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Both blood- and milk-based biomarkers have been analysed for decades in research settings, although often only in one herd, and without focus on the variation in the biomarkers that are specifically related to herd or diet. Biomarkers can be used to detect physiological imbalance and disease risk and may have a role in precision livestock farming (PLF). For use in PLF, it is important to quantify normal variation in specific biomarkers and the source of this variation. The objective of this study was to estimate the between- and within-herd variation in a number of blood metabolites (β-hydroxybutyrate (BHB), non-esterified fatty acids, glucose and serum IGF-1), milk metabolites (free glucose, glucose-6-phosphate, urea, isocitrate, BHB and uric acid), milk enzymes (lactate dehydrogenase and N-acetyl-β-D-glucosaminidase (NAGase)) and composite indicators for metabolic imbalances (Physiological Imbalance-index and energy balance), to help facilitate their adoption within PLF. Blood and milk were sampled from 234 Holstein dairy cows from 6 experimental herds, each in a different European country, and offered a total of 10 different diets. Blood was sampled on 2 occasions at approximately 14 days-in-milk (DIM) and 35 DIM. Milk samples were collected twice weekly (in total 2750 samples) from DIM 1 to 50. Multilevel random regression models were used to estimate the variance components and to calculate the intraclass correlations (ICCs). The ICCs for the milk metabolites, when adjusted for parity and DIM at sampling, demonstrated that between 12% (glucose-6-phosphate) and 46% (urea) of the variation in the metabolites’ levels could be associated with the herd-diet combination. Intraclass Correlations related to the herd-diet combination were generally higher for blood metabolites, from 17% (cholesterol) to approximately 46% (BHB and urea). The high ICCs for urea suggest that this biomarker can be used for monitoring on herd level. The low variance within cow for NAGase indicates that few samples would be needed to describe the status and potentially a general reference value could be used. The low ICC for most of the biomarkers and larger within cow variation emphasises that multiple samples would be needed - most likely on the individual cows - for making the biomarkers useful for monitoring. The majority of biomarkers were influenced by parity and DIM which indicate that these should be accounted for if the biomarker should be used for monitoring.

Unbalanced metabolic status in the weeks after calving predisposes dairy cows to metabolic and infectious diseases. Blood glucose, IGF-I, non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHB) are used as indicators of the metabolic status of cows. This work aims to (1) evaluate the potential of milk mid-IR spectra to predict these blood components individually and (2) to evaluate the possibility of predicting the metabolic status of cows based on the clustering of these blood components. Blood samples were collected from 241 Holstein cows on six experimental farms, at days 14 and 35 after calving. Blood samples were analyzed by reference analysis and metabolic status was defined by k-means clustering (k=3) based on the four blood components. Milk mid-IR analyses were undertaken on different instruments and the spectra were harmonized into a common standardized format. Quantitative models predicting blood components were developed using partial least squares regression and discriminant models aiming to differentiate the metabolic status were developed with partial least squares discriminant analysis. Cross-validations were performed for both quantitative and discriminant models using four subsets randomly constituted. Blood glucose, IGF-I, NEFA and BHB were predicted with respective R2 of calibration of 0.55, 0.69, 0.49 and 0.77, and R2 of cross-validation of 0.44, 0.61, 0.39 and 0.70. Although these models were not able to provide precise quantitative values, they allow for screening of individual milk samples for high or low values. The clustering methodology led to the sharing out of the data set into three groups of cows representing healthy, moderately impacted and imbalanced metabolic status. The discriminant models allow to fairly classify the three groups, with a global percentage of correct classification up to 74%. When discriminating the cows with imbalanced metabolic status from cows with healthy and moderately impacted metabolic status, the models were able to distinguish imbalanced group with a global percentage of correct classification up to 92%. The performances were satisfactory considering the variables are not present in milk, and consequently predicted indirectly. This work showed the potential of milk mid-IR analysis to provide new metabolic status indicators based on individual blood components or a combination of these variables into a global status. Models have been developed within a standardized spectral format, and although robustness should preferably be improved with additional data integrating different geographic regions, diets and breeds, they constitute rapid, cost-effective and large-scale tools for management and breeding of dairy cows.

