Shift Bias Research Articles

Potential application of artificial intelligence in cancer therapy.

This review underscores the critical role and challenges associated with the widespread adoption of artificial intelligence in cancer care to enhance disease management, streamline clinical processes, optimize data retrieval of health information, and generate and synthesize evidence. Advancements in artificial intelligence models and the development of digital biomarkers and diagnostics are applicable across the cancer continuum from early detection to survivorship care. Additionally, generative artificial intelligence has promised to streamline clinical documentation and patient communications, generate structured data for clinical trial matching, automate cancer registries, and facilitate advanced clinical decision support. Widespread adoption of artificial intelligence has been slow because of concerns about data diversity and data shift, model reliability and algorithm bias, legal oversight, and high information technology and infrastructure costs. Artificial intelligence models have significant potential to transform cancer care. Efforts are underway to deploy artificial intelligence models in the cancer practice, evaluate their clinical impact, and enhance their fairness and explainability. Standardized guidelines for the ethical integration of artificial intelligence models in cancer care pathways and clinical operations are needed. Clear governance and oversight will be necessary to gain trust in artificial intelligence-assisted cancer care by clinicians, scientists, and patients.

Enhancing care: evaluating the impact of True North Sexual Health and Rehabilitation eTraining for healthcare providers working with prostate cancer patients and partners.

Educational programs that enhance healthcare providers' competence in managing the care of patients with sexual dysfunction following prostate cancer treatments are needed to facilitate comprehensive sexual health treatments for patients and their partners. In this study we evaluated the impact of a real-world online sexual health educational intervention called the True North Sexual Health and Rehabilitation eTraining Program. This program is designed to increase healthcare providers' knowledge and self-efficacy in providing sexual healthcare to prostate cancer patients and their partners. Healthcare providers were invited to join a 12-week virtual training program. Participants completed precourse surveys (n = 89), retrospective prepost surveys (n = 58), and a 3-month follow-up survey (subset n = 18) to assess retention of relevant outcomes. Additionally, a course satisfaction survey was administered to participants (n = 57) at the end of the course. The main outcomes focused on participants' perceived knowledge and self-efficacy in conducting assessments and providing interventions for various relevant physical, functional, psychological, and relational domains of sexual dysfunction in prostate cancer patients and their partners. According to the retrospective analysis of post-then-pre-survey results, graduates perceived that their knowledge of and self-efficacy in providing sexual health counseling improved after completing the course. The 3-month follow-up survey indicated that the course graduate self-efficacy remained high 3 months after the course. Furthermore, the satisfaction survey indicated that a vast majority (98.2%) of participants were satisfied with the educational intervention. This real-world sexual health educational intervention can increase self-efficacy and knowledge in healthcare providers who are supporting prostate cancer patients dealing with sexual dysfunction. The use of a retrospective post-then-pre-survey helped to mitigate response shift bias while minimizing data gaps. However, it is important to note that this investigation was not a traditional research study and lacked a control group, thus limiting causal attributions. The True North Sexual Health and Rehabilitation eTraining program acts as an accessible and effective resource for healthcare providers seeking specialized training in providing sexual healthcare for prostate cancer patients and their partners.

Deep learning for head and neck semi-supervised semantic segmentation

Objective. Radiation therapy (RT) represents a prevalent therapeutic modality for head and neck (H&N) cancer. A crucial phase in RT planning involves the precise delineation of organs-at-risks (OARs), employing computed tomography (CT) scans. Nevertheless, the manual delineation of OARs is a labor-intensive process, necessitating individual scrutiny of each CT image slice, not to mention that a standard CT scan comprises hundreds of such slices. Furthermore, there is a significant domain shift between different institutions’ H&N data, which makes traditional semi-supervised learning strategies susceptible to confirmation bias. Therefore, effectively using unlabeled datasets to support annotated datasets for model training has become a critical issue for preventing domain shift and confirmation bias. Approach. In this work, we proposed an innovative cross-domain orthogon-based-perspective consistency (CD-OPC) strategy within a two-branch collaborative training framework, which compels the two sub-networks to acquire valuable features from unrelated perspectives. More specifically, a novel generative pretext task cross-domain prediction (CDP) was designed for learning inherent properties of CT images. Then this prior knowledge was utilized to promote the independent learning of distinct features by the two sub-networks from identical inputs, thereby enhancing the perceptual capabilities of the sub-networks through orthogon-based pseudo-labeling knowledge transfer. Main results. Our CD-OPC model was trained on H&N datasets from nine different institutions, and validated on the four local intuitions’ H&N datasets. Among all datasets CD-OPC achieved more advanced performance than other semi-supervised semantic segmentation algorithms. Significance. The CD-OPC method successfully mitigates domain shift and prevents network collapse. In addition, it enhances the network’s perceptual abilities, and generates more reliable predictions, thereby further addressing the confirmation bias issue.

