Miracle-dz/UniFAR-FTUs huggingface dataset:
Absolute quantification of E. coli virulence and housekeeping genes to determine pathogen loads in enumerated environmental samples

Quantifying pathogenic genes with q-PCR in complex samples to determine the pathogen loads is influenced by a wide range of factors, including choice of extraction method, standard curve, and the decision to use relative versus absolute quantification of the genes. The aim was to investigate the standardisation of q-PCR methods to determine enumerated E. coli gene ratios grown with the IDEXX Colilert® Quanti-Trays® using enteropathogenic E. coli as the model pathogen. q-PCR targeting the eaeA and gadAB genes was used to calculate the eaeA: gadAB ratios for clinical strains collected between [2005–2006 (n = 55)] and [2008–2009 (n = 19)] using the LinRegPCR software and Corbett Research Thermal cycler software. Both programs grouped the isolates into two distinct groups based on the gene ratios although the Corbett Research Thermal cycler software gave results one log higher than the LinRegPCR program. Although the eaeA: gadAB ratio range was determined using extracted E. coli DNA, the impact of free DNA and other bacteria present in the sample needed to be understood. Standard curve variations using serially diluted extracted E. coli DNA, serially diluted pure E. coli culture followed by DNA extraction from each dilution with or without other bacteria was tested using the eaeA q-PCR to quantify the genes. Comparison of the standard curves showed no significant difference between standard curves prepared with diluted DNA or with cells diluted before the DNA is extracted (P = 0.435). Significant differences were observed when background DNA was included in the diluent or Coliform cells added to the diluent to dilute cells before the DNA is extracted (P < 0.001). The “carrier” DNA and Coliform cells enhanced the DNA extraction results resulting in better PCR efficiency. This will have an influence on the quantification of gene ratios and pathogen load in samples containing lower numbers of E. coli.
Gene expression signatures as candidate biomarkers of response to PD-1 blockade in non-small cell lung cancers

Although anti-PD-1/PD-L1 monotherapy has achieved clinical success in non-small cell lung cancer (NSCLC), definitive predictive biomarkers remain to be elucidated. In this study, we performed whole-transcriptome sequencing of pretreatment tumor tissue samples and pretreatment and on-treatment whole blood samples (WB) samples obtained from a clinically annotated cohort of NSCLC patients (n = 40) treated with nivolumab (anti-PD-1) monotherapy. Using a single-sample gene set enrichment scoring method, we found that the tumors of responders with lung adenocarcinoma (LUAD, n = 20) are inherently immunogenic to promote antitumor immunity, whereas those with lung squamous cell carcinoma (LUSC, n = 18) have a less immunosuppressive tumor microenvironment. These findings suggested that nivolumab may function as a molecular targeted agent in LUAD and as an immunomodulating agent in LUSC. In addition, our study explains why the reliability of PD-L1 expression on tumor cells as a predictive biomarker for the response to nivolumab monotherapy is quite different between LUAD and LUSC.
High Rates of Aminoglycoside Methyltransferases Associated with Metallo-Beta-Lactamases in Multidrug-Resistant and Extensively Drug-Resistant Pseudomonas aeruginosa Clinical Isolates from a Tertiary Care Hospital in Egypt

Background Multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains of Pseudomonas aeruginosa are the leading cause of healthcare-associated infections worldwide. Objective The aim was to identify the resistant phenotypes among P. aeruginosa and to characterize different aminoglycosides and carbapenem resistance genes as major mechanisms of resistance in these isolates, in Theodor Bilharz Research Institute (TBRI), a tertiary care hospital in Cairo, Egypt. Methods During a period of 11 months, 42 P. aeruginosa clinical isolates were collected from the microbiology laboratory by routine culture. Antimicrobial sensitivity testing to the aminoglycosides gentamicin and amikacin, and other classes of antibiotics, was performed by a disk diffusion method. Isolates were tested for aminoglycoside resistance genes, aac(6ʹ)-lb, aac-(3)-lla, rmtB, rmtC, armA, rmtD, and rmtF, and carbapenemase resistance genes blaNDM, blaVIM, and blaIMP, using conventional PCR. Results Thirty-three (78.5%) of the clinical P. aeruginosa isolates showed MDR and XDR phenotypes at 42.4% and 57.65%, respectively, and these were included in the study. Aminoglycoside resistance was found in 97%, whereas carbapenem resistance was found in 81% of the isolates phenotypically. Only 59.4% (19/26) of the aminoglycoside-resistant isolates harbored resistance genes; none of the amikacin-susceptible isolates harbored any of the tested aminoglycoside resistance genes. Aminoglycoside resistance genes rmtB, armA, aac(6ʹ)-lb, and rmtF were found at rates of 17/33 (51.5%), 3/33 (9%), 2/33 (6%), and 2/33 (6%), respectively, whereas rmtD, acc(3)-II, and rmtC were not detected. Only 40.7% (11/27) of the carbapenem-resistant isolates harbored resistance genes. Carbapenem resistance genes, blaNDM andblaVIM, were found at rates of 7/33 (21.2%) and 6/33 (18.1%), respectively, and blaIMP was not detected. Conclusion Rates of MDR and XDR P. aeruginosa and resistance to aminoglycosides and carbapenems in our setting are high. Methyltransferases and metallo-beta-lactamases are the main mechanisms of resistance to aminoglycosides and carbapenems, respectively. The presence of blaNDM and rmtF in the strains confirms their rapid dissemination in the Egyptian environment.
Characterization of void space, large-scale structure, and transport properties of maximally random jammed packings of superballs

