Lifetime Resilience Research Articles

Quantification of the effect of in utero events on lifetime resilience in dairy cows

Currently, the dairy industry is facing many challenges that could affect its sustainability, including climate change and public perception of the industry. As a result, interest is increasing in the concept of identifying resilient animals, those with a long productive lifespan, as well as good reproductive performance and milk yield. There is much evidence that events in utero, that is, the developmental origins of health and disease hypothesis, alter the life-course health of offspring and we hypothesized that these could alter resilience in calves, where resilience is identified using lifetime data. The aim of this study was to quantify lifetime resilience scores (LRS) using an existing scoring system, based on longevity with secondary corrections for age at first calving and calving interval, and to quantify the effects of in utero events on the LRS using 2 datasets. The first was a large dataset of cattle on 83 farms in Great Britain born from 2006 to 2015 and the second was a smaller, more granular dataset of cattle born between 2003 and 2015 in the Langhill research herd at Scotland's Rural College. Events during dam's pregnancy included health events (lameness, mastitis, use of an antibiotic or anti-inflammatory medication), the effect of heat stress as measured by temperature-humidity index, and perturbations in milk yield and quality (somatic cell count, percentage fat, percentage protein and fat:protein ratio). Daughters born to dams that experienced higher temperature-humidity indexes while they were in utero during the first and third trimesters of pregnancy had lower LRS. Daughter LRS were also lower where milk yields or median fat percentages in the first trimester were low, and when milk yields were high in the third trimester. Dam LRS was positively associated with LRS of their offspring; however, as parity of the dam increased, LRS of their calves decreased. Similarly, in the Langhill herd, dams of a higher parity produced calves with lower LRS. Additionally, dams that recorded a high maximum locomotion score in the third trimester of pregnancy were negatively associated with lower calf LRS in the Langhill herd. Our results suggest that events that occur during pregnancy have lifelong consequences for the calf's lifetime performance. However, experience of higher temperature-humidity indexes, higher dam LRS, and mothers in higher parities explained a relatively small proportion of variation in offspring LRS, which suggests that other factors play a substantial role in determining calf LRS. Although "big data" can contain a considerable amount of noise, similar findings between the 2 datasets indicate it is likely these findings are real.

Genetic associations of ewe body condition score and lamb rearing performance in extensively managed meat sheep

Many small ruminant production systems are relatively low input and rely heavily on animals grazing pastures often in extensive and harsh environmental conditions, unsuitable for any other type of agriculture. New breeding goal traits for lifetime resilience for maternal sheep breeding programmes have been investigated, focusing on body tissue mobilisation phenotypes and their genetic relationships with maternal production traits. Performance records of 8,355 Scottish Blackface ewes, from 2 extensively reared hill sheep flocks, collected over a 20-year period, were used to quantify relationships between body condition score (BCS) and the ewe's ability to successfully rear lambs. Between 14,000 and 25,000 data records per trait were available, measured across the annual sheep reproductive cycle. The pedigree file used for the analyses included sire and dam information for 50,207 animals. Most heritability estimates for the BCS traits - measured at key time points within the annual production cycle, or considering BCS changes between time points - were low, but significantly different from zero, ranging between 0.07 and 0.17. The heritability estimate for the number of lambs at pregnancy scanning was 0.09. Heritabilities for foetal loss from pregnancy scanning to lambing, lamb loss from lambing to weaning and number of lambs weaned were 0.02, 0.02 and 0.06, respectively. These estimates indicate that genetic control over the reproductive and lamb survival traits (expressed as a trait of the dam), is lower than that for BCS (and BCS changes) over the reproductive cycle. The genetic relationships amongst the BCS of ewes at pre-mating, pregnancy scan and at pre-lambing, with litter size at pregnancy scan and number of lambs weaned, were consistent. They indicate that ewes of higher body condition score (potentially over-fat, within the range recorded in these flocks) conceive, and rear, fewer lambs, with genetic correlations ranging between -0.18 and -0.58. However, post-weaning, animals with genetic propensity for greater gain in BCS from weaning to pre-mating produce larger litters and rear more lambs. In addition, the selection of ewes that lose less condition during pregnancy will reduce foetal losses. The results from this study paint a complex picture that should be interpreted in the context of specific management practices for extensively managed hill sheep, at critical times in their reproductive cycle. The traits relating to body tissue mobilisation, as assessed using BCS, are mostly heritable and their inclusion in future breeding programmes, to aid future selection for resilience, should be considered.

