Understanding SEL to Create a Sense of Belonging: The Role Teachers Play in Addressing Students’ Social and Emotional Well-Being

This inquiry examines the benefits of utilizing social-emotional learning in today’s classrooms to address students’ academic and social-emotional needs. Now, more than ever, as teachers have transitioned from remote instruction to face-to-face or blended learning, particular emphasis must be placed on addressing students’ social and emotional needs (Fagell, 2021) while addressing learning loss. Social-emotional learning (SEL) may have been in place in school systems before the COVID-19 pandemic; however, understanding SEL and the instructional practices that contribute to developing a learning environment that nourishes students’ sense of belonging is necessary to inform teacher pedagogical practices post-pandemic. Teachers play a critical role in establishing a learning environment suitable for nurturing students’ sense of belonging (Jennings & Greenberg, 2009). As teachers attempt to provide a sense of normalcy in the classroom, they must skillfully balance designing instruction, creating an inviting classroom community, and helping students “feel” safe and supported

Volatile compound diversity and conserved alarm behaviour in Triatoma dimidiata

Background: Triatoma dimidiata (Latreille) is a key vector complex of Trypanosoma cruzi, etiologic agent of Chagas disease, as it spans North, Central, and South America. Although morphological and genetic studies clearly indicate existence of at least five clades within the species, there has been no robust or systematic revision, or appropriate nomenclature change for species within the complex. Three of the clades (haplogroups) are distributed in Mexico, and recent evidence attests to dispersal of clades across previously "presumed"monotypic geographic regions. Evidence of niche conservatism among sister species of this complex suggests that geographic dispersal is possible for non-sympatric populations, although no information is available on the behavioural aspects of potential interclade interactions, for instance whether differentiation of chemical signaling or response to these signals could impede communication among the haplogroups. Methods: Volatiles emitted by disturbed bugs, Brindley's (BGs), and metasternal (MGs) glands were identified using solid-phase micro-extraction (SPME) and gas chromatography coupled mass spectrometry (GC-MS). Volatile compounds emitted by BGs and MGs, and those secreted by disturbed nymphs and adults, of the three Mexican T. dimidiata haplogroups were tested for avoidance behaviour by conspecific nymphs and adults using an olfactometer. Results: Triatoma dimidiata haplogroups all have three age-related alarm responses: absence of response by early stage nymphs, stage-specific response by 4-5th stage nymphs, and a shared 4-5th nymph and adult response to adult compounds. Disturbed bugs released 15 to 24 compounds depending on the haplogroup, among which were three pyrazines, the first report of these organoleptics in Triatominae. Isobutyric acid from BGs was the most abundant molecule in the response in all haplogroups, in addition to 15 (h1) to 21 (h2 and h3) MG compounds. Avoidance behaviour of disturbed bugs and volatiles emitted by BGs were haplogroup specific, while those from the MG were not. Conclusions: Discriminant and cluster analysis of BG +MG compounds indicate significant separation among the three haplogroups, while alarm response compounds were similar between h2 and h3, both distinct from h1. This latter haplogroup is ancestral phylogenetically to the other two. Our results suggest that alarm responses are a conserved behaviour in the Triatoma dimidiata complex.Fil: May Concha, Irving Jesus. Provincia de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Universidad Autónoma de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción; Argentina. Instituto Nacional de Salud Pública; MéxicoFil: Rojas, Julio C.. El Colegio de la Frontera Sur; MéxicoFil: Cruz López, Leopoldo. El Colegio de la Frontera Sur; MéxicoFil: Ibarra-Cerdeña, Carlos N.. Instituto Politécnico Nacional. Centro de Investigación y de Estudios Avanzados; MéxicoFil: Ramsey, Janine. Instituto Nacional de Salud Pública; Méxic

Computing with cells: membrane systems - some complexity issues.

