Mapping Of Dynamics Research Articles

Machine learning for predicting control landscape maps of quantum molecular dynamics: Laser-induced three-dimensional alignment of asymmetric-top molecules

Machine learning for predicting control landscape maps of quantum molecular dynamics: Laser-induced three-dimensional alignment of asymmetric-top molecules

CIrrMap250: annual maps of China's irrigated cropland from 2000 to 2020 developed through multisource data integration

Abstract. Accurate maps of irrigation extent and dynamics are crucial for studying food security and its far-reaching impacts on Earth systems and the environment. While several efforts have been made to map irrigated area in China, few have provided multiyear maps, incorporated national land surveys, addressed data discrepancies, and considered the fractional coverage of cropland within coarse-resolution pixels. Here, we addressed these important gaps and developed new annual maps of China's irrigated cropland from 2000 to 2020, named CIrrMap250 (China's irrigation map with a 250 m resolution). We harmonized irrigation statistics and surveys and reconciled them with remote sensing data. The refined estimates of irrigated area were then integrated with multiple remote sensing data (i.e. vegetation indices, hybrid cropland products, and paddy field maps) and an irrigation suitability map by means of a semi-automatic training approach. We evaluated our CIrrMap250 maps using ∼ 20 000 reference samples, high-resolution irrigation water withdrawal data, and existing local to nationwide maps. Our CIrrMap250 maps demonstrated an overall accuracy of 0.79–0.88 for the years 2000, 2010, and 2020 and outperformed currently available maps. The CIrrMap250-estimated irrigation area explained 50 %–60 % of the variance in irrigation water withdrawal across China. CIrrMap250 revealed that China's irrigation area increased by about 180 000 km2 (or 25 %) from 2000 to 2020, with the majority (61 %) occurring in the water-unsustainable regions facing severe to extreme water stress. Moreover, our product unveiled a noticeable northward shift of China's irrigation area, attributed to substantial expansions in irrigated cropland across northeastern and northwestern China. The accurate representation of irrigation extent in CIrrMap250 will greatly support hydrologic, agricultural, and climate studies in China, aiding in improved water and land resources management. CIrrMap250 can be accessed at https://doi.org/10.6084/m9.figshare.24814293.v2 (Zhang et al., 2023a).

The snow depth and its dynamics on the continental part of the Russian Arctic under conditions of the present-day climate

Based on the data of route snow surveys for the period 1966–2020, the comparison of the average long-term maximum depths of snow cover, the depths of snow cover for individual months and the dynamics of snow accumulation (the ratio of the depths of snow cover to the maximum value) on the continental part of the Russian Arctic for two representative periods (1966–1990 and 1991–2020) was made. Maps of snow cover depths and snow accumulation dynamics have been constructed for both periods. These maps made possible to analyze influence of the climatic changes on the depths of snow cover and the dynamics of snow accumulation. A comparison of these values for the first (1991–2020) period with the same of the second one showed that in October–November in the European part of the Russian Arctic, the snow depths decreased by an average of 22% (first period) and 8% (second period), and in some areas the decrease reached 70 and 20%. In the Arctic part of Western Siberia, these characteristics of snow cover increased. Growth of snow cover depths in November/January/March averaged as 11/20/23%, and in some areas it exceeded 40%. The dynamics of snow accumulation in the Arctic for the whole period 1991–2020 averages 18/37%, in October/November, and 71/91% in January/March. In the European part of the Arctic, these values are smaller: 13/29% and 68/90%, respectively. The dynamics of snow accumulation in the west and in the center of the European Arctic territory by the end of the autumn period does not reach 30%, while in the Arctic part of Siberia this mainly exceeds 50%. In October/November 1991–2020, the dynamics of snow accumulation decreased in several regions of the European Arctic and the Arctic part of Western Siberia as compared to 1966–1990. On average over the entire territory of the Arctic, the decrease in the dynamics of snow accumulation compared to 1966–1990 amounted to 13/4% in October/November, and 3/1% in January/March.

Challenges in classifying cavitation: Correlating high-speed optical imaging and passive acoustic mapping of cavitation dynamics.

