Miracle-dz/UniFAR-FTUs huggingface dataset:
Age-Dependent Behavioral and Metabolic Assessment of AppNL−G−F/NL−G−F Knock-in (KI) Mice

Mitochondria play a crucial role in Alzheimer's disease (AD) onset and progression. Traditional transgenic AD mouse models which were widely used in the past decades share a common limitation: The overexpression of APP and overproduction of amyloid-beta (Aβ) are accompanied by other APP peptide fragments, which could introduce artificial and non-clinically relevant phenotypes. Here, we performed an in-depth and time-resolved behavioral and metabolic characterization of a clinically relevant AD mouse model engineered to express normal physiological levels of APP harboring humanized Swedish (K670N/M671L), Beyreuther/Iberian (I716F), and Arctic (E693G) mutations (AppNL−G−F/NL−G−F), termed APP knock-in (APPKI) mice. Our result showed that APPKI mice exhibited fear learning deficits at 6-m age and contextual memory deficit at 12-m age. Histopathological analysis revealed mild amyloidosis (6E10) accompanied by microgliosis (Iba1) as early as 3 months, which progressed significantly together with significant astrocytosis at 6 and 12 m. We further analyzed hippocampal mitochondrial dysfunction by multiple assays, while 3-m APPKI mice brain mitochondrial function remains a similar level as WT mice. Significant mitochondrial dysfunction characterized by decreased ATP production and higher membrane potential with subsequent overproduction of reactive oxygen species (ROS) was observed in mitochondria isolated from 7-m APPKI mice hippocampal tissue. Morphologically, these mitochondria were larger in volume with a decreased level of mitochondrial fusion protein mitofusin-2 (MFN2). At 12 months, APPKI mice exhibit a significantly decreased total mitochondrial oxygen consumption rate (OCR) in isolated hippocampal mitochondria detected by high-resolution respirometry. These data indicate early mitochondrial dysfunction in the brain at pre-symptomatic age in the AppNL−G−F/NL−G−mice, which may play a key role in the progression of the disease. Moreover, the identified behavioral and bioenergetic alterations in this clinically relevant AD mouse model provide a valuable tool to optimize the temporal component for therapeutic interventions to treat AD.
Milne-Eddington inversion of the Fe I line pair at 630~nm

The iron lines at 630.15 and 630.25 nm are often used to determine the physical conditions of the solar photosphere. A common approach is to invert them simultaneously under the Milne-Eddington approximation. The same thermodynamic parameters are employed for the two lines, except for their opacities, which are assumed to have a constant ratio. We aim at investigating the validity of this assumption, since the two lines are not exactly the same. We use magnetohydrodynamic simulations of the quiet Sun to examine the behavior of the ME thermodynamic parameters and their influence on the retrieval of vector magnetic fields and flow velocities. Our analysis shows that the two lines can be coupled and inverted simultaneously using the same thermodynamic parameters and a constant opacity ratio. The inversion of two lines is significantly more accurate than single-line inversions because of the larger number of observables.
Beyond the Fast Lane Narrative - A Temporal Perspective on the Unfolding of Digital Innovation in Digital Innovation Units

To develop and implement digital innovations many incumbent companies have set up digital innovation units (DIUs) primarily as part of their digital transformation journey. Despite a steadily growing body of knowledge, however, extant literature is in a nascent stage to explain the unfolding of digital innovations in such units. Drawing on empirical data from a multiple case study we adopt a temporal perspective to contribute to a better understanding of digital innovation in DIUs. To do so, we studied the DIUs of five manufacturing companies and were able to identify several other temporal aspects besides speed that influence or result from DIU’s digital innovation activities. We generalize our findings in the form of five propositions that depict the special role of time to inform extant literature
Seed viability of feathertop Rhodes grass (Chloris virgata Sw.) reduced by silage, digestion, and sheep rumen digestion

