical tests were then performed using different and complementary methods based on either site frequency (FST and Median homozygosity) or haplotype (iHS and XP-EHH). Results from candidate regions on chromosome 5 and X revealed clear evidence of selection with the derived haplotypes that were consistent with selection to near fixation for the haplotypes affecting fat tail size in the fat tail breed. Analysis of the candidate region on chromosome 7 indicated that selection differentiated the beneficial alleles between breeds and homozygosity has increased in the thin tail breed which also had the ancestral haplotype. These results enabled us to confirm the signature of selection in these regions and refine the critical intervals from 113kb, 201kb and 2,831kb to 28kb, 142kb and 1,006kb on chromosome 5, 7 and X respectively. These regions contain several genes associated with fat metabolism or developmental processes consisting TCF7 and PPP2CA (OAR5), PTGDR and NID2 (OAR7), AR, EBP, CACNA1F, HSD15B, SLC35A2, BMP15, WDR13 and RBM3 (OAR X), each of which could potentially be the actual target of selection. Study of core haplotypes alleles in our regions of interest also supported the hypothesis that the first domesticated sheep were thin tailed and fat tail animals were developed later. Conclusions Our results provide a comprehensive assessment of how and where selection has affected the patterns of variation in candidate regions associated with fat deposition in thin and fat tail sheep breeds. The hitchhiking mapping approach in this study was novel in the sense that most of the exploratory genome scan studies in domestic animals have not clarified the signal from the candidate regions, probably due to the lack of suitable genomic resources.

Epistemic artifacts and the modal dimension of modeling

The epistemic value of models has traditionally been approached from a representational perspective. This paper argues that the artifactual approach evades the problem of accounting for representation and better accommodates the modal dimension of modeling. From an artifactual perspective, models are viewed as erotetic vehicles constrained by their construction and available representational tools. The modal dimension of modeling is approached through two case studies. The first portrays mathematical modeling in economics, while the other discusses the modeling practice of synthetic biology, which exploits and combines models in various modes and media. Neither model intends to represent any actual target system. Rather, they are constructed to study possible mechanisms through the construction of a model system with built-in dependencies.

Exploring the learner profile of the English Home Language classroom in select urban secondary schools

In a democratic South Africa, English has increasingly become the preferred medium of instruction despite the majority of South African learners being mother tongue speakers of other languages. Many learners in urban areas are enrolled to take English Home Language and especially novice teachers expect them to have mother tongue proficiency. However, the reality is that learners come from diverse backgrounds and a single class comprises learners with varying levels of English proficiency. This study seeks to establish who is the actual target audience seated in the socalled English Home Language class. Quantitative data were collected through a questionnaire completed by 642 Grade 8 and 9 respondents at three suburban schools in Gauteng. Cross tabulations were used to compare different variables. Key findings indicate that respondents – although multilingual – do not considerthemselves adequately proficient in English. Secondly, the role of the caregiver as initial source of learning English has been underestimated. Although smallscale, the study highlights the mismatchbetween classroom reality and curriculum requirements. Results suggest that the national education authorities need to adapt policy documents so that what is currently expected of learners might be more easily accomplished in the English class. The questionnaire may serve as a useful resource to determine the linguistic profile of a particular target group.

Acyclic nucleoside phosphonates with adenine nucleobase inhibit Trypanosoma brucei adenine phosphoribosyltransferase in vitro

All medically important unicellular protozoans cannot synthesize purines de novo and they entirely rely on the purine salvage pathway (PSP) for their nucleotide generation. Therefore, purine derivatives have been considered as a promising source of anti-parasitic compounds since they can act as inhibitors of the PSP enzymes or as toxic products upon their activation inside of the cell. Here, we characterized a Trypanosoma brucei enzyme involved in the salvage of adenine, the adenine phosphoribosyl transferase (APRT). We showed that its two isoforms (APRT1 and APRT2) localize partly in the cytosol and partly in the glycosomes of the bloodstream form (BSF) of the parasite. RNAi silencing of both APRT enzymes showed no major effect on the growth of BSF parasites unless grown in artificial medium with adenine as sole purine source. To add into the portfolio of inhibitors for various PSP enzymes, we designed three types of acyclic nucleotide analogs as potential APRT inhibitors. Out of fifteen inhibitors, four compounds inhibited the activity of the recombinant APRT1 with Ki in single µM values. The ANP phosphoramidate membrane-permeable prodrugs showed pronounced anti-trypanosomal activity in a cell-based assay, despite the fact that APRT enzymes are dispensable for T. brucei growth in vitro. While this suggests that the tested ANP prodrugs exert their toxicity by other means in T. brucei, the newly designed inhibitors can be further improved and explored to identify their actual target(s).

