The cytoplasmic adaptor protein Caskin mediates Lar signal transduction during Drosophila motor axon guidance

The multiprotein complexes that receive and transmit axon pathfinding cues during development are essential to circuit generation. Here, we identify and characterize the Drosophila sterile α-motif (SAM) domain-containing protein Caskin, which shares homology with vertebrate Caskin, a CASK [calcium/calmodulin-(CaM)-activated serine-threonine kinase]-interacting protein. Drosophila caskin (ckn) is necessary for embryonic motor axon pathfinding and interacts genetically and physically with the leukocyte common antigen-related (Lar) receptor protein tyrosine phosphatase. In vivo and in vitro analyses of a panel of ckn loss-of-function alleles indicate that the N-terminal SAM domain of Ckn mediates its interaction with Lar. Like Caskin, Liprin-α is a neuronal adaptor protein that interacts with Lar via a SAM domain-mediated interaction. We present evidence that Lar does not bind Caskin and Liprin-α concurrently, suggesting they may assemble functionally distinct signaling complexes on Lar. Furthermore, a vertebrate Caskin homolog interacts with LAR family members, arguing that the role of ckn in Lar signal transduction is evolutionarily conserved. Last, we characterize several ckn mutants that retain Lar binding yet display guidance defects, implying the existence of additional Ckn binding partners. Indeed, we identify the SH2/SH3 adaptor protein Dock as a second Caskin-binding protein and find that Caskin binds Lar and Dock through distinct domains. Furthermore, whereas ckn has a nonredundant function in Lar-dependent signaling during motor axon targeting, ckn and dock have overlapping roles in axon outgrowth in the CNS. Together, these studies identify caskin as a neuronal adaptor protein required for axon growth and guidance

Magnetoresistance from Fermi Surface Topology

Extremely large non-saturating magnetoresistance has recently been reported for a large number of both topologically trivial and non-trivial materials. Different mechanisms have been proposed to explain the observed magnetotransport properties, yet without arriving to definitive conclusions or portraying a global picture. In this work, we investigate the transverse magnetoresistance of materials by combining the Fermi surfaces calculated from first principles with the Boltzmann transport theory approach relying on the semiclassical model and the relaxation time approximation. We first consider a series of simple model Fermi surfaces to provide a didactic introduction into the charge-carrier compensation and open-orbit mechanisms leading to non-saturating magnetoresistance. We then address in detail magnetotransport in three representative materials: (i) copper, a prototypical nearly free-electron metal characterized by the open Fermi surface that results in an intricate angular magnetoresistance, (ii) bismuth, a topologically trivial semimetal in which very large magnetoresistance is known to result from charge-carrier compensation, and (iii) tungsten diphosphide WP2, a recently discovered type-II Weyl semimetal that holds the record of magnetoresistance in compounds. In all three cases our calculations show excellent agreement with both the field dependence of magnetoresistance and its anisotropy measured at low temperatures. Furthermore, the calculations allow for a full interpretation of the observed features in terms of the Fermi surface topology. These results will help addressing a number of outstanding questions, such as the role of the topological phase in the pronounced large non-saturating magnetoresistance observed in topological materials.Comment: 13 pages, 9 figure

Utilization-Based Scheduling of Flexible Mixed-Criticality Real-Time Tasks

Mixed-criticality models are an emerging paradigm for the design of real-time systems because of their significantly improved resource efficiency. However, formal mixed-criticality models have traditionally been characterized by two impractical assumptions: once \textit{any} high-criticality task overruns, \textit{all} low-criticality tasks are suspended and \textit{all other} high-criticality tasks are assumed to exhibit high-criticality behaviors at the same time. In this paper, we propose a more realistic mixed-criticality model, called the flexible mixed-criticality (FMC) model, in which these two issues are addressed in a combined manner. In this new model, only the overrun task itself is assumed to exhibit high-criticality behavior, while other high-criticality tasks remain in the same mode as before. The guaranteed service levels of low-criticality tasks are gracefully degraded with the overruns of high-criticality tasks. We derive a utilization-based technique to analyze the schedulability of this new mixed-criticality model under EDF-VD scheduling. During runtime, the proposed test condition serves an important criterion for dynamic service level tuning, by means of which the maximum available execution budget for low-criticality tasks can be directly determined with minimal overhead while guaranteeing mixed-criticality schedulability. Experiments demonstrate the effectiveness of the FMC scheme compared with state-of-the-art techniques.Comment: This paper has been submitted to IEEE Transaction on Computers (TC) on Sept-09th-201

