Trained Observers Research Articles

Living Alone, Environmental Hazards, and Falls Among U.S. Older Adults.

Physical conditions of living environments can affect the incidence of falls; however, prior work has focused typically on 1 domain at a time-either neighborhood or home, capturing limited environmental boundaries of older adults. We examined how neighborhood together with the home environment affect the incidence of falls over time and whether living arrangement modifies the influence of the environmental risks on falls. Using the 2012-2020 waves of the Health and Retirement Study (HRS; N = 1,893), we fitted logistic regression to estimate the incidence of falls over an 8-year study period. We used the neighborhood and housing data that are collected systematically by trained observers in the HRS to assess environmental hazards. Sidewalk quality, neighborhood disorder, and the presence of green space were measured to capture outdoor environmental hazards. Indoor environmental hazards included the presence of housing decay and poorly maintained stairways. All models were stratified by living arrangement. Neighborhood and housing environment were independently associated with the odds of falls net of demographic characteristics and preexisting health conditions, and effects were significant for people living alone only. The presence of green space and poorly maintained stairways were associated with greater odds of falling, net of covariates during 8 years of follow-up (odds ratios = 2.10 and 2.65, p < .05, respectively). None of the environmental risk factors were significant for those living with others. Falls in old age may be determined in part by a combination of outdoor and indoor risk factors. More research is needed to understand pathways that lead to greater vulnerability among older adults living alone to environmental hazards.

Development and Evaluation of a Pragmatic Measure of Adherence to Dialectical Behavior Therapy: The DBT Adherence Checklist for Individual Therapy.

This paper presents two studies conducted to develop and evaluate a new pragmatic measure of therapist adherence to Dialectical Behavior Therapy (DBT): the DBT Adherence Checklist for Individual Therapy (DBT AC-I). Study 1 used item response analysis to select items from the gold standard DBT Adherence Coding Scale (DBT ACS) using archival data from 1271 DBT sessions. Items were then iteratively refined based on feedback from 33 target end-users to ensure relevance, usability, and understandability. Study 2 examined the psychometric properties of the DBT AC-I as a therapist self-report and observer-rated measure in 100 sessions from 50 therapist-client dyads, while also evaluating predictors of therapist accuracy in self-rated adherence. When used as a therapist self-report measure, concordance between therapist and observer ratings was at least moderate (AC1 ≥ 0.41) for all DBT AC-I items but overall concordance (ICC = 0.09) as well as convergent (r = 0.05) and criterion validity (AUC = 0.54) with the DBT ACS were poor. Higher therapist accuracy was predicted by greater DBT knowledge and adherence as well as more severe client suicidal ideation. When used by trained observers, the DBT AC-I had excellent interrater reliability (ICC = 0.93), convergent validity (r = 0.90), and criterion validity (AUC = 0.94). While therapists' self-rated adherence on the DBT AC-I should not be assumed to reflect their actual adherence, some therapists may self-rate accurately. The DBT AC-I offers an effective and relatively efficient method of evaluating adherence to DBT when used by trained observers.

Time Series Prediction of Lung Cancer Death Rates on the Basis of SEER Data.

The purpose of this study was to apply different time series analytical techniques to SEER US lung cancer death rate data to develop a best fit model. Three models for yearly time series predictions were built: autoregressive integrated moving average (ARIMA), simple exponential smoothing (SES), and Holt's double expansional smoothing (HDES) models. The three models were built using Python 3.9, on the basis of Anaconda 2022.10. This study used SEER data from 1975 to 2018 and included 545,486 patients with lung cancer. The best parameters for ARIMA are ARIMA (p, d, q) = (0, 2, 2). In addition, the best parameter for SES was α = .995, whereas the best parameters for HDES were α = .4 and β = .9. The HDES was the model that best fit the lung cancer death rate data, with a root mean square error (RMSE) of 132.91. Including monthly diagnoses, death rates, and years in SEER data increases the number of observations for training and test sets, enhancing the performance of time series models. The reliability of the RMSE was based on the mean lung cancer mortality rate. Owing to the high mean lung cancer death rate of 8,405 patients per year, it is acceptable for reliable models to have large RMSEs.

