Fitness Score Research Articles

MazeGen: A Low-Code Framework for Bootstrapping Robotic Navigation Scenarios for Smart Manufacturing Contexts

In this research, we describe the MazeGen framework (as a maze generator), which generates navigation scenarios using Grammatical Evolution for robots or drones to navigate. The maze generator uses evolutionary algorithms to create robotic navigation scenarios with different semantic levels along a scenario profile. Grammatical Evolution is a Machine Learning technique from the Evolutionary Computing branch that uses a BNF grammar to describe the language of the possible scenario universe and a numerical encoding of individual scenarios along that grammar. Through a mapping process, it converts new numerical individuals obtained by operations on the parents’ encodings to a new solution by means of grammar. In this context, the grammar describes the scenario elements and some composition rules. We also analyze associated concepts of complexity, understanding complexity as the cost of production of the scenario and skill levels needed to move around the maze. Preliminary results and statistics evidence a low correlation between complexity and the number of obstacles placed, as configurations with more difficult obstacle dispositions were found in the early stages of the evolution process and also when analyzing mazes taking into account their semantic meaning, earlier versions of the experiment not only resulted as too simplistic for the Smart Manufacturing domain, but also lacked correlation with possible real-world scenarios, as was evidenced in our experiments, where the most semantic meaning results had the lowest fitness score. They also show the emerging technology status of this approach, as we still need to find out how to reliably find solvable scenarios and characterize those belonging to the same class of equivalence. Despite being an emerging technology, MazeGen allows users to simplify the process of building configurations for smart manufacturing environments, by making it faster, more efficient, and reproducible, and it also puts the non-expert programmer in the center of the development process, as little boilerplate code is needed.

Cryptojacking Detection Using Genetic Search Algorithm

Mining cryptocurrency with an unauthorized and unlawful access to a victim’s computer’s processing power is called cryptojacking. With the rise of cryp- tocurrency in the markets people took advantage of this new piece of technol- ogy through mining it and earning from it. When the need for more perfor- mance and money emerged people came out with unlawful activities to mine cryptocurrency using other’s devices without their consent. These activities have led people and their devices vulnerable for the sake of greed. This is a dis- tributive approach which uses victim’s machine to mine cryptocurrency using all its resources i.e., CPU, GPU etc. This doesn’t need any software installa- tion but just to visit sites which are embedded with the malicious scripts that helps accessing user’s device. The analogy of this study is not limited to detect the presence of cryptojacking but also to enable users to detect the presence of any kind of suspicious activity is performed on the device. Through Genetic Search algorithm we extracted some of the key metrics used by computers for monitor its resources and further used these metrics to classify the presence of any unauthorized activity and achieved 100% accuracy for classifying these instances. The classification was done using several classification algorithms such as SVM (kernel – “linear”), SVM (kernel – “Radial Bias Function”), Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Multi-Layer Perceptron. The Genetic Search algorithm mentioned earlier is a machine learning iterative technique which is based on natural selection it selects individuals from the current population as parents and uses them to generate the next generation of offspring and all this through a method called CfsSubsetEval which returns a fitness score to the child based on their dependency on other attributes. For comparison we also used several methods which also serves the same objective

Comparative Analysis of Soccer and Futsal Extracurriculars: A Survey Study of Physical Fitness Profiles