Both the biological effects and acoustic emissions generated by cavitation are functions of bubble dynamics. Monitoring of acoustic emissions is therefore desirable to improve treatment safety and efficacy. The relationship between the emission spectra and bubble dynamics is, however, complex. The aim of this study was to characterise this relationship for single microbubbles using simultaneous ultra-high-speed optical imaging and passive acoustic mapping of cavitation emissions. As expected, both the number of discrete harmonics and broadband content in the emissions increased with increasing amplitude of bubble oscillation, but the spectral content was also dependent upon other variables, including the frequency of bubble collapse and receiving transducer characteristics. Moreover, phenomena, such as fragmentation and microjetting, could not be distinguished from spherical oscillations when using the full duration acoustic waveform to calculate the emission spectra. There was also no correlation between the detection of broadband noise and widely used thresholds for distinguishing bubble dynamics. It is therefore concluded that binary categorisations, such as stable and inertial cavitation, should be avoided, and different types of bubble behavior should not be inferred on the basis of frequency content alone. Treatment monitoring criteria should instead be defined according to the relevant bioeffect(s) for a particular application.

An improved framework for mapping and assessment of dynamics in cropping pattern and crop calendar from NDVI time series across a heterogeneous agro-climatic region.

The absence of spatial and temporal cropping information in semi-arid regions poses a significant challenge in assessing the dynamics of agricultural systems at river basin scales. Satellite remote sensing provides qualitative and quantitative information to derive vegetation dynamics over extensive areas of inherent complexities due to limitations in the availability of field data and the diverse nature of agricultural cropping practices. Utilizing phenological information derived from MODIS Normalized Difference Vegetation Index (NDVI) time series data from 2003-2004 to 2021-2022, this study derives major crop types, and cropping calendar (sowing, maturity, and harvest dates) for each season and year at 250-m resolution. This study introduces an integrated function-fitting model based on the Fast Fourier Transform (FFT) and a Lagrangian 3-point derivative-based approach to extract phenological events from the NDVI time series automatically. The derived phenological information is used in a hybrid crop classification algorithm that combines a rule-based decision tree approach (using the phenological events/dates) and a random forest classifier (using the phenological metrics) to generate the classified crop map at a river basin scale for multiple seasons and years. The implemented approach successfully captured sowing and harvesting dates for every crop growing season over 19years, with an RMSE of 9days, as observed with the available field survey data from 2015 to 2021. The classification results from this hybrid approach demonstrate an overall 82% accuracy. The proposed method shows a substantial improvement in cropping area estimation with a 60% reduction in MAE and RMSE compared to the existing algorithm. From the long time series of derived seasonal crop data (19-year analysis period), the influence of monsoonal activities and shifts on the spatial and temporal dynamics of sowing time and cropping patterns are assessed for a large river basin, demonstrating the utility of this continuous crop calendar. Further, an extensive analysis of results highlights farmers' adaptive strategies in response to dry and wet years, providing foundational insights for future studies on assessing the impacts of climate change. Hence, the hybrid framework adopted in this study for deriving a continuous crop calendar holds immense relevance for parameterizing and utilizing such information for developing river basin scale hydrologic and crop growth models for water resources planning and assessment.

Latent dynamics of primary sensory cortical population activity structured by fluctuations in the local field potential.

As emerging technologies enable measurement of precise details of the activity within microcircuits at ever-increasing scales, there is a growing need to identify the salient features and patterns within the neural populations that represent physiologically and behaviorally relevant aspects of the network. Accumulating evidence from recordings of large neural populations suggests that neural population activity frequently exhibits relatively low-dimensional structure, with a small number of variables explaining a substantial fraction of the structure of the activity. While such structure has been observed across the brain, it is not known how reduced-dimension representations of neural population activity relate to classical metrics of "brain state," typically described in terms of fluctuations in the local field potential (LFP), single-cell activity, and behavioral metrics. Hidden state models were fit to spontaneous spiking activity of populations of neurons, recorded in the whisker area of primary somatosensory cortex of awake mice. Classic measures of cortical state in S1, including the LFP and whisking activity, were compared to the dynamics of states inferred from spiking activity. A hidden Markov model fit the population spiking data well with a relatively small number of states, and putative inhibitory neurons played an outsize role in determining the latent state dynamics. Spiking states inferred from the model were more informative of the cortical state than a direct readout of the spiking activity of single neurons or of the population. Further, the spiking states predicted both the trial-by-trial variability in sensory responses and one aspect of behavior, whisking activity. Our results show how classical measurements of brain state relate to neural population spiking dynamics at the scale of the microcircuit and provide an approach for quantitative mapping of brain state dynamics across brain areas.