Influence of Corrosion Deterioration on Lifetime Seismic Resilience of Highway Bridges

Highway bridges across the globe have demonstrated vulnerability to damage during earthquake events. While earthquakes occur infrequently, environmental degradation mechanisms such as chloride-induced corrosion deterioration are continuous and decrease the structural performance over time. There is a need to assess the performance of existing older bridges in India, taking into account the combined threats of corrosion degradation and seismic hazard. In this regard, resilience has emerged as a powerful tool to determine the bridge performance integrating the seismic vulnerability, losses, and recovery functions. This study presents a case-study for lifetime resilience assessment considering the effect of environmental and seismic hazards using a representative two-span semi-integral bridge. Finite element models of the bridge is developed at pristine and four degraded conditions considering chloride-induced corrosion. Nonlinear time-history analyses are conducted on the bridge models to develop time-dependent seismic fragility curves. Further, these fragility curves are used to estimate post-earthquake direct and indirect losses that are combined with suitable recovery models to estimate the functionality and resilience of the bridge. Results obtained from the analysis revealed a degrading trend of the resilience of the bridge as it deteriorates with age, representing the significance of lifetime seismic resilience assessment for estimating the performance of existing aging bridges.

An overarching framework to assess the life-time resilience of deteriorating transportation networks in seismic-prone regions

This study develops a comprehensive framework to assess the resilience of transportation networks consisting of deteriorating bridges subjected to earthquake events. For this purpose, the structural capacity of highway bridges is estimated during their service life using a set of detailed finite-element models that simulate the progress of deterioration. The developed models take into consideration the main environmental stressors and determine the extent of capacity loss as a function of time. Based on the degraded state of structural components, seismic fragility analyses are performed to obtain a probabilistic evaluation of the extent of damageability of the existing bridges under seismic events. Since each transportation link normally consists of a number of bridges, the state of damage in the individual bridges is mapped to the corresponding links and a scenario-based approach is employed to estimate the resilience of the entire transportation network. To demonstrate how the consequences of structural degradation can be integrated into the developed framework, the large-scale transportation network of Los Angeles and Orange counties is investigated under a series of aging and earthquake scenarios. The outcome of this study indicates how the estimates associated with the functionality measures of a transportation network can be improved if the age factor is properly integrated into the framework used for resilience assessment.

Selection on functional longevity in a commercial population of dairy goats translates into significant differences in longevity in a common farm environment

Resilience is the ability of an animal to cope with environmental disturbances, such as pathogens or negative energy balance. To improve resilience through breeding, we need resilience indicators. Functional longevity might be a good indicator of a dairy goat's lifetime resilience as it results from the ability to cope with and recover from all the challenges faced throughout its lifetime. The aim of this study was to validate the use of functional longevity as an indicator of resilience for selection. To address this question, we created 2 genetic lines of Alpine goats using hyperselected artificial insemination bucks with the most extreme estimated breeding values for functional longevity and the same milk yield performance. A total of 440 goats, 228 in the high longevity (high_LGV) and 221 in the low longevity (low_LGV) lines, were bred and monitored for 4 yr. Health treatments, serum IgG concentration as a proxy of passive immune transfer in early life, kidding, age, and reason of culling were systematically noted. Weight and body morphology were monitored. Weight and growth during the first year of life were similar in both goat lines. In contrast, the low_LGV goats had a lower weight during the beginning of first lactation than high_LGV goats. The milk fat-to-protein ratio was also significantly higher in low_LGV goats during first lactation. A multivariable Cox regression was fitted to the data to decipher survival at different stages of life in the 2 lines. The overall survival of high_LGV goats was significantly better than low_LGV goats (hazard ratio = 0.63, confidence interval = 0.47; 0.86) even after we included treatment, growth, serum IgG concentration at birth, and year effects in the model. The line effect was not constant over time; no significant effect was found during the first year, and the difference was observed after first kidding. This result suggested that survival at an early stage of life and during later productive life are under different genetic regulation. Altogether, this monitoring of the goat lines indicated that functional longevity-based selection helps to improve resilience by improving survival and mitigating some indicators of fat mobilization during early lactation.

Lifetime Resilience Migration Quantification Using Nonparametric Distance Metrics and Application for River-Crossing Bridges

Quantitative resilience assessment is an essential to the lifetime management of civil structures. With the uncertainties arising from both physical and socioeconomic dimensions, resilience needs to be measured probabilistically. Although existing resilience frameworks have addressed this need, none statistically characterize how much a system’s resilience migrates and in what direction it migrates. This paper proposes a lifetime resilience migration quantification (LRMQ) framework based on nonparametric distance metrics. In this framework, system resilience positioning is proposed by comparing the resilience of a variable system against two reference systems. Two decision-making analytics, a binary resilience classification (BRC) diagram and a lifetime resilience attenuation (LRA) model, are proposed. The proposed method is evaluated with success by performing numerical experimentation over a representative river-crossing bridge. It is observed that the bridge’s system resilience attenuates progressively in its lifetime; with the consideration of a scour countermeasure, resilience is effectively enhanced via visualizing the proposed BRC diagram and LRA model.

