Miracle-dz/UniFAR-FTUs huggingface dataset:
Co-obligate symbioses have repeatedly evolved across aphids, but partner identity and nutritional contributions vary across lineages

Aphids are a large family of phloem-sap feeders. They typically rely on a single bacterial endosymbiont, Buchnera aphidicola, to supply them with essential nutrients lacking in their diet. This association with Buchnera was described in model aphid species from the Aphidinae subfamily and has been assumed to be representative of most aphids. However, in two lineages, Buchnera has lost some essential symbiotic functions and is now complemented by additional symbionts. Though these cases break our view of aphids harbouring a single obligate endosymbiont, we know little about the extent, nature, and evolution of these associations across aphid subfamilies. Here, using metagenomics on 25 aphid species from nine subfamilies, re-assembly and re-annotation of 20 aphid symbionts previously sequenced, and 16S rRNA amplicon sequencing on 223 aphid samples (147 species from 12 subfamilies), we show that dual symbioses have evolved anew at least six times. We also show that these secondary co-obligate symbionts have typically evolved from facultative symbiotic taxa. Genome-based metabolic inference confirms interdependencies between Buchnera and its partners for the production of essential nutrients but shows contributions vary across pairs of co-obligate associates. Fluorescent in situ hybridisation microscopy shows a common bacteriocyte localisation of two newly acquired symbionts. Lastly, patterns of Buchnera genome evolution reveal that small losses affecting a few key genes can be the onset of these dual systems, while large gene losses can occur without any co-obligate symbiont acquisition. Hence, the Buchnera-aphid association, often thought of as exclusive, seems more flexible, with a few metabolic losses having recurrently promoted the establishment of a new co-obligate symbiotic partner.
Parallel streams of raphe VGLUT3-positive inputs target the dorsal and ventral hippocampus in each hemisphere

The hippocampus (HP) receives neurochemically diverse inputs from the raphe nuclei, including glutamatergic fibers characterized by the expression of the vesicular glutamate transporter VGLUT3. These raphe-HP VGLUT3 (VGLUT3HP) projections have been suggested to play a critical role in HP functions, yet a complete anatomical overview of raphe VGLUT3 projections to the forebrain, and in particular the HP, is lacking. Using anterograde viral tracing, we describe largely non-overlapping VGLUT3-positive projections from the dorsal raphe (DR) and median raphe (MnR) to the forebrain, with the HP receiving inputs from the MnR. A limited subset of forebrain regions such as the amygdaloid complex, claustrum and hypothalamus receive projections from both the DR and MnR that remain largely segregated. This highly complementary anatomical pattern suggests contrasting roles for DR and MnR VGLUT3 neurons. To further analyse the topography of VGLUT3 raphe projections to the HP, we used retrograde tracing and found that VGLUT3HP neurons distribute over several raphe sub-regions (including the MnR, paramedian raphe and B9 nucleus) and lack co-expression of serotonergic markers. Strikingly, two-color retrograde tracing unraveled two parallel streams of VGLUT3-positive projections targeting the dorsal and ventral poles of the HP. These results demonstrate highly organized and segregated VGLUT3-positive projections to the HP, suggesting independent modulation of HP functions such as spatial memory and emotion-related behavior.
Identifiability and inference of phylogenetic birth-death models

Recent theoretical work on phylogenetic birth-death models offers differing viewpoints on whether they can be estimated using lineage-through-time data. Louca and Pennell (2020) showed that the class of models with continuously differentiable rate functions is nonidentifiable: any such model is consistent with an infinite collection of alternative models, which are statistically indistinguishable regardless of how much data are collected. Legried and Terhorst (2022a) qualified this grave result by showing that identifiability is restored if only piecewise constant rate functions are considered. Here, we contribute new theoretical results to this discussion, in both the positive and negative directions. Our main result is to prove that models based on piecewise polynomial rate functions of any order and with any (finite) number of pieces are statistically identifiable. In particular, this implies that spline-based models with an arbitrary number of knots are identifiable. The proof is simple and self-contained, relying mainly on basic algebra. We complement this positive result with a negative one, which shows that even when identifiability holds, rate function estimation is still a difficult problem. To illustrate this, we prove some rates-of-convergence results for hypothesis testing using birth-death models. These results are information-theoretic lower bounds which apply to all potential estimators.
Evidence for the Existence of a Bacterial Etiology for Alzheimer’s Disease and for a Temporal-Spatial Development of a Pathogenic Microbiome in the Brain

