Miracle-dz/UniFAR-FTUs huggingface dataset:
Arousal as a universal embedding for spatiotemporal brain dynamics

Neural activity in awake organisms shows widespread and spatiotemporally diverse correlations with behavioral and physiological measurements. We propose that this covariation reflects in part the dynamics of a unified, arousal-related process that regulates brain-wide physiology on the timescale of seconds. Taken together with theoretical foundations in dynamical systems, this interpretation leads us to a surprising prediction: that a single, scalar measurement of arousal (e.g., pupil diameter) should suffice to reconstruct the continuous evolution of multimodal, spatiotemporal measurements of large-scale brain physiology. To test this hypothesis, we perform multimodal, cortex-wide optical imaging and behavioral monitoring in awake mice. We demonstrate that spatiotemporal measurements of neuronal calcium, metabolism, and blood-oxygen can be accurately and parsimoniously modeled from a low-dimensional state-space reconstructed from the time history of pupil diameter. Extending this framework to behavioral and electrophysiological measurements from the Allen Brain Observatory, we demonstrate the ability to integrate diverse experimental data into a unified generative model via mappings from an intrinsic arousal manifold. Our results support the hypothesis that spontaneous, spatially structured fluctuations in brain-wide physiology—widely interpreted to reflect regionally-specific neural communication—are in large part reflections of an arousal-related process. This enriched view of arousal dynamics has broad implications for interpreting observations of brain, body, and behavior as measured across modalities, contexts, and scales.
Emergence and spread of feline infectious peritonitis due to a highly pathogenic canine/feline recombinant coronavirus

Cross-species transmission of coronaviruses (CoVs) poses a serious threat to both animal and human health1-3. Whilst the large RNA genome of CoVs shows relatively low mutation rates, recombination within genera is frequently observed and demonstrated4-7. Companion animals are often overlooked in the transmission cycle of viral diseases; however, the close relationship of feline (FCoV) and canine CoV (CCoV) to human hCoV-229E5,8, as well as their susceptibility to SARS-CoV-29 highlight their importance in potential transmission cycles. Whilst recombination between CCoV and FCoV of a large fragment spanning orf1b to M has been previously described5,10, here we report the emergence of a novel, highly pathogenic FCoV-CCoV recombinant responsible for a rapidly spreading outbreak of feline infectious peritonitis (FIP), originating in Cyprus11. The recombination, spanning spike, shows 97% sequence identity to the pantropic canine coronavirus CB/05. Infection is spreading fast and infecting cats of all ages. Development of FIP appears rapid and likely non-reliant on biotype switch12. High sequence identity of isolates from cats in different districts of the island is strongly supportive of direct transmission. A deletion and several amino acid changes in spike, particularly the receptor binding domain, compared to other FCoV-2s, indicate changes to receptor binding and likely cell tropism.
Decomposition of retinal ganglion cell electrical images for cell type and functional inference

Identifying neuronal cell types and their biophysical properties based on their extracellular electrical features is a major challenge for experimental neuroscience and the development of high-resolution brain-machine interfaces. One example is identification of retinal ganglion cell (RGC) types and their visual response properties, which is fundamental for developing future electronic implants that can restore vision. The electrical image (EI) of a RGC, or the mean spatio-temporal voltage footprint of its recorded spikes on a high-density electrode array, contains substantial information about its anatomical, morphological, and functional properties. However, the analysis of these properties is complex because of the high-dimensional nature of the EI. We present a novel optimization-based algorithm to decompose electrical image into a low-dimensional, biophysically-based representation: the temporally-shifted superposition of three learned basis waveforms corresponding to spike waveforms produced in the somatic, dendritic and axonal cellular compartments. Large-scale multi-electrode recordings from the macaque retina were used to test the effectiveness of the decomposition. The decomposition accurately localized the somatic and dendritic compartments of the cell. The imputed dendritic fields of RGCs correctly predicted the location and shape of their visual receptive fields. The inferred waveform amplitudes and shapes accurately identified the four major primate RGC types (ON and OFF midget and parasol cells), a substantial advance. Together, these findings may contribute to more accurate inference of RGC types and their original light responses in the degenerated retina, with possible implications for other electrical imaging applications.
Fast-slow traits predict competition network structure and its response to resources and enemies

