Dense prediction of label noise for learning building extraction from aerial drone imagery

Label noise is a commonly encountered problem in learning building extraction tasks; its presence can reduce performance and increase learning complexity. This is especially true for cases where high resolution aerial drone imagery is used, as the labels may not perfectly correspond/align with the actual objects in the imagery. In general machine learning and computer vision context, labels refer to the associated class of data, and in remote sensing-based building extraction refer to pixel-level classes. Dense label noise in building extraction tasks has rarely been formalized and assessed. We formulate a taxonomy of label noise models for building extraction tasks, which incorporates both pixel-wise and dense models. While learning dense prediction under label noise, the differences between the ground truth clean label and observed noisy label can be encoded by error matrices indicating locations and type of noisy pixel-level labels. In this work, we explicitly learn to approximate error matrices for improving building extraction performance; essentially, learning dense prediction of label noise as a subtask of a larger building extraction task. We propose two new model frameworks for learning building extraction under dense real-world label noise, and consequently two new network architectures, which approximate the error matrices as intermediate predictions. The first model learns the general error matrix as an intermediate step and the second model learns the false positive and false-negative error matrices independently, as intermediate steps. Approximating intermediate error matrices can generate label noise saliency maps, for identifying labels having higher chances of being mis-labelled. We have used ultra-high-resolution aerial images, noisy observed labels from OpenStreetMap, and clean labels obtained after careful annotation by the authors. When compared to the baseline model trained and tested using clean labels, our intermediate false positive-false negative error matrix model provides Intersection-Over-Union gain of 2.74% and F1-score gain of 1.75% on the independent test set. Furthermore, our proposed models provide much higher recall than currently used deep learning models for building extraction, while providing comparable precision. We show that intermediate false positive-false negative error matrix approximation can improve performance under label noise

Particle swarm optimization based LSTM networks for water level forecasting: A case study on Bangladesh river network

Data availability: Data will be made available on request.Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S2590123023000786#appsec1 .Floods are one of the most catastrophic natural disasters. Water level forecasting is an essential method of avoiding floods and disaster preparedness. In recent years, models for predicting water levels have been developed using artificial intelligence techniques like the artificial neural network (ANN). It has been demonstrated that more advanced and sequenced-based deep learning techniques, like long short-term memory (LSTM) networks, are superior at forecasting hydrological data. However, historically, most LSTM hyperparameters were based on experience, which typically did not produce the best outcomes. The Particle Swarm Optimization (PSO) method was utilized to adjust the LSTM hyperparameter to increase the capacity to learn data sequence characteristics. Utilizing water level observation data from stations along Bangladesh's Brahmaputra, Ganges, and Meghna rivers, the model was utilized to estimate flood dynamics. The Nash Sutcliffe efficiency (NSE) coefficient, root mean square error (RMSE), and MAE were used to assess the model's performance, where PSO-LSTM model outperforms the ANN, PSO-ANN, and LSTM models in predicting water levels in all stations. The PSO-LSTM model provides improved prediction accuracy and stability and improves water level forecasting accuracy at varying lead times. The findings may aid in sustainable flood risk mitigation in the study region in the future.Ministry of Post, Telecommunication and Information Technology, Bangladesh through ICT Innovation Fund (2020–21) round 3: Grant Number 12

Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search Algorithm

River flow prediction is essential in many applications of water resources planning and management. In this paper, the accuracy of multivariate adaptive regression splines (MARS), model 5 regression tree (M5RT), and conventional multiple linear regression (CMLR) is compared with a hybrid least square support vector regression-gravitational search algorithm (HLGSA) in predicting monthly river flows. In the first part of the study, all three regression methods were compared with each other in predicting river flows of each basin. It was found that the HLGSA method performed better than the MARS, M5RT, and CMLR in river flow prediction. The effect of log transformation on prediction accuracy of the regression methods was also examined in the second part of the study. Log transformation of the river flow data significantly increased the prediction accuracy of all regression methods. It was also found that log HLGSA (LHLSGA) performed better than the other regression methods. In the third part of the study, the accuracy of the LHLGSA and HLGSA methods was examined in river flow estimation using nearby river flow data. On the basis of results of all applications, it was found that LHLGSA and HLGSA could be successfully used in prediction and estimation of river flow.</jats:p

