Founder-Leader Transitions: The Role of Succession Planning in Nonprofit Organizations

There are both opportunities and challenges associated with executive transitions. Succession planning is becoming an increasingly significant topic for many nonprofit organizations in the Twin Cities. The retirement of the large generation of baby-boom leaders during the next decade is likely to have a direct impact on the capacity of organizations to sustain their work. This is especially critical for smaller organizations and those with founders or long-term executive directors who leave. These leaders have shaped their organizations throughout their tenure and are seen as synonymous with their organizations. Founders and long-term executives have a strong presence and vision for an organization, but when their time has come to move on, it\u27s critical to be prepared for the transition. Understanding the traits of founders and long-term executives, and knowing how to engage the next generation of leaders can facilitate a positive transition, which creates a sustainable organization. This study examines the factors that are critical for successful leadership transition planning in nonprofit organizations with founders or long-term executives

Accurate contact predictions using covariation techniques and machine learning.

Here we present the results of residue-residue contact predictions achieved in CASP11 by the CONSIP2 server, which is based around our MetaPSICOV contact prediction method. On a set of 40 target domains with a median family size of around 40 effective sequences, our server achieved an average top-L/5 long-range contact precision of 27%. MetaPSICOV method bases on a combination of classical contact prediction features, enhanced with three distinct covariation methods embedded in a two-stage neural network predictor. Some unique features of our approach are (1) the tuning between the classical and covariation features depending on the depth of the input alignment and (2) a hybrid approach to generate deepest possible multiple-sequence alignments by combining jackHMMer and HHblits. We discuss the CONSIP2 pipeline, our results and show that where the method underperformed, the major factor was relying on a fixed set of parameters for the initial sequence alignments and not attempting to perform domain splitting as a preprocessing step. Proteins 2015. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc

Computational Investigations of Backbone Dynamics in Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs), due to their dynamic nature, play important roles in molecular recognition, signalling, regulation, or binding of nucleic acids. IDPs have been extensively studied computationally in terms of binary disorder/order classification. This approach has proven to be fruitful and enabled researchers to estimate the amount of disorder in prokaryotic and eukaryotic genomes. Other computational methods – molecular dynamics, or other simulation techniques, require a starting structure. However, there are no approaches permitting insight into the behaviour of disordered ensembles from sequence alone. Such a method would facilitate the study of proteins of unknown structures, help to obtain a better classification of the disordered regions, and the design disorder-to-order transitions. In this work, I develop FRAGFOLD-IDP, a method to address this issue. Using a fragment-based structure prediction approach – FRAGFOLD, I generate the ensembles of IDPs and show that the features extracted from them correspond well with the backbone dynamics of NMR ensembles deposited in the PDB. FRAGFOLD-IDP predictions significantly improve over a naïve approach and help to get a better insight into the dynamics of the disordered ensembles. The results also show it is not necessary to predict the correct fold of the protein to reliably assign per-residue fluctuations to the sequence in question. This suggests that disorder is a local property and it does not depend on the protein fold. Next, I validate FRAGFOLD-IDP on the disorder classification task and show that the method performs comparably to machine learning-based approaches designed specifically for this task. I also found that FRAGFOLD-IDP produces results on par with DynaMine, a machine learning approach to predict the NMR order parameters and that the results of both methods are not correlated. Thus, I constructed a consensus neural network predictor, which takes the results of FRAGFOLD-IDP, DynaMine and physicochemical features to predict per-residue fluctuations, improving upon both input methods

Statistical evaluation of longitudinal data (1969-2011) from a non-denominational christian, regional crisis call center for frequency distributions of various call parameters

Anonymous calls to a non-denominational Christian, regional crisis call center were documented in Volunteer Call Logs (VCLs). VCLs (N=629,710) were coded for age, gender, marital status and category of distress for each call. Additionally, VCLs reported parameters such as time of day, day of week, month of year for each call. VCLs were tabulated for frequency and grouped according to parameters by administrative personnel. Frequency distributions of all parameters were summarized in an annual statistical report and made available to the general public. The frequency distributions of the archival annual statistical reports (1969-2011) were used to generate a description of the population served by the crisis call center. The statistical descriptions of the population were compared to a variety of regional, state, and national data

Gut microbiome in serious mental illnesses: A systematic review and critical evaluation.