Weed seeds can be spread by different vectors, and seed dispersal is an important mechanism for the weed to persist. Weed seeds passaging through the digestive tract of a ruminant animal is expected to result in reduced viability. Two separate experiments were conducted to determine the germinability and viability of the mature seeds of feathertop Rhodes grass (Chloris virgata Sw.) after exposure to four treatments, that is, 3 months in silage, 48 h in the rumen of steers, silage plus digestion, and passing through the digestive tract of sheep. Our results showed that three different treatments (silage, digestion, and silage plus digestion) can inhibit 90%–100% of the seed germination of feathertop Rhodes grass. Both silage and digestion reduced seed viability by 65%–90%, depending on the population. Silage followed by digestion reduced viability by 80%–97%. The sheep feeding study showed that total viable seeds from the daily recovery of feces for 12 consecutive days after ingestion was only 0.084% and 0.022% in the 2020 and 2021 experiments, respectively. In comparison with the untreated control, the seed viability of feathertop Rhodes grass was reduced by more than 99.9% after feeding through sheep, indicating that the spreading of feathertop Rhodes grass seeds via sheep feces is minimal. These results indicate that silage, digestion, silage followed by digestion, and the ingestion of mature seeds are effective non-chemical weed management options for an integrated weed management package for feathertop Rhodes grass.
Approximate Inference in Variational Autoencoders

Approximate Inference in Variational Autoencoders Chris Cremer Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2020 A deep latent variable model is a powerful tool for modelling complex distributions. However, in order to train this model, we must perform approximate inference of the latent variable. A variational autoencoder (VAE) is a framework for learning both the generative and inference models for a latent variable model. This thesis provides novel analyses, applications, and interpretations of approximate inference in VAEs. This thesis reviews many of the recent developments made to improve VAEs. One such improvement is the importance-weighted autoencoder. The standard interpretation of importanceweighted autoencoders is that they maximize a tighter lower bound on the marginal likelihood than the standard evidence lower bound. The first contribution of this thesis is to provide an alternative interpretation: that it optimizes the standard variational lower bound, but using a stochastic importance-weighted variational distribution. In order to improve approximate inference in VAEs, it is important to understand what causes inference to be suboptimal. Two important factors that determine the quality of approximate inference in VAEs are: a) whether the variational distribution is expressive enough to match the true posterior and b) the ability of the recognition network to produce good variational parameters for each datapoint. This thesis contributes to our understanding of approximate inference in VAEs by using these factors to diagnose suboptimal inference across a number of settings. Finally, we investigate the task of using a latent variable to learn a joint distribution over two data modalities: images and text. The contribution of this work is to develop a model for this task and analyze how to perform approximate inference in this model. Specifically, we identify a problem arising from the mismatch between the posteriors of each modality and we demonstrate how the problem can be largely addressed by modelling the aggregate of the image posteriors.
Developing and Coordinating Autonomous Agents for Efficient Electricity Markets

Aggressive greenhouse gas reduction targets will necessitate a transformation of energy use systems, with increasing emphasis on electricity, which can be decarbonized more efficiently than other energy sources. Mr. Perrault argues that deploying consumer-representing autonomous agents can make this transformation less expensive by allowing attention-limited consumers to respond to changes in market conditions. The talk has two parts: in Part I, he develops a cooperative game theoretic model that illustrates the value of such agents in electricity markets. In Part II, he focuses on the problem of training such an agent using a new variant of preference elicitation called experiential elicitation.
A Bi-Directional 300-GHz-Band Phased-Array Transceiver in 65-nm CMOS With Outphasing Transmitting Mode and LO Emission Cancellation

