Binary Process Research Articles

Metal contamination in a sediment core from Osaka Bay during the last 400 years

Osaka Bay adjacent to the Kyoto–Osaka–Kobe metropolitan area was affected by severe metal pollution during the twentieth century; yet little is known about the trace metal sources and pre-industrial human activities. We have determined the elemental concentrations and zinc stable isotope ratios (δ66Zn) in bulk sediments and the trace metal concentrations in chemical fractions of a 9-m-long sediment core from Osaka Bay. Our goals were (1) to reconstruct the historical trace metal contamination, and (2) to identify anthropogenic Zn sources and the solid phases of anthropogenic trace metals. The core provided a continuous environmental record of the last 2300 years based on radiocarbon dating of molluscan shells. Copper, Zn, and Pb showed an initial enrichment from the 1670s AD, which could be caused by human activities due to an increasing population. In agreement with previous findings, the trace metal concentrations slightly increased from the 1870s, strongly increased from the beginning of the twentieth century, and peaked around 1960 before environmental pollution control laws were enacted. Increasing trace metal concentrations in the acid-labile and reducible fractions obtained by the Community Bureau of Reference (BCR) sequential extraction procedure toward the surface indicate carbonates and Mn oxyhydroxides were the primary fractions for anthropogenic trace metals. The δ66Zn values (1) were constant until the 1940s, suggesting that the average δ66Zn of industrial sources was indistinguishable from that value of the natural background, (2) showed a slight decrease from the 1950s and remained constant until the present, and (3) fell in a binary mixing process between a lithogenic (~ + 0.27‰) and an anthropogenic endmember (~ + 0.17‰), the latter likely representing a mixture of various Zn sources such as road dust, tire wear, industrial effluents, and effluents from wastewater treatment plants. We conclude the combination of Zn stable isotopes together with chemical fractions obtained by the BCR method represents a promising approach to assess the trace metal sources and their potential mobility in sediment cores from anthropogenically affected coastal areas.

A semi-supervised adaptive Markov Gaussian embedding process (SAMGEP) for prediction of phenotype event times using the electronic health record

While there exist numerous methods to identify binary phenotypes (i.e. COPD) using electronic health record (EHR) data, few exist to ascertain the timings of phenotype events (i.e. COPD onset or exacerbations). Estimating event times could enable more powerful use of EHR data for longitudinal risk modeling, including survival analysis. Here we introduce Semi-supervised Adaptive Markov Gaussian Embedding Process (SAMGEP), a semi-supervised machine learning algorithm to estimate phenotype event times using EHR data with limited observed labels, which require resource-intensive chart review to obtain. SAMGEP models latent phenotype states as a binary Markov process, and it employs an adaptive weighting strategy to map timestamped EHR features to an embedding function that it models as a state-dependent Gaussian process. SAMGEP’s feature weighting achieves meaningful feature selection, and its predictions significantly improve AUCs and F1 scores over existing approaches in diverse simulations and real-world settings. It is particularly adept at predicting cumulative risk and event counting process functions, and is robust to diverse generative model parameters. Moreover, it achieves high accuracy with few (50–100) labels, efficiently leveraging unlabeled EHR data to maximize information gain from costly-to-obtain event time labels. SAMGEP can be used to estimate accurate phenotype state functions for risk modeling research.

On the relations between letter, word, and sentence-level processing during reading

Much prior research on reading has focused on a specific level of processing, with this often being letters, words, or sentences. Here, for the first time in adult readers, we provide a combined investigation of these three key component processes of reading comprehension. We did so by testing the same group of participants in three tasks thought to reflect processing at each of these levels: alphabetic decision, lexical decision, and grammatical decision. Participants also performed a non-reading classification task, with an aim to partial-out common binary decision processes from the correlations across the three main tasks. We examined the pairwise partial correlations for response times (RTs) in the three reading tasks. The results revealed strong significant correlations across adjacent levels of processing (i.e., letter-word; word-sentence) and a non-significant correlation between non-adjacent levels (letter-sentence). The results provide an important new benchmark for evaluating computational models that describe how letters, words, and sentences contribute to reading comprehension.

