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Aerial inspection of agricultural regions can provide crucial information to safeguard from numerous obstacles to efficient farming. Farmland anomalies such as standing water, weed clusters, hamper the farming practices, which causes improper use of farm area and disrupts agricultural planning. Monitoring of farmland and crops through Internet-of-Things (IoT)-enabled smart systems has potential to increase the efficiency of modern farming techniques. Unmanned Aerial Vehicle (UAV)-based remote sensing is a powerful technique to acquire farmland images on a large scale. Visual data analytics for automatic pattern recognition from the collected data is useful for developing Artificial intelligence (AI)-assisted farming models, which holds great promise in improving the farming outputs by capturing the crop patterns, farmland anomalies and providing predictive solutions to the inherent challenges faced by farmers. In this work, we propose a deep learning framework AgriSegNet for automatic detection of farmland anomalies using multiscale attention semantic segmentation of UAV acquired images. The proposed model is useful for monitoring of farmland and crops to increase the efficiency of precision farming techniques.

Travel has individual, societal and planetary health implications. We explored socioeconomic and gendered differences in travel behaviour in Africa, to develop an understanding of travel-related inequity. We conducted a mixed-methods systematic review (PROSPERO CRD42019124802). In 2019, we searched MEDLINE, TRID, SCOPUS, Web of Science, LILACS, SciELO, Global Health, Africa Index Medicus, CINAHL and MediCarib for studies examining travel behaviour by socioeconomic status and gender in Africa. We appraised study quality using Critical Appraisal Skills Programme checklists. We synthesised qualitative data using meta-ethnography, followed by a narrative synthesis of quantitative data, and integrated qualitative and quantitative strands using pattern matching principles. We retrieved 103 studies (20 qualitative, 24 mixed-methods, 59 quantitative). From the meta-ethnography, we observed that travel is: intertwined with social mobility; necessary to access resources; associated with cost and safety barriers; typified by long distances and slow modes; and dictated by gendered social expectations. We also observed that: motorised transport is needed in cities; walking is an unsafe, ‘captive’ mode; and urban and transport planning are uncoordinated. From these observations, we derived hypothesised patterns that were tested using the quantitative data, and found support for these overall. In lower socioeconomic individuals, travel inequity entailed reliance on walking and paratransit (informal public transport), being unable to afford travel, travelling less overall, and travelling long distances in hazardous conditions. In women and girls, travel inequity entailed reliance on walking and lack of access to private vehicles, risk of personal violence, societally-imposed travel constraints, and household duties shaping travel. Limitations included lack of analytical rigour in qualitative studies and a preponderance of cross-sectional quantitative studies (offering a static view of an evolving process). Overall, we found that travel inequity in Africa perpetuates socioeconomic and gendered disadvantage. Proposed solutions focus on improving the safety, efficiency and affordability of public transport and walking., Highlights • Utilised meta-ethnography and pattern-matching principles. • Revealed travel patterns differed by socioeconomic status and gender. • Travel inequity compounded disadvantage. • Females and poor people more likely to rely on walking. • Cost, safety and cultural factors were barriers to travel.

