Uncovering the Hidden Conflicts in Securities Class Action Litigation: Lessons from the State Street Case

Courts, Congress, and commentators have long worried that stockholder plaintiffs in securities and M&A litigation and their counsel may pursue suits that benefit themselves rather than absent stockholders or the corporations in which they invest. Following congressional reforms that encouraged the appointment of institutional stockholders as lead plaintiffs in securities actions, significant academic commentary has focused on the problem of “pay to play”—the possibility that class action law firms encourage litigation by making donations to politicians with influence over institutional stockholders, particularly public sector pension funds. A recent federal securities class action in the District of Massachusetts, however, suggests that the networks of influence between class plaintiffs and their counsel are much more complex and difficult to detect. After appointing a special master to look into fee issues, the court discovered that a large class action firm had paid over $4 million in “bare referral” fees to an attorney who did little work on the case but had recommended the larger firm to a public sector pension fund “after considerable favors, political activity, money spent and time dedicated in Arkansas.” This is only one of the less-visible ways that class counsel may route benefits to class plaintiffs. Current class action processes do not routinely identify these potential conflicts of interest. Instead, they tend to surface when nonlitigants bring them to public attention. Because neither the lead plaintiff nor the defendants have a strong incentive to voluntarily address these conflicts, we propose revisions to the class certification process that would require class plaintiffs to disclose more information regarding their relationships with class counsel. We also propose that courts routinely appoint special masters or class guardians as part of the settlement approval process to ensure that class plaintiffs’ statements are subject to discovery and adversarial review

Near-Optimal Evasion of Convex-Inducing Classifiers

Classifiers are often used to detect miscreant activities. We study how an adversary can efficiently query a classifier to elicit information that allows the adversary to evade detection at near-minimal cost. We generalize results of Lowd and Meek (2005) to convex-inducing classifiers. We present algorithms that construct undetected instances of near-minimal cost using only polynomially many queries in the dimension of the space and without reverse engineering the decision boundary.Comment: 8 pages; to appear at AISTATS'201

Exploiting Machine Learning to Subvert Your Spam Filter

Using statistical machine learning for making security decisions introduces new vulnerabilities in large scale systems. This paper shows how an adversary can exploit statistical machine learning, as used in the SpamBayes spam filter, to render it useless—even if the adversary’s access is limited to only 1 % of the training messages. We further demonstrate a new class of focused attacks that successfully prevent victims from receiving specific email messages. Finally, we introduce two new types of defenses against these attacks.

“We are fingers of a hand that make a fist": Working class alliances in Colorado River water protests in the Mexicali Valley, Mexico

This article explores recent water protests across northern Mexico which emanated from the Mexicali Valley in Baja California, Mexico. Beginning in 2015, communal farmers and industrial labourers, among other groups, aligned under the banner of Defense of Water to protest the construction of a United States-based beverage production facility. Through interviews, participant observation and archival research, we study this social movement through a class-based, historical lens to show how the meaning of water presupposes and represents a century of class politics that has allowed seemingly disparate groups to find meaning and build alliances within it. It is this history that has allowed protesters to achieve shared goals

Dimeric 2G12 as a Potent Protection against HIV-1

We previously showed that broadly neutralizing anti-HIV-1 antibody 2G12 (human IgG1) naturally forms dimers that are more potent than monomeric 2G12 in in vitro neutralization of various strains of HIV-1. In this study, we have investigated the protective effects of monomeric versus dimeric 2G12 against HIV-1 infection in vivo using a humanized mouse model. Our results showed that passively transferred, purified 2G12 dimer is more potent than 2G12 monomer at preventing CD4 T cell loss and suppressing the increase of viral load following HIV-1 infection of humanized mice. Using humanized mice bearing IgG “backpack” tumors that provided 2G12 antibodies continuously, we found that a sustained dimer concentration of 5–25 µg/ml during the course of infection provides effective protection against HIV-1. Importantly, 2G12 dimer at this concentration does not favor mutations of the HIV-1 envelope that would cause the virus to completely escape 2G12 neutralization. We have therefore identified dimeric 2G12 as a potent prophylactic reagent against HIV-1 in vivo, which could be used as part of an antibody cocktail to prevent HIV-1 infection

DIDA: Distributed Indexing Dispatched Alignment

One essential application in bioinformatics that is affected by the high-throughput sequencing data deluge is the sequence alignment problem, where nucleotide or amino acid sequences are queried against targets to find regions of close similarity. When queries are too many and/or targets are too large, the alignment process becomes computationally challenging. This is usually addressed by preprocessing techniques, where the queries and/or targets are indexed for easy access while searching for matches. When the target is static, such as in an established reference genome, the cost of indexing is amortized by reusing the generated index. However, when the targets are non-static, such as contigs in the intermediate steps of a de novo assembly process, a new index must be computed for each run. To address such scalability problems, we present DIDA, a novel framework that distributes the indexing and alignment tasks into smaller subtasks over a cluster of compute nodes. It provides a workflow beyond the common practice of embarrassingly parallel implementations. DIDA is a cost-effective, scalable and modular framework for the sequence alignment problem in terms of memory usage and runtime. It can be employed in large-scale alignments to draft genomes and intermediate stages of de novo assembly runs. The DIDA source code, sample files and user manual are available through http://www.bcgsc.ca/platform/bioinfo/software/dida. The software is released under the British Columbia Cancer Agency License (BCCA), and is free for academic use

