Temporal Input Research Articles

A Model of Memory Linking Time to Space.

The storage of temporally precise spike patterns can be realized by a single neuron. A spiking neural network (SNN) model is utilized to demonstrate the ability to precisely recall a spike pattern after presenting a single input. We show by using a simulation study that the temporal properties of input patterns can be transformed into spatial patterns of local dendritic spikes. The localization of time-points of spikes is facilitated by phase-shift of the subthreshold membrane potential oscillations (SMO) in the dendritic branches, which modifies their excitability. In reference to the points in time of the arriving input, the dendritic spikes are triggered in different branches. To store spatially distributed patterns, two unsupervised learning mechanisms are utilized. Either synaptic weights to the branches, spatial representation of the temporal input pattern, are enhanced by spike-timing-dependent plasticity (STDP) or the oscillation power of SMOs in spiking branches is increased by dendritic spikes. For retrieval, spike bursts activate stored spatiotemporal patterns in dendritic branches, which reactivate the original somatic spike patterns. The simulation of the prototypical model demonstrates the principle, how linking time to space enables the storage of temporal features of an input. Plausibility, advantages, and some variations of the proposed model are also discussed.

Seasonal Variability in Benthic–Pelagic Coupling: Quantifying Organic Matter Inputs to the Seafloor and Benthic Macrofauna Using a Multi-Marker Approach

The exchange between the water column and the seafloor is a complex process, and is particularly intensive in the shallow waters of highly productive coastal areas, where the temporal variability in the inputs of pelagic organic matter will determine many aspects of the benthic community structure. However, few studies have focused on the seasonality of inputs of organic matter to the seafloor, and on the consequent dynamics and time scales of response of benthic consumers. We conducted a one-year study where we repeatedly sampled multiple organic compounds traditionally used as markers to study the link between the pelagic organic matter inputs and the seafloor, and the potential response of benthic macrofauna to seasonal trends in phytoplankton biomass. We simultaneously quantified the particulate organic matter in the water column, the sinking material and different seafloor compartments, and analysed it for pigments, organic carbon and nitrogen content, C/N ratio, and stable isotopes. Seafloor sediment was also analysed for total lipids, and the dominant macrobenthic species for isotopic signatures. Results showed a major deposition of fresh organic matter during the spring bloom followed by more degraded organic matter inputs during the late summer bloom and even lower quality of the organic matter reaching the seafloor during winter. Strong positive relationships between water column and sedimentary pigments suggest that phytoplankton was the main source of carbon to the seafloor. The isotopic signatures of the dominant macrobenthic species suggest a fast response to the organic matter inputs from the water column. However, different species responded differently to the deposition of organic matter. Macoma balthica and Marenzelleria spp. fed on more reworked and degraded sedimentary material, while Monoporeia affinis showed a shift in the feeding habits according to its life stage, with adult individuals feeding on fresher material than juveniles did. Our study highlights the seasonal variability of the benthic-pelagic coupling and the utility of a multi-marker approach to follow the temporal inputs of organic matter from the water column to the seafloor and benthic organisms.

Electroformed, Self‐Connected Tin Oxide Nanoparticle Networks for Electronic Reservoir Computing

AbstractInterconnected SnOx nanoparticle (NP) networks are electroformed within a semi‐insulating SnOx thin‐film and between lateral electrodes. During this top‐down process, Joule heating, disproportionation, and de‐wetting of the SnOx thin‐film precede the formation of the NP networks. The same lateral electrodes used for electroforming are used to probe the network and reveal its complex electrical characteristics. Higher‐order harmonic generation is observed and the internal short‐term memory effects of the NP networks enable temporal inputs to be mapped into reservoir states for subsequent linear readout without training. Reservoir computing functionality is demonstrated with no requirement for high‐vacuum or cryo‐cooled environments.

Tunable compression of THz chirped pulses using a helical graphene ribbon-loaded hollow-core waveguide.

