Individual Variation Research Articles

Do the best of all together: Hierarchical spatial-frequency fusion transformers for animal re-identification

Animal re-identification is fundamental in the ecology and ethology community for understanding the Earth’s ecosystems. Due to the uncertainty of photographing animals in the wild, such as the position and angle of capture and the variations in animal individuals as well as in their poses and habitats, building image-based visual models for re-identifying animal individuals is a challenging task. We propose a novel framework, hierarchical spatial-frequency fusion transformers, to address animal re-identification. We first use spatial and frequency representation learning to capture effective deep features and then employ hierarchical transformer-based representation learning to consolidate low-level detailed information into high-level semantic information from a global perspective. Through this process, we construct a deeply supervised nonlinear aggregation method to enhance finer multi-scale, multi-level and cross-domain features. Our method illustrates the case of “do the best of all together”, meaning that only when both the frequency and spatial domains are combined can we achieve the best performance. The experimental results demonstrate that our approach achieves significantly higher performance than other state-of-the-art methods.

Integrated line-field optical coherence tomography and scheimpflug imaging for corneal imaging

Previously we demonstrated a combined scanning-point optical coherence tomography (OCT) and Scheimpflug Imaging (SI) system, to provide cross-section images of the corneal layers and the whole anterior segment of the eye simultaneously. However, to fully realise the benefits of this dual imaging modality concept towards clinical practice, it is required that the OCT part of the system can achieve ultra-high axial resolution (<3 µm in air) and faster imaging speeds, without using prohibitively expensive components. To achieve this, a new imaging device integrating line-field OCT and SI, using a supercontinuum light source, is demonstrated. This line-field configuration enables both OCT and SI B-scan to be captured using the same light illumination in a single shot, which is the most significant improvement over single point scanning. This achieves an OCT axial resolution down to 2.1 µm in air (1.6 µm in corneal tissue) and an imaging speed up to 213 kA-Scans/s. Since the OCT and SI capture the identical corneal position and serve as cross-validation, this technique offers a great method for accurately determining the individual corneal thickness and refractive index, thereby minimizing individual variations, which can help determine the extent of cutting or correction needed in vision correction. Moreover, the precise measurement of corneal thickness provided by this technique allows for a better understanding of the biomechanical properties of the cornea. We demonstrate the efficacy of the proposed system by evaluating porcine and bovine eyes ex-vivo for determining the corneal refractive index and thickness.

A dependency distance approach to the syntactic complexity variation in the connected speech of Alzheimer’s disease

Previous research on the syntax complexity variation in individuals with Alzheimer’s disease primarily focuses on isolated language features like sentence length or syntactic structure, with limited exploration of their connection to memory and cognitive deficits. This study investigated the syntactic complexity variation in AD by associating working memory load with fine-grained syntactic features adopting a dependency distance approach. The findings include: (1) AD patients had shorter mean dependency distances (MDD) and more head-final dependencies compared to the HC group; (2) The patients still produced their speech under the DDM tendency but exhibited a stronger preference for simple syntactic structures; (3) Sentences produced by AD patients were statistically shorter than the HC group, and the patients exhibited greater difficulty handling syntactic structures as sentence length increases; (4) The patients could employ different syntactic structures but perform worse in extending them, particularly in nominal groups and hypotactic constructions; (5) The patients showed stronger reliance on sentence-initial adjuncts and inverted constructions. These findings suggest that syntactic impairments in AD exhibit a potential hierarchical decline along the continuum of syntactic complexity, with working memory deficits being a critical factor.

Patients' Experiences of Health After Surgical Treatment for Paraesophageal Hernia Grades III and IV: An Interview Based Study.

