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Helical growth of the root tip (circumnutation) that permits surface exploration facilitates root penetration into soil. Here, we reveal that rice actin-binding protein RMD aids in root circumnutation, manifested by wavy roots as well as compromised ability to efficiently explore and avoid obstacles in rmd mutants. We demonstrate that root circumnutation defects in rmd depend on brassinosteroid (BR) signaling, which is elevated in mutant roots. Suppressing BR signaling via pharmacological (BR inhibitor) or genetic (knockout of BR biosynthetic or signaling components) manipulation rescues root defects in rmd. We further reveal that mutations in MAPK6 suppress BR signaling and restore normal root circumnutation in rmd, which may be mediated by the interaction between MAPK6, MAPKK4 and BR signaling factor BIM2. Our study thus demonstrates that RMD and MAPK6 control root circumnutation by modulating BR signaling to facilitate early root growth.

Helical growth of the root tip (circumnutation) that permits surface exploration facilitates root penetration into soil. Here, we reveal that rice actin-binding protein RMD aids in root circumnutation, manifested by wavy roots as well as compromised ability to efficiently explore and avoid obstacles in rmd mutants. We demonstrate that root circumnutation defects in rmd depend on brassinosteroid (BR) signaling, which is elevated in mutant roots. Suppressing BR signaling via pharmacological (BR inhibitor) or genetic (knockout of BR biosynthetic or signaling components) manipulation rescues root defects in rmd. We further reveal that mutations in MAPK6 suppress BR signaling and restore normal root circumnutation in rmd, which may be mediated by the interaction between MAPK6, MAPKK4 and BR signaling factor BIM2. Our study thus demonstrates that RMD and MAPK6 control root circumnutation by modulating BR signaling to facilitate early root growth.

BackgroundNational Malaria Control Programmes (NMCPs) currently make limited use of parasite genetic data. We have developed GenRe-Mekong, a platform for genetic surveillance of malaria in the Greater Mekong Subregion (GMS) that enables NMCPs to implement large-scale surveillance projects by integrating simple sample collection procedures in routine public health procedures.MethodsSamples from symptomatic patients are processed by SpotMalaria, a high-throughput system that produces a comprehensive set of genotypes comprising several drug resistance markers, species markers and a genomic barcode. GenRe-Mekong delivers Genetic Report Cards, a compendium of genotypes and phenotype predictions used to map prevalence of resistance to multiple drugs.ResultsGenRe-Mekong has worked with NMCPs and research projects in eight countries, processing 9623 samples from clinical cases. Monitoring resistance markers has been valuable for tracking the rapid spread of parasites resistant to the dihydroartemisinin-piperaquine combination therapy. In Vietnam and Laos, GenRe-Mekong data have provided novel knowledge about the spread of these resistant strains into previously unaffected provinces, informing decision-making by NMCPs.ConclusionsGenRe-Mekong provides detailed knowledge about drug resistance at a local level, and facilitates data sharing at a regional level, enabling cross-border resistance monitoring and providing the public health community with valuable insights. The project provides a rich open data resource to benefit the entire malaria community.FundingThe GenRe-Mekong project is funded by the Bill and Melinda Gates Foundation (OPP11188166, OPP1204268). Genotyping and sequencing were funded by the Wellcome Trust (098051, 206194, 203141, 090770, 204911, 106698/B/14/Z) and Medical Research Council (G0600718). A proportion of samples were collected with the support of the UK Department for International Development (201900, M006212), and Intramural Research Program of the