Subducted Front of the Indian Continental Crust Beneath the Tibetan Plateau in the Early Eocene

AbstractThe immense Tibetan Plateau is a masterpiece of the ongoing India–Asia collision. The timing of the collision is a critical parameter to reconstruct the plateau evolution, but has been not well understood yet. Here, we report a newly discovered Eocene (53.8 Ma) shoshonitic intrusion with the input of Indian continental material in the Yangbajing area of the Lhasa Terrane, south Tibet. The reverse 207Pb/206Pb zoning exhibited by feldspars, the linear isotopic arrays between whole‐rock 187Os/188Os and 1/188Os, the in‐situ feldspar 208Pb/204Pb and 206Pb/204Pb values, and the in‐situ zircon εHf(t) values and δ18O values strongly demonstrate a binary interaction process for the generation of the Yangbajing shoshonitic intrusion. Our Os isotopes further illustrate that the interaction process was dominated by recycled crustal melt assimilating minor mantle melt. In addition, different isotopic systems consistently point to an end‐member represented by the underthrust crust slice of Indian continent, which indicates that the Indian lithosphere must have already subducted beneath the Lhasa Terrane at 53.8 Ma. This is also supported by the abrupt isotopic shift toward the enriched values and the composition variation toward the high potassium contents in the subsequent Tibetan magmatism, corresponding to an increasing involvement of the Indian continental crust into the upper plate magmatism. Therefore, the Yangbajing shoshonitic intrusion is one of the earliest known magmatic evidence for the onset of the India–Asia collision.

T-channel singularities in cosmology and particle physics

The t-channel singularity of a cross section for a binary 2→2 scattering occurs when the particle exchanged in the t-channel is kinematically allowed to be on its mass shell, that is when the process can be viewed as a sequence of two physical subprocesses: a two-body decay 1→2 and an inverse decay 2→1. We derive conditions for the singularity to be present in a binary process. A class of divergent cross sections for Standard Model processes has been determined and illustrated by the weak analog of the Compton scattering Ze−→Ze−. After critically reviewing regularization methods proposed in literature, we discuss singular processes that occur in a medium composed of particles in thermal equilibrium. The medium is shown to regulate the singularities naturally as particles acquire a non-zero width. We demonstrate a possible cosmological application by calculating thermally averaged cross section for an elastic scattering within a simple scalar model. The transition probability, which is divergent in vacuum, becomes finite when the process occurs in a thermal bath due to the imaginary part of the t-channel mediator's self-energy computed within the Keldysh-Schwinger formalism.

Uncertainty-of-Information Scheduling: A Restless Multiarmed Bandit Framework

This paper proposes using the uncertainty of information (UoI), measured by Shannon’s entropy, as a metric for information freshness. We consider a system in which a central monitor observes M binary Markov processes through m communication channels (m<M). The UoI of a Markov process corresponds to the monitor’s uncertainty about its state. At each time step, only m Markov processes can be selected to update its state to the monitor; hence there is a tradeoff among the UoIs of the processes that depend on the scheduling policy used to select the processes to be updated. The age of information (AoI) of a process corresponds to the time since its last update. In general, the associated UoI can be a non-increasing function, or even an oscillating function, of its AoI, making the scheduling problem particularly challenging. This paper investigates scheduling policies that aim to minimize the average sum-UoI of the processes over the infinite time horizon. We formulate the problem as a restless multi-armed bandit (RMAB) problem, and develop a Whittle index policy that is near-optimal for the RMAB after proving its indexability. We further provide an iterative algorithm to compute the Whittle index for the practical deployment of the policy. Although this paper focuses on UoI scheduling, our results apply to a general class of RMABs for which the UoI scheduling problem is a special case. Specifically, this paper’s Whittle index policy is valid for any RMAB in which the bandits are binary Markov processes and the penalty is a concave function of the belief state of the Markov process. Numerical results demonstrate the excellent performance of the Whittle index policy for this class of RMABs.

