Individual Effects Research Articles

Group-common and individual-specific effects of structure-function coupling in human brain networks with graph neural networks.

The human cerebral cortex is organized into functionally segregated but synchronized regions bridged by the structural connectivity of white matter pathways. While structure-function coupling has been implicated in cognitive development and neuropsychiatric disorders, it remains unclear to what extent the structure-function coupling reflects a group-common characteristic or varies across individuals, at both the global and regional brain levels. By leveraging two independent, high-quality datasets, we found that the graph neural network accurately predicted unseen individuals' functional connectivity from structural connectivity, reflecting a strong structure-function coupling. This coupling was primarily driven by network topology and was substantially stronger than that of the correlation approaches. Moreover, we observed that structure-function coupling was dominated by group-common effects, with subtle yet significant individual-specific effects. The regional group and individual effects of coupling were hierarchically organized across the cortex along a sensorimotor-association axis, with lower group and higher individual effects in association cortices. These findings emphasize the importance of considering both group and individual effects in understanding cortical structure-function coupling, suggesting insights into interpreting individual differences of the coupling and informing connectivity-guided therapeutics.

When an AI Doctor Gets Personal: The Effects of Social and Medical Individuation in Encounters With Human and AI Doctors

How do we know when someone knows us? Does it matter whether the knower is a human or a machine? Following the theory of interpersonal knowledge, a between-subjects experiment investigated whether a doctor’s incorporation of individualized knowledge about a patient’s social or medical history enhances doctor-patient relationships in online conversations. Patients in this study conversed with either a human doctor, an AI doctor, or an AI-assisted human doctor. Following previous research, additional factors such as perceptions of effort, relational closeness, privacy intrusiveness, and the provision of privacy control were assessed. Results showed that an AI doctor enhanced patient satisfaction when it employed social individuation messages, which triggered perceptions of increased effort, but only when patients could activate privacy control. Perception of relational closeness with a human doctor and an AI-assisted human doctor did not seem to require social individuation and privacy control. The study concludes with implications for the theory of interpersonal knowledge and AI-mediated communication research, as well as practical implications for improving chatbot medical systems.

Individualistic powerfulness and collectivistic powerlessness corrupts: how power and cultural orientation influence corruption

ABSTRACT Evidence from individualistic cultures suggests that power corrupts. Using a goals-based perspective, here we argue that power and culture jointly predict corrupt attitudes and behavior. Four studies (N = 447) and one meta-analysis were conducted to test these hypotheses. Study 1 investigated the joint effects of power and individuals’ cultural orientations on corruption proclivity. Studies 2 and 3 assessed if power and cultural orientations affect actual corrupt behaviors (i.e. abuse of discretion in Study 2 and bribe-taking in Study 3). Study 4 tested the hypothesis at a national level, using monocultural samples both in the UK and China. The results consistently showed that the effects of power on corruption depend on culture: for collectivistic individual orientations and cultures, holding power predicts less corruption than lacking power; in contrast, holding power predicts more corruption for individualist orientations and cultures. Our findings represent the first direct experimental and correlational evidence regarding the links between power, culture, and corruption.

Exploration of the individual and combined effects of predictors of engagement, dropout, and change from digital interventions for recurrent binge eating.

Our ability to predict responsiveness to digital interventions for eating disorders has thus far been poor, potentially for three reasons: (1) there has been a narrow set of predictors explored; (2) prediction has mostly focused on symptom change, ignoring other aspects of the user journey (uptake, early engagement); and (3) there is an excessive focus on the unique effects of predictors rather than the combined contributions of a predictor set. We evaluated the univariate and multivariate effects of outcome predictors in the context of a randomized trial (n = 398) of digitally delivered interventions for recurrent binge eating. Thirty baseline variables were selected as predictors, ranging from specific symptoms, to key protective factors, to technological acceptance, and to online treatment attitudes. Outcomes included uptake, early engagement, and remission. Univariate (d) and multivariate (D) standardized mean differences were calculated to estimate the individual and combined effects of predictors, respectively. At the univariate level, few predictors produced an effect size larger than what is considered small (d > .20) across outcomes. However, our multivariate approach enhanced prediction (Ds = .65 to 1.12), producing accuracy rates greater than chance (63%-71% accuracy). Less than half of the chosen variables proved to be useful in contributing to predictions in multivariate models. Findings suggest that accuracy in outcome prediction from digitally delivered interventions may be better driven by the aggregation of many small effects rather than one or several largely influential predictors. Replication with different data streams (sensor, neuroimaging) would be useful. Our ability to predict who will and will not benefit from digital interventions for eating disorders has been poor. We highlight the viability of a multivariate approach to outcome prediction, whereby prediction may be better driven by the aggregation of many small effects rather than one or a few influential predictors.