Background: Futsal and soccer are sports that require endurance and good physical fitness. However, the striking difference, especially in terms of the field used, causes various assumptions. Objectives: The purpose of this study is to compare the levels of physical fitness required for futsal and soccer as extracurricular activities. Methods: The Indonesian Physical Fitness Test (TKJI), In this study, a survey administered to extracurricular football and futsal players aged 13 to 15 was used as a quantitative method. Male students from two separate schools who were involved in extracurricular activities participated in this study, including soccer at State Junior High School 3 Sukadana, and futsal at Madrasah Tsanawiyah State 1 Pontianak. Applying a purposive sampling strategy to collect the sample, 20 futsal extracurricular students and 20 soccer extracurricular students were obtained. The SPSS version 26 program was used to aid in the data analysis for this investigation. Results: According to the study's findings, the average physical fitness score for soccer extracurricular was 15.1, while the average for futsal extracurricular was 11.75. Additionally, the significance value is 0.000 0.05, demonstrating a significant distinction between football and futsal as extracurricular activities. Conclusion: The study on the degree of physical fitness in extracurricular futsal and football found that there is a big gap. These preliminary results might improve understanding of student fitness developments among exercise professionals.

Evaluating the effect of SARS-CoV-2 spike mutations with a linear doubly robust learner.

Driven by various mutations on the viral Spike protein, diverse variants of SARS-CoV-2 have emerged and prevailed repeatedly, significantly prolonging the pandemic. This phenomenon necessitates the identification of key Spike mutations for fitness enhancement. To address the need, this manuscript formulates a well-defined framework of causal inference methods for evaluating and identifying key Spike mutations to the viral fitness of SARS-CoV-2. In the context of large-scale genomes of SARS-CoV-2, it estimates the statistical contribution of mutations to viral fitness across lineages and therefore identifies important mutations. Further, identified key mutations are validated by computational methods to possess functional effects, including Spike stability, receptor-binding affinity, and potential for immune escape. Based on the effect score of each mutation, individual key fitness-enhancing mutations such as D614G and T478K are identified and studied. From individual mutations to protein domains, this paper recognizes key protein regions on the Spike protein, including the receptor-binding domain and the N-terminal domain. This research even makes further efforts to investigate viral fitness via mutational effect scores, allowing us to compute the fitness score of different SARS-CoV-2 strains and predict their transmission capacity based solely on their viral sequence. This prediction of viral fitness has been validated using BA.2.12.1, which is not used for regression training but well fits the prediction. To the best of our knowledge, this is the first research to apply causal inference models to mutational analysis on large-scale genomes of SARS-CoV-2. Our findings produce innovative and systematic insights into SARS-CoV-2 and promotes functional studies of its key mutations, serving as reliable guidance about mutations of interest.

Bayesian optimization for ternary complex prediction (BOTCP)

Proximity-inducing compounds (PICs) are an emergent drug technology through which a protein of interest (POI), often a drug target, is brought into the vicinity of a second protein which modifies the POI’s function, abundance or localisation, giving rise to a therapeutic effect. One of the best-known examples for such compounds are heterobifunctional molecules known as proteolysis targeting chimeras (PROTACs). PROTACs reduce the abundance of the target protein by establishing proximity to an E3 ligase which labels the protein for degradation via the ubiquitin-proteasomal pathway. Design of PROTACs in silico requires the computational prediction of the ternary complex consisting of POI, PROTAC molecule, and the E3 ligase.We present a novel machine learning-based method for predicting PROTAC-mediated ternary complex structures using Bayesian optimization. We show how a fitness score combining an estimation of protein-protein interactions with PROTAC conformation energy calculations enables the sample-efficient exploration of candidate structures. Furthermore, our method presents two novel scores for filtering and reranking which take PROTAC stability (Autodock-Vina based PROTAC stability score) and protein interaction restraints (the TCP-AIR score) into account. We evaluate our method using DockQ scores on a number of available ternary complex structures (including previously unevaluated cases) and demonstrate that even with a clustering that requires members to have a high similarity, i.e., with smaller clusters, we can assign high ranks to those clusters that contain poses close to the experimentally determined native structure of the ternary complexes. We also demonstrate the resultant improved yield of near-native poses33The near-native pose is defined as a pose that has DockQ score ≥0.23. in these clusters.

Identification of a PD1/PD-L1 inhibitor by Structure-Based Pharmacophore Modelling, Virtual Screening, Molecular docking and Biological Evaluation.

