Clinical outcomes of repetitive transcranial magnetic stimulation (rTMS) for treatment of treatment-resistant depression (TRD) vary widely and there is no mood rating scale that is standard for assessing rTMS outcome. It remains unclear whether TMS is as efficacious in older adults with late-life depression (LLD) compared to younger adults with major depressive disorder (MDD). This study examined the effect of age on outcomes of rTMS treatment of adults with TRD. Self-report and observer mood ratings were measured weekly in 687 subjects ages 16–100 years undergoing rTMS treatment using the Inventory of Depressive Symptomatology 30-item Self-Report (IDS-SR), Patient Health Questionnaire 9-item (PHQ), Profile of Mood States 30-item, and Hamilton Depression Rating Scale 17-item (HDRS). All rating scales detected significant improvement with treatment; response and remission rates varied by scale but not by age (response/remission ≥ 60: 38%–57%/25%–33%; <60: 32%–49%/18%–25%). Proportional hazards models showed early improvement predicted later improvement across ages, though early improvements in PHQ and HDRS were more predictive of remission in those < 60 years (relative to those ≥ 60) and greater baseline IDS burden was more predictive of non-remission in those ≥ 60 years (relative to those < 60). These results indicate there is no significant effect of age on treatment outcomes in rTMS for TRD, though rating instruments may differ in assessment of symptom burden between younger and older adults during treatment.

In 2016, the National Center for Advancing Translational Science launched the Trial Innovation Network (TIN) to address barriers to efficient and informative multicenter trials. The TIN provides a national platform, working in partnership with 60+ Clinical and Translational Science Award (CTSA) hubs across the country to support the design and conduct of successful multicenter trials. A dedicated Hub Liaison Team (HLT) was established within each CTSA to facilitate connection between the hubs and the newly launched Trial and Recruitment Innovation Centers. Each HLT serves as an expert intermediary, connecting CTSA Hub investigators with TIN support, and connecting TIN research teams with potential multicenter trial site investigators. The cross-consortium Liaison Team network was developed during the first TIN funding cycle, and it is now a mature national network at the cutting edge of team science in clinical and translational research. The CTSA-based HLT structures and the external network structure have been developed in collaborative and iterative ways, with methods for shared learning and continuous process improvement. In this paper, we review the structure, function, and development of the Liaison Team network, discuss lessons learned during the first TIN funding cycle, and outline a path toward further network maturity.

The Hierarchical Taxonomy of Psychopathology (HiTOP) has emerged out of the quantitative approach to psychiatric nosology. This approach identifies psychopathology constructs based on patterns of co-variation among signs and symptoms. The initial HiTOP model, which was published in 2017, is based on a large literature that spans decades of research. HiTOP is a living model that undergoes revision as new data become available. Here we discuss advantages and practical considerations of using this system in psychiatric practice and research. We especially highlight limitations of HiTOP and ongoing efforts to address them. We describe differences and similarities between HiTOP and existing diagnostic systems. Next, we review the types of evidence that informed development of HiTOP, including populations in which it has been studied and data on its validity. The paper also describes how HiTOP can facilitate research on genetic and environmental causes of psychopathology as well as the search for neurobiologic mechanisms and novel treatments. Furthermore, we consider implications for public health programs and prevention of mental disorders. We also review data on clinical utility and illustrate clinical application of HiTOP. Importantly, the model is based on measures and practices that are already used widely in clinical settings. HiTOP offers a way to organize and formalize these techniques. This model already can contribute to progress in psychiatry and complement traditional nosologies. Moreover, HiTOP seeks to facilitate research on linkages between phenotypes and biological processes, which may enable construction of a system that encompasses both biomarkers and precise clinical description.

Early in the COVID-19 pandemic, the World Health Organization stressed the importance of daily clinical assessments of infected patients, yet current approaches frequently consider cross-sectional timepoints, cumulative summary measures, or time-to-event analyses. Statistical methods are available that make use of the rich information content of longitudinal assessments. We demonstrate the use of a multistate transition model to assess the dynamic nature of COVID-19-associated critical illness using daily evaluations of COVID-19 patients from 9 academic hospitals. We describe the accessibility and utility of methods that consider the clinical trajectory of critically ill COVID-19 patients.