Automated dynamic motion correction improves repeatability and reproducibility of myocardial blood flow quantification with rubidium-82 PET imaging

BackgroundPatient motion reduces the accuracy of PET myocardial blood flow (MBF) measurements. This study evaluated the effect of automatic motion correction on test-retest repeatability and inter-observer variability in a clinically relevant population. MethodsPatients with known or suspected CAD underwent repeat rest 82Rb PET scans within minutes as part of their scheduled rest-stress perfusion study. Two trained observers evaluated the presence of heart motion in each scan. Global LV and per-vessel MBF were computed from the dynamic rest images before and after automatic motion correction. Test-retest and inter-observer variability were assessed using intra-class correlation and Bland-Altman analysis. Results140 pairs of test-retest scans were included, with visual motion noted in 18%. Motion correction decreased the global MBF values by 3.5% (0.80 ± 0.24 vs 0.82 ± 0.25 mL⋅min−1⋅g−1; P < 0.001) suggesting that the blood input function was underestimated in cases with patient motion. Test-retest repeatability of global MBF improved by 9.7% (0.25 vs 0.28 mL⋅min−1⋅g−1; P < 0.001) and inter-observer repeatability was improved by 7.1% (0.073 vs 0.079 mL⋅min−1⋅g−1; P = 0.012). There was a marked impact on both test-retest repeatability as well as inter-observer repeatability in the LCX territory, with improvements of 16.5% (0.30 vs 0.36 mL⋅min−1⋅g−1; P < 0.0000) and 18.4% (0.13 vs 0.16 mL⋅min−1⋅g−1; P < 0.001), respectively. ConclusionAutomatic motion correction improved test-retest repeatability and reduced differences between observers.

Bayesian neural tree models for nonparametric regression

SummaryFrequentist and Bayesian methods differ in many aspects but share some basic optimal properties. In real‐life prediction problems, situations exist in which a model based on one of the above paradigms is preferable depending on some subjective criteria. Nonparametric classification and regression techniques, such as decision trees and neural networks, have both frequentist (classification and regression trees (CARTs) and artificial neural networks) as well as Bayesian counterparts (Bayesian CART and Bayesian neural networks) to learning from data. In this paper, we present two hybrid models combining the Bayesian and frequentist versions of CART and neural networks, which we call the Bayesian neural tree (BNT) models. BNT models can simultaneously perform feature selection and prediction, are highly flexible, and generalise well in settings with limited training observations. We study the statistical consistency of the proposed approaches and derive the optimal value of a vital model parameter. The excellent performance of the newly proposed BNT models is shown using simulation studies. We also provide some illustrative examples using a wide variety of standard regression datasets from a public available machine learning repository to show the superiority of the proposed models in comparison to popularly used Bayesian CART and Bayesian neural network models.

A novel approach to detecting pulse onset in photoplethysmographic signal using an automatic non assisted method

An automatic method for determining the location of the pulse onset on arterial pulse waves is presented. The aim is to develop an automatic method for enhance the accuracy and precision of pulse onset detection without ECG assistance (R peaks as reference). The approach is composed by two stages: pulse delineator and onset decision. Pulse delineator is based on amplitude and time thresholds computation in time and frequency domain respectively, for locating each pulse wave. Decision stage is based on triangle area algorithm that computes on each pulse the triangle area successive with two fix points and one mobile. The mobile point that corresponds to higher area is the pulse onset. Pulse delineator stage of proposed method was evaluated with CSL database, and their results were comparable to other pulse detection algorithm. Simultaneous ECG and PPG records were used to evaluate the repeatability of proposed method for several signal-noise ratios, as well as the concordance respect to trained observers. The performance of proposed method was compared with ECG assisted methods, such as tangents intersection, diastolic point and second derivative. The approach obtained acceptable values of sensitivity (&gt; 97.99 %), positive predictivity (&gt; 97.91 %), failed rate detection (&lt; 4.11 %) and error (&lt; 5.78 ± 6.64 ms). This proposal found the repeatability condition for each signal-noise ratios. This method could be used on medical systems that need the pulse onset to compute diagnostic markers.

