Research centers in transition: patterns of convergence and diversity

Governments continue to play a central role in the way research is conducted and organized by defining new models for research centers. How do existing research centers adapt to changes in their environment? Institutional theory suggests that organizations pursue efficiency and legitimacy by conforming to isomorphic pressures in their organizational field, which will eventually lead to a reduction of diversity in organizational practices and strategies. Resource-dependence theory assumes a more active agency and calls attention to the diverse strategic responses of organizations to institutional processes. Based on funding microdata and qualitative information at center level, this study undertakes to analyze changes in two populations of Spanish research centers (government laboratories and technology centers) in a time of evolving policy paradigms, emergence of new models for research centers, and increasing competition in the field of R&D. We find that a large share of the existing government laboratories and technology centers have progressively conformed to a funding strategy based on diversifying sources and increasing competitive public funding, although both populations are still characterized by some degree of internal diversity regarding funding portfolios. Structural heterogeneity also remains as regards management practices such as research planning and agenda setting.     

The Motives Underlying Stereotype-Based Discrimination Against Members of Stigmatized Groups

We argue that the motivations that underlie stereotype-based discrimination against racial minorities and other stigmatized groups often fail to meet standard criteria for rational judgments. Stereotyping of such groups is often driven by threats to one’s self-esteem and a desire to rationalize inequality, and declines when the perceiver is motivated to be accurate. Also, Bayesian racism—the belief that it is rational to discriminate against individuals based on stereotypes about their racial group—correlates highly with negative feelings toward minorities and the desire to keep low-status groups in their place, and correlates negatively with indices of rational thinking. The motives that drive social judgments call into question whether people engage in stereotype-based discrimination for rational reasons.     

The 2D:4D Ratio as a Predictor of the Risk of Recidivism after Court‐mandated Intervention Program for Intimate Partner Violence Perpetrators

Differences in aggressive behavior could be partially explained by differential prenatal exposure to testosterone (T). A peripheral marker of prenatal T exposure is the 2D:4D ratio, which has shown a negative correlation with self‐reported aggression in violent men. This study tested the direct association of the 2D:4D ratio with anger expression and the risk of recidivism in intimate partner violence (IPV) perpetrators after psychotherapeutic intervention program. The sample consisted of IPV perpetrators, whose 2D:4D ratio was measured before the intervention. Moreover, after the intervention, anger expression and risk of recidivism in IPV were assessed. Smaller 2D:4D ratio, especially of the right hand, was related to higher anger expression and risk of recidivism in IPV perpetrators. The contribution of this prenatal marker together with other psychobiological variables could play a key role in the propensity to react aggressively when coping with environmental demands.     

A Morphological and Morphometric Study of Bite Marks Caused by Mice (Mus Musculus) on Different Baits for Forensic Purposes

In animal bites, the dental attributes can be fundamental in identifying the marks made by various species on different matrices. Although rodent bite marks have been studied in the context of postmortem interference, little research has used different baits to analyze these marks linking not only specific behavior patterns but also the possibility of structural damage. Twenty mice (Mus musculus) were exposed to different baits to study their bite marks in a controlled model. The known pattern of parallel and multiple grooves has been seen in all baits, but polyvinyl chloride and fiber‐optic cable were significantly different between each other and the other baits. Some baits showed patterns of anchorage of the upper incisors and space between the lower incisors when gnawing. This technical note represents a novel model of analysis where veterinarians and/or dentists may be asked to give an opinion on alleged animal bite marks.     

Why is Family Violence Lower Among Mexican Immigrants? The Protective Features of Mexican Culture

Although immigrants tend to be less involved in crime than the native-born, less is known about whether immigration is protective regarding family violence and, if so, why. This is especially problematic given that some cultural features of immigrants, such as machismo, may increase family violence. Using a random sample of adults in El Paso County, Texas, the present study finds that family violence is substantially lower among first generation Mexican immigrants compared to 1.5 generation immigrants, second generation Americans and third generation or higher Americans. Higher levels of acculturation to Mexico among first generation immigrants partially mediated, or explained, this finding. However, familism and machismo were not higher among first generation Mexican immigrants; and, while lower among first generation immigrants, acculturation to the US was not associated with higher levels of family violence. Implications of these findings are discussed.     

Evaluation and classification of fentanyl-related compounds using EC-SERS and machine learning

Multiple analytical techniques for the screening of fentanyl-related compounds exist. High discriminatory methods such as GC–MS and LC–MS are expensive, time-consuming, and less amenable to onsite analysis. Raman spectroscopy provides a rapid, inexpensive alternative. Raman variants such as electrochemical surface-enhanced Raman scattering (EC-SERS) can provide signal enhancements with 10¹⁰ magnitudes, allowing for the detection of low-concentration analytes, otherwise undetected using conventional Raman. Library search algorithms embedded in instruments utilizing SERS may suffer from accuracy when multicomponent mixtures involving fentanyl derivatives are analyzed. The complexing of machine learning techniques to Raman spectra demonstrates an improvement in the discrimination of drugs even when present in multicomponent mixtures of various ratios. Additionally, these algorithms are capable of identifying spectral features difficult to detect by manual comparisons. Therefore, the goal of this study was to evaluate fentanyl-related compounds and other drugs of abuse using EC-SERS and to process the acquired data using machine learning—convolutional neural networks (CNN). The CNN was created using Keras v 2.4.0 with Tensorflow v 2.9.1 backend. In-house binary mixtures and authentic adjudicated case samples were used to evaluate the created machine-learning models. The overall accuracy of the model was 98.4 ± 0.1% after 10-fold cross-validation. The correct identification for the in-house binary mixtures was 92%, while the authentic case samples were 85%. The high accuracies achieved in this study demonstrate the advantage of using machine learning to process spectral data when screening seized drug materials comprised of multiple components.