PD-1/PD-L1 is a critical druggable target for immunotherapy against sepsis. Chemoinformatics techniques involved the structure-based 3D pharmacophore model development followed by virtual screening of small molecule databases to identify the small molecules against PD-L1 pathway inhibition. Raltitrexed and Safinamide act as potent repurposed drugs, and three other Specs database compounds using in silico methods. These compounds were screened based on the pharmacophore fit score and binding affinity towards the active site of the PD-L1 protein. In silico pharmacokinetic profiling of these screened compounds was done to test their biological activity. Next, experimental validation of the best four virtually screened hits was done in vitro for its hemocompatibility and cytotoxicity. Among these, Raltitrexed, Safinamide and Specs compound (AK-968/40642641) effectively increased the proliferation of immune cells and IFN-γ production. These compounds can act as potent PDL-1 inhibitors for adjuvant therapy against sepsis.

Modelling of AgrA inhibitors to combat anti-microbial resistance in Staphylococcus aureus

Staphylococcus aureus is a Gram-positive bacterium found on human skin that causes skin and soft tissue infections, as well as pneumonia, osteomyelitis, and endocarditis. The prevalence of antibiotic resistant strains has made the treatments less effective. An efficient alternate method for battling these contagious diseases is anti-virulence strategy. The AgrA protein, a key activator of Accessory Gene Regulator system in S. aureus, is vital to the virulence of the organism and, consequently, its pathogenesis. Using a Machine Learning algorithm, the Support Vector Machine (SVM), and a ligand-based pharmacophore modelling method, prediction models of AgrA inhibitors were developed. The metrics of the SVM model were inadequate, hence it was not used for virtual screening. For ligand-based pharmacophore modelling, 14 of 29 compounds were removed from the active set due to a lack of shared pharmacophore properties, and 504 compounds were designated as decoys. A 3D pharmacophore model was created using LigandScout 4.4.5, with a fit score of 57.48, including a positive ionizable group, one hydrogen bond donor, and three hydrogen bond acceptors. The model after further validation was used to virtually screen an external database which resulted in six hits. These compounds were docked with the AgrA domain crystal structure to determine the inhibitor activity. Further, each docked complex was subjected to a 100 ns molecular dynamics simulation. CID238 and CID20510252 demonstrated potent inhibitory binding interactions and hence can be used to develop AgrA inhibitors in future after proper validation. Communicated by Ramaswamy H. Sarma

Impact of long-term exercise habits on cognitive test performance

Past research has supported the idea that exercise is beneficial for the mind and cognitive functions. Further, imaging and neurotransmitter studies have shown how acute exercise can increase white matter and monoamines in the brain, thus improving cognition. On this basis, it was inferred that exercise, specifically long-term exercise, will improve an individual’s cognitive performance. To determine this, college-aged subjects were assessed with baseline reaction time and simultaneous amplitude discrimination tests. They subsequently participated in an acute exercise, after completing the same tests, to examine how their performance was affected. To evaluate fitness levels, subjects took a survey reporting their fitness levels and history, and their answers were translated into a comprehensive fitness score. In analyzing fitness score with test data, results concluded that those with more robust fitness levels performed better in the amplitude discrimination task. In addition, their reaction time variability, a consistent measure of cognitive focus, decreased (i.e., improved) following acute exercise. In contrast, those with lower fitness levels witnessed an increase in variability following exercise. This data suggests that taking part in exercise, particularly from a young age, can have long-term beneficial effects on cognitive function.

