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Background. This article describes the redesign, implementation, and students' experiences with an elementary mathematics teaching methods course in the Pacific Northwest of the United States that was forced online due to COVID-19 restrictions. We share course design principles, changes to reflect online teaching, and students' experiences of relational equity, "the care and consideration students and teachers must take with each other to establish and maintain a culture of equitable learning." Methods: This study took place online. 50 out of a cohort of 54 pre-service elementary teacher candidates participated. Our methods included analysis of course documents, analytic memoing, and analysis of students' responses to online open response surveys. We qualitatively analyzed the responses from the surveys, coding for a priori and emergent themes (Charmaz, 1995; Emerson et al., 2011; Miles et al., 2019). Findings: The results share the ways the instructors modified the course for online instruction and indicate that our participants experienced successes or challenges to enacting relational equity in the following: (i) curriculum and assessments, (ii) instructors, (iii) generic reference to the course, (iv) technology limits, and (v) future teaching. Contribution: This work sheds light on how two mathematics teacher educators redesigned their course for online instruction while centering and modeling relational equity teaching practices.

Background: This article describes teachers' equity concerns related to teaching mathematics online as a result of the COVID-19 pivot to online/distance-based instruction. COVID-19 restrictions forced the creation of virtual education contexts that magnified existing equity issues related to access to technology and the challenges of providing inquiry-based, student-centered instruction. Methods: This study took place under conditions promulgated by the COVID-19 pandemic. Nine teachers agreed to share their observations and experiences with the sudden switch to teaching mathematics online. Our methods included two online open response surveys. We qualitatively analyzed the responses from the surveys, coding for a priori, and emergent themes (Charmaz, 1995; Emerson et al., 2011). Findings: The results indicate that our participants experienced concerns for students and families struggling to effectively engage with and access online education, and shared the practices and online tools they found most and least helpful in enacting equitable instruction. Contribution: This work sheds light on how skilled and caring teachers leveraged prior experiences, collegial support, and technological tools to meet the challenges brought by the sudden transition to online mathematics pedagogy.

There is a growing need for teacher preparation courses that support teacher candidates (TCs) in meeting requirements to obtain a supplemental teaching credential. In particular, given the demands for secondary mathematics teachers, developing effective cross-disciplinary coursework within preparation programs is timely and important work. Our study examines the development and evolution of TCs' experiences in a mathematics teaching methods course designed for TCs seeking a supplemental credential. We describe the design principles and practices used to create, and modify, a course to meet (a) state licensure requirements, (b) the goals of the teacher education program, and (c) the individual needs of the TCs. Using survey and interview data, we report on TCs' experiences with the course and their future plans for obtaining a supplemental teaching credential in mathematics. Our study contributes to discussions on teacher preparation for interdisciplinary teaching, licensure requirements, conceptual and practical tools for teaching, and effective course design.

As institutions strive to improve teaching and learning for all, educators must consider equitable instruction. This includes equitable distributions of authority and agency among students. The authors define distribution of authority as shared opportunities for decision-making in enacting mathematical tasks and agency as the power to carry out these self-made decisions. Equitable distributions of authority and agency can be enhanced in mathematics public sensemaking classrooms where students participate in discourse as active members of the classroom. In public sensemaking classrooms, students understand and acknowledge one another's ideas, present and revise arguments, and take risks by sharing ideas. This study investigates one group of students and how they positioned one another during mathematical groupwork in a public sensemaking classroom as well as how the positioning impacted the distribution of agency and authority. At the time of data collection, the students attended a school for grades 6-12 situated in an urban public school that focuses largely on preparing students for careers in the health sciences and other health-related fields. Data was collected as video footage and analyzed using a priori codes. Analyses indicate that one student replicates the role of teacher by redistributing authority and agency back to other students through self-removal. This is contrasted with other motivations for self-removal while doing mathematics in a team. The findings inform researchers and classroom teachers of potential metacognitive awareness of equity between students, positioning patterns that may occur during collaborative groupwork, and the effectiveness of public sensemaking classrooms on distributing authority and agency equitably during groupwork.