Assessment of natural and anthropogenic impacts of the Irganay Hydroelectric Power Station reservoir zone on eco‐ and geosystems using remote sensing data, Dagestan, Russia

Aim. The research and mapping of natural object dynamics using aerial and satellite imagery is one of the long‐established and actively developing fields of remote sensing and geographic research. The current phase is characterised by the wide availability of remote sensing data processing software, which offers almost unlimited possibilities for transforming and integrating datasets from different periods.Material and Methods. For the effective use of archival maps in studying territorial dynamics, two interconnected factors are critical: the date of the map’s creation and the positional accuracy of the mapped objects. The materials used in this study were TM/Landsat satellite images from the periods 2002–2017 and 2006–2014.Results. Reservoirs with a surface area of up to 60–70 km² and a volume of up to 250 million m³ increase absolute air humidity by 10–15 %. In areas with a hot climate, the impact of a reservoir on air humidity is even more pronounced.Conclusions. The anticipated significant changes in the microclimate are expected to affect the structure of agricultural production in the Irganay valley and in the villages of Maidanskoye, Untsukul, and Gimry, particularly through changes in precipitation distribution during the growing season.

A Macrocyclic Hybrid PET/MRI Probe for Quantitative Perfusion Imaging In Vivo.

Perfusion dynamics play a vital role in delivering essential nutrients and oxygen to tissues while removing metabolic waste products. Imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) use contrast agents to visualize perfusion and clearance patterns; however, each technique has specific limitations. Hybrid PET/MRI combines the quantitative power and sensitivity of PET with the high functional and anatomical detail of MRI and holds great promise for precision in molecular imaging. However, the development of dual PET/MRI probes has been hampered by challenging synthesis and radiolabeling. Here, we present a novel PET/MRI probe, [18F][Gd(FL1)], which exhibits excellent stability comparable to macrocyclic MRI contrast agents used in clinical practice. The unique molecular design of [18F][Gd(FL1)] allows selective and expeditious radiolabeling of the gadolinium chelate in the final synthetic step. Leveraging the strengths of MRI and PET signals, the probe enables quantitative in vivo mapping of perfusion and excretion dynamics through an innovative voxel-based analysis. The diagnostic capabilities of [18F][Gd(FL1)] were demonstrated in a pilot study on healthy mice, successfully detecting early cases of unilateral renal dysfunction, a condition that is typically challenging to diagnose. This study introduces a new approach for PET/MRI and emphasizes a streamlined probe design for practical synthesis and improved diagnostic accuracy.

Transparent MXene Microelectrode Arrays for Multimodal Mapping of Neural Dynamics.

Transparent microelectrode arrays have proven useful in neural sensing, offering a clear interface for monitoring brain activity without compromising high spatial and temporal resolution. The current landscape of transparent electrode technology faces challenges in developing durable, highly transparent electrodes while maintaining low interface impedance and prioritizing scalable processing and fabrication methods. To address these limitations, we introduce artifact-resistant transparent MXene microelectrode arrays optimized for high spatiotemporal resolution recording of neural activity. With 60% transmittance at 550 nm, these arrays enable simultaneous imaging and electrophysiology for multimodal neural mapping. Electrochemical characterization shows low impedance of 563 ± 99 kΩ at 1 kHz and a charge storage capacity of 58 mC cm⁻² without chemical doping. In vivo experiments in rodent models demonstrate the transparent arrays' functionality and performance. In a rodent model of chemically-induced epileptiform activity, we tracked ictal wavefronts via calcium imaging while simultaneously recording seizure onset. In the rat barrel cortex, we recorded multi-unit activity across cortical depths, showing the feasibility of recording high-frequency electrophysiological activity. The transparency and optical absorption properties of Ti₃C₂Tx MXene microelectrodes enable high-quality recordings and simultaneous light-based stimulation and imaging without contamination from light-induced artifacts.

Comprehensive maps of material stock dynamics reveal increasingly coordinated urban development in the Yangtze River Delta of China

Sustainable urban development critically depends on effectively managing the interplay between material stock (MS) and economic growth. This study combined convolutional neural network model and nighttime lights data to map building MS of Yangtze River Delta (YRD) urban agglomeration in China from 2000 to 2020 across 1 km × 1 km pixel scale, then uncovered the spatiotemporal dynamics of MS and its correlation with economic development. Our findings indicate that the model performed robustly on the test set (R2 > 0.88). YRD's MS surged over tenfold, reaching 20,772 teragram, primarily expanding along northwest-southeast developmental axes. Most YRD cities exhibited synchronized growth in material stock and GDP, suggesting an emergent pattern of sustainable urban expansion. However, cities at the developmental extremes highlighted the need for optimizing urban development strategies. By categorizing YRD cities into four distinct development modes, our study offers deep insights into the dynamics of urban development, underpinning targeted strategies that could guide cities towards more sustainable and resource-efficient growth trajectories.

