Miracle-dz/UniFAR-FTUs huggingface dataset:
Dectin-1/CARD9 induction of the TFEB and TFE3 gene network is dispensable for phagocyte anti-Aspergillus activity in the lung

ABSTRACT Myeloid phagocytes of the respiratory immune system, such as neutrophils, monocytes, and alveolar macrophages, are essential for immunity to Aspergillus fumigatus, the most common etiologic agent of mold pneumonia worldwide. Following the engulfment of A. fumigatus conidia, fusion of the phagosome with the lysosome is a critical process for killing conidia. TFEB and TFE3 are transcription factors that regulate lysosomal biogenesis under stress and are activated by inflammatory stimuli in macrophages, but it is unknown whether TFEB and TFE3 contribute to anti-Aspergillus immunity during infection. We found that lung neutrophils express TFEB and TFE3, and their target genes were upregulated during A. fumigatus lung infection. In addition, A. fumigatus infection induced nuclear accumulation of TFEB and TFE3 in macrophages in a process regulated by Dectin-1 and CARD9. Genetic deletion of Tfeb and Tfe3 impaired macrophage killing of A. fumigatus conidia. However, in a murine immune-competent Aspergillus infection model with genetic deficiency of Tfeb and Tfe3 in hematopoietic cells, we surprisingly found that lung myeloid phagocytes had no defects in conidial phagocytosis or killing. Loss of TFEB and TFE3 did not impact murine survival or clearance of A. fumigatus from the lungs. Our findings indicate that myeloid phagocytes activate TFEB and TFE3 in response to A. fumigatus, and while this pathway promotes macrophage fungicidal activity in vitro, genetic loss can be functionally compensated in the lung, resulting in no measurable defect in fungal control and host survival.
Analysis of E. coli growth dynamics during Lambda(vir) phage infection reveals phage decay

Biological interactions like those between a pathogen and a host are intrinsically dynamic. The Luria-Delbruck experiment is a classic quantitative analysis demonstrating that the mutation rates of bacterial hosts are independent of the presence of their bacteriophage pathogens. However, this experiment was performed as an endpoint analysis. To determine if new insights could be gained from measuring the dynamics of bacterial-host interactions, we used a high-throughput spectrophotometer to repeat this classic experiment. We examined interactions between Escherichia coli bacteria and the lytic lambda(vir) phage to confirm the fundamental finding of the Luria-Delbruck experiment. Specifically, we demonstrated that the variance in the timing required for phage-resistant mutants to emerge increased as the size of the starting bacterial population decreased. In addition to confirming the Luria-Delbruck results, our analysis of the dynamics of bacterial growth in the presence of phage revealed unexpected findings, including the significant decay in lambda(vir) potency and the substantial impact of dead bacterial debris on culture density measurements. Phage are typically thought to be extremely stable, such that our discovery that their potency rapidly decays in experimental settings may have implications for real-world applications where phage potency is important, such as phage therapy pharmacology. Our results thus establish a more rapid method of analyzing bacterial resistant mutant fluctuations and illustrate how dynamical measurements can provide new insights into even extremely well-characterized experimental systems.
Population coding of distinct categories of behavior in the frontal eye field