Pulse shaping is important for communications, spectroscopy, and other applications that require high peak power and pulsed operation, such as radar systems. Unfortunately, pulse shaping remains largely elusive for terahertz (THz) frequencies. To address this void, a comprehensive study on the dispersion tunability properties of THz chirped pulses traveling through a dielectric-lined hollow-core waveguide loaded with a helical graphene ribbon is presented. It is demonstrated that there is an optimal compression waveguide length over which THz chirped pulses reach the maximum compression. The optimal length is dependent on the chirp pulse duration. It is shown that by applying an electrostatic controlling gate voltage (Vg) of 0 and 30 V on the helical graphene ribbon, the temporal input pulses of width 8 and 12 ps, propagating through two different lengths, can be tuned by 5.9% and 8%, respectively, in the frequency range of 2.15-2.28 THz.

Anisotropy in tactile time perception

Spatial distortions in touch have been investigated since the 19th century. For example, two touches applied to the hand dorsum feel farther apart when aligned with the mediolateral axis (i.e., across the hand) than when aligned with the proximodistal axis (along the hand). Stimulations to our sensory receptors are usually dynamic, where spatial and temporal inputs closely interact to establish our percept. For example, physically bigger tactile stimuli are judged to last longer than smaller stimuli. Given such links between space and time in touch, we investigated whether there is a tactile anisotropy in temporal perception analogous to the anisotropy described above. In this case, the perceived duration between the onset of two touches should be larger when they are aligned with the mediolateral than with the proximodistal axis of the hand dorsum. To test this hypothesis, we asked participants to judge which of two tactile temporal sequences, having the same spatial separation along and across the dorsum, felt longer. A clear anisotropy of the temporal perception was observed: temporal intervals across the hand were perceived as longer than those along the hand. Consistent with the spatial anisotropy, the temporal anisotropy did not appear on the palm side of the hand, indicating that the temporal anisotropy was based on perceptual processes rather than top-down modulations such as attentional or decisional/response biases. Contrary to our predictions, however, we found no correlation between the magnitudes of the temporal and spatial anisotropies. Our results demonstrated a novel type of temporal illusion in touch, which is strikingly similar in nature to the previously reported spatial anisotropy. Thus, qualitatively similar distorted somatosensory representations appear to underlie both temporal and spatial processing of touch.

The emission of volatile halocarbons by seaweeds and their response towards environmental changes

Volatile halocarbons can deplete the protective stratospheric ozone layer contributing to global climate change and may even affect local climate through aerosol production. These compounds are produced through anthropogenic and biogenic processes. Biogenic halocarbons may be produced as defence compounds, anti-oxidants or by-products of metabolic processes. These compounds include very short-lived halocarbons (VSLH), e.g. bromoform (CHBr3), dibromomethane (CH2Br2), methyl iodide (CH3I), diiodomethane (CH2I2). Efforts to quantify the biogenic sources of these compounds, especially those of marine origin, e.g. seaweeds, phytoplankton and seagrass meadows, are often complicated by inherent biological variability as well as spatial and temporal changes in emissions. The contribution of the coastal region and the oceans to the stratospheric load of halocarbons has been widely debated. This highlights the need to understand the factors affecting the release of these compounds from marine sources for which data for modelling purposes are generally lacking. Seaweeds are important sources of biogenic halocarbons subjected to changing environmental conditions. Huge uncertainties in the prediction of current and future global halocarbon pool exist due to the lack of spatial and temporal data input from coastal and oceanic sources. Therefore, investigating the effect of changing environmental conditions on the emission of VSLH by the seaweeds could help towards better estimations of halocarbon emissions. This is especially important in light of global changes in both climate and the environment, the expansion of seaweed cultivation industry and the interactions between halocarbon emission and their environment. In this paper, we review current knowledge of seaweed halocarbon emissions, how environmental factors affect these emissions and identify gaps in understanding. Our aim is to direct much needed research to improve understanding of the contribution of marine biogenic sources of halocarbons and their impact on the environment.