Plants interact in complex networks but how their structure depends on resources, natural enemies and species resource-use strategy remains poorly understood. Here, we quantified competition networks among 18 plants varying in fast-slow strategy, by testing how increased nutrient availability and reduced foliar pathogens affected intra- and inter-specific interactions. Our results show that nitrogen and pathogens altered several aspects of network structure, often in unexpected ways due to fast and slow growing species responding differently. Nitrogen addition increased competition asymmetry in slow growing networks, as expected, but decreased it in fast growing networks. Pathogen reduction made networks more even and less skewed because pathogens targeted weaker competitors. Surprisingly, pathogens and nitrogen dampened each other’s effect. Our results show that plant growth strategy is key to understand how competition respond to resources and enemies, a prediction from classic theories which has rarely been tested by linking functional traits to competition networks.
Divergence time and environmental similarity predict the strength of morphological convergence in stick and leaf insects

Independent evolution of similar traits in lineages inhabiting similar environments (convergent evolution) is often taken as evidence for adaptation by natural selection, and used to illustrate the predictability of evolution. Yet convergence is rarely perfect. Environments may not be as similar as they appear (e.g., habitats scored the same may be heterogenous to the organisms). And lineages can evolve in different ways even when submitted to the same environmental challenges, because responses to selection are contingent upon available genetic variation and independent lineages may differ in the alleles, genetic backgrounds, and even the developmental mechanisms responsible for the phenotypes in question. Both impediments to convergence are predicted to increase as the length of time separating two lineages increases, making it difficult to discern their relative importance. We quantified environmental similarity and the extent of convergence to show how habitat and divergence time each contribute to observed patterns of morphological evolution in stick and leaf insects (order Phasmatodea). Dozens of phasmid lineages independently colonized similar habitats, repeatedly evolving in parallel directions on a 26-trait morphospace, though the magnitude and direction of these shifts varied. Lineages converging towards more similar environments ended up closer on the morphospace, as did closely related lineages, and closely related lineages followed more parallel trajectories to arrive there. Remarkably, after accounting for habitat similarity, we show that divergence time reduced convergence at a constant rate across more than 60 million years of separation, suggesting even the magnitude of contingency can be predictable, given sufficient spans of time. Significance statement Phasmids (stick and leaf insects) exemplify the extraordinary power of natural selection to shape organismal phenotypes. The animals themselves are charismatic champions of crypsis and masquerade; and our characterization of their adaptive radiation reveals dozens of instances of convergence, as lineages adapted to similar changes in habitat by repeatedly evolving similar body forms. Our findings show that the similarity of environmental conditions experienced by the organisms – the closeness of the invaded niches – and the extent of elapsed time since divergence, both predict the strength of morphological convergence. The phasmid radiation reveals an evolutionary process that is surprisingly predictable, even when lineages have been evolving independently for tens of millions of years.
An Efficient Rigorous Approach for Identifying Statistically Significant Frequent Itemsets

As advances in technology allow for the collection, storage, and analysis of vast amounts of data, the task of screening and assessing the significance of discovered patterns is becoming a major challenge in data mining applications. In this work, we address significance in the context of frequent itemset mining. Specifically, we develop a novel methodology to identify a meaningful support threshold s* for a dataset, such that the number of itemsets with support at least s* represents a substantial deviation from what would be expected in a random dataset with the same number of transactions and the same individual item frequencies. These itemsets can then be flagged as statistically significant with a small false discovery rate. We present extensive experimental results to substantiate the effectiveness of our methodology.
Active Classification for POMDPs: a Kalman–like State Estimator