Genome-Wide Mapping of Main Histone Modifications and Coordination Regulation of Metabolic Genes under Salt Stress in Pea (Pisum sativum L)

Abstract Pea occupy a key position in modern biogenetics, playing a multifaceted roles as food, vegetable, fodder, and green manure. However, due to the complex nature of its genome and the prolonged unveiling of high-quality genetic maps, research into the molecular mechanisms underlying pea development and stress responses has been significantly delayed. Furthermore, the exploration of its epigenetic modification profiles and associated regulatory mechanisms remains uncharted. This research conducted a comprehensive investigation of four specific histone marks, namely H3K4me3, H3K27me3, H3K9ac, and H3K9me2 and the transcriptome in pea under normal condition, and established a global map of genome-wide regulatory elements, chromatin states, and dynamics based on these major modifications. Our analysis identified epigenomic signals across approximately 82.6% of the genome. Each modification exhibits distinct enrichment patterns: H3K4me3 is predominantly associated with the gibberellin response pathway, H3K27me3 is primarily associated with auxin and ethylene responses, and H3K9ac is primarily associated with negative regulatory stimulus responses. We also identified a novel bivalent chromatin state (H3K9ac-H3K27me3) in pea, which is related to their development and stress response. Additionally, we unveil that these histone modifications synergistically regulate metabolic-related genes, influencing metabolite production under salt stress conditions. Our findings offer a panoramic view of the major histone modifications in pea, elucidate their interplay, and highlight their transcriptional regulatory roles during salt stress.

Connecting the dots in green food purchasing behavior literature: A system thinking approach for systematic literature reviews

AbstractRecent years have been characterized by an ever‐growing interest in consumers' behavior while purchasing green food products. Although existing research has produced a great number of papers on this topic, the knowledge generated in the field appears fragmented and, in certain cases, ambiguous. The main reasons can be traced back to the lack of reference frameworks that clarify the most used concepts, thus providing a shared language in this research domain. Despite other literature reviews that have been carried out on the domain of green food purchasing behavior, these works mainly rely on narrative summaries and qualitative analyses, which can overlook the complexity and interdependencies inherent in this research domain. The complexity of interconnected factors within this research domain poses challenges in effectively understanding and representing the underlying dynamics. To overcome these limitations, this paper proposes the use of a System Thinking based approach as a valuable method to provide a complete view of dynamics occurring in the green consumption behavior domain. In particular, we propose the integration of causal loop diagrams as a powerful visual tool to augment the conventional Systematic Literature Review process to provide a view at a glance of dynamics and capture the complex interdependencies occurring in a specific research domain. We carried out a systematic literature review by analyzing a set of 67 papers. We find nine relevant themes in the investigated research domain. Besides a descriptive picture of the scientific activity, a map of the main dynamics occurring in green food purchasing behavior has been provided.

Expansion microscopy reveals characteristic ultrastructural features of pathogenic budding yeast species.

Candida albicans is the most prevalent fungal pathogen associated with candidemia. Similar to other fungi, the complex life cycle of C. albicans has been challenging to study with high-resolution microscopy due to its small size. Here, we employed ultrastructure expansion microscopy (U-ExM) to directly visualise subcellular structures at high resolution in the yeast and during its transition to hyphal growth. N-hydroxysuccinimide (NHS)-ester pan-labelling in combination with immunofluorescence via snapshots of various mitotic stages provided a comprehensive map of nucleolar and mitochondrial segregation dynamics and enabled the resolution of the inner and outer plaque of spindle pole bodies (SPBs). Analyses of microtubules (MTs) and SPBs suggest that C. albicans displays a side-by-side SPB arrangement with a short mitotic spindle and longer astral MTs (aMTs) at the pre-anaphase stage. Modifications to the established U-ExM protocol enabled the expansion of six other human fungal pathogens, revealing that the side-by-side SPB configuration is a plausibly conserved feature shared by many fungal species. We highlight the power of U-ExM to investigate subcellular organisation at high resolution and low cost in poorly studied and medically relevant microbial pathogens.

Multi-shank 1024 channels active SiNAPS probe for large multi-regional topographical electrophysiological mapping of neural dynamics.