Surgery is the only available treatment for the longstanding chronic symptoms associated with large paraesophageal hernias except for reflux disease. The aim of this study was to illuminate how patients who previously suffered from grade III-IV hiatal hernia experience their life and health 2-6months after surgery. The study is based on semi-structured interviews with 17 patients who received elective laparoscopic hernia repair for a large paraesophageal hernia. The data were analyzed using qualitative content analysis, resulting in three main themes: "Experiences of health," "Being unable to leave the disease behind," and "Still feeling unwell" and seven subthemes: "Escaping suffering"; "Learning to interpret bodily signals"; "Looking to the future with confidence"; "Finding oneself in a vicious circle of worry"; "The fear of relapse as a constant companion"; "Lingering disabling symptoms," and "New and frightening symptoms." Our study demonstrates large individual variations in the way patients experience their life and health after laparoscopic hernia repair. Central to the patients' descriptions is that simply feeling physically healthy is insufficient for achieving overall health. Health care personnel can benefit from learning about patients' experiences of health and suffering after surgery.

Divergent patterns of locomotor activity in cave isopods (Oniscidea: Styloniscidae) in Neotropics

ABSTRACT In cave environments, stable conditions devoid of light-dark cycles and temperature fluctuations sustain circadian clock mechanisms across various species. However, species adapted to these conditions may exhibit disruption of circadian rhythm in locomotor activity. This study examines potential rhythm loss due to convergent evolution in five semi-aquatic troglobitic isopod species (Crustacea: Styloniscidae), focusing on its impact on locomotor activity. The hypothesis posits that these species display aperiodic locomotor activity patterns. Isopods were subjected to three treatments: constant red light (DD), constant light (LL), and light-dark cycles (LD 12:12), totaling 1656 h. Circadian rhythm analysis employed the Sokolove and Bushell periodogram chi-square test, Hurst coefficient calculation, intermediate stability (IS), and activity differences for each species. Predominantly, all species exhibited an infradian rhythm under DD and LL. There was synchronization of the locomotor rhythm in LD, likely as a result of masking. Three species displayed diurnal activity, while two exhibited nocturnal activity. The Hurst coefficient indicated rhythmic persistence, with LD showing higher variability. LD conditions demonstrated higher IS values, suggesting synchronized rhythms across species. Significant individual variations were observed within species across the three conditions. Contrary to the hypothesis, all species exhibited synchronization under light-dark conditions. Analyzing circadian activity provides insights into organism adaptation to non-cyclical environments, emphasizing the importance of exploring underlying mechanisms.

N of 1: Optimizing Methodology for the Detection of Individual Response Variation in Resistance Training.

Most resistance training research focuses on inference from average intervention effects from observed group-level change scores (i.e., mean change of group A vs group B). However, many practitioners are more interested in training responses (i.e., causal effects of an intervention) on the individual level (i.e., causal effect of intervention A vs intervention B for individual X). To properly examine individual response variation, multiple confounding sources of variation (e.g., random sampling variability, measurement error, biological variability) must be addressed. Novel study designs where participants complete both interventions and at least one intervention twice can be leveraged to account for these sources of variation (i.e., n of 1 trials). Specifically, the appropriate statistical methods can separate variability into the signal (i.e., participant-by-training interaction) versus the noise (i.e., within-participant variance). This distinction can allow researchers to detect evidence of individual response variation. If evidence of individual response variation exists, researchers can explore predictors of the more favorable intervention, potentially improving exercise prescription. This review outlines the methodology necessary to explore individual response variation to resistance training, predict favorable interventions, and the limitations thereof.

Sex differences in early-onset atrial fibrillation in Norwegian primary care: a retrospective national database analysis

BackgroundIndividual variation in the need for healthcare constitutes knowledge gaps for young atrial fibrillation (AF) patients. We aimed to estimate the prevalence and primary care burden of early-onset AF in...

Roles of empathy in altruistic cooperation in adults with and without autism spectrum disorder