Lifetime Resilience Measurement of River-Crossing Bridges with Scour Countermeasures under Multiple Hazards

River-crossing bridges are often subject to multiple hazards, including foundation scour, seismic attacks, and environmental degradation. When river-crossing bridges are in service, they should be restored rapidly after any disruption over their lifetime. To achieve this resilient quality, it is necessary to assess the system resilience subjected to multihazard impacts and the beneficial effects of any retrofitting or hazard-countermeasure in a lifetime context. To river-crossing bridges, one important intervention is to implement scour countermeasures. This study presents a probabilistic framework to quantify the lifetime system resilience of river-crossing bridges subject to multiple hazards. Notably, the framework is designed to reveal how progressive and abrupt hazards interact and result in resilience degradation and how scour countermeasures contribute to resilience enhancement. Experimental outcomes reveal the positive and distinct effects of implementing scour countermeasures at different times. The proposed framework is expected to assist civil engineers in conducting lifecycle management of river-crossing bridges that are subject to hydraulic scour and demand timely countermeasures.

A data-driven prediction of lifetime resilience of dairy cows using commercial sensor data collected during first lactation

Reliable prediction of lifetime resilience early in life can contribute to improved management decisions of dairy farmers. Several studies have shown that time series sensor data can be used to predict lifetime resilience rankings. However, such predictions generally require the translation of sensor data into biologically meaningful sensor features, which involve proper feature definitions and a lot of preprocessing. The objective of this study was to investigate the hypothesis that data-driven random forest algorithms can equal or improve the prediction of lifetime resilience scores compared with ordinal logistic regression, and that these algorithms require considerably less effort for data preprocessing. We studied this by developing prediction models that forecast lifetime resilience of a cow early in her productive life using sensor data from the first lactation. We used an existing data set from a Dutch experimental herd, with data of culled cows for which birth dates, insemination dates, calving dates, culling dates, and health treatments were available to calculate lifetime resilience scores. Moreover, 4 types of first-lactation sensor data, converted to daily aggregated values, were available: milk yield, body weight, activity, and rumination. For each sensor, 14 sensor features were calculated, of which part were based on absolute daily values and part on relative to herd average values. First, we predicted lifetime resilience rank with stepwise logistic regression using sensor features as predictors and a P-value of <0.2 as the cut-off. Next, we applied a random forest with the 6 features that remained in the final logistic regression model. We then applied a random forest with all sensor features, and finally applied a random forest with daily aggregated values as features. All models were validated with stratified 10-fold cross-validation with 90% of the records in the training set and 10% in the validation set. Model performances expressed in percentage of correctly classified cows (accuracy) and percentage of cows being critically misclassified (i.e., high as low and vice versa) ± standard deviation were 45.1 ± 8.1% and 10.8% with the ordinal logistic regression model, 45.7 ± 8.4% and 16.0% with the random forest using the same 6 features as the logistic regression model, 48.4 ± 6.7% and 10.0% for the random forest with all sensor features, and 50.5 ± 6.3% and 8.4% for the random forest with daily sensor values. This random forest also revealed that data collected in early and late stages of first lactation seem to be of particular importance in the prediction compared with that in mid lactation. Accuracies of the models were not significantly different, but the percentage of critically misclassified cows was significantly higher for the second model than for the other models. We concluded that a data-driven random forest algorithm with daily aggregated sensor data as input can be used for the prediction of lifetime resilience classification with an overall accuracy of ~50%, and provides at least as good prediction as models with sensor features as input.

Productive life span and resilience rank can be predicted from on-farm first-parity sensor time series but not using a common equation across farms