Isolating the effect of confounding from the observed survival benefit of screening participants — a methodological approach illustrated by data from the German mammography screening programme

BackgroundMammography screening programmes (MSP) aim to reduce breast cancer mortality by shifting diagnoses to earlier stages. However, it is difficult to evaluate the effectiveness of current MSP because analyses can only rely on observational data, comparing women who participate in screening with women who do not. These comparisons are subject to several biases: one of the most important is self-selection into the MSP, which introduces confounding and is difficult to control for. Here, we propose an approach to quantify confounding based on breast cancer survival analyses using readily available routine data sources.MethodsUsing data from the Cancer Registry of North Rhine-Westphalia, Germany, we estimate the relative contribution of confounding to the observed survival benefit of participants of the German MSP. This is accomplished by comparing non-participants, participants with screen-detected and participants with interval breast cancers for the endpoints “death from breast cancer” and “death from all causes other than breast cancer” — the latter being assumed to be unrelated to any MSP effect. By using different contrasts, we eliminate the effects of stage shift, lead and length time bias. The association of breast cancer detection mode with survival is analysed using Cox models in 68,230 women, aged 50–69 years, with breast cancer diagnosed in 2006–2014 and followed up until 2018.ResultsThe hazard of dying from breast cancer was lower in participants with screen-detected cancer than in non-participants (HR = 0.21, 95% CI: 0.20–0.22), but biased by lead and length time bias, and confounding. When comparing participants with interval cancers and non-participants, the survival advantage was considerably smaller (HR = 0.62, 95% CI: 0.58–0.66), due to the elimination of stage shift and lead time bias. Finally, considering only mortality from causes other than breast cancer in the latter comparison, length time bias was minimised, but a survival advantage was still present (HR = 0.63, 95% CI: 0.56–0.70), which we attribute to confounding.ConclusionsThis study shows that, in addition to stage shift, lead and length time bias, confounding is an essential component when comparing the survival of MSP participants and non-participants. We further show that the confounding effect can be quantified without explicit knowledge of potential confounders by using a negative control outcome.

Unraveling the safety of adjuvant radiotherapy in prostate cancer: impact of older age and hypofractionated regimens on acute and late toxicity - a multicenter comprehensive analysis.

The objective of this study was to assess the impact of age and other patient and treatment characteristics on toxicity in prostate cancer patients receiving adjuvant radiotherapy (RT). This observational study (ICAROS-1) evaluated both acute (RTOG) and late (RTOG/EORTC) toxicity. Patient- (age; Charlson's comorbidity index) and treatment-related characteristics (nodal irradiation; previous TURP; use, type, and duration of ADT, RT fractionation and technique, image-guidance systems, EQD2 delivered to the prostate bed and pelvic nodes) were recorded and analyzed. A total of 381 patients were enrolled. The median EQD2 to the prostate bed (α/β=1.5) was 71.4 Gy. The majority of patients (75.4%) were treated with intensity-modulated radiation therapy (IMRT) or volumetric-modulated arc therapy (VMAT). Acute G3 gastrointestinal (GI) and genitourinary (GU) toxicity rates were 0.5% and 1.3%, respectively. No patients experienced >G3 acute toxicity. The multivariable analysis of acute toxicity (binomial logistic regression) showed a statistically significant association between older age (> 65) and decreased odds of G≥2 GI acute toxicity (OR: 0.569; 95%CI: 0.329-0.973; p: 0.040) and decreased odds of G≥2 GU acute toxicity (OR: 0.956; 95%CI: 0.918-0.996; p: 0.031). The 5-year late toxicity-free survival rates for G≥3 GI and GU toxicity were 98.1% and 94.5%, respectively. The only significant correlation found (Cox's regression model) was a reduced risk of late GI toxicity in patients undergoing hypofractionation (HR: 0.38; 95% CI: 0.18-0.78; p: 0.008). The unexpected results of this analysis could be explained by a "response shift bias" concerning the protective effect of older age and by treatment in later periods (using IMRT/VMAT) concerning the favorable effect of hypofractionation. However, overall, the study suggests that age should not be a reason to avoid adjuvant RT and that the latter is well-tolerated even with moderately hypofractionated regimens.