Solving a classification task by spiking neural network with STDP based on rate and temporal input encoding

This paper develops local learning algorithms to solve a classification task with the help of biologically inspired mathematical models of spiking neural networks involving the mechanism of spike‐timing‐dependent plasticity (STDP). The advantages of the models are their simplicity and, hence, the potential ability to be hardware‐implemented in low‐energy‐consuming biomorphic computing devices. The methods developed are based on two key effects observed in neurons with STDP: mean firing rate stabilization and memorizing repeating spike patterns. As the result, two algorithms to solve a classification task with a spiking neural network are proposed: the first based on rate encoding of the input data and the second based on temporal encoding. The accuracy of the algorithms is tested on the benchmark classification tasks of Fisher's Iris and Wisconsin breast cancer, with several combinations of input data normalization and preprocessing. The respective accuracies are 99% and 94% by F1‐score.

P53 Integrates Temporal WDR5 Inputs during Neuroectoderm and Mesoderm Differentiation of Mouse Embryonic Stem Cells

SUMMARYHow ubiquitous transcription factors (TFs) coordinate temporal inputs from broadly expressed epigenetic factors to control cell fate remains poorly understood. Here, we uncover a molecular relationship between p53, an abundant embryonic TF, and WDR5, an essential member of the MLL chromatin modifying complex, that regulates mouse embryonic stem cell fate. Wild-type Wdr5 or transient Wdr5 knockout promotes a distinct pattern of global chromatin accessibility and spurs neuroectodermal differentiation through an RbBP5-dependent process in which WDR5 binds to, and activates transcription of, neural genes. Wdr5 rescue after its prolonged inhibition targets WDR5 to mesoderm lineage-specifying genes, stimulating differentiation toward mesoderm fates in a p53-dependent fashion. Finally, we identify a direct interaction between WDR5 and p53 that enables their co-recruitment to, and regulation of, genes known to control cell proliferation and fate. Our results unmask p53-dependent mechanisms that temporally integrate epigenetic WDR5 inputs to drive neuroectoderm and mesoderm differentiation from pluripotent cells.

Approach to Reasoning about Uncertain Temporal Data in OWL 2

Abstract In this paper, we propose an ontology-based approach for representing and reasoning about certain and uncertain temporal data. It handles temporal data in terms of quantitative time intervals and points and the qualitative relations between them (e.g., “before”). It includes three parts. (1) We extend the 4D-fluents approach with certain ontological components to represent the handled temporal data in OWL 2. (2) We extend the Allen’s interval algebra to reason about certain and uncertain time intervals. We adapt these relations to allow relating a time interval and a time point, and two time points. All relations can be used for temporal reasoning by means of transitivity tables. (3) The extended Allen’s algebra instantiates the 4D-fluents-based representation. Inferences are based on SWRL rules. Based on this ontology, a prototype is implemented and integrated into an ontology-based memory prosthesis for Alzheimer’s patients to handle certain and uncertain temporal data inputs.

Investigation on Surface Smoothing of Mold Material by Pulsed Laser Irradiation of 532 nm

Manual polishing has been commonly applied as finishing process for metal molds with complicated-shape, but it takes a long time to achieve required surface roughness. Thus, high-efficient polishing method and surface smoothing in micro-areas are required for precise molds. Therefore, the reduction method of surface roughness for metal mold was experimentally investigated by controlling the pulse duration and the waveform in microsecond pulsed Nd:YAG laser irradiation of 532 nm wavelength, which can achieve stable absorption of laser energy even for difficult-to-weld materials with high reflectance. The maximum height roughness can be reduced from 4.0 μm to 1.0 μm by setting an appropriate pulse duration. Excessive heat input generates the edge at the circumference of laser irradiated area due to the movement of molten material. However, control of pulse duration enables the reduction of edge formation. Shorter pulse duration from 2.0 μs to 4.5 μs can perform a wider range of power density without edge formation, compared with longer pulse duration. The range of power density without edge formation can be increased by controlling the temporal input power, which can improve the process controllability of surface smoothing of mold material.