Abstract 3938: In vivo genome-wide CRISPR screen in pancreatic ductal adenocarcinoma defines HSPE1 as a potential oncogene by acting through G2/M cell cycle arrest

Abstract Superior knowledge of cancer biology has enabled unprecedented innovations in therapies targeting mutated driver genes. Despite the attempt of targeting cancer-inducing genes such as KRAS, the life expectancy of patients diagnosed with pancreatic ductal adenocarcinoma (PDAC) has poorly improved. We performed an unbiased genome wide in-vivo loss-of-function CRISPR screen to discover novel oncogenes and identified heat-shock protein HSPE1, whose function is still unknown in PDAC. By decreasing HSPE1 expression with shRNA and CRISPR technology, we detected a slowdown in cell growth of PDAC cancer cells. HSPE1 knock-out cells injected in mice displayed a drastic reduction in tumor volume. We exploited this vulnerability by disrupting HSPE1 function by using KHS101, a validated HSPD1-HSPE1 complex inhibitor. Several PDAC cell lines cultured in vitro were sensitive upon KHS101 treatment and in vivo administration of KHS101 reduced tumorigenesis. Compiling the negative fitness scores from DepMap database revealed a co-dependency between HSPE1 and PLK1, a protein involved in regulating G2/M checkpoint. Indeed, we detected a protein-level decrease of PLK1 in our experimental model of PDAC cells having either a drug-induced HSPE1 deficiency by KHS101 exposure or a HSPE1 gene-expression reduction by shRNA. We found that the cell cycle was disrupted through G2/M arrest in PDAC cells treated with KHS101. Our findings highlight a new role underlying PDAC tumorigenesis for HSPE1 and could unlock a new area of research towards precision medicine. Citation Format: Julien Boudreault, Ni Wang, Gang Yan, Meiou Dai, Sophie Poulet, Girija Daliah, Jean-Jacques Lebrun. In vivo genome-wide CRISPR screen in pancreatic ductal adenocarcinoma defines HSPE1 as a potential oncogene by acting through G2/M cell cycle arrest. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3938.

ChemGAPP: a tool for chemical genomics analysis and phenotypic profiling.

High-throughput chemical genomic screens produce informative datasets, providing valuable insights into unknown gene function on a genome-wide level. However, there is currently no comprehensive analytic package publicly available. We developed ChemGAPP to bridge this gap. ChemGAPP integrates various steps in a streamlined and user-friendly format, including rigorous quality control measures to curate screening data. ChemGAPP provides three sub-packages for different chemical-genomic screens: ChemGAPP Big for large-scale screens; ChemGAPP Small for small-scale screens; and ChemGAPP GI for genetic interaction screens. ChemGAPP Big, tested against the Escherichiacoli KEIO collection, revealed reliable fitness scores which displayed biologically relevant phenotypes. ChemGAPP Small demonstrated significant changes in phenotype in a small-scale screen. ChemGAPP GI was benchmarked against three sets of genes with known epistasis types and successfully reproduced each interaction type. ChemGAPP is available at https://github.com/HannahMDoherty/ChemGAPP, as a standalone Python package as well as Streamlit applications.

Turkish Adaptation of Dissociative Subtype of Post Traumatic Stress Disorder Scale

ABSTRACT After the changes in DSM-5, dissociative subtype was added to post-traumatic stress disorder. That caused a necessity for a scale to measure the mentioned change. A scale named Dissociative Subtype of Post-Traumatic Stress Disorder (DSPS) was developed to measure this subtype and help the diagnosis. The purpose of this study is to adapt the Dissociative Subtype of Post-Traumatic Stress Disorder to Turkish and examine its reliability and validity. The Dissociative Subtype of PTSD (DSPS) was translated into Turkish. DSPS, Turkish forms of The Posttraumatic Diagnostic Scale and Dissociative Experiences Scale were sent to participants via Google Forms and data from 279 people aged 18–45 were analyzed. Reliability tests and factor analysis were conducted. Factor analysis showed that scale has good model fit scores and items were loaded to the factors the same as the original study. Scales internal consistency was examined, and a good score was obtained (α=.84). Fit index values of confirmatory factor analysis were found as χ2/df = 2.51, GFI=.90, RMSEA=.07, RMR=.02. As a result of the high reliability scores and sufficient model fit scores, this scale is considered as a dependable measure to evaluate the dissociative subtype of PTSD.

