Spatio-temporal Context Research Articles

Pesticide contamination and their botanical sources in pollen loads collected by honeybees in Kenya: A spatio-temporal context

Honeybees are generalists, and therefore, a wide range of flowering plants can easily be identified from their collected pollen loads. In this study, the levels of pesticide contamination on corbicular pollen were investigated using two approaches; (i) unsorted colony level collected and (ii) sorted pollen samples (according to botanical origin). Sorted samples were palynologically identified up to the family level to establish the types of pesticides used across landscapes and identify their botanical sources. This study was carried out between November 2019 and October 2020 in Murang’a county, Kenya, across three landscape types. The landscape was characterized according to the degree of greenness using the Normalized Difference Vegetation Index (NDVI) into high, medium, and low classes. Pollen Hazard Quotient (PHQ) was used to estimate the risk to honeybees of each detected pesticide. In the unsorted samples, five different pesticides were detected with concentrations ranging between 0.12 and 37.97 µg kg -1 . From the results, 11 pesticides were detected, nine insecticides and two fungicides. These pesticides were further traced to 11 plant families, from which Poaceae, Rubiceae, and Astereceae were contaminated with more than 70% of the 11 detected pesticides. Acetamiprid concentration in March was found to be extremely higher (1202.50 µg kg -1 ) the recommended EU limit (50 µg kg -1 ). Additionally, chlorpyriphos concentration was found to be higher than the EU set limit of 10 µg kg -1 in months of July, September, and October. Additionally, pollen from Rubiaceae and Poaceae plant families were the most collected during the period of this study. It was further noted that pesticides and plant families identified varied across sampling time, but not across landscapes. • Insecticides were the most widely used as compared to fungicides. • Asteraceae, Poaceae and Rubiaceae were the most contaminated plant families, with more than 70% of the detected pesticides. • Pollen loads from different apiaries could appear similar in color, but do not necessarily originate from the same family. • Type of pesticide present and their levels, and plant families identified varied across sampling time, but not landscapes. • All detected pesticides are not restricted products as per the Pest Control Products Board (PCPB) of Kenya.

Role of the membrane anchor in the regulation of Lck activity

Theoretical work suggests that collective spatiotemporal behavior of integral membrane proteins should be modulated by boundary lipids sheathing their membrane anchors. Here, we show evidence for this prediction while investigating the mechanism for maintaining a steady amount of the active form of integral membrane protein Lck kinase (LckA) by Lck trans-autophosphorylation regulated by the phosphatase CD45. We used super-resolution microscopy, flow cytometry, and pharmacological and genetic perturbation to gain insight into the spatiotemporal context of this process. We found that LckA is generated exclusively at the plasma membrane, where CD45 maintains it in a ceaseless dynamic equilibrium with its unphosphorylated precursor. Steady LckA shows linear dependence, after an initial threshold, over a considerable range of Lck expression levels. This behavior fits a phenomenological model of trans-autophosphorylation that becomes more efficient with increasing LckA. We then challenged steady LckA formation by genetically swapping the Lck membrane anchor with structurally divergent ones, such as that of Src or the transmembrane domains of LAT, CD4, palmitoylation-defective CD4 and CD45 that were expected to drastically modify Lck boundary lipids. We observed small but significant changes in LckA generation, except for the CD45 transmembrane domain that drastically reduced LckA due to its excessive lateral proximity to CD45. Comprehensively, LckA formation and maintenance can be best explained by lipid bilayer critical density fluctuations rather than liquid-ordered phase-separated nanodomains, as previously thought, with "like/unlike" boundary lipids driving dynamical proximity and remoteness of Lck with itself and with CD45.

