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Research has shown that media portrayals greatly influence viewers’ beliefs, but few have studied stepfamily portrayals or how viewer demographics might impact what they notice. In this exploratory sequential mixed methods research, undergraduates reported perceptions of media portrayals of stepmothers, stepfathers and stepfamilies in 107 narratives. Those categories created a way to quantitatively distinguish between perceptions based on viewer sex and family type in the second study with 341 college student participants. The first study revealed that perceptions of stepparent portrayals in media might often align with stereotypes (e.g., stepmonsters) while also highlighting a mix of negative and positive perceptions and narratives about the depicted normalcy of stepfamilies. Study Two identified that sex or family type might slightly influence what some notice in television portrayals. However, media often portrays unambiguous views of stepfamilies, and these portrayals are noticed and remembered by viewers with only a slight impact from demographic differences.

This study aims to investigate the prevalence of weight control attempts along with their strategies and motivations. This study used a retrospective observational qualitative method, and adults who had body mass index (BMI) of at least 25 kg/m2 were included. The qualitative data were analyzed using a descriptive phenomenological approach. The summaries and emergence of major concepts identified by the participants were analyzed in-depth using a systematized retrieval and review of the data. NVivo was used to establish interrater reliability and percent agreement and analyze and manage the data. 91.7% of participants had tried to lose weight, and 72% were successful at least in one of their attempts. Health, appearance, and sports training were salient motivating factors. Their attempts to lose weight ranged from 2 to 30 times (median 4 times). Different dietary strategies and physical exercises were often tried. Among the most notable strategies were diet methods, following weight management programs, and consulting with dietitians. A number of barriers were reported, including ineffective weight loss, non-sustainability, demotivation, and unaffordability. For advancing evidence-based long-term weight control, it is vital to evaluate the quality and nature of weight management strategies and services from the perspective of users.

The MGnify platform (https://www.ebi.ac.uk/metagenomics) facilitates the assembly, analysis and archiving of microbiome-derived nucleic acid sequences. The platform provides access to taxonomic assignments and functional annotations for nearly half a million analyses covering metabarcoding, metatranscriptomic, and metagenomic datasets, which are derived from a wide range of different environments. Over the past 3 years, MGnify has not only grown in terms of the number of datasets contained but also increased the breadth of analyses provided, such as the analysis of long-read sequences. The MGnify protein database now exceeds 2.4 billion non-redundant sequences predicted from metagenomic assemblies. This collection is now organised into a relational database making it possible to understand the genomic context of the protein through navigation back to the source assembly and sample metadata, marking a major improvement. To extend beyond the functional annotations already provided in MGnify, we have applied deep learning-based annotation methods. The technology underlying MGnify's Application Programming Interface (API) and website has been upgraded, and we have enabled the ability to perform downstream analysis of the MGnify data through the introduction of a coupled Jupyter Lab environment.

Advancements in DNA synthesis and sequencing technologies have enabled a novel paradigm of protein design where machine learning (ML) models trained on experimental data are used to guide exploration of a protein fitness landscape. ML-guided directed evolution (MLDE) builds on the success of traditional directed evolution and unlocks strategies which make more efficient use of experimental data. Building an MLDE pipeline involves many design choices across the design-build-test-learn loop ranging from data collection strategies to modeling, each of which has a large impact on the success of designed sequences. The cost of collecting experimental data makes benchmarking every component of these pipelines on real data prohibitively difficult, necessitating the development ofsyntheticlandscapes where MLDE strategies can be tested. In this work, we develop a framework called SLIP (“Synthetic Landscape Inference for Proteins”) for constructing biologically-motivated synthetic landscapes with tunable difficulty based on Potts models. This framework can be extended to any protein family for which there is a sequence alignment. We show that without tuning, Potts models are easy to optimize. In contrast, our tuning framework provides landscapes sufficiently challenging to benchmark MLDE pipelines. SLIP is open-source and is available athttps://github.com/google-research/slip.

Modern experimental technologies can assay large numbers of biological sequences, but engineered protein libraries rarely exceed the sequence diversity of natural protein families. Machine learning (ML) models trained directly on experimental data without biophysical modeling provide one route to accessing the full potential diversity of engineered proteins. Here we apply deep learning to design highly diverse adeno-associated virus 2 (AAV2) capsid protein variants that remain viable for packaging of a DNA payload. Focusing on a 28-amino acid segment, we generated 201,426 variants of the AAV2 wild-type (WT) sequence yielding 110,689 viable engineered capsids, 57,348 of which surpass the average diversity of natural AAV serotype sequences, with 12–29 mutations across this region. Even when trained on limited data, deep neural network models accurately predict capsid viability across diverse variants. This approach unlocks vast areas of functional but previously unreachable sequence space, with many potential applications for the generation of improved viral vectors and protein therapeutics. Viable AAV capsids are designed with a machine learning approach.

