Spatiotemporal Mapping Research Articles

Spatiotemporal Mapping of Lymphatic Metastases in Gastric Cancer Using Tumor-Trackable and Enzyme-Activatable Near-Infrared Fluorescent Nanoprobes.

Sentinel lymph node biopsy holds significant importance in cancer management, yet the challenge persists in early detection and precise resection of metastasis lymph nodes (LNs) due to the absence of specific and sensitive optical probes. This study reports metastatic LN reporters (MLRs) with an activatable optical output for accurate spatiotemporal mapping of lymphatic metastases in gastric cancer. MLRs are self-assembled entities incorporating mixed amphiphiles with a lipophilic tail and a tumor-targeting ligand or a fluorescent moiety that is caged with a switch cleavable by tumor-specific β-galactosidase (β-Gal). After draining into LNs, MLRs selectively activate their near-infrared fluorescence in the presence of spreading tumor cells. In orthotopic gastric cancer mouse models, the representative reporter MLR1 distinguishes macro/micrometastatic LNs from benign LNs and enables early detection of skip LNs metastasis patterns in a spatial-dependent manner. Such an active sensing mechanism provides a high level of sensitivity and specificity comparable to those of flow cytometry analysis. In surgically resected patient specimens, MLR1 differentiates cancerous tissues and metastatic LNs from normal tissues and benign LNs within 1 h. This study thus presents NIRF nanoprobes that permit facile detection of LN metastases in GC patient samples and highlights a generic translatable nanoprobe design for understanding metastatic progression.

Spatiotemporal Mapping of the Evolution of Silver Nanoparticles in Living Cells.

Bioaccumulated silver nanoparticles (AgNPs) can undergo transformation and release toxic Ag+, which can be further reduced and form secondary AgNPs (Ag0NPs). However, the intricate interconversions among AgNPs, Ag+, and Ag0NPs remain speculative. Herein, we developed a bioimaging method by coupling the aggregation-induced emission method with the label-free confocal scattering and hyperspectral imaging techniques to quantitatively visualize the biodistribution and biotransformation of AgNPs, Ag0NPs, and Ag+ in living cells. We demonstrated that AgNPs were first dissolved in the medium, and the released Ag+ was converted into Ag0NPs with the presence of algal extracellular polymeric substances and light. Under these conditions, AgNPs alone accounted for 12.4% of the total AgNP toxicity, a percentage comparable to that of Ag0NPs (15.6%). However, Ag+ remained the primary contributor to overall AgNP toxicity. Additionally, we found that about 9.00% of the accumulated AgNPs within the algal cells were transformed after 24 h exposure. Of these transformed AgNPs, 4.70% remained as Ag+ forms (located in the apical region, nucleus, and pyrenoid), while 4.30% persisted as Ag0NP forms (located in the cytosol) that were only detectable after a 4 h exposure. We further showed that AgNP exposure upregulated algal glutathione production with a 38.3-fold increase in glutathione reductase activity, which potentially resulted in Ag0NP formation at the active site. Overall, this study differentiated the toxicity of AgNPs, Ag+, and Ag0NPs and directly visualized the ongoing transformation and translocation of AgNPs, Ag+, and Ag0NPs within living cells, which are critical in unveiling the toxicity mechanisms of AgNPs.

Spatial Transcriptome and Single Nucleus Transcriptome Sequencing Reveals Tetrahydroxy Stilbene Glucoside Promotes Ovarian Organoids Development Through the Vegfa-Ephb2 Pair.

Ovarian dysfunction is a major factor leading to female infertility. Understanding how to improve or reshape ovarian function has become an important entry point for preventing and treating female infertility caused by ovarian dysfunction. Here, plant-derived compounds are screened for in vitro activity upon ovarian organoids derived from feeder-free female germline stem cells. Tetrahydroxy stilbene glucoside (TSG) is found to promote the development of ovarian organoids. Single nucleus transcriptome sequencing and spatial transcriptome sequencing are used to establish a comprehensive spatiotemporal map to elucidate the role of TSG in ovarian organoid development, encompassing cell types and subtypes, transcription factors, pseudo-time sequence, and cell communication dynamics. This analysis indicates that TSG promotes ovarian organoid development through the vascular endothelial growth factor A-Eph receptor B2 ligand-receptor pair between granulosa cells and oocytes. This study has enhanced the understanding of the mechanisms of ovarian organoid development, establishes a technical platform for screening compounds for treating infertility and related diseases, and lays a foundation for clinically applying plant-derived compounds.

