Miracle-dz/UniFAR-FTUs huggingface dataset:
Diversity of the bacterial and viral communities in the tropical horse tick, Dermacentor nitens in Colombia

Ticks are obligatory hematophagous ectoparasites that transmit pathogens among various vertebrates, including humans. The composition of the microbial and viral communities in addition to the pathogenic microorganisms is highly diverse in ticks, but the factors driving the diversity are not well understood. The tropical horse tick, Dermacentor nitens, is distributed throughout the Americas and it is recognized as a natural vector of Babesia caballi and Theileria equi, the causal agents of equine piroplasmosis. We characterized the bacterial and viral communities associated with partially-fed D. nitens females collected by a passive survey on horses from field sites representing three distinct geographical areas in Colombia (Bolivar, Antioquia, and Cordoba). RNA-seq and sequencing of the V3 and V4 hypervariable regions of the 16S rRNA gene were performed using the Illumina-Miseq platform. A total of 356 operational taxonomic units (OTUs) were identified, in which the presumed endosymbiotic Francisellaceae/Francisella spp. was predominantly found. Nine contigs corresponding to six different viruses were identified in three viral families: Chuviridae, Rhabdoviridae, and Flaviviridae. Differences in the relative abundance of the microbial composition among the geographical regions were found to be independent of the presence of Francisella-Like Endosymbiont (FLE). The most prevalent bacteria found on each region were Corynebacterium in Bolivar, Staphylococcus in Antioquia, and Pseudomonas in Cordoba. Rickettsia-like endosymbionts, mainly recognized as the etiological agent of rickettsioses in Colombia were detected in the Cordoba samples. Metatranscriptomics revealed 13 contigs containing FLE genes, suggesting a trend of regional differences. These findings suggest regional distinctions among the ticks and their bacterial compositions.
When population growth intensifies intergroup competition, female colobus monkeys free-ride less

In many social species, intergroup aggression is a cooperative activity that produces public goods such as a safe and stable social environment and a home range containing the resources required to survive and reproduce. In this study, we investigate temporal variation in intergroup aggression in a growing population of colobus monkeys to ask a novel question: “Who stepped-up to produce these public goods when the competitive landscape changed? ”. Both whole-group encounters and male incursions occurred more frequently as the population grew. Males and females were both more likely to participate in whole-group encounters when monopolizable food resources were available, indicating both sexes engaged in food defence. However, only females increasingly did so over time, suggesting that when intergroup competition intensified, it was females who increasingly invested in home range defence. Females were also more active in male incursions at high population densities, suggesting they also worked harder to maintain a safe and stable social environment over time. This is not to say that males were chronic free-riders when it came to maintaining public goods. Males consistently participated in the majority of intergroup interactions throughout the study period, indicating they may have lacked the capacity to invest more time and effort.
Lexical semantic content, not syntactic structure, is the main contributor to ANN-brain similarity of fMRI responses in the language network

Representations from artificial neural network (ANN) language models have been shown to predict human brain activity in the language network. To understand what aspects of linguistic stimuli contribute to ANN-to-brain similarity, we used an fMRI dataset of responses to n=627 naturalistic English sentences (Pereira et al., 2018) and systematically manipulated the stimuli for which ANN representations were extracted. In particular, we i) perturbed sentences’ word order, ii) removed different subsets of words, or iii) replaced sentences with other sentences of varying semantic similarity. We found that the lexical semantic content of the sentence (largely carried by content words) rather than the sentence’s syntactic form (conveyed via word order or function words) is primarily responsible for the ANN-to-brain similarity. In follow-up analyses, we found that perturbation manipulations that adversely affect brain predictivity also lead to more divergent representations in the ANN’s embedding space and decrease the ANN’s ability to predict upcoming tokens in those stimuli. Further, results are robust to whether the mapping model is trained on intact or perturbed stimuli, and whether the ANN sentence representations are conditioned on the same linguistic context that humans saw. The critical result—that lexical- semantic content is the main contributor to the similarity between ANN representations and neural ones—aligns with the idea that the goal of the human language system is to extract meaning from linguistic strings. Finally, this work highlights the strength of systematic experimental manipulations for evaluating how close we are to accurate and generalizable models of the human language network.
Non-pathogenic leaf-colonising bacteria elicit pathogen-like responses in a colonisation density-dependent manner