A wetland permanence classification tool to support prairie wetland conservation and policy implementation

AbstractWetland permanence, the duration and frequency that surface water is present, affects biological communities and whether wetlands are protected under legislation in some jurisdictions. Wetland drainage in the Prairie Pothole Region (PPR) has changed the distribution of wetlands because smaller and more temporary wetlands are more likely to be drained. This change in distribution affects biodiversity and other wetland ecosystem services. In Manitoba, Canada, wetlands are treated differently under the Water Rights Act based on permanence classification and can either be drained with a simplified registration (temporary and ephemeral wetlands), drained with a permit requiring mitigation (seasonal wetlands), or are protected from drainage (semipermanent and permanent wetlands). To facilitate implementing a conservation program targeting the most vulnerable wetlands, we built a classification model using LiDAR and Sentinel‐2 data (1312 training observations). Our random forest model had 73% accuracy on 563 test observations and is applicable across the agricultural region of southwestern Manitoba. We predicted the wetland permanence class of 365,499 wetlands and built an online tool to help practitioners implement a conservation program that pays producers to conserve temporary and ephemeral wetlands. Our approach is applicable elsewhere in the PPR and other regions with variation in wetland permanence.

Associations between stress, resources, and hair cortisol concentration in teachers

Challenging interactions are the main source of teacher’ stress in the classroom. We investigated the association of chronic stress and characteristics of teacher-student interactions with teachers’ Hair Cortisol Concentration (HCC). Forty-one teachers (27 women; Mage = 39.65 ± 12.14 years; Mlesson number = 23.15 ± 3.99 lessons per week; grade: elementary, secondary, high, and vocational school teachers) participated in the present study, with participation lasting over the length of one year. HCC was assessed from a 3 cm hair segment near the scalp. Self-reported chronic stress in the last three months was further assessed using the ‘Trier Inventory for Chronic Stress’ (TICS). Additionally, four consecutive, same-day lectures of each teacher were videotaped and coded offline in an event sampling procedure by trained external observers. The videos were analyzed for two stressors, i.e., classroom disruptions and total student aggression, as well as two resources, i.e., teacher-student relationship and classroom management. Overall, hair samples were collected M = 120.34 days (SD = 84.39) after the distribution of the questionnaires, and M = 67.63 days (SD = 18.40) prior to the observations. Lesson number, classroom disruptions, as well as total student aggression were all significantly positively correlated with HCC. In addition, both teacher-student relationship and classroom management were significantly negatively related to HCC. With regard to self-rated chronic stress, only the TICS subscale ‘Pressure to perform’ was positively related to HCC. Exploratory moderation analyses revealed that an increasingly good, observed teacher-student relationship buffered the positive association between lesson number and HCC. Our findings show significant associations between HCC and mainly objectively assessable stress, supporting HCC as a biological indicator of chronic stress. In this association, a good relationship between teachers and students acts as a buffer. While the findings underline the importance of examining objective and behavioral data for better understanding the psychobiology of stress, they also support the importance of boostering teachers’ (social) resources to increase their overall resilience.

A six-step process to explore facial expressions performances to detect pain in dairy cows with lipopolysaccharide-induced clinical mastitis

This study aimed to explore the performances of Facial Action Units (FAUs) to detect pain in cows under lipopolysaccharide (LPS) challenge though a six-step process based on expression, selectivity (construct validity, responsiveness), intra- and inter-observer reliability, and measurement error (specificity, Sp; sensitivity, Se). Twenty-seven cows received an intra-mammary infusion of 25 µg E. coli LPS in one healthy udder quarter. Then, 14 cows received a placebo (LPS cows) and 13 cows received 3 mg/kg BW ketoprofen i.m. (LPS+NSAID cows). Each cow’s face was video-recorded for 40 s at three time points before (T-21 = 11:30 h, T-19 = 13:30 h, T-16 = 16:30 h) and after (T3 = 11:30 h, T5 = 13:30 h, T8 = 16:30 h) infusion. Three trained observers scored the duration, frequency or presence/absence of 43 FAUs on 40 second video segments. We kept only the selective FAUs (construct validity) and transformed them into binary variables (below / above a threshold determined by ROC curves and Youden index). Intra- and inter-observer reliability were assessed by percentage of agreement (PA) and Fleiss’ kappa (k). We calculated Sp and Se. The process therefore consisted in a waterfall method with expression, selectivity, intra-observer reliability being an eliminative step, while inter-observer reliability, Sp and Se were not. FAUs were kept if expressed (>5% of the videos), and kept for intra-observer reliability if PA≥ 75% and k ≥ 0.41. Two too rarely expressed FAUs were excluded. LPS infusion induced changes in 7 FAUs of orbital, auricular, and mouth-and-muzzle regions. Compared to before challenge (T-19), at T5, LPS cows spent significantly less time with ‘muzzle in motion’ (P = 0.045), tended to display more ‘nostril dilation’ (P = 0.097), spend more time with ‘motionless muzzle’ (P = 0.068) and less time in ‘ear: position 8′ (i.e. backwards /central/pinna to the side) (P = 0.057). At T8, LPS cows spent significantly less time with ‘eye open’ (P = 0.036) and tended to less frequently display ‘eye blinking’ (P = 0.071) and ‘eye movements’ (P = 0.091) compared to T-16. Four of these 7 FAUs (‘eye open’, ‘motionless muzzle’, ‘muzzle in motion’, ‘nostril dilatation’) satisfied all following steps of the process except sensitivity. Two other FAUs (‘eye blinking’, ‘eye movements’) satisfied intra- and inter-observer reliability; depending on the time point considered they were either sensitive or specific but not both simultaneously. The last FAU (‘ear: position 8′) satisfied intra-observer reliability and sensitivity but not inter-observer reliability nor specificity. This study identified 7 FAUs as potential candidate for detecting mild pain associated with induced inflammatory mastitis in dairy cows.