Incorporating networks in semantic understanding of streetscapes: Contextualising active mobility decisions

Planning for active mobility satisfies many fundamental tenets of good urban design and planning. However, planning for active mobility is a complex endeavour due to numerous local, place-based factors that influence active mobility decisions. Recent advancements in urban data research have demonstrated the effectiveness of deep learning methods in evaluating active mobility potential for urban environments. At present, the incorporation of semantic information from deep learning models and street view imagery into spatio-temporal contexts remains a challenge. In particular, knowledge extraction from deep learning models remains an open question for urban planning and decision-making. Towards this issue, we propose a functional deep learning and network science workflow that employs open data from OpenStreetMap and Mapillary to assess factors affecting active mobility decisions and route planning. We demonstrate the generalisability of our analytical workflow through two case studies focusing on urban greenery in Nerima city (Japan) and urban visual complexity in Pasir Ris town (Singapore). Our results reveal clear patterns of heterogeneity in urban streetscapes and identify unevenness in street infrastructure provision based on destination types. Using this information, we propose specific areas for design intervention to improve active mobility planning. Our workflow is applicable for a diverse range of use cases making it relevant to a wide range of stakeholders, not limited to, urban researchers, policy makers and urban planners.

Temporal Organization of Episodic and Experience-near Semantic Autobiographical Memories: Neural Correlates and Context-dependent Connectivity.

Autobiographical memory includes a representation of personal life events with a unique spatiotemporal context (episodic autobiographical memory) and factual self-knowledge (personal semantics). Whereas "experience-far" personal semantics have undergone complete abstraction, "experience-near" personal semantics are still linked to a spatiotemporal context. The representation of one's own past involves an autobiographical knowledge base, in the form of a personal timeline, along which autobiographical information is temporally organized into different lifetime periods. Commonalities and differences between brain networks supporting this temporal organization for autobiographical information with different contextual specificity, however, have not been investigated to date. Here, we used task-based fMRI to assess neural substrates of temporal ordering along the personal timeline for real autobiographical episodic and experience-near personal semantic memories. Within a distributed network, the left calcarine cortex was more strongly activated for episodic autobiographical memory than personal semantics, whereas the left ventromedial pFC and right posterior cingulate cortex (PCC), angular gyrus (AG), and anterior middle temporal gyrus (aMTG) showed stronger activation for personal semantics than episodic autobiographical memory. Findings were confirmed by analyses in independently derived ROIs. Generalized psychophysiological interaction analyses between the same regions showed that, during personal semantics compared with episodic autobiographical memory, memory category modulated activity in the left PCC and right PCC, AG, and aMTG. Findings provide insights on how personal events and facts are represented in the timescale of years, suggesting that the temporal organization of autobiographical memory exploits properties of situation models developed within posteromedial, lateral parietal, and medial prefrontal regions.

Spatio-Temporal Context Based Adaptive Camcorder Recording Watermarking

Video watermarking technology has attracted increasing attention in the past few years, and a great deal of traditional and deep learning-based methods have been proposed. However, these existing methods usually suffer from the following two challenges: First, most algorithms cannot resist camcorder recording attack, which limits their practical application. Second, watermark embedding may cause substantial degradation of video quality. Through analyzing the unique distortions presented in the camcorder recording process, including geometric distortion, temporal sampling distortion, sensor distortion and processing distortion, this paper proposes a novel spatio-temporal context based adaptive camcorder recording watermarking scheme STACR. In STACR, considering the geometric distortion and video visual quality, we embed the watermark by constructing a spatio-temporal histogram and incorporate a content features based adaptive locating algorithm to select embedding blocks and embedding strengths. As for the temporal sampling attack, we put forward a watermark correlation-based synchronization algorithm and combine it with cross-validation. Moreover, to resist the sensor distortion, we design a local matching-based algorithm to improve the extraction accuracy. In addition, grouped and repeated embedding strategies are combined to cope with the processing distortion. Experimental results compared with the state-of-the-art show that the proposed scheme achieves high video quality and is robust to geometric attacks, compression, scaling, transcoding, recoding, frame rate changes and especially for camcorder recording.

Joint Attention Networks with Inherent and Contextual Preference-Awareness for Successive POI Recommendation

Nowadays recording and sharing personal lives using mobile devices on the Internet is becoming increasingly popular, and successive POI recommendation is gaining growing attention from academia and industry. In mobile scenarios, multiple influencing factors including the diversity of user preferences, the changeability of user behavior and the dynamic of spatiotemporal context bring great challenges to the POI recommender system. In order to accurately capture both the stable and the contextual preferences of mobile users in dynamic contexts, we propose a fusion framework JANICP (Joint Attention Networks with Inherent and Contextual Preferences) for successive POI recommendation by jointly training an offline/nearline user inherent interest perception model and an online user contextual interest prediction model. The offline model is trained based on the global historical behavior data to achieve stable interest representation, while the online model is trained based on the instantly selected context-sensitive data to achieve dynamic interest perception. An attention aggregation and matching module is used to fully connect the two kinds of preference representations and generate the final POI recommendation. Extensive experiments were conducted on three real datasets and experimental results show that the proposed JANICP outperforms existing state-of-the-art methods.