Electronic Tuning of Mixed Quinoidal‐Aromatic Conjugated Polyelectrolytes: Direct Ionic Substitution on Polymer Main‐Chains

The synthesis of conjugated polymers with ionic substituents directly bound to their main chain repeat units is a strategy for generating strongly electron-accepting conjugated polyelectrolytes, as demonstrated through the synthesis of a series of ionic azaquinodimethane (iAQM) compounds. The introduction of cationic substituents onto the quinoidal para-azaquinodimethane (AQM) core gives rise to a strongly electron-accepting building block, which can be employed in the synthesis of ionic small molecules and conjugated polyelectrolytes (CPEs). Electrochemical measurements alongside theoretical calculations indicate notably low-lying LUMO values for the iAQMs. The optical band gaps measured for these compounds are highly tunable based on structure, ranging from 2.30 eV in small molecules down to 1.22 eV in polymers. The iAQM small molecules and CPEs showcase the band gap reduction effects of combining the donor-acceptor strategy with the bond-length alternation reduction strategy. As a demonstration of their utility, the iAQM CPEs so generated were used as active agents in photothermal therapy

Modeling pulsar time noise with long term power law decay modulated by short term oscillations of the magnetic fields of neutron stars

We model the evolution of the magnetic fields of neutron stars as consisting of a long term power-law decay modulated by short term small amplitude oscillations. Our model predictions on the timing noise $\ddot\nu$ of neutron stars agree well with the observed statistical properties and correlations of normal radio pulsars. Fitting the model predictions to the observed data, we found that their initial parameter implies their initial surface magnetic dipole magnetic field strength ~ 5E14 G at ~0.4 year old and that the oscillations have amplitude between E-8 to E-5 and period on the order of years. For individual pulsars our model can effectively reduce their timing residuals, thus offering the potential of more sensitive detections of gravitational waves with pulsar timing arrays. Finally our model can also re-produce their observed correlation and oscillations of the second derivative of spin frequency, as well as the "slow glitch" phenomenon.Comment: 10 pages, 6 figures, submitted to IJMPD, invited talk in the 3rd Galileo-XuGuangqi Meeting}, Beijing, China, 12-16 October 201

Active Multi-Field Learning for Spam Filtering

Ubiquitous spam messages cause a serious waste of time and resources. This paper addresses the practical spam filtering problem, and proposes a universal approach to fight with various spam messages. The proposed active multi-field learning approach is based on: 1) It is cost-sensitive to obtain a label for a real-world spam filter, which suggests an active learning idea; and 2) Different messages often have a similar multi-field text structure, which suggests a multi-field learning idea. The multi-field learning framework combines multiple results predicted from field classifiers by a novel compound weight, and each field classifier calculates the arithmetical average of multiple conditional probabilities predicted from feature strings according to a data structure of string-frequency index. Comparing the current variance of field classifying results with the historical variance, the active learner evaluates the classifying confidence and regards the more uncertain message as the more informative sample for which to request a label. The experimental results show that the proposed approach can achieve the state-of-the-art performance at greatly reduced label requirements both in email spam filtering and short text spam filtering. Our active multi-field learning performance, the standard (1-ROCA) % measurement, even exceeds the full feedback performance of some advanced individual classifying algorithm

Transmission line condition prediction based on semi-supervised learning

Transmission line state assessment and prediction are of great significance for the rational formulation of operation and maintenance strategy and improvement of operation and maintenance level. Aiming at the problem that existing models cannot take into account the robustness and data demand, this paper proposes a state prediction method based on semi-supervised learning. Firstly, for the expanded feature vector, the regular matrix is used to fill in the missing data, and the sparse coding problem is solved by representation learning. Then, with the help of a small number of labelled samples to initially determine the category centers of line segments in different defective states. Finally, the estimated parameters of the model are corrected using unlabeled samples. Example analysis shows that this method can improve the recognition accuracy and use data more efficiently than the existing models