Co-occurring PTSD in intensive OCD treatment: Impact on treatment trajectory vs. response.

Obsessive-compulsive disorder (OCD) and co-occurring posttraumatic stress disorder (PTSD) is associated with more severe and chronic OCD. However, findings regarding treatment effectiveness of cognitive behavioral treatment (CBT) with exposure and response prevention (ERP) with this comorbidity are mixed. Research aimed at understanding the precise barriers to OCD treatment effectiveness for individuals with co-occurring PTSD may help elucidate unique treatment needs. The current study used linear regression and latent growth curve analysis comparing treatment response and trajectory from patients with OCD (n=3083, 94.2%) and OCD+PTSD (n=191, 5.2%) who received CBT with ERP in two major intensive OCD treatment programs. Although patients with OCD+PTSD evidenced similar trajectories of overall severity change, patients at one site required nearly 11 additional treatment days to achieve comparable reduction in OCD severity. Further, at the dimensional level, those with OCD+PTSD had poorer treatment response for unacceptable thoughts and symmetry symptoms. The moderate effect for unacceptable thoughts, indicating the widest gap in treatment response, suggests these symptoms may be particularly relevant to PTSD. Findings are limited by a naturalistic treatment sample with variation in treatment provision. Findings emphasize caution in using a one-size-fits-all approach for patients with co-occurring OCD+PTSD within intensive OCD treatment programs, as broadly defined outcomes (e.g., reduction in overall severity) may not translate to reduction in the nuanced symptom dimensions likely to intersect with trauma. Unacceptable thoughts and symmetry symptoms, when co-occurring with PTSD, may require a trauma-focused treatment approach within intensive OCD treatment.

Marriage and childbearing in patients with epilepsy in Turkey.

For epilepsy, a common neurological disorder, brings psychosocial challenges like stigma, employment difficulties, and barriers to marriage and childbearing. Stigma often stems from misconceptions and societal beliefs, particularly in less developed regions like Turkey. However, research on the marital and childbearing experiences of epilepsy patients in such settings is limited. We aimed to research the marriage and childbearing behaviors of men and women with epilepsy. We conducted a cross-sectional study involving 215 adult epilepsy patients at Antalya Training and Research Hospital between 2019 and 2022. Patients were asked questions about marriage and having children on prepared questionnaires. The gender distribution of the 215 patients included in the study was revealed to be 62.3% (134) females and 37.7% (81) males. 71.6% of patients were married, and 12.7% had no children. 33.3% of these patients stated that they did not desire children because of the disease. A statistically significant correlation was observed between the duration of the disease and being unmarried. A significant correlation was observed between age at disease onset and number of children. Our study revealed the effects of individuals with epilepsy on marriage and childbearing, and as we know, it is the first study conducted in Turkey on childbearing attitudes in individuals with epilepsy. Despite medical and social developments, epilepsy is still one of the most stigmatized diseases, and the disease has considerable negative effects on marriage and fertility. Our study supported the findings of a small number of previous similar studies on this subject and additionally showed that the likelihood of having children decreased in patients using multiple ASM, and on the other hand, it showed that marriage positively affected patients in terms of social support.

State of the Art Review for Titanium Fluorine Glasses and Glass Ceramics.