Introduction Psychiatric drugs, including antipsychotics and antidepressants, are widely prescribed, even in young and adolescent populations at early or subthreshold disease stages. However, their impact on brain structure remains elusive. Elucidating the relationship between psychotropic medication and structural brain changes could enhance the understanding of the potential benefits and risks associated with such treatment. Objectives Investigation of the associations between psychiatric drug intake and longitudinal grey matter volume (GMV) changes in a transdiagnostic sample of young individuals at early stages of psychosis or depression using an unbiased data-driven approach. Methods The study sample comprised 247 participants (mean [SD] age = 25.06 [6.13] years, 50.61% male), consisting of young, minimally medicated individuals at clinical high-risk states for psychosis, individuals with recent-onset depression or psychosis, and healthy control individuals. Structural magnetic resonance imaging was used to obtain whole-brain voxel-wise GMV for all participants at two timepoints (mean [SD] time between scans = 11.15 [4.93] months). The multivariate sparse partial least squares (SPLS) algorithm (Monteiro et al. JNMEDT 2016; 271:182-194) was embedded in a nested cross-validation framework to identify parsimonious associations between the cumulative intake of psychiatric drugs, including commonly prescribed antipsychotics and antidepressants, and change in GMV between both timepoints, while additionally factoring in age, sex, and diagnosis. Furthermore, we correlated the retrieved SPLS results to personality domains (NEO-FFI) and childhood trauma (CTQ). Results SPLS analysis revealed significant associations between the antipsychotic classes of benzamides, butyrophenones and thioxanthenes and longitudinal GMV decreases in cortical regions including the insula, posterior superior temporal sulcus as well as cingulate, postcentral, precentral, orbital and frontal gyri (Figure 1A-C). These brain regions corresponded most closely to the dorsal and ventral attention, somatomotor, salience and default network (Figure 1D). Furthermore, the medication signature was negatively associated with the personality domains extraversion, agreeableness and conscientiousness and positively associated with the CTQ domains emotional and physical neglect. Image: Conclusions Psychiatric drug intake over a period of one year was linked to distinct GMV reductions in key cortical hubs. These patterns were already visible in young individuals at early or subthreshold stages of mental illness and were further linked to childhood neglect and personality traits. Hence, a better and more in-depth understanding of the structural brain implications of medicating young and adolescent individuals might lead to more cautious, sustainable and targeted treatment strategies. Disclosure of Interest None Declared

Introduction An early and comprehensive neurobiological characterization of severe mental disorders could elucidate mechanistic pathways, aid the development of novel therapeutics, and therefore enable timely and targeted intervention in at-risk youth and young adults. Therefore, we present an unsupervised transdiagnostic machine learning approach to investigate shared and distinct patterns of early-stage depressive and psychotic disorders on multiple clinical and neurobiological levels. Objectives To derive multi-level neurobiological and clinical signatures of early-stage affective and psychotic disorders in adolescents and young adults. Methods From the multicenter prospective European PRONIA cohort, we acquired data from 678 individuals (51% female) comprising young, minimally medicated in- and outpatients with clinical high-risk (CHR) states for psychosis, with recent-onset depression (ROD) or psychosis (ROP), and healthy control (HC) individuals. Within repeated nested cross-validation frameworks, we employed Sparse Partial Least Squares Analysis to detect associations between blood markers and grey matter volume (GMV), followed by support vector machine prediction of these signatures using biographical, clinical, neurocognitive, proteomic, and functional data. Results Our results demonstrated a psychosis staging signature separating ROP from CHR individuals via GMV patterns in the cortico-thalamo-cerebellar circuitry with a blood marker set of elevated of IL-6, TNF-α and CRP (ρ = 0.272; P = 0.002). A depression signature separated ROD from HC individuals via altered GMV in the limbic system with a blood marker set of elevated IL-1ß, IL-2, IL-4, S100B and BDNF (ρ = 0.186; P = 0.021). Only the psychosis staging signature showed a distinct proteomic enrichment regarding innate immune response, abnormal neutrophil function, cellular senescence, and anti-inflammatory drugs (Balanced Accuracy (BAC) = 87.73%; Area Under the Curve (AUC) = 0.94). Childhood trauma differentially predicted psychosis and depression signatures, while past level of functioning, personality and quality of life was predictive of both signatures (BAC = 67.19-78.00%; AUC = 0.71-0.83). Image: Image 2: Image 3: Conclusions Psychosis and depression exhibit distinct multi-level signatures evident in early disease stages. Enhanced insight into these signatures could help delineate individual trajectories and potentially new mechanisms for pharmacological treatment. Disclosure of Interest None Declared

