Signature Patterns Research Articles

Non-contact imaging detection of thermal Hall effect signature by periodic heating method using lock-in thermography

We demonstrate non-contact imaging detection of the thermal Hall effect by means of the lock-in thermography with rotationally asymmetric periodic heating. In the lock-in thermography measurements, we applied periodic laser heating in an elliptical pattern to a sample and measured the temperature modulation oscillating at the same frequency as the heating under a magnetic field. From the magnetic field dependence of the elliptical heating pattern, the temperature modulation due to the thermal Hall effect can be extracted. We confirm the validity of this method by detecting a signature pattern specific to the thermal Hall effect in a Bi slab at room temperature.

Comparing different approaches for operationalizing subjective cognitive decline: Impact on syndromic and biomarker profiles

AbstractBackgroundA framework for research on subjective cognitive decline (SCD) was published in 2014, but different SCD operationalization approaches still need to be compared. We compared two hypotheses‐driven and two data‐driven approaches in the same sample of individuals.MethodsNine cognitive complaints were assessed on 399 cognitively healthy individuals from a community‐based cohort. Four SCD operationalization approaches were applied and the resulting groups were characterized using multivariate methods on comprehensive demographic, clinical, cognitive, and neuroimaging data.ResultsTwo main phenotypes were identified: first, an amnestic form associated with an Alzheimer’s Disease (AD) signature pattern of brain atrophy; second, an anomic form mainly related to cerebrovascular pathology. Complaints on other language components also allowed further identifying a subgroup with subclinical impairment in cognition and activities of daily living, as well as with an AD signature pattern of atrophy.ConclusionsThe phenotype of SCD, including syndromic and biomarker profiles, varies depending on the operationalization approach. We discuss how these findings may be used in clinical practice and research.

The Self-Reflexive Art of Don DeLillo. Graley Herren. New York: Bloomsbury Academic, 2019. Pp. viii+268.

<i>The Self-Reflexive Art of Don DeLillo</i>. Graley Herren. New York: Bloomsbury Academic, 2019. Pp. viii+268.

Genetic source completeness of HIV-1 circulating recombinant forms (CRFs) predicted by multi-label learning.

Infection with strains of different subtypes and the subsequent crossover reading between the two strands of genomic RNAs by host cells' reverse transcriptase are the main causes of the vast HIV-1 sequence diversity. Such inter-subtype genomic recombinants can become circulating recombinant forms (CRFs) after widespread transmissions in a population. Complete prediction of all the subtype sources of a CRF strain is a complicated machine learning problem. It is also difficult to understand whether a strain is an emerging new subtype and if so, how to accurately identify the new components of the genetic source. We introduce a multi-label learning algorithm for the complete prediction of multiple sources of a CRF sequence as well as the prediction of its chronological number. The prediction is strengthened by a voting of various multi-label learning methods to avoid biased decisions. In our steps, frequency and position features of the sequences are both extracted to capture signature patterns of pure subtypes and CRFs. The method was applied to 7185 HIV-1 sequences, comprising 5530 pure subtype sequences and 1655 CRF sequences. Results have demonstrated that the method can achieve very high accuracy (reaching 99%) in the prediction of the complete set of labels of HIV-1 recombinant forms. A few wrong predictions are actually incomplete predictions, very close to the complete set of genuine labels. https://github.com/Runbin-tang/The-source-of-HIV-CRFs-prediction. Supplementary data are available at Bioinformatics online.

Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator

BackgroundPerforming a statistical test requires a null hypothesis. In cancer genomics, a key challenge is the fast generation of accurate somatic mutational landscapes that can be used as a realistic null hypothesis for making biological discoveries.ResultsHere we present SigProfilerSimulator, a powerful tool that is capable of simulating the mutational landscapes of thousands of cancer genomes at different resolutions within seconds. Applying SigProfilerSimulator to 2144 whole-genome sequenced cancers reveals: (i) that most doublet base substitutions are not due to two adjacent single base substitutions but likely occur as single genomic events; (ii) that an extended sequencing context of ± 2 bp is required to more completely capture the patterns of substitution mutational signatures in human cancer; (iii) information on false-positive discovery rate of commonly used bioinformatics tools for detecting driver genes.ConclusionsSigProfilerSimulator’s breadth of features allows one to construct a tailored null hypothesis and use it for evaluating the accuracy of other bioinformatics tools or for downstream statistical analysis for biological discoveries. SigProfilerSimulator is freely available at https://github.com/AlexandrovLab/SigProfilerSimulator with an extensive documentation at https://osf.io/usxjz/wiki/home/.

