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\"Using the most extensive and currently available survey opinion data, this book empirically supports the argument that Latinos have emerged as a convergent panethnic political group, beyond the individual national origin identities dating to the time of the 1990 Latino National Political Survey when Mexican-Americans, Puerto Ricans, and Cuban Americans were treated conceptually as politically distinct groups. Replete with data and supplemented by an extensive online resource, this book offers scholars, students, and sophisticated general readers evidence and inspiration for understanding the dynamics of Latino politics in the US today\"-- Provided by publisher.

Dual language learners (DLLs) are sensitive to teachers’ language influence in early childhood classrooms. In this mixed methods study incorporating 53 teachers from 28 preschools in Northern California, we investigated the characteristics of teachers’ language use in preschools teaching Chinese–English and Spanish–English DLLs. We further examined the links of teachers’ language use to the DLLs’ expressive vocabulary in English and their heritage language (HL), controlling for home language exposure and other confounding variables. Finally, we conducted interviews with teachers to understand how they make meaning of their daily language practices. The sample of children consisted of 190 Chinese–English (N = 125) and Spanish–English (N = 65) DLLs (mean age = 48.3 months; 48% females). The teacher survey showed that most teachers spoke two or more languages and used a mix of English and their HL during their interactions with DLLs. The results of random-intercept models showed that teachers’ language use did not uniquely predict children’s vocabulary, controlling for family-level factors. However, the teachers with more years of teaching DLLs oversaw children with a higher HL vocabulary. The interview data revealed that teachers employ several strategies to communicate with DLLs and support HL maintenance. Our study reveals the multilingual backgrounds of preschool teachers and the rich language strategies they implement in multilingual classrooms. Future directions concerning the quality and development of teachers’ language use are discussed.

Family caregivers of patients with Alzheimer disease and related dementias (AD and ADRD) often experience high stress and are at high risk for depression. Technologically delivered therapy is attractive for AD and ADRD caregivers because of the time demands associated with in-person participation. We aimed to study the feasibility and conduct limited efficacy testing of a mobile app intervention delivering mentalizing imagery therapy (MIT) for family caregivers. A 4-week trial of the MIT app for family AD and ADRD caregivers was conducted to assess the feasibility of use and investigate changes in depression symptoms, mood, and caregiving experience. Semistructured interviews were conducted to characterize participants' perceived feasibility and benefits. A total of 17 of the 21 (80%) consented participants (mean age 67 years, range 54-79) utilized the app at least once and were further analyzed. Average usage of audio recordings was on 14 (SD 10) days out of 28 possible and comprised 29 (SD 28) individual sessions. There were improvements in depression with a large effect size for those who used the app at least moderately (P=.008), increases in positive mood postintervention (P<.05), and acute increases in mood following daily guided imagery practice (Stretching and Breathing, P<.001; Eye in the Center, P<.001; Nesting Doll, P=.002; Situation Solver, P=.003; and Life Globe, P=.006). Semistructured interviews revealed perceived benefits such as greater ability to remain \"centered\" despite caregiving challenges and positive reframing of the caregiver experience. App delivery of MIT is feasible for family AD and ADRD caregivers, including aging seniors. Results showed moderate to high usage of the app for a majority of users. Limited efficacy testing provides justification for studying the MIT app for AD and ADRD caregivers to improve mood and reduce depression in larger, controlled trials.

The UNESCO Universal Declaration on Bioethics and Human Rights (UDBHR) was a well-intentioned but controversial document. Some scholars appreciated UNESCO’s attempts at providing an internationally recognized set of ethical guidelines. Other critics maintained the UDBHR, in particular Article 12, was yet another representation of Western ideological hegemony and an ill-informed attempt at embracing universality at the expense of diversity. This paper acknowledged this debate but approaches the UDBHR in trans-cultural manner, framing the UDBHR not as an imposing guideline, but as a seed around which dialogue can coalesce. Such dialogue might engage similarities, differences, and nuances between different ethical perspectives, and consider why these phenomena exist in a way that avoids excessive generalizations or dichotomizations. Using the UDBHR as a topical anchor, this paper explores and juxtaposes ethical perspectives from the United States and South Korea through qualitative, semi-structured interviews with American and Korean students involved in the health sector. Interview questions prompted participants to reflect on their ethical values and application of those values in difficult dilemmas. Responses were organized based on the UDBHR principles and examined further. The UDBHR principles all emerged in both Korean and American participant responses, although some responses embodied unique nuances. Although Korean and American perspectives on topics such as benefit, harm, and transparency were relatively comparable, discussions around individual ethics and solidarity had slightly different flavors: Koreans commonly considered individual ethics in the context of COVID-19, while solidarity was the main focus in American discussions around COVID-19. This paper concludes with a reflection on these observed nuances, and potential implications for future global ethics education.

The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, \"trained\" networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale's principle), which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural activity patterns and behavior that can be modeled, and suggest a unified setting in which diverse cognitive computations and mechanisms can be studied.