BackgroundNational Malaria Control Programmes (NMCPs) currently make limited use of parasite genetic data. We have developed GenRe-Mekong, a platform for genetic surveillance of malaria in the Greater Mekong Subregion (GMS) that enables NMCPs to implement large-scale surveillance projects by integrating simple sample collection procedures in routine public health procedures.MethodsSamples from symptomatic patients are processed by SpotMalaria, a high-throughput system that produces a comprehensive set of genotypes comprising several drug resistance markers, species markers and a genomic barcode. GenRe-Mekong delivers Genetic Report Cards, a compendium of genotypes and phenotype predictions used to map prevalence of resistance to multiple drugs.ResultsGenRe-Mekong has worked with NMCPs and research projects in eight countries, processing 9623 samples from clinical cases. Monitoring resistance markers has been valuable for tracking the rapid spread of parasites resistant to the dihydroartemisinin-piperaquine combination therapy. In Vietnam and Laos, GenRe-Mekong data have provided novel knowledge about the spread of these resistant strains into previously unaffected provinces, informing decision-making by NMCPs.ConclusionsGenRe-Mekong provides detailed knowledge about drug resistance at a local level, and facilitates data sharing at a regional level, enabling cross-border resistance monitoring and providing the public health community with valuable insights. The project provides a rich open data resource to benefit the entire malaria community.FundingThe GenRe-Mekong project is funded by the Bill and Melinda Gates Foundation (OPP11188166, OPP1204268). Genotyping and sequencing were funded by the Wellcome Trust (098051, 206194, 203141, 090770, 204911, 106698/B/14/Z) and Medical Research Council (G0600718). A proportion of samples were collected with the support of the UK Department for International Development (201900, M006212), and Intramural Research Program of the

Flag leaf angle impacts the photosynthetic capacity of densely grown plants and is thus an important agronomic breeding trait for crop architecture and yield. The hormone auxin plays a key role in regulating this trait, yet the underlying molecular and cellular mechanisms remain unclear. Here, we report that two rice (Oryza sativa) auxin response factors (ARFs), OsARF6 and OsARF17, which are highly expressed in lamina joint tissues, control flag leaf angle in response to auxin. Loss-of-function double osarf6 osarf17 mutants displayed reduced secondary cell wall levels of lamina joint sclerenchymatous cells (Scs), resulting in an exaggerated flag leaf angle and decreased grain yield under dense planting conditions. Mechanical measurements indicated that the mutant lamina joint tissues were too weak to support the weight of the flag leaf blade, resembling the phenotype of the rice increased leaf angle1 (ila1) mutant. We demonstrate that OsARF6 and OsARF17 directly bind to the ILA1 promoter independently and synergistically to activate its expression. In addition, auxin-induced ILA1 expression was dependent on OsARF6 and OsARF17. Collectively, our study reveals a mechanism that integrates auxin signaling with the secondary cell wall composition to determine flag leaf angle, providing breeding targets in rice, and potentially other cereals, for this key trait.

Flag leaf angle impacts the photosynthetic capacity of densely grown plants and is thus an important agronomic breeding trait for crop architecture and yield. The hormone auxin plays a key role in regulating this trait, yet the underlying molecular and cellular mechanisms remain unclear. Here, we report that two rice (Oryza sativa) auxin response factors (ARFs), OsARF6 and OsARF17, which are highly expressed in lamina joint tissues, control flag leaf angle in response to auxin. Loss-of-function double osarf6 osarf17 mutants displayed reduced secondary cell wall levels of lamina joint sclerenchymatous cells (Scs), resulting in an exaggerated flag leaf angle and decreased grain yield under dense planting conditions. Mechanical measurements indicated that the mutant lamina joint tissues were too weak to support the weight of the flag leaf blade, resembling the phenotype of the rice increased leaf angle1 (ila1) mutant. We demonstrate that OsARF6 and OsARF17 directly bind to the ILA1 promoter independently and synergistically to activate its expression. In addition, auxin-induced ILA1 expression was dependent on OsARF6 and OsARF17. Collectively, our study reveals a mechanism that integrates auxin signaling with the secondary cell wall composition to determine flag leaf angle, providing breeding targets in rice, and potentially other cereals, for this key trait.

