Encouraging Dialogue around Social Issues with Latinx Students Through Literature Discussion and Culturally Relevant Literature

This teacher research study examines literature discussions with fourth and fifth grade Latinx students about books that reflect Latinx experiences. For this study, the following questions are explored (1) What social issues do students discuss in literature groups? (2) How do literary response strategies influence student dialogue? And (3) How does my theoretical frame influence my decision making as a teacher? Theories that informed the construction of the literature discussions and the decision making occurring throughout the study are examined closely. The theories intertwine and bridge education and students’ experiences as a resource in learning more about the educational setting. In this study, the discussions of students and the literature response strategies are explored as the data is analyzed to examine the student discussions around issues of immigration, family separation, borders, and so much more. The findings in this study indicate that the experiences of Latinx students are integral to the educational setting and an education that invites who they are enhances their learning experiences. Latinx students are eager for learning opportunities that invite their voices and stories. It is through the construction and assessment of the educational setting that educators can promote culturally responsive, relevant, and sustaining teaching experiences that go beyond the classroom setting. Latinx students build relationships with each other and their teachers as they engage in discussions that allow them to share and learn with each other. This study is a reminder of the crucial role teachers play in creating such powerful spaces and the value that Latinx students bring into the classroom when invited to discuss, engage, and create powerful learning experiences

Internally Controlled, Generic Real-Time PCR for Quantification and Multiplex Real-Time PCR with Serotype-Specific Probes for Serotyping of Dengue Virus Infections

Dengue has become a global public health problem and a sensitive diagnostic test for early phase detection can be life saving. An internally controlled, generic real-time PCR was developed and validated by testing serial dilutions of a DENV positive control RNA in the presence of a fixed amount of IC with results showing a good linearity (R2 = 0.9967) and a LOD of at least 1.95 × 104 copies/mL. Application of the generic PCR on 136 patient samples revealed a sensitivity of 95.8% and specificity of 100%. A newly developed multiplex real-time PCR with serotype-specific probes allowed the serotyping of DENV for 80 out of 92 (87%) generic real-time PCR positive patients. Combined these real-time PCRs offer a convenient diagnostic tool for the sensitive and specific quantification of DENV in clinical specimens with the possibility for serotyping

Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks

Federated Learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet-of-Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning efficiency, reduce communication overheads and enhance privacy for cyberattack detection systems. Challenges in implementation of FL in such systems include unavailability of labeled data and dissimilarity of data features in different IoT networks. In this paper, we propose a novel collaborative learning framework that leverages Transfer Learning (TL) to overcome these challenges. Particularly, we develop a novel collaborative learning approach that enables a target network with unlabeled data to effectively and quickly learn knowledge from a source network that possesses abundant labeled data. It is important that the state-of-the-art studies require the participated datasets of networks to have the same features, thus limiting the efficiency, flexibility as well as scalability of intrusion detection systems. However, our proposed framework can address these problems by exchanging the learning knowledge among various deep learning models, even when their datasets have different features. Extensive experiments on recent real-world cybersecurity datasets show that the proposed framework can improve more than 40% as compared to the state-of-the-art deep learning based approaches.Comment: 12 page

Electrophysiological Excitability and Parallel Fiber Synaptic Properties of Zebrin-Positive and -Negative Purkinje Cells in Lobule VIII of the Mouse Cerebellar Slice

Heterogeneous populations of cerebellar Purkinje cells (PCs) are arranged into separate longitudinal stripes, which have different topographic afferent and efferent axonal connections presumably involved in different functions, and also show different electrophysiological properties in firing pattern and synaptic plasticity. However, whether the differences in molecular expression that define heterogeneous PC populations affect their electrophysiological properties has not been much clarified. Since the expression pattern of many of such molecules, including glutamate transporter EAAT4, replicates that of aldolase C or zebrin II, we recorded from PCs of different “zebrin types” (zebrin-positive = aldolase C-positive = Z+; and Z−) in identified neighboring stripes in vermal lobule VIII, in which Z+ and Z− stripes occupy similar widths, in the Aldoc-Venus mouse cerebellar slice preparation. Regarding basic cellular electrophysiological properties, no significant differences were observed in input resistance or in occurrence probability of types of firing patterns between Z+ and Z− PCs. However, the firing frequency of the tonic firing type was higher in Z− PCs than in Z+ PCs. In the case of parallel fiber (PF)-PC synaptic transmission, no significant differences were observed between Z+ and Z− PCs in interval dependency of paired pulse facilitation or in time course of synaptic current measured without or with the blocker of glutamate receptor desensitization. These results indicate that different expression levels of the molecules that are associated with the zebrin type may affect the intrinsic firing property of PCs but not directly affect the basic electrophysiological properties of PF-PC synaptic transmission significantly in lobule VIII. The results suggest that the zebrin types of PCs in lobule VIII is linked with some intrinsic electrophysiological neuronal characteristics which affect the firing frequency of PCs. However, the results also suggest that the molecular expression differences linked with zebrin types of PCs does not much affect basic electrophysiological properties of PF-PC synaptic transmission in a physiological condition in lobule VIII

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues

This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In Part I, an extensive background of social distancing is provided, and enabling wireless technologies are thoroughly surveyed. In this Part II, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we discuss open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in their standard architectures/designs

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part I: Fundamentals and Enabling Technologies

Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In this Part I, we provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effect- in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. The companion paper Part II surveys other emerging and related technologies, such as machine learning, computer vision, thermal, ultrasound, etc., and discusses open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part I: Fundamentals and Enabling Technologies

Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In this Part I, we provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effect- in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. The companion paper Part II surveys other emerging and related technologies, such as machine learning, computer vision, thermal, ultrasound, etc., and discusses open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues

This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In Part I, an extensive background of social distancing is provided, and enabling wireless technologies are thoroughly surveyed. In this Part II, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we discuss open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in their standard architectures/designs