An instance segmentation dataset of cabbages over the whole growing season for UAV imagery.

Crop growth monitoring is essential for both crop and supply chain management. Conventional manual sampling is not feasible for assessing the spatial variability of crop growth within an entire field or across all fields. Meanwhile, UAV-based remote sensing enables the efficient and nondestructive investigation of crop growth. A variety of crop-specific training image datasets are needed to detect crops from UAV imagery using a deep learning model. Specifically, the training dataset of cabbage is limited. This data article includes annotated cabbage images in the fields to recognize cabbages using machine learning models. This dataset contains 458 images with 17,621 annotated cabbages. Image sizes are approximately 500 to 1000 pixel squares. Since these cabbage images were collected from different cultivars during the whole growing season over the years, deep learning models trained with this dataset will be able to recognize a wide variety of cabbage shapes. In the future, this dataset can be used not only in UAVs but also in land-based robot applications for crop sensing or associated plant-specific management.

The BioRECIPE Knowledge Representation Format.

The BioRECIPE (Biological system Representation for Evaluation, Curation, Interoperability, Preserving, and Execution) knowledge representation format was introduced to standardize and facilitate human-machine interaction while creating, verifying, evaluating, curating, and expanding executable models of intra- and intercellular signaling. This format allows a human user to easily preview and modify any model component, while it is at the same time readable by machines and can be processed by a suite of model development and analysis tools. The BioRECIPE format is compatible with multiple representation formats, natural language processing tools, modeling tools, and databases that are used by the systems and synthetic biology communities.

The benefits of socioemotional learning strategies and video formats for older digital immigrants learning a novel smartphone application.

The need to continually learn and adjust to new technology can be an arduous demand, particularly for older adults who did not grow up with digital technology ("older digital immigrants" or ODIs). This study tests the efficacy of socioemotional learning strategies (i.e., encoding information in a socially- or emotionally-meaningful way) for ODIs learning a new software application from an instructional video (Experiment 1) or a written manual (Experiment 2). An experiment-by-condition effect was identified, where memory was greatest for participants engaging socioemotional learning strategies while learning from a video, suggesting a synergistic effect of these manipulations. These findings serve as a first step toward identifying and implementing an optimal learning context for ODIs to learn new technologies in everyday life.

Interaction between overconfidence effects and training formats in nurses' education in hand hygiene.

BACKGROUND: Undergraduate training in hand hygiene is a keystone of infection control. Several studies have shown overconfidence effects in hand hygiene practices, which can impair metacognition. We hypothesized that overconfidence might be prevalent in the early education stages of nursing students and that these effects could be reduced through frequent interactive learning formats, such as learning groups. METHODS: We conducted a multicenter cross-sectional questionnaire with 196 German nursing students, including general, surgical, and anesthetic nursing specializations. RESULTS: Overconfidence was observed in nursing students across all specialties and years of education. The cluster analyses showed three different types of learners: two characterized by overconfidence and one demonstrating justifiable confidence. Furthermore, the moderation analysis indicated that providing feedback and promoting metacognition regarding students' learning achievements could mitigate overplacement, particularly through the frequent implementation of interactive teaching formats. DISCUSSION: Despite some limitations, these findings highlight the prevalence of overconfidence effects in nursing students, the presence of different learning profiles, and the importance of incorporating feedback within interactive learning formats concerning hand hygiene. Accordingly, educators need to be trained and supervised to deliver these learning formats and provide feedback to students effectively.

Thermomechanical Material Characterization of Polyethylene Terephthalate Glycol with 30% Carbon Fiber for Large-Format Additive Manufacturing of Polymer Structures.

