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Changing practice patterns caused by the pandemic have created an urgent need for guidance in prescribing stimulants using telepsychiatry for attention-deficit hyperactivity disorder (ADHD). A notable spike in the prescribing of stimulants accompanied the suspension of the Ryan Haight Act, allowing the prescribing of stimulants without a face-to-face meeting. Competing forces both for and against prescribing ADHD stimulants by telepsychiatry have emerged, requiring guidelines to balance these factors. On the one hand, factors weighing in favor of increasing the availability of treatment for ADHD via telepsychiatry include enhanced access to care, reduction in the large number of untreated cases, and prevention of the known adverse outcomes of untreated ADHD. On the other hand, factors in favor of limiting telepsychiatry for ADHD include mitigating the possibility of exploiting telepsychiatry for profit or for misuse, abuse, and diversion of stimulants. This Expert Consensus Group has developed numerous specific guidelines and advocates for some flexibility in allowing telepsychiatry evaluations and treatment without an in-person evaluation to continue. These guidelines also recognize the need to give greater scrutiny to certain subpopulations, such as young adults without a prior diagnosis or treatment of ADHD who request immediate-release stimulants, which should increase the suspicion of possible medication diversion, misuse, or abuse. In such cases, nonstimulants, controlled-release stimulants, or psychosocial interventions should be prioritized. We encourage the use of outside informants to support the history, the use of rating scales, and having access to a hybrid model of both in-person and remote treatment.
Although the link between alcohol involvement and behavioral phenotypes (e.g. impulsivity, negative affect, executive function [EF]) is well-established, the directionality of these associations, specificity to stages of alcohol involvement, and extent of shared genetic liability remain unclear. We estimate longitudinal associations between transitions among alcohol milestones, behavioral phenotypes, and indices of genetic risk.
Methods
Data came from the Collaborative Study on the Genetics of Alcoholism (n = 3681; ages 11–36). Alcohol transitions (first: drink, intoxication, alcohol use disorder [AUD] symptom, AUD diagnosis), internalizing, and externalizing phenotypes came from the Semi-Structured Assessment for the Genetics of Alcoholism. EF was measured with the Tower of London and Visual Span Tasks. Polygenic scores (PGS) were computed for alcohol-related and behavioral phenotypes. Cox models estimated associations among PGS, behavior, and alcohol milestones.
Results
Externalizing phenotypes (e.g. conduct disorder symptoms) were associated with future initiation and drinking problems (hazard ratio (HR)⩾1.16). Internalizing (e.g. social anxiety) was associated with hazards for progression from first drink to severe AUD (HR⩾1.55). Initiation and AUD were associated with increased hazards for later depressive symptoms and suicidal ideation (HR⩾1.38), and initiation was associated with increased hazards for future conduct symptoms (HR = 1.60). EF was not associated with alcohol transitions. Drinks per week PGS was linked with increased hazards for alcohol transitions (HR⩾1.06). Problematic alcohol use PGS increased hazards for suicidal ideation (HR = 1.20).
Conclusions
Behavioral markers of addiction vulnerability precede and follow alcohol transitions, highlighting dynamic, bidirectional relationships between behavior and emerging addiction.
The U.S. Department of Agriculture–Agricultural Research Service (USDA-ARS) has been a leader in weed science research covering topics ranging from the development and use of integrated weed management (IWM) tactics to basic mechanistic studies, including biotic resistance of desirable plant communities and herbicide resistance. ARS weed scientists have worked in agricultural and natural ecosystems, including agronomic and horticultural crops, pastures, forests, wild lands, aquatic habitats, wetlands, and riparian areas. Through strong partnerships with academia, state agencies, private industry, and numerous federal programs, ARS weed scientists have made contributions to discoveries in the newest fields of robotics and genetics, as well as the traditional and fundamental subjects of weed–crop competition and physiology and integration of weed control tactics and practices. Weed science at ARS is often overshadowed by other research topics; thus, few are aware of the long history of ARS weed science and its important contributions. This review is the result of a symposium held at the Weed Science Society of America’s 62nd Annual Meeting in 2022 that included 10 separate presentations in a virtual Weed Science Webinar Series. The overarching themes of management tactics (IWM, biological control, and automation), basic mechanisms (competition, invasive plant genetics, and herbicide resistance), and ecosystem impacts (invasive plant spread, climate change, conservation, and restoration) represent core ARS weed science research that is dynamic and efficacious and has been a significant component of the agency’s national and international efforts. This review highlights current studies and future directions that exemplify the science and collaborative relationships both within and outside ARS. Given the constraints of weeds and invasive plants on all aspects of food, feed, and fiber systems, there is an acknowledged need to face new challenges, including agriculture and natural resources sustainability, economic resilience and reliability, and societal health and well-being.
Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact.
Methods:
We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations.
Results:
BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI.
Conclusions:
We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.
We investigated whether people’s risk taking tendency established in one domain (gains or losses) carries over to the other domain. Participants played a game in which they made repeated decisions between a fixed payoff and a risky option, where the outcome of the risky option depended on whether they had responded correctly on a difficult perceptual-memory task. In some trials, participants played to gain points; on others, they played to avoid losing points. In two studies, we observed the following pattern of results. 1) Participants risked less on gain trials than on loss trials. 2) This difference in risk taking persisted (carried over) when the domain changed from gains to losses and vice versa (with the effect of experiencing losses first being stronger than the effect of experiencing gains first). 3) There was no analogous carryover effect on responses to a delay discounting measure, but there was a carryover effect on responses on a risk attitude measure. We compare these results with those from other recent studies and discuss various ways of explaining them.
Psychological stress has an established bi-directional relationship with obesity. Mindfulness techniques reduce stress and improve eating behaviours, but their long-term impact remains untested. CALMPOD (Compassionate Approach to Living Mindfully for Prevention of Disease) is a psychoeducational mindfulness-based course evidenced to improve eating patterns across a 6-month period, possibly by reducing stress. However, no long-term evaluation of impact exists.
Aims
This study retrospectively evaluates 2-year outcomes of CALMPOD on patient engagement, weight and metabolic markers.
Method
All adults with a body mass index >35 kg/m2 attending an UK obesity service during 2016–2020 were offered CALMPOD. Those who refused CALMPOD were offered standard lifestyle advice. Routine clinic data over 2 years, including age, gender, 6-monthly appointment attendance, weight, haemoglobin A1C and total cholesterol, were pooled and analysed to evaluate CALMPOD.
Results
Of 289 patients, 163 participated in the CALMPOD course and 126 did not. No baseline demographic differences existed between the participating and non-participating groups. The CALMPOD group had improved attendance across all 6-monthly appointments compared with the non-CALMPOD group (P < 0.05). Mean body weight reduction at 2 years was 5.6 kg (s.d. 11.2, P < 0.001) for the CALMPOD group compared with 3.9 kg (s.d. 10.5, P < 0.001) for the non-CALMPOD group. No differences in haemoglobin A1C and fasting serum total cholesterol were identified between the groups.
Conclusions
The retrospective evaluation of CALMPOD suggests potential for mindfulness and compassion-based group educational techniques to improve longer-term patient and clinical outcomes. Prospective large-scale studies are needed to evaluate the impact of stress on obesity and the true impact of CALMPOD.
Tree-ring chronologies encode interannual variability in forest growth rates over long time periods from decades to centuries or even millennia. However, each chronology is a highly localized measurement describing conditions at specific sites where wood samples have been collected. The question whether these local growth variabilites are representative for large geographical regions remains an open issue. To overcome the limitations of interpreting a sparse network of sites, we propose an upscaling approach for annual tree-ring indices that approximate forest growth variability and compute gridded data products that generalize the available information for multiple tree genera. Using regression approaches from machine learning, we predict tree-ring indices in space and time based on climate variables, but considering also species range maps as constraints for the upscaling. We compare various prediction strategies in cross-validation experiments to identify the best performing setup. Our estimated maps of tree-ring indices are the first data products that provide a dense view on forest growth variability at the continental level with 0.5° and 0.0083° spatial resolution covering the years 1902–2013. Furthermore, we find that different genera show very variable spatial patterns of anomalies. We have selected Europe as study region and focused on the six most prominent tree genera, but our approach is very generic and can easily be applied elsewhere. Overall, the study shows perspectives but also limitations for reconstructing spatiotemporal dynamics of complex biological processes. The data products are available at https://www.doi.org/10.17871/BACI.248.