Nomogram for Predicting Viral Shedding Time of SARS-CoV-2 Delta Variant in Patients with COVID-19: A Retrospective Study

Abstract Background The coronavirus disease 2019 (COVID-19) cases continue to rise, and the demand for medical treatment and resources in healthcare systems surges. Assessing the viral shedding time (VST) of patients with COVID-19 can facilitate clinical decision making. Although some studies have been conducted on the factors affecting the VST of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), few prediction models are currently available. Methods This retrospective study included the consecutive patients with COVID-19 admitted to Xi’an Chest Hospital in Shaanxi, China, for treatment between December 19, 2021 and February 5, 2022. The clinical data of the patients were extracted from their electronic medical records. Combining significant factors affecting the VST, a nomogram was developed to predict the VST of the SARS-CoV-2 Delta variant in patients with COVID-19. Results We included 332 patients in this study. The average VST was 21 d. VST was significantly prolonged in patients with severe clinical symptoms, sore throat, old age, long time from onset to diagnosis, and an abnormal white blood cell count. Consequently, we developed a nomogram prediction model using these 5 variables. The concordance index (C-index) of this nomogram was 0.762, and after internal validation using bootstrapping (1000 resamples), the adjusted C-index was 0.762. The area under the nomogram’s receiver operator characteristic curve showed good discriminative ability (0.965). The calibration curve showed high consistency. The VST was prolonged in the group with lower model fitting scores according to the Kaplan-Meier curve (χ2=286, log-rank P < 0.001). Conclusions We developed a nomogram for predicting VST based on 5 easily accessible factors. It can effectively estimate the appropriate isolation period, control viral transmission, and optimize clinical strategies.

Inverse Kinematic of 1-DOF Robot Manipulator Using Sparse Identification of Nonlinear System

Robot Manipulator is the most robot used in industry since it can act like a human arm that can move objects. Research on robot manipulator has been widely carried out in various problems such as control systems, intelligence robots, degrees of freedom, mechanics-electronics systems and various other problems. In control systems there are studies to design of robot motion through kinematics. However, modeling the kinematic motion which has nonlinear characteristics will be more difficult if the number of degrees of freedom increases. To overcome this problem, this research will proposed sparse regression to modeling the kinematics of a robotic arm with the black box principle modeling. The results obtained indicate that the method The proposed one has the ability to identify robots manipulator with a fitness score of up to 100%. This matter shows that the proposed method can modeling the kinematic inverse of the manipulator robot without through complex calculations. From this research is expected can provide other research opportunities related to identification kinematics with the identification system method

Metabolic profiling, antioxidant, and enzyme inhibition potential of Iris pseudacorus L. from Egypt and Japan: A comparative study

Genus Iris comprises numerous and diverse phytoconstituents displaying marked biological activities. The rhizomes, and aerial parts of Iris pseudacorus L. cultivars from Egypt and Japan were subjected to comparative metabolic profiling using UPLC-ESI-MS/MS. The antioxidant capacity was determined using DPPH assay. In vitro enzyme inhibition potential against α-glucosidase, tyrosinase and lipase was evaluated. In silico molecular docking was conducted on the active sites of human α-glucosidase and human pancreatic lipase. Forty-three compounds were tentatively identified including flavonoids, isoflavonoids, phenolics and xanthones. I. pseudacorus rhizomes extracts (IPR-J and IPR-E) exhibited the highest radical scavenging activity with IC50 values of 40.89 µg/mL and 97.97 µg/mL, respectively (Trolox IC50 value was 14.59 µg/mL). Moreover, IPR-J and IPR-E exhibited promising α-glucosidase inhibitory activity displaying IC50 values of 18.52 µg/mL, 57.89 µg/mL, respectively being more potent as compared to acarbose with IC50 value of 362.088 µg/mL. All extracts exerted significant lipase inhibitory activity exhibiting IC50 values of 2.35, 4.81, 2.22 and 0.42 µg/mL, respectively compared to cetilistat with IC50 value of 7.47 µg/mL. However, no tyrosinase inhibitory activity was observed for all I. pseudacorus extracts up to 500 µg/mL. In silico molecular modelling revealed that quercetin, galloyl glucose, and irilin D exhibited the highest fitting scores within the active sites of human α-glucosidase and pancreatic lipase. ADMET prediction (absorption, distribution, metabolism, excretion, and toxicity) showed that most of the phytoconstituents exhibited promising pharmacokinetic, pharmacodynamics and tolerable toxicity properties. According to our findings, I. pseudacorus might be considered as a valuable source for designing novel phytopharmaceuticals.