Brain regions frequently contribute to the control of diverse behaviors. Within these regions, individual neurons often respond to various motor tasks, suggesting that the ability to control multiple behaviors is rooted in the organization of neuronal populations. To study this organization, we examined how the frontal eye field (FEF) encodes distinct behaviors by recording the activity of 1200 neurons during various eye movement tasks in two female fascicularis monkeys. We focused on three behaviors: smooth pursuit, pursuit suppression, and saccades. We found that single neurons tended to respond in all three tasks, thus challenging the notion that the FEF is organized into task-specific clusters. We then identified the low-dimensional subspaces that contained most of the population activity during each behavior and quantified the extent of overlap between these spaces across behaviors. Population activity during pursuit and pursuit suppression exhibited a substantial overlap, with highly correlated directional tuning at the single-neuron level, as reflected by similar outcomes for the population decoders. These distinct behaviors combined with similar encoding suggest that the suppression of movement occurs mostly downstream from the FEF. By contrast, pursuit and saccades mostly occupied orthogonal subspaces, prompting an independent linear readout of saccades and pursuit. Thus overall, these results indicate that distinct behaviors can exhibit either separate or overlapping population codes within a specific brain region, hence emphasizing the importance of the system-level organization of behavior. Significance statement How do brain areas control multiple behaviors? We investigated the organization of the FEF in monkeys during three different types of eye movements: smooth pursuit, pursuit suppression, and saccades. We found that individual neurons tended to respond to all three tasks, thus challenging a task-specific FEF cluster organization. Further, the low-dimensional subspaces that contained most of the population activity during pursuit and pursuit suppression overlapped substantially, implying that movement suppression occurs downstream from the FEF. In contrast, pursuit and saccades occupied orthogonal subspaces, prompting independent linear readouts. These results underscore the importance of adopting a system-level perspective to comprehend how diverse behaviors are encoded in the brain.
Intraspecific chemical variation of Tanacetum vulgare affects plant growth and reproductive traits in field plant communities

Intraspecific plant chemodiversity plays a fundamental role in interactions between plants and their interaction partners. However, how chemodiversity at the stand level (plant communities that vary in the number and type of plant chemotypes that grow in them, i.e., chemotype richness) affects ecosystem functioning is not fully understood. We describe a biodiversity experiment using six chemotypes of common tansy (Tanacetum vulgare L., Asteraceae) to manipulate intraspecific plant chemodiversity at the plot level. We tested the effects of chemotype identity and plot-level chemotype richness (1-6) on plant growth and reproductive traits at plant and plot levels. We found that chemotypes differed in growth and reproductive traits and that traits were affected by the plot-level chemotype richness. Although morphological differences became less pronounced over time, reproductive phenology patterns persisted. It suggests that chemotypes initially adopted different growth strategies, which may facilitate their establishment in nature. Although chemotype richness did not lead to overyielding effects, plot-level trait means were affected by the presence or absence of certain chemotypes in a plot, and the direction of the effect depended on the chemotype. We analyzed plot-level headspace emissions and found that blends released from plant communities were neither richer nor more diverse with increasing plot-level chemotype richness. However, we found that plots became more dissimilar in their headspace terpenoids as they were more dissimilar in their leaf-terpenoid profiles. This long-term field experiment will allow further investigation into plant-insect interactions and insect community assembly in response to intraspecific chemodiversity.
When Time Warps: The Lived Experience of Gender, Race, and Sexual Violence

Megan Burke locates When Time Warps at the juncture between two major developments in feminist phenomenology. First, Burke joins the recent movement in Simone de Beauvoir studies to read The Second Sex as a rigorous phenomenology of women’s oppression, replacing previous cultural and sociological interpretations with specifically philosophical analysis.1 Second, Burke identifies their work with the emerging constellation of critical phenomenology, which investigates lived realities of oppression by supplementing phenomenology’s attention to first-person perceptual experience with “a reflexive inquiry into how power relations structure experience as well as our ability to analyze that experience. ”2 For feminists, critical phenomenology marks a shift away from pure phenomenological descriptions of female-bodied experience and women’s oppression under patriarchy, mobilizing instead an increasingly interdisciplinary attention to intersectionality and complex sociohistorical interrelations, especially colonialism. While Beauvoir’s socially situated phenomenology has inspired many identifying their work as critical phenomenology, critical phenomenology’s interdisciplinarity and its critique of the exclusions of the classical phenomenological method put it in tension with the contemporary reading of Beauvoir as a thoroughgoing existential phenomenologist.3
Dehydrogenase Activity in a Litter Manipulation Experiment in Temperate Forest Soil

