Miracle-dz/UniFAR-FTUs huggingface dataset:
A neuronal substrate for translating nutrient state and resource density estimations into foraging decisions

Foraging animals must balance the costs of exploring their surroundings with the potential benefits of finding nutritional resources. Each time an animal encounters a food source it must decide whether to initiate feeding or continue searching for potentially better options. Experimental evidence and patch foraging models predict that this decision depends on both nutritional state and the density of available resources in the environment. How the brain integrates such internal and external states to adapt the so-called exploration-exploitation trade-off remains poorly understood. We use video-based tracking to show that Drosophila regulates the decision to engage with food patches based on nutritional state and travel time between food patches, the latter being a measure of food patch density in the environment. To uncover the neuronal basis of this decision process, we performed a neurogenetic silencing screen of more than 400 genetic driver lines with sparse expression patterns in the fly brain. We identified a population of neurons in the central complex that acts as a key regulator of the decision to engage with a food patch. We show that manipulating the activity of these neurons alters the probability to engage, that their activity is modulated by the protein state of the animal, and that silencing these neurons perturbs the ability of the animal to adjust foraging decisions to the fly’s travel time between food patches. Taken together, our results reveal a neuronal substrate that integrates nutritional state and patch density information to control a specific foraging decision, and therefore provide an important step towards a mechanistic explanation of the cognitive computations that resolve complex cost-benefit trade-offs.
Into the deep: The subthalamic and para-subthalamic nuclei in behavioral avoidance

The subthalamic nucleus (STN) is a key component of the brain network for movement control. However, the STN is strikingly heterogeneous and also intricately engaged in limbic and cognitive functions. The STN shows aberrant firing activity in several neurological and neuropsychiatric disorders, including Parkinsońs disease (PD). Deep brain stimulation (DBS) in the STN alleviates motor impairment in PD, but patients have reported altered mood as adverse side-effect. Recent observations suggest that optogenetic STN activation in mice induces flight behavior. We hypothesized that STN activation stand at risk of causing an aversive response with behavioral avoidance as consequence. The STN is directly adjoined with the para-STN (pSTN), a hypothalamic area correlated with appetitive and aversive behavior. STN-DBS aiming to correct STN might thereby also modulate pSTN. To dissociate the impact of STN and pSTN, we took advantage of selective promoters in mice, identified in our recent RNA- sequencing of the subthalamic area, to selectively direct optogenetic excitation. Acute photostimulation resulted in aversion via both the STN and pSTN, but only STN- stimulation-paired cues resulted in conditioned avoidance. Viral-genetic tracing coupled with electrophysiological recordings identified a polysynaptic pathway from the STN to the lateral habenula, a critical hub for aversion and associated with clinical depression. This study demonstrates that STN activation is directly correlated with aversion, and thereby contributes neurobiological underpinnings to emotional affect upon STN manipulation with implications for STN-targeted treatment outcome.
Identification of Lactuca sativa transcription factors impacting resistance to Botrytis cinerea through predictive network inference

Lettuce is susceptible to a wide range of plant pathogens including the fungal pathogens Botrytis cinerea and Sclerotinia sclerotiorum, causal agents of grey mould and lettuce drop, respectively. Chemical control is routinely used but there is an urgent need to develop varieties with enhanced resistance given the economic and environmental costs of preventative pesticide sprays, the prevalence of fungicide-resistant isolates of both pathogens in the field, and the increasing withdrawal of approved fungicides through legislation. Resistance against Botrytis cinerea and Sclerotinia sclerotiorum is quantitative, governed by multiple small-medium impact loci, with plant responses involving large-scale transcriptional reprogramming. The elucidation of the gene regulatory networks (GRNs) mediating these responses will not only identify key transcriptional regulators but also interactions between regulators and show how the defence response is fine-tuned to a particular pathogen. We generated high-resolution (14 time points) time series expression data from lettuce leaves following mock-inoculation or inoculation with B. cinerea, capturing the dynamics of the transcriptional response to infection. Integrating this data with a time series dataset from S. sclerotiorum infection of lettuce identified a core set of 4362 genes similarly differentially expressed in response to both pathogens. Using the expression data for these core genes (with additional single time point data from 21 different lettuce accessions) we inferred a GRN underlying the lettuce defence response to these pathogens. Using the GRN, we have predicted and validated key regulators of lettuce immunity, identifying both positive (LsBOS1) and negative (LsNAC53) regulators of defence against B. cinerea, as well as downstream target genes. These data provide a high level of detail on defence-induced transcriptional change in a crop species and a GRN with the ability to predict transcription factors mediating disease resistance both in lettuce and other species.
Gamma responses to colored natural stimuli can be predicted from local low-level stimulus features