Large-format additive manufacturing (LFAM) is used to print large-scale polymer structures. Understanding the thermal and mechanical properties of polymers suitable for large-scale extrusion is needed for design and production capabilities. An in-house-built LFAM printer was used to print polyethylene terephthalate glycol with 30% carbon fiber (PETG CF30%) samples for thermomechanical characterization. Thermogravimetric analysis (TGA) shows that the samples were 30% carbon fiber by weight. X-ray microscopy (XRM) and porosity studies find 25% voids/volume for undried material and 1.63% voids/volume for dry material. Differential scanning calorimetry (DSC) shows a glass transition temperature (Tg) of 66 °C, while dynamic mechanical analysis (DMA) found Tg as 82 °C. The rheology indicated that PETG CF30% is a good printing material at 220-250 °C. Bending experiments show an average of 48.5 MPa for flexure strength, while tensile experiments found an average tensile strength of 25.0 MPa at room temperature. Comparison with 3D-printed PLA and PETG from the literature demonstrated that LFAM-printed PETG CF30% had a comparative high Young's modulus and had similar tensile strength. For design purposes, prints from LFAM should consider both material choice and print parameters, especially when considering large layer heights.

Linear Factor Analytic Thurstonian Forced-Choice Models: Current Status and Issues.

Thurstonian forced-choice modeling is considered to be a powerful new tool to estimate item and person parameters while simultaneously testing the model fit. This assessment approach is associated with the aim of reducing faking and other response tendencies that plague traditional self-report trait assessments. As a result of major recent methodological developments, the estimation of normative trait scores has become possible in addition to the computation of only ipsative scores. This opened up the important possibility of comparisons between individuals with forced-choice assessment procedures. With item response theory (IRT) methods, a multidimensional forced-choice (MFC) format has also been proposed to estimate individual scores. Customarily, items to assess different traits are presented in blocks, often triplets, in applications of the MFC, which is an efficient form of item presentation but also a simplification of the original models. The present study provides a comprehensive review of the present status of Thurstonian forced-choice models and their variants. Critical features of the current models, especially the block models, are identified and discussed. It is concluded that MFC modeling with item blocks is highly problematic and yields biased results. In particular, the often-recommended presentation of blocks with items that are keyed in different directions of a trait proves to be counterproductive considering the goal to reduce response tendencies. The consequences and implications of the highlighted issues are further discussed.

Customizing Bayesian multivariate generalizability theory to mixed-format tests.

Mixed-format tests, which typically include dichotomous items and polytomously scored tasks, are employed to assess a wider range of knowledge and skills. Recent behavioral and educational studies have highlighted their practical importance and methodological developments, particularly within the context of multivariate generalizability theory. However, the diverse response types and complex designs of these tests pose significant analytical challenges when modeling data simultaneously. Current methods often struggle to yield reliable results, either due to the inappropriate treatment of different types of response data separately or the imposition of identical covariates across various response types. Moreover, there are few software packages or programs that offer customized solutions for modeling mixed-format tests, addressing these limitations. This tutorial provides a detailed example of using a Bayesian approach to model data collected from a mixed-format test, comprising multiple-choice questions and free-response tasks. The modeling was conducted using the Stan software within the R programming system, with Stan codes tailored to the structure of the test design, following the principles of multivariate generalizability theory. By further examining the effects of prior distributions in this example, this study demonstrates how the adaptability of Bayesian models to diverse test formats, coupled with their potential for nuanced analysis, can significantly advance the field of psychometric modeling.

Is the effect of cognitive behaviour therapy for chronic fatigue syndrome (ME/CFS) moderated by the presence of comorbid depressive symptoms? A meta-analysis of three treatment delivery formats.

