Miracle-dz/UniFAR-FTUs huggingface dataset:
Variance estimates are similar using pedigree or genomic relationships with or without the use of metafounders or the algorithm for proven and young animals1

Abstract With an increase in the number of animals genotyped there has been a shift from using pedigree relationship matrices (A) to genomic ones. As the use of genomic relationship matrices (G) has increased, new methods to build or approximate G have developed. We investigated whether the way variance components are estimated should reflect these changes. We estimated variance components for maternal sow traits by solving with restricted maximum likelihood, with four methods of calculating the inverse of the relationship matrix. These methods included using just the inverse of A (A−1), combining A−1 and the direct inverse of G (HDIRECT−1), including metafounders (HMETA−1), or combining A−1 with an approximated inverse of G using the algorithm for proven and young animals (HAPY−1). There was a tendency for higher additive genetic variances and lower permanent environmental variances estimated with A−1 compared with the three H−1 methods, which supports that G−1 is better than A−1 at separating genetic and permanent environmental components, due to a better definition of the actual relationships between animals. There were limited or no differences in variance estimates between HDIRECT−1, HMETA−1, and HAPY−1. Importantly, there was limited differences in variance components, repeatability or heritability estimates between methods. Heritabilities ranged between <0.01 to 0.04 for stayability after second cycle, and farrowing rate, between 0.08 and 0.15 for litter weight variation, maximum cycle number, total number born, total number still born, and prolonged interval between weaning and first insemination, and between 0.39 and 0.44 for litter birth weight and gestation length. The limited differences in heritabilities suggest that there would be very limited changes to estimated breeding values or ranking of animals across models using the different sets of variance components. It is suggested that variance estimates continue to be made using A−1, however including G−1 is possibly more appropriate if refining the model, for traits that fit a permanent environmental effect.
Mechanical stimulation in Brachypodium distachyon: Implications for fitness, productivity, and cell wall properties

Abstract Mechanical stimulation, including exposure to wind, is a common environmental variable for plants. However, knowledge about the morphogenetic response of the grasses (Poaceae) to mechanical stimulation and impact on relevant agronomic traits is very limited. Two natural accessions of Brachypodium distachyon were exposed to wind and mechanical treatments. We surveyed a wide range of stem‐related traits to determine the effect of the two treatments on plant growth, development, and stem biomass properties. Both treatments induced significant quantitative changes across multiple scales, from the whole plant down to cellular level. The two treatments resulted in shorter stems, reduced biomass, increased tissue rigidity, delayed flowering, and reduced seed yield in both accessions. Among changes in cell wall‐related features, a substantial increase in lignin content and pectin methylesterase activity was most notable. Mechanical stimulation also reduced the enzymatic sugar release from the cell wall, thus increasing biomass recalcitrance. Notably, treatments had a distinct and opposite effect on vascular bundle area in the two accessions, suggesting genetic variation in modulating these responses to mechanical stimulation. Our findings highlight that exposure of grasses to mechanical stimulation is a relevant environmental factor affecting multiple traits important for their utilization in food, feed, and bioenergy applications.
Cortical laminar resting‐state signal fluctuations scale with the hypercapnic blood oxygenation level‐dependent response

Calibrated functional magnetic resonance imaging can remove unwanted sources of signal variability in the blood oxygenation level‐dependent (BOLD) response. This is achieved by scaling, using information from a perfusion‐sensitive scan during a purely vascular challenge, typically induced by a gas manipulation or a breath‐hold task. In this work, we seek for a validation of the use of the resting‐state fluctuation amplitude (RSFA) as a scaling factor to remove vascular contributions from the BOLD response. Given the peculiarity of depth‐dependent vascularization in gray matter, BOLD and vascular space occupancy (VASO) data were acquired at submillimeter resolution and averaged across cortical laminae. RSFA from the primary motor cortex was, thus, compared to the amplitude of hypercapnia‐induced signal changes (tSDhc) and with the M factor of the Davis model on a laminar level. High linear correlations were observed for RSFA and tSDhc ( R2 = 0.92 ± 0.06) and somewhat reduced for RSFA and M ( R2 = 0.62 ± 0.19). Laminar profiles of RSFA‐normalized BOLD signal changes yielded good agreement with corresponding VASO profiles. Overall, this suggests that RSFA contains strong vascular components and is also modulated by baseline quantities contained in the M factor. We conclude that RSFA may replace the scaling factor tSDhc for normalizing the laminar BOLD response.
Biotechnology and Bioengineering