We investigated how the transition to remote instruction during the COVID-19 pandemic affected students' engagement, self-appraisals, and learning in advanced placement (AP) Statistics courses. Participants included 681 (M[subscript age]=16.7 years, SD[subscript age]=0.90; %female=55.4) students enrolled in the course during 2017-2018 (N=266), 2018-2019 (N=200), and the pandemic-affected 2019-2020 (N=215) school years. Students enrolled during the pandemic-affected year reported a greater improvement in affective engagement but a decrease in cognitive engagement in the spring semester relative to a previous year. Females enrolled in the pandemic-affected year experienced a greater negative change in affective and behavioral engagement. Students enrolled during the pandemic-affected year reported a greater decrease in their anticipated AP exam scores and received lower scores on a practice exam aligned with the AP exam compared to a prior year. Although students were resilient in some respects, their self-appraisal and learning appear to have been negatively affected by pandemic circumstances.

Background: Students' tendencies to seek feedback are associated with improved learning. Yet, how soon this association becomes robust enough to make predictions about learning is not fully understood. Such knowledge has strong implications for early identification of students at-risk for underachievement via digital learning platforms. Objectives: We sought to understand how early in the academic year students' end-of-year learning outcomes could be predicted by their performance and feedback-seeking behaviours within a digital learning platform. We analysed data collected at different time points in the academic year and across different cohorts of students within the context of high school advanced placement (AP) Statistics courses. Methods: High school students enrolled in AP Statistics spanning three academic years between 2017 and 2020 (N = 726; M[subscript age] = 16.72 years) completed 3 or 4 homework assignments, each 2 and 3 months apart. Results and conclusions: Across the three cohorts, and even as early as the first assignment, a model consisting of demographic variables (gender, race/ethnicity, parental education), assignment performance, and interaction with the digital score report explained significant variation in students' final course grades (R[superscript 2] = 0.314-0.412) and AP exam scores (? = 0.583-0.689). Students' assignment performance was positively associated with end-of-year learning outcomes. Students who more frequently checked their digital score reports tended to receive better learning outcomes, though not consistently across cohorts. Implications: These findings further an understanding of how students' early performance and feedback-seeking behaviours within a digital learning platform predict end-of-year learning outcomes.

Preimplantation genetic testing for aneuploidy (PGT-A) has become a useful approach for embryo selection following IVF and ICSI. However, the biopsy process associated with PGT-A is expensive, prone to errors in embryo ploidy determination, and potentially damaging, impacting competence and implantation potential. Therefore, a less invasive method of PGT-A would be desirable and more cost-effective. Noninvasive methods for PGT-A (ni-PGT-A) have been well-studied but present limitations in terms of cf-DNA origin and diagnostic accuracy. Minimally invasive pre-implantation genetic testing (mi-PGT-A) for frozen-thawed embryo transfer is a promising, less studied approach that utilizes a combination of spent culture media (SCM) and blastocoelic fluid (BF)-derived cell-free (CF)-DNA for genetic testing. This study aimed to optimize the effectiveness of mi-PGT-A for aneuploidy diagnosis by investigating the optimal temporal sequence for this protocol. SCM+BF was collected at either 48 or 72 h of culture after thawing day 3 preimplantation embryos. cf-DNA in the SCM+BF was amplified, analyzed by next-generation sequencing (NGS) and compared with results from the corresponding whole embryos (WEs) obtained from human embryos donated for research. Fifty-three (42 expanded blastocysts, 9 early blastocysts, and 2 morula) WE and SCM+BF samples were analyzed and compared. The overall concordance rate between SCM+BF and WE was 60%. Gender and ploidy concordance improved with extended culture time from 48 h (73% and 45%) to 72 h (100% and 64%), respectively. These results demonstrate that SCM+BF-derived cf-DNA can be successfully used for mi-PGT-A. Our findings indicate that longer embryo culture time prior to SCM+BF-derived cf-DNA analysis improves DNA detection rate and concordance with WEs and decreases the proportion of false positive results.