Background Over the last few decades, a growing body of evidence suggests a role for various infectious agents in Alzheimer’s Disease (AD) pathogenesis. Despite diverse pathogens (virus, bacteria, or fungi) being detected in AD subjects’ brains, most research has focused on individual pathogens and only a few studies investigated the hypothesis of a bacterial brain microbiome. We profiled the bacterial communities present in non-demented controls and AD subjects’ brains. Results We obtained post-mortem samples from the brains of 32 individual subjects, comprising 16 AD and 16 control aged-matched subjects with a total of 130 samples from the frontal and temporal lobes and entorhinal cortex. We used full-length 16S rRNA gene amplification with Pacific Biosciences sequencing technology to identify the bacteria. We detected bacteria in the brains of both cohorts with the principal bacteria comprising Propionibacterium acnes (recently renamed Cutibacterium acnes) and two species each of Acinetobacter and Comamonas genera. We used a hierarchical Bayesian method to detect differences in relative abundance among AD and control groups. Because of large abundance variances we also employed an unconventional analysis approach that utilized Latent Dirichlet Allocation, often used in computational linguistics. This allowed us to identify 5 classes of samples, each revealing a different microbiome. Assuming that samples represented infections that potentially began at different times, we ordered these classes in time, finding that the last class exclusively explained the existence or non-existence of AD. Conclusions The AD-related pathogenicity of the brain microbiome seems to be based on a complex polymicrobial dynamic. The time ordering revealed a rise and fall of the abundance of Propionibacterium acnes with pathogenicity occurring for an off-peak abundance level in association with at least one other bacterium from a set of genera that included: Methylobacterium, Bacillus, Caulobacter, Delftia, and Variovorax. P. acnes may also be involved with outcompeting the Comamonas species, which were strongly associated with non-demented brain microbiome, whose early destruction could be the first stage of the disease. The statistical results are also consistent with a leaky blood brain barrier or lymphatic network that allows bacteria, viruses, fungi, or other pathogens to enter the brain.
BNIP3L-mediated mitophagy triggered by Brucella in host cells is required for bacterial egress

The facultative intracellular pathogen Brucella abortus interacts with several organelles of the host cell to reach its replicative niche inside the endoplasmic reticulum. However, little is known about the interplay between the bacteria and the host cell mitochondria. Here, we showed that B. abortus triggers a strong mitochondrial network fragmentation accompanied by mitophagy and the formation of mitochondrial Brucella-containing vacuoles in the late steps of cellular infection. The expression of the mitophagy receptor BNIP3L induced by B. abortus is essential for these events and relies on the iron-dependent stabilization of the hypoxia-inducible factor 1 alpha. Functionally, BNIP3L-mediated mitophagy appears to be advantageous for bacterial exit of the host cell as BNIP3L depletion drastically reduced the number of reinfection events. Altogether, these findings highlight the intricate link between Brucella trafficking and the mitochondria during host cell infection.
Local Field Potentials, Spiking Activity, and Receptive Fields in Human Visual Cortex