Microscopic examination of blood smears remains the gold standard for laboratory inspection and diagnosis of malaria. Smear inspection is, however, time-consuming and dependent on trained microscopists with results varying in accuracy. We sought to develop an automated image analysis method to improve accuracy and standardization of smear inspection that retains capacity for expert confirmation and image archiving. Here, we present a machine learning method that achieves red blood cell (RBC) detection, differentiation between infected/uninfected cells, and parasite life stage categorization from unprocessed, heterogeneous smear images. Based on a pretrained Faster Region-Based Convolutional Neural Networks (R-CNN) model for RBC detection, our model performs accurately, with an average precision of 0.99 at an intersection-over-union threshold of 0.5. Application of a residual neural network-50 model to infected cells also performs accurately, with an area under the receiver operating characteristic curve of 0.98. Finally, combining our method with a regression model successfully recapitulates intraerythrocytic developmental cycle with accurate lifecycle stage categorization. Combined with a mobile-friendly web-based interface, called PlasmoCount, our method permits rapid navigation through and review of results for quality assurance. By standardizing assessment of Giemsa smears, our method markedly improves inspection reproducibility and presents a realistic route to both routine lab and future field-based automated malaria diagnosis.

Over the past decade, a growing interest has developed on the archaeology, palaeontology, and palaeoenvironments of the Arabian Peninsula. It is now clear that hominins repeatedly dispersed into Arabia, notably during pluvial interglacial periods when much of the peninsula was characterised by a semiarid grassland environment. During the intervening glacial phases, however, grasslands were replaced with arid and hyperarid deserts. These millennial-scale climatic fluctuations have subjected bones and fossils to a dramatic suite of environmental conditions, affecting their fossilisation and preservation. Yet, as relatively few palaeontological assemblages have been reported from the Pleistocene of Arabia, our understanding of the preservational pathways that skeletal elements can take in these types of environments is lacking. Here, we report the first widespread taxonomic and taphonomic assessment of Arabian fossil deposits. Novel fossil fauna are described and overall the fauna are consistent with a well-watered semiarid grassland environment. Likewise, the taphonomic results suggest that bones were deposited under more humid conditions than present in the region today. However, fossils often exhibit significant attrition, obscuring and fragmenting most finds. These are likely tied to wind abrasion, insolation, and salt weathering following fossilisation and exhumation, processes particularly prevalent in desert environments.

Timing of weed emergence and seed persistence in the soil influence the ability to implement timely and effective control practices. Emergence patterns and seed persistence of kochia populations were monitored in 2010 and 2011 at sites in Kansas, Colorado, Wyoming, Nebraska, and South Dakota. Weekly observations of emergence were initiated in March and continued until no new emergence occurred. Seed was harvested from each site, placed into 100-seed mesh packets, and buried at depths of 0, 2.5, and 10 cm in fall of 2010 and 2011. Packets were exhumed at 6-mo intervals over 2 yr. Viability of exhumed seeds was evaluated. Nonlinear mixed-effects Weibull models were fit to cumulative emergence (%) across growing degree days (GDD) and to viable seed (%) across burial time to describe their fixed and random effects across site-years. Final emergence densities varied among site-years and ranged from as few as 4 to almost 380,000 seedlings m−2. Across 11 site-years in Kansas, cumulative GDD needed for 10% emergence were 168, while across 6 site-years in Wyoming and Nebraska, only 90 GDD were needed; on the calendar, this date shifted from early to late March. The majority (>95%) of kochia seed did not persist for more than 2 yr. Remaining seed viability was generally >80% when seeds were exhumed within 6 mo after burial in March, and declined to <5% by October of the first year after burial. Burial did not appear to increase or decrease seed viability over time but placed seed in a position from which seedling emergence would not be possible. High seedling emergence that occurs very early in the spring emphasizes the need for fall or early spring PRE weed control such as tillage, herbicides, and cover crops, while continued emergence into midsummer emphasizes the need for extended periods of kochia management.