Daily Light Integral Maps for Agriculture Lighting Design in Spain

The Daily Light Integral (DLI) is a key metric to optimise plant cultivation, influence photosynthesis, morphology, yield, and nutritional quality. Our study focuses on (i) the creation of the spatiotemporal DLI map for a representative Mediterranean country, Spain, (ii) the presentation of the improved semi-automatic DLI mapping workflow, (iii) the challenge of the comparability of different DLI value scalings in global (continent) and local context (country). DLI maps provide essential insights into the spatial and temporal distribution of natural and artificial light, critical for optimizing agricultural practices and enhancing crop yields. Spain's diverse topography and climate create significant regional and seasonal variations in DLI values. Using data from SunTracker Technologies and global weather stations, we developed detailed DLI maps at 30-meter resolution. These maps reveal high DLI values in coastal and southern regions, especially in summer, and lower values in northern and interior regions during winter. Our findings emphasize the importance of tailored lighting strategies in agriculture, suggesting higher scaling (from 5 to 2 mol·m−2·d−1) resolution for European contexts. This research underpins the development of smart agricultural systems that both quantitatively and qualitatively regulate lighting conditions (supplementary light and shading management) to maximize crop productivity and sustainability. Integrating DLI maps with IoT and AI technologies offers predictive insights for precision agriculture, highlighting the need for advanced training to leverage these tools effectively.

Assessment of Land Use Development Trends along Transport Corridors in Chennai Metropolitan Area

The Chennai Metropolitan Area (CMA) is a rapidly evolving region that experiences significant changes in land use patterns over time. Analogous to other Indian cities, these transformations are particularly evident in the periphery of Chennai's urban core, where substantial land use shifts have occurred between 2001 and 2021. These changes were assessed utilizing LANDSAT 7 Thematic Mapper (TM), LANDSAT 5 Enhanced Thematic Mapper plus (ETM+) for the years 2001 and 2011, and LANDSAT 8 (OLI) for 2021. This study employs unsupervised classification to investigate major land use/land cover alterations. Findings indicate that CMA has undergone substantial land use modifications over the past two decades. Notably, there has been a marked decline in open land and vegetation due to extensive reclamation for urban and commercial development. The results demonstrate that the area covered by open land and vegetation decreased by 70.23% and 4.56%, respectively, while the built-up area increased by 56%. The study also identified a pattern of urban expansion primarily along the GST road, OMR road, and Thirupati highway. The research further examines land use changes by creating buffer zones at distances of 500 meters, 1 km, 1.5 km, and 2 km. The detailed analysis, along with the spatio-temporal differentiation maps and tables generated through geospatial data, will be invaluable for understanding the growth dynamics of CMA. This information will also support the decision-making process for local planners, stakeholders, and academicians.

Spatiotemporal analysis of atmospheric methane concentrations and key influencing factors using machine learning in the Middle East (2010–2021)

Methane (CH4) is a potent greenhouse gas that significantly impacts climate change due to its rising atmospheric concentrations. Hence, it is crucial to comprehend the spatial and temporal fluctuations in atmospheric CH4 concentration (XCH4) at both national and international levels. This study investigates the correlation between atmospheric XCH4 concentrations (XCH4) and key influencing factors to identify the primary sources and sinks of CH4 across the Middle East (ME). Initially, XCH4 data from the GOSAT satellite, covering the period from 2010 to 2021, were employed to generate spatiotemporal distribution maps of XCH4 across the ME region. Subsequently, the study investigated the single and simultaneous relationship between XCH4 and relevant environmental factors, such as vegetation, temperature, precipitation, and others, across different months using correlation analysis and the Permutation Feature Importance (PFI) method to identify the key factors influencing XCH4 variations. The results reveal significant spatial and temporal variations in XCH4 concentrations, with higher levels detected in the central and southern regions of the ME during the summer months. The results also highlight the presence of both peak positive and negative correlations with temperature and moisture during winter months. Additionally, both precipitation and vegetation demonstrated negative correlations with XCH4, especially during the winter and plant-growing seasons. According to the PFI results, temperature emerged as the most significant factor, accounting for over 40% of the variance in XCH4 concentrations during summer. At the same time, anthropogenic activities exerted minimal influence on these patterns. This comprehensive spatiotemporal analysis provides crucial insights into the variation of CH4 and its primary drivers in this climatically vulnerable region. Identifying emission patterns can support the development of targeted mitigation policies to curb the future rise of CH4.