This article introduces a four-element 300-GHz-band bi-directional phased-array transceiver (TRX). The TRX utilizes the same antenna, signal path, and local oscillator (LO) circuitry to operate either in transmitter (TX) mode or receiver (RX) mode. The TX mode adopts the outphasing technique to increase the average output power for higher order modulation schemes by utilizing the two mixers that are connected directly to the antenna in a mixer-last fashion. The two signal paths also enable the canceling of the LO feed-through (LOFT). The RX mode also benefits from the LOFT cancellation technique to suppress the LO emission, which is a common issue of the mixer-first RXs. The RX has a separate Hartley operation mode to reject the image signal coming from the TX. The TRX chip was implemented using CMOS 65-nm process, and a four-element phased array was implemented by stacking liquid crystal polymer (LCP) flexible printed circuit boards (PCBs). The stacked structure provides the required narrow antenna pitch at the 300-GHz band. The measured beam angle range is from −18° to 18°. The single-element power consumption is 750 mW for both TX mode and RX mode.
Lattice Boltzmann Flux Solver: An Efficient Approach for Numerical Simulation of Fluid Flows

A lattice Boltzmann flux solver(LBFS)is presented for simulation of fluid flows.Like the conventional computational fluid dynamics(CFD)solvers,the new solver also applies the finite volume method to discretize the governing differential equations,but the numerical flux at the cell interface is not evaluated by the smooth function approximation or Riemann solvers.Instead,it is evaluated from local solution of lattice Boltzmann equation(LBE)at cell interface.Two versions of LBFS are presented in this paper.One is to locally apply one-dimensional compressible lattice Boltzmann(LB)model along the normal direction to the cell interface for simulation of compressible inviscid flows with shock waves.The other is to locally apply multi-dimensional LB model at cell interface for simulation of incompressible viscous and inviscid flows.The present solver removes the drawbacks of conventional lattice Boltzmann method(LBM)such as limitation to uniform mesh,tie-up of mesh spacing and time interval,limitation to viscous flows.Numerical examples show that the present solver can be well applied to simulate fluid flows with non-uniform mesh and curved boundary.
Soil bacterial and fungal communities show within field heterogeneity that varies by land management and distance metric

Increasing interest in the use of microbial metrics to evaluate soil health raises the issue of how fine-scale heterogeneity can affect microbial community measurements. Here we analyse bacterial and fungal communities of over 100 soil samples across 17 pasture farms and evaluate beta diversity at different scales. We find large variation in microbial communities between different points in the same field, and if Aitchison distance is used we find that within-field variation is as high as between-farm variation. However, if Bray-Curtis or Jaccard distance are used this variation is partially explained by differences in soil pH and vegetation and is higher under mob grazing for fungi. Hence, field scale variation in microbial communities can impact the evaluation of soil health.
PROTEIN CODING VARIATION IN OUTBRED LABORATORY MOUSE STOCKS PROVIDES A MOLECULAR BASIS FOR DISTINCT RESEARCH APPLICATIONS

Outbred laboratory mice (Mus musculus) are readily available and have high fecundity, making them a popular choice for biomedical research, especially toxicological and pharmacological applications. Direct high throughput genome sequencing (HTS) of these widely used research animals is an important genetic quality control measure that ensures research reproducibility. HTS data have been used to confirm the common origin of outbred stocks and to molecularly define distinct outbred populations. But these data have also revealed unexpected population structure and homozygosity in some populations; genetic features that emerge when outbred stocks are not properly maintained. We used exome sequencing to discover and interrogate protein coding variation in a newly established population of Swiss-derived outbred stock (J:ARC) that is closely related to other, commonly used CD-1 outbred populations. We used these data to describe the genetic architecture of the J:ARC population including heterozygosity, minor allele frequency, LD decay, and we defined the novel, protein-coding sequence variation. These data reveal the expected genetic architecture for a properly maintained outbred stock and provide a basis for on-going genetic quality control. We also compared these data to protein coding variation found in a multiparent outbred stock, the Diversity Outbred (J:DO). We found that the more recently derived, multiparent outbred stock has significantly higher interindividual variability, greater overall genetic variation, higher heterozygosity, and fewer novel variants than the Swiss derived J:ARC stock. However, among the novel variants found in the J:DO stock, significantly more are predicted to be protein-damaging. That individuals from this population can tolerate a higher load of potentially damaging variants highlights the buffering effects of allelic diversity and the differing selective pressures in these stocks. While both outbred stocks offer significant individual heterozygosity, our data provide a molecular basis for their intended applications, where the J:DO are best suited for studies requiring maximum, population-level genetic diversity and power for mapping, while the J:ARC are best suited as a general-purpose outbred stock with robust fecundity, relatively low allelic diversity, and less potential for extreme phenotypic variability.
Bacterial cell widening alters periplasmic size and activates envelope stress responses