Funding information National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health, Grant/Award Number: F32EB026916; Shriners Hospitals for Children, Grant/Award Numbers: 84295, 84311, 85105, 85210 Abstract Tissue engineering scaffolds are intended to provide mechanical and biological support for cells to migrate, engraft and ultimately regenerate the tissue. Development of scaffolds with sustained delivery of growth factors and chemokines would enhance the therapeutic benefits, especially in wound healing. In this study, we incorporated our previously designed therapeutic particles, composed of fusion of elastin‐like peptides (ELPs) as the drug delivery platform to keratinocyte growth factor (KGF), into a tissue scaffold, alloderm. The results demonstrated that sustained KGF–ELP release was achieved and the bioactivity of the released therapeutic particles was shown via cell proliferation assay, as well as a mouse pouch model in vivo, where higher cellular infiltration and vascularization were observed in scaffolds functionalized with KGF–ELPs.
Overexpression of transcription factor Foxa1 and target genes remediate therapeutic protein production bottlenecks in Chinese hamster ovary cells

Despite extensive research conducted to increase protein production from Chinese hamster ovary (CHO) cells, cellular bottlenecks often remain, hindering high yields. In this study, a transcriptomic analysis led to the identification of 32 genes that are consistently upregulated in high producer clones and thus might mediate high productivity. Candidate genes were associated with functions such as signaling, protein folding, cytoskeleton organization, and cell survival. We focused on two engineering targets, Erp27, which binds unfolded proteins and the Erp57 disulfide isomerase in the endoplasmic reticulum, and Foxa1, a pioneering transcription factor involved in organ development. Erp27 moderate overexpression increased production of an easy‐to‐express antibody, whereas Erp27 and Erp57 co‐overexpression increased cell density, viability, and the yield of difficult‐to‐express proteins. Foxa1 overexpression increased cell density, cell viability, and easy‐ and difficult‐to‐express protein yields, whereas it decreased reactive oxygen species late in fed‐batch cultures. Foxa1 overexpression upregulated two other candidate genes that increased the production of difficult‐ and/or easy‐to‐express proteins, namely Ca3, involved in protecting cells from oxidative stress, and Tagap, involved in signaling and cytoskeleton remodeling. Overall, several genes allowing to overcome CHO cell bottlenecks were identified, including Foxa1, which mediated multiple favorable metabolic changes that improve therapeutic protein yields.
Alzheimer’s disease: review of current nanotechnological therapeutic strategies

ABSTRACT Introduction: Alzheimer’s Disease (AD) is a progressive neurodegenerative pathology characterized by the presence of neuritic plaques and neurofibrillary tangles. The most important markers in AD pathology include excessive accumulation of amyloid beta (Aβ42) and phosphorylated tau (P-tau) proteins. One of the possible therapeutic strategies entails the elimination of such deposits by inhibiting Aβ aggregation. For years, one of the major problems in the treatment of AD has been the limited ability to deliver drugs to the brain for reasons related to poor solubility, low bioavailability, and the impact of the blood-brain barrier (BBB). Areas covered: In recent years, the authors have observed an increasing scientific interest in nanotechnological solutions as the factors potentially capable of facilitating the treatment of neurodegenerative diseases. The authors discuss recent reports regarding the use of nanotechnology in the therapy and treatment of AD. Expert opinion: The current advances in nanotechnology promise a chance to overcome the obstacles posed by said limitations. The size and diversity of nanoparticles in terms of both composition and shape create new possibilities for a variety of therapeutic applications, also in the context of the treatment and diagnostics of neurodegenerative diseases, for instance in combination with magnetic resonance imaging.
Construct Hybrid MoS2 Phase Coupled with SiC Heterojunctions with Promoted Photocatalytic Activity for 4-Nitrophenol Degradation.