Automated bank cheque verification using image processing and deep learning methods

Automated bank cheque verification using image processing is an attempt to complement the present cheque truncation system, as well as to provide an alternate methodology for the processing of bank cheques with minimal human intervention. When it comes to the clearance of the bank cheques and monetary transactions, this should not only be reliable and robust but also save time which is one of the major factor for the countries having large population. In order to perform the task of cheque verification, we developed a tool which acquires the cheque leaflet key components, essential for the task of cheque clearance using image processing and deep learning methods. These components include the bank branch code, cheque number, legal as well as courtesy amount, account number, and signature patterns. our innovation aims at benefiting the banking system by re-innovating the other competent cheque-based monetary transaction system which requires automated system intervention. For this research, we used institute of development and research in banking technology (IDRBT) cheque dataset and deep learning based convolutional neural networks (CNN) which gave us an accuracy of 99.14% for handwritten numeric character recognition. It resulted in improved accuracy and precise assessment of the handwritten components of bank cheque. For machine printed script, we used MATLAB in-built OCR method and the accuracy achieved is satisfactory (97.7%) also for verification of Signature we have used Scale Invariant Feature Transform (SIFT) for extraction of features and Support Vector Machine (SVM) as classifier, the accuracy achieved for signature verification is 98.10%.

Supervised data-driven approach for hyperspectral band selection using quantization

Hyperspectral image (HSI), acquired in narrow and contiguous bands, contains redundant information in neighbouring bands. In order to overcome the processing overhead of this data like Hughes phenomenon, dimension of HSI is reduced either by using feature extraction or selection technique. Most of the existing dimensionality reduction methods depend on the user input to provide the reduced set of bands. However, perceiving the required number of bands in advance is difficult, since this number varies with dataset. In order to cope up with this limitation some wrapper-based methods are suggested which are again computationally inefficient. Hence, the present research focuses on a supervised data-driven approach that enables in selecting required number of bands without any user intervention. In proposed method, signature patterns with minimum and maximum reflectance values are extracted for each class from the labeled data, which are subsequently quantized. The quantization process continues till unique patterns are obtained for each class. Finally, bands having maximum correlation and minimum variance are eliminated to ensure minimum redundancy among selected bands. The experimental result shows that, an improved classification accuracy is obtained while using the proposed method on two real HSIs as compared to other state-of-the-art methods.

Global volatile signature and polyphenols patterns in Vespolina wines according to vintage

SummaryThe global volatile signature of Vespolina wines from different vintages was established using solid‐phase microextraction combined with gas chromatography–mass spectrometry (HS‐SPME/GC‐qMS). Wines were also characterised in terms of bioactive compounds (such as individual polyphenols, biogenic amines and their precursors) by high‐performance liquid chromatography (RP‐HPLC). In addition, some physicochemical parameters, such as the total phenolic content, total tannins and antioxidant capacity, were evaluated. Seventy‐one volatile compounds and thirty‐three bioactive compounds were identified in Vespolina wines. The application of multivariate analysis to the obtained data revealed that 2‐phenylethyl acetate, ethyl nonanoate, 2‐hexanol, isoamyl octanoate and ethyl 2‐hydroxymethylbutanoate were the primary compounds responsible for Vespolina wines classification, mainly indicative for wines of 2015 and 2013 vintages. Conversely, wines from 2008 and 2009 vintages showed highest values of procyanidin B1, catechin, gallic acid, trans‐piceid and trans‐resveratrol.

Molecular and phylogenetic analysis of Vietnamese canine parvovirus 2C originated from dogs reveals a new Asia-IV clade.

