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

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

Role of oxidative stress in oxaliplatin-induced enteric neuropathy and colonic dysmotility in mice

BACKGROUND AND PURPOSE: Oxaliplatin is a platinum‐based chemotherapeutic drug used as a first‐line therapy for colorectal cancer. However, its use is associated with severe gastrointestinal side‐effects resulting in dose limitations and/or cessation of treatment. In this study, we tested whether oxidative stress, caused by chronic oxaliplatin treatment, induces enteric neuronal damage and colonic dysmotility. EXPERIMENTAL APPROACH: Oxaliplatin (3 mg·kg(−1) per day) was administered in vivo to Balb/c mice intraperitoneally three times a week. The distal colon was collected at day 14 of treatment. Immunohistochemistry was performed in wholemount preparations of submucosal and myenteric ganglia. Neuromuscular transmission was studied by intracellular electrophysiology. Circular muscle tone was studied by force transducers. Colon propulsive activity studied in organ bath experiments and faeces were collected to measure water content. KEY RESULTS: Chronic in vivo oxaliplatin treatment resulted in increased formation of reactive oxygen species (O(2)ˉ), nitration of proteins, mitochondrial membrane depolarisation resulting in the release of cytochrome c, loss of neurons, increased inducible NOS expression and apoptosis in both the submucosal and myenteric plexuses of the colon. Oxaliplatin treatment enhanced NO‐mediated inhibitory junction potentials and altered the response of circular muscles to the NO donor, sodium nitroprusside. It also reduced the frequency of colonic migrating motor complexes and decreased circular muscle tone, effects reversed by the NO synthase inhibitor, Nω‐Nitro‐L‐arginine. CONCLUSION AND IMPLICATIONS: Our study is the first to provide evidence that oxidative stress is a key player in enteric neuropathy and colonic dysmotility leading to symptoms of chronic constipation observed in oxaliplatin‐treated mice

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

Cancer symptom awareness and barriers to symptomatic presentation in England – Are we clear on cancer?

Background: Low cancer awareness may contribute to delayed diagnosis and poor cancer survival. We aimed to quantify socio-demographic differences in cancer symptom awareness and barriers to symptomatic presentation in the English population. Methods: Using a uniquely large data set (n=49?270), we examined the association of cancer symptom awareness and barriers to presentation with age, gender, marital status and socio-economic position (SEP), using logistic regression models to control for confounders. Results: The youngest and oldest, the single and participants with the lowest SEP recognised the fewest cancer symptoms, and reported most barriers to presentation. Recognition of nine common cancer symptoms was significantly lower, and embarrassment, fear and difficulties in arranging transport to the doctor’s surgery were significantly more common in participants living in the most deprived areas than in the most affluent areas. Women were significantly more likely than men to both recognise common cancer symptoms and to report barriers. Women were much more likely compared with men to report that fear would put them off from going to the doctor. Conclusions: Large and robust socio-demographic differences in recognition of some cancer symptoms, and perception of some barriers to presentation, highlight the need for targeted campaigns to encourage early presentation and improve cancer outcomes

Multicentre evaluations of two new rapid IgG4 tests (WB rapid and panLF rapid) for detection of lymphatic filariasis

In the global effort to eliminate lymphatic filariasis (LF), rapid field-applicable tests are useful tools that will allow on-site testing to be performed in remote places and the results to be obtained rapidly. Exclusive reliance on the few existing tests may jeopardize the progress of the LF elimination program, thus the introduction of other rapid tests would be useful to address this issue. Two new rapid immunochromatographic IgG4 cassette tests have been produced, namely WB rapid and panLF rapid, for detection of bancroftian filariasis and all three species of lymphatic filaria respectively. WB rapid was developed using BmSXP recombinant antigen, while PanLF rapid was developed using BmR1 and BmSXP recombinant antigens. A total of 165 WB rapid and 276 panLF rapid tests respectively were evaluated at USM and the rest were couriered to another university in Malaysia (98 WB rapid, 129 panLF rapid) and to universities in Indonesia (56 WB rapid, 62 panLF rapid), Japan (152 of each test) and India (18 of each test) where each of the tests underwent independent evaluations in a blinded manner. The average sensitivities of WB rapid and panLF rapid were found to be 97.6% (94%–100%) and 96.5% (94%–100%) respectively; while their average specificities were both 99.6% (99%–100%). Thus this study demonstrated that both the IgG4 rapid tests were highly sensitive and specific, and would be useful additional tests to facilitate the global drive to eliminate this disease

A distributed optimization method for the geographically distributed data centres problem