Leaves are colonised by a complex mix of microbes, termed the leaf microbiota. Even though the leaf microbiota is increasingly recognised as an integral part of plant life and health, our understanding of its interactions with the plant host is still limited. Here, mature, axenically grown Arabidopsis thaliana plants were spray-inoculated with six diverse leaf-colonising bacteria. The transcriptomic changes in leaves were tracked over time and significant changes in ethylene marker (ARL2) expression were observed only two to four days after spray-inoculation. Whole transcriptome sequencing revealed that four days after inoculation, leaf transcriptional changes to colonisation by non-pathogenic and pathogenic bacteria differed in strength but not in the type of response. Inoculation of plants with different densities of the non-pathogenic bacterium Williamsia sp. Leaf354 showed that high bacterial titers caused disease phenotypes and led to severe transcriptional reprogramming with a strong focus on plant defence. An in silico epigenetic analysis of the data was congruent with the transcriptomic analysis. These findings suggest (1) that plant responses are not rapid after spray-inoculation, (2) that plant responses only differ in strength and (3) that plants respond to high titers of non-pathogenic bacteria with pathogen-like responses. Plain Language Summary Plants are colonised by diverse bacteria affecting many aspects of plant life. Here we show that plants do not differentiate between different bacteria but measure their quantities to keep bacterial numbers in check.
Regulatory network structure and environmental signals constrain transcription factor innovation

Evolutionary innovation of transcription factors frequently drives phenotypic diversification and adaptation to environmental change. Rewiring – that is gaining or losing connections to transcriptional target genes – is a key mechanism by which transcription factors evolve and innovate. However the frequency of functional adaptation varies between different regulators, even when they are closely related. To identify factors influencing propensity for rewiring, we utilise a Pseudomonas fluorescens SBW25 strain rendered incapable of flagellar mediated motility in soft-agar plates via deletion of the flagellar master regulator (fleQ). This bacterium can evolve to rescue flagellar motility via gene regulatory network rewiring of an alternative transcription factor to rescue activity of FleQ. Previously, we have identified two members (out of 22) of the RpoN-dependent enhancer binding protein (RpoN-EBP) family of transcription factors (NtrC and PFLU1132) that are capable of innovating in this way. These two transcription factors rewire repeatably and reliably in a strict hierarchy – with NtrC the only evolved rewiring route in a ΔfleQ background, and PFLU1132 the only evolved rewiring route in a ΔfleQΔntrC background. However, why other members in the same transcription factor family have not been observed to rescue flagellar activity is unclear. Previous work shows that protein homology cannot fully explain this pattern, and mutations in rewired strains suggested high levels of transcription factor expression and activation drive rewiring. We predict that mutations that increase expression of the rewired transcription factor are vital to unlock rewiring potential. Here, we construct titratable expression mutant lines for 11 of the RpoN-EBPs in P. fluorescens. We show that in 5 additional RpoN-EBPs (HbcR, GcsR, DctD, AauR and PFLU2209), high expression levels result in different mutations conferring motility rescue, suggesting alternative rewiring pathways. Our results indicate that expression levels (and not protein homology) of RpoN-EBPs are a key constraining factor in determining rewiring potential. This suggests that transcription factors that can achieve high expression through few mutational changes, or transcription factors that are active in the selective environment, are more likely to innovate and contribute to adaptive gene regulatory network rewiring.
Spatio-temporal patterns of ovarian development and VgR gene silencing reduced fecundity of parthenogenetic Artemia