DistB-Condo: Distributed Blockchain-based IoT-SDN Model for Smart Condominium

Condominium network refers to intra-organization networks, where smart buildings or apartments are connected and share resources over the network. Secured communication platform or channel has been highlighted as a key requirement for a reliable condominium which can be ensured by the utilization of the advanced techniques and platforms like Software-Defined Network (SDN), Network Function Virtualization (NFV) and Blockchain (BC). These technologies provide a robust, and secured platform to meet all kinds of challenges, such as safety, confidentiality, flexibility, efficiency, and availability. This work suggests a distributed, scalable IoT-SDN with Blockchain-based NFV framework for a smart condominium (DistB-Condo) that can act as an efficient secured platform for a small community. Moreover, the Blockchain-based IoT-SDN with NFV framework provides the combined benefits of leading technologies. It also presents an optimized Cluster Head Selection (CHS) algorithm for selecting a Cluster Head (CH) among the clusters that efficiently saves energy. Besides, a decentralized and secured Blockchain approach has been introduced that allows more prominent security and privacy to the desired condominium network. Our proposed approach has also the ability to detect attacks in an IoT environment. Eventually, this article evaluates the performance of the proposed architecture using different parameters (e.g., throughput, packet arrival rate, and response time). The proposed approach outperforms the existing OF-Based SDN. DistB-Condo has better throughput on average, and the bandwidth (Mbps) much higher than the OF-Based SDN approach in the presence of attacks. Also, the proposed model has an average response time of 5% less than the core model

Development and in-vitro Evaluation of Once Daily Tablet Dosage Form of Loxoprofen Sodium

Purpose: To formulate and characterize once daily controlled release tablet of loxoprofen sodium.Methods: Eudragit RS-100, hydroxylpropyl methylcellulose (HPMC) and pectin were used as release retarding polymers. All the formulations were prepared by direct compression method. Various precompression studies were carried out to determine Hausner’s ratio, Carr’s index, angle of repose, bulk density and tapped density Differential scanning calorimetry (DSC) studies and also post-compression studies to evaluate hardness, friability, weight variation, drug content, in-vitro drug release were conducted on the tablets. The drug release data were subjected to kinetic models, including zero order, first order, Hixon Crowell, Higuchi and Korsmeyer-Peppas.Results: Compressibility index (7.6 ± 1.32 - 12.5 ± 1.43%), Hausner’s ratio (1.08 ± 0.04 - 1.14 ± 0.03), angle of repose (27.78 ± 0.47 - 30.49 ± 0.46°), hardness (6.25 ± 0.27 - 7.21±0.21 kg/cm2), friability (0.14 ± 0.06 - 0.28 ± 0.0 %), weight variation (249.5 ± 2.09 - 251.35 ± 2.41 mg) and drug content  (97.30 ± 0.28 - 103.70 ± 0.31 %) were within generally accepted limits for the pre-and post-compression formulations, respectively. The tablets having the maximum amount of among the three polymers tested as matrix materials, HPMC, represented by F3 tablets, exerted better sustained release properties after 12 h. Release pattern was more of Fickian diffusion followed by Higuchi mechanism.Conclusion: The release of the loxoprofen sodium was optimized up to 12 h.Keywords: Loxoprofen, Sustained release, hydroxypropyl methylcelluose, Pectin, Eudragit, Matrix tablet

Long-term remission of myopic choroidal neovascular membrane after treatment with ranibizumab: a case report

<p>Abstract</p> <p>Introduction</p> <p>Myopia has become a big public health problem in certain parts of the world. Sight-threatening complications like choroidal neovascularisation membranes occur in up to 10% of pathological myopia, and natural history studies show a trend towards progressive visual loss. There are long-term financial and quality-of-life implications in this group of patients, and treatment strategies should aim for long-term preservation of vision.</p> <p>Case presentation</p> <p>A 56-year-old Caucasian woman presented with a best-corrected visual acuity of 6/6-1 in her right eye and 6/24 in her left. Fundal examination revealed pathological myopia in both eyes and an elevated lesion associated with pre-retinal haemorrhage in the left macula. Ocular coherence tomography and fundus fluorescein angiogram confirmed a subfoveal classic choroidal neovascularisation membrane. The patient decided to proceed with intravitreal ranibizumab (0.5 mg) therapy. One month after treatment, best-corrected visual acuity improved to 6/12 in her left eye, with complete resolution subretinal fluid on ocular coherence tomography. After three months, best-corrected visual acuity further improved to 6/9, which was maintained up to 16 months post-treatment.</p> <p>Conclusion</p> <p>We suggest intravitreal ranibizumab as an alternative treatment for long-term remission of myopic choroidal neovascular membrane. It also suggests that myopic choroidal neovascularisation membranes may require fewer treatments to achieve sustained remission. Furthermore, this could serve as a feasible long-term management option if used in conjunction with ocular coherence tomography.</p

The use of watershed geomorphic data in flash flood susceptibility zoning: a case study of the Karnaphuli and Sangu river basins of Bangladesh

