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Objective The expanding power and accessibility of personal technology provide an opportunity to reduce burdens and costs of traditional clinical site‐centric therapeutic trials in Parkinson’s disease and generate novel insights. The value of this approach has never been more evident than during the current COVID‐19 pandemic. We sought to (1) establish and implement the infrastructure for longitudinal, virtual follow‐up of clinical trial participants, (2) compare changes in smartphone‐based assessments, online patient‐reported outcomes, and remote expert assessments, and (3) explore novel digital markers of Parkinson’s disease disability and progression. Methods Participants from two recently completed phase III clinical trials of inosine and isradipine enrolled in Assessing Tele‐Health Outcomes in Multiyear Extensions of Parkinson’s Disease trials (AT‐HOME PD), a two‐year virtual cohort study. After providing electronic informed consent, individuals complete annual video visits with a movement disorder specialist, smartphone‐based assessments of motor function and socialization, and patient‐reported outcomes online. Results From the two clinical trials, 226 individuals from 42 states in the United States and Canada enrolled. Of these, 181 (80%) have successfully downloaded the study’s smartphone application and 161 (71%) have completed patient‐reported outcomes on the online platform. Interpretation It is feasible to conduct a large‐scale, international virtual observational study following the completion of participation in brick‐and‐mortar clinical trials in Parkinson’s disease. This study, which brings research to participants, will compare established clinical endpoints with novel digital biomarkers and thereby inform the longitudinal follow‐up of clinical trial participants and design of future clinical trials.

Atmospheric aqueous chemistry can have profound effects on our environment. The importance of chemistry within the atmospheric aqueous phase was first realized in the 1970s as there was growing concern over the negative impacts on ecosystem health from acid deposition. Research at mountaintop observatories including Whiteface Mountain (WFM) showed that gas phase sulfur dioxide emissions react in cloud droplets to form sulfuric acid, which also impacted aerosol mass loadings. Cloud chemistry research has experienced a major resurgence in scientific interest due to the potential for aqueous chemical processes to fill the gap between modeled and observed organic aerosol mass. The current study updates the long-term trends in cloud water composition at WFM for the past 28 years (1994–2021). Substantial decreases in sulfate (SO42-) and nitrate (NO3-) concentrations have not been matched by an equivalent decrease in ammonium (NH4+) concentrations, leading to significantly higher cloud droplet pH. Meanwhile, Total Organic Carbon (TOC) concentrations may be increasing (both in relative and absolute terms). In the past, samples were excluded from trend analysis if they did not meet an approximate ion balance, which resulted in approximately half of samples being excluded in recent years. We show that, when including the entire available dataset, decreasing trends in SO42-, NO3- and NH4+ become more modest, TOC concentrations increase at a faster rate, and increasing trends in Ca2+ and Mg2+ emerge. A growing trend in cation / anion ratios is also observed, implying that a significant fraction of anions are not being measured with the current suite of measurements, and these missing anions are growing in importance. Organic acids are identified as the most likely candidates for the missing anions, since the measured ion imbalance correlates strongly with measured TOC concentrations. The TOC trend becomes statistically insignificant when evaluating cloud water loadings (or air equivalent mass loadings), possibly due to the complex role that LWC may play in TOC concentrations. We highlight the emergence of a new chemical regime characterized by low acidity and relatively high conductivity, and increasing TOC and base cation concentrations. With the increasing impact from Ca2+ and Mg2+ on the bulk cloud water pH, which are largely thought to reside within coarse mode aerosol that only represent a small fraction of cloud droplets, an "inferred cloud droplet pH" is introduced, to better represent the pH of the vast majority of cloud droplets as they reside in the atmosphere. While measured pH has increased during the history of the monitoring site, the cloud droplet pH has remained relatively flat since 2009. We also show that there is a missing source of acidity in the system that correlates with TOC. The chemical system at WFM has shifted away from a system dominated by SO42- to a system controlled by base cation, nitrogen containing species and TOC. Further research is required to understand the effects on air quality, climate, and ecosystem health.