Canine parvovirus type 2 (CPV-2) is a small, single-stranded DNA virus causing fatal haemorrhagic enteritis in dogs. Currently, CPV-2 is classified into CPV-2a, CPV-2b and CPV-2c based on genetic variation in the VP2 gene. The CPV-2c variant has become ubiquitous worldwide and gained attention for monitoring parvoviral evolution. In this study, we characterized the full-length genome sequences of CPV-2c strains obtained from 59 dogs in Vietnam. Molecular analysis revealed that Vietnamese CPV-2c shared a common evolutionary pattern with the Asian CPV-2 clade, which is marked by genetic signature patterns in the structural and nonstructural proteins. In addition, these Vietnamese CPV-2c strains exhibited unique Thr112Ile and Ile447Met mutations in the VP1 and VP2 sequence, respectively. Interestingly, phylogenetic analysis indicated that the mutations of amino acid residues in both the structural and nonstructural genes have contributed to the emergence of a new clade, designated here as the Asia-IV clade. The substitution rates, estimated from a dataset containing 199 sequences over the last 42years, confirmed that CPV-2 showed a high rate of nucleotide substitution, at about 2.49×10-4 nucleotide substitutions per site per year (nt/s/y), with VP1/2 and NS1/2 estimates of 3.06×10-4 and 3.16×10-4 nt/s/y, respectively. Even though no evidence of genetic recombination in these Vietnamese CPV-2c strains was established, potential positive selection sites were observed in both the structural and nonstructural genes, suggesting the viral evolutionary process has occurred in both the structural and nonstructural proteins. Genetic and evolutionary analysis of the full-length genome sequence is necessary to gain evolutionary insight of CPV-2.

In Silico Analysis of HSP70 Gene Family in Bovine Genome

Heat shock proteins (HSPs), members of molecular chaperones families fulfill essential roles under normal conditions and provide protection and adaptation during and after stress. Among different HSPs, HSP70 kDa family of proteins is most abundant and well-studied in human and mouse but has not yet been characterized in bovines. In silico analysis was performed to characterize members of HSP70 gene family in bovine genome and a total of 17 genes of bovine HSP70 gene family were identified. The members of HSP70 family were distributed over 12 chromosomes with gene size ranging from 1911 (HSPA2) to 54,017bp (HSPA4). Five genes were intronless, while rest of 12 genes were multiexonic. Phylogenetic analysis of HSP70 gene family distinguished them into eight major evolutionary groups wherein members of group 1 were most divergent and quite dissimilar than from rest of the HSP70 sequences. Domain structure of all bovine HSP70 genes was conserved and three signature patterns HSP70_1, HSP70_2, and HSP70_3 were identified. HSPA8, HSP9, and HSPA1A showed comparatively higher expression in majority of tissues. Like humans, bovine HSP70 family was characterized by remarkable evolutionary diversity. The analysis also suggested resemblance of bovine HSP70 family to that of human compared to mouse. Overall, the study indicates the presence of diversity for structure, function, localization, and expression in the bovine HSP70 family chaperons which could form the basis to understand thermotolerance/adaptive changes in the bovines.

Pre-trauma Growth under Terror Threat: Suggestive Communication Method in Anticipatory Trauma

ABSTRACT Counterterror measures put communities in a state of alertness for several years. Jewish schools in Europe respond with higher security measures, going through transformations in their daily routines and environments, marked by security rituals. Anticipating the trauma creates extreme situations that polarizes the sense of agency and alters the state of consciousness, having a collective impact. Analyzing the anticipatory trauma within the framework of the altered state of consciousness opens the possibility for intervening with suggestive communication methods that aim to increase the sense of agency. The positive effect of teachers learning the suggestive communication methods on the sense of agency was measured with the Sense of Agency Scale. The altered state of consciousness in anticipatory trauma opens the mind for the sense of agency changing in a signature pattern, in a way that is typical for a group, allowing cultural reproduction.