OBJECTIVE: Cognitive behaviour therapy (CBT) for fatigue in chronic fatigue syndrome (ME/CFS) leads to a significant reduction of fatigue and disability and is available in different treatment delivery formats, i.e. internet-based, individual face-to-face and group face-to-face. The aim of this study was to investigate whether moderation of the effects of CBT by clinically relevant depressive symptoms varies between CBT delivery formats. METHODS: Data from six randomised controlled trials (n = 1084 patients) were pooled. Moderation of clinically relevant depressive symptoms (Brief Depression Inventory for Primary Care) in different treatment formats on fatigue severity (Checklist Individual Strength, subscale fatigue severity), functional impairment (Sickness Impact Profile-8) and physical functioning (Short Form-36, subscale physical functioning) was investigated using linear mixed model analyses and interaction tests. Differences in percentages of patients no longer severely fatigued post-CBT were studied by calculating relative risks. RESULTS: The moderator effect of depressive symptoms on fatigue severity varied by delivery format. In internet-based CBT, ME/CFS patients with depressive symptoms showed less reduction in fatigue, and were more often still severely fatigued post-treatment than patients without depressive symptoms. In individual and group face-to-face CBT, no significant difference in treatment effect on fatigue severity was found between patients with and without depressive symptoms. No moderation was found for the other outcomes. CONCLUSION: In internet-based CBT, ME/CFS patients with comorbid depressive symptoms benefit less, making face-to-face CBT currently the first-choice delivery format for these patients. Internet-based CBT should be further developed to improve its effectiveness for ME/CFS patients with depressive symptoms.

Analysis-ready VCF at Biobank scale using Zarr.

Background: Variant Call Format (VCF) is the standard file format for interchanging genetic variation data and associated quality control metrics. The usual row-wise encoding of the VCF data model (either as text or packed binary) emphasises efficient retrieval of all data for a given variant, but accessing data on a field or sample basis is inefficient. Biobank scale datasets currently available consist of hundreds of thousands of whole genomes and hundreds of terabytes of compressed VCF. Row-wise data storage is fundamentally unsuitable and a more scalable approach is needed. Results: We present the VCF Zarr specification, an encoding of the VCF data model using Zarr which makes retrieving subsets of the data much more efficient. Zarr is a cloud-native format for storing multi-dimensional data, widely used in scientific computing. We show how this format is far more efficient than standard VCF based approaches, and competitive with specialised methods for storing genotype data in terms of compression ratios and calculation performance. We demonstrate the VCF Zarr format (and the vcf2zarr conversion utility) on a subset of the Genomics England aggV2 dataset comprising 78,195 samples and 59,880,903 variants, with a 5X reduction in storage and greater than 300X reduction in CPU usage in some representative benchmarks. Conclusions: Large row-encoded VCF files are a major bottleneck for current research, and storing and processing these files incurs a substantial cost. The VCF Zarr specification, building on widely-used, open-source technologies has the potential to greatly reduce these costs, and may enable a diverse ecosystem of next-generation tools for analysing genetic variation data directly from cloud-based object stores.

Instructional approach, sleep, and perceived academic well-being in adolescents during COVID-19: Evidence from the NESTED study.

OBJECTIVES: At the peak of COVID-19, adolescent life was disrupted as schools adapted their instructional approaches such as online, in-person, or hybrid instruction. We and others have previously commented on how these shifts facilitated longer, later and (more developmentally appropriate) sleep. Here, we report how sleep contributed to associations between remote instruction and broader academic well-being (e.g., cognitive function, school connectedness, and stress). METHODS: Adolescents from all 50 U.S. states (n = 4068) completed online self-report surveys in fall 2020. Instructional approach was operationalized from fully in-person instruction to fully asynchronous online education. Sleep parameters included sleep timing and duration, sleep disturbances, and sleep-related impairments. Perceived academic well-being was defined as cognitive function, school connectedness, and school-related stress. Sleep and perceived academic well-being are examined across instructional approaches, in their association, and in structural models. RESULTS: Sleep and perceived academic well-being differed between hybrid and online instruction groups. Less variable or disturbed sleep was associated both with in-person instruction, and with positive outcomes in cognitive function, school connectedness, and stress domains. Sleep mediated a substantial portion of variance in perceived academic well-being attributable to instructional approach. CONCLUSION: These data highlight the need to protect both healthy sleep and in-person instruction. Appropriate sleep timing and duration, fewer sleep disturbances and sleep-related impairments accounted for a substantial degree of variance in the association between remote instruction on academic outcomes. While many students experienced "lost learning" because of COVID-19, this study joins a broader discussion of ensuring developmentally appropriate school-start times to support both sleep and achievement.