BackgroundThe emergence and spread of multidrug-resistant Plasmodium falciparum in the Greater Mekong Subregion (GMS) threatens global malaria elimination efforts. Mass drug administration (MDA), the presumptive antimalarial treatment of an entire population to clear the subclinical parasite reservoir, is a strategy to accelerate malaria elimination. We report a cluster randomised trial to assess the effectiveness of dihydroartemisinin-piperaquine (DP) MDA in reducing falciparum malaria incidence and prevalence in 16 remote village populations in Myanmar, Vietnam, Cambodia, and the Lao People's Democratic Republic, where artemisinin resistance is prevalent.Methods and findingsAfter establishing vector control and community-based case management and following intensive community engagement, we used restricted randomisation within village pairs to select 8 villages to receive early DP MDA and 8 villages as controls for 12 months, after which the control villages received deferred DP MDA. The MDA comprised 3 monthly rounds of 3 daily doses of DP and, except in Cambodia, a single low dose of primaquine. We conducted exhaustive cross-sectional surveys of the entire population of each village at quarterly intervals using ultrasensitive quantitative PCR to detect Plasmodium infections. The study was conducted between May 2013 and July 2017. The investigators randomised 16 villages that had a total of 8,445 residents at the start of the study. Of these 8,445 residents, 4,135 (49%) residents living in 8 villages, plus an additional 288 newcomers to the villages, were randomised to receive early MDA; 3,790 out of the 4,423 (86%) participated in at least 1 MDA round, and 2,520 out of the 4,423 (57%) participated in all 3 rounds. The primary outcome, P. falciparum prevalence by month 3 (M3), fell by 92% (from 5.1% [171/3,340] to 0.4% [12/2,828]) in early MDA villages and by 29% (from 7.2% [246/3,405] to 5.1% [155/3,057]) in control villages. Over the following 9 months, the P.

BackgroundThe emergence and spread of multidrug-resistant Plasmodium falciparum in the Greater Mekong Subregion (GMS) threatens global malaria elimination efforts. Mass drug administration (MDA), the presumptive antimalarial treatment of an entire population to clear the subclinical parasite reservoir, is a strategy to accelerate malaria elimination. We report a cluster randomised trial to assess the effectiveness of dihydroartemisinin-piperaquine (DP) MDA in reducing falciparum malaria incidence and prevalence in 16 remote village populations in Myanmar, Vietnam, Cambodia, and the Lao People's Democratic Republic, where artemisinin resistance is prevalent.Methods and findingsAfter establishing vector control and community-based case management and following intensive community engagement, we used restricted randomisation within village pairs to select 8 villages to receive early DP MDA and 8 villages as controls for 12 months, after which the control villages received deferred DP MDA. The MDA comprised 3 monthly rounds of 3 daily doses of DP and, except in Cambodia, a single low dose of primaquine. We conducted exhaustive cross-sectional surveys of the entire population of each village at quarterly intervals using ultrasensitive quantitative PCR to detect Plasmodium infections. The study was conducted between May 2013 and July 2017. The investigators randomised 16 villages that had a total of 8,445 residents at the start of the study. Of these 8,445 residents, 4,135 (49%) residents living in 8 villages, plus an additional 288 newcomers to the villages, were randomised to receive early MDA; 3,790 out of the 4,423 (86%) participated in at least 1 MDA round, and 2,520 out of the 4,423 (57%) participated in all 3 rounds. The primary outcome, P. falciparum prevalence by month 3 (M3), fell by 92% (from 5.1% [171/3,340] to 0.4% [12/2,828]) in early MDA villages and by 29% (from 7.2% [246/3,405] to 5.1% [155/3,057]) in control villages. Over the following 9 months, the P.

