Miracle-dz/UniFAR-FTUs huggingface dataset:
Analog Logic: Continuous-Time Analog Circuits for Statistical Signal Processing

This thesis proposes an alternate paradigm for designing computers using continuoustime analog circuits. Digital computation sacrifices continuous degrees of freedom. A principled approach to recovering them is to view analog circuits as propagating probabilities in a message passing algorithm. Within this framework, analog continuous-time circuits can perform robust, programmable, high-speed, low-power, cost-effective, statistical signal processing. This methodology will have broad application to systems which can benefit from low-power, high-speed signal processing and offers the possibility of adaptable/programmable high-speed circuitry at frequencies where digital circuitry would be cost and power prohibitive. Many problems must be solved before the new design methodology can be shown to be useful in practice: Continuous-time signal processing is not well understood. Analog computational circuits known as “soft-gates” have been previously proposed, but a complementary set of analog memory circuits is still lacking. Analog circuits are usually tunable, rarely reconfigurable, but never programmable. The thesis develops an understanding of the convergence and synchronization of statistical signal processing algorithms in continuous time, and explores the use of linear and nonlinear circuits for analog memory. An exemplary embodiment called the Noise Lock Loop (NLL) using these design primitives is demonstrated to perform direct-sequence spread-spectrum acquisition and tracking functionality and promises order-of-magnitude wins over digital implementations. A building block for the construction of programmable analog gate arrays, the “soft-multiplexer” is also proposed. Thesis Supervisor: Neil Gershenfeld Title: Associate Professor
PI control of a tailless fighter aircraft with dynamic inversion and neural networks

This paper presents an application of dynamic inversion control with adaptive neural networks to a tailless fighter aircraft. We propose an extension of the dynamic inversion control using proportional-integral desired dynamics. The performance of the PI adaptive dynamic inversion controller is compared to an adaptive dynamic inversion controller using PD desired dynamics. The controller was tested in a 6-DOF simulation of the ICE tailless fighter aircraft. Simulation results are given for simultaneous longitudinal and lateral manoeuvres performed under nominal conditions and also with the simulated failure of a major control surface. The PI dynamic inversion controller with neural network adaptation outperforms both the baseline PI controller without adaptation and also a PD dynamic inversion controller with neural network adaptation.
The Rhodobacter sphaeroides methionine sulfoxide reductase MsrP can reduce R- and S-diastereomers of methionine sulfoxide from a broad-spectrum of protein substrates

Methionine (Met) is prone to oxidation and can be converted to Met sulfoxide (MetO), which exists as R- and S-diastereomers. MetO can be reduced back to Met by the ubiquitous methionine sulfoxide reductase (Msr) enzymes. Canonical MsrA and MsrB were shown as absolutely stereospecific for the reduction of S- and R-diastereomer, respectively. Recently, the molybdenum-containing protein MsrP, conserved in all gram-negative bacteria, was shown to be able to reduce MetO of periplasmic proteins without apparent stereospecificity in Escherichia coli. Here, we describe the substrate specificity of the Rhodobacter sphaeroides MsrP. Proteomics analysis coupled to enzymology approaches indicate that it reduces a broad spectrum of periplasmic oxidized proteins. Moreover, using model proteins, we demonstrated that RsMsrP preferentially reduces unfolded oxidized proteins and we confirmed that this enzyme, like its E. coli homolog, can reduce both R- and S-diastereomers of MetO with similar efficiency.
Metabolic engineering of Escherichia coli for production of valerenadiene