Psychometric sensitivity analyses can identify bias related to measurement properties in trials that use patient-reported outcome measures: a secondary analysis of a clinical trial using the disabilities of the arm, shoulder, and hand questionnaire

ObjectivesDemonstrate psychometric sensitivity analyses for testing the stability of study findings to assumptions made about patient-reported outcome measures. Study Design and SettingWe performed secondary analyses of Disability of Arm, Shoulder, and Hand (DASH) data collected within the Prevention of Shoulder Problems clinical trial, which compared upper limb function scores in women who had undergone breast cancer surgery, randomized to either an exercise program or usual care. We repeated the principal trial analyses after grouping DASH items into subscales suggested by factorial analyses in this dataset and applied item response theory to account for unequal item weighting. We checked for measurement invariance by participant age and response shift bias using established techniques. ResultsOur analyses suggested that the DASH measured two constructs: motor function and sensory symptoms. The majority of the six-month difference in DASH score was driven by motor function. With item response theory scoring, we found differences in both constructs at 12 months (P = 0.019 and P = 0.007), but in neither construct at 6 months, contrary to the original trial results. We found no differential item function by age or between baseline and 12-month measurements. ConclusionsPsychometric sensitivity analyses aid in the interpretation of the Prevention of Shoulder Problems trial’s results.

Undergraduate teaching of surgical skills in the UK: systematic review.

Students must be proficient in surgical skills according to General Medical Council and Royal College of Surgeons of England guidelines. If these skills are not appropriately taught, there is a risk of an incoming junior workforce with inadequate surgical skills. This paper aimed to review the literature relating to undergraduate teaching of surgical skills in the UK and summarize future suggested training methods. The databases MEDLINE, Embase and SCOPUS were searched, and the existing literature relating to methodology of undergraduate teaching of surgical skills in the UK over the past 10 years was summarized. The Medical Education Research Quality Instrument was used to assess research quality. A total of 19 papers were included. Cross-sectional evaluations and survey-based studies highlight a clear deficit in surgical skills teaching in the UK. Medical students are currently unable to fulfil their own learning needs and meet requirements set out by the General Medical Council. This lack of surgical teaching appears to negatively affect student desire to pursue a surgical career. The three main themes for improvement are extracurricular surgical skills days, near-peer teaching and simulation. Each method appeared to improve learning, although no studies utilized medium- to long-term follow-up to demonstrate efficacy and there lacks a clear consensus as to the 'standard' of undergraduate surgical skill education. There was also potential for selection bias and response shift bias in many of the studies assessing pre- and postintervention confidence and opinions. There is a concerning lack of surgical skills teaching that has resulted in medical students and junior doctors not having the necessary surgical skills as per General Medical Council guidance and students feel that their own learning needs are not met. This failure to address the learning deficit may be responsible for the fall in surgical competition ratios. While surgical skills teaching must be improved urgently, more robust evidence is required to evaluate the optimal ways of approaching this issue.

Counterfactual Generation Framework for Few-Shot Learning

Few-shot learning (FSL) that aims to recognize novel classes with few labeled samples is troubled by its data scarcity. Though recent works tackle FSL with data augmentation-based methods, these models fail to maintain the discrimination and diversity of the generated samples due to the distribution shift and intra-class bias caused by the data scarcity, therefore greatly undermining the performance. To this end, we use causal mechanisms, which are constant among independent variables across data distribution, to alleviate such effects. In this sense, we decompose the image information into two independent components: sample-specific and class-agnostic information, and further propose a novel Counterfactual Generation Framework (CGF) to learn the underlying causal mechanisms to synthesize faithful samples for FSL. Specifically, based on the counterfactual inference, we design a class-agnostic feature extractor to capture the sample-specific information, together with a counterfactual generation network to simulate the data generation process from a causal perspective. Moreover, to leverage the power of CGF in counterfactual inference, we further develop a novel classifier that classifies samples based on their distributions of counterfactual generations. Extensive experiments demonstrate the effectiveness of CGF on four FSL benchmarks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., 80.12/86.13% accuracy on 5-way 1/5-shot miniImageNet FSL tasks, significantly improving the performance. Our codes and models are available at https://github.com/eric-hang/CGF.