This chapter provides an overview of electromigration in metals, starting from the early studies on bulk metals to the current studies on copper interconnects. Asmicroelectronics technology advances, electromigration becomes an important reliability problem for on-chip interconnects, evolving from the microscale to the nanoscale in copper lines. Key concepts are introduced, including the electron wind force, the Blech short-length effects, and copper damascene interconnects.
In Chapter 8, the statistical nature of electromigration is described. Along with understanding of the basic physical degradation mechanisms, this is an important area of research due to the need for extrapolations from simple test structures to the product level. One has to keep in mind that electromigration testing is usually done on single-link structures. In some instances, a few links are stitched together in a series or parallel fashion, but massive-scale studies with large interconnect arrays have not been implemented yet as a standard testing methodology. Only the application of very large test structures with an extended amount of interconnect links encompassing metal lines and contacts/vias can lead to the detection of “early” or “extrinsic” failures, which are the limiting factor in the extrapolation to product-level interconnect systems. The detection of these early failures in electromigration and the complicated statistical nature of this important reliability phenomenon have been difficult issues to treat for decades in the past. In Chapter 8, an innovative technique utilizing large interconnect arrays in conjunction with the well-known Wheatstone Bridge are discussed, and both Al- and Cu-based interconnect technologies are described.
The momentum exchange between lattice atoms and conduction electrons together with the stress gradient along the metal wire embedded into the rigid confinement are two major driving forces for electromigration-induced evolution of stress and vacancy concentration. The growth of mechanical stress causes an evolution of a variety of defects that are inevitably present in the metal, leading to void formation. It affects the electrical properties of the interconnect. In order to estimate the time to failure caused by voiding, the kinetics of stress evolution should be resolved until the first void is nucleated. Then the analysis of the void size evolution should be performed in order to trace changes in resistances of individual voided lines and vias. In this chapter, we review the major results that have been achieved with the 1D phenomenological EM model. We demonstrate its capability to predict the transient and steady-state distributions of the vacancy concentration and the hydrostatic stress, a void nucleation, and its growth, and also a drift of small voids along a metal wire. Despite its simplified nature, the 1D model is capable of addressing the confinement effect of ILD/IMD dielectric on EM-induced degradation, and also the effect of metal grain structure.
A very different picture of the redistribution of metal density and stress, caused by electric stressing, can be expected in multibranch interconnect structures formed by connected metal lines within the same metal layer. The absence of diffusion barriers in line junctions allows atoms to freely migrate between lines along the trajectories of the current carriers. When a multibranch structure includes metal lines that are connected in parallel, the creation of a void in one of the parallel branches does not necessarily result in a failure, which contrasts with what happens in a single line segment, because current can continue to flow in the unvoided parallel lines. The on-chip power/ground (p/g) grid is an example of such electrically redundant multibranch structures. In this chapter, we review a recently developed assessment methodology of the p/g grid MTTF and describe a novel experimental technique that could validate the proposed methodology. EM assessment performed on the grids with tens of millions of nodes has shown that the formation of the first void alone didn’t cause a grid failure. A failure criterion of 10% voltage drop increase was met due to cumulative effect of nucleation of several voids and their growth in the failed branches.
In Chapter 5, we show that the microstructure and interfaces are important in controlling EM reliability of Cu damascene interconnects where the EM lifetime can be significantly improved with metal capping or alloying. In this chapter, we investigate the scaling effect on microstructure and the implication on EM reliability for Cu and Co damascene lines. The scaling effect on Cu microstructure was investigated using a high-resolution electron microdiffraction technique down to 22 nm linewidth for the 14 nm node. The results showed a systematic trend of microstructure evolution in Cu damascene lines with continued scaling. A Monte Carlo simulation was carried out to investigate grain growth in Cu interconnects beyond 22 nm linewidth based on total energy minimization. The simulation results enabled us to understand how the interface energy counteracts the strain and grain boundary energies to control the microstructure evolution in Cu lines with continued scaling. Then the scaling effect on microstructure evolution of Co damascene lines was investigated beyond the 10nm node using both electron microdiffraction and simulation. The simulated microstructures of Cu and Co interconnects are used to project the scaling effect on EM reliability beyond the 10 nm node.