Hasil untuk "Maps"

J. Grant, E. Enns, E. Marinier et al.

Abstract Proksee (https://proksee.ca) provides users with a powerful, easy-to-use, and feature-rich system for assembling, annotating, analysing, and visualizing bacterial genomes. Proksee accepts Illumina sequence reads as compressed FASTQ files or pre-assembled contigs in raw, FASTA, or GenBank format. Alternatively, users can supply a GenBank accession or a previously generated Proksee map in JSON format. Proksee then performs assembly (for raw sequence data), generates a graphical map, and provides an interface for customizing the map and launching further analysis jobs. Notable features of Proksee include unique and informative assembly metrics provided via a custom reference database of assemblies; a deeply integrated high-performance genome browser for viewing and comparing analysis results at individual base resolution (developed specifically for Proksee); an ever-growing list of embedded analysis tools whose results can be seamlessly added to the map or searched and explored in other formats; and the option to export graphical maps, analysis results, and log files for data sharing and research reproducibility. All these features are provided via a carefully designed multi-server cloud-based system that can easily scale to meet user demand and that ensures the web server is robust and responsive.

C. Cantalapiedra, Ana Hernández-Plaza, Ivica Letunic et al.

Even though automated functional annotation of genes represents a fundamental step in most genomic and metagenomic workflows, it remains challenging at large scales. Here, we describe a major upgrade to eggNOG-mapper, a tool for functional annotation based on precomputed orthology assignments, now optimized for vast (meta)genomic data sets. Improvements in version 2 include a full update of both the genomes and functional databases to those from eggNOG v5, as well as several efficiency enhancements and new features. Most notably, eggNOG-mapper v2 now allows: (i) de novo gene prediction from raw contigs, (ii) built-in pairwise orthology prediction, (iii) fast protein domain discovery, and (iv) automated GFF decoration. eggNOG-mapper v2 is available as a standalone tool or as an online service at http://eggnog-mapper.embl.de.

Max J. Karlsson, Cheng Zhang, Loren Méar et al.

Single-cell RNA analysis has been integrated with spatial protein profiling to create a single–cell type map of human tissues. Advances in molecular profiling have opened up the possibility to map the expression of genes in cells, tissues, and organs in the human body. Here, we combined single-cell transcriptomics analysis with spatial antibody-based protein profiling to create a high-resolution single–cell type map of human tissues. An open access atlas has been launched to allow researchers to explore the expression of human protein-coding genes in 192 individual cell type clusters. An expression specificity classification was performed to determine the number of genes elevated in each cell type, allowing comparisons with bulk transcriptomics data. The analysis highlights distinct expression clusters corresponding to cell types sharing similar functions, both within the same organs and between organs.

M. Malecela, Camilla Ducker

To neglect something is to fail to take care of it properly, particularly through inattention. Twenty diseases and disease groups comprise the WHO’s portfolio of neglected tropical diseases (NTDs) and each could provide a case study for that definition. Historical inattention by policymakers to these disabling, disfiguring and sometimes lethal health problems is probably explained by the fact that most NTDs predominantly affect people whose individual political power is wielded only very lightly, if at all. The 20 NTDs are each aetiologically, epidemiologically and clinically unique. It can be difficult, at times, to gather them together. They do share one defining characteristic, however: they impact the poorest communities and trap those communities in cycles of poverty. They collectively affect more than 1 billion people in such communities worldwide, heaping devastating health, social and economic consequences on affected individuals and their families. Since 2010, significant progress has been made, catalysed in part by a road map for intervention published by the WHO in 2012.1 Today, 500 million people no longer require interventions against one or more NTDs, and 42 countries, territories and areas have eliminated at least one NTD.2 Dracunculiasis is on the verge of eradication, with just 54 human cases reported in four countries in 2019. Lymphatic filariasis and trachoma have been eliminated as public health problems in 16 and 10 countries, respectively. Onchocerciasis has been eliminated in four countries. The annual number of cases of human African trypanosomiasis has fallen from more than 7000 in 2012 to fewer than 1000 in 2018, eclipsing the previous target of 2000 cases by 2020. The number of new leprosy cases reported globally has also continued to decline since 2010, at an average of 1% per year. These are notable achievements. They have required some extraordinary efforts at all levels. They should in noway be underestimated or downplayed. They should not obscure either, however, the fact that an immense task remains before us. The road map for NTDs 2021–20302 sets out global targets and milestones to prevent, control, eliminate and eradicate these diseases. It also defines cross-cutting targets, aligned with the WHO’s 13th General Programme of Work (2019–2023) and the United Nations’ Sustainable Development Goals (SDGs).3 Crucially, the road map proposes strategies for attaining these targets over the next decade.2 This new road map was drafted after an extensive global consultation that began in 2018 and culminated in the document’s endorsement bymember states at the 73thWorld Health Assembly in November 2020. The process involved regional workshops with managers of NTD programmes, country workshops with stakeholders in NTDs and related areas of work, as well as detailed input from disease experts, disease modellers, donors and partners. More than 100 bilateral interviews were conducted and more than 300 written responses, from two rounds of online consultations, were received and analysed. It is to be hoped, therefore, that the new road map is an accurate and thorough depiction of the perspectives of member states and a wide range of their supporters. The process was designed to ensure that the vision embodied in the 2021–2030 road map belongs not to the WHO alone, but to the entire global community dedicated to fighting NTDs. As the NTD community began to plan and develop the 2021– 2030 road map, it became clear that fundamental shifts in strategy were indicated. Three shifts, in fact, form its pillars. The first is a shift frommeasuring process to measuring impact. The second is a shift from vertical programming to horizontal, cross-cutting programming. The third shift, and potentially the most significant, calls for a move away from partner-led to country-driven and country-owned work. During the road map’s development, a thorough review of existing evidence was undertaken to identify and analyse critical gaps—both within and across diseases—which have led to bottlenecks in progress. Without addressing these, no meaningful advance seems likely. This resulted in the production of a heat map, offering an at-a-glance view of the global position.2 As the road map is rolled out, the WHO and partners will support countries to develop analogous national-level heat maps, allowing investments to be directed to areas where they will have the greatest possible impact. This may be particularly vital