Valeriana officinalis is a medicinal herb which produces a suite of compounds in its root tissue useful for treatment of anxiety and insomnia. The sesquiterpene components of the root extract, valerenic acid and valerena-1,10-diene, are thought to contribute to most of the observed anxiolytic of Valerian root preparations. However, valerenic acid and its biosynthetic intermediates are only produced in low quantities in the roots of V. officinalis. Thus, in this report, Escherichia coli was metabolically engineered to produce substantial quantities of valerena-1,10-diene in shake flask fermentations with decane overlay. Expression of the wildtype valerenadiene synthase gene (pZE-wvds) resulted in production of 12 μg/mL in LB cultures using endogenous FPP metabolism. Expression of a codon-optimized version of the valerenadiene synthase gene (pZE-cvds) resulted in 3-fold higher titers of valerenadiene (32 μg/mL). Co-expression of pZE-cvds with an engineered methyl erythritol phosphate (MEP) pathway improved valerenadiene titers 65-fold to 2.09 mg/L valerenadiene. Optimization of the fermentation medium to include glycerol supplementation enhanced yields by another 5.5-fold (11.0 mg/L valerenadiene). The highest production of valerenadiene resulted from engineering the codon-optimized valerenadiene synthase gene under strong Ptrc and PT7 promoters and via co-expression of an exogenous mevalonate (MVA) pathway. These efforts resulted in an E. coli production strain that produced 62.0 mg/L valerenadiene (19.4 mg/L/OD600 specific productivity). This E. coli production platform will serve as the foundation for the synthesis of novel valerenic acid analogues potentially useful for the treatment of anxiety disorders.
An environment for comprehending the behavior of software systems

Modern software systems are often large, distributed, written in more than one programming language, and developed using pre-built components (e.g., dynamically-linked libraries). The characteristics of such systems pose unique challenges to software comprehension, where the current practice typically involves the manual inspection and possibly static analysis of the system's source code. Our research addresses the characteristics of these systems by developing a software comprehension framework that facilitates dynamic analysis, feature-driven analysis, and tool integration. The framework is instantiated as a software comprehension environment that supports the dynamic analysis of distributed systems, multi-language systems, and systems that use binary components. The environment supports an extensive collection of dynamic views, which are derived from the runtime data collected during the execution of the software system undergoing scrutiny. The environment is implemented as a set of integrated tools consisting of three subsystems: data gathering, data repository, and analysis/visualization. The key issues addressed by our environment include: (a) the development of a data repository for storing static and dynamic data from distributed and multi-language applications, (b) the construction of a distributed profiler that collects runtime data from components that are distributed across a network, (c) the development of a software modeling query language that facilitates data retrieval, modeling, and analysis to create software views, (d) the development of an extensive set of software views to assist engineers in the comprehension of software systems, and (e) the decoupling of the environment's subsystems to enhance the extensibility and ease the integration of new tools. The software views produced by our environment include feature-interaction, feature-implementation, and feature-similarity views, as well as remote-interaction, thread-interaction, structural, and class-usage views. To demonstrate the effectiveness of our environment, a case study involving three significant software systems was conducted (i.e., the Mozilla web browser, the Jext text editor, and the TechReport technical report archiving system).
Using competition assays to quantitatively model cooperative binding by transcription factors and other ligands

BACKGROUND The affinities of DNA binding proteins for target sites can be used to model the regulation of gene expression. These proteins can bind to DNA cooperatively, strongly impacting their affinity and specificity. However, current methods for measuring cooperativity do not provide the means to accurately predict binding behavior over a wide range of concentrations. METHODS We use standard computational and mathematical methods, and develop novel methods as described in Results. RESULTS We explore some complexities of cooperative binding, and develop an improved method for relating in vitro measurements to in vivo function, based on ternary complex formation. We derive expressions for the equilibria among the various complexes, and explore the limitations of binding experiments that model the system using a single parameter. We describe how to use single-ligand binding and ternary complex formation in tandem to determine parameters that have thermodynamic relevance. We develop an improved method for finding both single-ligand dissociation constants and concentrations simultaneously. We show how the cooperativity factor can be found when only one of the single-protein dissociation constants can be measured. CONCLUSIONS The methods that we develop constitute an optimized approach to accurately model cooperative binding. GENERAL SIGNIFICANCE The expressions and methods we develop for modeling and analyzing DNA binding and cooperativity are applicable to most cases where multiple ligands bind to distinct sites on a common substrate. The parameters determined using these methods can be fed into models of higher-order cooperativity to increase their predictive power. HIGHLIGHTS Hill plots remain prominent in biology, but can mask cooperativity Effective modeling of binding by two ligands requires the use of 3 parameters We develop novel ways to find these parameters for two cooperating ligands We show how they can be used to enhance the power of established methods We describe how this framework can be extended to multiple cooperating ligands
Predictive current control of distribution static compensator for reactive power compensation