Validation of the neuroconnective endophenotype questionnaire (NEQ): a new clinical tool for medicine and psychiatry resulting from the contribution of Ehlers-Danlos syndrome.

The link between anxiety disorders and joint hypermobility syndrome (now under hypermobility spectrum disorders, which include hypermobile Ehlers-Danlos syndrome) has been widely replicated over the past 30 years and has grown beyond the initial nosological limits. To integrate clinical and research progress in this field, a new neuroconnective endophenotype (NE) and its corresponding instrument, the Neuroconnective Endophenotype Questionnaire (NEQ), have been developed. This new clinical construct, created with the active participation of patients, includes both somatic and psychological dimensions and symptoms and resilience items. The NE includes five dimensions: (1) sensorial sensitivity, (2) body signs and symptoms, (3) somatic conditions, (4) polar behavioral strategies, and (5) psychological and psychopathological dimensions. The NEQ information is collected through four self-administered questionnaires (sensorial sensitivity, body signs and symptoms, polar behavioral strategies, and psychological characteristics) and a structured diagnostic part that should be completed by a trained observer. This hetero-administered part incorporates (a) psychiatric diagnoses (using structured criteria, e.g., MINI), (b) somatic disorders diagnosis, using structured criteria, and (c) assessment of joint hypermobility criteria. In a sample of 36 anxiety cases with 36 matched controls, the NEQ obtained high scores for test-retest, inter-rater reliability, and internal consistency. As for predictive validity, cases and controls significantly differed in all five dimensions and hypermobility measurements. We can conclude that the NEQ has achieved acceptable reliability and validity values and, therefore, is ready to be used and tested in different samples. This original and consistent construct including somatic and mental items may improve clinical specificity, the search for more comprehensive therapies, and their genetic and neuroimaging bases.

Relationships between empathy and creativity in collective games: a comparison between handball and sitting ball.

In collective motor situations, creativity and empathy are central and strongly connected to cognitive and affective processes. Indeed, in the environment of high social uncertainty of games and sports, empathy would allow the player to anticipate motor behaviors in order to promote creative decision-making, i.e., to destabilize his opponents. On this basis, this study pursues two objectives. The first is to propose indicators to question the links between sociomotor empathy and motor creativity in an ecological situation. The second is to investigate the potential influence of the internal logic of two very different collective games (handball and Sitting ball) on the type of links that are woven between empathy and creativity. Two groups of students were recruited (n = 22 and 23) and participated in each of the games mentioned. The games were video recorded. The praxical communications made by each player were recorded and sorted by two trained observers. The results revealed major differences between the two studied collective games. In handball, there was a correlation between instrumental empathy (valuing cognitive aspects) and indicators of motor creativity (p < 0.05). The more creative the players are (quantity, diversity and quality of performance), the more they manage to accurately anticipate the behavior of other players. In Sitting Ball, there was no correlation between creativity indicators and instrumental empathy. On the other hand, it is noticed that instrumental empathy was correlated with socio-affective empathy (p < 0.001). To make their motor decisions, the players do not rely exclusively on the decoding of behaviors but significantly mobilize the feelings that they ascribe to the other co-participants. The results of this work invite reflection on the diversity of playful reading grids to be offered to students in order to develop their motor adaptability.

Adapting domestic abuse training to remote delivery during the COVID-19 pandemic: a qualitative study of views from general practice and support services.