The Rcs signal transduction system is a phosphorelay responsible for sensing a wide variety of enterobacterial cell envelope stresses. In Escherichia coli, the Rcs system is required to survive A22 and mecillinam treatment, two drugs that perturb cell size. To test whether cell size changes might be correlated with envelope damage and thereby sensed by the Rcs system, we tuned E. coli cell size via drug inhibition with A22, point mutations to the cell-shape determinant MreB, and mechanically confined growth. In all conditions, cell width was strongly correlated with Rcs activation, with wider cells exhibiting more activation than wild-type. In all conditions, RcsF, the outer membrane-localized upstream component of the Rcs system, was essential for responding to cell width changes. Consistently, several envelope gene deletions known to induce the Rcs system via RcsF resulted in cells that were wider than wild-type. Cryo- electron microscopy revealed that the periplasm of a wide MreB mutant was on average ∼3 nm thinner than wild-type, thereby bringing RcsF closer to the downstream components of the signaling cascade in the inner membrane. Conversely, extending the flexible linker region of RcsF by ∼3 nm increased Rcs activity in wild-type cells. In summary, we propose that the Rcs system responds to changes in cell width because of altered periplasmic thickness.
Multi-Molecular Hyperspectral PRM-SRS Imaging

Lipids play crucial roles in many biological processes under physiological and pathological conditions. Mapping spatial distribution and examining metabolic dynamics of different lipids in cells and tissues in situ are critical for understanding aging and diseases. Commonly used imaging methods, including mass spectrometry-based technologies or labeled imaging techniques, tend to disrupt the native environment of cells/tissues and have limited spatial or spectral resolution, while traditional optical imaging techniques still lack the capacity to distinguish chemical differences between lipid subtypes. To overcome these limitations, we developed a new hyperspectral imaging platform that integrates a Penalized Reference Matching algorithm with Stimulated Raman Scattering (PRM-SRS) microscopy. With this new approach, we directly visualized and identified multiple lipid species in cells and tissues in situ with high chemical specificity and subcellular resolution. High density lipoprotein (HDL) particles containing non-esterified cholesterol was observed in the kidney, indicating that these pools of cholesterol are ectopic deposits, or have yet to be enriched. We detected a higher Cholesterol to phosphatidylethanolamine (PE) ratio inside the granule cells of hippocampal samples in old mice, suggesting altered membrane lipid synthesis and metabolism in aging brains. PRM-SRS imaging also revealed subcellular distributions of sphingosine and cardiolipin in the human brain sample. Compared with other techniques, PRM-SRS demonstrates unique advantages, including faster data processing and direct user-defined visualization with enhanced chemical specificity for distinguishing clinically relevant lipid subtypes in different organs and species. Our method has broad applications in multiplexed cell and tissue imaging.
Endosome positioning coordinates spatially selective GPCR signaling