Single-shot isotropic differential interference contrast microscopy

Differential interference contrast (DIC) microscopy allows high-contrast, low-phototoxicity, and label-free imaging of transparent biological objects, and has been applied in the field of cellular morphology, cell segmentation, particle tracking, optical measurement and others. Commercial DIC microscopy based on Nomarski or Wollaston prism resorts to the interference of two polarized waves with a lateral differential offset (shear) and axial phase shift (bias). However, the shear generated by these prisms is limited to the rectilinear direction, unfortunately resulting in anisotropic contrast imaging. Here we propose an ultracompact metasurface-assisted isotropic DIC (i-DIC) microscopy based on a grand original pattern of radial shear interferometry, that converts the rectilinear shear into rotationally symmetric along radial direction, enabling single-shot isotropic imaging capabilities. The i-DIC presents a complementary fusion of typical meta-optics, traditional microscopes and integrated optical system, and showcases the promising and synergetic advancements in edge detection, particle motion tracking, and label-free cellular imaging.

The Draft Report by the Institute for Quality and Efficiency in Healthcare Does Not Provide Any Evidence That Graded Exercise Therapy and Cognitive Behavioral Therapy Are Safe and Effective Treatments for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

The German Institute for Quality and Efficiency in Healthcare (IQWiG) recently published its draft report to the government about myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The IQWiG concluded that graded exercise therapy (GET) and cognitive behavioral therapy (CBT) should be recommended in the treatment for mild and moderate ME/CFS based on two CBT and two GET studies. In this article, we reviewed the evidence used by IQWiG to support their claims, because their conclusion is diametrically opposed to the conclusion by the British National Institute for Health and Care Excellence (NICE) in its recently updated ME/CFS guidelines. Our analysis shows that the trials IQWiG used in support suffered from serious flaws, which included badly designed control groups; relying on subjective primary outcomes in non-blinded studies; alliance and response shift bias, including patients in their trials who did not have the disease under investigation, selective reporting, making extensive endpoint changes and low to very low adherence of treatments. Our analysis also shows that the report itself used one CBT and one GET study that both examined a different treatment. The report also used a definition of CBT that does not reflect the way it is being used in ME/CFS or was tested in the studies. The report noted that one study used a wrong definition of post-exertional malaise (PEM), the main characteristic of the disease, according to the report. Yet, it ignored the consequence of this, that less than the required minimum percentage of patients had the disease under investigation in that study. It also ignored the absence of improvement on most of the subjective outcomes, as well as the fact that the IQWiG methods handbook states that one should use objective outcomes and not rely on subjective outcomes in non-blinded studies. The report concluded that both treatments did not lead to objective improvement in the six-minute walk test but then ignored that. The report did not analyze the other objective outcomes of the studies (step test and occupational and benefits status), which showed a null effect. Finally, the report states that the studies do not report on safety yet assumes that the treatments are safe based on a tendency towards small subjective improvements in fatigue and physical functioning, even though the adherence to the treatments was (very) low and the studies included many patients who did not have the disease under investigation and, consequently, did not suffer from exertion intolerance contrary to ME/CFS patients. At the same time, it ignored and downplayed all the evidence that both treatments are not safe, even when the evidence was produced by a British university. In conclusion, the studies used by the report do not provide any evidence that CBT and GET are safe and effective. Consequently, the report and the studies do not provide any support for the recommendation to use CBT and GET for ME/CFS or long COVID, which, in many cases, is the same or resembles ME/CFS, after an infection with SARS-CoV-2.