Abstract Soil enzyme activities are “sensors” of soil organic matter (SOM) decomposition since they integrate information about microbial status and physico-chemical condition of soils. We measured dehydrogenase enzyme activity in a deciduous temperate oak forest in Hungary under litter manipulation treatments. The Síkfőkút Detritus Input and Removal Treatments (DIRT) Project includes treatments with doubling of leaf litter and woody debris inputs as well as removal of leaf litter and trenching to prevent root inputs. We hypothesized that increased detrital inputs increase labile carbon substrates to soils and would increase enzyme activities particularly that of dehydrogenase, which has been used as an indicator of soil microbial activity. We also hypothesized that enzyme activities would decrease with detritus removal plots and decrease labile carbon inputs to soil. After ten years of treatments, litter removal had a stronger effect on soil dehydrogenase activity than did litter additions. These results showed that in this forest ecosystem the changed litter production affected soil microbial activity: reduced litter production decreased the soil dehydrogenase activity; increased litter production had no significant effect on the enzyme activity. Kivonat Az avar mennyiségének hatása egy cseres-tölgyes erdő talajában a dehidrogenáz enzim aktivitására. A talajenzimek a talajban lévő szerves anyag (SOM) bomlásának "szenzorai", mivel információt adnak a talaj mikrobiológiai és fizikai-kémiai állapotáról. Egy magyarországi mérsékelt övi tölgyerdőben mértük a dehidrogenáz enzim aktivitását az avar mennyiségének csökkentésével és növelésével. A Síkfőkút DIRT Projectben (Detritus Input and Removal Treatments) az alábbi kezeléseket alkalmaztuk: dupla mennyiségű levél avar, dupla mennyiségű ágavar, valamint levél és gyökér megvonásos kezelések, ahol a gyökereknek a parcellákra történő benövését akadályozzuk meg. Azt feltételeztük, hogy a megnövelt avar input hatására megnövekszik a talajban a labilis, azaz a könnyen bontható szén szubsztrátok mennyisége és az enzimek aktivitása. Különösen a dehidrogenáz enzim aktivitásának változását vártuk, ami az egyik legáltalánosabban használt mutató a talaj mikrobiális aktivitásának mérésére. Továbbá azt feltételeztük, hogy a talaj enzim aktivitása csökkenni fog az avarmegvonásos kezelésekben, a labilis szén szubsztrátok mennyiségének csökkenésével. Eredményeink azt mutatják, hogy tíz év elteltével az avar produkció csökkenése ebben az erdei ökoszisztémában a talaj dehidrogenáz enzim aktivitásának csökkenését okozta, ugyanakkor az avarprodukció növekedése nem okozott szignifikáns változást az enzim aktivitásában
Towards Generating Realistic Geosocial Networks

The proliferation of location-based services and social networks have given rise to geosocial networks, which model not only the social interactions between users but also their spatial activities. Examples include traditional social networks extended with geo-annotated posts such as Twitter and Facebook, and networks such as Foursquare and Yelp that directly offer geosocial services. Despite the ubiquity of such networks in everyday life and the strong interest by the research community, a limited number of datasets are in fact publicly available. In view of this, we investigate the generation of realistic geosocial networks which find application in benchmarking and testing of analysis tasks, "what-if" scenarios and simulations. The contributions of our work are twofold. We first identify three types of synthetic geosocial networks which mimic the characteristics of real ones and second, we develop a prototype which combines graph and spatial generators, to construct such networks.
Low-Complexity Distributed Scheduling Algorithms for Wireless Networks