Titanium (Ti) and fluorine (F) have the potential to provide a variety of desirable physical, chemical, mechanical, and biological properties applicable to a broad range of indications. Consequently, Ti- and F-containing glasses and glass ceramics are currently under investigation for use in nuclear, optical, electrochemical, dental, and industrial fields. Accordingly, significant interest exists with respect to understanding the individual and interaction effects that these elements have on material structure and properties to support the accelerated design, development, and deployment of these materials. This review aims to serve as a foundational reference across multiple disciplines, highlighting the fundamental properties and versatility of Ti- and F-containing glasses and glass ceramics. By consolidating our current knowledge of these materials, this broad overview will identify areas in which we can further our understanding to support the a priori prediction and effective design of these systems. Finally, this paper will introduce the potential to improve material design by integrating experimentation, modelling, and computational approaches in a manner commensurate with the principles of the Materials Genome Initiative.

Defect tolerance and fatigue limit prediction for laser powder bed fusion Ti6Al4V

This study is focused on the individual effect of gas porosity size and location on fatigue strength of a laser powder bed fusion Ti6Al4V alloy. Tensile and fatigue samples were manufactured by artificially seeding gas pores either at the centre or close to surface. Seeded porosity reduced the ductility by 20% and fatigue strength by 33% but had little influence on the static strength. Despite seeded pores, 50% fatigue samples had crack initiation from process inherent defects that also caused fatigue data scatter. A modified Kitagawa-Takahashi diagram was established to predict the fatigue strength under the influence of porosity defects.

Drug-Drug Interactions and Actual Harm to Hospitalized Patients: A Multicentre Study Examining the Prevalence Pre- and Post-Electronic Medication System Implementation.

Drug-drug interactions (DDIs) have potential to cause patient harm, including lowering therapeutic efficacy. This study aimed to (i) determine the prevalence of potential DDIs (pDDIs); clinically relevant DDIs (cDDIs), that is, DDIs that could lead to patient harm, taking into account a patient's individual clinical profile, drug effects and severity of potential harmful outcome; and subsequent actual harm among hospitalized patients and (ii) examine the impact of transitioning from paper-based medication charts to electronic medication management (eMM) on DDIs and patient harms. This was a secondary analysis of the control arm of a controlled pre-post study. Patients were randomly selected from three Australian hospitals. Retrospective chart review was conducted before and after the implementation of an eMM system, without accompanying clinical decision support alerts for DDIs. Harm was assessed by an expert panel. Of 1186 patient admissions, 70.1% (n = 831) experienced a pDDI, 42.6% (n = 505) a cDDI and 0.9% (n = 11) an actual harm in hospital. Of 15,860 pDDIs identified, 27.0% (n = 4285) were classified as cDDIs. The median number of pDDIs and cDDIs per 10 drugs were 6 [interquartile range (IQR) 2-13] and 0 (IQR 0-2), respectively. In cases where a cDDI was identified, both drugs were 44% less likely to be co-administered following eMM (adjusted odds ratio 0.56, 95% confidence interval 0.46-0.73). Although most patients experienced a pDDI during their hospital stay, less than one-third of pDDIs were clinically relevant. The low prevalence of harm identified raises questions about the value of incorporating DDI decision support into systems given the potential negative impacts of DDI alerts.

Strategy to remove ammonium compounds released from fish feed in aquaculture using natural filtration materials.

The effects of addition of adsorbent mixture at two different stages in aquaculture system were investigated for the first time in this study. In the first stage, in order to determine the effects of natural adsorbents in an environment without feed, a trial consisting of four treatment groups with three replications was conducted. In this trial, individual and combined effects of two natural adsorbents on water parameters in absence of feed were assessed for five days.In the second stage for 9 experimental days, a total of 18 aquaria consisting of three treatment groups with three replications for two different fish feeds were used. Of these aquaria, the first six received only two types of feeds containing 33 and 40% protein, designated as two control groups (C1 and C2). Other two groups (T1 and T2) were prepared by adding a leonardite: zeolite mixture (at 2:1 ratio) to next six replicates simultaneously. The last six replicates received a leonardite: zeolite mixture (at 2:1 ratio) after the 8th day of the study, and formed the last two treatment groups (T3 and T4).In this study, mean pH values varied between 7.01 and 7.82 and ammonia values were found to be maximum of 94.96% and minimum of 38.73% lower compared to the control group when 3g adsorbent mixture (2L: 1Z) was used to balance pH and ammonium (NH4+) values in an aquatic environment containing 0.5l freshwater and 0.5g fish feed with 33-40% protein contents. It was demonstrated that the combined use of zeolite and leonardite had positive effects on ammonium removal and providing optimum pH levels for aquaculture. The combined use of these two adsorbents helped balance the pH-reducing effect of leonardite with zeolite, and the pH-reducing effect of leonardite contributed to the NH4+ adsorption efficiency of zeolite.