BackgroundAltruistic cooperation (AC) is essential in human social interactions. Previous studies have investigated AC-related behavior in children with autism spectrum disorder (ASD), revealing that there is considerable individual variability in the behavior. However, this issue is still largely unexplored especially in the adult population. AimsTo investigate individual differences in AC-related behavior, we conducted the resource allocation task (RAT) and modified version of the ultimatum game (mUG) among adults with and without ASD. Methods and proceduresThe study employed a cross-sectional design, involving 27 adults with ASD (mean age 29.1 ± 4.3 years; three females) and 27 adults with typical development (TD) (mean age 25.8 ± 6.7 years; two females), who completed the RAT and mUG tasks. Beyond clinical characteristics, we assessed three primary psychological metrics: the interpersonal reactivity index (IRI), Barratt impulsiveness scale, and the behavioral inhibition and activation systems. Outcomes and resultsNo significant differences were observed in the proportions of participants with high AC when assessed by RAT (p = 0.15) and mUG (p = 0.59) between the TD and ASD groups. Participants with high AC from the RAT demonstrated higher perspective-taking scores on the IRI than those with low AC within both the TD (p = 0.04) and ASD groups (p = 0.03). In the TD group, high AC individuals also scored higher on the IRI's fantasy subscale as per the mUG (p = 0.03); however, this trend was not present in the ASD group. Conclusions and implicationsThe present findings indicate that empathy plays an important role in individual differences in AC-related behavior among adults with and without ASD, although the role could be different depending on the types of AC-related behavior between TD and ASD populations.

The interacting effects of talker, contrast, and listener variability in cross-language speech perception.

Building on previous observations of variability in speech research, we examine variability in speech perception study materials associated with the specific talker and contrast under examination. English-speaking listeners completed a web-based auditory AXB task involving Hindi dental-retroflex stop contrasts produced by four talkers. Main effects of talker and contrast, as well as the interaction of the two, were observed. Further, there was a great deal of individual listener variation. These findings complicate our ability to characterize the difficulty that Hindi dental-retroflex contrasts pose for English speakers, and lead to critical questions concerning the generalizability of speech perception study findings.

Characteristics Associated With Mental Health Treatment Prior to Suicide Among Youth in the United States

To examine individual and contextual characteristics associated with receipt of mental health treatment prior to youth suicide. Data from the US National Violent Death Reporting System, Area Health Resource File, and Social Vulnerability Index were used to examine characteristics associated with receipt of mental health treatment within 2 months before death among youth suicide decedents aged 5 to 17 years from 2013 to 2020 (N= 6,229). The association between individual (demographic, precipitating circumstances, and clinical characteristics) and contextual-level variables (county health resources, Social Vulnerability Index) and mental health service use was modeled using logistic regression. Mental health treatment was received by 31.6% of youth suicide decedents (n= 1,967) in the 2 months before suicide. Male individuals and youth from all racial and ethnic minority groups were less likely to receive mental health treatment in the 2 months prior to suicide, as were youth residing in non-metropolitan counties and living in counties characterized by high compared to low levels of social vulnerability. A history of family problems, a recent crisis, criminal/legal problems, and suicidal thoughts and attempts were associated with increased odds of receiving mental health services. Youth suicide decedents who were male, members of a racial or ethnic minority group, and residing in counties that are non-metropolitan and/or socially disadvantaged were less likely to have received mental health services in the months prior to death. Suicide prevention efforts that focus on improving access to care are essential for these vulnerable populations at risk for suicide.

Using Deep Learning Techniques as an Attempt to Create the Most Cost-Effective Screening Tool for Cognitive Decline.

This study aimed to use deep learning (DL) to develop a cost-effective and accessible screening tool to improve the detection of cognitive decline, a precursor of Alzheimer's disease (AD). This study integrating a comprehensive battery of neuropsychological tests adjusted for individual demographic variables such as age, sex, and education level. A total of 2,863 subjects with subjective cognitive complaints who underwent a comprehensive neuropsychological assessment were included. A random forest classifier was used to discern the most predictive test combinations to distinguish between dementia and nondementia cases. The model was trained and validated on this dataset, focusing on feature importance to determine the cognitive tests that were most indicative of decline. Subjects had a mean age of 72.68 years and an average education level of 7.62 years. The DL model achieved an accuracy of 82.42% and an area under the curve of 0.816, effectively classifying dementia. Feature importance analysis identified significant tests across cognitive domains: attention was gauged by the Trail Making Test Part B, language by the Boston Naming Test, memory by the Rey Complex Figure Test delayed recall, visuospatial skills by the Rey Complex Figure Test copy score, and frontal function by the Stroop Test Word reading time. This study showed the potential of DL to improve AD diagnostics, suggesting that a wide range of cognitive assessments could yield a more accurate diagnosis than traditional methods. This research establishes a foundation for future broader studies, which could substantiate the approach and further refine the screening tool.