AIMWe aimed to document current clinical practice and needs in screening for visual perception problems after stroke to inform development of new screening tools.METHODSWe interviewed 25 health care professionals (12 occupational therapists, 13 orthoptists) from 16 organisations in England. Interviews were transcribed and coded in NVivo Software. Data were thematically analysed using the Value Proposition Canvas, a model which establishes what people want to achieve, the challenges they face and what facilitates their jobs.RESULTSParticipants’ understanding of visual perception varied and often included sensory and cognitive deficits. Occupational therapists commonly screened for visual field deficits and hemispatial neglect, while other aspects of visual cognition were rarely assessed. They decided on referrals to orthoptists for further assessment. Screening generally occurred during functional assessments and/or with in-house developed tools. Challenges to practice were: lack of time, lack of training, environmental and stroke survivor factors (e.g. aphasia), insufficient continuation of care, and test characteristics (e.g. not evidence-based). Facilitators to practice were: quick and practical tools, experienced staff or tools with minimal training requirements, a streamlined care pathway between a stroke unit and eye hospital supported by occupational therapists and orthoptists.CONCLUSIONScreening employs non-standardised assessments and rarely covers visual perceptual deficits in higher order perception. Our service evaluation demonstrates the need for a standardised visual perception screen, which should ideally be 15 minutes or less, be portable, and require minimal equipment. The screen should be suitable for bedside testing in noisy environments, inclusive for participants with aphasia and evidence-based.

Machine-learning-guided protein design is rapidly emerging as a strategy to find high-fitness multi-mutant variants. In this issue of Cell Systems, Wittman et al. analyze the impact of design decisions for machine-learning-assisted directed evolution (MLDE) on its ability to navigate a fitness landscape and reliably find global optima.

Predicting the function of a protein from its amino acid sequence is a long-standing challenge in bioinformatics. Traditional approaches use sequence alignment to compare a query sequence either to thousands of models of protein families or to large databases of individual protein sequences. Here we instead employ deep convolutional neural networks to directly predict a variety of protein functions – EC numbers and GO terms – directly from an unaligned amino acid sequence. This approach provides precise predictions which complement alignment-based methods, and the computational efficiency of a single neural network permits novel and lightweight software interfaces, which we demonstrate with an in-browser graphical interface for protein function prediction in which all computation is performed on the user’s personal computer with no data uploaded to remote servers. Moreover, these models place full-length amino acid sequences into a generalised functional space, facilitating downstream analysis and interpretation. To read the interactive version of this paper, please visit https://google-research.github.io/proteinfer/Abstract FigureQR code for the interactive version of this preprint at https://google-research.github.io/proteinfer/

PURPOSE Visual perceptual deficits are frequently underdiagnosed in stroke survivors compared to sensory vision deficits or visual neglect. To better understand this imparity, we evaluated current practice for screening post-stroke visual perceptual deficits. METHODS We conducted a survey targeted at professionals working with stroke survivors involved in screening visual perceptual deficits across the United Kingdom and the Republic of Ireland. RESULTS Forty orthoptists and 174 occupational therapists responded to the survey. Visual perceptual deficit screening was primarily conducted by occupational therapists (94%), with 75∼100% of stroke survivors screened per month. Respondents lacked consensus on whether several common post-stroke visual deficits were perceptual or not. During the screening, respondents primarily relied on self-reports and observation (94%), while assessment batteries (58%) and screening tools were underutilised (56%) and selected inappropriately (66%). Respondents reported lack of training in visual perception screening (20%) and physical/cognitive condition of stroke survivors (19%) as extremely challenging during screening. CONCLUSIONS Visual perceptual deficits are screened post-stroke at a similar rate to sensory vision or visual neglect. Underdiagnosis of visual perceptual deficits may stem from both reliance on subjective and non-standardised screening approaches, and conflicting definitions of visual perception held among clinicians. We recommend increased training provision and use of brief performance-based screening tools.IMPLICATIONS FOR REHABILITATIONLack of agreement among clinicians on what constitutes as visual perceptual or sensory vision deficits may prove problematic, as precise and exact language is often required for clinical decision-making (e.g., referrals).Biases for more familiar visual (perceptual) deficits held among clinicians during the screening process may lead to other visual deficits being missed.To avoid problems being missed, clinicians should aim to use standardised assessments rather than stroke survivor self-report and observations of function when screening for visual perceptual difficulties.