The role of gamma rhythm (30-80 Hz) in visual processing is debated; stimuli like gratings and hue patches generate strong gamma, but many natural images do not. Could image gamma responses be predicted by approximating images as gratings or hue patches? Surprisingly, this question remains unanswered, since the joint dependance of gamma on multiple features is poorly understood. We recorded local field potentials and electrocorticogram from two monkeys while presenting natural images and parametric stimuli varying along several feature dimensions. Gamma responses to different grating/hue features were separable, allowing for a multiplicative model based on individual features. By fitting a hue patch to the image around the receptive field, this simple model could predict gamma responses to chromatic images across scales with reasonably high accuracy. Our results provide a simple “baseline” model to predict gamma from local image properties, against which more complex models of natural vision can be tested.
Accurate non-maximum suppression for object detection in high-resolution remote sensing images

ABSTRACT Non-maximum suppression (NMS) is widely adopted as a post-processing step in the state-of-the-art object detection pipelines to merge the nearby detections around one object. However, its performance is affected by objects that are highly overlapped with each other, and its localization accuracy depends solely on the highest scored detection. To tackle this, an accurate NMS method is proposed in this letter, which gradually merges the highly overlapped detections in an iterative way. In each iteration, detections overlapped with the highest scored one are grouped with a harder threshold to regress for a new proposal, and then the scores within the group are softly suppressed. This process is recursively applied on the remaining detections. The proposed method can not only detect more overlapped objects, but also achieve better object localization accuracy. Experimental results demonstrate that this simple and unsupervised method can gain obvious performance improvement on the majority of classes, compared with the state-of-the-art NMS methods.
Multiple objects evoke fluctuating responses in several regions of the visual pathway

How neural representations preserve information about multiple stimuli is mysterious. Because tuning of individual neurons is coarse (for example, visual receptive field diameters can exceed perceptual resolution), the populations of neurons potentially responsive to each individual stimulus can overlap, raising the question of how information about each item might be segregated and preserved in the population. We recently reported evidence for a potential solution to this problem: when two stimuli were present, some neurons in the macaque visual cortical areas V1 and V4 exhibited fluctuating firing patterns, as if they responded to only one individual stimulus at a time (Jun et al., 2022). However, whether such an information encoding strategy is ubiquitous in the visual pathway and thus could constitute a general phenomenon remains unknown. Here we provide new evidence that such fluctuating activity is also evoked by multiple stimuli in visual areas responsible for processing visual motion (middle temporal visual area, MT), and faces (middle fundus and anterolateral face patches in inferotemporal cortex – areas MF and AL), thus extending the scope of circumstances in which fluctuating activity is observed. Furthermore, consistent with our previous results in the early visual area V1, MT exhibits fluctuations between the representations of two stimuli when these form distinguishable objects but not when they fuse into one perceived object, suggesting that fluctuating activity patterns may underlie visual object formation. Taken together, these findings point toward an updated model of how the brain preserves sensory information about multiple stimuli for subsequent processing and behavioral action. Impact Statement Neural fluctuations in multiple areas along the visual cortical hierarchy could allow the brain to represent distinct co-occurring visual stimuli.
Entrance length in an acoustic waveguide

This communication presents measurements by Particle Image Velocimetry (PIV) of acoustic fields used to estimate the entrance length along the oscillation cycle. The entrance length is defined as the distance from the exit for which the centerline acoustic velocity is significantly perturbed due to end effects. The acoustic wave was driven by a loudspeaker box in a square cross-section tube, for two terminations: baffled and no baffled. The entrance length was found to be deeply dependent on the geometry of the termination (baffled or unbaffled). It was found to be independent on velocity amplitude for the range of studied level whereas it is frequency dependent for both the terminations.
Fetal Growth and Adverse Birth Outcomes in Women Receiving Prescriptions for Acetaminophen During Pregnancy

We studied the association between acetaminophen exposure during pregnancy and the prevalence of congenital abnormalities and fetal growth. Our study included 123 women who had received a prescription of acetaminophen during pregnancy and /or 30 days before conception and 13,329 controls who did not receive any prescription at all. We found more malformations among those who received a prescription with an odds ratio of 2.3 (95% Cl 1.0-5.4), but the type of malformations did not indicate a causal link. When restricting the study to first time pregnancies, we identified 58 women who received a prescription of acetaminophen during pregnancy and 30 days before conception and 7472 controls. We found no excess risk of malformation [OR = 0.7 (95% Cl 0.1-5.5)], and no evidence that acetaminophen should influence fetal growth.
Optimal phase cloning machine in a three‐dimensional transmon qubit system