Do machine learning methods lead to similar individualized treatment rules? A comparison study on real data.

Identifying patients who benefit from a treatment is a key aspect of personalized medicine, which allows the development of individualized treatment rules (ITRs). Many machine learning methods have been proposed to create such rules. However, to what extent the methods lead to similar ITRs, that is, recommending the same treatment for the same individuals is unclear. In this work, we compared 22 of the most common approaches in two randomized control trials. Two classes of methods can be distinguished. The first class of methods relies on predicting individualized treatment effects from which an ITR is derived by recommending the treatment evaluated to the individuals with a predicted benefit. In the second class, methods directly estimate the ITR without estimating individualized treatment effects. For each trial, the performance of ITRs was assessed by various metrics, and the pairwise agreement between all ITRs was also calculated. Results showed that the ITRs obtained via the different methods generally had considerable disagreements regarding the patients to be treated. A better concordance was found among akin methods. Overall, when evaluating the performance of ITRs in a validation sample, all methods produced ITRs with limited performance, suggesting a high potential for optimism. For non-parametric methods, this optimism was likely due to overfitting. The different methods do not lead to similar ITRs and are therefore not interchangeable. The choice of the method strongly influences for which patients a certain treatment is recommended, drawing some concerns about their practical use.

Effect of Perceived Organizational Support and Workload on Employee Performance with Moderation of Person Organization Fit

The purpose of this study was to investigate and analyze the impact of perceived oganizational support and workload on employee performance with moderation by the individual's organizational fit. The study population included a sample of 43 respondents aged 24-47 who were employees and employees of North Jakarta BNNK. The datasprocessing methodsin thissresearch uses smartPLS for the purpose of hypothesis verification. The resultssof this study show that perceived organizational support has a positive and significant impact on employee performance, and workload has a positive and significant impact on employee performance, and that an individual's organizational fit hassa positive and significant effect on employee performance. positive and significant impact on employee performance, and person organization fit weakens but has an effect and can moderate between perceived organizational support on employee performance, person organization fit is able to strengthen and can moderate the workload on employee performance. This study recommends that if you want to improve employeesperformance, it is advisable to be able to provide psychological differentiation to employees who work, because some of the respondents to this study stated that agencies have not fully providedsgood support, causing employee performance to be less than optimal, but this can be minimized by providing support and rewards to employees.

Perceptions, Experiences, and Patterns of Cannabis Use in Individuals with Mood and Anxiety Disorders in the Context of Cannabis Legalization and Medical Cannabis Program in Canada - A Qualitative Study.

Perceptions of cannabis as a potential medical treatment for mood and anxiety disorders have been increasing in the context of legalizations, availability, and medical cannabis programs, though current evidence predominately indicates risks and negative effects of cannabis use (CU) on mental health outcomes. This study aims to understand motivations, perceptions, effects, and patterns of CU in individuals with mood and anxiety disorders. Thirty-six adult patients diagnosed with mood or anxiety disorders, obsessive-compulsive disorder, or posttraumatic stress disorder who were currently using cannabis completed an in-depth qualitative interview on individual motivations, perceptions, experiences, effects, and patterns of their CU. The thematic analysis focused on phases of CU and sources of cannabis products and information. Reported motivations for initiation of CU included curiosity, peer pressure, and dissatisfaction with conventional treatments. Factors such as psychotropic effects and coping with mental health symptoms and insomnia contributed to the continuation of CU. More negative effects, including cognitive dysfunction, worsening of mood, and anxiety symptoms, were acknowledged with ongoing CU. Concerning findings included common initiation of CU before age 18, combined medical and recreational CU, rare consultation of medical professionals on CU, and potential effects and harms. Findings indicate individual complexity of motivations, perceptions, and patterns of CU in the study population. The reported potential beneficial effects of specific cannabis products should be further investigated. Findings emphasize patient-provider dialogue on both CU and conventional treatments. Information from this study can contribute to and inform the development of education, prevention, and intervention strategies.