Enhanced bioretention cell modeling with DRAINMOD-Urban: Moving from water balances to hydrograph production

Bioretention systems have become a leading stormwater control measure for mitigating urban hydrology. Although these systems have performed well in many site-scale field studies, less investigation has been directed toward effectively modeling these systems. This is critical, as modeling of bioretention systems provides an avenue for evaluating their effectiveness prior to devoting time and resources into installation. Many hydrologic models capable of simulating bioretention consist of lumped parameters and simplifications that do not fully account for fundamental hydrologic processes such as soil-water interactions. DRAINMOD has shown promise for obtaining detailed daily water balances within bioretention systems under continuous simulations. One significant advantage of DRAINMOD is that it uses the soil-water characteristic curve to account for fluctuations in soil moisture instead of assuming saturation; however, the model historically only produces daily outputs. For this study, DRAINMOD was modified to develop DRAINMOD-Urban, which allows high temporal resolution inputs and outputs, more closely matching the residence time of runoff in urban systems. DRAINMOD-Urban simulations of a bioretention cell in Ohio, USA, revealed that DRAINMOD-Urban could effectively produce hydrographs with a cumulative Nash-Sutcliffe Efficiency (NSE) of 0.60 for the 12 events that produced drainage over a 7-month monitoring period. Overflow was also modeled by DRAINMOD-Urban, but additional overflow data are necessary to derive conclusions about model effectiveness in predicting this hydrologic component. Input parameters previously calibrated for the DRAINMOD model did not translate well to DRAINMOD-Urban with the top-down approach applied in this study (NSE = 0.31 for drainage and NSE = −1.83 for overflow), but the bottom-up approach showed that parameters calibrated with DRAINMOD-Urban (NSE = 0.60 for drainage and NSE = −0.1 for overflow) could be used in DRAINMOD to obtain reasonable drainage volumes (25.6% error compared to measured values). This study suggests DRAINMOD-Urban is an effective tool for modeling bioretention hydrographs and demonstrates the importance of temporal scale in bioretention modeling by illustrating multiple model calibration approaches. Despite the promising results of this study, additional studies are recommended where validation of the model is performed at more sites, in particular for events with overflow. Further, sensitivity analysis of input parameters and comparison of DRAINMOD-Urban to other commonly used bioretention models would inform future modeling efforts.

Affective Video Content Analysis With Adaptive Fusion Recurrent Network

Affective video content analysis is an important research topic in video content analysis and has extensive applications. Intuitively, multimodal features can depict elicited emotions, and the accumulation of temporal inputs influences the viewer's emotion. Although a number of research works have been proposed for this task, the adaptive weights of modalities and the correlation of temporal inputs are still not well studied. To address these issues, a novel framework is designed to learn the weights of modalities and temporal inputs from video data. Specifically, three network layers are designed, including statistical-data layer to improve the robustness of data, temporal-adaptive-fusion layer to fuse temporal inputs, and multimodal-adaptive-fusion layer to combine multiple modalities. In particular, the feature vectors of three input modalities are respectively extracted from three pre-trained convolutional neural networks and then fed to three statistical-data layers. Then, the output vectors of these three statistical-data layers are separately connected to three recurrent layers, and the corresponding outputs are fed to a fully-connected layer which shares parameters across modalities and temporal inputs. Finally, the outputs of the fully-connected layer are fused by the temporal-adaptive-fusion layer and then combined by the multimodal-adaptive-fusion layer. To discover the correlation of both multiple modalities and temporal inputs, adaptive weights of modalities and temporal inputs are introduced into loss functions for model training, and these weights are learned by an optimization algorithm. Extensive experiments are conducted on two challenging datasets, which demonstrate that the proposed method achieves better performances than baseline and other state-of-the-art methods.

A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level.