Objectives/AimsSemantic dementia (SD) is characterised by impairment in conceptual (semantic) knowledge due to anterior temporal lobe (ATL) neurodegeneration.1Here we show, by analysis of the drawings-from-name of 19 patients with SD, that knowledge of individual object characteristics is lost in a transmodal, graded fashion, correlating with volume loss in the anterior-inferior temporal lobe.Previous studies1in SD have used the technique of delayed copy drawing: showing patients a line drawing of a camel; removing it from view; then 10 seconds later, asking: ‘Please draw what you were looking at.’ This yielded informative results, but drawings in this procedure can rely on visuospatial working memory and episodic declarative memory, as well as conceptual knowledge. Here we gave requests like: ‘Please draw a picture of a camel,’ where there is no perceptual connection between the word and the object other than through semantic knowledge.MethodsLine drawings of 19 patients with diagnoses of SD at Addenbrooke’s Hospital were analysed and compared with drawings of the same items from 3 age-matched healthy controls. For a subset of 10 patients, MRI-brain results were used to correlate semantic performance with cortical atrophy.ResultsQualitative analysesPatient drawings demonstrated a pattern of degradation similar to that observed in previous delayed-copy studies. Rare and distinctive features (such as the hump on a camel) were lost earliest in disease course, and there was an increase in the intrusion of prototypical features (such as normal-sized ears on an elephant) with more advanced disease, either cross-sectionally or longitudinally.Crucially, patient drawings showed a continuum of degradation rather than a binary ‘present’ or ‘absent’ state (figure 1).Quantitative analysesCore features of each object were identified by studying commonality across control drawings, and patient drawings were scored to quantify the proportion of core features correctly drawn by patients, normalised to object familiarity and difficulty. This composite score was then regressed against MRI-extracted grey-matter volume within ventral stream regions-of-interest. Left inferior temporal gyrus atrophy was significantly (p = 0.01) correlated with drawing score.Abstract 41 Figure 1An example single SD patient’s drawing of a dog (top row) and duck (bottom row) over a four- year timespan. The dog, a common ‘template’ animal, preserves its most typical features, but gains a human-like face in late illness. The duck, a moderate frequency animal, initially has wings and a beak, but even at the start demonstrates intrusion in the form of animal-typical four legs. With time, it degrades further to become more similar to the dog (more representative of the prototypical ‘animal’), but retains a beak, a feature prototypical of birdsConclusionsProgressive atrophy of the inferior temporal lobe correlated quantitively and qualitatively with a transmodal continuum of degradation in semantic knowledge. These results therefore support the idea of a population code for semantic memory, whereby a graded ATL ‘hub’ binds modality-specific features of concepts stored across various cortical areas (the ‘spokes’).2ReferencesBozeat S,et al. A duck with four legs: Investigating the structure of conceptual knowledge using picture drawing in semantic dementia.Cogn. Neuropsychol.2003;20: 27–47.Ralph MAL, Jefferies E, Patterson K, & Rogers TT. The neural and computational bases of semantic cognition.Nat. Rev. Neurosci. 2017;18:42–55.

Due to the increase in crime and terrorism in most parts of the world, security surveillance is becoming increasingly important. A computer vision-based system for detecting weapons for real-time security surveillance is designed in this work. For identification, detection, and notifying the appropriate authorities, the system employs the YOLO V4 (You Only Look Once) algorithm. This neural network can be trained using images, videos, and live streaming videos. This model incorporates Internet-of-Things (IoT) smart devices that are interconnected and automated in weapon detection. This model's accuracy varies depending on the quality of the images and videos used in the detection process. Here, the proposed research work has discovered that the detection process is affected by the type of hardware that has been utilized to run the algorithm, ranging from low-quality image/video detection with 70% accuracy to high-quality image/video detection with 95% accuracy.

Deriving mechanisms of immune‐mediated disease from GWAS data remains a formidable challenge, with attempts to identify causal variants being frequently hampered by strong linkage disequilibrium. To determine whether causal variants could be identified from their functional effects, we adapted a massively parallel reporter assay for use in primary CD4 T cells, the cell type whose regulatory DNA is most enriched for immune‐mediated disease SNPs. This enabled the effects of candidate SNPs to be examined in a relevant cellular context and generated testable hypotheses into disease mechanisms. To illustrate the power of this approach, we investigated a locus that has been linked to six immune‐mediated diseases but cannot be fine‐mapped. By studying the lead expression‐modulating SNP, we uncovered an NF‐κB‐driven regulatory circuit which constrains T‐cell activation through the dynamic formation of a super‐enhancer that upregulates TNFAIP3 (A20), a key NF‐κB inhibitor. In activated T cells, this feedback circuit is disrupted—and super‐enhancer formation prevented—by the risk variant at the lead SNP, leading to unrestrained T‐cell activation via a molecular mechanism that appears to broadly predispose to human autoimmunity., Little progress has been made in resolving causal SNPs, genes and disease mechanisms at GWAS loci. An adapted massively‐parallel reporter assay (MPRA) allows to study immune‐mediated disease loci in CD4 T cells, the cell‐type whose regulatory DNA is most highly enriched for disease‐associated SNPs.