Human activity recognition from sensor data using spatial attention-aided CNN with genetic algorithm.

Capturing time and frequency relationships of time series signals offers an inherent barrier for automatic human activity recognition (HAR) from wearable sensor data. Extracting spatiotemporal context from the feature space of the sensor reading sequence is challenging for the current recurrent, convolutional, or hybrid activity recognition models. The overall classification accuracy also gets affected by large size feature maps that these models generate. To this end, in this work, we have put forth a hybrid architecture for wearable sensor data-based HAR. We initially use Continuous Wavelet Transform to encode the time series of sensor data as multi-channel images. Then, we utilize a Spatial Attention-aided Convolutional Neural Network (CNN) to extract higher-dimensional features. To find the most essential features for recognizing human activities, we develop a novel feature selection (FS) method. In order to identify the fitness of the features for the FS, we first employ three filter-based methods: Mutual Information (MI), Relief-F, and minimum redundancy maximum relevance (mRMR). The best set of features is then chosen by removing the lower-ranked features using a modified version of the Genetic Algorithm (GA). The K-Nearest Neighbors (KNN) classifier is then used to categorize human activities. We conduct comprehensive experiments on five well-known, publicly accessible HAR datasets, namely UCI-HAR, WISDM, MHEALTH, PAMAP2, and HHAR. Our model significantly outperforms the state-of-the-art models in terms of classification performance. We also observe an improvement in overall recognition accuracy with the use of GA-based FS technique with a lower number of features. The source code of the paper is publicly available here.

Diversification of the Pristimantis conspicillatus group (Anura: Craugastoridae) within distinct neotropical areas throughout the Neogene

Determining the relative importance of dispersal and vicariance events across neotropical regions is a major goal in biogeography. These events are thought to be related to important landscape changes, notably the transition of Amazonia toward its modern hydrological configuration ca. 10 million years ago. We investigated the spatio-temporal context of the diversification of one of the major lineages of Pristimantis, a widespread and large genus of direct-developing Neotropical frogs. We gathered a spatially and taxonomically extensive sampling of mitochondrial DNA sequences from 754 Pristimantis gr. conspicillatus specimens, which led to delimiting 75 Operational Taxonomic Units (OTUs). Complete mitogenomes of 35 of these OTUs were assembled and collated with two nuDNA loci to reconstruct a time-calibrated phylogeny. We identified five major clades that diverged around the Oligocene-Miocene transition and that are largely restricted to distinct Neotropical regions i.e. Western Amazonia (P. conspicillatus clade), the Brazilian Shield (P. fenestratus clade), the Atlantic Forest (P. ramagii clade), the Guiana Shield (P. vilarsi clade) and the northern Andes (P. nicefori clade). The majority of the diversification events within these clades occurred in-situ from the early Miocene onward. Yet, a few ancient dispersal/vicariance events are inferred to have occurred among trans-Andean forests, the Atlantic Forest, the Brazilian and the Guiana Shields, but almost none in the last 10 Ma. The radical landscape transformations during the Miocene caused by the Andean orogeny and hydrological barriers such as the Pebas System and the subsequent transcontinental configuration of the Amazon drainage is a likely explanation for the isolation of the different clades within the P. gr. conspicillatus.

Mapping volumes to planes: Camera-based strategies for snapshot volumetric microscopy

Optical microscopes allow us to study highly dynamic events from the molecular scale up to the whole animal level. However, conventional three-dimensional microscopy architectures face an inherent tradeoff between spatial resolution, imaging volume, light exposure and time required to record a single frame. Many biological processes, such as calcium signalling in the brain or transient enzymatic events, occur in temporal and spatial dimensions that cannot be captured by the iterative scanning of multiple focal planes. Snapshot volumetric imaging maintains the spatio-temporal context of such processes during image acquisition by mapping axial information to one or multiple cameras. This review introduces major methods of camera-based single frame volumetric imaging: so-called multiplane, multifocus, and light field microscopy. For each method, we discuss, amongst other topics, the theoretical framework; tendency towards optical aberrations; light efficiency; applicable wavelength range; robustness/complexity of hardware and analysis; and compatibility with different imaging modalities, and provide an overview of applications in biological research.