Phase engineering has been recognized as a promising method for boosting the catalytic activity of molybdenum sulfide (MoS2) in the field of electrocatalysts and photocatalysts. The metallic 1T-MoS2 exhibits much higher catalytic activity than natural semiconducting 2H-MoS2, but suffers from harsh synthetic conditions and metastable physical/chemical properties. Hybrid 1T/2H-MoS2 phase shows higher catalytic activity than the 2H-MoS2 and exhibits better stability than the 1T-MoS2, which is more favorable than the 2H-MoS2 in the photocatalytic reactions. In this study, we report a hydrothermal synthesis of hybrid 1T/2H-MoS2 phase coupled with SiC as a heterojunction photocatalyst for 4-nitrophenol (4-NP) degradation. SiC acts as a counterpart of heterojunction structure and morphology modifier, which dramatically promotes the reaction rate and visible light responsibility, providing new candidates and strategies in photocatalysis.
Membrane interactions of the globular domain and the hypervariable region of KRAS4b define its unique diffusion behavior

The RAS proteins are GTP-dependent switches that regulate signaling pathways and are frequently mutated in cancer. RAS proteins concentrate in the plasma membrane via lipid-tethers and hypervariable region side-chain interactions in distinct nano-domains. However, little is known about RAS membrane dynamics and the details of RAS activation of downstream signaling. Here, we characterize RAS in live human and mouse cells using single-molecule-tracking methods and estimate RAS mobility parameters. KRAS4b exhibits confined mobility with three diffusive states distinct from the other RAS isoforms (KRAS4a, NRAS, and HRAS); and although most of the amino acid differences between RAS isoforms lie within the hypervariable region, the additional confinement of KRAS4b is largely determined by the protein’s globular domain. To understand the altered mobility of an oncogenic KRAS4b, we used complementary experimental and molecular dynamics simulation approaches to reveal a detailed mechanism.
Multisite pacing and myocardial scars: a computational study

Abstract Cardiac resynchronization therapy (CRT) is a frequently effective treatment modality for dyssynchronous heart failure, however, 30% of patients do not respond, usually due to suboptimal activation of the left ventricle (LV). Multisite pacing (MSP) may increase the response rate, but its effect in the presence of myocardial scars is not fully understood. We use a computational model to study the outcome of MSP in an LV with scars in two different locations and of two different sizes. The LV was stimulated from anterior, posterior and lateral locations individually and in pairs, while a septal stimulation site represented right ventricular (RV) pacing. Intraventricular pressures were measured, and outcomes evaluated in terms of maximum LV pressure gradient (dP/dtmax)- change compared to isolated RV pacing. The best result obtained using various LV pacing locations included a combination of sites remote from scars and the septum. The highest dP/dtmax increase was achieved, regardless of scar size, using MSP with one pacing site located on the LV free wall opposite to the scar and one site opposite to the septum. These in silico modelling results suggest that making placement of pacing electrodes dependent on location of scarring, may alter acute haemodynamics and that such modelling may contribute to future CRT optimization.
Teaching the affective domain in community-based medical education: A scoping review

Abstract Background: The affective domain is one of the essential areas in the assessment of the learning outcomes of medical students, apart from the cognitive and psychomotor domains. Community-based medical education (CBME) is a common instructional program for medical students in learning about these domains. However, preceptors and researchers pay less attention to the affective domain as compared to the other two learning domains. Aim: To describe the state of the literature on teaching the affective domain through CBME and to develop an initial model for instructional purposes. Methods: A scoping review of the literature was conducted. Out of the 971 references initially retrieved, 22 published references were selected. Relevant data from these references were extracted and analyzed through thematic analysis. Results and Conclusion: The various affective outcomes of CBME in the literature are commonly taught through role modelling and mentoring, providing opportunity to apply knowledge, and immersing in local organizations and communities. However, these teaching strategies will be optimized through a structured and rigorous process of reflection. Reflection is central to the learning experience of medical students, especially that affective outcomes are commonly less apparent. The findings of this review resulted to a proposed initial model in teaching the affective domain in CBME.
Cation Diffusion Guides Hybrid Halide Perovskite Crystallization during Gel Stage.

