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Here we describe a new method for evaluating rate constants for activated processes using numerical simulation--the absorbing boundary method. While approximate, this method is shown to be accurate and faster than previously existing methods. It is applied to several distinct physical systems. Rate constants for a one-dimensional non-Markovian system with exponential friction are calculated and shown to deviate from theoretical predictions. A qualitative analysis of trajectories gives a physical explanation for the deviations and indicates what an accurate theory must include. Also, rate constants for a two-dimensional Markovian system are calculated and used to test the validity of two conflicting predictions for the rate of energy activation. A useful criterion is proposed for determining the regions of validity of each theory. Finally, a theory for the calculation of non-adiabatic effects in activated barrier crossing is presented. We compare our theory with recent simulation results and find excellent agreement.

Frontotemporal dementia is a heterogenous neurodegenerative disorder, with about a third of cases being genetic. Most of this genetic component is accounted for by mutations in GRN, MAPT, and C9orf72. In this study, we aimed to complement previous phenotypic studies by doing an international study of age at symptom onset, age at death, and disease duration in individuals with mutations in GRN, MAPT, and C9orf72. In this international, retrospective cohort study, we collected data on age at symptom onset, age at death, and disease duration for patients with pathogenic mutations in the GRN and MAPT genes and pathological expansions in the C9orf72 gene through the Frontotemporal Dementia Prevention Initiative and from published papers. We used mixed effects models to explore differences in age at onset, age at death, and disease duration between genetic groups and individual mutations. We also assessed correlations between the age at onset and at death of each individual and the age at onset and at death of their parents and the mean age at onset and at death of their family members. Lastly, we used mixed effects models to investigate the extent to which variability in age at onset and at death could be accounted for by family membership and the specific mutation carried. Data were available from 3403 individuals from 1492 families: 1433 with C9orf72 expansions (755 families), 1179 with GRN mutations (483 families, 130 different mutations), and 791 with MAPT mutations (254 families, 67 different mutations). Mean age at symptom onset and at death was 49·5 years (SD 10·0; onset) and 58·5 years (11·3; death) in the MAPT group, 58·2 years (9·8; onset) and 65·3 years (10·9; death) in the C9orf72 group, and 61·3 years (8·8; onset) and 68·8 years (9·7; death) in the GRN group. Mean disease duration was 6·4 years (SD 4·9) in the C9orf72 group, 7·1 years (3·9) in the GRN group, and 9·3 years (6·4) in the MAPT group. Individual age at onset and at death was significantly correlated with both parental age at onset and at death and with mean family age at onset and at death in all three groups, with a stronger correlation observed in the MAPT group (r=0·45 between individual and parental age at onset, r=0·63 between individual and mean family age at onset, r=0·58 between individual and parental age at death, and r=0·69 between individual and mean family age at death) than in either the C9orf72 group (r=0·32 individual and parental age at onset, r=0·36 individual and mean family age at onset, r=0·38 individual and parental age at death, and r=0·40 individual and mean family age at death) or the GRN group (r=0·22 individual and parental age at onset, r=0·18 individual and mean family age at onset, r=0·22 individual and parental age at death, and r=0·32 individual and mean family age at death). Modelling showed that the variability in age at onset and at death in the MAPT group was explained partly by the specific mutation (48%, 95% CI 35–62, for age at onset; 61%, 47–73, for age at death), and even more by family membership (66%, 56–75, for age at onset; 74%, 65–82, for age at death). In the GRN group, only 2% (0–10) of the variability of age at onset and 9% (3–21) of that of age of death was explained by the specific mutation, whereas 14% (9–22) of the variability of age at onset and 20% (12–30) of that of age at death was explained by family membership. In the C9orf72 group, family membership explained 17% (11–26) of the variability of age at onset and 19% (12–29) of that of age at death. Our study showed that age at symptom onset and at death of people with genetic frontotemporal dementia is influenced by genetic group and, particularly for MAPT mutations, by the specific mutation carried and by family membership. Although estimation of age at onset will be an important factor in future pre-symptomatic therapeutic trials for all three genetic groups, our study suggests that data from other members of the family will be particularly helpful only for individuals with MAPT mutations. Further work in identifying both genetic and environmental factors that modify phenotype in all groups will be important to improve such estimates. UK Medical Research Council, National Institute for Health Research, and Alzheimer's Society.