Data Format Standardization and DICOM Integration for Hyperpolarized 13C MRI.

Hyperpolarized (HP) 13C MRI has shown promise as a valuable modality for in vivo measurements of metabolism and is currently in human trials at 15 research sites worldwide. With this growth it is important to adopt standardized data storage practices as it will allow sites to meaningfully compare data. In this paper we (1) describe data that we believe should be stored and (2) demonstrate pipelines and methods that utilize the Digital Imaging and Communications in Medicine (DICOM) standard. This includes proposing a set of minimum set of information that is specific to HP 13C MRI studies. We then show where the majority of these can be fit into existing DICOM Attributes, primarily via the "Contrast/Bolus" module. We also demonstrate pipelines for utilizing DICOM for HP 13C MRI. DICOM is the most common standard for clinical medical image storage and provides the flexibility to accommodate the unique aspects of HP 13C MRI, including the HP agent information but also spectroscopic and metabolite dimensions. The pipelines shown include creating DICOM objects for studies on human and animal imaging systems with various pulse sequences. We also show a python-based method to efficiently modify DICOM objects to incorporate the unique HP 13C MRI information that is not captured by existing pipelines. Moreover, we propose best practices for HP 13C MRI data storage that will support future multi-site trials, research studies and technical developments of this imaging technique.

Optimal format design in green tourism supply chain in the presence of a dominant online travel agency.

Due to a large consumer base owned by a dominant online travel agency(OTA), green hotels widely cooperate with the OTA to sell their products. This study develops a green tourism supply chain model involving an OTA and a green hotel. The study aims to provide valuable insights for the OTA and hotel in optimizing their decision-making process when they come to reach an agreement on cooperation formats among price-only, cost sharing, and revenue sharing. The findings indicate that cost sharing and revenue sharing formats are more effective than price-only format in incentivizing the hotel to improve its environmental efforts. Moreover, these two formats result in higher wholesale prices determined by the hotel compared to the wholesale price set in price-only format. This study also recommends the revenue sharing format as an optimal format, which could lead to a win-win outcome for both the OTA and hotel, achieving profit maximization under specific conditions. Overall, by effectively implementing cooperation formats, the OTA and hotel can work together to promote green tourism supply chain performances.

Measuring Unipolar Traits With Continuous Response Items: Some Methodological and Substantive Developments.

In recent years, some models for binary and graded format responses have been proposed to assess unipolar variables or "quasi-traits." These studies have mainly focused on clinical variables that have traditionally been treated as bipolar traits. In the present study, we have made a proposal for unipolar traits measured with continuous response items. The proposed log-logistic continuous unipolar model (LL-C) is remarkably simple and is more similar to the original binary formulation than the graded extensions, which is an advantage. Furthermore, considering that irrational, extreme, or polarizing beliefs could be another domain of unipolar variables, we have applied this proposal to an empirical example of superstitious beliefs. The results suggest that, in certain cases, the standard linear model can be a good approximation to the LL-C model in terms of parameter estimation and goodness of fit, but not trait estimates and their accuracy. The results also show the importance of considering the unipolar nature of this kind of trait when predicting criterion variables, since the validity results were clearly different.

Data Format Standardization and DICOM Integration for Hyperpolarized 13C MRI.

Hyperpolarized (HP) 13C MRI has shown promise as a valuable modality for in vivo measurements of metabolism and is currently in human trials at 15 research sites worldwide. With this growth, it is important to adopt standardized data storage practices as it will allow sites to meaningfully compare data. In this paper, we (1) describe data that we believe should be stored and (2) demonstrate pipelines and methods that utilize the Digital Imaging and Communications in Medicine (DICOM) standard. This includes proposing a set of minimum set of information that is specific to HP 13C MRI studies. We then show where the majority of these can be fit into existing DICOM attributes, primarily via the "Contrast/Bolus" module. We also demonstrate pipelines for utilizing DICOM for HP 13C MRI. DICOM is the most common standard for clinical medical image storage and provides the flexibility to accommodate the unique aspects of HP 13C MRI, including the HP agent information but also spectroscopic and metabolite dimensions. The pipelines shown include creating DICOM objects for studies on human and animal imaging systems with various pulse sequences. We also show a python-based method to efficiently modify DICOM objects to incorporate the unique HP 13C MRI information that is not captured by existing pipelines. Moreover, we propose best practices for HP 13C MRI data storage that will support future multi-site trials, research studies, and technical developments of this imaging technique.