Lead halide perovskites have attracted tremendous attention to find their promising potential in various applications as optoelectronics. Unfortunately, this materials with mixed cations/anions often suffer from phase segregation, which is detrimental to the device efficiency and long-term stability. During perovskite film growth, the gel stage (in between liquid and crystalline) correlates to the phase segregation, which has been rarely explored. Herein, we systematically investigate the cation diffusion kinetics at the gel stage to develop a diffusion model obeying Fick's Second Law. Take 2D layered perovskite as an example, we combine theoretical and experimental results to reveal the impacts of diffusion coefficient, temperature, and gel duration on the film growth and phase formation. In light of this finding, we successfully fabricate a homogenous 2D perovskite thin film without significant phase segregation. This in-depth understanding of gel stage and relevant cation diffusion kinetics would further guide the design and processing of halide perovskites with mixed composition to meet requirements for optoelectronic applications.
Leaf carbon and water status control stomatal and non-stomatal limitations of photosynthesis in trees.

Photosynthetic rate is concurrently limited by stomatal and non-stomatal limitations. However, the controls on non-stomatal limitations to photosynthesis (NSL) and their coordination with stomatal control on different timescales remain poorly understood. According to a recent optimization hypothesis, NSL depends on leaf osmotic or water status, and is coordinated with stomatal control so as to maximize leaf photosynthesis. Drought and notching experiments were conducted on Pinus sylvestris, Picea abies, Betula Pendula, and Populus tremula seedlings in greenhouse conditions to study the dependence of NSL on leaf osmotic and water status, and its coordination with stomatal control, on timescales of minutes and weeks, to test the assumptions and predictions of the optimization hypothesis. Both NSL and stomatal conductance followed power-law functions of leaf osmotic concentration and leaf water potential. Moreover, stomatal conductance was proportional to the square root of soil-to-leaf hydraulic conductance, as predicted by the optimization hypothesis. While the detailed mechanisms underlying the dependence of NSL on leaf osmotic or water status lie outside the scope of this study, our results support the hypothesis that NSL and stomatal control are coordinated to maximize leaf photosynthesis, and allow the effect of NSL to be included in models of tree gas-exchange.
Model‐Informed Artificial Intelligence: Reinforcement Learning for Precision Dosing

The availability of multidimensional data together with the development of modern techniques for data analysis represent an exceptional opportunity for clinical pharmacology. Data science—defined in this special issue as the novel approaches to the collection, aggregation, and analysis of data—can significantly contribute to characterize drug‐response variability at the individual level, thus enabling clinical pharmacology to become a critical contributor to personalized healthcare through precision dosing. We propose a minireview of methodologies for achieving precision dosing with a focus on an artificial intelligence technique called reinforcement learning, which is currently used for individualizing dosing regimen in patients with life‐threatening diseases. We highlight the interplay of such techniques with conventional pharmacokinetic/pharmacodynamic approaches and discuss applicability in drug research and early development.
gQSPSim: A SimBiology‐Based GUI for Standardized QSP Model Development and Application