Background The emergence and spread of multidrug-resistant Plasmodium falciparum in the Greater Mekong Subregion (GMS) threatens global malaria elimination efforts. Mass drug administration (MDA), the presumptive antimalarial treatment of an entire population to clear the subclinical parasite reservoir, is a strategy to accelerate malaria elimination. We report a cluster randomised trial to assess the effectiveness of dihydroartemisinin-piperaquine (DP) MDA in reducing falciparum malaria incidence and prevalence in 16 remote village populations in Myanmar, Vietnam, Cambodia, and the Lao Peoples Democratic Republic, where artemisinin resistance is prevalent. Methods and findings After establishing vector control and community-based case management and following intensive community engagement, we used restricted randomisation within village pairs to select 8 villages to receive early DP MDA and 8 villages as controls for 12 months, after which the control villages received deferred DP MDA. The MDA comprised 3 monthly rounds of 3 daily doses of DP and, except in Cambodia, a single low dose of primaquine. We conducted exhaustive cross-sectional surveys of the entire population of each village at quarterly intervals using ultrasensitive quantitative PCR to detect Plasmodium infections. The study was conducted between May 2013 and July 2017. The investigators randomised 16 villages that had a total of 8,445 residents at the start of the study. Of these 8,445 residents, 4,135 (49%) residents living in 8 villages, plus an additional 288 newcomers to the villages, were randomised to receive early MDA; 3,790 out of the 4,423 (86%) participated in at least 1 MDA round, and 2,520 out of the 4,423 (57%) participated in all 3 rounds. The primary outcome, P. falciparum prevalence by month 3 (M3), fell by 92% (from 5.1% [171/3,340] to 0.4% [12/2,828]) in early MDA villages and by 29% (from 7.2% [246/3,405] to 5.1% [155/3,057]) in control villages. Over the following 9 months, the P, Funding Agencies|Wellcome Trust [101148/Z/13/Z]; Bill and Melinda Gates Foundation [OPP1081420]; National Health and Medical Research Council [1104975]

Ductal carcinoma in situ (DCIS) is considered a pre-invasive breast cancer and sometimes it can develop into an invasive ductal carcinoma. The analysis of histopathological images to detect tumour border of DCIS could provide important information for better diagnosis of patients. We present a deep learning based system to automatically identify DCIS in histopathological images. Specifically, a convolutional neural network (CNN) is first trained to predict labels of small patches cropped out of a histopathological whole slide image. Next, a sliding window method is used to produce a probability map of DCIS. Finally, given the probability map, a tumor border of DCIS is produced and delineated with the method of Marching Cubes to facilitate pathologists' review and assessment. Evaluation of cross validation demonstrates that the CNN model of GoogleNet performs well in histology image patch classification with an overall accuracy of (98.46 +/- 0.40)% and identifies the DCIS tissue patches with a Fl-score of (97.40 +/- 1.18)% (mean +/- variance). Moreover, around 95.6% tumour tissue within the enclosed tumour regions can be identified by our developed method. Finally, the goal of tumor border detection can be well achieved with a few post-processing steps.

Single image rain streaks removal has recently witnessed substantial progress due to the development of deep convolutional neural networks. However, existing deep learning based methods either focus on the entrance and exit of the network by decomposing the input image into high and low frequency information and employing residual learning to reduce the mapping range, or focus on the introduction of cascaded learning scheme to decompose the task of rain streaks removal into multi-stages. These methods treat the convolutional neural network as an encapsulated end-to-end mapping module without deepening into the rationality and superiority of neural network design. In this paper, we delve into an effective end-to-end neural network structure for stronger feature expression and spatial correlation learning. Specifically, we propose a non-locally enhanced encoder-decoder network framework, which consists of a pooling indices embedded encoder-decoder network to efficiently learn increasingly abstract feature representation for more accurate rain streaks modeling while perfectly preserving the image detail. The proposed encoder-decoder framework is composed of a series of non-locally enhanced dense blocks that are designed to not only fully exploit hierarchical features from all the convolutional layers but also well capture the long-distance dependencies and structural information. Extensive experiments on synthetic and real datasets demonstrate that the proposed method can effectively remove rain-streaks on rainy image of various densities while well preserving the image details, which achieves significant improvements over the recent state-of-the-art methods., Comment: Accepted to ACM Multimedia 2018

Object categories inherently form a hierarchy with different levels of concept abstraction, especially for fine-grained categories. For example, birds (Aves) can be categorized according to a four-level hierarchy of order, family, genus, and species. This hierarchy encodes rich correlations among various categories across different levels, which can effectively regularize the semantic space and thus make prediction less ambiguous. However, previous studies of fine-grained image recognition primarily focus on categories of one certain level and usually overlook this correlation information. In this work, we investigate simultaneously predicting categories of different levels in the hierarchy and integrating this structured correlation information into the deep neural network by developing a novel Hierarchical Semantic Embedding (HSE) framework. Specifically, the HSE framework sequentially predicts the category score vector of each level in the hierarchy, from highest to lowest. At each level, it incorporates the predicted score vector of the higher level as prior knowledge to learn finer-grained feature representation. During training, the predicted score vector of the higher level is also employed to regularize label prediction by using it as soft targets of corresponding sub-categories. To evaluate the proposed framework, we organize the 200 bird species of the Caltech-UCSD birds dataset with the four-level category hierarchy and construct a large-scale butterfly dataset that also covers four level categories. Extensive experiments on these two and the newly-released VegFru datasets demonstrate the superiority of our HSE framework over the baseline methods and existing competitors., Comment: Accepted at ACM MM 2018 as oral presentation

