Securities Regulation: Shareholder Derivative Actions Against Insiders Under Rule 10b-5

After a general examination of Rule 10b-5 in the context of its traditional application, this comment focuses on the recent developments concerning the rule\u27s function as a weapon for the enforcement of controlling insiders\u27 duties to their corporation

Influence of Different Stabilization Systems and Multiple Ultraviolet A (UVA) Aging/Recycling Steps on Physicochemical, Mechanical, Colorimetric, and Thermal-Oxidative Properties of ABS

Commercially mass-polymerized acrylonitrile–butadiene–styrene (ABS) polymers, pristine or modified by stabilization systems, have been injection molded and repeatedly exposed to ultraviolet A (UVA) radiation, mechanical recycling, and extra injection molding steps to study the impact of such treatments on the physicochemical, mechanical, colorimetric, and thermal-oxidative characteristics. The work focus on mimicking the effect of solar radiation behind a window glass as relevant during the lifetime of ABS polymers incorporated in electrical and electronic equipment, and interior automotive parts by using UVA technique. The accelerated aging promotes degradation and embrittlement of the surface exposed to radiation and causes physical aging, deteriorating mechanical properties, with an expressive reduction of impact strength (unnotched: up to 900%; notched: up to 250%) and strain at break (>1000%), as well as an increase in the yellowing index (e.g., 600%). UV-exposition promotes a slight increase in the tensile modulus (e.g., 10%). The addition of antioxidants (AOs) leads to a limited stabilization during the first UVA aging, although the proper AO formulation increases the thermal-oxidative resistance during all the cycles. Mechanical recycling promotes an increase in strain at break and unnotched impact strength alongside a slight decrease in tensile modulus, due to disruption of the brittle surface and elimination of the physical aging.This research was funded by the European Union’s Horizon 2020 Research and Innovation Program, grant number 730308

Automatic C-Plane Detection in Pelvic Floor Transperineal Volumetric Ultrasound

Transperineal volumetric ultrasound (US) imaging has become routine practice for diagnosing pelvic floor disease (PFD). Hereto, clinical guidelines stipulate to make measurements in an anatomically defined 2D plane within a 3D volume, the so-called C-plane. This task is currently performed manually in clinical practice, which is labour-intensive and requires expert knowledge of pelvic floor anatomy, as no computer-aided C-plane method exists. To automate this process, we propose a novel, guideline-driven approach for automatic detection of the C-plane. The method uses a convolutional neural network (CNN) to identify extreme coordinates of the symphysis pubis and levator ani muscle (which define the C-plane) directly via landmark regression. The C-plane is identified in a postprocessing step. When evaluated on 100 US volumes, our best performing method (multi-task regression with UNet) achieved a mean error of 6.05 mm and 4.81 ∘ and took 20 s. Two experts blindly evaluated the quality of the automatically detected planes and manually defined the (gold standard) C-plane in terms of their clinical diagnostic quality. We show that the proposed method performs comparably to the manual definition. The automatic method reduces the average time to detect the C-plane by 100 s and reduces the need for high-level expertise in PFD US assessment

Automatic C-Plane Detection in Pelvic Floor Transperineal Volumetric Ultrasound

© 2020, Springer Nature Switzerland AG. Transperineal volumetric ultrasound (US) imaging has become routine practice for diagnosing pelvic floor disease (PFD). Hereto, clinical guidelines stipulate to make measurements in an anatomically defined 2D plane within a 3D volume, the so-called C-plane. This task is currently performed manually in clinical practice, which is labour-intensive and requires expert knowledge of pelvic floor anatomy, as no computer-aided C-plane method exists. To automate this process, we propose a novel, guideline-driven approach for automatic detection of the C-plane. The method uses a convolutional neural network (CNN) to identify extreme coordinates of the symphysis pubis and levator ani muscle (which define the C-plane) directly via landmark regression. The C-plane is identified in a postprocessing step. When evaluated on 100 US volumes, our best performing method (multi-task regression with UNet) achieved a mean error of 6.05 mm and 4.81 and took 20 s. Two experts blindly evaluated the quality of the automatically detected planes and manually defined the (gold standard) C-plane in terms of their clinical diagnostic quality. We show that the proposed method performs comparably to the manual definition. The automatic method reduces the average time to detect the C-plane by 100 s and reduces the need for high-level expertise in PFD US assessment

Pushing forward the predictive power of kinetic Monte Carlo simulations for detailed (de)polymerization chemistries

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Performance analysis of kinetic Monte Carlo algorithms for synthesis of linear polymers