Identifying and responding to patients affected by domestic violence and abuse (DVA) is vital in primary care. There may have been a rise in the reporting of DVA cases during the COVID-19 pandemic and associated lockdown measures. Concurrently general practice adopted remote working that extended to training and education. IRIS (Identification and Referral to Improve Safety) is an example of an evidence-based UK healthcare training support and referral programme, focusing on DVA. IRIS transitioned to remote delivery during the pandemic. To understand the adaptations and impact of remote DVA training in IRIS-trained general practices by exploring perspectives of those delivering and receiving training. Qualitative interviews and observation of remote training of general practice teams in England were undertaken. Semi-structured interviews were conducted with 21 participants (three practice managers, three reception and administrative staff, eight general practice clinicians, and seven specialist DVA staff), alongside observation of eight remote training sessions. Analysis was conducted using a framework approach. Remote DVA training in UK general practice widened access to learners. However, it may have reduced learner engagement compared with face-to-face training and may challenge safeguarding of remote learners who are domestic abuse survivors. DVA training is integral to the partnership between general practice and specialist DVA services, and reduced engagement risks weakening this partnership. The authors recommend a hybrid DVA training model for general practice, including remote information delivery alongside a structured face-to-face element. This has broader relevance for other specialist services providing training and education in primary care.

Promoting Occupancy Detection Accuracy Using On-Device Lifelong Learning

Our recently developed Synchronized Low Energy Electronically chopped Passive Infra-Red (SLEEPIR) sensor node enables the stationary occupancy detection capability of traditional Passive Infra-Red sensors. A Machine Learning (ML) algorithm reports occupancy based on a locally collected dataset from the sensor node. Though promising, the ML algorithm’s detection accuracy depends on the diversity of the collected dataset- provided the dataset contains a wide variety of infrared noise and occupancy patterns. Thus, it is challenging to train a universal ML model that contains all possible patterns. We propose an efficient K-Nearest Neighbor (KNN) occupancy classifier that incrementally adapts to the novel data from the sensor. The proposed algorithm ensures that only the relevant noise and occupancy patterns are learned. The fact that training observations are gathered on the same sensor node where the inference is made, keeps the proposed classifier accurate even with the bounded size of the dataset. A small dataset and an architecture like KNN, both enable the training and inference to be executed on a resource-constrained Internet of Things device. Thus, the proposed On-Device Lifelong Learning approach eliminates the need for over-the-cloud ML model updates. The dataset was collected for two distinct floorplans over two months. Results indicate an average occupancy accuracy improvement of 20.8% compared to a statically trained Long Short-Term Memory (LSTM) model. The proposed KNN model delivers comparable detection accuracy while remaining orders of magnitude faster in terms of computational performance when compared to the LSTM-based occupancy detection algorithm.

108 Effects of equine assisted activities on stress-related behaviors exhibited by horses

The various benefits of equine assisted activities (EAA) programs on humans have been studied and documented, whereas the effects of these programs on their equine counterparts have not been as thoroughly investigated. The objective of this study was to investigate the effects of EAA sessions with veterans with posttraumatic stress disorder (PTSD) on stress-related behaviors exhibited by horses. During the 8-week study, horses were assigned to 30-min horsemanship sessions with veterans with PTSD (EAA, n = 5) or a 30-min standing control with little to no human interaction in a small section of the same arena (CON, n = 5). Sessions occurred weekly, with EAA and CON horses participating regularly in similar work. EAA horses were videotaped from 3 different angles to capture the entirety of the arena used for the sessions, whereas CON horses were videotaped at one angle capturing the entirety of the stall they were enclosed in. Video was captured for the duration of the 30-min sessions. Video was then edited using Adobe Premiere Pro and the veterans’ faces redacted for privacy. Frequency and duration of stress-related behaviors was logged by trained observers using an ethogram in the BehavioralObservation Research Interactive Software (BORIS). Data were analyzed using a Poisson distribution repeated measures mixed model ANOVA with fixed effects of week and treatment and their interaction, and random effect of horse. Statistically different means were separated using Tukey's method. Significance was set at P ≤ 0.05. The total number of stress-related behaviors exhibited by horses was significantly different between EAA (19.8 ± 2.3 behaviors) and CON (35.3 ± 2.6 behaviors) (P = 0.0273). The number of unique stress-related behaviors exhibited by horses was also significantly different between EAA (5.9 ± 0.4 unique behaviors) and CON (7.7 ± 0.3 unique behaviors) (P = 0.0488). There was no difference between EAA and CON horses for the duration of stress-related behaviors. Horses appeared to exhibit more stress-related behaviors under isolation with little human interaction than when working with veterans with PTSD.