Traditional donors have made wide debates and attempts to coordinate aid to reduce transaction cost and increase aid effectiveness. Several obstacles have been studied, one of which is emerging donors' bilateral approach with recipients. This not only complicates existing aid coordination, but also increases transaction cost for recipients. Relation between China and traditional donors is mostly heated discussed due to its prominence in aid provision. This paper, applying the geothermal financing in Kenya as a case, tests the hypothesis that trilateral cooperation based on comparative advantages can provide a configuration to coordinate traditional and Chinese donors at project or sectoral level, with expected results of reducing transaction cost and increasing aid effectiveness. This paper contributes to the field by showing potential improvement of transaction costs and aid effectiveness through trilateral cooperation engaging emerging donors in energy project or sector, which also sheds lights on aid coordination in infrastructure sector.

This paper proposes a classification network to image semantic retrieval (NIST) framework to counter the image retrieval challenge. Our approach leverages the successful classification network GoogleNet based on Convolutional Neural Networks to obtain the semantic feature matrix which contains the serial number of classes and corresponding probabilities. Compared with traditional image retrieval using feature matching to compute the similarity between two images, NIST leverages the semantic information to construct semantic feature matrix and uses the semantic distance algorithm to compute the similarity. Besides, the fusion strategy can significantly reduce storage and time consumption due to less classes participating in the last semantic distance computation. Experiments demonstrate that our NIST framework produces state-of-the-art results in retrieval experiments on MIRFLICKR-25K dataset., Comment: 4 pages

This paper addresses the problem of large-scale image retrieval. We propose a two-layer fusion method which takes advantage of global and local cues and ranks database images from coarse to fine (C2F). Departing from the previous methods fusing multiple image descriptors simultaneously, C2F is featured by a layered procedure composed by filtering and refining. In particular, C2F consists of three components. 1) Distractor filtering. With holistic representations, noise images are filtered out from the database, so the number of candidate images to be used for comparison with the query can be greatly reduced. 2) Adaptive weighting. For a certain query, the similarity of candidate images can be estimated by holistic similarity scores in complementary to the local ones. 3) Candidate refining. Accurate retrieval is conducted via local features, combining the pre-computed adaptive weights. Experiments are presented on two benchmarks, \emph{i.e.,} Holidays and Ukbench datasets. We show that our method outperforms recent fusion methods in terms of storage consumption and computation complexity, and that the accuracy is competitive to the state-of-the-arts.

This paper describes an effective and efficient image classification framework nominated distributed deep representation learning model (DDRL). The aim is to strike the balance between the computational intensive deep learning approaches (tuned parameters) which are intended for distributed computing, and the approaches that focused on the designed parameters but often limited by sequential computing and cannot scale up. In the evaluation of our approach, it is shown that DDRL is able to achieve state-of-art classification accuracy efficiently on both medium and large datasets. The result implies that our approach is more efficient than the conventional deep learning approaches, and can be applied to big data that is too complex for parameter designing focused approaches. More specifically, DDRL contains two main components, i.e., feature extraction and selection. A hierarchical distributed deep representation learning algorithm is designed to extract image statistics and a nonlinear mapping algorithm is used to map the inherent statistics into abstract features. Both algorithms are carefully designed to avoid millions of parameters tuning. This leads to a more compact solution for image classification of big data. We note that the proposed approach is designed to be friendly with parallel computing. It is generic and easy to be deployed to different distributed computing resources. In the experiments, the largescale image datasets are classified with a DDRM implementation on Hadoop MapReduce, which shows high scalability and resilience.