Summary: Background: The increasing resistance of Enterobacterales to third-generation cephalosporins and carbapenems in sub-Saharan Africa (SSA) is a major public health concern. We did a systematic review and meta-analysis of studies to estimate the carriage prevalence of Enterobacterales not susceptible to third-generation cephalosporins or carbapenems among paediatric populations in SSA. Methods: We performed a systematic literature review and meta-analysis of cross-sectional and cohort studies to estimate the prevalence of childhood (0–18 years old) carriage of extended-spectrum cephalosporin-resistant Enterobacterales (ESCR-E) or carbapenem-resistant Enterobacterales (CRE) in SSA. Medline, EMBASE and the Cochrane Library were searched for studies published from 1 January 2005 to 1 June 2022. Studies with

Summary: Background: The burden of antimicrobial resistance (AMR) has been estimated to be the highest in sub-Saharan Africa (SSA). The current study estimated the proportion of drug-resistant Enterobacterales causing infections in SSA children. Methods: We searched MEDLINE/PubMed, Embase and the Cochrane Library to identify retrospective and prospective studies published from 01/01/2005 to 01/06/2022 reporting AMR of Enterobacterales causing infections in sub-Saharan children (0–18 years old). Studies were excluded if they had unclear documentation of antimicrobial susceptibility testing methods or fewer than ten observations per bacteria. Data extraction and quality appraisal were conducted by two authors independently. The primary outcome was the proportion of Enterobacterales resistant to antibiotics commonly used in paediatrics. Proportions were combined across studies using mixed-effects logistic regression models per bacteria and per antibiotic. Between-study heterogeneity was assessed using the I2 statistic. The protocol was registered with PROSPERO (CRD42021260157). Findings: After screening 1111 records, 122 relevant studies were included, providing data on more than 30,000 blood, urine and stool isolates. Escherichia coli and Klebsiella spp. were the predominant species, both presenting high proportions of resistance to third-generation cephalosporins, especially in blood cultures: 40.6% (95% CI: 27.7%–55%; I2: 85.7%, number of isolates (n): 1032) and 84.9% (72.8%–92.2%; I2: 94.1%, n: 2067), respectively. High proportions of resistance to other commonly used antibiotics were also observed. E. coli had high proportions of resistance, especially for ampicillin (92.5%; 95% CI: 76.4%–97.9%; I2: 89.8%, n: 888) and gentamicin (42.7%; 95% CI: 30%–56.5%; I2: 71.9%, n: 968). Gentamicin-resistant Klebsiella spp. were also frequently reported (77.6%; 95% CI: 65.5%–86.3%; I2: 91.6%, n: 1886). Interpretation: High proportions of resistance to antibiotics commonly used for empirical treatment of infectious syndromes were found for Enterobacterales in sub-Saharan children. There is a critical need to better identify local patterns of AMR to inform and update clinical guidelines for better treatment outcomes. Funding: No funding was received.

Abstract Structural MRI studies in first-episode psychosis and the clinical high-risk state have consistently shown volumetric abnormalities. Aim of the present study was to introduce radiomics texture features in identification of psychosis. Radiomics texture features describe the interrelationship between voxel intensities across multiple spatial scales capturing the hidden information of underlying disease dynamics in addition to volumetric changes. Structural MR images were acquired from 77 first-episode psychosis (FEP) patients, 58 clinical high-risk subjects with no later transition to psychosis (CHR_NT), 15 clinical high-risk subjects with later transition (CHR_T), and 44 healthy controls (HC). Radiomics texture features were extracted from non-segmented images, and two-classification schemas were performed for the identification of FEP vs. HC and FEP vs. CHR_NT. The group of CHR_T was used as external validation in both schemas. The classification of a subject’s clinical status was predicted by importing separately (a) the difference of entropy feature map and (b) the contrast feature map, resulting in classification balanced accuracy above 72% in both analyses. The proposed framework enhances the classification decision for FEP, CHR_NT, and HC subjects, verifies diagnosis-relevant features and may potentially contribute to identification of structural biomarkers for psychosis, beyond and above volumetric brain changes.

What does it mean to be "good-at-math," and how is it determined? Cobb et al. (2009) defined the normative identity of mathematics classrooms as the obligations that students must meet to be considered good-at-math. Obligations are negotiated between teachers and students through series of bids. Normative identities reveal distributions of agency and authority within classrooms, which affect learning opportunities for students. Traditionally, mathematics teachers held the predominance of agency and authority in classrooms. Research supports shifting toward more equitable teaching and learning (e.g., National Council of Teachers of Mathematics, 2018). Clear examples of enacting and supporting changes are helpful. This article shares how sixth-grade students and their teacher co-constructed good-at-math to invite and obligate students to become active agents in mathematical argumentation.