The problem of state tracking with active observation contr l is considered for a system modeled by a discrete–time, finite–state Markov chain observed thro ugh conditionally Gaussian measurement vectors. The measurement model statistics are shaped by the underlying state and an exogenous control input, which influence the observations’ quality. Exploiti ng an innovations approach, an approximate minimum mean-squared error (MMSE) filter is derived to estim a e the Markov chain system state. To optimize the control strategy, the associated mean–square d er or is used as an optimization criterion in a partially observable Markov decision process formulation . A stochastic dynamic programming algorithm is proposed to solve for the optimal solution. To enhance the quality of system state estimates, approximate MMSE smoothing estimators are also derived. Finally, the pe rformance of the proposed framework is illustrated on the problem of physical activity detection i n wireless body sensing networks. The power of the proposed framework lies within its ability to accommoda te broad spectrum of active classification applications including sensor management for object class ification and tracking, estimation of sparse signals and radar scheduling.
Non – linear Time Series Analysis : Methods and Applications to Atrial Fibrillation

Division of Image Processing, Department of Radiology, Leiden University Medical Center, The Netherlands Center for Nonlinear Dynamics in Economics and Finance, Department of Economics and Econometrics, University of Amsterdam, The Netherlands Cardiovascular Research Institute, Department of Physiology, Maastricht University, The Netherlands Department of Physiology, University of Leiden, The Netherlands
Cuticle cracking in tomato fruit

The effects of fruit pruning and deleafing practices on fruit cuticle cracking were investigated in greenhouse tomato plants. Three treatments were established: (1) a standard practice of fruit pruning to three or four fruit per truss, with deleafing up to the ripening truss, (2) increased fruit pruning to one or two fruit per truss and deleafed to the ripening truss or (3) increased deleafing up to the fifth leaf above the ripening truss with fruit pruning to three or four fruit per truss. Fruit pruning substantially increased the plant leaf/ fruit ratio and significantly increased the incidence of cracking. The increased cracking with fruit pruning was not attributable to the increase in fruit size and growth rate observed in that treatment. Increased deleafing slightly reduced the plant leaf/fruit ratio and did not significantly affect cracking incidence. Further analysis of the individual treatments revealed a relationship between fruit size and cracking only in the deleafed plants. No correlations we.
Effects of meteorological factors on singing behavior of eastern black crested gibbons ( Nomascus nasutus)

Gibbons are characterized by their species-specific calls,which are known to be affected by both biotic and abiotic factors ( such as meteorological factors) . To identify the effects of meteorological factors on the singing behavior of eastern black crested gibbons ( Nomascus nasutus) ,we monitored three groups in Bangliang Nature Reserve,Guangxi,from August 2008 to October 2009 using all-occurrence recording. Groups sang during 69. 7% of days monitored,and sang an average of 1. 24 bouts per singing day. Over 90% of the songs were produced between 30 minutes before and three hours after the sunrise. The average duration of the songs was 18. 3 min,and females produced 4. 4 great calls/duet bout. Gibbons called later relative to sunrise on foggy days than on cloudy or sunny days. They produced shorter bouts and called later on rainy days,presumably because the energy loss and weaker solar radiation. We detected no effect of temperature on singing behavior.
Characteristics of Inter-Annual and Seasonal Changes in Temperature and Precipitation over the Nantong Region during the Period 1960-2007