Spatiotemporal Dynamics and Driving Factors of Soil Salinization: A Case Study of the Yutian Oasis, Xinjiang, China

Soil salinization is a critical global environmental issue, exacerbated by climatic and anthropogenic factors, and posing significant threats to agricultural productivity and ecological stability in arid regions. Therefore, remote sensing-based dynamic monitoring of soil salinization is crucial for timely assessment and effective mitigation strategies. This study used Landsat imagery from 2001 to 2021 to evaluate the potential of support vector machine (SVM) and classification and regression tree (CART) models for monitoring soil salinization, enabling the spatiotemporal mapping of soil salinity in the Yutian Oasis. In addition, the land use transfer matrix and spatial overlay analysis were employed to comprehensively analyze the spatiotemporal trends of soil salinization. The geographical detector (Geo Detector) tool was used to explore the driving factors of the spatiotemporal evolution of salinization. The results indicated that the CART model achieved 5.3% higher classification accuracy than the SVM, effectively mapping the distribution of soil salinization and showing a 26.76% decrease in salinized areas from 2001 to 2021. Improvements in secondary salinization and increased vegetation coverage were the primary contributors to this reduction. Geo Detector analysis highlighted vegetation (NDVI) as the dominant factor, and its interaction with soil moisture (NDWI) has a significant impact on the spatial and temporal distribution of soil salinity. This study provides a robust method for monitoring soil salinization, offering critical insights for effective salinization management and sustainable agricultural practices in arid regions.

Pspatreg: R Package for Semiparametric Spatial Autoregressive Models

This article introduces the R package pspatreg, which is publicly available for download from the Comprehensive R Archive Network, for estimating semiparametric spatial econometric penalized spline (P-Spline) models. These models can incorporate a nonparametric spatiotemporal trend, a spatial lag of the dependent variable, independent variables, noise, and time-series autoregressive noise. The primary functions in this package cover the estimation of P-Spline spatial econometric models using either Restricted Maximum Likelihood (REML) or Maximum Likelihood (ML) methods, as well as the computation of marginal impacts for both parametric and nonparametric terms. Additionally, the package offers methods for the graphical display of estimated nonlinear functions and spatial or spatiotemporal trend maps. Applications to cross-sectional and panel spatial data are provided to illustrate the package’s functionality.

Spatiotemporal Variability and Trend of Ozone Pollution from Satellite Data (2003- 2021) in Iraq

Ozone (O3) occurs naturally in the Earth's upper atmosphere, and at ground level is a dangerous pollutant hurts plants and lung tissue and it’s a major component of smog. The purpose of this study to analyze the time series, trend, and spatial-temporal changes of monthly Relative Humidity (RH), Outgoing Longwave Radiation (OLR), and O3 in Iraq using the Atmospheric Infrared Sounder (AIRS) ascending AIRS3STM data during 2003 - 2021. The time series over six stations (Mosul, Sulaymaniyah, Khanaqin, Rutba, Baghdad, and Basra) have been analyzed and showed similar changes and fluctuation in O3 and OLR. The results of the study obtained were: minimum (decreasing, January–December) and maximum (increasing, May–August), and the mean and standard deviation were (0.044±0.003 ppmv) for O3 and (298± 22 Wm-2) for OLR during the study period. During December to February, the highest RH values were recorded, while the lowest RH values occurred from June to August. The monthly mean and standard deviation of RH was (28.66±29.54 g/kg). Further O3 trends revealed negative results in their annual series over all stations, except for Khanaqin and Basra, which had a positive trend. The O3 concentrations were consistently connected with other meteorological variables (the O3 has a negative correlation with RH and a beneficial correlation with temperature). The O3 spatiotemporal maps showed recorded the highest values recorded in April and May over Mosul and Sulaymaniyah (0.055–0.057), and in December, at middle and southern regions (Baghdad and Basra), there were the lowest O3 value (0.033). These results were due to meteorological and geographical factors. The results showed the efficient use of AIRS data to analyze the variations and distributions of atmospheric factors over different regions. In addition, AIRS observations can be used to obtain atmospheric, climatic, and environmental scientific reports and analyses for large, regional, and global regions and for long, continuous periods.