GMM estimation for high-dimensional panel data models

In this paper, we study a class of high dimensional moment restriction panel data models with interactive effects, where the factors are unobserved and these factor loadings are nonparametrically unknown smooth functions of individual characteristic variables. We allow the dimension of the parameter vector and the number of moment conditions to diverge with the sample size. This is a very general framework and is closely related to many existing linear and nonlinear panel data models. In order to estimate the unknown parameters, factors and factor loadings, we propose a sieve-based generalized method of moments estimation method and we show that under a set of simple identification conditions, all those unknown quantities can be consistently estimated. Further we establish asymptotic distributions of the proposed estimators. In addition, we propose tests for over-identification, specification of factor loading functions, and establish their large sample properties. Moreover, a number of simulation studies are conducted to examine the performance of the proposed estimators and test statistics in finite samples. An empirical example on stock return prediction is studied to demonstrate both the empirical relevance and the applicability of the proposed framework and corresponding estimation and testing methods.

Telemedicine and Teamwork Among Health Care Professionals: State of the Science.

Background: As telemedicine becomes further rooted in standard patient care delivery, it is critical to understand how it may affect teamwork among health care providers. Understanding the state of the science between telemedicine and teamwork is an important first step. Obejctive: The purpose of this state-of-the-science review was to synthesize the published research on teamwork within the context of telemedicine. Methods: Data abstraction and analysis were structured following the Virtual Team Performance and the "Big Five" of Teamwork theoretical frameworks. The concepts within these models were used to organize data extraction. This state of the science used the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Results: A total of 14 studies met the inclusion criteria after final review. The most common variables were communication, tasks, leadership, team orientation, and team cohesion. Despite variable commonalities across the included articles, there was a discrepancy between improved and reduced teamwork outcomes with telemedicine. Conclusions: Multiple teamwork variables are influential across health care teams using telemedicine technologies-those that include communication, task facilitation, leadership, team orientation, and cohesion appear to have the greatest impact. However, it is not an individual teamwork variable, type of technology, or care environment alone that influences positive or negative outcomes among health care teams using telemedicine. Instead, it is a combination of factors and mechanisms that facilitate or hinder teamwork outcomes. A comprehensive model that describes the interaction of these common variables in teamwork among blended virtual and in-person health care teams is needed.

Assessment of Clinical Reasoning in Undergraduate Medical Education: A Pragmatic Approach to Programmatic Assessment.

Clinical reasoning, a complex construct integral to the practice of medicine, has been challenging to define, teach, and assess. Programmatic assessment purports to overcome validity limitations of judgments made from individual assessments through proportionality and triangulation processes. This study explored a pragmatic approach to the programmatic assessment of clinical reasoning. The study analyzed data from 2 student cohorts from the University of Utah School of Medicine (UUSOM) (n = 113 in cohort 1 and 119 in cohort 2) and 1 cohort from the University of Colorado School of Medicine (CUSOM) using assessment data that spanned from 2017 to 2021 (n = 199). The study methods included the following: (1) asking faculty judges to categorize student clinical reasoning skills, (2) selecting institution-specific assessment data conceptually aligned with clinical reasoning, (3) calculating correlations between assessment data and faculty judgments, and (4) developing regression models between assessment data and faculty judgments. Faculty judgments of student clinical reasoning skills were converted to a continuous variable of clinical reasoning struggles, with mean (SD) ratings of 2.93 (0.27) for the 232 UUSOM students and 2.96 (0.17) for the 199 CUSOM students. A total of 67 and 32 discrete assessment variables were included from the UUSOM and CUSOM, respectively. Pearson r correlations were moderate to strong between many individual and composite assessment variables and faculty judgments. Regression models demonstrated an overall adjusted R2 (standard error of the estimate) of 0.50 (0.19) for UUSOM cohort 1, 0.28 (0.15) for UUSOM cohort 2, and 0.30 (0.14) for CUSOM. This study represents an early pragmatic exploration of regression analysis as a potential tool for operationalizing the proportionality and triangulation principles of programmatic assessment. The study found that programmatic assessment may be a useful framework for longitudinal assessment of complicated constructs, such as clinical reasoning.