Structure-based virtual screening and molecular dynamics simulations for detecting novel candidates for allosteric inhibition of EGFRT790M

Structure-based virtual screening (SBVS) was applied to predict lead compounds for the allosteric inhibition of epidermal growth factor receptor (EGFR) by screening the library of chemical compounds prepared from the e-molecules chemical database. The library of chemical compounds consisting of 133,083 ligands was composed by evaluating the chemical and physical properties of e-molecules chemicals. The prepared library was screened by CCDC Gold software in the allosteric binding site of EGFRT790M using the library and virtual screening default parameters to filter out, respectively. The GOLD fitness scores 75 and 80 were selected as threshold values for the library and virtual screening processes, respectively. After the docking study, molecular dynamics simulations (MDS) of the top 25 compounds were built for calculating binding free energies from their MDS trajectories. MM-GBSA binding free energies for the compounds were computed from 20 ns MDS, 50 ns MDS and 200 ns MDS trajectories to filter out the candidates. Following MM-GBSA/MM-PBSA binding free energy calculations, six compounds were detected as the most promising candidates for allosteric inhibition of EGFRT790M. The dynamic behaviors of final compounds inside EGFR T790M were searched using structure stability, binding modes and energy decomposition analysis. Besides, the estimated inhibitors were exposed to docking study and MM-GBSA/MM-PBSA binding free energy calculations inside wild-type EGFR, respectively, to be determined their selectivity towards mutant form. Five of the estimated inhibitors displayed estimated selectivity towards EGFRT790M. Besides the ADMET properties of the estimated inhibitors were predicted by PreAdmet tools. Communicated by Ramaswamy H. Sarma

Impact of physical and sports rehabilitation on the level of physical fitness of combatants

The anti-terrorist operation, the operation of the joint forces, and the full-scale war in Ukraine have a significant physical and psychological impact on the participants of the hostilities. One of the negative consequences of war is the loss of physical health which further affects the process of socialization during a peaceful life. At the same time, the process of restoring physical health with the help of physical culture and sports rehabilitation has not been researched enough, which confirms the relevance of the chosen topic. Objective: to determine the impact of physical culture and sports rehabilitation on the level of physical fitness of combatants. Material and methods: the research was carried out on the basis of the National Sports Complex "Olympiyskyi" in the group "Veteran Ten" during 2020-2021. The main sports competitions the combatants were preparing for were various marathons. 40 combatants who had completed their military service took part in the research. During the research, there were used the following methods: pedagogical experiment, and methods of mathematical statistics. Results: During a year, combatants participated in the research in the framework of which they trained and prepared to participate in mass races. The content of the training included both running exercises and exercises aimed at developing strength qualities (mainly strength endurance), flexibility, and coordination. The level of physical fitness of combatants was tested in dynamics (before the start of training and after a year of training). The level of physical fitness of combatants was determined according to the approved tests and standards of the "Annual Assessment of Physical Fitness of the Population of Ukraine". Based on the results of the research, it was established that at the initial stage, 75% of the combatants had a low level of physical fitness, 23% had a medium level, and 2% had a sufficient level. Upon the completion of the pedagogical experiment, the level of physical fitness of the combatants improved: 33% of the combatants had a low level of physical fitness, 50% had a medium level, and 17% had a sufficient level, which demonstrates the effectiveness of training sessions as a means of physical culture and sports rehabilitation. Conclusions. The positive impact of physical culture and sports rehabilitation on the level of physical fitness of combatants was determined. With the help of the tests and standards of the "Annual Assessment of Physical Fitness of the Population of Ukraine", it was discovered that the indicators of physical fitness of the combatants at the first stage of the research were in the range from 2 to 21 and on average were (11.83; 4.49) points. In the second stage, they ranged from 11 to 24, their average physical fitness score was (11.28; 3.20) points. At the same time, 46.1% of the participants of the research got an observed increase in the indicator which turned out to be statistically significant (t=13.563; df=39; p<0.05). Therefore, it can be stated that running sessions have a positive impact on the level of physical fitness of combatants.