Understanding large-scale crop growth and its responses to climate change are critical for yield estimation and prediction, especially under the increased frequency of extreme climate and weather events. County-level corn phenology varies spatially and interannually across the Corn Belt in the United States, where precipitation and heat stress presents a temporal pattern among growth phases (GPs) and vary interannually. In this study, we developed a long short-term memory (LSTM) model that integrates heterogeneous crop phenology, meteorology, and remote sensing data to estimate county-level corn yields. By conflating heterogeneous phenology-based remote sensing and meteorological indices, the LSTM model accounted for 76% of yield variations across the Corn Belt, improved from 39% of yield variations explained by phenology-based meteorological indices alone. The LSTM model outperformed least absolute shrinkage and selection operator (LASSO) regression and random forest (RF) approaches for end-of-the-season yield estimation, as a result of its recurrent neural network structure that can incorporate cumulative and nonlinear relationships between corn yield and environmental factors. The results showed that the period from silking to dough was most critical for crop yield estimation. The LSTM model presented a robust yield estimation under extreme weather events in 2012, which reduced the root-mean-square error to 1.47Mg/ha from 1.93Mg/ha for LASSO and 2.43Mg/ha for RF. The LSTM model has the capability to learn general patterns from high-dimensional (spectral, spatial, and temporal) input features to achieve a robust county-level crop yield estimation. This deep learning approach holds great promise for better understanding the global condition of crop growth based on publicly available remote sensing and meteorological data.

Temporal tuning of repetition suppression across the visual cortex.

The visual system adapts to its recent history. A phenomenon related to this is repetition suppression (RS), a reduction in neural responses to repeated compared with nonrepeated visual input. An intriguing hypothesis is that the timescale over which RS occurs across the visual hierarchy is tuned to the temporal statistics of visual input features, which change rapidly in low-level areas but are more stable in higher level areas. Here, we tested this hypothesis by studying the influence of the temporal lag between successive visual stimuli on RS throughout the visual system using functional (f)MRI. Twelve human volunteers engaged in four fMRI sessions in which we characterized the blood oxygen level-dependent response to pairs of repeated and nonrepeated natural images with interstimulus intervals (ISI) ranging from 50 to 1,000 ms to quantify the temporal tuning of RS along the posterior-anterior axis of the visual system. As expected, RS was maximal for short ISIs and decayed with increasing ISI. Crucially, however, and against our hypothesis, RS decayed at a similar rate in early and late visual areas. This finding challenges the prevailing view that the timescale of RS increases along the posterior-anterior axis of the visual system and suggests that RS is not tuned to temporal input regularities.NEW & NOTEWORTHY Visual areas show reduced neural responses to repeated compared with nonrepeated visual input, a phenomenon termed repetition suppression (RS). Here we show that RS decays at a similar rate in low- and high-level visual areas, suggesting that the short-term decay of RS across the visual hierarchy is not tuned to temporal input regularities. This may limit the specificity with which the mechanisms underlying RS could optimize the processing of input features across the visual hierarchy.

Temporal data classification and forecasting using a memristor-based reservoir computing system

Time-series analysis including forecasting is essential in a range of fields from finance to engineering. However, long-term forecasting is difficult, particularly for cases where the underlying models and parameters are complex and unknown. Neural networks can effectively process features in temporal units and are attractive for such purposes. Reservoir computing, in particular, can offer efficient temporal processing of recurrent neural networks with a low training cost, and is thus well suited to time-series analysis and forecasting tasks. Here, we report a reservoir computing hardware system based on dynamic tungsten oxide (WOx) memristors that can efficiently process temporal data. The internal short-term memory effects of the WOx memristors allow the memristor-based reservoir to nonlinearly map temporal inputs into reservoir states, where the projected features can be readily processed by a linear readout function. We use the system to experimentally demonstrate two standard benchmarking tasks: isolated spoken-digit recognition with partial inputs, and chaotic system forecasting. A high classification accuracy of 99.2% is obtained for spoken-digit recognition, and autonomous chaotic time-series forecasting has been demonstrated over the long term. A reservoir computer system based on dynamic tungsten oxide memristors can be used to perform time-series analysis, demonstrating isolated spoken-digit recognition with partial inputs and chaotic system forecasting.