G protein-coupled receptors (GPCRs), a class of critical regulators of mammalian physiology, can initiate unique functional responses depending on the subcellular compartment of their activation. Yet, how endosomal receptors transduce location-biased outcomes remains poorly understood. Efforts to uncover the mechanistic basis of compartmentalized GPCR signaling have largely focused on the biochemical aspect of this regulation through dissection of the relevant factors. Here, we assess the biophysical positioning of receptor-containing endosomes as an alternative salient mechanism coordinating the transduction of spatially biased responses. We focus on the prototypical beta2-adrenergic receptor (β2-AR), which preferentially mediates transcriptional reprogramming via cyclic AMP (cAMP) production from early endosomes. We overcome a technical challenge that has hindered the direct assessment of the role of endosome positioning in this paradigm by devising a strategy to selectively and rapidly redistribute endosomes ‘on command’ in intact cells without perturbing their biochemical composition. Next, we present two complementary optical readouts that enable robust measurements of bulk- and gene-specific GPCR/cAMP-dependent transcription with single-cell resolution. We then combine these readouts with rapid endosome relocalization to establish that increasing endosome distance from the nucleus inhibits the initiation of the endosome-dependent response. Lastly, we demonstrate a prominent mechanistic role of phosphodiesterase (PDE)-mediated cAMP hydrolysis in this process. Our study, therefore, illuminates a novel mechanism regulating GPCR function by identifying endosome positioning as a principal mediator of spatially selective receptor signaling. Summary G protein-coupled receptors (GPCRs) orchestrate essential aspects of mammalian physiology. GPCR function is tightly controlled by endocytic trafficking, where the ligand-activated receptor engages arrestins and clathrin machinery and is subsequently internalized into endosomal compartments1. While the endosome-associated receptor pool was classically presumed to be functionally inactive, it is now clear that receptors can also signal from endosomes2-4. Moreover, endosomal receptors can initiate cellular responses that are distinct from those activated at the plasma membrane. Transcriptional reprogramming was one of the first location-biased GPCR responses to be identified and shown to be stimulated from intracellular receptors5, 6. Since then, compartmentalized signaling has been implicated in the transduction of distinct phosphosignaling7, 8 and in the coordination of unique physiologies and drug actions8-17. Yet, how the endosome selectively facilitates these responses compared to other subcellular compartments remains unclear.
Replication origin mapping in the malaria parasite Plasmodium falciparum

The malaria parasite Plasmodium falciparum replicates via schizogony: a fundamentally unusual type of cell cycle involving asynchronous replication of multiple nuclei within the same cytoplasm. It also has one of the most A/T-biased genomes ever sequenced. Here, we present the first comprehensive study of the specification and activation of DNA replication origins during Plasmodium schizogony. Potential replication origins were found to be abundant, with ORC1-binding sites detected every ∼800 bp throughout the genome. They had no motif enrichment, but were biased towards areas of higher G/C content. Origin activation was then measured at single-molecule resolution via new DNAscent technology that measures fork movement by detecting base analogues BrdU and EdU in DNA sequenced on the Oxford nanopore platform (https://github.com/MBoemo/DNAscent). DNAscent-called origins were found to be much less dense than ORC1-binding sites, with origins activated preferentially in areas of low transcriptional activity. Consistently, replication forks moved fastest through the most lowly transcribed genes, suggesting that conflicts between transcription and origin firing inhibit efficient replication, and that P. falciparum has evolved its S-phase to minimise such conflicts.
Hypoxia potentiates the inflammatory fibroblast phenotype promoted by pancreatic cancer cell-derived cytokines

Cancer-associated fibroblasts (CAFs) are a major cell type in the stroma of solid tumors and can exert both tumor-promoting and tumor-restraining functions. This functional heterogeneity is correlated with the existence of transcriptionally distinct subpopulations of CAFs. CAF heterogeneity is observed in pancreatic ductal adenocarcinoma (PDAC), a tumor characterized by a remarkably dense and hypoxic stroma that features tumor-restraining myofibroblastic CAFs (myCAFs) and tumor-supporting inflammatory CAFs (iCAFs). While CAF heterogeneity can be driven in part by tumor cell-produced cytokines, other determinants shaping CAF identity and function are largely unknown. In vivo, we found that iCAFs display a hypoxic gene expression and biochemical profile and are enriched in hypoxic regions of PDAC tumors. Hypoxia leads fibroblasts to acquire an inflammatory gene expression signature and synergizes with cancer cell-derived cytokines to promote an iCAF phenotype in a HIF-1α dependent fashion. Furthermore, we show that HIF-1α stabilization is sufficient to induce an iCAF phenotype in stromal cells introduced into PDAC organoid co-cultures and to promote PDAC tumor growth. These findings indicate hypoxia-induced HIF-1α as a regulator of CAF heterogeneity and promoter of tumor progression in PDAC.