Cytoplasmic Accumulation and Aggregation of TDP-43 upon Proteasome Inhibition in Cultured Neurons

Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are characterized by intraneuronal deposition of the nuclear TAR DNA-binding protein 43 (TDP-43) caused by unknown mechanisms. Here, we studied TDP-43 in primary neurons under different stress conditions and found that only proteasome inhibition by MG-132 or lactacystin could induce significant cytoplasmic accumulation of TDP-43, a histopathological hallmark in disease. This cytoplasmic accumulation was accompanied by phosphorylation, ubiquitination and aggregation of TDP-43, recapitulating major features of disease. Proteasome inhibition produced similar effects in both hippocampal and cortical neurons, as well as in immortalized motor neurons. To determine the contribution of TDP-43 to cell death, we reduced TDP-43 expression using small interfering RNA (siRNA), and found that reduced levels of TDP-43 dose-dependently rendered neurons more vulnerable to MG-132. Taken together, our data suggests a role for the proteasome in subcellular localization of TDP-43, and possibly in disease

Signaling in Secret: Pay-for-Performance and the Incentive and Sorting Effects of Pay Secrecy

Key Findings: Pay secrecy adversely impacts individual task performance because it weakens the perception that an increase in performance will be accompanied by increase in pay; Pay secrecy is associated with a decrease in employee performance and retention in pay-for-performance systems, which measure performance using relative (i.e., peer-ranked) criteria rather than an absolute scale (see Figure 2 on page 5); High performing employees tend to be most sensitive to negative pay-for- performance perceptions; There are many signals embedded within HR policies and practices, which can influence employees’ perception of workplace uncertainty/inequity and impact their performance and turnover intentions; and When pay transparency is impractical, organizations may benefit from introducing partial pay openness to mitigate these effects on employee performance and retention

The multiplicity of performance management systems:Heterogeneity in multinational corporations and management sense-making

This field study examines the workings of multiple performance measurement systems (PMSs) used within and between a division and Headquarters (HQ) of a large European corporation. We explore how multiple PMSs arose within the multinational corporation. We first provide a first‐order analysis which explains how managers make sense of the multiplicity and show how an organization's PMSs may be subject to competing processes for control that result in varied systems, all seemingly functioning, but with different rationales and effects. We then provide a second‐order analysis based on a sense‐making perspective that highlights the importance of retrospective understandings of the organization's history and the importance of various legitimacy expectations to different parts of the multinational. Finally, we emphasize the role of social skill in sense‐making that enables the persistence of multiple systems and the absence of overt tensions and conflict within organizations

Machine Learning in Automated Text Categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey

Toll-like receptor engagement converts T-cell autoreactivity into overt autoimmune disease

Autoimmune diabetes mellitus in humans is characterized by immunological destruction of pancreatic beta islet cells. We investigated the circumstances under which CD8(+) T cells specific for pancreatic beta-islet antigens induce disease in mice expressing lymphocytic choriomeningitis virus (LCMV) glycoprotein (GP) as a transgene under the control of the rat insulin promoter. In contrast to infection with LCMV, immunization with LCMV-GP derived peptide did not induce autoimmune diabetes despite large numbers of autoreactive cytotoxic T cells. Only subsequent treatment with Toll-like receptor ligands elicited overt autoimmune disease. This difference was critically regulated by the peripheral target organ itself, which upregulated class I major histocompatibility complex (MHC) in response to systemic Toll-like receptor-triggered interferon-alpha production. These data identify the 'inflammatory status' of the target organ as a separate and limiting factor determining the development of autoimmune disease

Are mice good models for human neuromuscular disease? Comparing muscle excursions in walking between mice and humans

The mouse is one of the most widely used animal models to study neuromuscular diseases and test new therapeutic strategies. However, findings from successful pre-clinical studies using mouse models frequently fail to translate to humans due to various factors. Differences in muscle function between the two species could be crucial but often have been overlooked. The purpose of this study was to evaluate and compare muscle excursions in walking between mice and humans