The paper describes modelling and current control techniques of a distribution static compensator (D-STATCOM). Current control is based on the predictive and the space vector pulsewidth modulation (PWM) scheme. The predictive current-controlled PWM D-STATCOM can maintain its performance with reactive power compensation and a fixed switching frequency. By using the space vector control, low ripple and offset in the current and the voltage as well as fast dynamic responses are achieved using a small DC link capacitance.
mRNA detection in budding yeast with single fluorophores

Quantitative measurement of mRNA levels in single cells is necessary to understand phenotypic variability within an otherwise isogenic population of cells. Single-molecule mRNA Fluorescence In Situ Hybridization (FISH) has been established as the standard method for this purpose, but current protocols require a long region of mRNA to be targeted by multiple DNA probes. Here, we introduce a new single-probe FISH protocol termed sFISH for budding yeast, Saccharomyces cerevisiae using a single DNA probe labeled with a single fluorophore. In sFISH, we markedly improved probe specificity and signal-to-background ratio by using methanol fixation and inclined laser illumination. We show that sFISH reports mRNA changes that correspond to protein levels and gene copy number. Using this new FISH protocol, we can detect more than 50% of the total target mRNA. We also demonstrate the versatility of sFISH using FRET detection and mRNA isoform profiling as examples. Our FISH protocol with single-fluorophore sensitivity significantly reduces cost and time compared to the conventional FISH protocols and opens up new opportunities to investigate small changes in RNA at the single cell level.
Spatial organization of the somatosensory cortex revealed by cyclic smFISH

The global efforts towards the creation of a molecular census of the brain using single-cell transcriptomics is generating a large catalog of molecularly defined cell types lacking spatial information. Thus, new methods are needed to map a large number of cell-specific markers simultaneously on large tissue areas. Here, we developed a cyclic single molecule fluorescence in situ hybridization methodology and defined the cellular organization of the somatosensory cortex using markers identified by single-cell transcriptomics.
Silencing of a unique integrated domain nucleotide-binding leucine-rich repeat gene in wheat abolishes Diuraphis noxia resistance

Plants respond in a similar manner to aphid feeding as to pathogen attack. Diuraphis noxia is a specialist aphid, feeding only on selected grasses that include wheat, barley, and oats. The wheat-Diuraphis noxia interaction is characterized by very similar responses as seen in wheat-pathogen interactions with none of the underlying resistance pathways and genes characterized yet. From wheat harboring the Dn1 resistance gene, we have identified a nucleotide-binding leucine-rich repeat (NLR) gene containing two integrated domains (IDs). These are three C-terminus ankyrin repeat-domains and an N-terminus WRKY domain. The NLR core of the gene can be traced through speciation events within the grass family, with a recent WRKY domain integration that is Triticum specific. Virus induced gene silencing of the gene in resistant wheat lines resulted in the abolishment of localized cell death. Silenced plants supported a higher number of aphids similar to the susceptible NIL and the intrinsic rate of increase of the aphids matched that of aphids feeding on the susceptible NIL. The presence of the gene is necessary for Dn1 resistance and we have named the gene Associated with Dn resistance 1 (Adr1) to reflect this function.
Pre-post synaptic alignment through neuroligin tunes synaptic transmission efficiency