Parkinson’s disease (PD) is a neurodegenerative disorder associated with motor and non-motor symptoms. Current treatments primarily focus on managing motor symptom severity such as tremor, bradykinesia, and rigidity. However, as the disease progresses, treatment side-effects can emerge such as on/off periods and dyskinesia. The objective of the Levodopa Response Study was to identify whether wearable sensor data can be used to objectively quantify symptom severity in individuals with PD exhibiting motor fluctuations. Thirty-one subjects with PD were recruited from 2 sites to participate in a 4-day study. Data was collected using 2 wrist-worn accelerometers and a waist-worn smartphone. During Days 1 and 4, a portion of the data was collected in the laboratory while subjects performed a battery of motor tasks as clinicians rated symptom severity. The remaining of the recordings were performed in the home and community settings. To our knowledge, this is the first dataset collected using wearable accelerometers with specific focus on individuals with PD experiencing motor fluctuations that is made available via an open data repository., Measurement(s) body movement coordination trait • Movement Disorder Society Unified Parkinson’s Disease Rating Scale Questionnaire • Medication • motor coordination/balance trait • sleep pattern • MDS-UPDRS Tasks and Simulated Activities of Daily Living (in-clinic) • Activity of Daily Living Technology Type(s) Accelerometer • body movement/behavior method • Clinical Observation • smartphone • Subject Diary Factor Type(s) age of patient • gender of patient • timing of medication intake Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13342055

SUMMARY We present a consensus atlas of the human brain transcriptome in Alzheimer’s disease (AD), based on meta-analysis of differential gene expression in 2,114 postmortem samples. We discover 30 brain coexpression modules from seven regions as the major source of AD transcriptional perturbations. We next examine overlap with 251 brain differentially expressed gene sets from mouse models of AD and other neurodegenerative disorders. Human-mouse overlaps highlight responses to amyloid versus tau pathology and reveal age- and sex-dependent expression signatures for disease progression. Human coexpression modules enriched for neuronal and/or microglial genes broadly overlap with mouse models of AD, Huntington’s disease, amyotrophic lateral sclerosis, and aging. Other human coexpression modules, including those implicated in proteostasis, are not activated in AD models but rather following other, unexpected genetic manipulations. Our results comprise a cross-species resource, highlighting transcriptional networks altered by human brain pathophysiology and identifying correspondences with mouse models for AD preclinical studies., Graphical Abstract, In Brief Wan et al. develop a consensus atlas of the human brain transcriptome in Alzheimer’s disease, based on 2,114 postmortem samples, and examine overlap with 251 expression signatures from mouse brain RNA sequencing experiments, including many neurodegenerative disease models.

SUMMARYAlzheimer’s disease (AD) is a complex and heterogenous brain disease that affects multiple inter-related biological processes. This complexity contributes, in part, to existing difficulties in the identification of successful disease-modifying therapeutic strategies. To address this, systems approaches are being used to characterize AD-related disruption in molecular state. To evaluate the consistency across these molecular models, a consensus atlas of the human brain transcriptome was developed through coexpression meta-analysis across the AMP-AD consortium. Consensus analysis was performed across five coexpression methods used to analyze RNA-seq data collected from 2114 samples across 7 brain regions and 3 research studies. From this analysis, five consensus clusters were identified that described the major sources of AD-related alterations in transcriptional state that were consistent across studies, methods, and samples. AD genetic associations, previously studied AD-related biological processes, and AD targets under active investigation were enriched in only three of these five clusters. The remaining two clusters demonstrated strong heterogeneity between males and females in AD-related expression that was consistently observed across studies. AD transcriptional modules identified by systems analysis of individual AMP-AD teams were all represented in one of these five consensus clusters except ROS/MAP-identified Module 109, which was specific for genes that showed the strongest association with changes in AD-related gene expression across consensus clusters. The other two AMP-AD transcriptional analyses reported modules that were enriched in one of the two sex-specific Consensus Clusters. The fifth cluster has not been previously identified and was enriched for genes related to proteostasis. This study provides an atlas to map across biological inquiries of AD with the goal of supporting an expansion in AD target discovery efforts.

Digital technologies such as smartphones are transforming the way scientists conduct biomedical research. Several remotely conducted studies have recruited thousands of participants over a span of a few months allowing researchers to collect real-world data at scale and at a fraction of the cost of traditional research. Unfortunately, remote studies have been hampered by substantial participant attrition, calling into question the representativeness of the collected data including generalizability of outcomes. We report the findings regarding recruitment and retention from eight remote digital health studies conducted between 2014–2019 that provided individual-level study-app usage data from more than 100,000 participants completing nearly 3.5 million remote health evaluations over cumulative participation of 850,000 days. Median participant retention across eight studies varied widely from 2–26 days (median across all studies = 5.5 days). Survival analysis revealed several factors significantly associated with increase in participant retention time, including (i) referral by a clinician to the study (increase of 40 days in median retention time); (ii) compensation for participation (increase of 22 days, 1 study); (iii) having the clinical condition of interest in the study (increase of 7 days compared with controls); and (iv) older age (increase of 4 days). Additionally, four distinct patterns of daily app usage behavior were identified by unsupervised clustering, which were also associated with participant demographics. Most studies were not able to recruit a sample that was representative of the race/ethnicity or geographical diversity of the US. Together these findings can help inform recruitment and retention strategies to enable equitable participation of populations in future digital health research.