A dairy cow's lifetime resilience and her ability to recalve gain importance on dairy farms, as they affect all aspects of the sustainability of the dairy industry. Many modern farms today have milk meters and activity sensors that accurately measure yield and activity at a high frequency for monitoring purposes. We hypothesized that these same sensors can be used for precision phenotyping of complex traits such as lifetime resilience or productive life span. The objective of this study was to investigate whether lifetime resilience and productive life span of dairy cows can be predicted using sensor-derived proxies of first-parity sensor data. We used a data set from 27 Belgian and British dairy farms with an automated milking system containing at least 5 yr of successive measurements. All of these farms had milk meter data available, and 13 of these farms were also equipped with activity sensors. This subset was used to investigate the added value of activity meters to improve the model's prediction accuracy. To rank cows for lifetime resilience, a score was attributed to each cow based on her number of calvings, her 305-d milk yield, her age at first calving, her calving intervals, and the DIM at the moment of culling, taking her entire lifetime into account. Next, this lifetime resilience score was used to rank the cows within their herd, resulting in a lifetime resilience ranking. Based on this ranking, cows were classified in a low (last third), moderate (middle third), or high (first third) resilience category within farm. In total, 45 biologically sound sensor features were defined from the time series data, including measures of variability, lactation curve shape, milk yield perturbations, activity spikes indicating estrous events, and activity dynamics representing health events (e.g., drops in daily activity). These features, calculated on first-lactation data, were used to predict the lifetime resilience rank and, thus, to predict the classification within the herd (low, moderate, or high). Using a specific linear regression model progressively including features stepwise selected at farm level (cutoff P-value of 0.2), classification performances were between 35.9 and 70.0% (46.7 ± 8.0, mean ± SD) for milk yield features only, and between 46.7 and 84.0% (55.5 ± 12.1, mean ± SD) for lactation and activity features together. This is, respectively, 13.7 and 22.2% higher than what random classification would give. Moreover, using these individual farm models, only 3.5 and 2.3% of cows were classified high when they were actually low, or vice versa, whereas respectively 91.8 and 94.1% of wrongly classified animals were predicted in an adjacent category. The sensor features retained in the prediction equation of the individual farms differed across farms, which demonstrates the variability in culling and management strategies across farms and within farms over time. This lack of a common model structure across farms suggests the need to consider local (and evidence-based) culling management rules when developing decision support tools for dairy farms. With this study we showed the potential of precision phenotyping of complex traits based on biologically meaningful features derived from readily available sensor data. We conclude that first-lactation milk and activity sensor data have the potential to predict cows' lifetime resilience rankings within farms but that consistency between farms is currently lacking.

Life-cycle management of deteriorating civil infrastructure considering resilience to lifetime hazards: A general approach based on renewal-reward processes

Civil infrastructure during its service life is subject to progressive deterioration due to aggressive environments and sudden deterioration due to natural and/or manmade hazards. This paper presents a general approach to perform life-cycle management considering both types of deterioration. As an important aspect of life-cycle asset management under hazards, the present study introduces a novel concept, referred to as lifetime resilience. The lifetime resilience of a deteriorating structure is characterized by its cumulative losses to lifetime hazards. By modeling lifetime hazards and life-cycle performance as renewal-reward processes, the proposed approach resorts to the renewal theory to formulate analytical expressions of expected values of lifetime intervention costs, lifetime failure risks, and lifetime resilience losses. Owing to the efficiency in evaluating these expressions, a generic life-cycle management framework is proposed using multi-objective optimization. This proposed framework is applicable to a wide range of civil infrastructure systems under various types of hazards. The proposed approach is illustrated by using a numerical example.

Assessment of subjective emotional valence and long-lasting impact of life events: development and psychometrics of the Stralsund Life Event List (SEL)

BackgroundLife events (LEs) are associated with future physical and mental health. They are crucial for understanding the pathways to mental disorders as well as the interactions with biological parameters. However, deeper insight is needed into the complex interplay between the type of LE, its subjective evaluation and accompanying factors such as social support. The “Stralsund Life Event List” (SEL) was developed to facilitate this research.MethodsThe SEL is a standardized interview that assesses the time of occurrence and frequency of 81 LEs, their subjective emotional valence, the perceived social support during the LE experience and the impact of past LEs on present life. Data from 2265 subjects from the general population-based cohort study “Study of Health in Pomerania” (SHIP) were analysed. Based on the mean emotional valence ratings of the whole sample, LEs were categorized as “positive” or “negative”. For verification, the SEL was related to lifetime major depressive disorder (MDD; Munich Composite International Diagnostic Interview), childhood trauma (Childhood Trauma Questionnaire), resilience (Resilience Scale) and subjective health (SF-12 Health Survey).ResultsThe report of lifetime MDD was associated with more negative emotional valence ratings of negative LEs (OR = 2.96, p < 0.0001). Negative LEs (b = 0.071, p < 0.0001, β = 0.25) and more negative emotional valence ratings of positive LEs (b = 3.74, p < 0.0001, β = 0.11) were positively associated with childhood trauma. In contrast, more positive emotional valence ratings of positive LEs were associated with higher resilience (b = − 7.05, p < 0.0001, β = 0.13), and a lower present impact of past negative LEs was associated with better subjective health (b = 2.79, p = 0.001, β = 0.05). The internal consistency of the generated scores varied considerably, but the mean value was acceptable (averaged Cronbach’s alpha > 0.75).ConclusionsThe SEL is a valid instrument that enables the analysis of the number and frequency of LEs, their emotional valence, perceived social support and current impact on life on a global score and on an individual item level. Thus, we can recommend its use in research settings that require the assessment and analysis of the relationship between the occurrence and subjective evaluation of LEs as well as the complex balance between distressing and stabilizing life experiences.