Determination of Fiber Content in 3-D Printed Composite Parts Using Image Analysis

Fiber-reinforced composite parts used in drones, automobiles, and sports equipment are now being manufactured by additive manufacturing (AM), where the material parameters such as fiber direction can be changed within a layer or from one layer to the other. Nondestructive evaluation methods are required to assess the quality of such customized printed parts. In this work, a microcomputed tomography ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> CT) dataset is analyzed to determine the fiber content in a 3-D printed composite material part using a digital binary image processing method. The existing literature on binary image analysis methods to measure the fiber volume fraction is limited to continuous fiber reinforced composites. Discontinuous fiber reinforced 3-D printing filaments are popular in manufacturing parts with increased strength. The methods developed in this work expands the binary image process to scans that show fibers embedded lengthwise in different directions in the 3-D printed layers. An optimized thresholding method is trained on the filament sample and then applied to 3-D printed samples. The results show fiber volume fraction measurements with standard deviations below 0.15%. The results in this work will be useful for product quality validation.

Activation Energy and Inclination Magnetic Dipole Influences on Carreau Nanofluid Flowing via Cylindrical Channel with an Infinite Shearing Rate

Background: The infinite shear viscosity model of Carreau fluid characterizes the attitude of fluid flow at a very high/very low shear rate. This model has the capacity for interpretation of fluid at both extreme levels, and an inclined magnetic dipole in fluid mechanics has its valuable applications such as magnetic drug engineering, cold treatments to destroy tumors, drug targeting, bio preservation, cryosurgery, astrophysics, reaction kinetics, geophysics, machinery efficiency, sensors, material selection and cosmology. Novelty: This study investigates and interprets the infinite shear rate of Carreau nanofluid over the geometry of a cylindrical channel. The velocity is assumed to be investigated through imposing an inclined magnetic field onto cylindrical geometry. Activation energy is utilized because it helps with chemical reactions and mass transport. Furthermore, the effects of thermophoresis, the binary chemical process and the Brownian movement of nanoparticles are included in this attempt. Formulation: The mathematics of the assumed Carreau model is derived from Cauchy stress tensor, and partial differential equations (PDEs) are obtained. Similarity transformation variables converted these PDEs into a system of ordinary differential equations (ODEs). Passing this system under the bvp4c scheme, we reached at numerical results of this research attempt. Findings: Graphical debate and statistical analysis are launched on the basis of the obtained computed numerical results. The infinite shear rate aspect of Carreau nanofluid gives a lower velocity. The inclined magnetic dipole effect shows a lower velocity but high energy. A positive variation in activation energy amplifies the concentration field.

Sistem Pencocokan Aksara Bali Menggunakan Template Matching Dan Komputasi Paralel

Balinese script is a traditional script used to write Balinese language and literature. Balinese script is also one of the richness of Balinese culture. However, Balinese script has a unique shape that is almost the same as each other, and some writings are only distinguished by one line stroke. This can complicate learning as well as in developing applications in this modern era. So because I did research in making Balinese script matching systems using template matching and parallel computation in order to find out what the value of the average dissimilarity of each Balinese script letter can be used as a reference in developing applications related to Balinese script.&#x0D; In this study I used the template matching method to find out the average error value of each Balinese accented letter contained in the bali simbar font. From the method, each pixel value of the Balinese script that has been processed in the binary process will match each pixel value to obtain an error value. The error value will be looked for the average and will be made a table to make it easier to find the largest MSE value or Balinese script that has a small similarity value. In the template matching process, parallel computation is also inserted as well as making comparisons between applications that use parallel computing and do not use parallel computing in order to know whether there is a difference in processing time or not.&#x0D; Based on the research conducted, the results obtained that the largest MSE value of 0.071354167 has a similarity of 93%. Where this shows that the similarity of the Balinese script to the others has similarities above 90% and the results of the data processing time for systems that use parallel computing are 4977 and systems that do not use parallel computing are 4789.&#x0D; &#x0D; Keywords: Template matching, parallel computing, Balinese script matching&#x0D;

The Supernatural after the Neuro-Turn edited by Pieter F. Craffert, John R. Baker, Michael J. Winkelman