A compact, palm-sized isothermal fluorescent diagnostic intelligent IoT device for personal health monitoring and beyond via one-tube/one-step LAMP-CRISPR assay

The demand for accurate, user-friendly, and sensitive at-home nucleic acid testing solutions is rising due to occasional outbreaks of various infectious diseases and a growing desire for an improved quality of life. In response, we developed the WeD-mini, a compact, palm-sized isothermal fluorescent diagnostic IoT device that weighs just 61 g. The WeD-mini features a uniquely designed, highly sensitive optical sensing system, ultra-low power consumption, a minimalist industrial design, and an intelligent operating algorithm. It integrates real-time fluorescence detection and automatic result interpretation via a smartphone, with results seamlessly uploaded to the ‘EzDx Cloud’ for comprehensive health management and spatio-temporal disease mapping. The device supports various assays that operate at different temperatures and with varying fluorescence emission intensities, such as RPA (39 °C, low intensity), LAMP (65 °C, high intensity), and LAMP-PfAgo (65/95 °C, high intensity), while maintaining precise temperature control and exceptional fluorescence detection sensitivity. Additionally, we engineered a more thermostable AapCRISPR-Cas12b variant that operates effectively at 63 °C, enhancing compatibility with LAMP to create a robust One-Tube/One-Step LAMP-CRISPR assay. Adaptable for at-home testing of SARS-CoV-2 and influenza viruses, the WeD-mini achieved 100% sensitivity and specificity with the newly established One-Tube/One-Step LAMP-CRISPR assay. Furthermore, the WeD-mini shows potential applications in detecting meat adulteration, monitoring respiratory diseases in pets, and conducting wastewater surveillance, making it suitable for a wide range of personal and public health use cases.

Data-driven inference of high-dimensional spatiotemporal state of plasma systems

Many plasma systems and technologies, such as Hall thrusters for spacecraft propulsion, exhibit complex underlying physics that affect the global operation. When characterizing such systems in an experiment, obtaining full spatiotemporal maps of the involved state variables can be, thus, highly informative. However, this goal is not practically realizable because of various experimental limitations, e.g., finite spatial resolution of the diagnostics and geometrical accessibility constraints. Therefore, having the capability to reconstruct the full high-dimensional states of plasma systems from low-dimensional time-history measurements is greatly desirable. Compressed sensing is a signal processing technique that can answer this crucial need. However, existing compressed sensing approaches have several limitations that restrict their effectiveness for complex physical systems like plasma technologies. These include the need for abundant sensor measurements and a principled sensor placement. In this paper, we demonstrate the capabilities of Shallow Recurrent Decoder (SHRED) architecture for compressed sensing. We show in several plasma test cases that SHRED can robustly infer full high-dimensional spatiotemporal state vectors of these systems (i.e., all macroscopic plasma properties) from minimal system information. This minimal information can consist of three finite time-history measurements of either local values of a plasma property or the global plasma properties (spatially averaged or performance parameters). An application of SHRED's inference capability in the numerical plasma simulation context is “super-resolution” enhancement. We will discuss this application by presenting how SHRED can effectively establish mappings between a low-resolution and a high-resolution simulation, recovering detailed spatial plasma features that are below the simulation's grid size.

μMap-Interface: Temporal Photoproximity Labeling Identifies F11R as a Functional Member of the Transient Phagocytic Surfaceome.