Event-Triggered Reduced-Order Filtering for Continuous Semi-Markov Jump Systems With Imperfect Measurements.

This article conducts the issue of event-triggered reduced-order filtering for continuous-time semi-Markov jump systems with imperfect measurements as well as randomly occurring uncertainties (ROUs). Specifically, the sojourn-time-dependent transition probability matrix (TPM) is presumed to be polytopic and a quantizer is introduced to quantize output signals aiming to reflect the reality. Both ROUs and sensor failures are generated by individual random variables belonging to be mutually independent Bernoulli-distributed white sequences. First, sufficient conditions for the existence of the event-triggered reduced-order filter are obtained by utilizing the dissipativity-based technique to ensure the asymptotical stability with a strictly dissipative performance of the filtering error system. The time-varying TPM is then fractionalized, which enhances the results as stated. Furthermore, the required reduced-order filter parameters are obtained by introducing slack symmetric matrix as well as cone complementarity linearization algorithm. The effectiveness of the suggested event-triggered reduced-order filter design method is shown through simulation results.

Linking Multi-Dimensional Religiosity in Childhood and Later Adulthood: Implications for Later Life Health.

This study examines religiosity patterns across childhood and later adulthood and their associations with later-life health using an experimental module from the 2016 Health and Retirement Study (N = 1649; Mean Age = 64.0). Latent class analysis is used to categorize individuals by commonalities in religious attendance, religious identity, and spiritual identity. Cross-sectional and longitudinal associations are then explored using probable depression, disability, and mortality as health indicators. Results reveal complex patterns, often characterized by declining attendance and fluctuating identity. Relationships with health appear stronger in cross-sectional analyses, suggesting that some associations may be non-causal. Individuals with consistently strong religiosity show significantly better psychological health compared to their relatively non-religious counterparts. Moreover, the absence of religiosity in later adulthood is associated with an increased risk of mortality. Overall, the findings support the promotion of religiosity whilst acknowledging individual variations and highlighting the need for more individualistic approaches to the study of religion and health.

An accurate tidal peak localization method in radial arterial pulse signals based on hybrid neural networks

Background: cardiovascular diseases (CVDs) have become the leading causes of death worldwide. Arterial stiffness and elasticity are important indicators of cardiovascular health. Pulse wave analysis (PWA) is essential for analyzing arterial stiffness and elasticity, which are highly dependent on the tidal peak (P 2). P 2 is one of the four key physiological points, which also include percussion peaks (P 1), diastolic notches (P 3), and diastolic peaks (P 4). P 1, P 3, and P 4 are often local maxima or minima, facilitating their identification via the second derivatives method, a classic localization method for key physiological points. Classic methods such as the second derivative method, Empirical Mode Decomposition (EMD), and Wavelet Transform (WT), have been employed for the extraction and analysis of the P 2. Due to individual variation and arterial stiffness, locating the P 2 using classic methods is particularly challenging. Methods: we propose a hybrid neural network based on Residual Networks (ResNet) and bidirectional Long Short-Term Memory Networks (Bi-LSTM), successfully achieving high-precision localization of the P 2 in radial artery pulse signals. Meanwhile, we compared our method with the second derivative method, EMD, WT, Convolutional Neural Networks (CNN) and the hybrid model with ResNet and LSTM. Results: the results indicate that our proposed model exhibits significantly higher accuracy compared to other algorithms. Overall, MAEs and RMSEs for our proposed method are 62.60% and 58.84% on average less than those for other algorithms. The average R Adj 2 is 29.20% higher. The outcomes of the efficiency evaluation suggest that the hybrid model performs more balancedly without any significant shortcomings, which indicates that the Bi-LSTM structure upgrades the performances of LSTM. Significance: our hybrid model can provide the medical field with improved diagnostic tools and promote the development of clinical practice and research.

Within-population variation in an invasive fish' sociability when associating with conspecifics or heterospecifics.