Implantable active dense CMOS neural probes unlock the possibility of spatiotemporally resolving the activity of hundreds of single neurons in multiple brain circuits to investigate brain dynamics. Mapping neural dynamics in brain circuits with anatomical structures spanning several millimeters, however, remains challenging. Here, we demonstrate the first CMOS neural probe for mapping intracortical neural dynamics (both LFPs and spikes) in awake, behaving mice from an area >4 mm2. By taking advantage of the modularity of our SiNAPS technology, we realized an eight shanks probe with 1024 electrode channels arranged on each shank in regular arrays with an electrode pitch <30 μm. Low-noise recordings from all electrodes at 20 kHz/channel demonstrate a field of view spanning the 2D lattice of the entire mice hippocampal circuit, together with cortical and thalamic regions. This arrangement allows combining large population unit recording across distributed networks with precise intra- and interlaminar/nuclear mapping of the oscillatory dynamics.

Arrhythmogenic Potential of Myocardial Edema: The Interstitial Osmolality Induces Spiral Waves and Multiple Excitation Wavelets.

Myocardial edema is a common symptom of pathological processes in the heart, causing aggravation of cardiovascular diseases and leading to irreversible myocardial remodeling. Patient-based studies show that myocardial edema is associated with arrhythmias. Currently, there are no studies that have examined how edema may influence changes in calcium dynamics in the functional syncytium. We performed optical mapping of calcium dynamics on a monolayer of neonatal rat cardiomyocytes with Fluo-4. The osmolality of the solutions was adjusted using the NaCl content. The initial Tyrode solution contained 140 mM NaCl (1T) and the hypoosmotic solutions contained 105 (0.75T) and 70 mM NaCl (0.5T). This study demonstrated a sharp decrease in the calcium wave propagation speed with a decrease in the solution osmolality. The successive decrease in osmolality also showed a transition from a normal wavefront to spiral wave and multiple wavelets of excitation with wave break. Our study demonstrated that, in a cellular model, hypoosmolality and, as a consequence, myocardial edema, could potentially lead to fatal ventricular arrhythmias, which to our knowledge has not been studied before. At 0.75T spiral waves appeared, whereas multiple wavelets of excitation occurred in 0.5T, which had not been recorded previously in a two-dimensional monolayer under conditions of cell edema without changes in the pacing protocol.

Optical mapping of ground reaction force dynamics in freely behaving Drosophila melanogaster larvae

During locomotion, soft-bodied terrestrial animals solve complex control problems at substrate interfaces, but our understanding of how they achieve this without rigid components remains incomplete. Here, we develop new all-optical methods based on optical interference in a deformable substrate to measure ground reaction forces (GRFs) with micrometre and nanonewton precision in behaving Drosophila larvae. Combining this with a kinematic analysis of substrate-interfacing features, we shed new light onto the biomechanical control of larval locomotion. Crawling in larvae measuring ~1 mm in length involves an intricate pattern of cuticle sequestration and planting, producing GRFs of 1–7 µN. We show that larvae insert and expand denticulated, feet-like structures into substrates as they move, a process not previously observed in soft-bodied animals. These ‘protopodia’ form dynamic anchors to compensate counteracting forces. Our work provides a framework for future biomechanics research in soft-bodied animals and promises to inspire improved soft-robot design.

Mapping of soil carbon balances changes in the dry tropical forest ecosystem in Pernambuco Brazil

Maps of soil and vegetation carbon stock dynamics resulting from changes in land use in tropical dry areas are still scarce and virtually absent for the Brazilian Northeast region. The few data available were built on a scale that does not allow their use for decision-making and precision farming applications. Based on soil and land use data, we developed a geographical information system to estimate and map carbon balances in the large (86.135 km2) semiarid region of Pernambuco state, Brazil. Maps of carbon stocks for soil and vegetation for the years 2000 and 2016 were created on the scale of 1: 100000, stratified by land use and soil types. In this period, 28% of the area had decreases in soil and vegetation C stocks, 57% had no significant changes and only 13% had increases. Most of the change was associated with converting the open native forest vegetation (Caatinga) into pastures. The net C loss was 291 million Mg, representing an average loss of 2 Mg C ha-1 year-1. Water bodies, urban areas, and other unclassified uses were not accounted for but amounted to only 2% of the area. Overall, the method proved to be a fast and feasible approach to monitoring carbon balances derived from land use changes on a regional scale.

Optical mapping of ground reaction force dynamics in freely behaving Drosophila melanogaster larvae.