Phagocytosis is usually carried out by professional phagocytic cells in the context of pathogen response or wound healing. The transient surface proteins that regulate phagocytosis pose a challenging proteomics target; knowledge thereof could lead to new therapeutic insights. Herein, we describe a novel photocatalytic proximity labeling method: "μMap-Interface", allowing for spatiotemporal mapping of phagocytosis. Utilizing photocatalyst-conjugated IGG-opsonized beads and initiating phagocytosis in a synchronized manner, we capture phagocytic interactome "snapshots" at the interface of the phagocyte and its target. This allows profiling of the dynamic surface proteome of human macrophages during the engulfment process. We reveal previously known phagocytic mediators as well as potential novel interactors and validate their presence with super-resolution microscopy. This includes F11R, an important cancer target yet to be investigated in the context of phagocytosis. Further, we demonstrate that knocking down F11R leads to an increased degree of phagocytosis; this insight could contribute to explaining its oncogenic activity. Lastly, we show capture of orthogonal phagocytic surfaceomes across different cells, using a neutrophil-like model. We believe this method will enable new insights into phagocytic processes in a variety of contexts.

The Kernel Density Estimation Technique for Spatio-Temporal Distribution and Mapping of Rain Heights over South Africa: The Effects on Rain-Induced Attenuation

The devastating effects of rain-induced attenuation on communication links operating above 10 GHz during rainy events can significantly degrade signal quality, leading to interruptions in service and reduced data throughput. Understanding the spatial and seasonal distribution of rain heights is crucial for predicting these attenuation effects and for network performance optimization. This study utilized ten years of atmospheric temperature and geopotential height data at seven pressure levels (1000, 850, 700, 500, 300, 200, and 100 hPa) obtained from the Copernicus Climate Data Store (CDS) to deduce rain heights across nine stations in South Africa. The kernel density estimation (KDE) method was applied to estimate the temporal variation of rain height. A comparison of the measured and estimated rain heights shows a correlation coefficient of 0.997 with a maximum percentage difference of 5.3%. The results show that rain height ranges from a minimum of 3.5 km during winter in Cape Town to a maximum of about 5.27 km during the summer in Polokwane. The spatial variation shows a location-dependent seasonal trend, with peak rain heights prevailing at the low-latitude stations. The seasonal variability indicates that higher rain heights dominate in the regions (Polokwane, Pretoria, Nelspruit, Mahikeng) where there is frequent occurrence of rainfall during the winter season and vice versa. Contour maps of rain heights over the four seasons (autumn, spring, winter, and summer) were also developed for South Africa. The estimated seasonal rain heights show that rain-induced attenuations were grossly underestimated by the International Telecommunication Union (ITU) recommended rain heights at most of the stations during autumn, spring, and summer but fairly overestimated during winter. Durban had a peak attenuation of 15.9 dB during the summer, while Upington recorded the smallest attenuation of about 7.7 dB during winter at a 0.01% time exceedance. Future system planning and adjustments of existing infrastructure in the study stations could be improved by integrating these localized, seasonal radio propagation data in link budget design.

A comparative study on the use of spatiotemporal maps for teaching data visualization in a hybrid learning context

ABSTRACT Data visualization is a complex matter and so is teaching it in the context of engineering and data science. Even more challenging is teaching in a midst of once in a century pandemic. In this paper, we show the results of a workshop conducted with second-year students on a university degree in data science, which took place in the context of pandemic restrictions in a hybrid learning environment. About half of the students were in the classroom and the other half at home. They learned to use STMaps, a tool developed in the scope of the SILKNOW project. We transferred research outcomes to the classroom; similarly, the results of evaluating the tool were transferred to the project. But more importantly, we found insights that the learning process of students that were online was not the same quality as of those that were physically in the classroom. While both groups performed well in terms of correctly fulfilling the tasks proposed during the workshop, the students at home were less motivated and perceived the tasks more cumbersome to achieve. From a research perspective, this activity was perceived as very useful, and similar activities are being planned for each academic year.