The nanoscale organization of neurotransmitter receptors relative to pre-synaptic release sites is a fundamental determinant of both the amplitude and reliability of synaptic transmission. How modifications in the alignment between pre- and post-synaptic machineries affect synaptic current properties has only been addressed with computer modeling, and therefore remains hypothetical. Using dual-color single molecule based super-resolution microscopy, we found a strong spatial correlation between AMPA receptor (AMPAR) nanodomains and the post-synaptic adhesion protein neuroligin-1 (NLG1). Expression of a C-terminal truncated form of NLG1 disrupted this correlation without affecting the intrinsic organization of AMPAR nanodomains. Moreover, this NLG1 dominant-negative mutant significantly shifted the pre-synaptic release machinery from AMPAR synaptic clusters. Electrophysiology and computer modeling show that this physical shift is sufficient to induce a significant decrease in synaptic transmission. Thus, our results suggest the necessity for synapses to release glutamate in front of AMPAR nanodomains, to maintain a high efficiency of synaptic responses.
TNER: A Novel Bayesian Background Error Suppression Method for Mutation Detection in Circulating Tumor DNA

The use of ultra-deep, next generation sequencing of circulating tumor DNA (ctDNA) holds great promise for early detection of cancer as well as a tool for monitoring disease progression and therapeutic responses. However, the low abundance of ctDNA in the bloodstream coupled with technical errors introduced during library construction and sequencing, complicates mutation detection. To achieve high accuracy of variant calling via better distinguishing low frequency ctDNA mutations from technical errors, we introduce TNER (Tri-Nucleotide Error Reducer), a novel Bayesian background error suppression method that provides a robust estimation of background noise to enhance the specificity for ctDNA mutation detection without sacrificing sensitivity. Results on both simulated and real healthy subjects’ data demonstrate that the proposed algorithm consistently outperforms current position-specific models, particularly when the sample of is small. TNER is publicly available at https://github.com/ctDNA/TNER.
The Evolutionary Genomic Dynamics of Peruvians Before, During, and After the Inca Empire

Native Americans from the Amazon, Andes, and coast regions of South America have a rich cultural heritage, but have been genetically understudied leading to gaps in our knowledge of their genomic architecture and demographic history. Here, we sequenced 150 high-coverage and genotyped 130 genomes from Native American and mestizo populations in Peru. A majority of our samples possess greater than 90% Native American ancestry and demographic modeling reveals, consistent with a rapid peopling model of the Americas, that most of Peru was peopled approximately 12,000 years ago. While the Native American populations possessed distinct ancestral divisions, the mestizo groups were admixtures of multiple Native American communities which occurred before and during the Inca Empire. The mestizo communities also show Spanish introgression only after Peruvian Independence. Thus, we present a detailed model of the evolutionary dynamics which impacted the genomes of modern day Peruvians.
Hal Shelton Revisited: Designing and Producing Natural-Color Maps with Satellite Land Cover Data

This paper examines natural-color maps by focusing on the painted map art of Hal Shelton, the person most closely associated with developing the genre during the mid twentieth century. Advocating greater use of natural-color maps by contemporary cartographers, we discuss the advantages of natural-color maps compared to physical maps made with hypsometric tints; why natural-color maps, although admired, have remained comparatively rare; and the inadequacies of using satellite images as substitutes for natural-color maps. Seeking digital solutions, the paper then introduces techniques for designing and producing natural-color maps that are economical and within the skill range of most cartographers. The techniques, which use Adobe Photoshop software and satellite land cover data, yield maps similar in appearance to those made by Shelton, but with improved digital accuracy. Full-color illustrations show examples of Shelton’s maps and those produced by digital techniques.
Earthquake-induced habitat migration in a riparian spawning fish: implications for conservation management