Dense, disordered packings of particles are useful models of low-temperature amorphous phases of matter, biological systems, granular media, and colloidal systems. The study of dense packings of nonspherical particles enables one to ascertain how rotational degrees of freedom aﬀect packing behavior. Here, we study superballs, a large family of deformations of the sphere, deﬁned in three dimensions by | x 1 | 2 p + | x 2 | 2 p + | x 3 | 2 p ≤ 1, where p ∈ (0 , ∞ ) is a deformation parameter indicating to what extent the shape deviates from a sphere. As p increases from the sphere point ( p = 1), the superball tends to a cuboidal shape and approaches a cube in the p → ∞ limit. As p → 0 . 5, it approaches an octahedron, becomes a concave body with octahedral symmetry for p < 0 . 5, and approaches a three-dimensional cross in the limit p → 0. Previous characterization of superball packings has shown that they have a maximally random jammed (MRJ) state, whose properties (e.g., packing fraction φ , average contact number ¯ Z ) vary nonanalytically as p diverges from unity. Here, we use an event-driven molecular dynamics algorithm to produce MRJ superball packings with 0 . 85 ≤ p ≤ 1 . 50. To supplement the previous work on such packings, we characterize their large-scale structure by examining the behaviors of their structure factors S ( Q ) and spectral densities ˜ χ V ( Q ), as the wave number Q tends to zero, and ﬁnd that these packings are eﬀectively hyperuniform for all values of p examined. We show that the mean width ¯ w is a useful length scale to make distances dimensionless in order to compare systematically superballs of diﬀerent shape. Moreover, we compute the complementary cumulative pore-size distribution F ( δ ) and ﬁnd that the pore sizes tend to decrease as | 1 − p | increases. From F ( δ ), we estimate how the ﬂuid permeability, mean survival time, and principal diﬀusion relaxation time vary as a function of p . Additionally, we compute the diﬀusion “spreadability” S ( t ) [Torquato, Phys. Rev. E, 104 , 054102, (2021)] of these packings and ﬁnd that the long-time power-law scaling indicates these packings are hyperuniform with a small- Q power law scaling of the spectral density ˜ χ V ( Q ) ∼ Q α with an exponent α that ranges from 0.64 at the sphere point to 0.32 at p = 1 . 50, and decreases as | 1 − p | increases. Each of the structural characteristics computed here exhibits an extremum at the sphere point and varies nonanalytically as p departs the sphere point. We ﬁnd the nonanalytic behavior in φ on either side of the sphere point is nearly linear, and determine that the rattler fraction φ R decreases rapidly as | 1 − p | increases. Our results can be used to help inform the design of colloidal or granular materials with targeted densities and transport properties.
Studying Hadronization by Machine Learning Techniques

Hadronization is a non-perturbative process, which theoretical description can not be deduced from first principles. Modeling hadron formation requires several assumptions and various phenomenological approaches. Utilizing state-of-the-art Computer Vision and Deep Learning algorithms, it is eventually possible to train neural networks to learn non-linear and non-perturbative features of the physical processes. In this study, results of two ResNet networks are presented by investigating global and kinematical quantities, indeed jet- and event-shape variables. The widely used Lund string fragmentation model is applied as a baseline in $\sqrt{s}= 7$ TeV proton-proton collisions to predict the most relevant observables at further LHC energies.
Contrasting Identifying Assumptions of Average Causal Effects: Robustness and Semiparametric Efficiency

Semiparametric inference on average causal effects from observational data is based on assumptions yielding identification of the effects. In practice, several distinct identifying assumptions may be plausible; an analyst has to make a delicate choice between these models. In this paper, we study three identifying assumptions based on the potential outcome framework: the back-door assumption, which uses pre-treatment covariates, the front-door assumption, which uses mediators, and the two-door assumption using pre-treatment covariates and mediators simultaneously. We provide the efficient influence functions and the corresponding semiparametric efficiency bounds that hold under these assumptions, and their combinations. We demonstrate that neither of the identification models provides uniformly the most efficient estimation and give conditions under which some bounds are lower than others. We show when semiparametric estimating equation estimators based on influence functions attain the bounds, and study the robustness of the estimators to misspecification of the nuisance models. The theory is complemented with simulation experiments on the finite sample behavior of the estimators. The results obtained are relevant for an analyst facing a choice between several plausible identifying assumptions and corresponding estimators. Our results show that this choice implies a trade-off between efficiency and robustness to misspecification of the nuisance models.
Bilingual Topic Models for Comparable Corpora