We consider the problem of distributed scheduling in wireless networks. We present two different algorithms whose performance is arbitrarily close to that of maximal schedules, but which require low complexity due to the fact that they do not necessarily attempt to find maximal schedules. The first algorithm requires each link to collect local queue-length information in its neighborhood, and its complexity is independent of the size and topology of the network. The second algorithm is presented for the node-exclusive interference model, does not require nodes to collect queue-length information even in their local neighborhoods, and its complexity depends only on the maximum node degree in the network.
NEW STATE OF PLAY IN INFORMATION SYSTEMS RESEARCH : THE PUSH TO THE EDGES 1

ion reflected in practice, such as a specific portal or a DSS. • These constructs, often used in models, have weak or no logic in applying taxonomical principles, so that building strict causal or evolutionary models is difficult (Bunge 1998). • These constructs are idiosyncratic to the context and offer good IT “stories,” but they are difficult to generalize. • Often, IS scholars treat the IT artifact as nominal (i.e., the presence or absence of the system) or in a way that is inconsistent across studies. • Work on developing strong and richer taxonomic principles for representing IT and its affordances so instantiations of IT can transcend contexts. • Several multidimensional views of IT effects offered by the classic DeLone-McLean model (DeLone and McLean 1992), the Burton-Jones and Gallivan (2007) model of IS use, or Dennis et al.’s (2008) representation of IT attributes in communication are examples of richer attempts to theorize around the IT construct in taxonomic ways. High levels of abstraction by reducing IT to a few basic high-level functions, such as storage, processing, and communication capability, and general changes in any of such capacities such as its quality or cost. • These functions, whereas useful in understanding the generic functions of IT as a general purpose technology alone, seldom make good constructs because of the inability to benchmark them well and the presence of complex interactions between them. • Sometimes terms are so abstract that without specific affordances or capabilities of IT we cannot build powerful theory. • Sometimes scholars seek to overcome this problem by relegating these functions into specific families of IT artifacts that are labeled with specific terms—such as a DSS or a portal. By doing so we are back to the problems described in the row above. • Work on identifying constructs that can represent variation in observable artifacts along welldefined dimensions based on a broader ontology of information technology. Maintaining inconsistent views of IT and its interactions with the context. • Developing rigorous higher abstractions such as “digital capabilities” requires advances in formulating systematic building blocks (e.g., IT features and their aggregations) that underlie the observed behaviors and the associated effects of digital technologies. Currently, such concepts and taxonomies are largely non-existent. • Often, concepts (like digital capabilities) are idiosyncratically conceptualized for each piece of research—with somewhat arbitrary relationships and abstraction levels— often using the language of the specific system described. • Engage in the conceptualization of IT-based constructs founded on features and their interrelationships that can be used consistently across technologies, including emerging technologies. • Build logic on how feature sets can interact with contexts in a value (or capability) hierarchy. • See Markus et al. (2002) and Shen et al. (2014) for some kernels of these ideas.
Platform or Wholesale? A Strategic Tool for Online Retailers to Bene(cid:12)t from Third-Party Information (cid:3)

Online retailing is dominated by a channel structure in which a retailer either buys products from competing manufacturers and resells to consumers (wholesale scheme) or lets manufacturers directly sell to consumers on its platform for a commission (plat-form scheme). Easy access to publicly available third-party information such as product reviews which facilitate consumers’ purchase decisions is another distinctive and ubiq-uitous characteristic of online retailing. We show that retailers can use the upstream pricing scheme, wholesale or platform, as a strategic tool to bene(cid:28)t from third-party information. Information on the quality dimension homogenizes consumers’ perceived utility di(cid:27)erences between competing products and increases the upstream competition, which bene(cid:28)ts the retailer under the wholesale scheme but hurts the retailer under the platform scheme. Information on the (cid:28)t dimension heterogenizes consumers’ estimated (cid:28)ts to the products and softens the upstream competition, which hurts the retailer under the wholesale scheme but bene(cid:28)ts the retailer under the platform scheme. Consequently, when the precision of the third-party information is high (low), a retailer can bene(cid:28)t from third-party information by adopting the wholesale (platform) scheme if the quality dimension plays a dominant role and by adopting the platform (wholesale) scheme if the (cid:28)t dimension plays a dominant role. The results reveal that the quality information and (cid:28)t information play very di(cid:27)erent roles in changing the upstream competition, and whether the retailer can bene(cid:28)t from the third-party information depends on its pricing scheme choice, the precision of the third-party information, and the relative importance of quality and (cid:28)t attributes in consumers’ evaluation of products.
Crowd-Squared: Amplifying the Predictive Power of Search Trend Data