Behavioural traits are key to promote invasion success because they are easier to adjust to changing environmental conditions than morphological or life history traits. Often, research has overlooked variance in behavioural traits within populations or has assumed it to be mere noise. However, a recent focus towards individual variation of behaviour of successful invaders has revealed new and more profound insights into the invasion process. Behavioural variation within a population could lead to more successful invasions, as they include individuals with diverse behaviours, which ensures at least some individuals could be able to cope with changing conditions. The aim of this research was to examine if invasive guppies (Poecilia reticulata) present within-population differences in their sociability (time spent associating with a shoal) when interacting with conspecifics or heterospecifics. Guppies presented significant differences in their individual tendencies to associate with conspecific or heterospecific shoals. There were among-individual differences in the time spent shoaling with conspecifics versus heterospecifics, where most individuals did not differ in their sociability with conspecifics or heterospecifics, and only 22% of individuals presented a higher tendency to associate with conspecifics. Our results are the first to show individual differences in fish' tendencies to associate with heterospecifics among individuals of the same population and rearing conditions. Given that associations with heterospecific natives have been found to be as beneficial as associations with conspecifics for invaders, our results contribute to the understanding of mechanisms behind heterospecific sociability between natives and invaders.

Enhancing multimodal depression detection with intra- and inter-sample contrastive learning

Multimodal depression detection (MDD) has garnered significant interest in recent years. Current methods typically integrate multimodal information within samples to distinguish positive from negative samples, but they often neglect the relationships between samples. Despite similarities within the same class, individual variations exist. By leveraging these relationships, we can provide supervision signals for both inter- and intra-class samples, thereby enhancing the discriminative power of user representations. Inspired by this observation, we introduce IISFD, a novel approach that concurrently exploits intra-sample contrastive learning and inter-sample contrastive learning with hard negative sampling. This method comprehensively considers information both within individual samples and across samples. Specifically, we decompose the multimodal inputs of each sample, including audio, vision and text, into modality-common features and modality-specific features. To obtain better decomposed feature representations, we integrate intra-sample contrastive learning and inter-sample contrastive learning with hard negative sampling. Additionally, detailed modal information is obtained through unimodal reconstruction. By passing the decomposed features through a carefully designed multimodal fusion module, we obtain more discriminative user representations. Experimental results on two publicly available datasets demonstrate the superiority of our model, highlighting its effectiveness in leveraging both intra- and inter-sample information for enhanced MDD.

In perspective of specific symptoms of major depressive disorder: Functional connectivity analysis of electroencephalography and potential biomarkers of treatment response

BackgroundThe symptom variability in major depressive disorder (MDD) complicates treatment assessment, necessitating a thorough understanding of MDD symptoms and potential biomarkers. MethodsIn this prospective study, we enrolled 54 MDD patients and 39 controls. Over the course of weeks 1, 2, and 4 participants underwent evaluations, with electroencephalograms (EEG) recorded at baseline and week 1. Our investigation considered five previously identified syndromal factors derived from the 17-item Hamilton Depression Rating Scale (17-item HAMD) for assessing depression: core, insomnia, somatic anxiety, psychomotor-insight, and anorexia. We assessed treatment response and EEG characteristics across all syndromal factors and total scores, all of which are based on the 17-item HAMD. To analyze the topology of brain networks, we employed functional connectivity (FC) and a graph theory-based method across various frequency bands. ResultsThe healthy control group had notably higher values in delta band EEG FC compared to the MDD patient group. Similar distinctions were observed between the responder and non-responder patient groups. Further exploration of baseline FC values across distinct syndromal factors revealed significant variations among the core, psychomotor-insight, and anorexia subgroups when using a specific graph theory-based approach, focusing on global efficiency and average clustering coefficient. LimitationsDifferent antidepressants were included in this study. Therefore, the results should be interpreted with caution. ConclusionsOur findings suggest that delta band EEG FC holds promise as a valuable predictor of antidepressant efficacy. It demonstrates an ability to adapt to individual variations in depressive symptomatology, offering insights into personalized treatment for patients with depression.