Abstract Extracellular vesicles (EVs) have been proposed to play dual roles in cellular homeostasis, functioning both to remove unwanted intracellular molecules, and to enable communication between cells as a means of modulating cellular responses in different physiological and pathological scenarios. EVs contain a broad range of cargoes, including multiple biotypes of RNA, which can vary depending on the cell status, and may function as signalling molecules. In this study, we carried out comparative transcriptomic analysis of Drosophila EVs and cells, demonstrating that the RNA profile of EVs is distinct from cells and shows dose‐dependent changes in response to oxidative stress. We identified a high abundance of snoRNAs in EVs, alongside an enrichment of intronic and untranslated regions (UTRs) of mRNAs under stress. We also observed an increase in the relative abundance of either aberrant or modified mRNAs under stress. These findings suggest that EVs may function both for the elimination of specific cellular RNAs, and for the incorporation of RNAs that may hold signalling potential.

Abstract Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson’s disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug–gene interactions. We performed automated ML on multimodal data from the Parkinson’s progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson’s Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available.

Friction stir processing (FSP) is a solid-state processing technique that has proven to be an efficient surface modification process for producing aluminium matrix composites (AMCs). However, practical challenges still occur during the processing of AMCs even though extensive progress has been made in recent years. In the present study, the influence of FSP process parameters on the tensile property of Al-Ni-Fe composite has been investigated. The process parameters studied were rotational speed and advancing speed. The rotational speed varied between 600 and 1000 rpm while the advancing speed varied between 70 and 210 mm/min. The rotational speed was kept constant at each setting and the advancing speed varied. Other processing parameters were kept constant throughout the experiments. The results were compared with those of the base metal (Al). The results showed that the tensile strength decreased as the advancing speed increased. The highest tensile strength was obtained at a rotational speed of 1000 rpm and an advancing speed of 70 mm/min.

Long-range optical coherence tomography has been developed to image the upper airway, obtaining high resolution, cross-sectional images of the hollow structure. The information obtained from the anatomical structure of the airway is important to objectively identify regions of airway obstruction. This paper describes a technique to create 3D reconstructions of the upper airway from LR-OCT images. Herein we outline the necessary steps to generate these 3D models, including image processing techniques, manual tissue segmentation in Mimics, anatomical curvature bending, and the final STL model rendition. These 3D models were used to qualitatively analyze structural changes before and after surgical interventions. The reconstructions could also be used for further computational fluid dynamics analysis.

The honey bee industry in Australia is small but has a big impact on both producers and consumers. Alarmingly, it has been recently reported that an international laboratory, specializing in honey authentication, found that almost half of the 28 blended and imported honey samples selected from Australian supermarket shelves were “adulterated”. The Official method of honey analysis (AOAC 998.12) is based on the stable-isotope ratio mass spectrometry analysis of δ13C value of honey versus δ13C of honey protein to detect the addition of C4 plant derived sugars, such as cane sugar or high fructose corn syrup. This method is used as the primary C4 sugar adulteration test around the world, but honey derived from some Australian plants, particularly Manuka (Leptospermum species), fails this process. Our research aims to examine the characteristics of Australian honey and develop a test that is fit for purpose, particularly where honeys derived from Leptospermum species are concerned. We have focused on the isotopic values of “proteins” precipitated using the standard AOAC method and “proteins” precipitated after incorporation of a further modification step which removes insoluble material (including pollen) from the honey before precipitation. Our modified method includes the analysis of different isotopes of the precipitated protein, sugar profiles and Manuka markers. A key advantage of the proposed modification is that it does not preclude the detection of residual sugar feeding of bees or extension of honey with C4 sugar. The proposed modification to the AOAC test will reduce false identification of C4 sugars and improve the overall reliability of Australian honey authentication.