The concept of receptive field (RF) is central to sensory neuroscience. Neuronal RF properties have been substantially studied in animals, while those in humans remain nearly unexplored. Here, we measured neuronal RFs with intracranial local field potentials (LFPs) and spiking activity in human visual cortex (V1/V2/V3). We recorded LFPs via macro-contacts and discovered that RF sizes estimated from low-frequency activity (LFA, 0.5 – 30 Hz) were larger than those estimated from low-gamma activity (LGA, 30 – 60 Hz) and high-gamma activity (HGA, 60 – 150 Hz). We then took a rare opportunity to record LFPs and spiking activity via microwires in V1 simultaneously. We found that RF sizes and temporal profiles measured from LGA and HGA closely matched those from spiking activity. In sum, this study reveals that spiking activity of neurons in human visual cortex could be well approximated by LGA and HGA in RF estimation and temporal profile measurement, implying the pivotal functions of LGA and HGA in early visual information processing.
Acoustic properties of the South Pole ice for astrophysical neutrino detection

Over the last few decades, several dedicated neutrino telescopes have been built to detect high energy astrophysical neutrinos which are predicted from a variety of astrophysical objects. Since the neutrino flux is predicted to be very low (about 1 per km² per year), the IceCube detector could detect only about 1 event per year. A detector with an effective volume of the order of 100 km³ is needed to detect a few events per year. Acoustic and radio methods can, in principle, be used to instrument a large hybrid neutrino telescope with a good sensitivity at a reasonable cost. The South Pole Acoustic Test Setup (SPATS) is the only acoustic activity to study the acoustic detection in ice so far. The acoustic attenuation length of the Antarctic ice is a fundamental quantity to design a future acoustic neutrino detector at the South Pole. The longitudinal waves in the South Pole ice are expected to be attenuated via absorption and scattering, where the attenuation due to scattering depends on the frequency (~f^4). In this work, recent measurements from SPATS was used to investigate the frequency dependence of sound in the South Pole ice. This will allow us to distinguish between the two different attenuation mechanisms (absorption or scattering.) Further analysis related to the sound speed frequency dependence and the ice fabric of the South Pole was done. The in-situ measured attenuation length was used to perform a detailed simulation of neutrino-induced cascades and the resulting acoustic signal in ice. Further simulations were done to investigate the feasibility of a large neutrino telescope in ice.
Self intersections of wavefront evolution

In this paper we generalise results of V. I. Arnold on functions defined on discriminant sets of simple singularities to the case where we have a transverse intersection of such sets. This generalization is very easy, but the results are of some geometric importance since they describe (amongst other things) the quasi global behaviour of wavefront evolution that is the changes in the self intersections of the wavefronts as time progresses. We first prove the required generalisation and then discuss how to proceed when considering functions on the transverse intersection of bifurcation sets. Familiarity with the results and notation of Arnold's paper (1976, Wavefront evolution and equivariant Morse lemma, "Commun. pure appl. Math." 29, 557-82) is assumed.
Intelligent Mobile Plant Disease Diagnostic System Using NASNet-Mobile Deep Learning

 Abstract -- Plant diseases remain a threat to global food supply, as it causes unimaginable loss of food and revenue. Intelligent mobile plant disease diagnostic system has however become valuable due to its usefulness for the early diagnosis and detection of plant diseases using leave images even when there are no availability of competent and adequate experts in such locations. The objective of this paper is to develop an intelligent mobile plant disease diagnostic system that runs on a smartphone. The diagnostic system is based on NASNet-Mobile, a lightweight convolutional neural network (CNN) architecture using the images of the plant leaves for plant disease diagnosis. A mobile application is developed for both android and iOS smartphones to capture the plant leaf images. The system runs on a web service that gets diagnosis from the CNN model. The plant leave images captured using the developed mobile application are sent through the web service and the recognition of the plant disease is achieved using NASNet-Mobile CNN model. The proposed NASNet-Mobile CNN model plant disease diagnostic system achieved an accuracy rate of 99.31%.
Social Engagement with climate change: principles for effective visual representation on social media