D. Gordon, Gwendolyn M. Jang, Mehdi Bouhaddou et al.

A newly described coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the causative agent of coronavirus disease 2019 (COVID-19), has infected over 2.3 million people, led to the death of more than 160,000 individuals and caused worldwide social and economic disruption 1 , 2 . There are no antiviral drugs with proven clinical efficacy for the treatment of COVID-19, nor are there any vaccines that prevent infection with SARS-CoV-2, and efforts to develop drugs and vaccines are hampered by the limited knowledge of the molecular details of how SARS-CoV-2 infects cells. Here we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins that physically associated with each of the SARS-CoV-2 proteins using affinity-purification mass spectrometry, identifying 332 high-confidence protein–protein interactions between SARS-CoV-2 and human proteins. Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (of which, 29 drugs are approved by the US Food and Drug Administration, 12 are in clinical trials and 28 are preclinical compounds). We screened a subset of these in multiple viral assays and found two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the sigma-1 and sigma-2 receptors. Further studies of these host-factor-targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19. A human–SARS-CoV-2 protein interaction map highlights cellular processes that are hijacked by the virus and that can be targeted by existing drugs, including inhibitors of mRNA translation and predicted regulators of the sigma receptors.

J. Redmon, Ali Farhadi

We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more accurate. It's still fast though, don't worry. At 320x320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 mAP@50 in 51 ms on a Titan X, compared to 57.5 mAP@50 in 198 ms by RetinaNet, similar performance but 3.8x faster. As always, all the code is online at this https URL

Peter J. Thul, Lovisa Åkesson, Mikaela Wiking et al.

J. Huerta-Cepas, K. Forslund, Luis Pedro Coelho et al.

Orthology assignment is ideally suited for functional inference. However, because predicting orthology is computationally intensive at large scale, and most pipelines relatively in accessible, less precise homology-based functional transfer is still the default for (meta-)genome annotation. We therefore developed eggNOG-mapper, a tool for functional annotation of large sets of sequences based on fast orthology assignments using precomputed clusters and phylogenies from eggNOG. To validate our method, we benchmarked Gene Ontology predictions against two widely used homology-based approaches: BLAST and InterProScan. Compared to BLAST, eggNOG-mapper reduced by 7% the rate of false positive assignments, and increased by 19% the ratio of curated terms recovered over all terms assigned per protein. Compared to InterProScan, eggNOG-mapper achieved similar proteome coverage and precision, while predicting on average 32 more terms per protein and increasing by 26% the rate of curated terms recovered over total term assignments per protein. Through strict orthology assignments, eggNOG-mapper further renders more specific annotations than possible from domain similarity only (e.g. predicting gene family names). eggNOG-mapper runs ~15x than BLAST and at least 2.5x faster than InterProScan. The tool is available standalone or as an online service at http://eggnog-mapper.embl.de.