Over the past half century,temperature rising has been observed in most parts of China,but temporal-spatial distribution of temperature change varies among different regions. In general,precipitation shows a greater regional difference than temperature,but the trend is not prominent. Examining trends in temperature and precipitation in Nantong located in the north wing of the Yangtze River delta would be greatly useful in predicting future climate change. There were 4 meteorological variables,i.e.,average temperature,average maximum temperature,average minimum temperature,and precipitation at the monthly scale obtained from seven meteorological stations across the study region for use in this analysis. Methods of linear trend,moving average methods,Mann-Kendall nonparametric,moving T-test,and wavelet analysis were employed to examine trend,mutation,and cycle changes in temperature and precipitation at inter-annual and seasonal scales across the Nantong Region during the period 1960 to 2007. Results show that 1) linear rates of annual average temperature,annual average maximum temperature,and annual average minimum temperature were 0.29℃/10a,0.24℃/10a,and 0.54℃/10a,respectively. Years of mutation in annual average temperature,annual average maximum temperature,and annual average minimum temperature were detected to be 1997,2001,and 1988,respectively;2) Linear rates of average temperature in spring,autumn,and winter were 0.37℃/10a,0.27℃/10a,and 0.43℃/10a,respectively. Average temperature in summer didn’t pass the test. Linear rates of average minimum temperature in autumn and winter were 0.85℃/10a and 0.54℃/10a,respectively,and the value in spring and summer didn’t pass the test. Linear rates of precipitation only passed the test in winter,showing a value of 13.84 mm/10a. Years of mutation in average temperature in spring,summer,autumn,and winter were in 2000,2003,1996-1997,and 1991,respectively. Years of mutation in average maximum temperature in summer and autumn were in 2000 and 1994,while they were not significant in spring and winter. The year of mutation in average minimum temperature in winter was found to be 1987,while the other seasons were not significant. Precipitation changes in autumn and winter occurred in 1991 and 1989,respectively;they decreased in autumn but increase in winter. Characteristics of seasonal change showed a significant increase in average minimum temperature in autumn and winter,precipitation in winter,and an earlier warming mutation in autumn and winter;3) For cycle change,temperature and precipitation showed stable changes at a longer time scale,such as cycle changes of annual average temperature in 21-30 years and precipitation in 14-25 years. Cycles in a small scale(e.g.,10 years) changed frequently,but not stable. Overall,mutation in temperature in the Nantong Region was later than western and central regions of China,while that in precipitation was not significant at inter-annual or seasonal scales.
Pan Arab Journal of Rhinology Pan Arab Journal of Rhinology

Obstructive sleep apnea (OSA) is a serious and prevalent public health disorder that can have long-term detrimental effects on the cardiovascular, nervous, and metabolic systems, and increase the risk of accidents and mortality. Newly identi ﬁ ed pathophysiological traits of OSA include decreased upper airway pharyngeal dilator muscle responsiveness. Myofunctional therapy (MT) has been proposed as a treatment to improve dilator tone by performing isotonic and isometric exercises of the tongue and other upper airway muscles. This article reviews the beginnings of this therapy and describes systematic reviews and meta-analyses published until February 2023 focusing on our experience using MT as an isolated therapy for treating OSA in adult patients. The search strategy was designed using the
Smectite formation in rhyolitic obsidian as inferred by microscopic (SEM-TEM-AEM) investigation

Abstract Experimental alteration of a rhyolitic obsidian by solutions containing variable concentrations of Mg was carried out at 100, 150 and 200°C, for 30, 60 and 90 days, to investigate the mechanism of smectite formation. The smectite exhibits two distinct morphologies: (1) small flakes (aggregates of a few crystals); and (2) large flakes (massive groups). The small flakes are the earlier alteration products. Both morphological types have distinct chemical compositions: the smallest laminae are dioctahedral and contain more Fe, whereas the particles from the rose-shaped aggregates contain Mg and are trioctahedral. Intermediate compositions have been observed between the extreme compositions. It is suggested that the two morphologies are the result of two distinct genetic processes: (1) the small flakes (Fe-rich smectite) form by solid state rearrangements of the hydrated external layer of glass and/or via the formation of domains within the glass; (2) the rose-shaped aggregates (Mg-rich smectite) form by precipitation from solution.
Distributional Cloning for Stabilized Imitation Learning via ADMM