The Energy Transition and Support for the Radical Right: Evidence from the Netherlands

Energy transition policies often have distributional effects that could have electoral consequences. I study this issue in the context of a Dutch policy change that increased taxes on household natural gas consumption and redistributed the revenues as subsidies for renewables. Radical right parties were the only source of political opposition. A Differences-in-Differences (DiD) analysis with panel data from 2007-2020 shows that after the policy change renters with individualized utility bills became 5–6 percentage points more likely to vote for the radical right compared to renters with utilities included in their rents. Renters with individualized utility bills also became relatively less sympathetic towards the Green party and more concerned about price increases but they did not alter their left-right self-placements nor their views on immigration or the European Union. A secondary analysis finds similar effects for individuals (including home-owners) who are energy poor. This suggests an emerging economically rooted political cleavage over energy transition policies.

Content-Based Text Extraction from Image using Deep Learning

Text extraction proves beneficial in various scenarios, as it allows the conversion of information stored in non-text formats, like images or documents, into machine-readable and searchable text. In contemporary times, this technique serves as a time-efficient tool across different sectors such as real estate, finance, law, food ordering and delivery, and e-commerce. Industries are increasingly adopting text extraction methods. Previously, numerous models centered around text extraction utilized OCR, CNN, and RNN. When it comes to extracting text from images using content-based approaches, CNNs play a crucial role in recognizing and locating text regions within the images. In situations where the identification and transcription of text from images are essential, RNNs prove valuable for content-based text extraction. While CNNs and RNNs independently yield accurate outcomes in content-driven text extraction from photos, the combined utilization of both methods surpasses the individual effectiveness of each. The proposed CRNN system stands out in various aspects compared to existing methodologies. It not only demonstrates heightened accuracy and efficiency but also exhibits superior performance overall. Our investigation's findings highlight that the CRNN methodology, when applied, outperforms previous approaches by recognizing text in images with a reduced latency and more precise recognition

Cooperative induction of CXCL chemokines by inflammatory cytokines and oxidized phospholipids.

Inflammation is initiated and driven by a mixture of mediators, which modify effects of each other. This study analysed in vitro pro-inflammatory activity of inflammatory cytokines (TNFα and IL-1β) in a combination with a lipid DAMP molecule, oxidized palmitoyl-arachidonoyl-phosphatidylcholine (OxPAPC). The study was performed on endothelial and monocytic cell lines. The cells were treated with different concentrations of TNFα or IL-1β, OxPAPC and their combinations, either in the presence or absence of drugs regulating inflammation. Pro-inflammatory effects of TNFα/IL-1β and OxPAPC were estimated by analysis of chemokines CXCL8, CXCL2 and CXCL3 by ELISA and RT-PCR. Toxicity was determined by analysis of metabolic activity. Statistical significance was estimated by ANOVA and Dunnett's test. OxPAPC was a much weaker chemokine inducer as compared to TNFα or IL-1β. However, OxPAPC and TNFα/IL-1β together induced effects that were significantly stronger than the arithmetical sum of individual effects. This cooperative action of OxPAPC and TNFαwas reversed by inhibitors of p38 MAPK. We hypothesise that the boosting of TNFα and IL-1β effects by OxPAPC may be more pathologically important than the action of the lipid alone. Inhibitors of p38 MAPK may become a tool for analysis of pathological role of oxidized phospholipids.

Investigating anti-cancer activity of dual-loaded liposomes with thymoquinone and vitamin C.

Background: Thymoquinone (TQ) and vitamin C (Vit C) have demonstrated individual anticancer effects in various studies. TQ exhibits inhibitory properties against tumor growth, induces apoptosis, while Vit C protects against DNA damage and oxidative stress. Aim: Formulation of TQ and Vit C combination into liposomes using two methods and investigate the synergistic anticancer. Method: Liposomal preparations were characterized, and the purity of drug components was confirmed using encapsulation efficiency (EE %). Results: In vitro cell viability studies demonstrated the inhibitory effect of TQ and Vit C against colorectal (HT29, 5.5±0.9μM) and lung cancer (A549, 6.25±0.9μM) cell lines with combination index <1. Conclusion: The formulation of TQ and Vit C displayed synergistic anticancer activity.