Quantifying the Direct Radiative Effect of Absorbing Aerosols for Numerical Weather Prediction: A Case Study

We conceptualize aerosol radiative transfer processes arising from the hypothetical coupling of a global aerosol transport model and a global numerical weather prediction model by applying the US Naval Research Laboratory Navy Aerosol Analysis and Prediction System (NAAPS) and the Navy Global Environmental Model (NAVGEM) meteorological and surface reflectance fields. A unique experimental design during the 2013 NASA Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) field mission allowed for collocated airborne sampling by the high spectral resolution Lidar (HSRL), the Airborne Multi-angle SpectroPolarimetric Imager (AirMSPI), up/down shortwave (SW) and infrared (IR) broadband radiometers, as well as NASA A-Train support from the Moderate Resolution Imaging Spectroradiometer (MODIS), to attempt direct aerosol forcing closure. The results demonstrate the sensitivity of modeled fields to aerosol radiative fluxes and heating rates, specifically in the SW, as induced in this event from transported smoke and regional urban aerosols. Limitations are identified with respect to aerosol attribution, vertical distribution, and the choice of optical and surface polarimetric properties, which are discussed within the context of their influence on numerical weather prediction output that is particularly important as the community propels forward towards inline aerosol modeling within global forecast systems

Contact inhibition of locomotion and mechanical cross-talk between cell-cell and cell-substrate adhesion determines the pattern of junctional tension in epithelial cell aggregates

We generated a computational approach to analyze the biomechanics of epithelial cell aggregates, either island or stripes or entire monolayers, that combines both vertex and contact-inhibition-of-locomotion models to include both cell-cell and cell-substrate adhesion. Examination of the distribution of cell protrusions (adhesion to the substrate) in the model predicted high order profiles of cell organization that agree with those previously seen experimentally. Cells acquired an asymmetric distribution of basal protrusions, traction forces and apical aspect ratios that decreased when moving from the edge to the island center. Our in silico analysis also showed that tension on cell-cell junctions and apical stress is not homogeneous across the island. Instead, these parameters are higher at the island center and scales up with island size, which we confirmed experimentally using laser ablation assays and immunofluorescence. Without formally being a 3-dimensional model, our approach has the minimal elements necessary to reproduce the distribution of cellular forces and mechanical crosstalk as well as distribution of principal stress in cells within epithelial cell aggregates. By making experimental testable predictions, our approach would benefit the mechanical analysis of epithelial tissues, especially when local changes in cell-cell and/or cell-substrate adhesion drive collective cell behavior.Comment: 39 pages, 8 Figures. Supplementary Information is include

Proteomics: a subcellular look at spermatozoa

<p>Abstract</p> <p>Background</p> <p>Male-factor infertility presents a vexing problem for many reproductively active couples. Many studies have focused on abnormal sperm parameters. Recent advances in proteomic techniques, especially in mass spectrometry, have aided in the study of sperm and more specifically, sperm proteins. The aim of this study was to review the current literature on the various proteomic techniques, and their usefulness in diagnosing sperm dysfunction and potential applications in the clinical setting.</p> <p>Methods</p> <p>Review of PubMed database. Key words: spermatozoa, proteomics, protein, proteome, 2D-PAGE, mass spectrometry.</p> <p>Results</p> <p>Recently employed proteomic methods, such as two-dimensional polyacrylamide gel electrophoresis, mass spectrometry, and differential in gel electrophoresis, have identified numerous sperm-specific proteins. They also have provided a further understanding of protein function involved in sperm processes and for the differentiation between normal and abnormal states. In addition, studies on the sperm proteome have demonstrated the importance of post-translational modifications, and their ability to bring about physiological changes in sperm function. No longer do researchers believe that in order for them to elucidate the biochemical functions of genes, mere knowledge of the human genome sequence is sufficient. Moreover, a greater understanding of the physiological function of every protein in the tissue-specific proteome is essential in order to unravel the biological display of the human genome.</p> <p>Conclusion</p> <p>Recent advances in proteomic techniques have provided insight into sperm function and dysfunction. Several multidimensional separation techniques can be utilized to identify and characterize spermatozoa. Future developments in bioinformatics can further assist researchers in understanding the vast amount of data collected in proteomic studies. Moreover, such advances in proteomics may help to decipher metabolites which can act as biomarkers in the detection of sperm impairments and to potentially develop treatment for infertile couples.</p> <p>Further comprehensive studies on sperm-specific proteome, mechanisms of protein function and its proteolytic regulation, biomarkers and functional pathways, such as oxidative-stress induced mechanisms, will provide better insight into physiological functions of the spermatozoa. Large-scale proteomic studies using purified protein assays will eventually lead to the development of novel biomarkers that may allow for detection of disease states, genetic abnormalities, and risk factors for male infertility. Ultimately, these biomarkers will allow for a better diagnosis of sperm dysfunction and aid in drug development.</p