The occurrence of heavy rainfall in the south-eastern hilly region of Bangladesh makes this area highly susceptible to recurrent flash flooding. As the region is the commercial capital of Bangladesh, these flash floods pose a significant threat to the national economy. Predicting this type of flooding is a complex task which requires a detailed understanding of the river basin characteristics. This study evaluated the susceptibility of the region to flash floods emanating from within the Karnaphuli and Sangu river basins. Twenty-two morphometric parameters were used. The occurrence and impact of flash floods within these basins are mainly associated with the volume of runoff, runoff velocity, and the surface infiltration capacity of the various watersheds. Analysis showed that major parts of the basin were susceptible to flash flooding events of a ‘moderate’-to-‘very high’ level of severity. The degree of susceptibility of ten of the watersheds was rated as ‘high’, and one was ‘very high’. The flash flood susceptibility map drawn from the analysis was used at the sub-district level to identify populated areas at risk. More than 80% of the total area of the 16 sub-districts were determined to have a ‘high’-to-‘very-high’-level flood susceptibility. The analysis noted that around 3.4 million people reside in flash flood-prone areas, therefore indicating the potential for loss of life and property. The study identified significant flash flood potential zones within a region of national importance, and exposure of the population to these events. Detailed analysis and display of flash flood susceptibility data at the sub-district level can enable the relevant organizations to improve watershed management practices and, as a consequence, alleviate future flood risk

Functional Amyloids Composed of Phenol Soluble Modulins Stabilize Staphylococcus aureus Biofilms

Staphylococcus aureus is an opportunistic pathogen that colonizes the skin and mucosal surfaces of mammals. Persistent staphylococcal infections often involve surface-associated communities called biofilms. Here we report the discovery of a novel extracellular fibril structure that promotes S. aureus biofilm integrity. Biochemical and genetic analysis has revealed that these fibers have amyloid-like properties and consist of small peptides called phenol soluble modulins (PSMs). Mutants unable to produce PSMs were susceptible to biofilm disassembly by matrix degrading enzymes and mechanical stress. Previous work has associated PSMs with biofilm disassembly, and we present data showing that soluble PSM peptides disperse biofilms while polymerized peptides do not. This work suggests the PSMs' aggregation into amyloid fibers modulates their biological activity and role in biofilms

Tuning fresh: radiation through rewiring of central metabolism in streamlined bacteria

Most free-living planktonic cells are streamlined and in spite of their limitations in functional flexibility, their vast populations have radiated into a wide range of aquatic habitats. Here we compared the metabolic potential of subgroups in the Alphaproteobacteria lineage SAR11 adapted to marine and freshwater habitats. Our results suggest that the successful leap from marine to freshwaters in SAR11 was accompanied by a loss of several carbon degradation pathways and a rewiring of the central metabolism. Examples for these are C1 and methylated compounds degradation pathways, the Entner–Doudouroff pathway, the glyoxylate shunt and anapleuretic carbon fixation being absent from the freshwater genomes. Evolutionary reconstructions further suggest that the metabolic modules making up these important freshwater metabolic traits were already present in the gene pool of ancestral marine SAR11 populations. The loss of the glyoxylate shunt had already occurred in the common ancestor of the freshwater subgroup and its closest marine relatives, suggesting that the adaptation to freshwater was a gradual process. Furthermore, our results indicate rapid evolution of TRAP transporters in the freshwater clade involved in the uptake of low molecular weight carboxylic acids. We propose that such gradual tuning of metabolic pathways and transporters toward locally available organic substrates is linked to the formation of subgroups within the SAR11 clade and that this process was critical for the freshwater clade to find and fix an adaptive phenotype.This work was supported by the Swedish Research Council (Grant Numbers 2012-4592 to AE and 2012-3892 to SB) and the Communiy Sequencing Programme of the US Department of Energy Joint Genome Institute. The work conducted by the US Department of Energy Joint Genome Institute, a DOE Office of Science User Facility, is supported under Contract No. DE-AC02-05CH11231

The role of the complement system in traumatic brain injury: a review

Traumatic brain injury (TBI) is an important cause of disability and mortality in the western world. While the initial injury sustained results in damage, it is the subsequent secondary cascade that is thought to be the significant determinant of subsequent outcomes. The changes associated with the secondary injury do not become irreversible until some time after the start of the cascade. This may present a window of opportunity for therapeutic interventions aiming to improve outcomes subsequent to TBI. A prominent contributor to the secondary injury is a multifaceted inflammatory reaction. The complement system plays a notable role in this inflammatory reaction; however, it has often been overlooked in the context of TBI secondary injury. The complement system has homeostatic functions in the uninjured central nervous system (CNS), playing a part in neurodevelopment as well as having protective functions in the fully developed CNS, including protection from infection and inflammation. In the context of CNS injury, it can have a number of deleterious effects, evidence for which primarily comes not only from animal models but also, to a lesser extent, from human post-mortem studies. In stark contrast to this, complement may also promote neurogenesis and plasticity subsequent to CNS injury. This review aims to explore the role of the complement system in TBI secondary injury, by examining evidence from both clinical and animal studies. We examine whether specific complement activation pathways play more prominent roles in TBI than others. We also explore the potential role of complement in post-TBI neuroprotection and CNS repair/regeneration. Finally, we highlight the therapeutic potential of targeting the complement system in the context of TBI and point out certain areas on which future research is needed