Transformer-based local-global guidance for image captioning

Image captioning is a difficult problem for machine learning algorithms to compress huge amounts of images into descriptive languages. The recurrent models are popularly used as the decoder to extract the caption with significant performance, while these models have complicated and inherently sequential overtime issues. Recently, transformers provide modeling long dependencies and support parallel processing of sequences compared to recurrent models. However, recent transformer-based models assign attention weights to all candidate vectors based on the assumption that all vectors are relevant and ignore the intra-object relationships. Besides, the complex relationships between key and query vectors cannot be provided using a single attention mechanism. In this paper, a new transformer-based image captioning structure without recurrence and convolution is proposed to address these issues. To this end, a generator network and a selector network to generate textual descriptions collaboratively are designed. Our work contains three main steps: (1) Design a transformer-based generator network as word-level guidance to generate next words based on the current state. (2) Train a latent space to learn the mapping of captions and images into the same embedding space to learn the text-image relation. (3) Design a selector network as sentence-level guidance to evaluate next words by assigning fitness scores to the partial captions through the embedding space. Compared with the architecture of existing methods, the proposed approach contains an attention mechanism without the dependencies of time. It executes each state to select the next best word using local–global guidance. In addition, the proposed model maintains dependencies between the sequences, and can be trained in parallel. Several experiments on the COCO and Flickr datasets demonstrate that the proposed approach can outperform various state-of-the-art models over well-known evaluation measures.

Abstract P5-07-04: Evaluation of Treatment Choices at Baseline Among Breast Cancer Patients Based on the Frailty Status of the Patients

Abstract Background: Previous studies have shown that the frailty scores aid in better patient assessment and in advising treatment strategies. The objective of this study is to evaluate the impact of frailty status on the treatment choices among breast cancer patients. Methods: Patient Reported Outcomes Mobile Platform (PROmpt®), an application for remote patient reporting, was used by breast cancer patients for completing surveys between September 2020 and April 2021. Out of 317 breast cancer patients, there were 84 women for whom frailty assessment was done at baseline based on age, Activities of Daily living (ADLS), Instrumental Activities of Daily living (IADLS) and comorbidities. Frailty status was stratified based on frailty score into fit (score=0), intermediate fit (score = 1) and frail (score >=2). Treatment choices at baseline were stratified into targeted and cytotoxic monotherapy, hormone therapy, and systemic combined therapy. Role of chemotherapy treatment as adjuvant or neoadjuvant therapy was also assessed. Descriptive analysis was performed to assess the association of frailty status on the treatment choices at baseline. Results: Around 24% of breast cancer patients were below 50 years of age and 76% patients were at least 50 years of age or older. The majority of the study population was White (84.15%) and non-Hispanic and Latino’s (63.09%). Around 61% of patients had early-stage breast cancer and around 36% of the patients had late-stage breast cancer. The majority of fit patients (70.59%) and intermediate fit patients (81.82%) receive systemic combined therapy at baseline. Out of fit patients, 67.80 % patients received adjuvant or neoadjuvant therapy, while 44.44 % of the intermediate fit patients received adjuvant or neoadjuvant therapy. Discussion/Conclusion: Results demonstrate the feasibility of gathering fitness and frailty data as part of routine breast cancer care. Overall, the treatment choices among fit, intermediate fit and frail groups of patients appear consistent, although adjuvant/neoadjuvant therapy is directionally higher in fit patients. This similarity in regimen choice may be due to sample size, as the number of frail and intermediate fit patients is relatively small when compared to the fit patients. Further exploration related to treatment choices is recommended in a study population with higher levels of patients assessed as frail for comparative evaluation. Frailty Assessment and Treatment Choice Citation Format: Jasmeen Kaur, Aaron Galaznik. Evaluation of Treatment Choices at Baseline Among Breast Cancer Patients Based on the Frailty Status of the Patients [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-07-04.