Abstract 5465: HRDetect as a predictive score of platinum response in advanced PDAC

Abstract Background: Predictors of response to DNA damaging agents may expand the population of patients who could benefit from platinum and/or PARP inhibition in pancreatic cancer (PDAC). The HRDetect score derived from whole genome sequencing (WGS) incorporates patterns of substitution base signatures and structural variation and is associated with tumours deficient in homologous recombination repair (HRD). Methods: The prospective COMPASS trial (NCT 02750657) enrolls patients with advanced PDAC prior to systemic treatment for WGS and RNAseq. Choice of combination chemotherapy is at the discretion of the physician. HRD tumours are identified by the presence of a number of known genomic characteristics including evidence of biallelic inactivation of BRCA/PALB2 or the RAD51 family of genes, the presence of signature 3, the number and proportion of indels that are deletions of at least 4 base pairs (bp), and the presence of rearrangement signatures 3 and 5. We ascertained the number of patients with high HRDetect scores (&amp;gt;0.7) and evaluated outcomes and response to chemotherapy including platinum regimens. Results: As of 1st May 2019 204 eligible patients were enrolled. High HRDetect scores (HRDetecthi ) were present in 26/204 (13%). Only half of these cases were represented by HRD tumours including: 9 germline BRCA1/2 or PALB2 cases with biallelic inactivation and 3 tumours with somatic biallelic inactivation of BRCA2, RAD51C and XRCC2. 1 additional HRD case was of unknown aetiology. All HRD cases were HRDetecthi. Two germline carriers of BRCA2 without a second somatic hit were HR intact with low HRDetect scores. Of the remaining 13 non-HRD tumours identified as HRDetecthi, 6 exhibited a tandem duplicator phenotype (TDP) with tandem duplications ranging from 10Kbp to 1Mbp in size. These tumours did not have any other genomic characteristics of HRD. Notably, of the 204 patients included in this analysis, a TDP was present in 15 (7%), of which 40% were HRDetecthi. In patients treated with modified FOLFIRINOX(mFFX) (n=111, *includes 2 patients treated with cisplatin/gemcitabine) the response rate in HRDetecthi patients (n=16) was 56% vs 20% in HRDetectlo patients (n=95) p=0.005. There was no difference in response rates according to HRDetect score in patients treated with gemcitabine-nab-paclitaxel; HRDetecthi (n=7) 14.3% vs. HRDetectlo(n=71) 32.8%, p=0.42. Median OS in all patients receiving mFFX (n=111) was 10.2 months; in patients with HRDetecthi PDAC, median OS was 15.5 mths vs. 9.9 mths in HRDetectlo PDAC, (HR 0.42, 95% CI 0.24-0.87, p=0.01). In all patients (n=78) receiving gemcitabine based combination treatment the median OS was 8.1 mths; in HRDetecthi patients median OS was 11.5mths vs 7.8 mths in HRDetectlo patients (HR 0.94, 95% CI 0.42-2.13, p=0.8). Conclusions: In patients with advanced PDAC with high HRDetect scores, response rate to FFX is over 50% and survival is significantly longer compared to patients with PDAC and low HRDetect scores. HRDetect identifies an additional population of patients with non-HRD PDAC who benefit from DNA damaging agents. Citation Format: Grainne M. O'Kane, Robert E. Denroche, Amy Zhang, Sarah Picardo, Robert C. Grant, MIchael J. Allen, Gun Ho Jang, Yifan Wang, Anna Dodd, Stephanie Rampotar, Shawn Hutchinson, Mustaphe Tehfe, James J. Biagi, Dianne Chadwick, Bernard Lam, Julie Wilson, Faiyaz Notta, Sandra E. Fischer, George Zogopoulos, Steven Gallinger, Jennifer J. Knox. HRDetect as a predictive score of platinum response in advanced PDAC [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5465.

Evidence of inverted gravity-driven variation in predictive sensorimotor function.

We move our eyes to place the fovea into the part of a viewed scene currently of interest. Recent evidence suggests that each human has signature patterns of eye movements like handwriting which depend on their sensitivity, allocation of attention and experience. Use of implicit knowledge of how earth's gravity influences object motion has been shown to aid dynamic perception. We used a projected ball-tracking task with a plain background offering no context cues to probe the effect of acquired experience about physical laws of gravitation on performance differences of 44 participants under a simulated gravity and an atypical (upward) antigravity condition. Performance measured by the unsigned difference between instantaneous eye and stimulus positions (RMSE) was consistently worse in the antigravity condition. In the vertical RMSE, participants took about 200ms longer to improve to the best performance for antigravity compared to gravity trials. The antigravity condition produced a divergence of individual performance which was correlated with levels of questionnaire-based quantified traits of schizotypy but not control traits. Grouping participants by high or low traits revealed a negative relationship between schizotypy trait level and both initiation and maintenance of tracking, a result consistent with trait-related impoverished sensory prediction. The findings confirm for the first time that where cues enabling exact estimation of acceleration are unavailable, knowledge of gravity contributes to dynamic prediction improving motion processing. With acceleration expectations violated, we demonstrate that antigravity tracking could act as a multivariate diagnostic window into predictive brain function.

