Genetic analysis of father-daughter incest using multifaceted STR markers and study of inheritance pattern of alleles

Background: Two cases involving father-daughter incest, a rare report in the Indian population, have been analyzed in the current study. STR markers on both autosomal and sex chromosomes were employed to expound the cases. Objective: To confirm the identity of the fetus as a product of father-daughter incest and to study the inheritance pattern of alleles in such cases. Results: In both cases, the aborted fetus was found to be the product of an incestuous father-daughter relationship. The probability of paternity as well as maternity was found to be >99.9999% in both cases. Analysis of other paternity and forensic parameters also substantiated the inclusion of the alleged individuals. Father-daughter incest had a tremendous effect on the genome as evidenced from the dramatical decrease in unrelated alleles between father/child [16.66% (Case 1), 20% (Case 2)] and mother/child [26.66% (Case 1), 21.66% (Case 2)]. Genetic evidence also suggested an increased biallelic match i.e., 26.66% (Case 1) and 33.33% (Case 2) between mother and fetus which are at par/ above the normal siblings’ values i.e., 26.66%. Conclusion: A significant increase in the percentage of homozygous alleles (53.33% in both cases) was observed in the product of father-daughter incest. Both daughters share the same X chromosome from the father, which also suggested the case to be of father-daughter incest. Similarly, the same Y-STR profile between the male fetus and alleged father confirmed the correct pattern of inherit1ance of the Y chromosome in this case.

Robustness of Laser Speckles as Unique Traceable Markers of Metal Components

The traceability of manufactured components is growing in importance with the greater use of digital service solutions offered and with an increased digitalization of manufacturing logistics. In this paper, we investigate the use of image-plane laser speckles as a tool to acquire a unique code from the surface of the component and the ability to use this pattern as a secure component-specific digital fingerprint. Intensity correlation is used as a numerical identifier. Metal sheets of different materials and steel pipes are considered. It is found that laser speckles are robust against surface alterations caused by surface compression and scratching and that the correct pattern reappears from a surface contaminated by oil after cleaning. In this investigation, the detectability is close to 100% for all surfaces considered, with zero false positives. The exception is a heavily oxidized surface wiped by a cotton cloth between recordings. It is further found that the main source for lost detectability is caused by misalignment between the registration and detection geometries where a positive match is lost by a change in angle in the order of 60 mrad. Therefore, as long as the registration and detection systems, respectively, use the same optical arrangement, laser speckles have the ability to serve as unique component identifiers without having to add extra markings or a dedicated sensor to the component.

Philibert de L’Orme’s Dome in the Chapel of the Château d’Anet: The Role of Stereotomy

ABSTRACTThe coffered dome designed by Philibert de L’Orme (1514-70) for the chapel of the Château d’Anet in northern France between 1549 and 1552 is a masterpiece of stereotomy — the stone vaulting technique characterised by the custom cutting (or dressing) of a vault’s components or voussoirs. The dome was executed by first individually dressing its large voussoirs, so that they would fit one another precisely, and then dry assembling them like the pieces of a three-dimensional jigsaw puzzle. The spiralling ribs that form the coffers added a layer of complexity to the work, for they are embedded in the voussoirs; thus the exact shape and position of the rib sections belonging to each voussoir had to be calculated precisely before dressing to ensure that, after assembling, they would form the correct pattern over the vault’s surface. The dome’s execution method continues to baffle historians, in particular with regard to the transfer of the complex pattern formed by the ribs on to the templates used by the stonecutters to shape the voussoirs. Based on a new 3D laser scan of the dome and on the analysis of late medieval and early modern stereotomic practices and theories, this article offers a new interpretation of the methods that de L’Orme adopted at Anet and of their significance within the panorama of sixteenth-century architectural practice and theory.

Automated Linescan Analysis for CMP Modeling

Many of the process steps used in semiconductor chip manufacturing require planar (smooth) surfaces on the wafer to ensure correct pattern printing and generation of multilevel interconnections in the chips during manufacturing. Chemical-mechanical polishing/planarization (CMP) is the primary process used to achieve these surface planarity requirements. Modeling of CMP processes allows users to detect and fix large surface planarity variations (hotspots) in the layout prior to manufacturing. Fixing hotspots before tape-out may significantly reduce turnaround time and the cost of manufacturing. Creating an accurate CMP model that takes into account complicated chemical and mechanical polishing mechanisms is challenging. Measured data analysis and extraction of erosion and dishing data from profile linescans from test chips are important steps in CMP model building. Measured linescans are often tilted and noisy, which makes the extraction of erosion and dishing data more difficult. The development and implementation of algorithms used to perform automated linescan analysis may significantly reduce CMP model building time and improve the accuracy of the models. In this work, an automated linescan analysis (ALSA) tool is presented that performs automated linescan delineation, test pattern separation, and automatic extraction of erosion and dishing values from linescan data.