In the book chapter "Neurocognitive processes and supernatural beliefs," Andrew Newberg and David Yaden describe a basic cognitive process they call the "binary process." According to their model, this should contribute to the development of supernatural beliefs in addition to other processes. They understand this to be a general structuring process that organizes objects and abstract ideas into into dyads; that is, into relations of two, e.g., good and bad, happy and sad, natural and supernatural, right and wrong. The latter dyad points precisely to a problem I have with large parts of the book. The texts of the authors involved—all of whom are men—are almost universally characterized by such a binary thought structure. One of the editors, Pieter Craffert, a neuroanthropologist who teaches in South Africa, puts it succinctly in Chapter, “The supernatural: A range of neurocultural phenomena,” when he writes:&#x0D; If transcendental theorists are correct, there is not only a whole range of entities and phenomena with powerful influences on the world that beg explanation, but the scientific enterprise as we know it needs radical transformation. If non-transcendental theorists are correct, their theories pose a challenge to nearly all religions as well as local and cultural explanations of the phenomena. (p. 24)&#x0D; All authors of the book represent non-transcendental positions and attribute the category of the supernatural to “this-worldly” causes. It is not clear to me whether Craffert is aware that he leaves the area of scientific argumentation with the statement quoted above. With this escalation, there is only an either-or: Either they or we are right. It does not seem to occur to the authors that many of the non-transcendental explanations for supernatural interpretations of events and the arising of corresponding beliefs may be plausible and, in many cases, sufficient, but still do not capture the full picture. At least a more modest and reflective attitude that allows for this possibility is not reflected in the texts—with few exceptions.

Binary multi-modal matrix factorization for fast item cold-start recommendation

Hashing technology can support large-scale recommendation very effectively due to its advantages of low storage cost and high recommendation efficiency. However, existing hashing-based recommendation methods often suffer from item cold-start problem. This is because they simply consider the user-item interaction and the single content information of the items, but the full interaction history is not always available and the single auxiliary information may be missing. To solve this issue, in this paper we propose a Binary Multi-modal Matrix Factorization (BMMF) method. First, we propose an efficient consensus multi-modal mapping to transform the heterogeneous multi-modal features to the unified factors by exploiting the complementarity of multiple modalities. Then, binary matrix factorization is simultaneously performed on the multi-modal features of the items and past user preferences to learn the compact binary codes of the users/items in a common Hamming space. In addition, inspired by the observation that similar instances often have similar binary codes within a short Hamming distance, we formulate a semantic structure regularization term to preserve the similarities of the items during the binary embedding process. Finally, we develop an effective Discrete Coordinate Descent (DCD) approach to tackle the formulated discrete hash optimization problem directly. Experiments on three publicly available real-world datasets demonstrate the superiority of the proposed method against the state-of-the-art methods. Our source codes and testing datasets are available athttps://github.com/pcm1217/BMMF.

Cu isotope records of Cu-based antifouling paints in sediment core profiles from the largest European Marina, The Port Camargue

The intensive use of copper (Cu) compounds as an alternative biocide in antifouling paints (APs) has resulted in wide Cu contamination into the marine environment, especially near marina harbor activities. In this work, the applicability of Cu isotopes to discriminate Cu origins related to the use of Cu-based APs in marine environments was tested. To this, Cu isotopes in APs, shipyard sludges, and sediment cores sampled in the Cu-contaminated Mediterranean marina of Port Camargue were determined. APs represent an important dominant anthropogenic source for metals in this site, making it ideal to test Cu isotopes as tracers. The overall isotope composition of four sediment cores and a surface sample varied between −0.13 and 0.44 ‰ (δ65Cu relative to NIST-976). Selected APs brands show a similar Cu concentration ~0.15 % and δ65Cu average of 0.54 ± 0.05 ‰. The plot of δ65Cu vs concentration for all datasets allowed dissociating natural and APs end-members. However, sample isotope systematics were not consistent with a conservative mixing binary source process. Heavily Cu-contaminated sediments show isotope signatures lighter than APs brands. However, the most Cu-contaminated sample, located directly above the careening area, shows a δ65Cu slightly lighter than APs (0.44 ‰ vs 0.54 ‰, respectively). Results suggest the preferential releasing of a heavy isotope pool by APs when these compounds are solubilized in seawater. The isotope fractionation was attributed to potential chemical Cu coordination changes during its elemental partition between paint and marina seawater and the fractionation induced by the organic ligands in the water column, before deposition. Further laboratory experiments are recommended to model the isotope fractionation mechanisms related to Cu release by APs. Because the APs' isotope signature is modified in marine environments, the use of Cu isotopes as tracers of AP in marine environments is challenging and needs more investigation.