Childhood Adversity and Factors Determining Resilience among Undergraduate Students

Childhood adversity experiences, when not properly managed may result to anti-social behaviour, health risk or psychological problems like severe depression, suicidal behavior in undergraduate students. Hence the study examined childhood adversity experiences and its factors determining resilience among undergraduate students in Oyo State. The study adopted a descriptive survey research; multi-stage random sampling technique was used to select 341 undergraduate students of Ladoke Akintola University of Technology, Ogbomosho and Oyo State. Two research instruments were used to test childhood adversity scale and factors determining resilience (protective family, birth order, community protective factors). The study formulated four research hypotheses which were tested with Pearson Product moment correlation analysis and multiple regression analysis at coefficient level of 0.05. The result of the analysis revealed that level of Adverse Childhood Experience (ACE) among respondents is slightly low with half of the respondents between the ages of 18 years to 20 years. The prominent ACE identified by respondents is physical assaults, home with incidence of substance abuse, victim of sexual abuse, and humiliation from parents. The level of resilience among the respondents is moderate. There was a positive significant relationship between childhood adversity and lifetime resilience undergraduate student in Oyo State (r = 0.272), protective family factor was also associated with lifetime resilience undergraduate student in Oyo State (r = 0.018) there was a significant relationship between birth order and lifetime resilience undergraduate student in Oyo State. (r = 0.794) there was a significant relationship between community protective factor and lifetime resilience undergraduate student o in Oyo State (r = 0.835). The magnitude of relative contribution showed protective family factor (β = 0.987), had significant relative contribution; community protective factor (β = 0.762), had significant relative contribution; childhood adversity (β = 0.724), had significant relative contribution; and then birth order (β = 0.687), had significant relative contribution to resilience of the undergraduate students. Educational institutions should tackle issues on educator-student relationships through various channels, especially social work, counseling units and students affairs department. There is need for initial psychosocial assessment for fresher to test resilience and adverse childhood experiences, mental health service should be contextualize in health care service for undergraduate students, the parents should be educated on the impact of childhood adversity on wellbeing and academic performance of undergraduate students in Nigeria.

Investigating Resilience to Depression in Adults With ADHD

Objective: ADHD is associated with elevated rates of comorbid depressive disorders, yet the nature and development of this comorbidity remain understudied. We hypothesized that a longer period of prior ADHD treatment, being less likely to engage in maladaptive cognitive/behavioral coping strategies, and less severe ADHD symptoms would predict greater likelihood of lifetime resilience to depression. Method: Seventy-seven adults with ADHD completed diagnostic interviews, clinician-administered symptom rating scales, a stressful life events measure, and self-report questionnaires. We used logistic regression analyses to identify factors associated with resilience to depression. Results: Adults with more extensive ADHD treatment histories were more likely to be resilient to depression. Those who were less likely to report ruminative thinking patterns and cognitive-behavioral avoidance were also more resilient. Severity of current or childhood ADHD symptoms and recent negative life events did not predict resilience. Conclusion: Results identify protective factors that may promote the resiliency to ADHD-depression comorbidity.

Discursive Resilience

Discursive Resilience

The Long-term Outcome for Emotional and Behavioural Problems

Long-term outcome of psychological problems in childhood is important in a variety of ways including understanding disease entities, managing treatment and implementing research. It is of particular importance to courts in cases involving children. Judges have the unenviable task of determining best action for the future of children. Research is now available showing linkages between childhood and adult disorder but also revealing increasing complexity of factors determining outcome. A model of child to adult continuity can be constructed showing linkages between genetic influence, intrauterine environment, perinatal risks, early upbringing and lifetime risk and resilience influences. Known continuities of behaviours are traced from child to adult life. The presentation is intended to give a glimpse of some of the data available to assist in decision-making. Childhood determination of disorders such as schizophrenia may enable treatment to prevent further damage. Exciting developments particularly in genetics and functional imaging, will radically improve data over the next few years, possibly challenging currently held beliefs. At present expert witness contribution is under challenge but knowledge of the continuities can save court time, help prevent future heartache for vulnerable children, and later cost to the taxpayer.