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Rizzo showed that the family of elliptic curves W (t) : y 2 = x 3 + t x 2 − (t + 3) x + 1 , a well-known example of Washington, has root number W (W (t)) = − 1 for all t ∈ Z [11]. In this paper we generalise this example and identify the families of small degree on which this phenomenon happens. Motivated by results from [2] and [5] , we study in detail the two families F s (t) : y 2 = x 3 + 3 t x 2 + 3 s x + s t and L w , s , v (t) : w y 2 = x 3 + 3 (t 2 + v) x 2 + 3 s x + s (t 2 + v) and describe necessary and sufficient conditions for which subfamilies of F s (t) have constant root number on integer fibres. We further prove similar but partial results on L w , s , v (t). Our results give examples of subfamilies for which there is rank elevation at integer fibres. [ABSTRACT FROM AUTHOR]

Increases in stimulant drug use (such as methamphetamine) and related deaths creates an imperative for community settings to adopt evidence-based practices to help people who use stimulants. Contingency management (CM) is a behavioral intervention with decades of research demonstrating efficacy for the treatment of stimulant use disorder, but real-world adoption has been slow, due to well-known implementation barriers, including difficulty funding reinforcers, and stigma. This paper describes the training and technical assistance (TTA) efforts and lessons learned for two state-wide stimulant-focused CM implementation projects in the Northwestern United States (Montana and Washington). A total of 154 providers from 35 community-based service sites received didactic training in CM beginning in 2021. Seventeen of these sites, ten of eleven in Montana (90.9%) and seven of 24 in Washington (29.2%), went on to implement contingency management programs adherent to their state's established CM protocol and received ongoing TTA in the form of implementation coaching calls. These findings illustrate that site-specific barriers such as logistical fit precluded implementation in more than 50% of the trained sites; however, strategies for site-specific tailoring within the required protocol aided implementation, resulting in successful CM program launch in a diverse cross-section of service sites across the states. The lessons learned add to the body of literature describing CM implementation barriers and solutions. • Contingency Management (CM) is an evidence-based intervention for stimulant use. • Montana and Washington embarked on state-wide rollouts of contingency management. • Training and technical assistance was delivered to support CM implementation. • Coaching calls were conducted to facilitate model adherence and support adaptation. • Lessons learned include recommendations for clinicians interested in CM implementation. [ABSTRACT FROM AUTHOR]

Maternity Care Homes (MCHs) intend to address clinical and psychosocial needs for perinatal patients and are commonly implemented for Medicaid beneficiaries. Rigorous evidence supporting MCHs' effectiveness for improving birth outcomes is thin, but most studies consider only clinical and demographic factors from administrative data. To assess birth outcomes with controls for psychosocial variables known to affect them, this paper considers quantitative participant-level data from the Strong Start for Mothers and Newborns prenatal care initiative, with qualitative case study data to further contextualize results. From 2013 to 2017, Strong Start served over 45,000 Medicaid beneficiaries in 32 states, D.C., and Puerto Rico though MCHs, group prenatal care, or freestanding birth centers. Participant data included risks screens for food insecurity, depression, anxiety, pregnancy intention, and intimate partner violence, in addition to clinical and demographic information. After clinical, demographic and psychosocial risks were controlled in a regression model, Strong Start birth center participants showed significantly lower rates of preterm birth, low birthweight, and cesarean section relative to MCH participants (p < .01). In group prenatal care, White participants showed lower rates of preterm birth (p < .01) and Black participants showed lower rates of low birthweight (p < .05) relative to MCH participants. Strong Start participants reported appreciation for MCH care managers' support, but community and clinical referrals often had long waiting lists or were inaccessible. Transformative care models focusing on provider continuity, relationship building, and patient activation may offer more promise for improving birth outcomes than supplementing medical models with care management and other resources. [ABSTRACT FROM AUTHOR]

Air pollution (AP) has been shown to increase the risk of type 2 diabetes mellitus, as well as other cardiometabolic diseases. AP is characterized by a complex mixture of components for which the composition depends on sources and metrological factors. The US Environmental Protection Agency (EPA) monitors and regulates certain components of air pollution known to have negative consequences for human health. Research assessing the health effects of these components of AP often uses traditional regression models, which might not capture more complex and interdependent relationships. Machine learning has the capability to simultaneously assess multiple components and find complex, non-linear patterns that may not be apparent and could not be modeled by other techniques. Here we use k-means clustering to assess the patterns associating PM 2.5 , PM 10 , CO, NO 2 , O 3 , and SO 2 measurements and changes in annual diabetes incidence at a US county level. The average age adjusted annual decrease in diabetes incidence for the entire US populations is −0.25 per 1000 but the change shows a significant geographic variation (range: −17.2 to 5.30 per 1000). In this paper these variations were compared with the local daily AP concentrations of the pollutants listed above from 2005 to 2015, which were matched to the annual change in diabetes incidence for the following year. A total of 134,925 daily air quality observations were included in the cluster analysis, representing 125 US counties and the District of Columbia. K-means successfully clustered AP components and indicated an association between exposure to certain AP mixtures with lower decreases on T2D incidence. • Epidemiological studies tend to focus on a single air pollution component, potentially missing synergistic effects. • The concurrent impact of PM 2.5 , PM 10 , CO, NO 2 , O 3 , and SO 2 on type-2 diabetes incidence was assessed at the US county level. • The cluster with the greatest CO, NO 2 , PM 2.5 , PM 10 , and SO 2 concentrations had the smallest decrease in diabetes incidence. • Individual cluster components did not trend with diabetes incidence, possibly indicating cooperative or threshold effects. • Machine learning methods can assess concurrent effects of air pollution components, revealing potentially non-linear patterns. [ABSTRACT FROM AUTHOR]

Abstract: We present a history of the concepts and developments that have led us to focus on the resting state as an object of study. We then discuss resting state research performed in our laboratory since 2005 with an emphasis on papers of particular interest. [Copyright &y& Elsevier]