As members of a research group taking initial steps for creating mathematics curriculum in an Indigenous language (Yakama Ichishkíin), we engaged with an unanticipated outcome: the ways Indigenous identities and homelands are fractionated, as part of ongoing colonizing harm. Our work centers on how mathematics instruction can help heal, by recognizing, valuing, and inviting learners to wholly engage in mathematics in ways that honor and reflect Indigenous ways of being and knowing.

The beginning of the school year is a time of intense work for students and teachers, especially if it means learning mathematics in new ways (Ruef 2016). Students need guidance and support if they are shifting beliefs about what it means to do mathematics. This article shows how Torres and her students co-created new ways to be good at mathematics. It describes an extended lesson and concludes with a menu of scaffolds to support risk taking.

This article describes the initial work and next steps for the development of a mathematics curriculum in Ichishkíin, an Indigenous Yakama language. Framed by the Ichishkíin concept of pinak'inut'áwaas (mirror/window), our work seeks to answer the following questions: How are mathematical concepts represented in Yakama culture, and how is Yakama culture reflected in mathematical concepts? How can we respectfully and reflectively craft new Ichishkíin words to express mathematical concepts not reflected in Yakama culture? This is decolonizing work, and joins a growing body of Culturally Sustaining and Culturally Reviving Pedagogies, which center teaching and learning on the lived experiences of students while critiquing culture for the ways it both sustains and diminishes. Given the dominant nature of mathematical culture, a key tenet of our project is privileging Yakama culture and the development of Ichishkíin language. We discuss the values and respect that are foundational to our ongoing collaboration to craft curriculum.

Mathematics Teacher Educators (MTEs) help preservice teachers in transitioning from students to teachers of mathematics. They support PSTs in shifting what they notice and envision to align with the collective vision encoded in the AMTE and NCTM standards. This study analyzes drawings and descriptions completed at the beginning and end of a one-year teacher education program--snapshots depicting optimized visions of teaching and learning mathematics. This study analyzed drawings-and-descriptions by cohort and by participants. The findings suggest that the task can be used as formative assessment to inform supports for specific PSTs such as choosing a cooperating teacher or coursework that challenges problematic beliefs. It can also be used as summative assessment to inform revision of coursework for the next cohort.

Abstract Continued cannabis use (CCu) is an important predictor for poor long-term outcomes in psychosis and clinically high-risk patients, but no generalizable model has hitherto been tested for its ability to predict CCu in these vulnerable patient groups. In the current study, we investigated how structured clinical and cognitive assessments and structural magnetic resonance imaging (sMRI) contributed to the prediction of CCu in a group of 109 patients with recent-onset psychosis (ROP). We tested the generalizability of our predictors in 73 patients at clinical high-risk for psychosis (CHR). Here, CCu was defined as any cannabis consumption between baseline and 9-month follow-up, as assessed in structured interviews. All patients reported lifetime cannabis use at baseline. Data from clinical assessment alone correctly classified 73% (p 0.093), and their addition to the interview-based predictor via stacking did not improve prediction significantly, either in the ROP or CHR groups (ps > 0.065). Lower functioning, specific substance use patterns, urbanicity and a lack of other coping strategies contributed reliably to the prediction of CCu and might thus represent important factors for guiding preventative efforts. Our results suggest that it may be possible to identify by clinical measures those psychosis-spectrum patients at high risk for CCu, potentially allowing to improve clinical care through targeted interventions. However, our model needs further testing in larger samples including more diverse clinical populations before being transferred into clinical practice.

Abstract Age plays a crucial role in the performance of schizophrenia vs. controls (SZ-HC) neuroimaging-based machine learning (ML) models as the accuracy of identifying first-episode psychosis from controls is poor compared to chronic patients. Resolving whether this finding reflects longitudinal progression in a disorder-specific brain pattern or a systematic but non-disorder-specific deviation from a normal brain aging (BA) trajectory in schizophrenia would help the clinical translation of diagnostic ML models. We trained two ML models on structural MRI data: an SZ-HC model based on 70 schizophrenia patients and 74 controls and a BA model (based on 561 healthy individuals, age range = 66 years). We then investigated the two models’ predictions in the naturalistic longitudinal Northern Finland Birth Cohort 1966 (NFBC1966) following 29 schizophrenia and 61 controls for nine years. The SZ-HC model’s schizophrenia-specificity was further assessed by utilizing independent validation (62 schizophrenia, 95 controls) and depression samples (203 depression, 203 controls). We found better performance at the NFBC1966 follow-up (sensitivity = 75.9%, specificity = 83.6%) compared to the baseline (sensitivity = 58.6%, specificity = 86.9%). This finding resulted from progression in disorder-specific pattern expression in schizophrenia and was not explained by concomitant acceleration of brain aging. The disorder-specific pattern’s progression reflected longitudinal changes in cognition, outcomes, and local brain changes, while BA captured treatment-related and global brain alterations. The SZ-HC model was also generalizable to independent schizophrenia validation samples but classified depression as control subjects. Our research underlines the importance of taking account of longitudinal progression in a disorder-specific pattern in schizophrenia when developing ML classifiers for different age groups.