Image-based textile decoding

A textile fabric consists of countless parallel vertical yarns (warps) and horizontal yarns (wefts). While common looms can weave repetitive patterns, Jacquard looms can weave the patterns without repetition restrictions. A pattern in which the warps and wefts cross on a grid is defined in a binary matrix. The binary matrix can define which warp and weft is on top at each grid point of the Jacquard fabric. The process can be regarded as encoding from pattern to textile. In this work, we propose a decoding method that generates a binary pattern from a textile fabric that has been already woven. We could not use a deep neural network to learn the process based solely on the training set of patterns and observed fabric images. The crossing points in the observed image were not completely located on the grid points, so it was difficult to take a direct correspondence between the fabric images and the pattern represented by the matrix in the framework of deep learning. Therefore, we propose a method that can apply the framework of deep learning viau the intermediate representation of patterns and images. We show how to convert a pattern into an intermediate representation and how to reconvert the output into a pattern and confirm its effectiveness. In this experiment, we confirmed that 93% of correct pattern was obtained by decoding the pattern from the actual fabric images and weaving them again.

Evaluation of Variances in VEGF-A-D and VEGFR-1-3 Expression in the Ishikawa Endometrial Cancer Cell Line Treated with Salinomycin and An-ti-Angiogenic/Lymphangiogenic Effect

Background: In cancer, an excessive and uncontrolled process of creating new blood and lymphatic vessels that play a key role in the metastasis process can be observed. The vascular endothelial growth factor (VEGF-A,-B,-C,-D) family together with their specific receptors (VEGFR-1,-2,-3) plays a key role in these processes, therefore it would be reasonable to determine the correct pattern of their expression. Objective: The study aimed to assess the use of salinomycin as an anti-angiogenic and anti-lymphangiogenic drug during endometrial cancer by examining changes in the expression pattern of VEGF-A, VEGF-B, VEGF-C, VEGF-D, VEGFR-1, VEGFR-2, VEGFR-3 depending on the treatment period of the Ishikawa endometrial cancer cells with salinomycin in comparison to the control culture. Materials and Methods: To determine how influential salinomycin was on the expression of both mRNAs, 1 µM of the drug was added to the cell culture and then it was cultured all together for 12,24 and 48 hour periods. The cells that made up the control culture were not treated with salinomycin. To determine the changes in the expression profile of the selected genes we used the microarray, techniques: RTqPCR and ELISA (p<0.05). Results: For all isoforms of VEGF-A-D as well as receptors of VEGFR-1-3, a decrease in expression under the influence of salinomycin was noted. For VEGF-A and VEGFR-1, the difference in the expression between the culture treated with salinomycin in comparison to the control was statistically significant (p=0.0004). In turn for VEGF-B, the difference between the culture exposed for 24 hours in comparison to the control (p=0.00000) as well as the comparison between H48 vs C (p=0.00000) was statistically significant. In reference to VEGF-C, VEGFR-2, VEGFR-3 the statistical analysis showed the significant difference in expression between the culture incubated with the drug for 12,24 and 48 hours in comparison to the control as well as between the selected times. For all of these comparisons, p=0.00000 was utilized. Conclusions: Salinomycin changes the expression pattern of VEGF-A, VEGF-B, VEGF-C, VEGF-D, VEGFR-1, VEGFR-2, and VEGFR-3 in endometrial cancer cells. The obtained results suggest that salinomycin might exert the effect via VEGF signaling pathways.