This paper introduces an effective processing framework nominated ICP (Image Cloud Processing) to powerfully cope with the data explosion in image processing field. While most previous researches focus on optimizing the image processing algorithms to gain higher efficiency, our work dedicates to providing a general framework for those image processing algorithms, which can be implemented in parallel so as to achieve a boost in time efficiency without compromising the results performance along with the increasing image scale. The proposed ICP framework consists of two mechanisms, i.e. SICP (Static ICP) and DICP (Dynamic ICP). Specifically, SICP is aimed at processing the big image data pre-stored in the distributed system, while DICP is proposed for dynamic input. To accomplish SICP, two novel data representations named P-Image and Big-Image are designed to cooperate with MapReduce to achieve more optimized configuration and higher efficiency. DICP is implemented through a parallel processing procedure working with the traditional processing mechanism of the distributed system. Representative results of comprehensive experiments on the challenging ImageNet dataset are selected to validate the capacity of our proposed ICP framework over the traditional state-of-the-art methods, both in time efficiency and quality of results.

In this paper, we propose a compact network called CUNet (compact unsupervised network) to counter the image classification challenge. Different from the traditional convolutional neural networks learning filters by the time-consuming stochastic gradient descent, CUNet learns the filter bank from diverse image patches with the simple K-means, which significantly avoids the requirement of scarce labeled training images, reduces the training consumption, and maintains the high discriminative ability. Besides, we propose a new pooling method named weighted pooling considering the different weight values of adjacent neurons, which helps to improve the robustness to small image distortions. In the output layer, CUNet integrates the feature maps gained in the last hidden layer, and straightforwardly computes histograms in non-overlapped blocks. To reduce feature redundancy, we implement the max-pooling operation on adjacent blocks to select the most competitive features. Comprehensive experiments are conducted to demonstrate the state-of-the-art classification performances with CUNet on CIFAR-10, STL-10, MNIST and Caltech101 benchmark datasets.

Tracing magnetic fields is crucial as magnetic fields play an important role in many astrophysical processes. Earlier studies have demonstrated that Ground State Alignment (GSA) is a unique way to detect weak magnetic fields (1G> B> 1exp(-15)G) in diffuse media, they consider the situation when the pumping source is a point source, which applies when the star is very far away from the diffuse media. In this paper, we explore the GSA in the presence of extended radiation fields. For the radiation fields with a clear geometric structure, we consider the alignment in circumstellar medium, binary systems, disc, and Local Interstellar Medium (LISM). For the radiation fields with unidentified pumping sources, we apply the method of multipole expansion and discuss the GSA induced by each component. We demonstrate that for general radiation fields, it is adequate to consider the contribution from dipole and quadrupole radiation components. We find that in general polarization of absorption arizing from GSA coincides with the projection of magnetic field in the 2D sky with 90 degree degeneracy. We conclude that the GSA is a unique tool to detect the direction of weak magnetic field, and it can be applied to diffuse media in any radiation field., Comment: 17pages, 17 Figures, ApJ published

Tracing magnetic fields is crucial as magnetic fields play an important role in many astrophysical processes. Earlier studies have demonstrated that Ground State Alignment (GSA) is a unique way to detect weak magnetic fields (1G> B> 1exp(-15)G) in diffuse media, they consider the situation when the pumping source is a point source, which applies when the star is very far away from the diffuse media. In this paper, we explore the GSA in the presence of extended radiation fields. For the radiation fields with a clear geometric structure, we consider the alignment in circumstellar medium, binary systems, disc, and Local Interstellar Medium (LISM). For the radiation fields with unidentified pumping sources, we apply the method of multipole expansion and discuss the GSA induced by each component. We demonstrate that for general radiation fields, it is adequate to consider the contribution from dipole and quadrupole radiation components. We find that in general polarization of absorption arizing from GSA coincides with the projection of magnetic field in the 2D sky with 90 degree degeneracy. We conclude that the GSA is a unique tool to detect the direction of weak magnetic field, and it can be applied to diffuse media in any radiation field., Comment: 17pages, 17 Figures, ApJ published