ABSTRACT Climate change communication on social media plays a prominent role in efforts undertaken by state agencies, NGOs, and international organizations, to make citizens aware of this phenomenon. The images used to communicate climate change are of great importance, since they can help to effectively raise citizen awareness. Building upon news values theory and the concept of availability heuristics, this research paper aims to identify principles that can be used for effective visual communication of climate change on social media on a cross-national scale, based upon analyses of characteristics of images that foster interaction on Twitter. We conducted a content analysis of a random selection of images (photographs, illustrations, and graphics, n = 380), posted on Twitter that were included in the so-called ‘top tweets’ about climate change. The results indicate that the types of images that are used on social media are relatively similar to those employed by conventional media, although images of identifiable people are less frequently shown on social media. We also deduced that four practical principles are especially relevant to foster user interaction on Twitter through images: (i) show ‘real people’ (i.e. non-staged images of people that transmit real emotions), (ii) tell a story, (iii) include a local connection, and (iv) show impacts or actions by people who are directly affected. These practical principles are based on the more general principles of meaningfulness and personification, two foundations that can help to overcome some of the main barriers to citizens’ perception of climate change as a relevant issue with serious consequences in their lives. Campaigns on social media that use imagery based on these practical and general principles can be effective in communicating the shared responsibility to address climate change. This can have a relevant impact on social perception, since it can encourage citizens to care about climate, which is regarded as necessary to increase participation in climate action. Key policy insights Social media can play a prominent role in campaigns to make citizens aware of climate change. Images can help to effectively raise citizen awareness of climate change. Four practical principles can be effective in increasing user interaction on social media with images about climate: show ‘real’ people, tell a story, include a local connection, and show people who are directly affected. Visual campaigns based on the more general principles of meaningfulness and personification can be effective in representing climate change as a relevant issue in citizens’ lives. These practical and more general principles can have a relevant impact on the social perception of climate change and increase citizen participation in climate debate and action.
Using PD-L1 full-length structure, enhanced induced fit docking and molecular dynamics simulations for structural insights into inhibition of PD-1/PD-L1 interaction by small-molecule ligands

ABSTRACT T-cell activation through the blockade of PD-1/PD-L1 interaction by monoclonal antibodies has demonstrated the clinical benefit for patients with diverse types of metastatic cancers. However, a number of limitations when producing and using antibodies hamper their broader clinical application. This awakened a significant interest in design and discovery of small-molecule inhibitors (SMIs) of PD-1/PD-L1 interaction, and several such inhibitors have been identified. However, up to now, many mechanistic details of their inhibitory action remain not fully understood. In this work, a new protocol of enhanced protein conformational sampling combining RosettaLigand and Schrodinger Induced Fit Docking approaches with Molecular Dynamics simulations, preceded by structural modelling of the full-length PD-L1 – dimer in the heterogeneous environment including the membrane and extracellular solution, are used for the prediction of atomistic structures of the complexes of the PD-L1 dimers with BMS-1016, BMS-2007, BMS-4121, BMS-40210, four SMIs with a high inhibitory activity in vitro against PD-1/PD-L1 interaction. Critical protein–ligand interactions responsible for the stabilisation of the complexes of the PD-L1 dimer with SMIs and high inhibitory activities of these SMIs against the PD-1/PD-L1 interaction are revealed. The roles of the membrane and of interaction of PD-L1 with its cis and trans protein partners are also addressed.
Effects of galvanostatic and artificial chloride environment methods on the steel corrosion spatial variability and probabilistic flexural capacity of RC beams

Abstract The galvanostatic method has been widely used for accelerating the corrosion of reinforcing bars in concrete to complete test studies within a reasonable timeframe. However, which level of current density induces characteristics of steel corrosion spatial variability and the associated structural performance of corroded reinforced concrete (RC) beams that are similar to those observed under natural conditions remains unknown. In this paper, comprehensive experimental research is conducted to compare the characteristics of spatial growth in steel weight loss and crack width and the structural behavior of corroded RC beams by two corrosion-accelerated methods (i.e. galvanostatic method at six current densities and artificial chloride environment method). The effects of these corrosion acceleration methods on Gumbel’s location and scale distribution parameters and the associated yield load capacity of corroded RC beams are investigated using Monte Carlo-based two-dimensional finite element analysis. Finally, a suitable current density is recommended to better simulate the steel corrosion distribution and the associated yield load capacity of RC beams in comparison to those observed under an artificial chloride environment.
Characteristics and roles of streamers in e-commerce live streaming