A clinimetric assessment of the validity and reliability of 3D technology for scar surface area measurement.

The quality of scars has become an important outcome of burn care. Objective scar assessment through scar surface area measurement enables quantification of scar formation and evaluation of treatment efficacy. 3D technology has proven valid and reliable but often remains cumbersome, expensive, and time-consuming. 3D technology with depth sensors on mobile devices has become available and might surpass these limitations. This study provides a clinimetric assessment of the validity and reliability of a 3D system with a depth sensor for scar surface area measurement. A technology involving a depth sensor mounted on a mobile device was used. Images and analyses were made with a custom-made software application. A standardized one-keyframe image capturing procedure was followed. To assess validity, stickers with predefined dimensions (8.01cm2 - 77.70cm2) were imaged in a single observer setting on various body parts of healthy volunteers. To assess reliability, hypertrophic scars, keloids, and normotrophic scars were imaged and rated by two observers independently. Data are expressed as mean (+/-SD), Coefficient of Variation (CV), Intraclass Correlation Coefficients (ICC), and Limits of Agreements (LoA). Eighty stickers placed on 20 healthy volunteers showed validity with CV between 0.62%-1.67% for observer A and 0.75%-1.19% for observer B. For the reliability study, 69 scars on 36 patients were included. Mean scar surface area ranged from 0.83cm2 to 155.59cm2. Mean scar surface area measurement was 13.83cm2 (SD 23.06) for observer A and 13.59cm2 (SD 23.31) for observer B. Adjusted interobserver CV for trained observers is estimated as 5.59%, with corresponding LoA =0±0.15 x mean surface area. Interobserver ICCs were 0.99-1.00. This 3D technology with a depth sensor for measuring scar surface area provides valid and reliable data and thereby surpasses expensive and time-consuming 3D cameras.

Intra-observer reproducibility and inter-observer agreement of Fels skeletal age assessments among male tennis players 8–16 years

BackgroundSkeletal age (SA) is an estimate of biological maturity status that is commonly used in sport-related medical examinations. This study considered intra-observer reproducibility and inter-observer agreement of SA assessments among male tennis players.MethodsSA was assessed with the Fels method in 97 male tennis players with chronological ages (CA) spanning 8.7–16.8 years. Radiographs were evaluated by two independent trained observers. Based on the difference between SA and CA, players were classified as late, average or early maturing; if a player was skeletally mature, he was noted as such as an SA is not assigned.ResultsThe magnitude of intra-individual differences between repeated SA assessments were d = 0.008 year (observer A) and d = 0.001 year (observer B); the respective coefficients of variation were 1.11% and 1.75%. Inter-observer mean differences were negligible (t = 1.252, p = 0.210) and the intra-class correlation coefficient was nearly perfect (ICC = 0.995). Concordance of classifications of players by maturity status between observers was 90%.ConclusionFels SA assessments were highly reproducible and showed an acceptable level of inter-observer agreement between trained examiners. Classifications of players by skeletal maturity status based on assessments of the two observers were highly concordant, though not 100%. The results highlight the importance of experienced observers in skeletal maturity assessments.

The Proposed Specifiers for Conduct Disorder - Parent (PSCD-P): Convergent Validity, Incremental Validity, and Reactions to Unfamiliar Peer Confederates.

Youth who experience psychopathy display multiple impairments across interpersonal (grandiose-manipulative [GM]), affective (callous-unemotional [CU]), lifestyle (daring-impulsive [DI]), and potentially antisocial and behavioral features. Recently, it has been acknowledged that the inclusion of psychopathic features can offer valuable information in relation to the etiology of Conduct Disorder (CD). Yet, prior work largely focuses on the affective component of psychopathy, namely CU. This focus creates uncertainty in the literature on the incremental value of a multicomponent approach to understanding CD-linked domains. Consequently, researchers developed the Proposed Specifiers for Conduct Disorder (PSCD; Salekin & Hare, 2016) as a multicomponent approach to assess GM, CU, and DI features in combination with CD symptoms. The notion of considering the wider set of psychopathic features for CD specification requires testing whether multiple personality dimensions predict domain-relevant criterion outcomes above-and-beyond a CU-based approach. Thus, we tested the psychometric properties of parents' reports on the PSCD (PSCD-P) in a mixed clinical/community sample of 134 adolescents (Mage = 14.49, 66.4% female). Confirmatory factor analyses resulted in a 19-item PSCD-P displaying acceptable reliability estimates and a bifactor solution consisting of GM, CU, DI, and CD factors. Findings supported the incremental validity of scores taken from the PSCD-P across multiple criterion variables, including (a) an established survey measure of parent-adolescent conflict; and (b) trained independent observers' ratings of adolescents' behavioral reactions to laboratory controlled tasks designed to simulate social interactions with unfamiliar peers. These findings have important implications for future research on the PSCD and links to adolescents' interpersonal functioning.