The presence of Merkel cell carcinoma polyomavirus is associated with a distinct phenotype in neoplastic Merkel cell carcinoma cells and their tissue microenvironment.

AimsMerkel cell carcinoma (MCC) is an aggressive primary neuroendocrine tumor of the skin, associated with Merkel cell polyomavirus (MCPyV) in 49–89% of cases, depending on the country of origin and the techniques of detection. The presence of MCPyV defines heterogeneity in MCC; MCPyV-negative cases bear a much higher mutational load, with a distinct ultraviolet signature pattern featuring C > T transitions, as a consequence of exposure to ultraviolet light radiation. MCC stroma has not been thoroughly studied, although MCC patients benefit from therapy targeting PD1/PDL1.Methods and resultsIn this study, using Tissue Microarrays and immunohistochemistry, we have analyzed a series of 219 MCC cases in relation to the presence of MCPyV, and confirmed that the presence of MCPyV is associated with changes not only in the neoplastic cells, but also in the composition of the tumor stroma. Thus, MCPyV, found in 101/176 (57,4%) analyzable cases, exhibits changes in its tumor morphology, the density of the inflammatory infiltrate, the phenotype of the neoplastic cells, and the cell composition of the tumor stroma. MCPyV presence is negatively correlated with a higher level of p53 expression, and associated with a very high frequency (86%) of HLA-I expression loss, a higher apoptotic index, and a stroma richer in T-cells, cytotoxic T-cells, macrophages, PDL1-positive macrophages, and B-cells.ConclusionsOur findings provide evidence of the basic heterogeneity of MCC, supporting the hypothesis that the presence of MCPyV may induce a rich inflammatory response, which is at least partially avoided through loss of HLA-I antigen expression. On the other hand, MCPyV-negative cases show a much higher frequency of stronger p53 expression and, probably, p53 alterations.

Horizontal Gene Transfer as a Source of Conflict and Cooperation in Prokaryotes.

Horizontal gene transfer (HGT) is one of the most important processes in prokaryote evolution. The sharing of DNA can spread neutral or beneficial genes, as well as genetic parasites across populations and communities, creating a large proportion of the variability acted on by natural selection. Here, we highlight the role of HGT in enhancing the opportunities for conflict and cooperation within and between prokaryote genomes. We discuss how horizontally acquired genes can cooperate or conflict both with each other and with a recipient genome, resulting in signature patterns of gene co-occurrence, avoidance, and dependence. We then describe how interactions involving horizontally transferred genes may influence cooperation and conflict at higher levels (populations, communities, and symbioses). Finally, we consider the benefits and drawbacks of HGT for prokaryotes and its fundamental role in understanding conflict and cooperation from the gene-gene to the microbiome level.

Personality Prediction System Based on Signatures Using Machine Learning

Many methods are used to assess a person’s character and personality. Some are through the face, body movements, body language, handwriting and signatures. Assessing a person’s character and personality by looking at the type of handwriting and signature can be learned with the science of graphology. Detecting a person’s personality system based on a signature pattern automatically is still difficult. This study aims to build a system of predicting personality based on signature patterns using machine learning. A person’s personality based on a signature has many features. In this study the analysed features consist of four features, namely curve start, end streak, middle stroke, and underline. The steps taken in developing a prediction system are model training and model testing. The method used to extract features is Principle Component Analysis (PCA) and the method for classifying is Support Vector Machine (SVM). Based on the test results using confusion matrix produces an accuracy value of 71%. It can be concluded that machine learning can be implemented to predict personality based on signatures with good accuracy.