Towards out of distribution generalization for problems in mechanics

There has been a massive increase in research interest towards applying data driven methods to problems in mechanics, with a particular emphasis on using data driven methods for predictive modeling and design of materials with novel functionality. While traditional machine learning (ML) methods have enabled many breakthroughs, they rely on the assumption that the training (observed) data and testing (unseen) data are independent and identically distributed (i.i.d). However, when these standard ML approaches are applied to real world mechanics problems with unknown test environments, they can be very sensitive to data distribution shifts, and can break down when evaluated on test datasets that violate the i.i.d. assumption. In contrast, out-of-distribution (OOD) generalization approaches assume that the data contained in test environments are allowed to shift (i.e., violate the i.i.d. assumption). To date, multiple methods have been proposed to improve the OOD generalization of ML methods. However, most of these OOD generalization methods have been focused on classification problems, driven in part by the lack of benchmark datasets available for OOD regression problems. Thus, the efficiency of these OOD generalization methods on regression problems, which are typically more relevant to mechanics research than classification problems, is unknown. To address this, we perform a fundamental study of OOD generalization methods for regression problems in mechanics. Specifically, we identify three OOD generalization problems: covariate shift, mechanism shift, and sampling bias. For each problem, we create two benchmark examples that extend the Mechanical MNIST dataset collection, and we investigate the performance of popular OOD generalization methods on these mechanics-specific regression problems. Our numerical experiments show that in most cases, while the OOD algorithms perform better compared to traditional ML methods on these OOD generalization problems, there is a compelling need to develop more robust OOD methods that can generalize the notion of invariance across multiple OOD scenarios. Overall, we expect that this study, as well as the associated open access benchmark datasets, will enable further development of OOD methods for mechanics specific regression problems.

Student teachers’ perceptions of motivation, independence, and supervision preferences: An exploratory study

The student teaching experience is one of the most impactful capstone experiences for the preparation of preservice teachers. The supervisor, either a cooperating teacher or university supervisor, plays a critically important role in the student teaching experience. The purpose of this study was to explore preservice teachers' perceived motivation and independence throughout their student teaching experience. It is recommended that early in the student teaching experience, a directive supervision style should be utilized. Then, as motivation starts to decline in the middle of the student teaching experience, the focus of supervision should shift to providing moral support and encouraging commitment to the profession of teaching. Recommendations for future research include replication of this study with future cohorts of student teachers across multiple institutions so data trends can be analyzed longitudinally. Additionally, it is recommended that future iterations of this study should administer a post-then version of the quantitative plotting instrument to control response shift bias.

P089 A Retrospective Pretest-Posttest Design as an Alternative Evaluation Method for EFNEP

<h3>Background</h3> The Expanded Food and Nutrition Education Program (EFNEP) evaluates its behavioral outcomes using prospective pretest-posttests, which can be prone to response shift bias. A retrospective survey design can be a potential solution. <h3>Objective</h3> To examine program participants' understanding and preferences regarding retrospective versions of the EFNEP Adult Questionnaire (AQ). <h3>Study Design, Setting, Participants</h3> One-on-one, semi-structured interviews were conducted with 23 EFNEP adult participants/graduates in four US states/territories in 2020-2021. Participants were shown retrospective pretests and posttests in the "side-by-side" and "separate page" formats, which included one question from each of the AQ domains of diet quality, physical activity, food resource management, food safety and food security. AQ questions were modified by adding "Before EFNEP" for the pretest and "Now" for the posttest timeframes. <h3>Measurable Outcome/Analysis</h3> Demographic characteristics (e.g., age, gender, education), and AQ format preferences were collected. Audio transcripts were analyzed using template analysis and NVivo 1.5. <h3>Results</h3> Most of the participants were female (87%) and high school graduates (70%); their mean age was 35 years. For the "Before EFNEP" questions, participants mentioned "any time before the past two months" or provided the particular month before EFNEP lessons took place. Regarding the "Now" questions, emerging themes were relevant to the past weeks or past month. Some participants indicated that additional descriptions such as "in the week before EFNEP" were helpful in pinpointing the intended timeframe. Most (18/23) of the participants could correctly differentiate the time periods between the "Before" and "Now" questions. Most participants preferred the "side-by-side" (vs "separate page") format. <h3>Conclusions</h3> A retrospective AQ, especially in a side-by-side format, may be an appropriate alternative to the prospective surveys to evaluate behavioral outcomes of adult EFNEP participants. Next steps include piloting the retrospective survey to examine its criterion validity and feasibility with a larger sample. <h3>Funding</h3> Agricultural Experiment Station, NC3169 Multistate Project

A Review of Single-Source Deep Unsupervised Visual Domain Adaptation.

Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. However, in many applications, it is prohibitively expensive and time-consuming to obtain large quantities of labeled data. To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain. Unfortunately, direct transfer across domains often performs poorly due to the presence of domain shift or dataset bias. Domain adaptation (DA) is a machine learning paradigm that aims to learn a model from a source domain that can perform well on a different (but related) target domain. In this article, we review the latest single-source deep unsupervised DA methods focused on visual tasks and discuss new perspectives for future research. We begin with the definitions of different DA strategies and the descriptions of existing benchmark datasets. We then summarize and compare different categories of single-source unsupervised DA methods, including discrepancy-based methods, adversarial discriminative methods, adversarial generative methods, and self-supervision-based methods. Finally, we discuss future research directions with challenges and possible solutions.

Retrospective Pretests: Recent Use in Visitor Studies Research and Ways to Make Them More Informative

Many visitor studies researchers use Retrospective Pretest (RPT) ­methods to document outcomes. Research literature compares the validity of RPT with traditional pretest-posttests. This article reviews visitor study and informal learning literature about how RPT has been used to evaluate programs in museums, aquariums, parks/recreation, zoos, and tourism. It furthers discussions about response shift bias, arguing that response shift can be either intentional or inadvertent. The type of response shift is important in determining when RPT should be used. We argue that RPT is the best choice when the program intends to shift participants’ understanding of constructs being measured, and when comparing intervention with non-intervention groups. The literature review found that RPT results usually focus on statistical significance testing. RPT data offer much more learning when the results are also examined from other perspectives. This article describes additional analyses of RPT data that can assess the applicability of programs to intended audiences.

Historical fidelity and future change of Amundsen Sea Low under 1.5 °C–4 °C global warming in CMIP6

The realistic simulation and projection of the Amundsen Sea Low (ASL) are essential for understanding the Antarctic climate and global climate change. Using 14 models that participated in phase 6 of the Coupled Model Intercomparison Project (CMIP6), this study evaluates the climatological characteristics of ASL with comparison to the ERA5 reanalysis and their CMIP5 versions and assesses the future change of ASL under 1.5 °C–4 °C global warming. The climatological spatial distribution of ASL is captured reasonably but with underestimated intensity by CMIP6 multi-model ensemble (MME). Among the CMIP6 models, EC-Earth3 has most accurate representation of ASL according to the pattern correlation and biases. The seasonal variation of the ASL depth and location are found to be reasonably reproduced by the CMIP6 models. CMIP6 MME has higher skills in simulating the seasonal cycle of absolute depth and zonal migration of the ASL center. The relative central pressure of ASL is underestimated in all seasons and there is a 4-degree northward shift bias of the ASL center in austral winter, which were also evident in the CMIP5. The semiannual cycle of ASL absolute depth with two deepest pressure in April and October is also captured by CMIP6 MME. However, the observed peak of pressure between the two months occurs in June, while it delays one month and appears until July in CMIP6 MME. Compared with CMIP5, CMIP6 MME exhibit evident reduced uncertainties and overall improvement in simulating absolute depth and location of the ASL center, which might be attributed to models' capability of representing the location of Southern Hemisphere westerlies, while the biases in relative depth become even large in CMIP6 MME. In response to future warming from 1.5 °C to 4 °C above pre-industrial levels, the absolute depth of ASL will very likely deepen with larger amplitude in all seasons, while the relative depth might enhance only under high-level warmer world in austral autumn to winter. The CMIP6 MME also projects that the ASL will shift poleward constantly in austral summer and migrate southwestward during austral autumn with the rising global mean temperature. Among all the seasons, the most prominent future changes in intensity and location of ASL are found in autumn. The enhancement and poleward movement of ASL could also be identified during the Ross Sea ice advance season under 1.5 °C–4 °C global warming. The results reveal the potential of CMIP6 models in the ASL study and the impact of ASL on Antarctic climate under different global warming levels.