CTIFTrack: Continuous Temporal Information Fusion for object track

In visual tracking tasks, researchers usually focus on increasing the complexity of the model or only discretely focusing on the changes in the object itself to achieve accurate recognition and tracking of the moving object. However, they often overlook the significant contribution of video-level linear temporal information fusion and continuous spatiotemporal mapping to tracking tasks. This oversight may lead to poor tracking performance or insufficient real-time ability of the model in complex scenes. Therefore, this paper proposes a real-time tracker, namely Continuous Temporal Information Fusion Tracker (CTIFTrack). The key of CTIFTrack lies in its well-designed Temporal Information Fusion (TIF) module, which cleverly performs a linear fusion of the temporal information between the (t-1)-th and the t-th frames and completes the spatiotemporal mapping. This enables the tracker to better understand the overall spatiotemporal information and contextual spatiotemporal correlations within the video, thereby having a positive impact on the tracking task. In addition, this paper also proposes the Object Template Feature Refinement (OTFR) module, which effectively captures the global information and local details of the object, and further improves the tracker’s understanding of the object features. Extensive experiments are conducted on seven benchmarks, such as LaSOT, GOT-10K, UAV123, NFS, TrackingNet, VOT2018 and OTB-100. The experimental results validate the significant contribution of the TIF module and OTFR module to the tracking task, as well as the effectiveness of CTIFTrack. It is worth noting that while maintaining excellent tracking performance, CTIFTrack also shows outstanding real-time tracking speed. On the Nvidia Tesla T4-16GB GPU, the FPS of CTIFTrack reaches 71.98. The code and demo materials will be available at https://github.com/vpsg-research/CTIFTrack.

A fast approach for analyzing spatio-temporal patterns in ischemic heart disease mortality across US counties (1999–2021)

Ischaemic heart disease (IHD) remains the primary cause of mortality in the US. This study focuses on using spatio-temporal disease mapping models to explore the temporal trends of IHD at the county level from 1999 to 2021. To manage the computational burden arising from the high-dimensional data, we employ scalable Bayesian models using a ”divide and conquer” strategy. This approach allows for fast model fitting and serves as an efficient procedure for screening spatio-temporal patterns. Additionally, we analyze trends in four regional subdivisions, West, Midwest, South and Northeast, and in urban and rural areas. The dataset on IHD contains missing data, and we propose a procedure to impute the omitted information. The results show a slowdown in the decrease of IHD mortality in the US after 2014 with a slight increase noted after 2019. However, differences exists among the counties, the four big geographical regions, and rural and urban areas.

Insights Into the Ecology of a Widespread but Poorly Known Aerial Insectivore and a Theoretical Basis for Range Expansion Following Repeated Vagrancy Events.

Over the course of the last two centuries, Northern Rough-winged Swallows (Stelgidopteryx serripennis) have expanded their range across the North American continent but have remained a relatively poorly known species. In this paper, I discuss two aspects of their nature history that has received little attention. First, I document an instance of Northern Rough-winged Swallows digging their own burrow, which was for the better part of the last century considered a behavior that was either lost or never occurred in the first place. Second, I review the natural history literature to document qualitative patterns evident in their expansion over the last two centuries. In doing so, I define three potentially useful concepts that can he applied to understand species' range shifts: an Expansion Chronology, which is a spatiotemporal map of a range shift; the concept of Vagrancy-induced Range Expansion, where a species undergoes repeated movements outside of their typical geographic range (defined as vagrancy events), leading to the regular occurrence in a new region (e.g., regular overwintering or nonbreeding occurrences); and the concept of Vagrancy-induced Long-distance Dispersal, which is specifically expansion of a species' breeding range following repeated vagrancy events.

Intelligent Monitoring Applications of Landslide Disaster Knowledge Graphs Based on ChatGLM2

Over the years, the field of landslide disaster research has amassed a wealth of data and specialized knowledge. However, these resources originate from a wide array of sources and often feature complex data structures, highlighting a persistent lack of methods to integrate multi-source, heterogeneous data. Traditional landslide monitoring methods typically focus on singular monitoring targets and data sources, which limits a comprehensive understanding of the complex processes involved in landslides. This paper introduces a landslide monitoring model based on a knowledge graph. This model employs P-Tuning to fine-tune ChatGLM2 for the extraction of triples. Differential InSAR (D-InSAR) is utilized to extract ground deformation data, which is then integrated with the knowledge graph for landslide monitoring and analysis. This study focuses on the co-seismic landslide in Jishishan, Gansu, China. By analyzing the landslide knowledge graph and the spatiotemporal deformation map, the results are as follows: (1) For this event, 106 entities and attributes were constructed, along with two recommended calculation routes. (2) The deformation at the earthquake’s central region reached up to 8.784 cm, with a slightly smaller deformation zone to the northwest peaking at 9.662 cm. Significant unilateral subsidence was observed in the mountain range to the southwest. (3) The area affected by the co-seismic landslide primarily includes farmland and villages, covering an area of 0.3408 square kilometers. (4) Analysis based on the knowledge graph indicates that this landslide was primarily caused by the rapid liquefaction of water-saturated soil layers due to the earthquake, resulting in instability. This study contributes to the analysis of post-disaster losses, attribution, and impacts.