Abstract A02: CBX7 binds TWIST-1’s E-box to inhibit TWIST-1 function and curtail tumorigenecity and metastatic potential in ovarian cancer

Abstract Introduction: Ovarian cancer metastasizes via the transcoelomic route and yields overwhelmingly numerous micrometastases. This clinical presentation limits the value of standard of care and contributes to patient mortality. Whereas EMT is required in the acquisition of a migratory phenotype, MET has been shown to occur on secondary sites leading to the establishment of metastasis. The nature of the ovarian cancer cells that result after MET is, however, yet to be fully characterized. Whether these secondary epithelial ovarian cancer cells (sEOC) are required to repeat EMT in order to generate additional metastatic sites is not known. The objectives of this study are twofold: to determine if full MET occurs on secondary sites, and to determine mechanisms that confer to disseminated ovarian cancer cells the ability to rapidly spread and form the characteristic micrometastatic disease. Materials and Methods: Ovarian cancer spheroids were derived from three epithelial ovarian cancer (EOC) cell lines (OVCAR3, R182, R2615). sEOC were derived from the spheroids by plating in tissue culture-treated plates. Gene signature pattern was determined by RNAseq. mRNA and protein levels were quantified by RT-qPCR and Western blot, respectively. TWIST-1 activity was measured by a luciferase reporter system. Promoter binding was determined by anti-CBX7 chromatin IP followed by PCR. CBX7 was knocked out (KO) using CRISPR-Cas9. Tumorigenic potential was quantified by measuring resulting intraperitoneal burden (g) in nude mice. Results: PCA and clustering analysis of RNA-seq data showed sEOC do not fully recapitulate the molecular signature of the primary EOC cells from which they are derived. Despite displaying an epithelial morphology, sEOC maintain a high expression of the classical mesenchymal effector, TWIST-1. TWIST-1 is, however, transcriptionally inactive in these cells as it is inhibited by the PcG protein, CBX7. KO of CBX7 in sEOC was sufficient to promote a mesenchymal morphology, significantly promote TWIST-1-induced transcription of mir-199A (p = 0.004, wt vs. CBX7KO), and induce higher protein levels of FOXC2. CBX7 precludes TWIST-1 from accessing its promoter by binding at or near the E box. Finally, KO of CBX7 was able to significantly enhance tumorigenicity and increase tumor load and metastatic potential in vivo (p=0.033, wt vs CBX7 KO). Conclusion: We demonstrate that full MET does not occur in ovarian cancer cells at secondary metastatic sites. Although sEOC display an epithelial morphology, they do not achieve the full molecular signature of the primary EOC cells from which they are derived. sEOC maintain high levels of TWIST-1 whose activity is inhibited by CBX7. This regulation allows secondary tumors to achieve an epithelial morphology while conferring the advantage to achieve a prompt reversal to a mesenchymal morphology and gain in tumorigenic potential upon perturbation of CBX7. This may explain ovarian cancer’s rapid progression and characteristic micrometastatic disease. Citation Format: Juanni Li, Ayesha B Alvero, Sudhakar Nuti, Roslyn Tedja, Cai M. Roberts, Mary Pitruzzello, Yimin Li, Qing Xiao, Sai Zhang, Yaqi Gan, Gil Mor, Gang Yin. CBX7 binds TWIST-1’s E-box to inhibit TWIST-1 function and curtail tumorigenecity and metastatic potential in ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr A02.

Forest fire flame and smoke detection from UAV-captured images using fire-specific color features and multi-color space local binary pattern

In recent years, the frequency and severity of forest fire occurrence have increased, compelling the research communities to actively search for early forest fire detection and suppression methods. Remote sensing using computer vision techniques can provide early detection from a large field of view along with providing additional information such as location and severity of the fire. Over the last few years, the feasibility of forest fire detection by combining computer vision and aerial platforms such as manned and unmanned aerial vehicles, especially low cost and small-size unmanned aerial vehicles, have been experimented with and have shown promise by providing detection, geolocation, and fire characteristic information. This paper adds to the existing research by proposing a novel method of detecting forest fire using color and multi-color space local binary pattern of both flame and smoke signatures and a single artificial neural network. The training and evaluation images in this paper have been mostly obtained from aerial platforms with challenging circumstances such as minuscule flame pixels, varying illumination and range, complex backgrounds, occluded flame and smoke regions, and smoke blending into the background. The proposed method has achieved F1 scores of 0.84 for flame and 0.90 for smoke while maintaining a processing speed of 19 frames per second. It has outperformed support vector machine, random forest, Bayesian classifiers and YOLOv3, and has demonstrated the capability of detecting challenging flame and smoke regions of a wide range of sizes, colors, textures, and opacity.