J. Redmon, Ali Farhadi

We introduce YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories. First we propose various improvements to the YOLO detection method, both novel and drawn from prior work. The improved model, YOLOv2, is state-of-the-art on standard detection tasks like PASCAL VOC and COCO. Using a novel, multi-scale training method the same YOLOv2 model can run at varying sizes, offering an easy tradeoff between speed and accuracy. At 67 FPS, YOLOv2 gets 76.8 mAP on VOC 2007. At 40 FPS, YOLOv2 gets 78.6 mAP, outperforming state-of-the-art methods like Faster RCNN with ResNet and SSD while still running significantly faster. Finally we propose a method to jointly train on object detection and classification. Using this method we train YOLO9000 simultaneously on the COCO detection dataset and the ImageNet classification dataset. Our joint training allows YOLO9000 to predict detections for object classes that dont have labelled detection data. We validate our approach on the ImageNet detection task. YOLO9000 gets 19.7 mAP on the ImageNet detection validation set despite only having detection data for 44 of the 200 classes. On the 156 classes not in COCO, YOLO9000 gets 16.0 mAP. YOLO9000 predicts detections for more than 9000 different object categories, all in real-time.

Yingying Zhang, Desen Zhou, Siqin Chen et al.

Raul Mur-Artal, J. D. Tardós

We present ORB-SLAM2, a complete simultaneous localization and mapping (SLAM) system for monocular, stereo and RGB-D cameras, including map reuse, loop closing, and relocalization capabilities. The system works in real time on standard central processing units in a wide variety of environments from small hand-held indoors sequences, to drones flying in industrial environments and cars driving around a city. Our back-end, based on bundle adjustment with monocular and stereo observations, allows for accurate trajectory estimation with metric scale. Our system includes a lightweight localization mode that leverages visual odometry tracks for unmapped regions and matches with map points that allow for zero-drift localization. The evaluation on 29 popular public sequences shows that our method achieves state-of-the-art accuracy, being in most cases the most accurate SLAM solution. We publish the source code, not only for the benefit of the SLAM community, but with the aim of being an out-of-the-box SLAM solution for researchers in other fields.

Mengde Xu, Zheng Zhang, Han Hu et al.

This paper presents an end-to-end semi-supervised object detection approach, in contrast to previous more complex multi-stage methods. The end-to-end training gradually improves pseudo label qualities during the curriculum, and the more and more accurate pseudo labels in turn benefit object detection training. We also propose two simple yet effective techniques within this framework: a soft teacher mechanism where the classification loss of each unlabeled bounding box is weighed by the classification score produced by the teacher network; a box jittering approach to select reliable pseudo boxes for the learning of box regression. On the COCO benchmark, the proposed approach outperforms previous methods by a large margin under various labeling ratios, i.e. 1%, 5% and 10%. Moreover, our approach proves to perform also well when the amount of labeled data is relatively large. For example, it can improve a 40.9 mAP baseline detector trained using the full COCO training set by +3.6 mAP, reaching 44.5 mAP, by leveraging the 123K unlabeled images of COCO. On the state-of-the-art Swin Transformer based object detector (58.9 mAP on test-dev), it can still significantly improve the detection accuracy by +1.5 mAP, reaching 60.4 mAP, and improve the instance segmentation accuracy by +1.2 mAP, reaching 52.4 mAP. Further incorporating with the Object365 pre-trained model, the detection accuracy reaches 61.3 mAP and the instance segmentation accuracy reaches 53.0 mAP, pushing the new state-of-the-art. The code and models will be made publicly available at https://github.com/microsoft/SoftTeacher.

T. Terwilliger, P. D. Adams, Randy J. Read et al.

Ashish P. Jadhav, Yosuke Yamazaki, S. Gurubaran et al.

Abstract This study establishes an observational connection between an ultra‐fast Kelvin wave (UFKW) and the occurrence of counter electrojet (CEJ) in the equatorial E region and planetary wave‐like oscillations in low‐latitude total electron content (TEC), based on a case study of CEJ events that occurred in December 2017. During this period, noontime CEJ was observed by ground‐based magnetometers with a periodicity of ∼2 days. The analysis of hourly wind data from the JAGUAR‐DAS Whole neutral Atmosphere Reanalysis reveals the presence of an eastward‐propagating UFKW with a zonal wavenumber of 1 (E1) and a period of ∼2 days along with an enhancement of eastward wind in the equatorial dynamo region which coincided with occurrence of CEJ. Observations from the Swarm satellite mission confirm the presence of equatorial electrojet (EEJ) oscillations associated with the UFKW. This is the first time that an UFKW has been identified as a source of day‐to‐day variability in CEJ. As CEJ is linked to a reduced daytime upward plasma drift over the magnetic equator, TEC at low latitudes also varies at a period of ∼2 days. The analysis of global TEC maps reveals that the 2‐day TEC variations consist not only of the eastward propagating wavenumber 1 component but also of the westward propagating wavenumber 2 (W2) component. The latter arises from the interaction of the E1 UFKW with the diurnally varying ionosphere. The 2‐day TEC variations exhibit significant longitudinal dependence due to the interplay of constructive and destructive interferences between the E1 and W2 2‐day oscillations.

