Biology subjects

Hakim, A.

Publications and source records attributed to Hakim, A..

9 recordsLinked to original sources

bioRxiv2026

Predicting the immediate and subsequent effects of commercials on product valuation using EEG and deep learning

Neuromarketing mainly seeks to enhance the prediction of marketing stimuli success, such as commercials and movie trailers, by integrating neurophysiological measures with traditional behavioral measures. In the current study, the authors tested whether neural activity could predict consumer valuation and how preferences change after watching commercials, both immediately and over time. Participants (n=161) watched images of products followed by commercials advertising those products while their neural activity was recorded using electroencephalograph (EEG). They watched the product images again a week later without EEG recordings. Immediately after each stimulus exposure, participants stated their willingness to pay (WTP) for the product and how much they liked the commercial. Standard EEG measures showed weak and inconsistent relationships with behavior and yielded near-chance predictions. In contrast, deep learning models applied to the raw neural data achieved substantially higher predictive accuracy, successfully predicting both immediate and delayed WTP and ad liking. Importantly, the authors were able to successfully predict preferences for new participants and/or new products the models were not trained on. However, accuracy declined as generalization demands increased. Moreover, prediction performance declined as value differences narrow. Together, these findings show that neural responses carry reliable and temporally persistent information about consumer valuation and its evolution over time.

neuroscience↗

bioRxiv2026

OPA1 controls mitochondrial dysfunction-driven liver fibrosis in MASLD

Progressive hepatic fibrosis is the principal determinant of morbidity and mortality in metabolic dysfunction-associated steatotic liver disease and steatohepatitis (MASLD/MASH). Mitochondrial dysfunction is a hallmark of MASH, and the release of mitochondrial damage-associated molecular patterns (mito-DAMPs) from injured hepatocytes can promote fibrosis. However, how mitochondrial dynamics and quality control shape the fibrotic response in MASLD/MASH remains unclear. Here, through large-scale genomic analyses of mitochondrial genes governing mitophagy, fusion and fission in human MASLD, with a power-equivalent sample size of approximately 700,000 individuals, we identify a strong association between hepatic fibrosis and the mitochondrial fusion factor dynamin-like GTPase optic atrophy 1 (OPA1). OPA1 transcripts and protein abundance in the liver epithelium were progressively dysregulated with advancing fibrosis. In mice, hepatocyte-specific OPA1 loss alone was sufficient to induce hepatic stellate cell activation and fibrosis in zone 3, promoted the release of mito-DAMPs into the circulation and exacerbated fibrosis in experimental MASH. These findings identify OPA1 as a central regulator of the hepatic fibrotic response and connect defective mitochondrial homeostasis to mito-DAMP release, hepatic stellate cell activation and fibrosis in MASLD.

genetics↗

bioRxiv2022

The trembling hand unraveled: the motor dynamics and neural sources of choice inconsistency

Extensive evidence shows that humans are inconsistent with their choices. Yet, the neural mechanism underlying this type of choices remains unknown. Here, we aim to show that inconsistent choice is tied to the valuation process, but can also arise from motor errors during task execution. We report the results from three behavioral and neuroimaging studies. Subjects completed a risky-choice task to test their inconsistency levels, followed by two novel tasks, explicitly designed to examine motor output. We recorded mouse trajectories during task execution and designed 34 features to analyze motor dynamics in an exploratory manner. We show that motor dynamics predict inconsistency levels, even when motor output was absent any valuation elements. In the neuroimaging study, we show that inconsistency is associated with value brain circuits, but at the same time, is also related to activity in motor circuits. These findings suggest that (at least) two neural sources of noise contribute to inconsistent choice behavior.