Spatiotemporal Mapping of Auditory Onsets during Speech Production.

The human auditory cortex is organized according to the timing and spectral characteristics of speech sounds during speech perception. During listening, the posterior superior temporal gyrus is organized according to onset responses, which segment acoustic boundaries in speech, and sustained responses, which further process phonological content. When we speak, the auditory system is actively processing the sound of our own voice to detect and correct speech errors in real time. This manifests in neural recordings as suppression of auditory responses during speech production compared with perception, but whether this differentially affects the onset and sustained temporal profiles is not known. Here, we investigated this question using intracranial EEG recorded from seventeen pediatric, adolescent, and adult patients with medication-resistant epilepsy while they performed a reading/listening task. We identified onset and sustained responses to speech in the bilateral auditory cortex and observed a selective suppression of onset responses during speech production. We conclude that onset responses provide a temporal landmark during speech perception that is redundant with forward prediction during speech production and are therefore suppressed. Phonological feature tuning in these "onset suppression" electrodes remained stable between perception and production. Notably, auditory onset responses and phonological feature tuning were present in the posterior insula during both speech perception and production, suggesting an anatomically and functionally separate auditory processing zone that we believe to be involved in multisensory integration during speech perception and feedback control.

Geospatial analysis of alarmingly increasing flood vulnerability and disaster risk within the northeast himalaya region of India

Geoenvironmetally the eastern part of the Himalaya region is highly vulnerable to flood and other natural disasters as it consists of fragmented and tectonically active geology and geomorphology, very high monsoon rainfall (>360 cm) and subsequent runoff, rugged hilly terrain with high ranges of elevation and slope, dense drainage density etc. Other hand, the unplanned developmental activities keep going and scaling up this vulnerability and risk to floods and other disasters. Addressing this burning issue, a geospatial technology-based case study of the Kohima district, Nagaland state (India), is presented here. The geospatial "technology-based" analyses employed in the study are thematic GIS mapping of flood hazard, vulnerability and risk controlling factors; and performing overlay operation using the AHP model in GIS software to generate spatio-temporal map layers of flood vulnerability and disaster risk. Results reveal that the region is under a high rate of monsoon climate change (increasing temperature, rainfall, rainy days, rainfall events and flood events with an annual rate of 0.35 %, 1.12 %, 0.36 %, 2.67 % and 4 % respectively), land use degradation (increasing built-up area with 0.60 %, annual rate decreasing forest, shrubs and water bodies with 0.80 % accumulated annual rates respectively) and demographic changes (increasing urban as well and rural population density with 0.53–2.10 % and 0.55–2.14 % respectively). Accumulated impacts of climate change, land use degradation and demographic changes causing an increase in flood hazard, vulnerability and disaster risk. Flood hazard zones and vulnerability zones extending with 0.50 % (4.89 km2) and 0.84 % (8.16 km2) annual rates respectively, subsequently the flood risk zones categorized as a moderate, high and very high potential risk, have been spreading out with a yearly rate of 0.07 % (0.65 km2), 0.13 % (1.31 km2) and 0.03 % (0.33 km2) respectively. It decreases the area under low and very low-risk zones by 0.17 % (1.63 km2) and 0.07 % (0.65 km2) annual rates respectively. Following up on these annual rates, the spatial distribution of flood hazard, vulnerability and risk zones for the next decade (2031–2040) have also been projected, revealing alarming situations, if flood disaster risk reduction (F-DRR) measures were not implemented in timely. It is strongly believed that the proposed study will be very useful for district-level planners and administrators to implement sustainable development planning, for the scientific fraternity to enhance their research work in the field of flood disaster management and for individuals for their safety in terms of life and property.