Reservoir Computing Using Diffusive Memristors

Reservoir computing (RC) is a framework that can extract features from a temporal input into a higher‐dimension feature space. The reservoir is followed by a readout layer that can analyze the extracted features to accomplish tasks such as inference and classification. RC systems inherently exhibit an advantage, since the training is only performed at the readout layer, and therefore they are able to compute complicated temporal data with a low training cost. Herein, a physical reservoir computing system using diffusive memristor‐based reservoir and drift memristor‐based readout layer is experimentally implemented. The rich nonlinear dynamic behavior exhibited by a diffusive memristor due to Ag migration and the robust in situ training of drift memristor arrays makes the combined system ideal for temporal pattern classification. It is then demonstrated experimentally that the RC system can successfully identify handwritten digits from the Modified National Institute of Standards and Technology (MNIST) dataset, achieving an accuracy of 83%.

Breeding rice for nitrogen use efficiency

The aim of present investigation was to study the gross morphology and incidence of canine mammary tumors (CMTs) based on age, sex, breed, reproductive status and location along with histopathological classification and immunohistochemical characteristics. A total of 56 CMTs samples were collected from 49 cases of dogs. Gross morphology was studied in 26 cases of canine mammary tumors. For histopathological classification, samples were fixed in 10% formalin, embedded in paraffin and sections (5 μm thick) obtained from each was stained with HandE stain. Immuno-histochemistry was carried out by using p63 antibody to confirm the histopathological types of CMTs. Malignant tumors and benign tumors were mostly observed in older dogs. Among 9 breeds affected, the highest incidence was recorded in a German shepherd. Caudal abdominal pair was most commonly affected. Most of the cases were observed in intact female dogs, except for one male dog. The tumors were oval and round in shape with 30–2000 g weight, soft to hard in consistency and grayish white cut surface. Out of 56 CMTs, the highest incidence was found of malignant neoplasms (36/56, 64.28%), followed by benign neoplasms (10/56, 17.85%) and non-neoplastic proliferation hyperplasia/dysplasia (10/56, 17.85%). Complex carcinoma, carcinoma, and malignant myoepithelioma and malignant myoepithelioma were confirmed by p63 antibody. In these neoplasms, myoepithelial cells showed strong immunoreactivity with p63.Development of nitrogen use efficient (NUE) rice varieties is inevitable for sustainability of environmental friendly and economical agricultural practices. Several management practices are being studied for increasing efficiency of spatial and temporal inputs of N under National Innovations for Climate Resilient Agriculture (NICRA). Attempts are being made to develop NUE rice varieties with multidisciplinary approach and conventional selection along with mapping and next generation sequencing strategies. Around 800 rice genotypes were characterized under low and recommended nitrogen for two consecutive seasons and the promising donors further evaluated to identify consistent NUE rice genotypes. Several mapping populations were developed using the NUE donors and popular rice varieties. QTL/genomic regions were identified for yield under low N using biparental and association mapping. Using minimum marker set of 50 rice SSR markers, 12 genomic regions were identified for yield and yield associated traits under low nitrogen. Several promising recombinants of yield and NUE were identified and these breeding lines were evaluated under multi-locations and stable performers were identified under AICRIP Trial - Evaluation of Radiation and Nitrogen use efficient promising rice genotypes -Plant Physiology during Kharif 2016, 2017 and 2018. As nitrogen is the building block of biomass, an optimum N is required for realizing the yield. The strategy should be to maximise uptake and improve utilization, so that remobilization of N to yield is achieved under low N.

Memory for temporal order in action is slow developing, sensitive to deviant input, and supported by foundational cognitive processes.