Synthesis of Neural Network Architecture for Recognition of Sea-Going Ship Images

Purpose of research. The current task is to monitor ships using video surveillance cameras installed along the canal. It is important for information communication support for navigation of the Moscow Canal. The main subtask is direct recognition of ships in an image or video. Implementation of a neural network is perspectively.Methods. Various neural network are described. images of ships are an input data for the network. The learning sample uses CIFAR-10 dataset. The network is built and trained by using Keras and TensorFlow machine learning libraries.Results. Implementation of curving artificial neural networks for problems of image recognition is described. Advantages of such architecture when working with images are also described. The selection of Python language for neural network implementation is justified. The main used libraries of machine learning, such as TensorFlow and Keras are described. An experiment has been conducted to train swirl neural networks with different architectures based on Google collaboratoty service. The effectiveness of different architectures was evaluated as a percentage of correct pattern recognition in the test sample. Conclusions have been drawn about parameters influence of screwing neural network on showing its effectiveness.Conclusion. The network with a single curl layer in each cascade showed insufficient results, so three-stage curls with two and three curl layers in each cascade were used. Feature map extension has the greatest impact on the accuracy of image recognition. The increase in cascades' number has less noticeable effect and the increase in the number of screwdriver layers in each cascade does not always have an increase in the accuracy of the neural network. During the study, a three-frame network with two buckling layers in each cascade and 128 feature maps is defined as an optimal architecture of neural network under described conditions. operability checking of architecture's part under consideration on random images of ships confirmed the correctness of optimal architecture choosing.

Interpreting patient-Specific risk prediction using contextual decomposition of BiLSTMs: application to children with asthma

Background Predictive modeling with longitudinal electronic health record (EHR) data offers great promise for accelerating personalized medicine and better informs clinical decision-making. Recently, deep learning models have achieved state-of-the-art performance for many healthcare prediction tasks. However, deep models lack interpretability, which is integral to successful decision-making and can lead to better patient care. In this paper, we build upon the contextual decomposition (CD) method, an algorithm for producing importance scores from long short-term memory networks (LSTMs). We extend the method to bidirectional LSTMs (BiLSTMs) and use it in the context of predicting future clinical outcomes using patients’ EHR historical visits. Methods We use a real EHR dataset comprising 11071 patients, to evaluate and compare CD interpretations from LSTM and BiLSTM models. First, we train LSTM and BiLSTM models for the task of predicting which pre-school children with respiratory system-related complications will have asthma at school-age. After that, we conduct quantitative and qualitative analysis to evaluate the CD interpretations produced by the contextual decomposition of the trained models. In addition, we develop an interactive visualization to demonstrate the utility of CD scores in explaining predicted outcomes. Results Our experimental evaluation demonstrate that whenever a clear visit-level pattern exists, the models learn that pattern and the contextual decomposition can appropriately attribute the prediction to the correct pattern. In addition, the results confirm that the CD scores agree to a large extent with the importance scores generated using logistic regression coefficients. Our main insight was that rather than interpreting the attribution of individual visits to the predicted outcome, we could instead attribute a model’s prediction to a group of visits. Conclusion We presented a quantitative and qualitative evidence that CD interpretations can explain patient-specific predictions using CD attributions of individual visits or a group of visits.

An Errors Analysis in Using Tenses of Students Writing Text at Senior High School

The objective of this investigation was to investigate students errors in writing recount text made by the eleventh grade students of social program Senior High School Number 1 and Senior High School Number 2 Muara Beliti in academic year 2016/2017. The method applied in this investigation was descriptive method. The subject of the investigation was all the eleventh grade students of social program Senior High School Number 1 and Senior High School Number 2 Muara Beliti. The number of the sample was 30 that consist of five students each class and each school. To get the data, the writer used essay test. The test given was an intruction to compose a paragraph in 150 words. Based on the finding of data analysis from the test. The majority errors done by the Eleventh Grade of Social Program Students of Senior High School Number 1Muara Beliti was Verb (55.36 percent) total errors was 62. Punctuation and spelling (4.46 percent) total errors was 5. Preposition (5,35 percent) total errors 6. Pronoun (34.83 percent) total errors was39.The majority errors donethe Eleventh of social program Grade Students of Senior High School Number 2 Muara Beliti wasVerb (61.67 percent) total errors was 37. Punctuation and spelling (1.66 percent) total errors was 1. Preposition (1.66 percent) total errors was1. Pronoun (35.01 percent) total errors was21. In conclusion, The students did such errors because the students translated directly from Indonesian language into English. Therefore, they forgot to use the correct pattern of a sentence that consist subject, verb, and object. Keywords: Writing, Errors, Tenses Analysis, Recount Text.