During locomotion, soft-bodied terrestrial animals solve complex control problems at substrate interfaces, but our understanding of how they achieve this without rigid components remains incomplete. Here, we develop new all-optical methods based on optical interference in a deformable substrate to measure ground reaction forces (GRFs) with micrometre and nanonewton precision in behaving Drosophila larvae. Combining this with a kinematic analysis of substrate-interfacing features, we shed new light onto the biomechanical control of larval locomotion. Crawling in larvae measuring ~1 mm in length involves an intricate pattern of cuticle sequestration and planting, producing GRFs of 1-7 µN. We show that larvae insert and expand denticulated, feet-like structures into substrates as they move, a process not previously observed in soft-bodied animals. These 'protopodia' form dynamic anchors to compensate counteracting forces. Our work provides a framework for future biomechanics research in soft-bodied animals and promises to inspire improved soft-robot design.

Visual Mapping of Operating Theater Team Dynamics and Communication for Reflexive Feedback and Surgical Practice Optimization.

Effective operating theater (OT) communication and teamwork are essential to optimal surgical outcomes. We mapped the OT team dynamics and infection control practices using visual methods to guide reflexive feedback and optimize perioperative practices. Data were gathered from adult gastrointestinal surgical teams at a tertiary hospital in India using observations, sociograms (communication mapping tool), and focus group discussions (FGDs). Our methods aimed to map team communication, roles and responsibilities in infection-related practices, and door openings. Qualitative data were thematically analyzed. Quantitative data were analyzed using descriptive statistics. Data were gathered from 10 surgical procedures (over 51 hours) using 16 sociograms, 15 traffic flow maps, and 3 FGDs. Senior surgeons directly influence team hierarchies, dynamics, and communication. While the surgeons, anesthetic residents, and technicians lead most tasks during procedures, the scrub nurse acts as a mediator coordinating activity among role players across hierarchies. Failing to provide the scrub nurse with complete details of the planned surgery leads to multiple door openings to fetch equipment and disposables. Traffic flow observed in 15-minute intervals corresponds to a mean frequency of 56 door openings per hour (min: 16; max: 108), with implications for infection control. Implementing the World Health Organization surgical safety checklist was inconsistent across pathways and does not match reported compliance data. Human factors research is important in optimizing surgical teamwork. Using visual methods to provide feedback to perioperative teams on their communication patterns and behaviors, provided an opportunity for contextualized enhancement of infection prevention and control practices.

The graded multidimensional geometry of phenotypic variation and progression in neurodegenerative syndromes.

Clinical variants of Alzheimer's disease and frontotemporal lobar degeneration display a spectrum of cognitive-behavioural changes varying between individuals and over time. Understanding the landscape of these graded individual-/group-level longitudinal variations is critical for precise phenotyping; however, this remains challenging to model. Addressing this challenge, we leverage the National Alzheimer's Coordinating Center database to derive a unified geometric framework of graded longitudinal phenotypic variation in Alzheimer's disease and frontotemporal lobar degeneration. We included three time-point, cognitive-behavioural and clinical data from 390 typical, atypical and intermediate Alzheimer's disease and frontotemporal lobar degeneration variants (114 typical Alzheimer's disease; 107 behavioural variant frontotemporal dementia; 42 motor variants of frontotemporal lobar degeneration; and 103 primary progressive aphasia patients). On this data, we applied advanced data-science approaches to derive low-dimensional geometric spaces capturing core features underpinning clinical progression of Alzheimer's disease and frontotemporal lobar degeneration syndromes. To do so, we first used principal component analysis to derive six axes of graded longitudinal phenotypic variation capturing patient-specific movement along and across these axes. Then, we distilled these axes into a visualisable 2D manifold of longitudinal phenotypic variation using Uniform Manifold Approximation and Projection. Both geometries together enabled the assimilation and inter-relation of paradigmatic and mixed cases, capturing dynamic individual trajectories, and linking syndromic variability to neuropathology and key clinical end-points such as survival. Through these low-dimensional geometries, we show that (i) specific syndromes (Alzheimer's disease and primary progressive aphasia) converge over time into a de-differentiated pooled phenotype, while others (frontotemporal dementia variants) diverge to look different from this generic phenotype; (ii) phenotypic diversification is predicted by simultaneous progression along multiple axes, varying in a graded manner between individuals and syndromes; and (iii) movement along specific principal axes predicts survival at 36 months in a syndrome-specific manner and in individual pathological groupings. The resultant mapping of dynamics underlying cognitive-behavioural evolution potentially holds paradigm-changing implications to predicting phenotypic diversification and phenotype-neurobiological mapping in Alzheimer's disease and frontotemporal lobar degeneration.