Assessing the assessment: a comparison of the IPASS and ICCAS to measure change in interprofessional competencies following a large-scale interprofessional forum

ABSTRACT Measuring the effectiveness of interprofessional education (IPE) experiences is essential but challenging. Surveying learners before (pretest) and after (posttest) an IPE experience may result in minimal change due to response shift bias. Retrospective pretest/posttest assessment may ameliorate response shift bias but may also result in inflated change scores due to social desirability bias. We studied a cohort of 675 students from 12 health professions who completed the Interprofessional Attitudes Scale (IPAS) within the 4 weeks before (pretest) and the 3 weeks after (posttest) an IPE forum and completed the Interprofessional Collaborative Competency Attainment Survey (ICCAS) within the 3 weeks after the forum as a retrospective pretest/posttest. We found higher scores on the pretest IPAS than the retrospective pretest ICCAS and greater change in scores on the ICCAS than the IPAS, indicating potential response shift and social desirability biases. Furthermore, we found few significant correlations between change scores in subscales of the two tests, but a high number of strong and significant correlation among the ICCAS subscale change scores. Our results indicate the timing of pretest administration may impact change scores or that subscales of IPAS may be more unique than those of ICCAS. These findings suggest that educators should consider the potential impact of response shift and social desirability biases when interpreting results of the IPAS and ICCAS in response to an IPE learning experience.

Attention guided for partial domain adaptation

How to effectively extract feature representations from unlabeled samples from the target domain is critical for unsupervised domain adaptation, specific for partial domain adaptation where source label space is a super-space of target label space, as it helps reduce large performance gap due to domain shift or domain bias. In this paper, a novel partial domain adaptation method named Multiple Self-Attention Networks (MSAN) based on adversarial learning is proposed. Unlike most existing partial domain adaptation methods which only focus on high-level features, MSAN focuses on effective high-level context features and low-level structural features from unlabeled target data with the help of labeled source data. Specifically, we present multiple self-attention network, a general approach to learning more fine-grained and transferable features in a manner of gradual feature enhancement so that domain shift can be relatively decreased to boost the model generalization power. Comprehensive experiments on Office-31 and Office-Home datasets demonstrate that the proposed approach significantly improves upon representation partial domain adaptation methods to yield state-of-the-art results for various partial transfer tasks.

Attentional bias to fearful faces in infants at high risk for autism spectrum disorder.

Individuals with autism spectrum disorder (ASD) and their first-degree relatives show differences from neurotypical individuals in emotional face processing. Prospective studies of infant siblings of children with ASD, a group at high risk for autism (HRA), allow researchers to examine the early emergence of these differences. This study used eye tracking to examine disengagement of attention from emotional faces (fearful, happy, neutral) at 6, 9, and 12 months in low-risk control infants (LRC) and HRA infants who received a subsequent clinical judgment of ASD (HRA+) or non-ASD (HRA-). Infants saw centrally presented faces followed by a peripheral distractor (with face remaining present). For each emotion, latency to shift to the distractor and percentage of trials with no shift were calculated. Results showed increased saccadic latency and a greater percentage of no-shift trials for fearful faces. No between-group differences were present for emotion; however, there was an interaction between age and group for disengagement latency, with HRA+ infants slower to shift at 12 months compared with the other 2 groups. Exploratory correlational analyses looking at shift biases to fearful faces alongside measures of social behavior at 12 and 18 months (from the Communication and Symbolic Behavior Scales) revealed that for HRA+ infants, 9- and 12-month fear biases were significantly related to 12- and 18-month social abilities, respectively. This work suggests that both low- and high-risk infants show biases to threat-relevant faces, and that for HRA+, differences in attention shifting emerge with age, and a stronger fear bias could potentially relate to less social difficulty. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Improving Electrical Stability of a-InGaZnO Thin-Film Transistors with Thermally Deposited Self-Assembled Monolayers

Amorphous InGaZnO (a-IGZO) TFTs become mainstream at the forefront of display backplanes and are actively expanding their area for next-generation optoelectronic devices such as flexible and transparent displays. For flexible displays, low temperature processed passivation technology is required to keep the reliability of the electrical properties in a-IGZO TFTs without damaging flexible plastic substrates. Here, we proposed a low-temperature passivation process using a dual-chamber system. A high-quality passivation layer composed of octadecyl-trichlorosilane was formed at 140 °C under vacuum on the back-channel of a-IGZO TFTs using the system. The thermally deposited self-assembled monolayers (SAMs) enable the formation of hydrophobic surfaces on a-IGZO TFTs, leading to the protection of the back-channel against water and oxygen efficiently. As a result, the electrical characteristics such as the threshold voltage shift, hysteresis, field-effect mobility, and negative bias stress of the SAM treated TFTs were significantly improved compared to those of the control TFTs.