Publisher Correction: Deep learning enables fast and dense single-molecule localization with high accuracy

In the version of this Article initially published, Jacob H. Macke and Jonas Ries were not listed as corresponding authors. Their contact information and designation as corresponding authors are now included. The error has been corrected in the online version of the Article

Robustness of Learning That Is Based on Covariance-Driven Synaptic Plasticity

It is widely believed that learning is due, at least in part, to long-lasting modifications of the strengths of synapses in the brain. Theoretical studies have shown that a family of synaptic plasticity rules, in which synaptic changes are driven by covariance, is particularly useful for many forms of learning, including associative memory, gradient estimation, and operant conditioning. Covariance-based plasticity is inherently sensitive. Even a slight mistuning of the parameters of a covariance-based plasticity rule is likely to result in substantial changes in synaptic efficacies. Therefore, the biological relevance of covariance-based plasticity models is questionable. Here, we study the effects of mistuning parameters of the plasticity rule in a decision making model in which synaptic plasticity is driven by the covariance of reward and neural activity. An exact covariance plasticity rule yields Herrnstein's matching law. We show that although the effect of slight mistuning of the plasticity rule on the synaptic efficacies is large, the behavioral effect is small. Thus, matching behavior is robust to mistuning of the parameters of the covariance-based plasticity rule. Furthermore, the mistuned covariance rule results in undermatching, which is consistent with experimentally observed behavior. These results substantiate the hypothesis that approximate covariance-based synaptic plasticity underlies operant conditioning. However, we show that the mistuning of the mean subtraction makes behavior sensitive to the mistuning of the properties of the decision making network. Thus, there is a tradeoff between the robustness of matching behavior to changes in the plasticity rule and its robustness to changes in the properties of the decision making network

Etoricoxib - preemptive and postoperative analgesia (EPPA) in patients with laparotomy or thoracotomy - design and protocols

<p>Abstract</p> <p>Background and Objective</p> <p>Our objective was to report on the design and essentials of the <it>Etoricoxib </it>protocol<it>- Preemptive and Postoperative Analgesia (EPPA) </it>Trial, investigating whether preemptive analgesia with cox-2 inhibitors is more efficacious than placebo in patients who receive either laparotomy or thoracotomy.</p> <p>Design and Methods</p> <p>The study is a 2 × 2 factorial armed, double blinded, bicentric, randomised placebo-controlled trial comparing (a) etoricoxib and (b) placebo in a pre- and postoperative setting. The total observation period is 6 months. According to a power analysis, 120 patients scheduled for abdominal or thoracic surgery will randomly be allocated to either the preemptive or the postoperative treatment group. These two groups are each divided into two arms. Preemptive group patients receive etoricoxib prior to surgery and either etoricoxib again or placebo postoperatively. Postoperative group patients receive placebo prior to surgery and either placebo again or etoricoxib after surgery (2 × 2 factorial study design). The Main Outcome Measure is the cumulative use of morphine within the first 48 hours after surgery (measured by patient controlled analgesia PCA). Secondary outcome parameters include a broad range of tests including sensoric perception and genetic polymorphisms.</p> <p>Discussion</p> <p>The results of this study will provide information on the analgesic effectiveness of etoricoxib in preemptive analgesia and will give hints on possible preventive effects of persistent pain.</p> <p>Trial registration</p> <p>NCT00716833</p

The Immunomodulatory Role of Adjuvants in Vaccines Formulated with the Recombinant Antigens Ov-103 and Ov-RAL-2 against Onchocerca volvulus in Mice.

BACKGROUND: In some regions in Africa, elimination of onchocerciasis may be possible with mass drug administration, although there is concern based on several factors that onchocerciasis cannot be eliminated solely through this approach. A vaccine against Onchocerca volvulus would provide a critical tool for the ultimate elimination of this infection. Previous studies have demonstrated that immunization of mice with Ov-103 and Ov-RAL-2, when formulated with alum, induced protective immunity. It was hypothesized that the levels of protective immunity induced with the two recombinant antigens formulated with alum would be improved by formulation with other adjuvants known to enhance different types of antigen-specific immune responses. METHODOLOGY/ PRINCIPAL FINDINGS: Immunizing mice with Ov-103 and Ov-RAL-2 in conjunction with alum, Advax 2 and MF59 induced significant levels of larval killing and host protection. The immune response was biased towards Th2 with all three of the adjuvants, with IgG1 the dominant antibody. Improved larval killing and host protection was observed in mice immunized with co-administered Ov-103 and Ov-RAL-2 in conjunction with each of the three adjuvants as compared to single immunizations. Antigen-specific antibody titers were significantly increased in mice immunized concurrently with the two antigens. Based on chemokine levels, it appears that neutrophils and eosinophils participate in the protective immune response induced by Ov-103, and macrophages and neutrophils participate in immunity induced by Ov-RAL-2. CONCLUSIONS/SIGNIFICANCE: The mechanism of protective immunity induced by Ov-103 and Ov-RAL-2, with the adjuvants alum, Advax 2 and MF59, appears to be multifactorial with roles for cytokines, chemokines, antibody and specific effector cells. The vaccines developed in this study have the potential of reducing the morbidity associated with onchocerciasis in humans

Deep learning enables fast and dense single-molecule localization with high accuracy

Single-molecule localization microscopy (SMLM) has had remarkable success in imaging cellular structures with nanometer resolution, but standard analysis algorithms require sparse emitters, which limits imaging speed and labeling density. Here, we overcome this major limitation using deep learning. We developed DECODE (deep context dependent), a computational tool that can localize single emitters at high density in three dimensions with highest accuracy for a large range of imaging modalities and conditions. In a public software benchmark competition, it outperformed all other fitters on 12 out of 12 datasets when comparing both detection accuracy and localization error, often by a substantial margin. DECODE allowed us to acquire fast dynamic live-cell SMLM data with reduced light exposure and to image microtubules at ultra-high labeling density. Packaged for simple installation and use, DECODE will enable many laboratories to reduce imaging times and increase localization density in SMLM