neuroscience↗

bioRxiv2022

Sentinel Cards Provide Practical SARS-CoV-2 Monitoring in School Settings

Accurate, high-resolution environmental monitoring of SARS-CoV-2 traces indoors through sentinel cards is a promising approach to help students safely return to in-person learning. Because SARS-CoV-2 RNA can persist for up to a week on several indoor surface types, there is a need for increased temporal resolution to determine whether consecutive surface positives arise from new infection events or continue to report past events. Cleaning sentinel cards after sampling would provide the needed resolution, but might interfere with assay performance. We tested the effect of three cleaning solutions (BZK wipes, wet wipes, RNase Away) at three different viral loads: "high" (4 x 104 GE/mL), "medium" (1 x 104 GE/mL), and "low" (2.5 x 103 GE/mL). RNAse Away, chosen as a positive control, was the most effective cleaning solution on all three viral loads. Wet wipes were found to be more effective than BZK wipes in the medium viral load condition. The low viral load condition was easily reset with all three cleaning solutions. These findings will enable temporal SARS-CoV-2 monitoring in indoor environments where transmission risk of the virus is high and the need to avoid individual-level sampling for privacy or compliance reasons exists. ImportanceBecause SARS-CoV-2, the virus that causes COVID-19, persists on surfaces, testing swabs taken from surfaces is useful as a monitoring tool. This approach is especially valuable in school settings, where there are cost and privacy concerns that are eliminated by taking a single sample from a classroom. However, the virus persists for days to weeks on surface samples, so it is impossible to tell whether positive detection events on consecutive days are persistent signal or new infectious cases, and therefore whether the positive individuals have been successfully removed from the classroom. We compare several methods for cleaning "sentinel cards" to show that this approach can be used to identify new SARS-CoV-2 signals day to day. The results are important for determining how to monitor classrooms and other indoor environments for SARS-CoV-2 virus.

bioinformatics↗

bioRxiv2021

Comparison of heat-inactivated and infectious SARS-CoV-2 across indoor surface materials shows comparable RT-qPCR viral signal intensity and persistence

Environmental monitoring in public spaces can be used to identify surfaces contaminated by persons with COVID-19 and inform appropriate infection mitigation responses. Research groups have reported detection of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) on surfaces days or weeks after the virus has been deposited, making it difficult to estimate when an infected individual may have shed virus onto a SARS-CoV-2 positive surface, which in turn complicates the process of establishing effective quarantine measures. In this study, we determined that reverse transcription-quantitative polymerase chain reaction (RT-qPCR) detection of viral RNA from heat-inactivated particles experiences minimal decay over seven days of monitoring on eight out of nine surfaces tested. The properties of the studied surfaces result in RT-qPCR signatures that can be segregated into two material categories, rough and smooth, where smooth surfaces have a lower limit of detection. RT-qPCR signal intensity (average quantification cycle (Cq)) can be correlated to surface viral load using only one linear regression model per material category. The same experiment was performed with infectious viral particles on one surface from each category, with essentially identical results. The stability of RT-qPCR viral signal demonstrates the need to clean monitored surfaces after sampling to establish temporal resolution. Additionally, these findings can be used to minimize the number of materials and time points tested and allow for the use of heat-inactivated viral particles when optimizing environmental monitoring methods. ImportanceEnvironmental monitoring is an important tool for public health surveillance, particularly in settings with low rates of diagnostic testing. Time between sampling public environments, such as hospitals or schools, and notifying stakeholders of the results should be minimal, allowing decisions to be made towards containing outbreaks of coronavirus disease 2019 (COVID-19). The Safer At School Early Alert program (SASEA) [1], a large-scale environmental monitoring effort in elementary school and child care settings, has processed > 13,000 surface samples for SARS-CoV-2, detecting viral signals from 574 samples. However, consecutive detection events necessitated the present study to establish appropriate response practices around persistent viral signals on classroom surfaces. Other research groups and clinical labs developing environmental monitoring methods may need to establish their own correlation between RT - qPCR results and viral load, but this work provides evidence justifying simplified experimental designs, like reduced testing materials and the use of heat-inactivated viral particles.

microbiology↗

bioRxiv2020

Novel Cluster AZ Arthrobacter phages Powerpuff, Lego, and YesChef exhibit close functional relationships with Microbacterium phages