Imitation learning for coordinated human–robot collaboration based on hidden state-space models

This paper proposes a novel coordinated human–robot collaboration framework based on the hidden state-space model, which probabilistically clones the human behaviour and presents dynamic features in a nonparametric form. Derived from the filter prediction techniques and the theory of exact moment matching, this framework could provide an analytical approximation of the posterior distribution, and hence infer the hidden state variables of the collaborative robot given the external observation and its uncertainties. Not akin to the other cutting-edge movement-primitive based algorithms or coordinated human–robot collaboration methods, our collaboration framework not only preserves the adaptation functionalities of imitation learning but also propagates state variables and their uncertainties during real-time coordinated implementation. By leveraging on the binary Gaussian process classification, additional functionality, such as multiple task recognition is proposed to enhance the generalisation capability of our framework. The application feasibility is verified from both theoretical comparison simulation and real-world experiments.

A binary tournament competition algorithm for solving partial differential equation constrained optimization via finite element method

Sometimes, in a tournament competition to form a single team from two teams, 50% players are selected from the union of both teams according to their performance (fitness). Considering this idea, the aim of this article is to design an algorithm based on binary tournament process using two different types of metaheuristic algorithms. Here, at first, four swarms of populations are considered. The first two swarms are updated by Gaussian quantum behaved particle swarm optimization (GQPSO) algorithm and other two swarms are updated by weighted quantum behaved particle swarm optimization (WQPSO) algorithm considering second strategy among six strategies of existing binary tournamenting process. Thereafter, a swarm is formed from the updated solutions found from GQPSO and also from WQPSO algorithms. Finally, an algorithm is selected randomly with equal probability to update the final swarm. To test the effectiveness of the proposed algorithm, it is applied on partial differential equation (PDE) constrained optimization problems transforming these into bound constrained optimization problems using finite element discretization method and the obtained results are compared with the parent algorithms, WQPSO and GQPSO numerically. Also, the results are compared with a number of existing metaheuristic algorithms. Finally, to check the significance of the results and also to draw the fruitful conclusion, nonparametric statistical tests are performed.

Modified local binary patterns based feature extraction and hyper parameters tuned attention segmental recurrent neural network classifier using flamingo search optimization algorithm for disease diagnosis model

SummaryIn this article, the modified local binary patterns based feature extraction and hyper parameters tuned attention segmental recurrent neural network classifier with flamingo search optimization algorithm (MLBPFE‐ASRNNFSOAC‐DDM) is proposed for the disease diagnosis model. Here, breast cancer, diabetic, chronic kidney diseases diagnosis model is implemented. Initially, images are considered as dataset for disease diagnosis, which is given to the altered phase preserving dynamic range compression (APPDRC) scheme for preprocessing process. This APPDRC is used to preserve local features for boundary detection, thus; recovers image quality. Then the morphological, grayscale statistical and Haralick texture features are taken from preprocessed image with the help of modified local binary pattern process. The extracted features are given with attention segmented recurrent neural network (ASRNN) for classification. Then the weight parameters of ASRNN classifier are optimized by flamingo search optimization algorithm (FSOA), which increases the classification accuracy. This simulation process is accomplished at MATLAB platform. The proposed method, during Diabetic Diagnosis Model attains higher accuracy 16.4%, 21.45%, 30.38%, and 21.01% compared with the existing methods like FE‐ResNetV2DNNMSOC‐DDM and CMVHHO‐DKMLC‐DDM, CNN‐CAD‐DDM, and AD‐CFDRI‐SDL, respectively. During breast cancer Diagnosis Model attains higher accuracy 27.3%, 20.56%, 31.34%, and 25.13% compared with the existing methods like CMVHHO‐DKMLC‐BCM and SVMFE‐OPFPSOC‐BCM, MLPNN‐CNN‐BCM, and DCNN‐EL‐BCM, respectively.

Exploration of the Moderating Effects of Physical Activity and Early Life Stress on the Relation between Brain-Derived Neurotrophic Factor (BDNF) rs6265 Variants and Depressive Symptoms among Adolescents.