AUTOGRAF—AUTomated Orthorectification of GRAFfiti Photos

Admired and despised, created and destroyed, legal and illegal: Contemporary graffiti are polarising, and not everybody agrees to label them as cultural heritage. However, if one is among the steadily increasing number of heritage professionals and academics that value these short-lived creations, their digital documentation can be considered a part of our legacy to future generations. To document the geometric and spectral properties of a graffito, digital photographs seem to be appropriate. This also holds true when documenting an entire graffiti-scape consisting of 1000s of individual creations. However, proper photo-based digital documentation of such an entire scene comes with logistical and technical challenges, certainly if the documentation is considered the basis for further analysis of the heritage assets. One main technical challenge relates to the photographs themselves. Conventional photographs suffer from multiple image distortions and usually lack a uniform scale, which hinders the derivation of dimensions and proportions. In addition, a single graffito photograph often does not reflect the meaning and setting intended by the graffitist, as the creation is frequently shown as an isolated entity without its surrounding environment. In other words, single photographs lack the spatio-temporal context, which is often of major importance in cultural heritage studies. Here, we present AUTOGRAF, an automated and freely-available orthorectification tool which converts conventional graffiti photos into high-resolution, distortion-free, and georeferenced graffiti orthophotomaps, a metric yet visual product. AUTOGRAF was developed in the framework of INDIGO, a graffiti-centred research project. Not only do these georeferenced photos support proper analysis, but they also set the basis for placing the graffiti in their native, albeit virtual, 3D environment. An experiment showed that 95 out of 100 tested graffiti photo sets were successfully orthorectified, highlighting the proposed methodology’s potential to improve and automate one part of contemporary graffiti’s digital preservation.

The hippocampal formation as a hierarchical generative model supporting generative replay and continual learning.

We advance a novel computational theory of the hippocampal formation as a hierarchical generative model that organizes sequential experiences, such as rodent trajectories during spatial navigation, into coherent spatiotemporal contexts. We propose that the hippocampal generative model is endowed with inductive biases to identify individual items of experience (first hierarchical layer), organize them into sequences (second layer) and cluster them into maps (third layer). This theory entails a novel characterization of hippocampal reactivations as generative replay: the offline resampling of fictive sequences from the generative model, which supports the continual learning of multiple sequential experiences. We show that the model learns and efficiently retains multiple spatial navigation trajectories, by organizing them into spatial maps. Furthermore, the model reproduces flexible and prospective aspects of hippocampal dynamics that are challenging to explain within existing frameworks. This theory reconciles multiple roles of the hippocampal formation in map-based navigation, episodic memory and imagination.

Ecosystem-based fisheries risk assessment and forecasting considering a spatio-temporal component in Korean waters

Integrated fisheries risk analysis method for ecosystems (IFRAME) was originally developed to accomplish the goals of an ecosystem approach to fisheries (EAF) to overcome the limitations of the single-species approach. This approach performs ecosystem-based fisheries assessment, forecasting, and management. However, this IFRAME approach does not explain spatial variations in ecosystem components. Although this approach has a number of advantages in mitigating the risks associated with developing macroscopic management measures, its limitation is apparent when microscopic management measures that reflect the spatial features of the species or the fisheries within the ecosystem are required. In this study, the IFRAME was extended to assess and forecast ecosystem dynamics and risk indices in a spatio-temporal context. The distribution of biomass and changes in risk of chub mackerel were predicted using the extended IFRAME, based on scenarios considering changes in climate and variations in fishing mortality. A total of 87% of chub mackerel resources were distributed in the East China Sea in 2019, but they are expected to rise northward due to the rise of the average sea surface water temperature (SST) as per the RCP4.5 and RCP8.5 IPCC scenarios. Based on these scenarios, a major fishing ground was predicted to form around the Yellow Sea in 2069. The species risk index (SRI) of mackerel in 2069 showed relatively low in the East and Yellow Seas, but as climate change continues, the risks in the East China Sea increased. The forecasting results can be used in the management component of IFRAME to assess alternative harvest strategies and tactics in the rapidly changing marine ecosystems. Since food web relationships and water temperature scenarios were only considered as input data for the Ecospace module used in the prediction of mackerel biomass distribution, more quantitative spatial information should be incorporated in the analysis to better explain the complex nature of the marine ecosystems. In conclusion, based on the spatio-temporal IFRAME approach, it could be possible to establish a proper fisheries management plan by incorporating spatial variations in the ecosystem for sustainable fisheries.

