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Flexible InGaAs/InAlAs avalanche photodiodes for short-wave infrared detection
Fatigue crack propagation analysis considering the dynamic crack-load coupling effect
Compassion as a guiding framework for the implementation of digital mental health interventions: An interview study with clients and professionals
Background Digital mental health interventions are often described in terms of their contribution to cost-effectiveness or innovation. Instead, many clients and professionals in mental healthcare seem to value the human connection highly. To implement technology in ways that align with values held by clients and professionals, a value-based framework for technology use in mental healthcare could be promising. The current study explores whether values of clients and professionals in mental healthcare match a framework of compassion, and whether this framework could be a suitable foundation for the implementation of digital mental health interventions. Method We conducted semi-structured interviews with 5 (former) clients and 15 professionals in mental healthcare. Values of both clients and professionals were analyzed inductively, and deductively linked to a compassion framework. Professionals were asked whether their values were congruent with their organization’s approach to technology. We coded their answers as matches and mismatches, and described the themes developed in both categories. Results Values held by clients and professionals showed many connections with the compassion framework. Clients highly valued feeling heard and understood, humanity, and openness from the professional. Professionals highly valued helping people, personalization, and offering transparency. Examples of how technology use could enhance or detract from compassion according to participants were also produced. Professionals experienced a match with their values if they felt that their organizations focused the adoption of technology on the client’s autonomy or meeting treatment needs. They experienced a mismatch if they felt that their organizations were more focused on financial benefit or a technology push. Conclusion Compassion seems a promising framework for integrating technology in mental healthcare in value-sensitive ways.
Visualizing influenza A virus assembly by in situ cryo-electron tomography
Abstract Influenza A virus (IAV) forms pleomorphic particles that package eight ribonucleoprotein complexes (vRNPs), each carrying a distinct RNA genome segment. vRNPs assemble in the nucleus and undergo selective sorting during Rab11a-mediated trafficking to the plasma membrane. Virion assembly is orchestrated by matrix protein 1 (M1), which forms a layer beneath the viral envelope containing hemagglutinin (HA) and neuraminidase (NA). However, molecular details of vRNP distribution, cytosolic trafficking, and coordination of IAV assembly remains unclear. Using in situ cryo-ET, we reveal that HA-containing membranes provide Rab11a-dependent platforms for membrane-assisted vRNP clustering, reducing inter-vRNP distances. In the absence of HA, vRNPs cluster on NA-containing membranes and virus assembly remains intact, indicating that vRNP clustering and trafficking is membrane-assisted but HA independent. The characteristic 7 + 1 vRNP bundle forms concomitantly with budding and is orchestrated by M1 layer assembly that precedes plasma membrane attachment. We further reveal that intracellular M1 forms multilayered helical assemblies of antiparallel dimers, structurally distinct from the M1 layer in virions. These assemblies are compact in the nucleus but partially dissociate in the cytoplasm, likely serving as a reservoir for budding. Together, our findings uncover membrane-assisted vRNP clustering and molecular details of M1 coordinated influenza virus assembly.
An optimized bidirectional recurrent neural network for kidney stone detection based on developed bald eagle search method in CT scan images
Retraction: Women engagement in agriculture and human capital development in developing countries: An African sub-regional analysis
Andean peoples hunted and gathered long after they embraced farming
A pair of readers of histone H3K4 methylation recruit Polycomb repressive complex 2 to regulate photoperiodic flowering
Virtual reality reduces climate indifference by making distant locations feel psychologically close
Exploring the heterogeneity in depression through value attached to agency and communion
Depression is a heterogeneous disorder with varying expressions and underlying factors. This study adopted an interpersonal perspective, examining how individual differences in the value attached to agency (getting ahead) and communion (getting along) explain variability in depressive experiences. Specifically, we explored whether these individual differences explain what kind of situations can feed into depression and whether, together with the frustration of these dimensions, they can help explain the heterogeneity in types of depressive experience, behaviors, and symptoms. In this preregistered study, 510 participants prescreened for depressive symptoms reported negative affect in response to vignettes depicting agentic and communal frustration, and completed questionnaires on value attached to and frustration of agency and communion, types of depressive experiences, behaviors, and depressive symptoms. The results suggested that valuing agency and communion appears to increase individuals’ susceptibility to situations that frustrate their agentic and communal motives, respectively, although the context in which these frustrations occurred played a key role. However, at the trait level, it was overall frustration of these motives, not the value attached to them or their interaction, that was associated with depression. If anything, strongly valuing agency and communion may instead reflect adaptive psychological profiles as they showed some buffering effects. Moreover, agentic and communal frustration were overall not differentially related to different manifestations of depression, and may reflect a general frustration of motives or general negativity.
