Evaluation of human acellular dermis versus porcine acellular dermis in an in vivo model for incisional hernia repair

Incisional hernias commonly occur following abdominal wall surgery. Human acellular dermal matrices (HADM) are widely used in abdominal wall defect repair. Xenograft acellular dermal matrices, particularly those made from porcine tissues (PADM), have recently experienced increased usage. The purpose of this study was to compare the effectiveness of HADM and PADM in the repair of incisional abdominal wall hernias in a rabbit model. A review from earlier work of differences between human allograft acellular dermal matrices (HADM) and porcine xenograft acellular dermal matrices (PADM) demonstrated significant differences (P < 0.05) in mechanical properties: Tensile strength 15.7 MPa vs. 7.7 MPa for HADM and PADM, respectively. Cellular (fibroblast) infiltration was significantly greater for HADM vs. PADM (Armour). The HADM exhibited a more natural, less degraded collagen by electrophoresis as compared to PADM. The rabbit model surgically established an incisional hernia, which was repaired with one of the two acellular dermal matrices 3 weeks after the creation of the abdominal hernia. The animals were euthanized at 4 and 20 weeks and the wounds evaluated. Tissue ingrowth into the implant was significantly faster for the HADM as compared to PADM, 54 vs. 16% at 4 weeks, and 58 vs. 20% for HADM and PADM, respectively at 20 weeks. The original, induced hernia defect (6 cm2) was healed to a greater extent for HADM vs. PADM: 2.7 cm2 unremodeled area for PADM vs. 1.0 cm² for HADM at 20 weeks. The inherent uniformity of tissue ingrowth and remodeling over time was very different for the HADM relative to the PADM. No differences were observed at the 4-week end point. However, the 20-week data exhibited a statistically different level of variability in the remodeling rate with the mean standard deviation of 0.96 for HADM as contrasted to a mean standard deviation of 2.69 for PADM. This was significant with P < 0.05 using a one tail F test for the inherent variability of the standard deviation. No significant differences between the PADM and HADM for adhesion, inflammation, fibrous tissue or neovascularization were noted

Rewriting a Deep Generative Model

A deep generative model such as a GAN learns to model a rich set of semantic and physical rules about the target distribution, but up to now, it has been obscure how such rules are encoded in the network, or how a rule could be changed. In this paper, we introduce a new problem setting: manipulation of specific rules encoded by a deep generative model. To address the problem, we propose a formulation in which the desired rule is changed by manipulating a layer of a deep network as a linear associative memory. We derive an algorithm for modifying one entry of the associative memory, and we demonstrate that several interesting structural rules can be located and modified within the layers of state-of-the-art generative models. We present a user interface to enable users to interactively change the rules of a generative model to achieve desired effects, and we show several proof-of-concept applications. Finally, results on multiple datasets demonstrate the advantage of our method against standard fine-tuning methods and edit transfer algorithms.Comment: ECCV 2020 (oral). Code at https://github.com/davidbau/rewriting. For videos and demos see https://rewriting.csail.mit.edu

Speed/Accuracy Trade-Off between the Habitual and the Goal-Directed Processes

Instrumental responses are hypothesized to be of two kinds: habitual and goal-directed, mediated by the sensorimotor and the associative cortico-basal ganglia circuits, respectively. The existence of the two heterogeneous associative learning mechanisms can be hypothesized to arise from the comparative advantages that they have at different stages of learning. In this paper, we assume that the goal-directed system is behaviourally flexible, but slow in choice selection. The habitual system, in contrast, is fast in responding, but inflexible in adapting its behavioural strategy to new conditions. Based on these assumptions and using the computational theory of reinforcement learning, we propose a normative model for arbitration between the two processes that makes an approximately optimal balance between search-time and accuracy in decision making. Behaviourally, the model can explain experimental evidence on behavioural sensitivity to outcome at the early stages of learning, but insensitivity at the later stages. It also explains that when two choices with equal incentive values are available concurrently, the behaviour remains outcome-sensitive, even after extensive training. Moreover, the model can explain choice reaction time variations during the course of learning, as well as the experimental observation that as the number of choices increases, the reaction time also increases. Neurobiologically, by assuming that phasic and tonic activities of midbrain dopamine neurons carry the reward prediction error and the average reward signals used by the model, respectively, the model predicts that whereas phasic dopamine indirectly affects behaviour through reinforcing stimulus-response associations, tonic dopamine can directly affect behaviour through manipulating the competition between the habitual and the goal-directed systems and thus, affect reaction time

Effort-related functions of nucleus accumbens dopamine and associated forebrain circuits

Background Over the last several years, it has become apparent that there are critical problems with the hypothesis that brain dopamine (DA) systems, particularly in the nucleus accumbens, directly mediate the rewarding or primary motivational characteristics of natural stimuli such as food. Hypotheses related to DA function are undergoing a substantial restructuring, such that the classic emphasis on hedonia and primary reward is giving way to diverse lines of research that focus on aspects of instrumental learning, reward prediction, incentive motivation, and behavioral activation. Objective The present review discusses dopaminergic involvement in behavioral activation and, in particular, emphasizes the effort-related functions of nucleus accumbens DA and associated forebrain circuitry. Results The effects of accumbens DA depletions on food-seeking behavior are critically dependent upon the work requirements of the task. Lever pressing schedules that have minimal work requirements are largely unaffected by accumbens DA depletions, whereas reinforcement schedules that have high work (e.g., ratio) requirements are substantially impaired by accumbens DA depletions. Moreover, interference with accumbens DA transmission exerts a powerful influence over effort-related decision making. Rats with accumbens DA depletions reallocate their instrumental behavior away from food-reinforced tasks that have high response requirements, and instead, these rats select a less-effortful type of food-seeking behavior. Conclusions Along with prefrontal cortex and the amygdala, nucleus accumbens is a component of the brain circuitry regulating effort-related functions. Studies of the brain systems regulating effort-based processes may have implications for understanding drug abuse, as well as energy-related disorders such as psychomotor slowing, fatigue, or anergia in depression