In quantum mechanics, although the no‐cloning theorem forbids us to clone an arbitrary quantum state ideally, it is still possible to partially reproduce an unknown quantum state by using a phase cloning machine. Since the no‐cloning theorem guarantees the safety of quantum key distribution (QKD), resulting in the famous Bennett–Brassard 1984 (BB84) protocol, phase cloning of BB84 states increases the probability to eavesdrop the transmitted information between correspondents, forcing us to recheck the security of the QKD protocol. In this work, we realize an optimal phase cloning machine for BB84 states in a superconducting quantum bit (qubit). The lowest three energy levels in this system are exploited as logical qubits to demonstrate this cloning procedure. Two microwaves are used to prepare and clone the states. In order to achieve the maximum fidelity, we use the state tomography to readout the experimental results. It is found that the state information is well preserved during this cloning process. The average fidelity between input and output states is 83.92%, making this a valid quantum cloning machine.
Dimension Reduction and Clustering using Non-Elliptical Mixtures

Dimension Reduction and Clustering using Non-Elliptical Mixtures Katherine Morris Advisor: University of Guelph, 2014 Prof. Paul D. McNicholas Finite mixtures of non-elliptical distributions (specifically the shifted asymmetric Laplace and the generalized hyperbolic) are considered to introduce dimension reduction methods for model-based clustering. The approaches are based on existing work on reducing dimensionality in the case of finite Gaussian mixtures. The methods rely on identifying a reduced subspace of the data by considering the extent to which group means and group covariances vary. This subspace contains linear combinations of the original data, which are ordered by importance via the associated eigenvalues. Observations can be projected onto the subspace and the resulting set of variables captures most of the clustering structure available in the data. The algorithms are illustrated using simulated and real data. Furthermore, methods of detecting outliers are developed for model-based clustering using mixtures of contaminated shifted asymmetric Laplace distributions, and mixtures of contaminated skew-normal distributions. The approaches are based on existing work for outlier detection in the context of contaminated Gaussian mixtures. The main idea is to introduce a contamination factor which increases the dispersion of the fitted distribution by altering the skewness and covariance parameters. An expectation-conditional maximization algorithm is employed to obtain maximum likelihood estimates for the parameters in the model. Thus each observation is given a posterior probability of belonging to a particular group, and of being an outlier or not. The performance of the methods is tested on simulated and real data. iv To Phillip, with love
Regularised inference for changepoint and dependency analysis in non-stationary processes

Multivariate correlated time series are found in many modern socio-scientific domains such as neurology, cyber-security, genetics and economics. The focus of this thesis is on efficiently modelling and inferring dependency structure both between data-streams and across points in time. In particular, it is considered that generating processes may vary over time, and are thus non-stationary. For example, patterns of brain activity are expected to change when performing different tasks or thought processes. Models that can describe such behaviour must be adaptable over time. However, such adaptability creates challenges for model identification. In order to perform learning or estimation one must control how model complexity grows in relation to the volume of data. To this extent, one of the main themes of this work is to investigate both the implementation and effect of assumptions on sparsity; relating to model parsimony at an individual time- point, and smoothness; how quickly a model may change over time. Throughout this thesis two basic classes of non-stationary model are stud- ied. Firstly, a class of piecewise constant Gaussian Graphical models (GGM) is introduced that can encode graphical dependencies between data-streams. In particular, a group-fused regulariser is examined that allows for the estima- tion of changepoints across graphical models. The second part of the thesis focuses on extending a class of locally-stationary wavelet (LSW) models. Un- like the raw GGM this enables one to encode dependencies not only between data-streams, but also across time. A set of sparsity aware estimators are developed for estimation of the spectral parameters of such models which are then compared to previous works in the domain.
Investigation of the memristor nonlinear properties

The study of nonlinear systems is an important research topic for scientists and researchers. Memristor, for a long time, it remained just as a theoretical element and rarely appeared in the literature because of having no simple and practical realization. In this paper, we reviewed the theoretical substantiation of the memristor and conducted a practical study of its nonlinear properties using the memristor company KNOWM of series BS-AF-W 16DIP. We also investigated the characteristics of the memristor via the LabView environment.
Tunable multiple plasmon resonances and local field enhancement of nanocrescent/nanoring structure*