Utility of a Physical Fitness Score in Screening for Chronic Diseases.

We developed a new Physical Score (PS) consisting of comprehensive physical fitness indicators and elucidated the association between the resultant PS and metabolic diseases, i.e., diabetes, hypertension, dyslipidemia, fatty liver, and metabolic syndrome (MetS), among Japanese. Analyzed were 49,850 persons (30,039 men) aged 30 to 69 y who underwent physical fitness tests. Principal component analysis was performed on the correlation matrix of the physical fitness test results (relative grip strength, single-leg balance with eyes closed, and forward bending) according to sex and age. We defined the PS as the first principal component score. A formula was developed for various age groups comprised of men and women from 30 to 69 years of age from which the PS for each age and sex was calculated. The PS for both men and women was normally distributed with a value of 0 ± 1.15-1.16. Multivariate logistic regression analysis showed that the risk of metabolic diseases increased approximately 1.1-1.6 times per each 1-point reduction in the PS. The association between PS and MetS was particularly strong in that a 1-point reduction in the PS increased the risk of MetS by 1.54 times (95% confidence interval 1.46 to 1.62) in men and by 1.21 times (1.15 to 1.28) in women. The association between a lower PS and disease risk was stronger in younger men for fatty liver and in older men for MetS. Conversely, in women, the association between a lower PS and disease risk was stronger in older women for fatty liver and in younger women for MetS. For diabetes, hypertension, and dyslipidemia, the change in the impact of PS reductions across age groups was small. The PS is a useful and simple non-invasive tool for screening Japanese people for metabolic diseases.

소도구 저항운동프로그램이 중년여성 건강관련체력과 FMS(Functional Movement Screen) 점수에 미치는 영향

The purpose of this study was to investigate the effect of props resistance exercise program on health-related fitness and FMS score in middle-age women. Subjects were women aged 30-65 living in C-City. The exercise group participating in the props resistance exercise program performed 60minute exercise 3times a week for 12weeks and the control group performed the exercise as usual. Data collection quantified health-related physical fitness and FMS score before and after exercise. The collected data was calculated using the SPSS 26.0 version program. A paired t-test was conducted to differences within the group. And one-way ANOVA was conducted to differences between groups. The results obtained in this study are as follows. First, the change in all factors of health-related physical fitness before and after exercise in the props resistance exercise group were statistically significant (p.05SUP*/SUP, p.01SUP**/SUP, p.001SUP***/SUP). Second, the change in Hurdle step, Rotary stability, total score of FMS before and after exercise in the props resistance exercise group were statistically significant (p.05SUP*/SUP, p.001SUP***/SUP). Finally, there was a statistically significant difference between the groups only in the fat% and Hurdle step(p.05SUP*/SUP). In conclusion, if an exercise program suitable for the characteristics of middle-age women is designed, it will have a positive effect on health-related physical fitness and FMS score, which will help prevent physical aging.