Parallel Optimal Calibration of Mixed-Format Items for Achievement Tests.

When large achievement tests are conducted regularly, items need to be calibrated before being used as operational items in a test. Methods have been developed to optimally assign pretest items to examinees based on their abilities. Most of these methods, however, are intended for situations where examinees arrive sequentially to be assigned to calibration items. In several calibration tests, examinees take the test simultaneously or in parallel. In this article, we develop an optimal calibration design tailored for such parallel test setups. Our objective is both to investigate the efficiency gain of the method as well as to demonstrate that this method can be implemented in real calibration scenarios. For the latter, we have employed this method to calibrate items for the Swedish national tests in Mathematics. In this case study, like in many real test situations, items are of mixed format and the optimal design method needs to handle that. The method we propose works for mixed-format tests and accounts for varying expected response times. Our investigations show that the proposed method considerably enhances calibration efficiency.

Impact of fed-batch process intensification on the productivity and product quality of two CHO cell lines expressing unique novel molecular format proteins.

While monospecific antibodies have long been the foundational offering of protein therapeutics, recent advancements in antibody engineering have allowed for the development of far more complex antibody structures. Novel molecular format (NMF) proteins, such as bispecific antibodies (BsAbs), are structures capable of multispecific binding, allowing for expanded therapeutic functionality. As demand for NMF proteins continues to rise, biomanufacturers face the challenge of increasing bioreactor process productivity while simultaneously maintaining consistent product quality. This challenge is exacerbated when producing structurally complex proteins with asymmetric modalities, as seen in NMFs. In this study, the impact of a high inoculation density (HID) fed-batch process on the productivity and product quality attributes of two CHO cell lines expressing unique NMFs, a monospecific antibody with an Fc-fusion protein and a bispecific antibody, compared to low inoculation density (LID) platform fed-batch processes was evaluated. It was observed that an intensified platform fed-batch process increased product concentrations by 33 and 109% for the two uniquely structured complex proteins in a shorter culture duration while maintaining similar product quality attributes to traditional fed-batch processes.

A review of antibody, aptamer, and nanomaterials synergistic systems for an amplified electrochemical signal.

The synergy between biomolecules with inorganic nanomaterials and nanoparticles has been investigated over the past years, primarily to improve biomarker reception, generate signals, and amplify the signals generated. In this paper, several articles on aptamer-based and antibody-based electrochemical biosensors that target antigens were examined. Among the key characteristics identified were the electrochemical platform development, which includes the usage of nanomaterials as electroactive or electrocatalytic labels, crosslinking of the biological agent with inorganic compounds, and electrode coating to provide an electronic source and support efficient electron transfer. A single approach using labeled or unlabeled biological receptors has become advantageous due to its simple architecture and more straightforward application method. However, the dual system approach allows the incorporation of more nanomaterials to boost the signal and add more features to the electrochemical system. The dual system approach uses a capture and reporter probe in a competitive or sandwich detection format. The reporter probe is often labeled by an electroactive or electrocatalytic compound or immobilized in a nanocarrier, resulting in an increase in measured peak current in proportion to the target's concentration. The reported limit of detection and linear range for each platform is presented to assess its efficiency. Generally, the dual system aptasensor showed higher sensitivity, stability, and reproducibility than the immunosensor in comparable settings. The aptasensor showed promising results for the development of point-of-care type applications.

Environmental Durability of Bio-Based and Synthetic Thermoplastic Composites in Large-Format Additive Manufacturing.