Probabilistic topic models like Latent Dirichlet Allocation (LDA) have been previously extended to the bilingual setting. A fundamental modeling assumption in several of these extensions is that the input corpora are in the form of document pairs whose constituent documents share a single topic distribution. However, this assumption is strong for comparable corpora that consist of documents thematically similar to an extent only, which are, in turn, the most commonly available or easy to obtain. In this paper we relax this assumption by proposing for the paired documents to have separate, yet bound topic distributions. % a binding mechanism between the distributions of the paired documents. We suggest that the strength of the bound should depend on each pair's semantic similarity. To estimate the similarity of documents that are written in different languages we use cross-lingual word embeddings that are learned with shallow neural networks. We evaluate the proposed binding mechanism by extending two topic models: a bilingual adaptation of LDA that assumes bag-of-words inputs and a model that incorporates part of the text structure in the form of boundaries of semantically coherent segments. To assess the performance of the novel topic models we conduct intrinsic and extrinsic experiments on five bilingual, comparable corpora of English documents with French, German, Italian, Spanish and Portuguese documents. The results demonstrate the efficiency of our approach in terms of both topic coherence measured by the normalized point-wise mutual information, and generalization performance measured by perplexity and in terms of Mean Reciprocal Rank in a cross-lingual document retrieval task for each of the language pairs.
Using Natural Sketch Recognition Software to Provide Instant Feedback on Statics Homework (Truss Free Body Diagrams): Assessment of a Classroom Pilot

Engineering students in large classes receive far too little feedback on hand-sketched Free Body Diagrams (FBDs.) Despite the importance of these diagrams for engineering education and practice, large class sizes often make providing detailed paper-based feedback impractical. Such issues are even further strained in MOOC classes. Relatively recently, computing technology has become powerful enough to enable instantaneous, detailed feedback on hand-sketched engineering diagrams. Researchers have recently developed the free “Mechanix 1 ” sketch recognition tutoring system for free body diagrams (FBDs) and trusses. The Mechanix online software provides instantaneous and detailed feedback using a natural sketch recognition engine 2,3,4,5,6,7,8,9,10,11,12 . This paper describes the process and results of piloting the Mechanix software at a primarily undergraduate university. The experimental group included 39 students in a Statics class, contrasted with a similar class of 34 as a control group. Students in the experimental group completed the Mechanix tutorial and then worked 4 truss problems with instantaneous and unlimited online feedback regarding the accuracy of their free-body diagram drawing and numerical answers. The control group used commonly available online software giving feedback only on numerical answers. Results included here include student attitudes towards technology, online homework scores, class test scores, and self-reported perceptions of the software efficacy. Both quantitative learning outcomes and qualitative comments from users suggest the software system is effective and has great potential if further developed. Students in the experimental group performed as well as the control group, more than 90% of students chose to use the new software (rather than opt-out), and most students self-reported a positive learning experience.
AutoDrop: Training Deep Learning Models with Automatic Learning Rate Drop

Modern deep learning (DL) architectures are trained using variants of the SGD algorithm that is run with a $\textit{manually}$ defined learning rate schedule, i.e., the learning rate is dropped at the pre-defined epochs, typically when the training loss is expected to saturate. In this paper we develop an algorithm that realizes the learning rate drop $\textit{automatically}$. The proposed method, that we refer to as AutoDrop, is motivated by the observation that the angular velocity of the model parameters, i.e., the velocity of the changes of the convergence direction, for a fixed learning rate initially increases rapidly and then progresses towards soft saturation. At saturation the optimizer slows down thus the angular velocity saturation is a good indicator for dropping the learning rate. After the drop, the angular velocity"resets"and follows the previously described pattern - it increases again until saturation. We show that our method improves over SOTA training approaches: it accelerates the training of DL models and leads to a better generalization. We also show that our method does not require any extra hyperparameter tuning. AutoDrop is furthermore extremely simple to implement and computationally cheap. Finally, we develop a theoretical framework for analyzing our algorithm and provide convergence guarantees.
Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization

Physical simulation-based optimization is a common task in science and engineering. Many such simulations produce image- or tensor-based outputs where the desired objective is a function of those outputs, and optimization is performed over a high-dimensional parameter space. We develop a Bayesian optimization method leveraging tensor-based Gaussian process surrogates and trust region Bayesian optimization to effectively model the image outputs and to efficiently optimize these types of simulations, including a radio-frequency tower configuration problem and an optical design problem.
Terahertz Fingerprint of Monolayer Wigner Crystals