Ambient biothermal stress, preconceptional thyrotropin abnormalities, and the risk of preterm birth: a nationwide Chinese cohort study
Rationally designed, ACE2 mimetic binder to the SARS Cov-2 associated Spike protein for COVID-19 therapeutics and beyond
Methodology for predicting hospital admissions and evaluating recovery rates for coronavirus disease in Japan
In this study, we aimed to propose a method to predict the number of patients needing hospitalization using a combination of available technologies. We developed a method to predict the number of hospital admissions by combining a simple susceptible-infected-recovered (SIR) model with the relationship between the number of new positive cases and the number of hospital admissions, increasing the reliability of each prediction. The accuracy of the concordance between the actual number of patients and the predicted number of hospitalized patients was 99%. Owing to the high accuracy, we were also able to establish a method to evaluate recovery rates. This facilitated determination of the effectiveness of measures implemented throughout Japan to reduce the number of treatment days. The model developed in this study facilitates immediate estimation of the maximum number and timing of hospitalizations based on the peak of new positive cases. Moreover, it provides a statistically true value of the recovery rate required by the mathematical model for investigating countermeasures.
Observation of continuous time crystals and quasi-crystals in spin gases
Parietal gamma oscillations decreased in cognitive biotype of depression
An exploratory study on predicting HER2-positive expression status of breast cancer using ultrasound radiomics combined with machine learning models
Objective This study aimed to investigate the feasibility and potential value of predictive models for human epidermal growth factor receptor 2 (HER2)-positive status in breast cancer (BC) based on radiomics features from conventional ultrasound images and machine learning models. Methods Ultrasound images of 437 patients with surgically and pathologically confirmed BC were retrospectively analyzed, including 144 HER2-positive and 293 HER2-negative cases, which were used as a training and validation dataset. Key features highly correlated with HER2-positive status were identified and selected using the least absolute shrinkage and selection operator (LASSO), t-test, and principal component analysis (PCA). After the selection of relevant features, the dataset was randomly split into five equal parts for five-fold cross-validation to identify the optimal machine learning method and hyperparameters. A predictive model was then developed based on ultrasound imaging and radiomics features. After feature selection and model development, an additional cohort of 88 patients from other hospitals was utilized as an external validation dataset. The model’s internal validation performance was assessed through receiver operating characteristic (ROC) curve analysis, and metrics including area under the curve (AUC), sensitivity, and specificity were calculated. The generalizability of the model was further evaluated using the external validation. Results Five radiomics features were found to correlate with HER2-positive status in BC and used for model construction. Among the machine learning models generated, the best predictive model achieved area under the ROC curve values of 0.893 (95% confidence interval [CI], 0.860–0.920) in the training and validation dataset and 0.854 (95% CI, 0.775–0.927) in the external validation dataset. Conclusion Machine learning models based on ultrasound radiomics features have potential clinical value for predicting HER2-positive status in BC.
Optimizing the effect of TiO2 nano powder in water-based paint manufacturing using response surface methodology
Gastric lavage may not be representative of total microplastic ingestion for a wild passerine bird
Microplastic pollution has become a global concern and understanding its impact on wildlife requires effective sampling techniques that quantify exposure. In particular, non-lethal sampling techniques are needed for passerines for which microplastic exposure is poorly understood. In this study, we evaluated whether non-lethal proventricular gastric lavage can provide a representative sample of total microplastic ingestion in passerine birds. We sampled Brown-headed Cowbirds ( Molothrus ater ) (n = 105) from Government Canyon State Natural Area in San Antonio, Texas, United States (US). We performed gastric lavage to recover microplastics from each bird, before euthanizing them and dissecting gastrointestinal tracts. We recovered microplastics from 99% of birds. Gastric lavage recovered an average of 50.4% of ingested microplastics although recovery rate was highly variable (range: 0–100%, coefficient of variation: 59.52%), indicating much uncertainty in estimating individual total microplastic loads from gastric lavage. Sampling date influenced microplastic loads and recovery rates, which may be due to untested microplastic-environment interactions or may be an artifact of sampling conditions. Recovery rate was unaffected by time of day, bird age, sex, or body condition, or microplastic shape. Overall, our findings suggest that gastric lavage provides highly variable estimates of total gastrointestinal microplastics, and may be more appropriate for studies of recently ingested microplastics, only, that should be contained within the proventriculus.