This research investigates the durability of large-format 3D-printed thermoplastic composite material systems under environmental exposure conditions of moisture and freeze-thaw. Durability was evaluated for two bio-based composite material systems, namely wood-fiber-reinforced semi-crystalline polylactic acid (WF/PLA) and wood-fiber-reinforced amorphous polylactic acid (WF/aPLA), and one conventionally used synthetic material system, namely short-carbon-fiber-reinforced acrylonitrile butadiene styrene (CF/ABS). The moisture absorption, coefficient of moisture expansion, and reduction of relevant mechanical properties-flexural strength and flexural modulus-after accelerated exposure were experimentally characterized. The results showed that the large-format 3D-printed parts made from bio-based thermoplastic polymer composites, compared to conventional polymer composites, were more susceptible to moisture and freeze-thaw exposure, with higher moisture absorption and greater reductions in mechanical properties.

Adapt/Exchange Decisions Depend on Structural and Surface Features: Effects of Solution Costs and Presentation Format.

Problem solvers often need to choose between adapting a current solution and exchanging it for a new one. However, previous studies have not considered how such decisions might depend on structural and surface features of the task. Therefore, the present study investigated the interplay between the costs of the two solutions (a structural feature) and the format in which this information is presented (a surface feature). In a computer-based modular plant scenario, participants chose between modifying process parameters (Adapt) and reconfiguring the module setup (Exchange). The solution costs were presented either as graphs depicting parameter relations, separate numbers for each parameter, or integrated numbers for each solution. It was hypothesised that graphs induce satisficing (i.e., basing decisions only on Adapt), whereas the numeric formats foster a comparison of the solutions (i.e., basing decisions on the Adapt/Exchange ratio). The hypothesised effects were restricted to situations with medium Adapt costs. A second experiment replicated these findings while adjusting the scale of the numeric formats. In conclusion, Adapt/Exchange decisions are shaped by an interaction of structural and surface features of the task. These findings contribute to a more detailed understanding of the influences on decision strategies in complex scenarios that require a balance between stability and flexibility.

Reducing trainee mistakes. Better performance with changing to a high-fidelity simulation system?

BACKGROUND: Postpartum hemorrhage is a significant cause of both maternal morbidity and mortality worldwide and is increasing in incidence. This study aimed to assess improvement and identify shortcomings in trainee performance in different simulation systems in the management of postpartum hemorrhage. OBJECTIVE: To perform a pilot study evaluating and comparing high- and low-fidelity simulation models, assessing improvement in repeated performance with high-fidelity mode and identifying mistakes made assessed using Objective Structured Assessment of Technical Skills and thereby exploring what aspects of emergency management of postpartum hemorrhage should be prioritized in teaching settings and assessing what simulation setup is most effective in achieving competence. STUDY DESIGN: This was a prospective randomized, single-blinded, single-institution trial in a population of 17 junior obstetrical trainees at the Charité University Hospital Obstetric Simulation Center in Berlin. Trainees were randomized into 2 groups, with either initial low-fidelity simulation or high-fidelity simulation, followed by repeated assessment of performance, using the high-fidelity model simulation system. Individual simulation sessions were video-recorded and transcribed, and the timing of interventions was documented. Strandardized Objective Structured Assessment of Technical Skills forms were used as a checklist for performance. RESULTS: There was a statistically significant general improvement in performance (P=.02; 24.7-27.2 of 31.0 points; average of 8.7%) in the second cycle of simulation assessment and a statistically significant training effect (P=.043; 24.4-28.4 of 31.0 points; average of 12.9%) in the group that underwent repeat simulation assessment from the initial low-fidelity system to the high-fidelity system compared with the group using the same high-fidelity setup (P=.276; 25.0-25.8 of 31.0; average of 2.4%). CONCLUSION: There was an improvement in the performance when trainees underwent a repeated cycle of simulation assessment changing from a low-fidelity system to a high-fidelity system. Simulation assessment can identify mistakes and learning gaps that are important for obstetrical trainees. This study found that trainees make the same mistakes, regardless of which simulation model was initially used.