ABSTRACT Streamers are the individuals at the core of e-commerce live streaming (ELS), which is a successful influencer marketing strategy. However, a systematic profile of ELS streamers is lacking. This study aims to investigate the roles and characteristics of ELS streamers, as well as their matching relationships, by applying the grounded theory method to analyze semi-structured interviews of 96 consumers. Eight characteristics of ELS streamers (expertise, attractiveness, credibility, interactivity, popularity, price support, affinity, and responsiveness) are identified and further classified into four roles (opinion leader, spokesperson, interactive friend, and salesperson). This study is the first to create a role set for ELS streamers and the first to systematically elucidate the roles of ELS streamers and their corresponding characteristics. It not only supplements e-commerce, influencer marketing, and role theory research by identifying new characteristics and roles of ELS streamers, but it also provides a clear framework practitioners can use to train streamers.
Smart Crowd Management: The Data, the Users and the Solution (Short Paper)

This research project is situated in the domain of smart crowd management, a domain that is gaining importance because of the challenges that arise from urbanization, but also the opportunities that come with smart cities. While our cities become more crowded every day, they also become smarter, for example by employing pedestrian tracking sensors. However, the datasets that are generated by these sensors do not allow smart crowd management yet, because they are sparse and not linked to the perception of the crowd. This research will tackle these issues in three steps. First, pedestrian counts will be estimated on streets that have no tracking data by use of deep learning and space syntax data. Next, the perception of crowdedness within the crowd will be linked to the objective pedestrian counts by conducting two user studies, and finally, the resulting subjective pedestrian counts will be used as weights for a routing algorithm. The last step has already been developed as a proof of concept. The routing algorithm, that uses partly simulated data and partly real-time tracking data, has been embedded in a webtool to show stakeholders the potential and goal of this innovative project.
Mapping mountain glaciers using an improved U-Net model with cSE

ABSTRACT Global warming is melting glaciers. Changes in mountain glaciers have a tremendous impact on human life. Regular identification and extraction of glaciers from satellite images are necessary. However, when studying glaciers, materials surrounding the glacier have high spectral similarity to glaciers and are easily misclassified in the identification process. Therefore, in this study of glacier extraction, we used an improved U-Net model (a channel-attention U-Net) to map glaciers. The model was trained on Landsat 8 Operational Land Imager (OLI) data and a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), and was tested on glaciers in the Pamir Plateau. The results show that the channel-attention U-Net identifies glaciers with relatively high accuracy compared to U-Net and GlacierNet. The obtained results were fine-tuned by the conditional random field model, effectively reducing background misidentification.
Worker and Establishment Wages: Estimates from a Multi-Level Model*

Empirical studies of wage determination have focussed mainly on either worker supply side factors or employer demand side considerations. Theory puts forward several reasons why employers may pay a premium to all workers rather than a selected few, based upon ideas of limited information, and employer, worker and job heterogeneity. Coherent modelling of both sides of the market is crucial in assessing the nature and importance of employer pay policy. A multi-level random effects model is estimated on a Danish representative matched panel of 1400 private sector establishments and all of their employees over 11 years. We find strong evidence of firm-specific pay policy in the form of establishment wage differentials after having controlled for worker and match effects. Furthermore we are able to decompose the variance in log wage according to observables (20%), unobservedheterogeneity amongworkers (38%), workplaces (26%)and worker-workplace matches (6%), leaving 10% unaccounted for.