The two leading solution paradigms for imitation learning (IL), BC and GAIL, each suffers from notable drawbacks. BC, a supervised learning approach to mimic expert actions, is vulnerable to covariate shift. GAIL applies adversarial training to minimize the discrepancy between expert and learner behaviors, which is prone to unstable training and mode collapse. In this work, we propose DC – Distributional Cloning – a novel IL approach for addressing the covariate shift and mode collapse problems simultaneously. DC directly maximizes the likelihood of observed expert and learner demonstrations, and gradually encourages the learner to evolve towards expert behaviors based on an averaging effect. The DC solution framework contains two stages in each training loop, where in stage one the mixed expert and learner state distribution is estimated via SoftFlow, and in stage two the learner policy is trained to match both the expert’s policy and state distribution via ADMM. Experimental evaluation of DC compared with several baselines in 10 different physics-based control tasks reveal superior results in learner policy performance, training stability, and mode distribution preservation.
Improved Combinatorial Algorithms for Facility Location Problems

We present improved combinatorial approximation algorithms for the uncapacitated facility location problem. Two central ideas in most of our results are cost scaling and greedy improvement. We present a simple greedy local search algorithm which achieves an approximation ratio of $2.414+\epsilon$ in $\tilde{O}(n^2/\epsilon)$ time. This also yields a bicriteria approximation tradeoff of $(1+\gamma,1+2/\gamma)$ for facility cost versus service cost which is better than previously known tradeoffs and close to the best possible. Combining greedy improvement and cost scaling with a recent primal-dual algorithm for facility location due to Jain and Vazirani, we get an approximation ratio of $1.853$ in $\tilde{O}(n^3)$ time. This is very close to the approximation guarantee of the best known algorithm which is linear programming (LP)-based. Further, combined with the best known LP-based algorithm for facility location, we get a very slight improvement in the approximation factor for facility location, achieving $1.728$. We also consider a variant of the capacitated facility location problem and present improved approximation algorithms for this.
Separations by Random Oracles and \almost" Classes for Generalized Reducibilities

Let r and s be two binary relations on 2 ! which are meant as reducibilities. Let both relations be closed under nite variation (of their set arguments) and consider the uniform distribution on 2 ! , which is obtained by choosing elements of 2 ! by independent tosses of a fair coin. Then we might ask for the probability that the lower r-cone of a randomly chosen set X, that is, the class of all sets A with A r X, diiers from the lower scone of X. By closure under nite variation, the Kolmogorov 0-1 law yields immediately that this probability is either 0 or 1; in case it is 1, the relations are said to be separable by random oracles. Again by closure under nite variation, for every given set A, the probability that a randomly chosen set X is in the upper cone of A w.r. t. r is either 0 or 1. Let Almostr be the class of sets for which the upper cone w.r. t. r has measure 1. In the following, results about separations by random oracles and about Almost classes are obtained in the context of generalized reducibilities, that is, for binary relations on 2 ! which can be deened by a countable set of total continuous functionals on 2 ! in the same way as the usual resource bounded reducibilities are deened by an enumeration of appropriate oracle Turing machines. The concept of generalized reducibility comprises all natural resource bounded reducibilities, but is more general ; in particular, it does not involve any kind of speciic machine model or even eeectivity. The results on generalized reducibilities yields corollaries about speciic resource bounded reducibilities, including several results which have been shown previously in the setting of time or space bounded Turing machine computations.
Detecting Distributed Denial of Service Attacks Using Source IP Address Monitoring

In this paper, we propose a simple but robust scheme to detect denial of service attacks (including distributed denial of service attacks) by monitoring the increase of new IP addresses. Unlike previous proposals for bandwidth attack detection schemes which are based on monitoring the traffic volume, our scheme is very effective for highly distributed denial of service attacks. Our scheme exploits an inherent feature of DDoS attacks, which makes it hard for the attacker to counter this detection scheme by changing their attack signature. Our scheme uses a sequential nonparametric change point detection method to improve the detection accuracy without requiring a detailed model of normal and attack traffic. We demonstrate that we can achieve high detection accuracy on a range of different network packet traces.