Halophilic zooplankton brine shrimp Artemia has been used as an experimental animal in multidisciplinary research. However, the reproductive patterns and regulation mechanisms of Artemia remain a mystery. In this study, the ovarian development process of parthenogenetic Artemia (P. Artemia) was distinguished into five stages, and oogenesis or egg formation process was identified with six phases. The mode of oogenesis was assumed polytrophic. Moreover, we traced the dynamic translocation of germline stem cells (GSCs) by EdU labeling, elucidated several key cytological events in oogenesis through HE staining and florescent image. Notably, distinguished from the ovary structure of insects and crustaceans, P. Artemia germarium originated from ovariole buds and located in the base of the ovarioles. RNA-seq based on five stages of ovarian development revealed that 2657 up-regulated genes related to reproduction were obtained by pair-to-pair comparison of RNA-seq. Furthermore, Gbb, Dpp, piwi, vasa, nanos, VgA and VgR associated with GSCs recognition and reproductive development genes were screened and also verified by qPCR. Silencing of the VgR gene in P. Artemia (PA-VgR) at stage L of ovarian development led to a low level of gene expression (<10%) within five days, which resulted variations of oogenesis-related gene expression and significantly inhibited vitellogenesis, impeded oocyte maturation, and eventually decreased the offspring number. In conclusion, we illustrated the patterns of ovarian development, outlined the key spatio-temporal features of oogenesis and established a platform for gene function research using P. Artemia as an experimental animal. These results lay the foundation for studying the reproductive biology of invertebrates, may also provide a novel insight in the reproductive regulation mechanism of aquatic animals in extreme environments. SUMMARY STATEMENT Artemia is a novel experimental platform and potential “model” crustacean for studying specific process and mechanism of reproduction.
Dopamine D2 receptors in mossy cells reduce excitatory transmission and are essential for hippocampal function

Hilar mossy cells (MCs) are principal excitatory neurons of the dentate gyrus (DG) that play critical roles in hippocampal function and have been implicated in brain disorders such as anxiety and epilepsy. However, the mechanisms by which MCs contribute to DG function and disease are poorly understood. Expression from the dopamine D2 receptor (D2R) gene (Drd2) promoter is a defining feature of MCs, and previous work indicates a key role for dopaminergic signaling in the DG. Additionally, the involvement of D2R signaling in cognition and neuropsychiatric conditions is well-known. Surprisingly, though, the function of MC D2Rs remain largely unexplored. In this study, we show that selective and conditional removal of Drd2 from MCs of adult mice impaired spatial memory, promoted anxiety-like behavior and was proconvulsant. To determine the subcellular expression of D2Rs in MCs, we used a D2R knockin mouse which revealed that D2Rs are enriched in the inner molecular layer of the DG, where MCs establish synaptic contacts with granule cells. D2R activation by exogenous and endogenous dopamine reduced MC to dentate granule cells (GC) synaptic transmission, most likely by a presynaptic mechanism. In contrast, removing Drd2 from MCs had no significant impact on MC excitatory inputs and passive and active properties. Our findings support that MC D2Rs are essential for proper DG function by reducing MC excitatory drive onto GCs. Lastly, impairment of MC D2R signaling could promote anxiety and epilepsy, therefore highlighting a potential therapeutic target. SIGNIFICANCE Growing evidence indicates that hilar mossy cells (MCs) of the dentate gyrus play critical but incompletely understood roles in memory and brain disorders, including anxiety and epilepsy. Dopamine D2 receptors (D2Rs), implicated in cognition and several psychiatric and neurological disorders, are considered to be characteristically expressed by MCs. Still, the subcellular localization and function of MC D2Rs are largely unknown. We report that removing the Drd2 gene specifically from MCs of adult mice impaired spatial memory and was anxiogenic and proconvulsant. We also found that D2Rs are enriched where MCs synaptically contact dentate granule cells (GC) and reduce MC-GC transmission. This work uncovered the functional significance of MC D2Rs, thus highlighting their therapeutic potential in D2R- and MC-associated pathologies.
Cross-modal feature based attention facilitates spatial transfer of perceptual learning in motion-domain figure-ground segregation