Schizophrenia and bipolar disorder (BD) are associated with debilitating psychiatric and cognitive dysfunction, worse health outcomes, and shorter life expectancies. The pathophysiological understanding of and therapeutic resources for these neuropsychiatric disorders are still limited. Humans harbor over 1000 unique bacterial species in our gut, which have been linked to both physical and mental/cognitive health. The gut microbiome is a novel and promising avenue to understand the attributes of psychiatric diseases and, potentially, to modify them. Building upon our previous work, this systematic review evaluates the most recent evidence of the gut microbiome in clinical populations with serious mental illness (SMI). Sixteen articles that met our selection criteria were reviewed, including cross-sectional cohort studies and longitudinal treatment trials. All studies reported alterations in the gut microbiome of patients with SMI compared to non-psychiatric comparison subjects (NCs), and beta-diversity was consistently reported to be different between schizophrenia and NCs. Ruminococcaceae and Faecalibacterium were relatively decreased in BD, and abundance of Ruminococcaceae was reported across several investigations of SMI to be associated with better clinical characteristics. Lactic acid bacteria were relatively more abundant in SMI and associated with worse clinical outcomes. There was very limited evidence for the efficacy of probiotic or prebiotic interventions in SMI. As microbiome research in psychiatry is still nascent, the extant literature has several limitations. We critically evaluate the current data, including experimental approaches. There is a need for more unified methodological standards in order to arrive at robust biological understanding of microbial contributions to SMI

Predictions of Backbone Dynamics in Intrinsically Disordered Proteins Using De Novo Fragment-Based Protein Structure Predictions

Intrinsically disordaered proteins (IDPs) are a prevalent phenomenon with over 30% of human proteins estimated to have long disordered regions. Computational methods are widely used to study IDPs, however, nearly all treat disorder in a binary fashion, not accounting for the structural heterogeneity present in disordered regions. Here, we present a new de novo method, FRAGFOLD-IDP, which addresses this problem. Using 200 protein structural ensembles derived from NMR, we show that FRAGFOLD-IDP achieves superior results compared to methods which can predict related data (NMR order parameter, or crystallographic B-factor). FRAGFOLD-IDP produces very good predictions for 33.5% of cases and helps to get a better insight into the dynamics of the disordered ensembles. The results also show it is not necessary to predict the correct fold of the protein to reliably predict per-residue fluctuations. It implies that disorder is a local property and it does not depend on the fold. Our results are orthogonal to DynaMine, the only other method significantly better than the naïve prediction. We therefore combine these two using a neural network. FRAGFOLD-IDP enables better insight into backbone dynamics in IDPs and opens exciting possibilities for the design of disordered ensembles, disorder-to-order transitions, or design for protein dynamics

IL-4Rα Blockade by Dupilumab Decreases Staphylococcus aureus Colonization and Increases Microbial Diversity in Atopic Dermatitis.

Dupilumab is a fully human antibody to interleukin-4 receptor α that improves the signs and symptoms of moderate to severe atopic dermatitis (AD). To determine the effects of dupilumab on Staphylococcus aureus colonization and microbial diversity on the skin, bacterial DNA was analyzed from swabs collected from lesional and nonlesional skin in a double-blind, placebo-controlled study of 54 patients with moderate to severe AD randomized (1:1) and treated with either dupilumab (200 mg weekly) or placebo for 16 weeks. Microbial diversity and relative abundance of Staphylococcus were assessed by DNA sequencing of 16S ribosomal RNA, and absolute S. aureus abundance was measured by quantitative PCR. Before treatment, lesional skin had lower microbial diversity and higher overall abundance of S. aureus than nonlesional skin. During dupilumab treatment, microbial diversity increased and the abundance of S. aureus decreased. Pronounced changes were seen in nonlesional and lesional skin. Decreased S. aureus abundance during dupilumab treatment correlated with clinical improvement of AD and biomarkers of type 2 immunity. We conclude that clinical improvement of AD that is mediated by interleukin-4 receptor α inhibition and the subsequent suppression of type 2 inflammation is correlated with increased microbial diversity and reduced abundance of S. aureus

Improved protein contact predictions with the MetaPSICOV2 server in CASP12

In this paper, we present the results for the MetaPSICOV2 contact prediction server in the CASP12 community experiment (http://predictioncenter.org). Over the 35 assessed Free Modelling target domains the MetaPSICOV2 server achieved a mean precision of 43.27%, a substantial increase relative to the server's performance in the CASP11 experiment. In the following paper, we discuss improvements to the MetaPSICOV2 server, covering both changes to the neural network and attempts to integrate contact predictions on a domain basis into the prediction pipeline. We also discuss some limitations in the CASP12 assessment which may have overestimated the performance of our method