Quantitative systems pharmacology (QSP) models are often implemented using a wide variety of technical workflows and methodologies. To facilitate reproducibility, transparency, portability, and reuse for QSP models, we have developed gQSPSim, a graphical user interface–based MATLAB application that performs key steps in QSP model development and analyses. The capabilities of gQSPSim include (i) model calibration using global and local optimization methods, (ii) development of virtual subjects to explore variability and uncertainty in the represented biology, and (iii) simulations of virtual populations for different interventions. gQSPSim works with SimBiology‐built models using components such as species, doses, variants, and rules. All functionalities are equipped with an interactive visualization interface and the ability to generate presentation‐ready figures. In addition, standardized gQSPSim sessions can be shared and saved for future extension and reuse. In this work, we demonstrate gQSPSim’s capabilities with a standard target‐mediated drug disposition model and a published model of anti‐proprotein convertase subtilisin/kexin type 9 (PCSK9) treatment of hypercholesterolemia.
Discovering hidden information in biosignals from patients using artificial intelligence

Biosignals such as electrocardiogram or photoplethysmogram are widely used for determining and monitoring the medical condition of patients. It was recently discovered that more information could be gathered from biosignals by applying artificial intelligence (AI). At present, one of the most impactful advancements in AI is deep learning. Deep learning-based models can extract important features from raw data without feature engineering by humans, provided the amount of data is sufficient. This AI-enabled feature presents opportunities to obtain latent information that may be used as a digital biomarker for detecting or predicting a clinical outcome or event without further invasive evaluation. However, the black box model of deep learning is difficult to understand for clinicians familiar with a conventional method of analysis of biosignals. A basic knowledge of AI and machine learning is required for the clinicians to properly interpret the extracted information and to adopt it in clinical practice. This review covers the basics of AI and machine learning, and the feasibility of their application to real-life situations by clinicians in the near future.
Behavioural pattern separation is strongly associated with familiarity-based decisions

ABSTRACT Pattern separation is the process that minimises interference between memory representations with similar features and is suggested to be associated with hippocampus-related recollection. We tested this hypothesis using the incidental Mnemonic Similarity Task with old (target), similar (lure), and new (foil) items presented on a recognition test, which is widely used for detecting individual differences in behavioural pattern separation performance. In Experiment 1, participants made old/similar/new decisions and rated decision confidence on a scale ranging from “not at all sure” to “very sure”. In Experiment 2, participants made recognition confidence judgments on a scale ranging between “sure it was new” and “sure it was old”. In Experiment 3, subjects gave old/similar/new decisions and made a secondary Remember/Know/Guess judgment. In Experiment 1, confidence ratings were higher for targets compared to lures when we analysed correct responses (old for targets and similar for lures). Additionally, we found a symmetrical ROC curve and a linear zROC curve for target-lure discrimination in Experiment 2. Finally, we found a bias toward Know responses when we analysed behavioural pattern separation performance (i.e., the rate of similar responses given to the lures). These findings suggest that familiarity, rather than recollection, contributes to behavioural pattern separation performance.
Toward point-of-care ultrasound estimation of fetal gestational age from the trans-cerebellar diameter using CNN-based ultrasound image analysis

Abstract. Obstetric ultrasound is a fundamental ingredient of modern prenatal care with many applications including accurate dating of a pregnancy, identifying pregnancy-related complications, and diagnosis of fetal abnormalities. However, despite its many benefits, two factors currently prevent wide-scale uptake of this technology for point-of-care clinical decision-making in low- and middle-income country (LMIC) settings. First, there is a steep learning curve for scan proficiency, and second, there has been a lack of easy-to-use, affordable, and portable ultrasound devices. We introduce a framework toward addressing these barriers, enabled by recent advances in machine learning applied to medical imaging. The framework is designed to be realizable as a point-of-care ultrasound (POCUS) solution with an affordable wireless ultrasound probe, a smartphone or tablet, and automated machine-learning-based image processing. Specifically, we propose a machine-learning-based algorithm pipeline designed to automatically estimate the gestational age of a fetus from a short fetal ultrasound scan. We present proof-of-concept evaluation of accuracy of the key image analysis algorithms for automatic head transcerebellar plane detection, automatic transcerebellar diameter measurement, and estimation of gestational age on conventional ultrasound data simulating the POCUS task and discuss next steps toward translation via a first application on clinical ultrasound video from a low-cost ultrasound probe.