Buyer behaviour and price expectations: a spatial analysis of the Athens residential market

PurposeThis paper examines the price setting behaviour over time and space in the Athens residential market. In periods of house price inflation asking prices are often based upon the last observed highest selling price achieved for a similar property in the same micro-location. However, in a falling market, prices may be rigid downwards and less sensitive to the most recent transaction prices, weakening spatial effects. Furthermore, the paper considers whether future price expectations affect price setting behaviour.Design/methodology/approachThe paper employs a dataset of approximately 24,500 property values from 2007 until 2014 in Athens incorporating characteristics and locational variables. The authors begin by estimating a baseline hedonic price model using property characteristics, neighbourhood amenities and location effects. Following this, a spatio-temporal autoregressive (STAR) model is estimated. Running separate models, the authors account for spatial dependence from historic valuations, contemporaneous peer effects and expectations effects.FindingsThe initial STAR model shows significant spatial and temporal effects, the former remaining important in a falling market contrasting with previous literature findings. In the second STAR model, whilst past sales effects remain significant although smaller, contemporaneous and price expectations effects are also found to be significant, the latter capturing anchoring and slow adjustment heuristics in price setting behaviour.Research limitations/implicationsAs valuations used in the database are based upon comparable sales, then in the recessionary periods covered in the dataset, finding comparables may have become more difficult, and hence this, in turn, may have impacted on valuation accuracy.Practical implicationsIn addition to past effects, contemporaneous transactions and expected future values need to be taken in consideration in analysing spatial interactions in housing markets. These factors will influence housing markets in different cities and countries.Social implicationsThe information content of property valuations should more carefully consider the relative importance of different components of asking prices.Originality/valueThis is the first paper to use transactions data over a period of falling house prices in Athens and to consider current and future values in addition to past values in a spatio-temporal context.

Role of autophagy in tumor response to radiation: Implications for improving radiotherapy.

Autophagy is an evolutionary conserved, lysosome-involved cellular process that facilitates the recycling of damaged macromolecules, cellular structures, and organelles, thereby generating precursors for macromolecular biosynthesis through the salvage pathway. It plays an important role in mediating biological responses toward various stress, including those caused by ionizing radiation at the cellular, tissue, and systemic levels thereby implying an instrumental role in shaping the tumor responses to radiotherapy. While a successful execution of autophagy appears to facilitate cell survival, abortive or interruptions in the completion of autophagy drive cell death in a context-dependent manner. Pre-clinical studies establishing its ubiquitous role in cells and tissues, and the systemic response to focal irradiation of tumors have prompted the initiation of clinical trials using pharmacologic modifiers of autophagy for enhancing the efficacy of radiotherapy. However, the outcome from the Phase I/II trials in many human malignancies has so far been equivocal. Such observations have not only precluded the advancement of these autophagy modifiers in the Phase III trial but have also raised concerns regarding their introduction as an adjuvant to radiotherapy. This warrants a thorough understanding of the biology of the cancer cells, including its spatio-temporal context, as well as its microenvironment all of which might be the crucial factors that determine the success of an autophagy modifier as an anticancer agent. This review captures the current understanding of the interplay between radiation induced autophagy and the biological responses to radiation damage as well as provides insight into the potentials and limitations of targeting autophagy for improving the radiotherapy of tumors.