Bacteriophages exhibit a vast spectrum of relatedness and there is increasing evidence of close genomic relationships independent of host genus. The variability in phage similarity at the nucleotide, amino acid, and gene content levels confounds attempts at quantifying phage relatedness, especially as more novel phages are isolated. This study describes three highly similar novel Arthrobacter globiformis phages-Powerpuff, Lego, and YesChef-which were assigned to Cluster AZ using a nucleotide-based clustering parameter. Phages in Cluster AZ and Microbacterium Cluster EH, as well as the former Microbacterium singleton Zeta1847, exhibited low nucleotide similarity but gene content similarity in excess of the recently adopted Microbacterium clustering parameter, which resulted in the reassignment of Zeta1847 to Cluster EH. Additionally, Clusters AZ and EH phages encode a shared integrase indicative of a lysogenic life cycle; in the first experimental verification of a Cluster AZ phages life cycle, we show that phage Powerpuff is a true temperate phage and forms stable lysogens that exhibit immunity to superinfection by related phages, despite lacking identifiable repressors typically required for lysogenic maintenance and superinfection immunity. The ability of phage Powerpuff to undergo and maintain lysogeny suggests that other closely related phages encoding an integrase but lacking an identified repressor may be temperate as well. Our findings further highlight the importance of using multiple metrics to capture phage relatedness, provide additional evidence of significant shared phage genomic content spanning multiple actinobacterial host genera, and demonstrate the continued need for verification and characterization of life cycles in newly isolated phages.

microbiology↗

bioRxiv2018

Phylogeny and distinct properties of major intrinsic proteins in the genomes of six Phytophthora species suggest their novel functions in Phytophthora

ABSTRACTMajor intrinsic proteins (MIPs), commonly known as aquaporins, facilitate the membrane diffusion of water and some other non- polar solutes. MIPs might be involved in host-pathogen interactions. Herein, we identified 17, 24, 27, 19, 19, and 22 full-length MIPs, respectively, in the genomes of six Phytophthora species, P. infestans, P. parasitica, P. sojae, P. ramorum, P. capsici, and P. cinnamomi. These Phytophthora species are devastating plant pathogens and members of oomycetes, a distinct lineage of fungus-like eukaryotic microbes. Phylogenetic analysis showed that the Phytophthora MIPs (PMIPs) formed a completely distinct clade from their counterparts in other taxa and were clustered into nine subgroups. Sequence and structural properties indicated that the primary selectivity-related constrictions, including aromatic arginine (ar/R) selectivity filter and Froger’s positions in PMIPs were distinct from those in other taxa. The substitutions in the conserved Asn-Pro-Ala motifs in loops B and E of many PMIPs were also divergent from those in plants. We further deciphered group-specific consensus sequences/motifs in different loops and transmembrane helices of PMIPs, which were distinct from those in plants, animals, and microbes. The data collectively supported the notion that PMIPs might have novel functions.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗

bioRxiv2018

More is Better: Using Machine Learning Techniques and Multiple EEG Metrics to Increase Preference Prediction Above and Beyond Traditional Measurements

A basic aim of marketing research is to predict consumers preferences and the success of marketing campaigns in the general population. However, traditional behavioral measurements have various limitations, calling for novel measurements to improve predictive power. In this study, we use neural signals measured with electroencephalography (EEG) in order to overcome these limitations. We record the EEG signals of subjects, as they watched commercials of six food products. We introduce a novel approach in which instead of using one type of EEG measure, we combine several measures, and use state-of-the-art machine learning algorithms to predict subjects individual future preferences over the products and the commercials population success, as measured by their YouTube metrics. As a benchmark, we acquired measurements of the commercials effectiveness using a standard questionnaire commonly used in marketing research. We reached 68.5% accuracy in predicting between the most and least preferred items and a lower than chance RMSE score for predicting the rank order preferences of all six products. We also predicted the commercials population success better than chance. Most importantly, we demonstrate for the first time, that for all of our predictions, the EEG measurements increased the prediction power of the questionnaires. Our analyses methods and results show great promise for utilizing EEG measures by managers, marketing practitioners, and researchers, as a valuable tool for predicting subjects preferences and marketing campaigns success.

neuroscience↗

bioRxiv2017

WorMachine: Machine Learning-Based Phenotypic Analysis Tool for Worms

While Caenorhabditis elegans nematodes are powerful model organisms, quantification of visible phenotypes is still often labor-intensive, biased, and error-prone. We developed \"WorMachine\", a three-step MATLAB-based image analysis software that allows automated identification of C. elegans worms, extraction of morphological features, and quantification of fluorescent signals. The program offers machine learning techniques which should aid in studying a large variety of research questions. We demonstrate the power of WorMachine using five separate assays: scoring binary and continuous sexual phenotypes, quantifying the effects of different RNAi treatments, and measuring intercellular protein aggregation. Thus, WorMachine is a \"quick and easy\", high-throughput, automated, and unbiased analysis tool for measuring phenotypes.

bioinformatics↗