This study tested the role of a cross-modal feature based attention (FBA) cue on perceptual learning and spatial transfer. The trained task was figure-ground segregation in the motion domain. The experiment involved a pre-test, ten days of training, and a post-test. Twelve visually intact participants were immersed in a virtual environment and tasked with identifying the location and motion direction of a peripheral 10◦aperture of semi-coherently moving dots embedded at randomized locations within whole-field random dot motion. The aperture contained both randomly moving dots and signal dots which had global leftward or rightward motion. To manipulate motion coherence, a 3-up-1-down staircase adjusted the direction range of the signal dots in response to segregation judgments. The dot stimulus was preceded by a 1s white-noise spatialized auditory cue emitted from the fixation point (neutral group), or from an emitter moving in the direction of signal dots at 80◦/s in a horizontal arc centered on the fixation point (FBA cue group). Visual feedback indicated the selected and true aperture locations, and correctness of the motion direction judgment. Analysis measured MD discrimination within the aperture as well as segregation ability, both measured in terms of direction range threshold (DRT). At trained locations, MD DRT improved similarly in FBA and neutral groups, and learning was retained when the pre-cue was removed (ΔDRT from pretest to posttest: 61±10◦(SD) FBA, 74±10◦neutral), and transferred to untrained locations (41±10◦FBA, 45±10◦neutral). DRT for localization also improved in both groups when pre-cues were removed (49±10◦FBA, 44±10◦neutral), but only the FBA group showed full transfer of learning to untrained locations in the segregation task (32±10◦FBA, 23±10◦neutral). In summary, transfer occurred for both motion direction and segregation tasks, but the segregation transfer required the presence of the cross-modal FBA cue during training.
Local changes in potassium ions modulate dendritic integration

During neuronal activity the extracellular concentration of potassium ions ([K+]o) increases substantially above resting levels, but it remains unclear what role these [K+]o changes play in dendritic integration of synaptic inputs. We used mathematical formulations and biophysical modeling to explore the role of activity-dependent K+ changes near dendritic segments of a visual cortex pyramidal neuron, receiving synaptic inputs tuned to stimulus orientation. We found that the fine-scale spatial arrangement of inputs dictates the magnitude of [K+]o changes around the dendrites: Dendritic segments with similarly-tuned inputs can attain substantially higher [K+]o increases than segments with diversely-tuned inputs. These [K+]o elevations in turn increase dendritic excitability, leading to more robust and prolonged dendritic spikes. Ultimately, these local effects amplify the gain of neuronal input-output transformations, causing higher orientation-tuned somatic firing rates without compromising orientation selectivity. Our results suggest that local activity-dependent [K+]o changes around dendrites may act as a “volume knob” that determines the impact of synaptic inputs on feature-tuned neuronal firing.
Experimental investigation of the structure and dynamics of laminar separation bubbles at the onset of bursting

A two-dimensional laminar separation bubble on a flat plate is studied experimentally using Particle Image Velocimetry (PIV) and flow visualization. The separation bubble was generated on a flat plate by an imposed adverse pressure gradient. The adverse pressure gradient was generated by using an inverted wing with a NACA 64 3 -618 airfoil mounted above the flat plate. A parametric study of the effect of the upstream flow velocity and the induced pressure gradient on the mean flow topology and the unsteady behaviour of the separation bubble was carried out in the low-speed water tunnel of the Hydrodynamics Laboratory at the University of Arizona. The structure and dynamics of the laminar separation bubble were found to depend strongly on the aforementioned parameters. As the flow velocity is reduced, at very low flow velocities the bubble is seen to undergo a drastic change in geometry, resulting in bubble bursting. An attempt is made in this work at understanding the physics of bubble bursting. For certain flow conditions, strong vortex shedding near the reattachment region of the bubble was observed, which is a characteristic behaviour of short bubbles. High-resolution spatio-temporal PIV measurements were made to analyze the formation and breakdown of these flow structures
SHEPHERD: Serving DNNs in the Wild