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A20/TNFAIP3 heterozygosity predisposes to behavioral symptoms in a mouse model for neuropsychiatric lupus

Background: Neuropsychiatric lupus (NPSLE) refers to the neurological and psychiatric manifestations that are commonly observed in patients with systemic lupus erythematosus (SLE). An important question regarding the pathogenesis of NPSLE is whether the symptoms are caused primarily by CNS-intrinsic mechanisms or develop as a consequence of systemic autoimmunity. Currently used spontaneous mouse models for SLE have already contributed significantly to unraveling how systemic immunity affects the CNS. However, they are less suited when interested in CNS primary mechanisms. In addition, none of these models are based on genes that are associated with SLE. In this study, we evaluate the influence of A20, a well-known susceptibility locus for SLE, on behavior and CNS-associated changes in inflammatory markers. Furthermore, given the importance of environmental triggers for disease onset and progression, the influence of an acute immunological challenge was evaluated. Methods: Female and male A20 heterozygous mice (A20+/−) and wildtype littermates were tested in an extensive behavioral battery. This was done at the age of 10±2weeks and 24 ​± ​2 weeks to evaluate the impact of aging. To investigate the contribution of an acute immunological challenge, LPS was injected intracerebroventricularly at the age of 10±2weeks followed by behavioral analysis. Underlying molecular mechanisms were evaluated in gene expression assays on hippocampus and cortex. White blood cell count and blood-brain barrier permeability were analyzed to determine whether peripheral inflammation is a relevant factor. Results: A20 heterozygosity predisposes to cognitive symptoms that were observed at the age of 10 ​± ​2 weeks and 24 ​± ​2 weeks. Young A20+/− males and females showed a subtle cognitive phenotype (10±2weeks) with distinct neuroinflammatory phenotypes. Aging was associated with clear neuroinflammation in female A20+/− mice only. The genetic predisposition in combination with an environmental stimulus exacerbates the behavioral impairments related to anxiety, cognitive dysfunction and sensorimotor gating. This was predominantly observed in females. Furthermore, signs of neuroinflammation were solely observed in female A20+/− mice. All above observations were made in the absence of peripheral inflammation and of changes in blood-brain barrier permeability, thus consistent with the CNS-primary hypothesis. Conclusions: We show that A20 heterozygosity is a predisposing factor for NPSLE. Further mechanistic insight and possible therapeutic interventions can be studied in this mouse model that recapitulates several key hallmarks of the disease

Continuous Ultrasound Speckle Tracking with Gaussian Mixtures

Speckle tracking echocardiography (STE) is now widely used for measuring strain, deformations, and motion in cardiology. STE involves three successive steps: acquisition of individual frames, speckle detection, and image registration using speckles as landmarks. This work proposes to avoid explicit detection and registration by representing dynamic ultrasound images as sparse collections of moving Gaussian elements in the continuous joint space-time space. Individual speckles or local clusters of speckles are approximated by a single multivariate Gaussian kernel with associated linear trajectory over a short time span. A hierarchical tree-structured model is fitted to sampled input data such that predicted image estimates can be retrieved by regression after reconstruction, allowing a (bias-variance) trade-off between model complexity and image resolution. The inverse image reconstruction problem is solved with an online Bayesian statistical estimation algorithm. Experiments on clinical data could estimate subtle sub-pixel accurate motion that is difficult to capture with frame-to-frame elastic image registration techniques

Feature- versus rule-based generalization in rats, pigeons and humans

Humans can spontaneously create rules that allow them to efficiently generalize what they have learned to novel situations. An enduring question is whether rule-based generalization is uniquely human or whether other animals can also abstract rules and apply them to novel situations. In recent years, there have been a number of high-profile claims that animals such as rats can learn rules. Most of those claims are quite weak because it is possible to demonstrate that simple associative systems (which do not learn rules) can account for the behavior in those tasks. Using a procedure that allows us to clearly distinguish feature-based from rule-based generalization (the Shanks-Darby procedure), we demonstrate that adult humans show rule-based generalization in this task, while generalization in rats and pigeons was based on featural overlap between stimuli. In brief, when learning that a stimulus made of two components ("AB") predicts a different outcome than its elements ("A" and "B"), people spontaneously abstract an opposites rule and apply it to new stimuli (e.g., knowing that "C" and "D" predict one outcome, they will predict that "CD" predicts the opposite outcome). Rats and pigeons show the reverse behavior-they generalize what they have learned, but on the basis of similarity (e.g., "CD" is similar to "C" and "D", so the same outcome is predicted for the compound stimulus as for the components). Genuinely rule-based behavior is observed in humans, but not in rats and pigeons, in the current procedure