Big Data generated by crowds provides a myriad of opportunities for monitoring and modeling people's intentions, preferences, and opinions. A crucial step in analyzing such “big data�? is selecting the relevant part of the data that should be provided as input to the modeling process. In this paper, we offer a novel, structured, crowd-based method to address the data selection problem in a widely used and challenging context: selecting search trend data. We label the method “crowd-squared,�? as it leverages crowds to identify the most relevant terms in search volume data that were generated by a larger crowd. We empirically test this method in two domains and find that our method yields predictions that are equivalent or superior to those obtained in previous studies (using alternative data selection methods) and to predictions obtained using various benchmark data selection methods. These results emphasize the importance of a structured data selection method in the prediction process, and demonstrate the utility of the crowd-squared approach for addressing this problem in the context of prediction using search trend data.
Towards Efficient Local 3D Conditioning

Recently, Neural Implicit Representations (NIRs) have gained popularity for learning-based 3D shape representation. General representations, i.e. ones that share a decoder across a family of geometries have multiple advantages such as ability of generating previously unseen samples and smoothly interpolating between training examples. These representations, however, impose a trade-off between quality of reconstruction and memory footprint stored per sample. Globally conditioned NIRs suffer from a lack of quality in capturing intricate shape details, while densely conditioned NIRs demand excessive memory resources. In this work we suggest using a Neural Network to approximate a grid of latent codes, while sharing the decoder across the entire category. Our model achieves a significantly better reconstruction quality compared to globally conditioned methods, while using less memory per sample to store single geometry.
Quantifying spatiotemporal dynamics of agricultural landscapes using remotely sensed data and landscape metrics

This study focuses on the spatiotemporal dynamics of agricultural lands and differences in rapidly developing urban and declining rural counties in Iowa, USA between 1984 and 2000. The study presents an analysis of land-cover maps derived from Landsat TM and ETM+ satellite imagery and different landscape metrics using FRAGSTATS and IDRISI software. The study provides evidence of both loss of croplands and change in fragmentation between 1984 and 2000. Fragmentation in agriculture-dominated areas increased with the development of urban centres and diversification of land uses. Fragmentation of landscapes, including agricultural land, was found to be higher in the urbanized counties, but was stable or even declined over time in these counties. In contrast, in the context of remote rural areas, agricultural landscapes experienced rapid increase in fragmentation and farmland loss. The urban–rural gradient analysis used in this study showed that the highest fragmentation occurred on the city edges. These findings suggest that farmland fragmentation is a complex process associated with socio-economic trends at regional and local scales. In addition, socio-economic determinants of landscape fragmentation differ between areas with diverging development trajectories. Intensive cropland fragmentation in remote agricultural regions, detected by this research, should be further studied and its possible effects on both agricultural productivity and biodiversity should be carefully considered.
Finding Concept Representations in Neural Networks with Self-Organizing Maps

In sufficiently complex tasks, it is expected that as a side effect of learning to solve a problem, a neural network will learn relevant abstractions of the representation of that problem. This has been confirmed in particular in machine vision where a number of works showed that correlations could be found between the activations of specific units (neurons) in a neural network and the visual concepts (textures, colors, objects) present in the image. Here, we explore the use of self-organizing maps as a way to both visually and computationally inspect how activation vectors of whole layers of neural networks correspond to neural representations of abstract concepts such as ‘female person’ or ‘realist painter’. We experiment with multiple measures applied to those maps to assess the level of representation of a concept in a network’s layer. We show that, among the measures tested, the relative entropy of the activation map for a concept compared to the map for the whole data is a suitable candidate and can be used as part of a methodology to identify and locate the neural representation of a concept, visualize it, and understand its importance in solving the prediction task at hand.