Deriving mechanisms of immune-mediated disease from GWAS data remains a formidable challenge, with attempts to identify causal variants being frequently hampered by strong linkage disequilibrium. To determine whether causal variants could be identified from their functional effects, we adapted a massively parallel reporter assay for use in primary CD4 T cells, the cell type whose regulatory DNA is most enriched for immune-mediated disease SNPs. This enabled the effects of candidate SNPs to be examined in a relevant cellular context and generated testable hypotheses into disease mechanisms. To illustrate the power of this approach, we investigated a locus that has been linked to six immune-mediated diseases but cannot be fine-mapped. By studying the lead expression-modulating SNP, we uncovered an NF-κB-driven regulatory circuit which constrains T-cell activation through the dynamic formation of a super-enhancer that upregulates TNFAIP3 (A20), a key NF-κB inhibitor. In activated T cells, this feedback circuit is disrupted-and super-enhancer formation prevented-by the risk variant at the lead SNP, leading to unrestrained T-cell activation via a molecular mechanism that appears to broadly predispose to human autoimmunity.

The problem faced by one farmer can also be the problem of some other farmer in other regions. Providing information to farmers and connecting them has always been a challenge. Crowdsourcing and community building are considered as useful solutions to these challenges. However, privacy concerns and inactivity of users can make these models inefficient. To tackle these challenges, we present a cost-efficient and blockchain-based secure framework for building a community of farmers and crowdsourcing the data generated by them to help the farmers’ community. Apart from ensuring privacy and security of data, a revenue model is also incorporated to provide incentives to farmers. These incentives would act as a motivating factor for the farmers to willingly participate in the process. Through integration of a deep neural network-based model to our proposed framework, prediction of any abnormalities present within the crops and their predicted possible solutions would be much more coherent. The simulation results demonstrate that the prediction of plant pathology model is highly accurate.

Numerous genomic loci have been implicated in autoimmune disorders, but attempts to identify causal variants, and thereby disease mechanisms, have been hampered by strong linkage disequilibrium; leaving most loci unresolved and the potential of GWAS largely unfulfilled. To overcome this, we developed a massively-parallel reporter assay for use in primary CD4 T-cells, a key effector of many autoimmune diseases, and sought to resolve potential causal variants via their functional effects. This provided testable hypotheses into disease mechanisms, which we illustrate using a multi-disease-associated locus on chromosome 6. By investigating the lead expression-modulating SNP, we uncover an NF-κB-driven regulatory circuit which constrains T-cell activation through the dynamic formation of a super-enhancer that upregulates expression of TNFAIP3 (A20), a key NF-κB inhibitor. In activated T-cells, this circuit is disrupted – and super-enhancer formation prevented – by the risk variant at the lead SNP, thus revealing a molecular mechanism that predisposes to multiple autoimmune diseases.

After methanol has been injected into a subsea tree, it flows back with produced fluid, partitioning between the oil, gas and water phases. Under production circumstances where water cut is low, high concentrations of methanol may be left in the oil phase. This leaves export crude contaminated with methanol which may result in penalties incurred due to pipeline export agreements. Several methods have been developed to control or minimize methanol concentration in the oil phase. The total volumes of methanol injected are compared against expected production flowrate values and a multiphase flow modeling exercise is carried out to track the arrival tendencies of the methanol injected at the tree. A model of the process system is created which considers GOR, water cut, fluid composition and production rates in order to track the partitioning behavior of the methanol. An assessment of the effect of several methanol control methods is conducted in order to select the most appropriate method while considering effect on CAPEX/OPEX, export agreements, and total system modification requirements. This paper will include a discussion of methanol partitioning behavior and the effect of water cut on export crude contamination levels. This is to provide a basis to identify whether or not methanol contamination may be an issue for a particular production scenario. This paper will discuss each control method in detail and outline the various benefits and drawbacks to each one. A discussion of the best option for methanol contamination control will investigate several considerations including topsides facilities, whether the owner of the field operates the platform, whether there are other water-producing wells on the platform, and what the methanol export limits are. Each method will have an assessment of the relative impact on CAPEX and OPEX required for implementation. Methanol injection at the subsea trees is a common solution for hydrate control, valve equalization, and annulus pressure management. Controlling methanol content in export crude will help to alleviate the negative consequences associated with methanol injection during low water cut production scenarios. An effective methanol contamination control method will reduce or even eliminate penalties associated with contaminated crude production.