Membrane computing is a branch of natural computing which abstracts computing models from the structure and the functioning of the living cell. The main ingredients of membrane systems, called P systems, are (i) the membrane structure, which consists of a hierarchical arrangements of membranes which delimit compartments where (ii) multisets of symbols, called objects, evolve according to (iii) sets of rules which are localised and associated with compartments. By using the rules in a nondeterministic/deterministic maximally parallel manner, transitions between the system configurations can be obtained. A sequence of transitions is a computation of how the system is evolving. Various ways of controlling the transfer of objects from one membrane to another and applying the rules, as well as possibilities to dissolve, divide or create membranes have been studied. Membrane systems have a great potential for implementing massively concurrent systems in an efficient way that would allow us to solve currently intractable problems once future biotechnology gives way to a practical bio-realization. In this paper we survey some interesting and fundamental complexity issues such as universality vs. nonuniversality, determinism vs. nondeterminism, membrane and alphabet size hierarchies, characterizations of context-sensitive languages and other language classes and various notions of parallelism

Dynamic Patterns of Transcript Abundance of Transposable Element Families in Maize.

Transposable Elements (TEs) are mobile elements that contribute the majority of DNA sequences in the maize genome. Due to their repetitive nature, genomic studies of TEs are complicated by the difficulty of properly attributing multi-mapped short reads to specific genomic loci. Here, we utilize a method to attribute RNA-seq reads to TE families rather than particular loci in order to characterize transcript abundance for TE families in the maize genome. We applied this method to assess per-family expression of transposable elements in >800 published RNA-seq libraries representing a range of maize development, genotypes, and hybrids. While a relatively small proportion of TE families are transcribed, expression is highly dynamic with most families exhibiting tissue-specific expression. A large number of TE families were specifically detected in pollen and endosperm, consistent with reproductive dynamics that maintain silencing of TEs in the germ line. We find that B73 transcript abundance is a poor predictor of TE expression in other genotypes and that transcript levels can differ even for shared TEs. Finally, by assessing recombinant inbred line and hybrid transcriptomes, complex patterns of TE transcript abundance across genotypes emerged. Taken together, this study reveals a dynamic contribution of TEs to maize transcriptomes

Using Biotic Interaction Networks for Prediction in Biodiversity and Emerging Diseases

Networks offer a powerful tool for understanding and visualizing inter-species interactions within an ecology. Previously considered examples, such as trophic networks, are just representations of experimentally observed direct interactions. However, species interactions are so rich and complex it is not feasible to directly observe more than a small fraction. In this paper, using data mining techniques, we show how potential interactions can be inferred from geographic data, rather than by direct observation. An important application area for such a methodology is that of emerging diseases, where, often, little is known about inter-species interactions, such as between vectors and reservoirs. Here, we show how using geographic data, biotic interaction networks that model statistical dependencies between species distributions can be used to infer and understand inter-species interactions. Furthermore, we show how such networks can be used to build prediction models. For example, for predicting the most important reservoirs of a disease, or the degree of disease risk associated with a geographical area. We illustrate the general methodology by considering an important emerging disease - Leishmaniasis. This data mining approach allows for the use of geographic data to construct inferential biotic interaction networks which can then be used to build prediction models with a wide range of applications in ecology, biodiversity and emerging diseases

More than colour attraction: behavioural functions of flower patterns

ReviewFlower patterns are thought to influence foraging decisions of insect pollinators. However, the resolution of insect compound eyes is poor. Insects perceive flower patterns only from short distances when they initiate landing or search for reward on the flower. From further away flower displays jointly form largersized patterns within the visual scene that will guide an insect’s flight behaviour. Chromatic and achromatic cues in such patterns may help insects to find, approach and learn rewarded locations in a flower patch, bringing them close enough to individual flowers. Flight trajectories and the spatial resolution of chromatic and achromatic vision in insects determine the effectiveness of floral displays, and both need to be considered in studies of plant-pollinator communication.BBSR