White Sparkling Wine Proteins and Glycoproteins and Their Behavior in Foam Expansion and Stability

The volume and stability of wine foams are influenced by many components of the matrix, especially proteins. However, the synergistic or inhibiting effects among these protein fractions, as well as their interactions with other wine components, are still under study. The present research aims to understand the individual and cooperative effects of different wine proteins and glycoproteins on the volume and stability of foams. To address this objective, different protein fractions were purified from a Chardonnay white wine and tested in different model wine conditions (with/without ethanol), along with a commercial yeast-based oenological additive. Different fractions were considered, including total protein fraction (FT), Mannoproteins (MP), and non-mannosylated proteins (NMP), as well as a protein fraction soluble in ammonium sulfate (FSA). These protein fractions were characterized, and their foaming properties were evaluated using a modified Rudin apparatus. The results showed that FT exhibited higher foam expansion (FE%) compared to its subfractions (NMP and MP) that, when tested individually, did not guarantee optimal foam formation. This suggests that foaming properties are enhanced when both glycosylated and non-glycosylated proteins are present in the system. Additionally, the foaming behavior was influenced by the presence of ethanol in the model wine. The FSA fraction demonstrated high foam expansion and stability, with ethanol enhancing foam expansion but reducing stability. A commercial yeast-based oenological additive, mainly containing glycoproteins, was also tested and behaved similarly to MP. This study provides valuable insights for sparkling wine producers to optimize practices for enhancing product quality and confirm previous research regarding the role of the synergy between MP and NMP in wine foam formation and stability.

Toward an Integrated Model of Healthy Food Purchase via the Impact of Online Nutrition Information Seeking

Healthy eating is critical to consumers’ overall health. The purpose of this study was to examine body mass index (BMI), obesity knowledge, and self-efficacy, along with online nutrition information seeking (ONIS), as antecedents to healthy food purchase (HFP) in a moderated mediation model. An online survey was conducted using Amazon Mechanical Turk to recruit 897 participants, with 484 women and 380 men. A moderated mediation analysis was then used to explore the mediating effect of ONIS, and the moderating effects of obesity knowledge and self-efficacy. Results found the impact of ONIS on HFP was significantly generated by obesity knowledge but not by BMI. Both ONIS and self-efficacy yielded individual and interactive effects on HFP, and ONIS did not only generate a direct effect on HFP but also interacted with self-efficacy for HFP. Practically, it was suggested that online health information should be strategically crafted to promote healthy eating behavior, given that consumers in various health conditions were activated to purchase heathier foods via ONIS. Through the ONIS’s mediation of the relationship between obesity knowledge and HFP, consumers with poor obesity knowledge would be cultivated well to further develop their better eating habits.

Mutations in the SARS-CoV-2 spike receptor binding domain and their delicate balance between ACE2 affinity and antibody evasion.

Intensive selection pressure constrains the evolutionary trajectory of SARS-CoV-2 genomes and results in various novel variants with distinct mutation profiles. Point mutations, particularly those within the receptor binding domain (RBD) of SARS-CoV-2 spike (S) protein, lead to the functional alteration in both receptor engagement and monoclonal antibody (mAb) recognition. Here, we review the data of the RBD point mutations possessed by major SARS-CoV-2 variants and discuss their individual effects on ACE2 affinity and immune evasion. Many single amino acid substitutions within RBD epitopes crucial for the antibody evasion capacity may conversely weaken ACE2 binding affinity. However, this weakened effect could be largely compensated by specific epistatic mutations, such as N501Y, thus maintaining the overall ACE2 affinity for the spike protein of all major variants. The predominant direction of SARS-CoV-2 evolution lies neither in promoting ACE2 affinity nor evading mAb neutralization but in maintaining a delicate balance between these two dimensions. Together, this review interprets how RBD mutations efficiently resist antibody neutralization and meanwhile how the affinity between ACE2 and spike protein is maintained, emphasizing the significance of comprehensive assessment of spike mutations.