Unlike familial Alzheimer’s disease, we have been unable to accurately predict symptom onset in presymptomatic familial frontotemporal dementia (f-FTD) mutation carriers, which is a major hurdle to designing disease prevention trials. We developed multimodal models for f-FTD disease progression and estimated clinical trial sample sizes in C9orf72 , GRN and MAPT mutation carriers. Models included longitudinal clinical and neuropsychological scores, regional brain volumes and plasma neurofilament light chain (NfL) in 796 carriers and 412 noncarrier controls. We found that the temporal ordering of clinical and biomarker progression differed by genotype. In prevention-trial simulations using model-based patient selection, atrophy and NfL were the best endpoints, whereas clinical measures were potential endpoints in early symptomatic trials. f-FTD prevention trials are feasible but will likely require global recruitment efforts. These disease progression models will facilitate the planning of f-FTD clinical trials, including the selection of optimal endpoints and enrollment criteria to maximize power to detect treatment effects. Empirically based models of disease progression in familial frontotemporal dementia reveal the relative ordering of clinical, neuroimaging, and fluid biomarker changes and facilitate novel clinical trial designs

The conect4children (c4c) initiative was established to facilitate the development of new drugs and other therapies for paediatric patients. It is widely recognised that there are not enough medicines tested for all relevant ages of the paediatric population. To overcome this, it is imperative that clinical data from different sources are interoperable and can be pooled for larger post hoc studies. c4c has collaborated with the Clinical Data Interchange Standards Consortium (CDISC) to develop cross-cutting data resources that build on existing CDISC standards in an effort to standardise paediatric data. The natural next step was an extension to disease-specific data items. c4c brought together several existing initiatives and resources relevant to disease-specific data and analysed their use for standardising disease-specific data in clinical trials. Several case studies that combined disease-specific data from multiple trials have demonstrated the need for disease-specific data standardisation. We identified three relevant initiatives. These include European Reference Networks, European Joint Programme on Rare Diseases, and Pistoia Alliance. Other resources reviewed were National Cancer Institute Enterprise Vocabulary Services, CDISC standards, pharmaceutical company-specific data dictionaries, Human Phenotype Ontology, Phenopackets, Unified Registry for Inherited Metabolic Disorders, Orphacodes, Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP), and Observational Medical Outcomes Partnership. The collaborative partners associated with these resources were also reviewed briefly. A plan of action focussed on collaboration was generated for standardising disease-specific paediatric clinical trial data. A paediatric data standards multistakeholder and multi-project user group was established to guide the remaining actions—FAIRification of metadata, a Phenopackets pilot with RDCA-DAP, applying Orphacodes to case report forms of clinical trials, introducing CDISC standards into European Reference Networks, testing of the CDISC Pediatric User Guide using data from the mentioned resources and organisation of further workshops and educational materials.

Blood-accessible biomarkers offer promising insights into the pathogenesis of Duchenne muscular dystrophy (DMD) and other muscle diseases. Here, we quantified the relative abundance of 7,289 serum proteins using SomaScan proteomics in pre-treatment samples from 51 boys with DMD (aged 4 to <7) and 13 healthy controls from the VISION DMD (VBP15-004) trial. An independent validation cohort of untreated DMD boys (aged 4 to <8) from the FOR-DMD trial was also analyzed. Of the proteins screened, 26% and 15% were significantly elevated and decreased, respectively, in the serum of young DMD boys compared to controls (adjusted p-value < 0.05). A high correlation (Spearman r = 0.85) in fold changes was observed between the two datasets. Many proteins with altered levels overlapped with known markers of muscle injury, inflammation, regeneration, and extracellular matrix remodeling. Selected biomarkers were queried in two published muscle mRNA and a muscle snRNAseq dataset in DMD biopsies. Novel factors involved in muscle regeneration and ECM remodeling were identified. This larger-scale, multi-clinical trial-based cohort study in untreated DMD boys substantially expands the catalog of circulating biomarkers, highlighting early-stage pathological processes. These findings can help identify new therapeutic targets and develop clinically actionable biomarkers to assess disease progression and response to therapies.

With 13 sets of debates confronting both large and small companies today, this excellent resource provides critical insights and various perspectives on leading human capital issues that are paramount to managing successful organizations. Written by authors, consultants, academics, and practitioners from across 14 countries, this record addresses various topics, including talent management, organizational effectiveness, leadership development, human resources strategy and planning, and building a strategic human resources function.

Understanding the influence of macromolecular crowding and nanoparticles on the formation of in-register \\(\\beta\\)-sheets, the primary structural component of amyloid fibrils, is a first step towards describing \\emph{in vivo} protein aggregation and interactions between synthetic materials and proteins. Using all atom molecular simulations in implicit solvent we illustrate the effects of nanoparticle size, shape, and volume fraction on oligomer formation of an amyloidogenic peptide from the transthyretin protein. Surprisingly, we find that inert spherical crowding particles destabilize in-register \\(\\beta\\)-sheets formed by dimers while stabilizing \\(\\beta\\)-sheets comprised of trimers and tetramers. As the radius of the nanoparticle increases crowding effects decrease, implying smaller crowding particles have the largest influence on the earliest amyloid species. We explain these results using a theory based on the depletion effect. Finally, we show that spherocylindrical crowders destabilize the ordered \\(\\beta\\)-sheet dimer to a greater extent than spherical crowders, which underscores the influence of nanoparticle shape on protein aggregation.