Testing of the Facial Expression Coding System (FACES) for middle school–aged special education students and development of a training protocol

Emotion regulation is a key developmental skillset, but many existing measures rely on self-report or laboratory-based measurement approaches. This study aimed to develop a training and implementation protocol for the widely used Facial Expression Coding System (FACES) to be used in real-world settings with pre-recorded video data. A revised coding system with supplemental guidelines and training procedures was developed to use FACES with video data recorded in special education classrooms. This system resulted in adequate interrater reliability as well as reduced training time for coders. Specific training methods included close study of code definitions, coding of practice video, quantitative analysis of observation data to generate interrater agreement and kappa statistics, review of comparison charts to identify discrepancies between coder and training observations using the Noldus Observer XT software, and post-observation discussions. The revised FACES protocol and new training method presented here offer a more robust, efficient, and versatile tool that can be applied to systematic behavior observations conducted of students in real-world classroom settings.

High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury

Recovery from acute kidney injury can vary widely in patients and in animal models. Immunofluorescence staining can provide spatial information about heterogeneous injury responses, but often only a fraction of stained tissue is analyzed. Deep learning can expand analysis to larger areas and sample numbers by substituting for time-intensive manual or semi-automated quantification techniques. Here we report one approach to leverage deep learning tools to quantify heterogenous responses to kidney injury that can be deployed without specialized equipment or programming expertise. We first demonstrated that deep learning models generated from small training sets accurately identified a range of stains and structures with performance similar to that of trained human observers. We then showed this approach accurately tracks the evolution of folic acid induced kidney injury in mice and highlights spatially clustered tubules that fail to repair. We then demonstrated that this approach captures the variation in recovery across a robust sample of kidneys after ischemic injury. Finally, we showed markers of failed repair after ischemic injury were correlated both spatially within and between animals and that failed repair was inversely correlated with peritubular capillary density. Combined, we demonstrate the utility and versatility of our approach to capture spatially heterogenous responses to kidney injury.

Validation of the Unesp-Botucatu pig composite acute pain scale (UPAPS) in piglets undergoing castration.

To accurately assess pain and support broadly-based analgesic protocols to mitigate swine pain, it is imperative to develop and validate a species-specific pain scale. The objective of this study was to investigate the clinical validity and reliability of an acute pain scale (UPAPS) adapted for newborn piglets undergoing castration. Thirty-nine male piglets (five days of age, 1.62 ± 0.23 kg BW) served as their own control, were enrolled in the study and underwent castration in conjunction with an injectable analgesic administered one-hour post-castration (flunixin meglumine 2.2 mg/kg IM). An additional 10, non-painful female piglets were included to account for the effect of natural behavioral variation by day on pain scale results. Behavior of each piglet was video recorded continuously at four recording periods (24 h pre-castration, 15 min post-castration, 3 and 24 h post-castration). Pre- and post-operative pain was assessed by using a 4-point scale (score 0-3) including the following six behavioral items: posture, interaction and interest in surroundings, activity, attention to the affected area, nursing, and miscellaneous behavior. Behavior was assessed by two trained blinded observers and statistical analysis was performed using R software. Inter-observer agreement was very good (ICC = 0.81). The scale was unidimensional based on the principal component analysis, all items except for nursing were representative (rs ≥ 0.74) and had excellent internal consistency (Cronbach's alpha ≥ 0.85). The sum of scores were higher in castrated piglets post-procedure compared to pre-procedure, and higher than in non-painful female piglets confirming responsiveness and construct validity, respectively. Scale sensitivity was good when piglets were awake (92.9%) and specificity was moderate (78.6%). The scale had excellent discriminatory ability (area under the curve > 0.92) and the optimal cut-off sum for analgesia was 4 out of 15. The UPAPS scale is a valid and reliable clinical tool to assess acute pain in castrated pre-weaned piglets.