Natural Products of Marine Origin for the Treatment of Colorectal and Pancreatic Cancers: Mechanisms and Potential

Gastrointestinal cancer refers to malignancy of the accessory organs of digestion, and it includes colorectal cancer (CRC) and pancreatic cancer (PC). Worldwide, CRC is the second most common cancer among women and the third most common among men. PC has a poor prognosis and high mortality, with 5-year relative survival of approximately 11.5%. Conventional chemotherapy treatments for these cancers are limited due to severe side effects and the development of drug resistance. Therefore, there is an urgent need to develop new and safe drugs for effective treatment of PC and CRC. Historically, natural sources—plants in particular—have played a dominant role in traditional medicine used to treat a wide spectrum of diseases. In recent decades, marine natural products (MNPs) have shown great potential as drugs, but drug leads for treating various types of cancer, including CRC and PC, are scarce. To date, marine-based drugs have been used against leukemia, metastatic breast cancer, soft tissue sarcoma, and ovarian cancer. In this review, we summarized existing studies describing MNPs that were found to have an effect on CRC and PC, and we discussed the potential mechanisms of action of MNPs as well as future prospects for their use in treating these cancers.     

Control and component selection for incremental motion systems

The main problem in the design of an incremental motion system is the heating of the motor which results from the start/stop type of operation. The question answered in this paper is how to select the components for this system and how to use them in order to minimize the operating temperature of the motor. In the analysis of the control selection, the optimal control was determined which minimizes the energy dissipation. It is shown how a triangular velocity profile, obtained using Bang-Bang control, corresponds to an efficiency of 0.75, whereas a slight change to trapezoidal velocity, increases the efficiency to 0.89 when compared with the optimal control. The optimal gear ratio will differ from the “inertial match” due to the presence of constant load torque. For a capstan drive, the inertia of the capstan must be much smaller than that of the motor, and not “inertially matched”.     

DISSOCIATION-RECOMBINATION IN NON-STATIC SYSTEMS: AN APPROXIMATE THEORY

An approximate theory for thermodynamically coupled heat and mass transfer in dissipative reacting flow systems (including a possible coupling between reaction rates), is applied here for the combined problems of Couette flow, channel flow and temperature difference between two parallel plates, wherein dissociation-recombination reactions take place. The analysis compares favorably with known solutions for reacting flow systems when the coupling is neglected. The influence of thermodynamic couplings on the temperature profile and on the reaction rate is then explained.     

Arachne: Planning the telephone network at NYNEX

Arachne provides an excellent example of the success that can be derived from an effective integration of expert systems and optimization techniques. Automating IOF planning at NYNEX was an extremely challenging problem involving conflicting constraints: (i) the IOF network planning problem is quite complex, and (ii) the size of NYNEX s IOF network is quite large. In Arachne, we resolve these conflicting constraints by decomposing the planning task in such a way that only two types of subtask remain: (i) subtasks in which the size of the data is large, and the equipment cost of decisions small, an (ii) subtasks in which the data size is small and the equipment cost of decisions high. We apply heuristics to the former types of subtasks and optimization techniques to the latter. Combining heuristics and optimization techniques in this way provides a very good solution that optimizes the IOF network where capital investment is greatest. Copyright     

Blind image deconvolution using machine learning for three-dimensional microscopy

In this work, we propose a novel method for the regularization of blind deconvolution algorithms. The proposed method employs example-based machine learning techniques for modeling the space of point spread functions. During an iterative blind deconvolution process, a prior term attracts the point spread function estimates to the learned point spread function space. We demonstrate the usage of this regularizer within a Bayesian blind deconvolution framework and also integrate into the latter a method for noise reduction, thus creating a complete blind deconvolution method. The application of the proposed algorithm is demonstrated on synthetic and real-world three-dimensional images acquired by a wide-field fluorescence microscope, where the need for blind deconvolution algorithms is indispensable, yielding excellent results.     

Interactive Learning for Point‐Cloud Motion Segmentation

Segmenting a moving foreground (fg) from its background (bg) is a fundamental step in many Machine Vision and Computer Graphics applications. Nevertheless, hardly any attempts have been made to tackle this problem in dynamic 3D scanned scenes. Scanned dynamic scenes are typically challenging due to noise and large missing parts. Here, we present a novel approach for motion segmentation in dynamic point‐cloud scenes designed to cater to the unique properties of such data. Our key idea is to augment fg/bg classification with an active learning framework by refining the segmentation process in an adaptive manner. Our method initially classifies the scene points as either fg or bg in an un‐supervised manner. This, by training discriminative RBF‐SVM classifiers on automatically labeled, high‐certainty fg/bg points. Next, we adaptively detect unreliable classification regions (i.e. where fg/bg separation is uncertain), locally add more training examples to better capture the motion in these areas, and re‐train the classifiers to fine‐tune the segmentation. This not only improves segmentation accuracy, but also allows our method to perform in a coarse‐to‐fine manner, thereby efficiently process high‐density point‐clouds. Additionally, we present a unique interactive paradigm for enhancing this learning process, by using a manual editing tool. The user explicitly edits the RBF‐SVM decision borders in unreliable regions in order to refine and correct the classification. We provide extensive qualitative and quantitative experiments on both real (scanned) and synthetic dynamic scenes.