Depression affects one in five persons at 18 years of age. Allele A of the brain-derived neurotrophic factor (BDNF) rs6265 is considered to be a risk factor for depression. Previous studies of the interaction between BDNF rs6265, early adversity, and/or physical activity have shown mixed results. In this study, we explored the relation between BDNF rs6265 polymorphism and childhood stress, as well as the moderating effect of physical activity in relation to depressive symptoms using binary logistic regressions and process models 1, 2 and 3 applied to data obtained at three times (waves 1, 2 and 3) from the Survey of Adolescent Life in Västmanland cohort study (SALVe). Results revealed that both childhood stress and physical activity had a moderation effect; physical activity in wave 1 with an R2 change = 0.006, p = 0.013, and the Johnson–Neyman regions of significance (RoS) below 1.259, p = 0.05 for 11.97%; childhood stress in wave 2 with the R2 change = 0.008, p = 0 002, and RoS below 1.561 with 26.71% and >4.515 with 18.20%; and a three-way interaction in wave 1 in genotype AA carriers. These results suggest that allele A is susceptible to physical activity (positive environment) and childhood stress (negative environment).

MDFNet: an unsupervised lightweight network for ear print recognition.

In this paper, we propose an unsupervised lightweight network with a single layer for ear print recognition. We refer to this method by MDFNet because it relies on gradient Magnitude and Direction alongside with responses of data-driven Filters. At first, we align ear using Convolution Neural Network (CNN) and Principal Component Analysis (PCA). MDFNet starts by generating a filter bank from training images using PCA. This is followed by a twofold layer, which comprises two operations namely convolution using learned filters and computation of gradient image. To prevent over-fitting, a binary hashing process is done by combining different filter responses into a single feature map. Then, we separately construct histograms for each of gradient magnitude and direction according to the feature map. These histograms are then normalized, using power-L2 rule, to cope with illumination disparity. Several fusion rules are evaluated to combine the two histograms. The main novelty of MDFNet lies in its simple architecture, effectiveness, the good compromise between processing time and performance it provides along with its high robustness to occlusion. We conduct extensive experiments on three public datasets namely AWE, AMI and IIT Delhi II. Experimental results demonstrate the effectiveness of MDFNet, which achieves high recognition rates (82.5%, 97.67% and 98.96%, respectively), and outperformed several state of the art methods with a high robustness to occlusion. Experiments revealed also the actual need for considering ear alignment.

Abstract 958: Exosomes from EGFR-mutated adenocarcinoma induce a hybrid EMT and MMP9-dependant tumour invasion

Abstract Exosomes are a class of extra cellular vesicles (EVs), with a multi vesicular endosomal origin, that are released by all cell types, with sizes ranging from 30-150nm and a lipid bilayer membrane. Recent studies document that tumour cells shed exosomes at considerably higher rates, and these exosomes play critical roles in several early and late events associated with tumour development and metastasis. Thus, circulating exosomes are emerging as a new paradigm of ‘liquid biopsy’ for non-invasive cancer diagnosis. They have been reported to influence epithelial mesenchymal transition (EMT) in pathological states such as metastatic lung cancer. We successfully isolated exosomes from an Epidermal Growth Factor Receptor (EGFR) mutated cell line (HCC827) and from serum of different lung cancer patients: EGFR-mutated adenocarcinoma (N=5), wild-type adenocarcinoma (N=5), squamous (N=3) and serum of patients with no cancer (N=5) and analysed their effects on cells from an A549 cell line. By zymography, we observed that exosomes derived from the EGFR-mutated adenocarcinoma patients contains significantly more MMP-9 and pro-MMP-9 than those derived from other patients. When incubated with A549 cells, all exosomes, derived from the EGFR mutated cell line and from patients (regardless of patient origin), induced an increase in MMP2 activity. However, the increase of MMP9 activity was seen only in A549 cells treated with exosomes derived from EGFR-mutated adenocarcinoma patients and from the EGFR mutated cell line. MMPs are one of the major attributes that epithelial cells acquire after undergoing EMT. Respectively by a modified Boyden chamber assay and by wound healing assay, we showed, that EGFR-mutated exosomes treatment increases the invasive capacity (fold change of 1.7; p-value=0.0008) and the migratory capacity [CM1] of A549 and HBE4 E6/E7 cells in an MMP-9 dependant pathway. By western blot and qPCR, we observed a significant increase of vimentin expression, a mesenchymal marker while retaining the epithelial characteristics as evidenced by the unaltered levels of E-cadherin and EPCAM. EMT is not a binary process but instead, cells often exhibit a spectrum of epithelial/mesenchymal phenotype(s). Such hybrid cells can move collectively as clusters which is thought to enhance their invasive properties. By qPCR, we observed an increase of NRF2, P-cadherin and cytokeratin 14, markers of hybrid-EMT and collective migration. Our results suggest that exosomes derived from EGFR-mutated adenocarcinoma serum could be a potential mediator of hybrid-EMT and tumour invasion and may provide insights into the mechanisms of lung cancer progression and metastasis. Further studies are required to precisely delineate these mechanisms and explore novel therapeutic avenues. Citation Format: Amina Jouida, Cormac McCarhty, Aurelie Fabre, Allan Kelly, Parthiban Nadarajan, Marissa O Callaghan, Michael P. Keane. Exosomes from EGFR-mutated adenocarcinoma induce a hybrid EMT and MMP9-dependant tumour invasion [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 958.