Emily R Bozich, Xiaolu Guo, Jennifer L Wilson et al.

Single-cell studies of signal transduction have revealed complex temporal dynamics that determine downstream biological function. For example, the stimulus-specific dynamics of the transcription factor NFκB specify stimulus-specific gene expression programs, and loss of specificity leads to disease. Thus, it is intriguing to consider drugs that may restore signaling specificity in disease contexts, or reduce activity but maintain signaling specificity to avoid unwanted side effects. However, while steady-state dose-response relationships have been the focus of pharmacological studies, there are no established methods for quantifying drug impact on stimulus-response signaling dynamics. Here we evaluated how drug treatments affect the stimulus-specificity of NFκB activation dynamics and its ability to accurately code ligand identity and dose. Specifically, we simulated the dynamic NFκB trajectories in response to 15 stimuli representing various immune threats under treatment of 10 representative drugs across 20 dosage levels. To quantify the effects on coding capacity, we introduced a Stimulus Response Specificity (SRS) score and a stimulus confusion score. We constructed stimulus confusion maps by employing epsilon network clustering in the trajectory space and in various dimensionally reduced spaces: canonical polyadic decomposition (CPD), functional principal component analysis (fPCA), and NFκB signaling codons (i.e., established, informative dynamic features). Our results indicated that the SRS score and the stimulus confusion map based on signaling codons are best-suited to quantify stimulus-specific NFκB dynamics confusion under pharmacological perturbations. Using these tools we found that temporal coding capacity of the NFκB signaling network is generally robust to a variety of pharmacological perturbations, thereby enabling the targeting of stimulus-specific dynamics without causing broad side-effects.

Ramyashree, S. Raghavendra, S. K. Abhilash et al.

Generating high-fidelity surround view images from text prompts is a complex task that requires balancing contextual coherence with computational efficiency. The proposed work introduces a novel methodology that combines recurrent attention-based encoder-decoder architectures with text-to-image diffusion models to produce coherent and continuous surround view images. The approach utilizes a custom text encoder to convert input text prompts into contextual embeddings, which are then processed by the proposed ViewNet Unet2d architecture within the decoder. This architecture employs dual cross-attention mechanisms: one aligns text embeddings with corresponding noise image latents, while the other integrates previously generated image latents to ensure continuity across the sequence. This method guarantees that each generated image adheres to its specific prompt, while maintaining coherence with preceding images. In addition, an annotation decoder was introduced that generates semantic segmentation maps, instance segmentation masks, and object detection annotations. The annotation decoder processes latent image maps using a shared feature extraction backbone and dedicated heads for each annotation task. Experimental results on the nuScenes validation set demonstrate the effectiveness of the proposed model in producing high-quality contextually aligned surround view images. The proposed model achieves an FVD of 99 and an FID of 12.6, outperforming existing methods such as Panacea+ and DriveDreamer-2. Furthermore, our approach improves segmentation and detection accuracy, achieving a PQ of 67.4, mIoU of 80.1, and mAP of 65.4, surpassing methods like OpenSeeD and D2Det. An ablation study highlights the contributions of key components in our architecture. Integrating positional encoding, self-attention, and concurrent attention significantly enhances generation quality, reducing FVD to 99 and FID to 12.6. Experimental results demonstrate the effectiveness of proposed work in producing high-quality, contextually aligned surround view images with comprehensive annotations, pushing the boundaries of text-to-image synthesis and scene understanding.

Iryna Voronenko, Alla Bohush, Oleksandr Voronenko et al.