The geographically distributed data centres problem (GDDC) is a naturally distributed resource allocation problem. The problem involves allocating a set of virtual machines (VM) amongst the data centres (DC) in each time period of an operating horizon. The goal is to optimize the allocation of workload across a set of DCs such that the energy cost is minimized, while respecting limitations on data centre capacities, migrations of VMs, etc. In this paper, we propose a distributed optimization method for GDDC using the distributed constraint optimization (DCOP) framework. First, we develop a new model of the GDDC as a DCOP where each DC operator is represented by an agent. Secondly, since traditional DCOP approaches are unsuited to these types of large-scale problem with multiple variables per agent and global constraints, we introduce a novel semi-asynchronous distributed algorithm for solving such DCOPs. Preliminary results illustrate the benefits of the new method

Differential phenotypic expression of a novel PDHA1 mutation in a female monozygotic twin pair

Pyruvate dehydrogenase complex (PDC) deficiency caused by mutations in the X-linked PDHA1 gene has a broad clinical presentation, and the pattern of X-chromosome inactivation has been proposed as a major factor contributing to its variable expressivity in heterozygous females. Here, we report the first set of monozygotic twin females with PDC deficiency, caused by a novel, de novo heterozygous missense mutation in exon 11 of PDHA1 (NM_000284.3: c.1100A>T). Both twins presented in infancy with a similar clinical phenotype including developmental delay, episodes of hypotonia or encephalopathy, epilepsy, and slowly progressive motor impairment due to pyramidal, extrapyramidal, and cerebellar involvement. However, they exhibited clear differences in disease severity that correlated well with residual PDC activities (approximately 60% and 20% of mean control values, respectively) and levels of immunoreactive E1α subunit in cultured skin fibroblasts. To address whether the observed clinical and biochemical differences could be explained by the pattern of X-chromosome inactivation, we undertook an androgen receptor assay in peripheral blood. In the less severely affected twin, a significant bias in the relative activity of the two X chromosomes with a ratio of approximately 75:25 was detected, while the ratio was close to 50:50 in the other twin. Although it may be difficult to extrapolate these results to other tissues, our observation provides further support to the hypothesis that the pattern of X-chromosome inactivation may influence the phenotypic expression of the same mutation in heterozygous females and broadens the clinical and genetic spectrum of PDC deficiency

Toward Human-Carnivore Coexistence: Understanding Tolerance for Tigers in Bangladesh

Fostering local community tolerance for endangered carnivores, such as tigers (Panthera tigris), is a core component of many conservation strategies. Identification of antecedents of tolerance will facilitate the development of effective tolerance-building conservation action and secure local community support for, and involvement in, conservation initiatives. We use a stated preference approach for measuring tolerance, based on the ‘Wildlife Stakeholder Acceptance Capacity’ concept, to explore villagers’ tolerance levels for tigers in the Bangladesh Sundarbans, an area where, at the time of the research, human-tiger conflict was severe. We apply structural equation modeling to test an a priori defined theoretical model of tolerance and identify the experiential and psychological basis of tolerance in this community. Our results indicate that beliefs about tigers and about the perceived current tiger population trend are predictors of tolerance for tigers. Positive beliefs about tigers and a belief that the tiger population is not currently increasing are both associated with greater stated tolerance for the species. Contrary to commonly-held notions, negative experiences with tigers do not directly affect tolerance levels; instead, their effect is mediated by villagers’ beliefs about tigers and risk perceptions concerning human-tiger conflict incidents. These findings highlight a need to explore and understand the socio-psychological factors that encourage tolerance towards endangered species. Our research also demonstrates the applicability of this approach to tolerance research to a wide range of socio-economic and cultural contexts and reveals its capacity to enhance carnivore conservation efforts worldwide

Male breast cancer

Male breast cancer (MBC) is a rare disease representing less than 1% of all breast cancers (BC) and less than 1% of cancers in men. Age at presentation is mostly in the late 60s. MBC is recognized as an estrogen-driven disease, specifically related to hyperestrogenism. About 20% of MBC patients have family history for BC. Mutations in BRCA1 and, predominantly, BRCA2, account for approximately 10% of MBC cases. Because of its rarity, MBC is often compared with female BC (FBC). Based on age-frequency distribution, age-specific incidence rate patterns and prognostic factors profiles, MBC is considered similar to late-onset, postmenopausal estrogen/progesterone receptor positive (ER+/PR+) FBC. However, clinical and pathological characteristics of MBC do not exactly overlap FBC. Compared with FBC, MBC has been reported to occur later in life, present at a higher stage, and display lower histologic grade, with a higher proportion of ER+ and PR+ tumors. Although rare, MBC remains a substantial cause for morbidity and mortality in men, probably because of its occurrence in advanced age and delayed diagnosis. Diagnosis and treatment of MBC generally is similar to that of FBC. Men tend to be treated with mastectomy rather than breast-conserving surgery. The backbone of adjuvant therapy or palliative treatment for advanced disease is endocrine, mostly tamoxifen. Use of FBC-based therapy led to the observation that treatment outcomes for MBC are worse and that survival rates for MBC do not improve like FBC. These different outcomes may suggest a non-appropriate utilization of treatments and that different underlying pathogenetic mechanisms may exist between male and female BC