Abstract Negative symptoms occur frequently in individuals at clinical high risk (CHR) for psychosis and contribute to functional impairments. The aim of this study was to predict negative symptom severity in CHR after 9 months. Predictive models either included baseline negative symptoms measured with the Structured Interview for Psychosis-Risk Syndromes (SIPS-N), whole-brain gyrification, or both to forecast negative symptoms of at least moderate severity in 94 CHR. We also conducted sequential risk stratification to stratify CHR into different risk groups based on the SIPS-N and gyrification model. Additionally, we assessed the models’ ability to predict functional outcomes in CHR and their transdiagnostic generalizability to predict negative symptoms in 96 patients with recent-onset psychosis (ROP) and 97 patients with recent-onset depression (ROD). Baseline SIPS-N and gyrification predicted moderate/severe negative symptoms with significant balanced accuracies of 68 and 62%, while the combined model achieved 73% accuracy. Sequential risk stratification stratified CHR into a high (83%), medium (40–64%), and low (19%) risk group regarding their risk of having moderate/severe negative symptoms at 9 months follow-up. The baseline SIPS-N model was also able to predict social (61%), but not role functioning (59%) at above-chance accuracies, whereas the gyrification model achieved significant accuracies in predicting both social (76%) and role (74%) functioning in CHR. Finally, only the baseline SIPS-N model showed transdiagnostic generalization to ROP (63%). This study delivers a multimodal prognostic model to identify those CHR with a clinically relevant negative symptom severity and functional impairments, potentially requiring further therapeutic consideration.

Participation is often presented as the solution for achieving social acceptance in energy infrastructure projects. Despite its broad recognition, participation might have very different meanings depending on the perspective taken. In this paper, we empirically explore different understandings of two central perspectives in a participatory process: those of the project managers and the residents. While project managers have a rather classic view of participatory formats, residents look for formats they can accommodate in their everyday practices. Put together, both perspectives expand common conceptualizations of participation in the literature and enable the detection of blind spots. Based on empirical data from geothermal energy, our findings show that perceptions and expectations of participation vary and, in some cases, diverge, which leads to misunderstandings about the nature and scope of participation. This, in turn, can delay or even block energy projects.

Naïve T cells (TN) constitutively recirculate through secondary lymphatic organs (SLOs), where they scan dendritic cells (DCs) for cognate peptide-loaded major histocompatibility complexes (pMHC). Continuous trafficking between SLOs not only enables rapid clonal selection but also ensures TN homeostasis by providing access to prosurvival signals from TCR, IL-7R, and the chemokine receptor CCR7. Inside the lymphoid tissue, CCR7-mediated TN motility is mainly driven by the Rac activator DOCK2, with a separate contribution by a phosphoinositide-3-kinase γ (PI3Kγ)-dependent pathway. Tec tyrosine kinases and the Rac activator Tiam1 constitute prominent downstream effectors of PI3K signaling. Yet, the precise role of Tec kinase versus Tiam1 signaling during CCR7-mediated TN migration and homeostasis remains incompletely understood. Here, we examined the function of the Tec family member interleukin-2-inducible T-cell kinase (Itk) and Tiam1 during TN migration in vitro and in vivo using intravital microscopy. Itk deficiency caused a mild decrease in CCR7-triggered TN migration, mirroring observations made with PI3Kγ;-/- T cells, while lack of Tiam1 did not affect TN motility. In silico modeling suggested that reduced migration in the absence of Itk does not result in a substantial decrease in the frequency of TN encounters with DCs within the lymphoid tissue. In contrast, Itk was important to maintain in vivo homeostasis of CD4+ TN, also in MHCII-deficient hosts. Taken together, our data suggest that Itk contributes to TN migration and survival by integrating chemokine receptor and TCR signaling pathways.