According to the plasmon hybridization theory, the plasmon resonance characteristics of the gold nanocrescent/nanoring (NCNR) structure are systematically investigated by the finite element method. It is found that the extinction spectra of NCNR structure exhibit multiple plasmon resonance peaks, which could be attributed to the result of the plasmon couplings between the multipolar plasmon modes of nanocrescent and the dipolar, quadrupolar, hexapolar, octupolar, decapolar plasmon modes of nanoring. By changing the geometric parameters, the intense and separate multiple plasmon resonance peaks are obtained and can be tuned in a wide wavelength range. It is further found that the plasmon coupling induces giant multipole electric field enhancements around the tips of the nanocrescent. The tunable and intense multiple plasmon resonances of NCNR structure may provide effective applications in multiplex biological sensing.
Computational modeling of dusty nuclear starburst disks and its consequences on observables

Active galactic nuclei (AGNs) are the most energetic non-explosive objects in the uni- verse whose comoving space density peaks near redshift z ∼ 2 (Wolf et al., 2003). They radiate energy across the electromagnetic spectrum from radio to gamma rays. The number density of AGNs and their luminosity function with redshift play a key role in the evolution of massive black holes. They can also provide a feedback mechanism that can regulate galaxy growth, thus playing an important role in galaxy formation and their evolutionary models. However, the study of AGNs can be extremely complicated. There are numerous AGN classes dependent on physical characteristics such as luminosity, amount of obscuration, and observed emission lines. Many AGNs are observed to be obscured by dust and gas (Alexander & Hickox, 2012; LaMassa et al., 2017). The geometrical and physical conditions of these regions are not well understood. Moreover, obscured AGNs play an important role in explaining the cosmic X-ray background (Gilli et al., 2007; Akylas et al., 2012). Dusty nuclear starburst disks (NSDs) could potentially obscure the AGN in Seyfert galaxies (Ballantyne, 2008). I worked on testing this hypothesis by modeling the two-dimensional structure of NSDs. Since these disks are dusty, the atmosphere of a disk can be expanded when the dust sublimates near mid-plane. The expanded dusty atmosphere (near the surface) can then be supported by stellar radiation pressure (Thompson et al., 2005). NSDs could be a key to understanding the relation among the observed AGN obscuration, cosmic X-ray background, and star-forming regions at intermediate redshift.
ESTIMATION OF GROWTH PARAMETERS USING NON-LINEAR MIXED MODEL AND COMPARISON WITH FIXED EFFECT MODEL IN ANIMALS

Modeling growth of an animal is a complex process, because it requires describing longitudinal measurements with few parameters with biological interpretation. With longitudinal data, the variance of observations may increase with time (age), and repeated measurements of an individual over time are correlated. The non-independence of data violates a key assumption underlying many statistical procedures and has been ignored in most traditional non-linear fixed effect models. A solution to this problem is the use of non-linear mixed effect models (NLMM). Mixed effects models for longitudinal data such as growth have become of interest because of their flexible covariance structure and their ability to handle unbalanced data. A NLMM makes it possible to account for random covariates before testing for fixed effects and control autocorrelation in repeated measures. Nonlinear mixed effect models also account for multiple sources of heterogeneity in data through the inclusion of random effects in the models. In this study, growth data of Goat and Pig has been used. Attempt has been made to develop the Von-bertalanffy mixed model. Logistic, Gompertz and Von-bertalanffy fixed and mixed models have been explored for these data. Comparison of the models i.e. between fixed and mixed type of the same model and among different fixed and mixed models has been attempted. The goodness of fit statistics like i.e. Mean Square Error (MSE) and Root Mean Square Error (RMSE) of the fitted models has been computed. The parameters of the best fitted models along with their corresponding standard error are estimated. It has been found that Logistic mixed effect model is performed best for the Goat and Pig data. The prediction of this model is also better. Computer programs in SAS and R have been developed for the data analysis. In literature it has been found that Logistic mixed model is most appropriate for animals. So it may be concluded that the logistic mixed effect model can be used for other animal.
OPTIMIZATION-BASED SYNTHESIS OF A DEEP-DIGGING TILLAGE MECHANISM

A quasi-Newton optimization method is employed to synthesize a four-bar tillage mechanism, a device for loosening the sub-soil in farm fields. The synthesis routine uses sequential parameter transformations that map from an unconstrained search variable space to a constrained design variable space. The sequential parameter transformations ensure that only a specific mechanism sub-type is considered, that Grashof criteria are satisfied, and that all mechanism parameters satisfy specified upper and lower constraints. Objective functions quantifying the level of satisfaction of the desired task displacements are minimized. It is shown that in addition to task satisfaction, further objective function terms can be added. The addition of objective function terms to increase the minimum transmission angle and to reduce the mechanism size are found to allow the synthesis of a practical mechanism for the deep-digging application. The use of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method with Fletcher’s Line Se.