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Cytoplasmic dynein is the primary minus-end-directed microtubule motor protein in animal cells, performing a wide range of motile activities, including transport of vesicular cargos, mRNAs, viruses, and proteins. Lissencephaly-1 (LIS1) is a highly conserved dynein-regulatory factor that binds directly to the dynein motor domain, uncoupling the enzymatic and mechanical cycles of the motor and stalling dynein on the microtubule track. Dynactin, another ubiquitous dynein-regulatory factor, releases dynein from an autoinhibited state, leading to a dramatic increase in fast, processive dynein motility. How these opposing activities are integrated to control dynein motility is unknown. Here, we used fluorescence single-molecule microscopy to study the interaction of LIS1 with the processive dynein-dynactin-BicD2N (DDB) complex. Surprisingly, in contrast to the prevailing model for LIS1 function established in the context of dynein alone, we found that binding of LIS1 to DDB does not strongly disrupt processive motility. Motile DDB complexes bound up to two LIS1 dimers, and mutational analysis suggested that LIS1 binds directly to the dynein motor domains during DDB movement. Interestingly, LIS1 enhanced DDB velocity in a concentration-dependent manner, in contrast to observations of the effect of LIS1 on the motility of isolated dynein. Thus, LIS1 exerts concentration-dependent effects on dynein motility and can synergize with dynactin to enhance processive dynein movement. Our results suggest that the effect of LIS1 on dynein motility depends on both LIS1 concentration and the presence of other regulatory factors such as dynactin and may provide new insights into the mechanism of LIS1 haploinsufficiency in the neurodevelopmental disorder lissencephaly.

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This paper approaches the problem of weed mapping for precision agriculture, using imagery provided by Unmanned Aerial Vehicles (UAVs) from sunflower and maize crops. Precision agriculture referred to weed control is mainly based on the design of early post-emergence site-specific control treatments according to weed coverage, where one of the most important challenges is the spectral similarity of crop and weed pixels in early growth stages. Our work tackles this problem in the context of object-based image analysis (OBIA) by means of supervised machine learning methods combined with pattern and feature selection techniques, devising a strategy for alleviating the user intervention in the system while not compromising the accuracy. This work firstly proposes a method for choosing a set of training patterns via clustering techniques so as to consider a representative set of the whole field data spectrum for the classification method. Furthermore, a feature selection method is used to obtain the best discriminating features from a set of several statistics and measures of different nature. Results from this research show that the proposed method for pattern selection is suitable and leads to the construction of robust sets of data. The exploitation of different statistical, spatial and texture metrics represents a new avenue with huge potential for between and within crop-row weed mapping via UAV-imagery and shows good synergy when complemented with OBIA. Finally, there are some measures (specially those linked to vegetation indexes) that are of great influence for weed mapping in both sunflower and maize crops.

Kinesin-5 motors organize mitotic spindles by sliding apart microtubules. They are homotetramers with dimeric motor and tail domains at both ends of a bipolar minifilament. Here, we describe a regulatory mechanism involving direct binding between tail and motor domains and its fundamental role in microtubule sliding. Kinesin-5 tails decrease microtubule-stimulated ATP-hydrolysis by specifically engaging motor domains in the nucleotide-free or ADP states. Cryo-EM reveals that tail binding stabilizes an open motor domain ATP-active site. Full-length motors undergo slow motility and cluster together along microtubules, while tail-deleted motors exhibit rapid motility without clustering. The tail is critical for motors to zipper together two microtubules by generating substantial sliding forces. The tail is essential for mitotic spindle localization, which becomes severely reduced in tail-deleted motors. Our studies suggest a revised microtubule-sliding model, in which kinesin-5 tails stabilize motor domains in the microtubule-bound state by slowing ATP-binding, resulting in high-force production at both homotetramer ends.

Abstract:The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous) from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86%) and notably higher than C4.5 (79%). The SVM+SVM classifier (best method) improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the OPEN ACCESS Remote Sens. 2014, 6 5020 most poorly classified class (minimum sensitivity). The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification tasks.

Currently, knowledge discovery in databases is an essential step to identify valid, novel and useful patterns for decision making. There are many real-world scenarios, such as bankruptcy prediction, option pricing or medical diagnosis, where the classification models to be learned need to fulfill restrictions of monotonicity (i.e. the target class label should not decrease when input attributes values increase). For instance, it is rational to assume that a higher debt ratio of a company should never result in a lower level of bankruptcy risk. Consequently,

Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field.