Abstract 961: TCF7 promotes a mammary cancer-stem-like phenotype leading to metastasis via potentiation of a hybrid epithelial/mesenchymal (E/M) phenotype

Abstract Cancer stem cells (CSCs) are a minor population of carcinoma cells that can self-renew and give rise to differentiated progenitors that form the bulk of the tumor. CSCs cause tumor recurrence, chemoresistance and metastasis. The epithelial-to-mesenchymal transition (EMT) promotes tumor cell migration, invasion and cancer stem cell properties. The Wnt signaling pathway is a major driver of cancer stemness and is regulated by the TCF/LEF family of transcription factors. Using loss and gain of function of TCF7 (TCF1) in mouse and human breast cancer cells, we were able to demonstrate that TCF7 promotes cancer stemness through striking regulation of the EMT process. TCF7 knockdown in PyMT mammary tumor cells induced luminal epithelial differentiation, resulting in inhibition of oncosphere formation, migration, tumor growth and lung colonization. However, in keeping with a changing view of EMT as a strictly binary process, we found that TCF7 shifts tumor cells towards a hybrid epithelial/mesenchymal state (E/M) rather than a mesenchymal (M) state. This E/M population was shown to carry stemness and metastatic properties and to be interestingly represented by a small “stem-like” subpopulation in triple negative breast cancer (TNBC) cells. Using CD44 and CD104 (integrin β4) flow cytometry to isolate E/M cells from normal MCF10A and basal-like MDA-MB-468 breast cancer cells, we show that TCF7, Snail, and GSK3β phosphorylation were all highly enriched in this population whereas Zeb-1 (a marker of mesenchymal cells), was absent from E/M cells. These data support a model whereby TCF7 potentiates Wnt signaling which in turn activates GSK3β phosphorylation, resulting in Snail upregulation and enrichment of cells in the E/M state that is conducive of stemness and metastasis. Citation Format: Huizhi Liang, Outhiriaradjou Benard, Zuen Ren, Kimita Suyama, Larry Norton, Rachel B. Hazan. TCF7 promotes a mammary cancer-stem-like phenotype leading to metastasis via potentiation of a hybrid epithelial/mesenchymal (E/M) phenotype [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 961.

Analysis of hand and face images for the purpose of engineering support for Parkinson's disease diagnosis

Engineering support in the field of recognizing Parkinson's disease against the background of other diseases, its progression and monitoring the effectiveness of drugs is currently widely implemented as part of work devoted to the use of re-cording and analysis devices equipped with sensors of movement parameters attached to the patient's body, e.g. accelerometers and gyroscopes. This material touches on an alternative approach, in which the concept of using techniques for processing selected image data obtained during a clinical examination evaluating a patient using the unified UPDRS number scale is proposed. The research was conducted on a material that corresponded to selected components of the scale and included images of faces recorded in the visible light range and images of the outer surfaces of the hand recorded with a thermal imaging camera. This was aimed at assessing the possibility of differentiating persons in terms of detecting Parkinson's disease on the basis of registered modalities. Thus, tasks aimed at developing characteristics important in the binary classification process were carried out. The assessment of features was made in a modality-dependent manner based on available tools in the field of statistics and machine learning.