Model serving systems observe massive volumes of inference requests for many emerging interactive web services. These systems need to be scalable, guarantee high system goodput and maximize resource utilization across compute units. However, achieving all three goals simultaneously is challenging since inference requests have very tight latency constraints (10–500ms), and production workloads can be extremely unpredictable at such small time granularities. We present S HEPHERD , a model serving system that achieves all three goals in the face of workload unpredictability. S HEPHERD uses a two-level design that decouples model serving into planning and serving modules. For planning, S HEPHERD exploits the insight that while individual request streams can be highly unpredictable, aggregating request streams into moderately-sized groups greatly improves pre-dictability, permitting high resource utilization as well as scalability. For serving, S HEPHERD employs a novel online algo-rithm that provides guaranteed goodput under workload un-predictability by carefully leveraging preemptions and model-specific batching properties. Evaluation results over production workloads show that S HEPHERD achieves up to 18 . 1 × higher goodput and 1 . 8 × better utilization compared to prior state-of-the-art, while scaling to hundreds of workers.
Data-driven energy demand forecasting for electric vehicle charging infrastructure

In the era of Big Data and electric vehicles growth by market, data-driven methodologies assume a crucial role to create valuable information. The focus is on supporting the decision-making process for the development of an accurate charging infrastructure. Forecast analysis allows prediction of energy demand over the network. This supports growing trends with a consequent increase in customer satisfaction. By anticipating potential breakdowns due to infrastructure overloads, maintenance costs are reduced. In this paper, we focus on analyzing charging sessions data together with external data ( weather and population information and energy/fuel prices ) collected from different sources. The proposed methodology, named GEORGE (enerGy dEmand fOrecasting foR charGing infrastructurE), offers a building-blocks based approach for the monthly energy demand forecasting. The approach is both generalisable and data-specific. We discuss the results of a classification learning approach to predict a belonging range of kwh for a charge point. In particular the most promising model has good performances in predicting high utilization and is more advantageous to support the company’s decision-making process. Many possible developments are discussed to improve the prediction.
Emotion-driven Harmonisation And Tempo Arrangement of Melodies Using Transfer Learning

We propose and assess deep learning models for harmonic and tempo arrangement generation given melodies and emotional constraints. A dataset of 4000 symbolic scores and emotion labels was gathered by expanding the HTPD3 dataset with mood tags from last.fm and allmusic.com. We explore how bi-directional LSTM and Transformer encoder architectures can learn relationships between symbolic melodies, chord progressions, tempo, and expressed emotions, with and without a transfer learning strategy leveraging symbolic music data without emotion labels. Three emotion annotation summarisation methods based on the Arousal/Valence (AV) representation are compared: Emotion Average, Emotion Surface, and Emotion Category. 20 participants (average age: 30.2, 7 females and 13 males from Japan) rated how well generated accom-paniments matched melodies (musical coherence) as well as perceived emotions for 75 arrangements corresponding to combinations of models and emotion summarisation methods. Musical coherence and match between target and perceived emotions were highest when melodies were encoded using a BLSTM model with transfer learning. The proposed method generates emotion-driven harmonic/tempo arrangements in a fast way, a keen advantage compared to state of the art. Applications of this work include AI-based composition assistant and live interactive music systems for entertainment such as video games.
Towards an Agent-Oriented Approach for Requirements Engineering in the Digital Era

As technology systems increasingly acquire human-like capabilities, requirements engineering frameworks need to adopt abstractions that are rich enough to support analysis of the complex relationship between systems and the social environment. The i* framework for social modeling, with its intentional, strategic actor abstraction, could provide a useful foundation. We suggest areas for extending agent-oriented modeling to respond to the challenges of the digital era, including the modeling of human values