Nature-inspired saccadic-like electrical stimulation paradigm promotes sustained retinal ganglion cell responses by spatiotemporally alternating activation of contiguous multi-electrode patterns

Objective. Retinal electrical stimulation using multi-electrode arrays (MEAs) aims to restore visual object perception in blind patients. However, the rate and duration of the artificial visual sensations are limited due to the rapid response decay of the stimulated neurons. Hence, we investigated a novel nature-inspired saccadic-like stimulation paradigm (biomimetic) to evoke sustained retinal responses. For implementation, the macroelectrode was replaced by several contiguous microelectrodes and activated non-simultaneously but alternating topologically. Approach. MEAs with hexagonally arranged electrodes were utilized to simulate and record mouse retinal ganglion cells (RGCs). Two shapes were presented electrically using MEAs: a 6e-hexagon (six hexagonally arranged 10 µm electrodes; 6e-hexagon diameter: 80 µm) and a double-bar (180 µm spaced, 320 µm in length). Electrodes of each shape were activated in three different modes (simultaneous, circular, and biomimetic (‘zig-zag’)), stimulating at different frequencies (1–20 Hz). Main results. The biomimetic stimulation generated enhanced RGC responses increasing the activity rate by 87.78%. In the spatiotemporal context, the electrical representation of the 6e-hexagon produced sustained and local RGC responses (∼130 µm corresponding to ∼2.5° of the human visual angle) for up to 90 s at 10 Hz stimulation and resolved the electrically presented double-bar. In contrast, during conventional simultaneous stimulation, the responses were poor and declined within seconds. Similarly, the applicability of the biomimetic mode for retinal implants (7 × 8 pixels) was successfully demonstrated. An object shape impersonating a smile was presented electrically, and the recorded data were used to emulate the implant’s performance. The spatiotemporal pixel mapping of the activity produced a complete retinal image of the smile. Significance. The application of electrical stimulation in the biomimetic mode produced locally enhanced RGC responses with significantly reduced fading effects and yielded advanced spatiotemporal performance reflecting the presented electrode shapes in the mapped activity imprint. Therefore, it is likely that the RGC responses persist long enough to evoke visual perception and generate a seamless image, taking advantage of the flicker fusion. Hence, replacing the implant’s macroelectrodes with microelectrodes and their activation in a topologically alternating biomimetic fashion may overcome the patient’s perceptual image fading, thereby enhancing the spatiotemporal characteristics of artificial vision.

JS04.6.A The landscape of tumor cell states and spatial organization in H3-K27M mutant diffuse midline glioma across age and location

Abstract Background Histone 3 lysine27-to-methionine mutations (H3-K27M) frequently occur in childhood diffuse midline gliomas (DMGs) of the pons, thalamus and spinal cord, presumed to be driven by the specific spatiotemporal context of these midline locations during postnatal development. While most common in the pons and at mid-childhood ages, the same oncohistone mutation is recurrently detected in adult DMGs and throughout different midline regions. The potential heterogeneity of tumors at different ages and in different anatomical locations of the midline are vastly understudied. Material and Methods Through dissecting the transcriptomic, epigenomic and spatial architectures of a comprehensive cohort of patient H3-K27M DMGs - spanning the age range from 2-68 years and locations from spinal cord to thalamus - at single cell resolution, we delineate how age- and location-dependent contexts shape glioma cell-intrinsic and -extrinsic features in light of the shared driver mutation. Results We identify that oligodendrocyte precursor (OPC)-like cells constitute the stem-like compartment in H3-K27M DMGs across all clinico-anatomical groups, however, depending on location, display varying levels of maturity resembling less differentiated pre-OPCs or more mature OPCs further differentiated along the oligodendroglial lineage. We further demonstrate increased mesenchymal cell states in adult tumors, which we link to age-related differences in glioma-associated immune cell compartments. We for the first time resolve the spatial organization of H3-K27M DMG cell types in intact patient tissues, identifying a local niche of the oligodendroglial lineage. Conclusion Our study provides a powerful resource for rational modeling and therapeutic frameworks taking into account determinants of age and location in this lethal glioma group.