Galaxias maculatus is a riparian spawning fish that supports an important recreational fishery in New Zealand with spawning habitat requirements strongly structured by salinity gradients at rivermouths. This study reports changes to the spawning habitat following a series of large earthquakes that resulted in widespread deformation of ground surfaces in the vicinity of waterways. Assessments of habitat recovery focussed on two rivers systems, the Avon and Heathcote, with pre-disturbance data available over a 20 year period. Recovery dynamics were assessed by field survey and mapping of spawning habitat prior to and on seven occasions after the disturbance event. Riparian land-use and management patterns were mapped and analysed using overlay methods in GIS. Habitat migration of up to 2 km occurred in comparison to all previous records and several anthropogenic land uses have become threats due to changed patterns of co-occurrence. Incompatible activities now affect more than half of the spawning habitat in both rivers, particularly in areas managed for flood control purposes and recreational use. The results are an example of landscape scale responses to salinity and water level changes driven by tectonic dynamics. These dynamics are not the source of the stress per se, rather, they have increased exposure to pre-existing stressors. The case illustrates important principles for managing subtle, yet widespread, change. Adaptive conservation methods and investments in information are priorities for avoiding management failure following environmental change.
Transcription-dependent spreading of canonical yeast GATA factor across the body of highly expressed genes

GATA transcription factors are highly conserved among eukaryotes and play roles in transcription of genes implicated in cancer progression and hematopoiesis. However, although their consensus binding sites have been well defined in vitro, the in vivo selectivity for recognition by GATA factors remains poorly characterized. Using ChIP-Seq, we identified the Dal80 GATA factor targets in yeast. Our data reveal Dal80 binding to a large set of promoters, sometimes independently of GATA sites. Strikingly, Dal80 was also detected across the body of promoter-bound genes, correlating with high, Dal80-sensitive expression. Mechanistic single-gene experiments showed that Dal80 spreading across gene bodies is independent of intragenic GATA sites but requires transcription elongation. Consistently, Dal80 co-purified with the post-initiation form of RNA Polymerase II. Our work suggests that GATA factors could play dual, synergistic roles during transcription initiation and post-initiation steps, promoting efficient remodeling of the gene expression program in response to environmental changes. Author Summary GATA transcription factors are highly conserved among eukaryotes and play key roles in cancer progression and hematopoiesis. In budding yeast, four GATA transcription factors are involved in the response to the quality of nitrogen supply. We have determined the whole genome binding profile of one of them, Dal80, and revealed that it also binds across the body or promoter-bound genes. Our observation that ORF binding correlated with elevated transcription levels and exquisite Dal80 sensitivity suggests that GATA factors could play other, unexpected roles at post-initiation stages in eukaryotes.
Automated high throughput animal DNA metabarcode classification

Until now, there has been difficulty assigning names to animal barcode sequences isolated directly from eDNA in a rapid, high-throughput manner, providing a measure of confidence for each assignment. To address this gap, we have compiled nearly 1 million marker gene DNA barcode sequences appropriate for classifying chordates, arthropods, and flag members of other major eukaryote groups. We show that the RDP naïve Bayesian classifier can assign the same number of queries 19 times faster than the popular BLAST top hit method and reduce the false positive rate by two-thirds. As reference databases become more representative of current species diversity, confidence in taxonomic assignments should continue to improve. We recommend that investigators can improve the performance of species-level assignments immediately by supplementing existing reference databases with full-length DNA barcode sequences from representatives of local fauna.
Guiding Professional Life-long Learning Projects: The Case of an Immersive Blended Learning Certificate | Accompagner des projets professionnels en formation continue : le cas d’un certificat de formation hybride et immersive

This case study presents a blended learning study program offered as a continuing education certificate of advanced studies for post-secondary educators and training professionals in the private, non-governmental, and public sectors. This accredited certificate program is unique in that it allows participants to propose and develop their own practical pedagogical projects. Another distinguishing characteristic is that it is offered in blended learning mode, i.e., alternating face-to-face phases with tutored distance learning phases. The pedagogical team includes one professor and one coordinator who supervise the entire program, as well as external instructors who provide individually tailored consulting on participants’ projects. During their studies, participants experience first-hand, the techno-pedagogical solutions proposed through their implementation within the program.