The occurrence of visuospatial neglect acts as a key predictor of recovery outcome following stroke. However, the specific behavioural profiles associated with various neglect subtypes are not well understood. This study aims to identify real-world functional impairments associated with neglect, to determine whether functional impairment profiles differ across patients with egocentric and allocentric neglect, and to investigate how neglect severity predicts functional impairments.Notes from 290 stroke patients' occupational therapy functional assessments were qualitatively and quantitatively analysed in the context of neglect type and severity as reported by the OCS Cancellation Task. Overall, neglect patients had more references to having difficulty initiating tasks, finding items, exhibiting spatial inattention, and having difficulty using both arms than patients without neglect. The proportion of theme references did not differ significantly across patients with egocentric and allocentric neglect. The quantitative severity of egocentric neglect was acted as a significant predictor of reference occurrence over and above stroke severity within difficulty finding items, spatial inattention, body inattention, and upper limb use.This study expands on previous findings by identifying real-world functional impairments differentiating patients with and without neglect. This data provides novel insight into the impact of neglect on functional abilities.

BackgroundArthrospira platensis (commonly known as spirulina) is a promising new platform for low-cost manufacturing of biopharmaceuticals. However, full realization of the platform’s potential will depend on achieving both high growth rates of spirulina and high expression of therapeutic proteins.ObjectiveWe aimed to optimize culture conditions for the spirulina-based production of therapeutic proteins.MethodsWe used a machine learning approach called Bayesian black-box optimization to iteratively guide experiments in 96 photobioreactors that explored the relationship between production outcomes and 17 environmental variables such as pH, temperature, and light intensity.ResultsOver 16 rounds of experiments, we identified key variable adjustments that approximately doubled spirulina-based production of heterologous proteins, improving volumetric productivity between 70% to 100% in multiple bioreactor setting configurations.ConclusionAn adaptive, machine learning-based approach to optimize heterologous protein production can improve outcomes based on complex, multivariate experiments, identifying beneficial variable combinations and adjustments that might not otherwise be discoverable within high-dimensional data.

The accurate prediction of RNA secondary structure from primary sequence has had enormous impact on research from the past 40 years. Although many algorithms are available to make these predictions, the inclusion of non-nested loops, termed pseudoknots, still poses challenges arising from two main factors: 1) no physical model exists to estimate the loop entropies of complex intramolecular pseudoknots, and 2) their NP-complete enumeration has impeded their study. Here, we address both challenges. First, we develop a polymer physics model that can address arbitrarily complex pseudoknots using only two parameters corresponding to concrete physical quantities—over an order of magnitude fewer than the sparsest state-of-the-art phenomenological methods. Second, by coupling this model to exhaustive enumeration of the set of possible structures, we compute the entire free energy landscape of secondary structures resulting from a primary RNA sequence. We demonstrate that for RNA structures of ∼80 nucleotides, with minimal heuristics, the complete enumeration of possible secondary structures can be accomplished quickly despite the NP-complete nature of the problem. We further show that despite our loop entropy model’s parametric sparsity, it performs better than or on par with previously published methods in predicting both pseudoknotted and non-pseudoknotted structures on a benchmark data set of RNA structures of ≤80 nucleotides. We suggest ways in which the accuracy of the model can be further improved.

The objective of this research was to investigate family communication patterns and interpersonal communication motives that college students use to communicate with their parents. In addition, thi...

Understanding the relationship between amino acid sequence and protein function is a long-standing challenge with far-reaching scientific and translational implications. State-of-the-art alignment-based techniques cannot predict function for one-third of microbial protein sequences, hampering our ability to exploit data from diverse organisms. Here, we train deep learning models to accurately predict functional annotations for unaligned amino acid sequences across rigorous benchmark assessments built from the 17,929 families of the protein families database Pfam. The models infer known patterns of evolutionary substitutions and learn representations that accurately cluster sequences from unseen families. Combining deep models with existing methods significantly improves remote homology detection, suggesting that the deep models learn complementary information. This approach extends the coverage of Pfam by9.5%, exceeding additions made over the last decade, and predicts function for 360 human reference proteome proteins with no previous Pfam annotation. These results suggest that deep learning models will be a core component of future protein annotation tools.