Differential Gene Expression and Allele Frequency Changes Favour Adaptation of a Heterogeneous Yeast Population to Nitrogen-Limited Fermentations.

Alcoholic fermentation is fundamentally an adaptation process, in which the yeast Saccharomyces cerevisiae outperforms its competitors and takes over the fermentation process itself. Although wine yeast strains appear to be adapted to the stressful conditions of alcoholic fermentation, nitrogen limitations in grape must cause stuck or slow fermentations, generating significant economic losses for the wine industry. One way to discover the genetic bases that promote yeast adaptation to nitrogen-deficient environments are selection experiments, where a yeast population undergoes selection under conditions of nitrogen restriction for a number of generations, to then identify by sequencing the molecular characteristics that promote this adaptation. In this work, we carried out selection experiments in bioreactors imitating wine fermentation under nitrogen-limited fermentation conditions (SM60), using the heterogeneous SGRP-4X yeast population, to then sequence the transcriptome and the genome of the population at different time points of the selection process. The transcriptomic results showed an overexpression of genes from the NA strain (North American/YPS128), a wild, non-domesticated isolate. In addition, genome sequencing and allele frequency results allowed several QTLs to be mapped for adaptation to nitrogen-limited fermentation. Finally, we validated the ECM38 allele of NA strain as responsible for higher growth efficiency under nitrogen-limited conditions. Taken together, our results revealed a complex pattern of molecular signatures favouring adaptation of the yeast population to nitrogen-limited fermentations, including differential gene expression, allele frequency changes and loss of the mitochondrial genome. Finally, the results suggest that wild alleles from a non-domesticated isolate (NA) may have a relevant role in the adaptation to the assayed fermentation conditions, with the consequent potential of these alleles for the genetic improvement of wine yeast strains.

Causal inferencing of -omics data from The Cancer Genome Atlas: Lung adenocarcinoma tumors for mechanistic disease characterization and feature engineering.

e21016 Background: Advances in high throughput measurement technologies (-omics data) have made it possible to generate high complexity, high volume data for oncology research. Researchers are often confronted many more measurements than samples (p &gt; &gt; &gt; n), which poses challenges for both modeling the complexity of disease at the molecular mechanism level, and overfitting when generating predictive models with complex data. Here, we applied a prior knowledge-driven approach to characterize and classify heavy versus light smokers with lung cancer from The Cancer Genome Atlas, an open source repository that catalogs, harmonizes and hosts -omics data collected from samples generously donated from cancer patients. Methods: We applied a reverse inferencing approach to systematically interrogate RNAseq measurements from tumor and control biopsies against a knowledgebase of directed gene networks curated from published experiments. If patterns observed in the data are significantly similar to those in a network, an inference about the directional activity of that network can be made; e.g., the increased transcriptional activity of NFKB. Our library was nucleated through an open sourced knowledge graph and enhanced with updated and relevant knowledge using the open sourced Biological Expression Language framework. Directed networks were either qualitatively scored and used to build disease models, or semi-quantitatively scored and used as classification features. Results: In LUAD tumors, we detected a pattern of gene signatures which indicated a tumor stem cell-like phenotype characterized by predicted decreases in the activity of pro-differentiation factors and an increased response to hypoxia. Analysis of patients with heavy ( &gt; 40) versus light ( &lt; 10) pack-year burden suggested an augmented dedifferentiation profile in heavy smokers. In this example, improved classification was observed through features compression through directed network scoring compared to using individual RNA measurements selected by filtration methods. Conclusions: In-silico analysis of lung cancer patient biopsies generated hypotheses implicating stem cell signaling in tumors, and a further stratification of this signal based on patient pack year burden. Mechanistic modeling may be a useful application to the overfitting problem often encountered with -omics data in translational studies. Data from other TCGA indications can be used to evaluate the consistency of this type of approach