Recently it has been proposed that information in working memory (WM) may not always be stored in persistent neuronal activity but can be maintained in ‘activity-silent’ hidden states, such as synaptic efficacies endowed with short-term synaptic plasticity. To test this idea computationally, we investigated recurrent neural network models trained to perform several WM-dependent tasks, in which WM representation emerges from learning and is not a priori assumed to depend on self-sustained persistent activity. We found that short-term synaptic plasticity can support the short-term maintenance of information, provided that the memory delay period is sufficiently short. However, in tasks that require actively manipulating information, persistent activity naturally emerges from learning, and the amount of persistent activity scales with the degree of manipulation required. These results shed insight into the current debate on WM encoding and suggest that persistent activity can vary markedly between short-term memory tasks with different cognitive demands.The role of persistent spiking activity in working memory has recently come under debate. Here the authors use biologically realistic recurrent neural networks to explain why the strength of persistent activity can vary markedly between tasks.

Acute upper gastrointestinal bleeding is a common medical emergency worldwide, a major cause of which are bleeding peptic ulcers. Endoscopic treatment and acid suppression with proton-pump inhibitors are cornerstones in the management of the disease, and both treatments have been shown to reduce mortality. The role of emergency surgery continues to diminish. In specialised centres, radiological intervention is increasingly used in patients with severe and recurrent bleeding who do not respond to endoscopic treatment. Despite these advances, mortality from the disorder has remained at around 10%. The disease often occurs in elderly patients with frequent comorbidities who use antiplatelet agents, non-steroidal anti-inflammatory drugs, and anticoagulants. The management of such patients, especially those at high cardiothrombotic risk who are on anticoagulants, is a challenge for clinicians. We summarise the published scientific literature about the management of patients with bleeding peptic ulcers, identify directions for future clinical research, and suggest how mortality can be reduced.

Clinical studies reveal changes in blood eosinophil counts and eosinophil cationic proteins that may serve as risk factors for human coronary heart diseases. Here we report an increase of blood or heart eosinophil counts in humans and mice after myocardial infarction (MI), mostly in the infarct region. Genetic or inducible depletion of eosinophils exacerbates cardiac dysfunction, cell death, and fibrosis post-MI, with concurrent acute increase of heart and chronic increase of splenic neutrophils and monocytes. Mechanistic studies reveal roles of eosinophil IL4 and cationic protein mEar1 in blocking H 2 O 2 - and hypoxia-induced mouse and human cardiomyocyte death, TGF-β-induced cardiac fibroblast Smad2/3 activation, and TNF-α-induced neutrophil adhesion on the heart endothelial cell monolayer. In vitro-cultured eosinophils from WT mice or recombinant mEar1 protein, but not eosinophils from IL4-deficient mice, effectively correct exacerbated cardiac dysfunctions in eosinophil-deficient ∆dblGATA mice. This study establishes a cardioprotective role of eosinophils in post-MI hearts. Blood eosinophil (EOS) counts may serve as risk factors for human coronary heart diseases. Here the authors show that increased circulating and myocardial EOS after myocardial infarction play a cardioprotective role by reducing cardiomyocyte death, cardiac fibroblast activation and fibrosis, and endothelium activation-mediated inflammatory cell accumulation.

Chest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based diagnostic error and lack of diagnostic expertise in areas of the world where radiologists are not available. Recently, deep learning approaches have been able to achieve expert-level performance in medical image interpretation tasks, powered by large network architectures and fueled by the emergence of large labeled datasets. The purpose of this study is to investigate the performance of a deep learning algorithm on the detection of pathologies in chest radiographs compared with practicing radiologists. We developed CheXNeXt, a convolutional neural network to concurrently detect the presence of 14 different pathologies, including pneumonia, pleural effusion, pulmonary masses, and nodules in frontal-view chest radiographs. CheXNeXt was trained and internally validated on the ChestX-ray8 dataset, with a held-out validation set consisting of 420 images, sampled to contain at least 50 cases of each of the original pathology labels. On this validation set, the majority vote of a panel of 3 board-certified cardiothoracic specialist radiologists served as reference standard. We compared CheXNeXt's discriminative performance on the validation set to the performance of 9 radiologists using the area under the receiver operating characteristic curve (AUC). The radiologists included 6 board-certified radiologists (average experience 12 years, range 4-28 years) and 3 senior radiology residents, from 3 academic institutions. We found that CheXNeXt achieved radiologist-level performance on 11 pathologies and did not achieve radiologist-level performance on 3 pathologies. The radiologists achieved statistically significantly higher AUC performance on cardiomegaly, emphysema, and hiatal hernia, with AUCs of 0.888 (95% confidence interval [CI] 0.863-0.910), 0.911 (95% CI 0.866-0.947), and 0.985 (95% CI 0.974-0.991), respectively, whereas CheXNeXt's AUCs were 0.831 (95% CI 0.790-0.870), 0.704 (95% CI 0.567-0.833), and 0.851 (95% CI 0.785-0.909), respectively. CheXNeXt performed better than radiologists in detecting atelectasis, with an AUC of 0.862 (95% CI 0.825-0.895), statistically significantly higher than radiologists' AUC of 0.808 (95% CI 0.777-0.838); there were no statistically significant differences in AUCs for the other 10 pathologies. The average time to interpret the 420 images in the validation set was substantially longer for the radiologists (240 minutes) than for CheXNeXt (1.5 minutes). The main limitations of our study are that neither CheXNeXt nor the radiologists were permitted to use patient history or review prior examinations and that evaluation was limited to a dataset from a single institution. In this study, we developed and validated a deep learning algorithm that classified clinically important abnormalities in chest radiographs at a performance level comparable to practicing radiologists. Once tested prospectively in clinical settings, the algorithm could have the potential to expand patient access to chest radiograph diagnostics.