In this work, the main trends in research and publication activity in digital transformation in the world and Ukraine are analyzed using meta- and bibliometric analysis. For this purpose, bibliometric data on scientific publications on the topic of digital transformations in the Google Scholar and Scopus databases were selected, which were additionally analyzed using the VOSviewer software package. Based on filtering the results obtained, an array was formed that included 366 scientific publications for 2019–2023 in Ukrainian in the Google Scholar database and 3,703 scientific publications in English for 2020–2023 in the Scopus database. Dynamic time analysis revealed a significant surge of scientific interest in the topic of digital transformations in recent years, while structural analysis revealed the multi-industry structure of existing research. The creation of bibliographic maps of keywords and publication maps allowed us to form an idea of the main thematic areas of research in the context of digital transformations and their opinion leaders. The data obtained became the basis for formulating recommendations for further areas of research in digital transformation, in particular, on the development of a unified roadmap for the digital transformation of education at different educational qualification levels and for various specialties. This will contribute to the formation of a single systemic approach to the digital transformation of Ukraine as a guarantee for the state’s sustainable development, well-being, strengthening of national security, speeding up the pace of European integration processes, as well as promoting national interests at the international level.

Luiz Henrique Werneck de Oliveira

Urban development surrounding commercial airports is a conflictual factor that hampers their operations, prevents their expansion, and may lead to their closure. São Paulo has 22 million in its metropolitan area. It is served by Congonhas (Domestic) and Guarulhos (International) Airports (22 and 46 million passengers, respectively, in 2019). Congonhas is surrounded by urban development. The hypothesis of this study was to verify if a similar process is happening around Guarulhos. This study investigates population density on runway alignments and the trend of real estate development from 1985 to 2013 by using “heat maps” superposed to an “Alignment Radial Model” (ARM) comprising a central circle from 0-2 km and concentrically radial rings at 2-4 km, 4-6 km, 6-8 km and 8-10 km radius. The 2010 population was computed in Census sectors within the ARM. Real estate development data of both runway-aligned and -dis-aligned was mapped in several years’ periods over the ARM. Results show intense real estate development Northwest of Congonhas from 1985 to 2004, with a similar pattern the Western alignment of Guarulhos airport’ runways since 2005. High population densities are noted in 2010 South of Congonhas and East of Guarulhos airports, where little or no real estate development occurred along 1985-2013, indicating informal dwellings. Evidence shows that the urban development conflicting with Congonhas airport is being replicated around Guarulhos airport, rendering both unable to expand their runways, restricting São Paulo’s airports system capacity. This brings further challenges both for airport and urban planners to increasing airport capacity at São Paulo.

Mohammod Ali, Md Asrakul Haque, Md Razob Ali et al.

Drip irrigation pressure is considered a key parameter for controlling and designing the drip irrigation system in sandy soils. Understanding soil water content (SWC) movements under varying pressures can enhance water use efficiency and support sustainable irrigation strategies for crops in arid regions. The objectives of this study were to investigate the effects of irrigation pressure on the spatial, vertical, and temporal variability of SWC in sandy loam soil using surface drip irrigation. Experiments were carried out in a soil bin located in a greenhouse. SWC sensors were placed at depths 10, 20, 30, 40, and 50 cm to monitor SWC variability under low, medium, and high drip irrigation pressures (25, 50, and 75 kPa) at a constant emitter flow rate of 3 L/h. A pressure controller was used to regulate drip irrigation pressure, while microcontrollers communicated with SWC sensors, collected experimental data, and automatically recorded the outputs. At low irrigation pressure, water content began to increase at 0.53 h and saturated at 3.5 h, with both values being significantly lower at medium and high pressures. The results indicated that lower pressures led to significant variability in water movement at shallow depths (10 to 30 cm), becoming uniform at deeper layers but requiring longer irrigation times. Competitively higher pressures showed uniform water distribution and retention statistically throughout the soil profiles with shorter irrigation times. The variation in water distribution resulting in non-uniform coverage across the irrigated area demonstrates how pressure changes affect the flow rate of the emitter. The results provide information maps with soil water data that can be adjusted with irrigation pressure to maximize water use efficiency in sandy loam soils, aiding farmers in better irrigation scheduling for different crops using surface drip irrigation techniques in arid environments.

Dietmar Offenhuber, Joy Mountford

The Future of Design Education working group on representation addressed the roles of data, maps, models, and interfaces as a continuum from representation to action. The article traces historical ideas of representation grounded by a linguistic paradigm to more recent approaches based on performance, embodiment, and sensory modalities other than vision. Discussions include the use of representations in the design process. Designers are able to use traditional forms of representation in the design of artifacts, such as sketches. These forms of representation are not sufficient for the design of systems. System design requires models that allow stakeholders to negotiate their view of a situation and design teams to iterate how things might work. Core ideas in the working group recommendations address issues of, substitution, formal rules, motivation, context dependency, materiality, provisionality, latency, performance, externalization, facilitation and negotiation, mediation, and measurement and evaluation. Discussions address the socio-political implications of representation and the expanding role of computing and data that call for a systems view.