Executing actions in a specific order is a critical component of many action sequences that children must acquire, the majority of which are learned through observation and imitation of others. Although a wealth of evidence indicates that children can process and represent temporal order in memory, relatively little is known about the development of this ability and the cognitive mechanisms that support it in the context of imitation. The present research investigated 4- through 8-year-old children's ability to learn the temporal order of novel, arbitrary action sequences via imitation. On Day 1, children observed and imitated four instances each for two different multistep sequences. One sequence was easy and the other was difficult, in terms of categorizing the items used in each instance. For one sequence, the experimenter also performed one instance in a deviant temporal order, which occurred either early or late in learning. Memory generalization for each sequence was assessed on Day 2. Results indicated significant effects of age and sequence difficulty on children's ability to recall the individual actions as well as the standard order. Experiencing the deviant order also uniquely disrupted children's ability to generalize the order. Experiencing the deviant early in learning globally lowered children's memory for both sequences. Thus, children's ability to learn temporal order develops slowly over childhood, is supported by foundational cognitive processes that operate in a hierarchical fashion, and is highly sensitive to variable temporal input. These results have implications for theories of imitation and cultural learning more broadly. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Development of a Stochastic Storm Generator Using High-Resolution Precipitation Records

Abstract.Sophisticated field and watershed scale environmental models for runoff, erosion control, environmental, and global-change investigations require detailed continuous temporal and spatial inputs of precipitation to drive the hydrologic processes. For accurate estimates of these processes, the resolution of the input data must allow the representation of the variability of precipitation as it represents a major source of variability in the model outputs. Currently, the use of stochastic weather generators is widespread to generate continuous series of meteorological data at gauged and ungauged locations. These weather simulators are designed to replicate the statistical properties of real weather data at monthly or daily time resolutions. However, daily values of precipitation do not represent the variability of storm parameters within a day, which is assumed to significantly influence the predictions of environmental or agricultural models where processes are sensitive to sub-daily values. This research proposes a parsimonious stochastic storm generator based on 5-min time resolution and correlated non-normal Monte Carlo-based numerical simulation. The model considers correlated non-normal random rainstorm characteristics such as time between storms, duration, and amount of precipitation, as well as the storm intensity structure. The accuracy of the model was verified by comparing the generated rainfall with rainfall data from a randomly selected 5-min weather station in North Carolina. Current results have shown that the proposed storm generator can capture the essential statistical features of rainstorms as well as their patterns followed by their intensities, preserving the first four moments of monthly storm events, good annual extreme event correspondence, and the correlation structure within each storm. Finally, as the proposed model depends on statistical properties at a site, this may allow the use of the synthetic storms in ungauged locations provided relevant information from a regional analysis is available. Keywords: Monte Carlo, Stochastic storm generator, Storm distribution.

P53 Coordinates Temporal WDR5 Inputs During Neuroectoderm and Mesoderm Differentiation of Mouse Embryonic Stem Cells

Cell-type specific transcription factors cooperate with chromatin-modifying proteins to orchestrate proper differentiation, but little is known how ubiquitous embryonic proteins coordinate inputs from broadly expressed epigenetic factors to control cell fate. Here, we uncover a previously unappreciated molecular relationship between p53, an abundant embryonic protein, and Wdr5, an essential member of the COMPASS chromatin modifying complex, that regulates mouse embryonic stem cell (ESC) fate. Intact, or brief disruption of, Wdr5 activity promotes a distinct pattern of global chromatin accessibility and spurs neuroectodermal specification through Wdr5 binding to, and transcription of, neural genes. This leads to RbBP5-dependent differentiation of 3D Rx+ neuroectodermal organoids. In contrast, prolonged Wdr5 inhibition transcriptionally impairs ribosome biogenesis, which upregulates p53 activity, triggering increased expression of pro-mesoderm p53 target genes and emergence of a chromatin accessibility state permissive for mesoderm specification. Rescue of Wdr5 function after its extended inhibition targets WDR5 to mesoderm lineage-specifying genes, stimulating differentiation toward contractile cardiogenic and hematopoietic mesoderm fates. This cell fate transition is p53-dependent, as p53 inactivation partially rescues neuroectoderm differentiation and inhibits mesoderm formation. Our results unmask a p53-dependent mechanism that temporally integrates an epigenetic WDR5 input to drive neuroectoderm and mesoderm differentiation from pluripotent cells.