Design and Simulation of InP and Silicon Nanowires With Different Channel Characteristic as Biosensors to Improve Output Sensitivity

This research contains a good comparison among technologies of SiNW-FET/InPNW-FET depending on the size of channel and dopants in channel for biosensing application based on the width and dopants for two types of silicon and InP materials in the nanowire channel. A device numerical modelling tool, Silvaco ATLAS is used in step one to design three p-type SiNW-FET/InPNW-FET biosensors with a channel width of 40­­­ nm, 60 nm and 70 nm for these two types of materials and in step two to design three p-type SiNW-FET/InPNW-FET biosensors with different dopants of 0.1×1014 cm-3, 1×1014 cm-3 and 10×1014 cm-3 for these two types of materials. Their sensing process is depended on the alteration in charge density that causes changing in the electric field at the surface of the SiNW-FET/InPNW-FET. The resistivity of the device is changed when a negatively charged biomolecules species has a chemical reaction with the external surface of a P-type SiNW-FET/InPNW-FET. To investigate the effect of different channel width and dopants on the performance of the SiNW-FET/InPNW-FET biosensor, several negatively interface charge densities, QF (-0.1×1012 cm-2, -0.5×1012 cm-2, and -1×1012 cm-2) are introduced on the surface of the SiNW-FET/InPNW-FET channel to represent as the actual target analytics (DNA) captured by the bioreceptor of the biosensor. Based on the results, these negatively QF attract the hole carriers below the surface of p-type nanowire causes to collect carriers in the channel, and make an increase in the device output ID. Increase of the applied negative charge density has allowed for more ID to flow across the channel between drain and source region. The changes of ID with the applied QF are utilized to determine the sensitivities for all designed biosensor with different channel width and channel dopants. The minimum nanowire width of 40 nm with the minimum nanowire dopants of 0.1×1014 cm-3 for the high sensitivity silicon state of 3.6 μA/cm-2 compared to the indium phosphide state of 2.8 μA/cm-2. So the best performance for detecting the desired analyte in the silicon state with the lowest width and dopant to be seen.

HDAC6 inhibitors sensitize non-mesenchymal triple-negative breast cancer cells to cysteine deprivation

Triple-negative breast cancer (TNBC) is a highly malignant type of breast cancer and lacks effective therapy. Targeting cysteine-dependence is an emerging strategy to treat the mesenchymal TNBC. However, many TNBC cells are non-mesenchymal and unresponsive to cysteine deprivation. To overcome such resistance, three selective HDAC6 inhibitors (Tubacin, CAY10603, and Tubastatin A), identified by epigenetic compound library screening, can synergize with cysteine deprivation to induce cell death in the non-mesenchymal TNBC. Despite the efficacy of HDAC6 inhibitor, knockout of HDAC6 did not mimic the synthetic lethality induced by its inhibitors, indicating that HDAC6 is not the actual target of HDAC6 inhibitor in this context. Instead, transcriptomic profiling showed that tubacin triggers an extensive gene transcriptional program in combination with erastin, a cysteine transport blocker. Notably, the zinc-related gene response along with an increase of labile zinc was induced in cells by the combination treatment. The disturbance of zinc homeostasis was driven by PKCγ activation, which revealed that the PKCγ signaling pathway is required for HDAC6 inhibitor-mediated synthetic lethality. Overall, our study identifies a novel function of HDAC6 inhibitors that function as potent sensitizers of cysteine deprivation and are capable of abolishing cysteine-independence in non-mesenchymal TNBC.

Airport Detection-Based Cosaliency on Remote Sensing Images

This paper proposes a contour extraction model based on cosaliency detection for remote sensing image airport detection and improves the traditional line segmentation detection (LSD) algorithm to make it more suitable for the goal of this paper. Our model consists of two parts, a cosaliency detection module and a contour extraction module. In the first part, the cosaliency detection module mainly uses the network framework of Visual Geometry Group-19 (VGG-19) to obtain the result maps of the interimage comparison and the intraimage consistency, and then the two result maps are multiplied pixel by pixel to obtain the cosaliency mask. In the second part, the contour extraction module uses superpixel segmentation and parallel line segment detection (PLSD) to refine the airport contour and runway information to obtain the preprocessed result map, and then we merge the result of cosaliency detection with the preprocessed result to obtain the final airport contour. We compared the model proposed in this article with four commonly used methods. The experimental results show that the accuracy of the model is 15% higher than that of the target detection result based on the saliency model, and the accuracy of the active contour model based on the saliency analysis is improved by 1%. This shows that the model proposed in this paper can extract a contour that closely matches the actual target.