Spatio-temporal fire detection based on brightness temperature change in Himawari-8 images

ABSTRACT The geostationary satellite Himawari-8 has become the primary source for fire remote-sensing detection because of its advantages of high frequency, large width and easy access. To solve the problem of difficulty in small fire detection and with a high omission on Himawari-8 image, a spatio-temporal fire detection (STFD) method based on Himawari-8 image brightness temperature change is proposed. Calculate the mean value difference of brightness temperature between multi-time series images before the target time, and use the least square model to fit the ideal mean value difference of brightness temperature between the target time and the image at the previous time. Then, the difference value of the actual brightness temperature between the target time and the last image time is calculated. The initial brightness temperature change pixels are obtained by the comparative analysis method. Based on the spatial statistical characteristics, the potential fire point is determined by the brightness temperature value ratio of the image, temperature mean and standard temperature deviation. Then, combined with the spatio-temporal context information, the persistent fire points are detected according to the threshold conditions to supplement undetected persistent fires. The results of experiments on the Himawari-8 images in Guizhou, China and southwestern Australia indicate that STFD exhibits superior performance on small fire detection points, and the accuracy is better than the several traditional methods.

Heri-Graphs: A Dataset Creation Framework for Multi-Modal Machine Learning on Graphs of Heritage Values and Attributes with Social Media

Values (why to conserve) and Attributes (what to conserve) are essential concepts of cultural heritage. Recent studies have been using social media to map values and attributes conveyed by the public to cultural heritage. However, it is rare to connect heterogeneous modalities of images, texts, geo-locations, timestamps, and social network structures to mine the semantic and structural characteristics therein. This study presents a methodological framework for constructing such multi-modal datasets using posts and images on Flickr for graph-based machine learning (ML) tasks concerning heritage values and attributes. After data pre-processing using pre-trained ML models, the multi-modal information of visual contents and textual semantics are modelled as node features and labels, while their social relationships and spatio-temporal contexts are modelled as links in Multi-Graphs. The framework is tested in three cities containing UNESCO World Heritage properties—Amsterdam, Suzhou, and Venice— which yielded datasets with high consistency for semi-supervised learning tasks. The entire process is formally described with mathematical notations, ready to be applied in provisional tasks both as ML problems with technical relevance and as urban/heritage study questions with societal interests. This study could also benefit the understanding and mapping of heritage values and attributes for future research in global cases, aiming at inclusive heritage management practices. Moreover, the proposed framework could be summarized as creating attributed graphs from unstructured social media data sources, ready to be applied in a wide range of use cases.

Co-creation as choreography

PurposeThis study aims to propose a novel concept of choreography as a way of understanding co-creation of value and thus develops the spatial analytical dimensions of co-creation theorising.Design/methodology/approachThis conceptual paper contemplates the meanings and possibilities of leveraging the theoretical underpinnings of value co-creation, from the viewpoint of value-in-experience.FindingsThe concept of choreography opens up a way to read knowledge as movement. It enables a way to elaborate on both the phenomenological and non-representational aspects of co-creation processes. Conceptualising co-creation through such a lens, where knowing is seen as an on-going, spatio-temporal and affective process formed in movement, posits opportunities to further understand the value co-creation practices of experiences. Choreography gives access to the kinaesthetic and affective nature of knowing gained in and through different spatio-temporal contexts and can, in turn, be mobilised in others.Originality/valueOnly a few studies have conceptualised co-creation in relation to a spatio-temporal phenomenon. Notably, this study connects co-creation with mobilities and thus constructs a novel view of knowledge and value creation.

The Role of RNA-Binding Proteins in Hematological Malignancies.

Hematological malignancies comprise a plethora of different neoplasms, such as leukemia, lymphoma, and myeloma, plus a myriad of dysplasia, such as myelodysplastic syndromes or anemias. Despite all the advances in patient care and the development of new therapies, some of these malignancies remain incurable, mainly due to resistance and refractoriness to treatment. Therefore, there is an unmet clinical need to identify new biomarkers and potential therapeutic targets that play a role in treatment resistance and contribute to the poor outcomes of these tumors. RNA-binding proteins (RBPs) are a diverse class of proteins that interact with transcripts and noncoding RNAs and are involved in every step of the post-transcriptional processing of transcripts. Dysregulation of RBPs has been associated with the development of hematological malignancies, making them potential valuable biomarkers and potential therapeutic targets. Although a number of dysregulated RBPs have been identified in hematological malignancies, there is a critical need to understand the biology underlying their contribution to pathology, such as the spatiotemporal context and molecular mechanisms involved. In this review, we emphasize the importance of deciphering the regulatory mechanisms of RBPs to pinpoint novel therapeutic targets that could drive or contribute to hematological malignancy biology.