The strong Coulomb interaction in monolayer semiconductors represents a unique opportunity for the realization of Wigner crystals without external magnetic fields. In this work, we predict that the formation of monolayer Wigner crystals can be detected by their terahertz response spectrum, which exhibits a characteristic sequence of internal optical transitions. We apply the density matrix formalism to derive the internal quantum structure and the optical conductivity of the Wigner crystal and to microscopically analyse the multi-peak shape of the obtained terahertz spectrum. Moreover, we predict a characteristic shift of the peak position as function of charge density for different atomically thin materials and show how our results can be generalized to an arbitrary two-dimensional system.
Online Learning for Receding Horizon Control with Provable Regret Guarantees

We address the problem of learning to con-trol an unknown linear dynamical system with time varying cost functions through the framework of online Receding Horizon Control (RHC). We consider the setting where the control algorithm does not know the true system model and has only access to a ﬁxed-length (that does not grow with the con-trol horizon) preview of the future cost functions. We characterize the performance of an algorithm using the metric of dynamic regret, which is deﬁned as the diﬀerence be-tween the cumulative cost incurred by the algorithm and that of the best sequence of actions in hindsight. We propose two diﬀer-ent online RHC algorithms to address this problem, namely Certainty Equivalence RHC (CE-RHC) algorithm and Optimistic RHC (O-RHC) algorithm. We show that under the standard stability assumption for the model estimate, the CE-RHC algorithm achieves O ( T 2 / 3 ) dynamic regret. We then extend this result to the setting where the stability assumption hold only for the true system model by proposing the O-RHC algorithm. We show that O-RHC algorithm achieves O ( T 2 / 3 ) dynamic regret but with some additional computation.
small: A Programmatic Nanostructure Design and Modelling Environment

Structural DNA nanotechnology has advanced to the extent that extremely complex structures can be designed. Much of this advancement has been due to the development of automated DNA design and simulation tools. Typically, the tools (e.g. NUPAK, cadnano, OxDNA) are created for a specific task. Ideally, there would be an environment that can integrate all such DNA tools, also with non-DNA tools - for example for modelling electromagnetic field along a zero-mode waveguide made of gold nanoparticles organized on a DNA breadboard. Such an environment would streamline design in DNA nanotechnology and enable applying DNA nanotechnology principles to construct high performance materials and devices from non-DNA components. Here we present small a programmatic tool that is a step towards building such an environment for designing arbitrary nanostructures. In particular we showcase how small has been used to create an integrated computational materials engineering (ICME) framework for DNA nanotechnology, allowing the hierarchical design, simulation and visualization of arbitrary DNA nanostructures. Furthermore we demonstrate the design and modeling of the mode profiles and band structure of hybrid DNA-nanoparticle materials through the integration of small with Maxwell solvers.
CO-SNE: Dimensionality Reduction and Visualization for Hyperbolic Data

Hyperbolic space can naturally embed hierarchies that often exist in real-world data and semantics. While high-dimensional hyperbolic embeddings lead to better representations, most hyperbolic models utilize low-dimensional embeddings, due to non-trivial optimization and visualization of high-dimensional hyperbolic data. We propose CO-SNE, which extends the Euclidean space visualization tool, t-SNE, to hyperbolic space. Like t-SNE, it converts distances between data points to joint probabilities and tries to minimize the Kullback-Leibler divergence between the joint probabilities of high-dimensional data $X$ and low-dimensional embedding $Y$. However, unlike Euclidean space, hyperbolic space is inhomogeneous: A volume could contain a lot more points at a location far from the origin. CO-SNE thus uses hyperbolic normal distributions for $X$ and hyperbolic Cauchy instead of t-SNE's Student's t-distribution for $Y$, and it additionally seeks to preserve $X$'s individual distances to the Origin in $Y$. We apply CO-SNE to naturally hyperbolic data and supervisedly learned hyperbolic features. Our results demonstrate that CO-SNE deflates high-dimensional hyperbolic data into a low-dimensional space without losing their hyperbolic characteristics, significantly outperforming popular visualization tools such as PCA, t-SNE, UMAP, and HoroPCA which is also designed for hyperbolic data.
Use of CEvNS to monitor spent nuclear fuel

Increasing amounts of spent nuclear fuel are stored in dry storage casks for prolonged periods of time. To date no effective technology exists to re-verify cask contents should this become necessary. We explore the applicability of Coherent-Elastic Neutrino-Nucleus Scattering (CEvNS) to monitor the content of spent nuclear fuel (SNF) from dry storage casks. SNF produces neutrinos chiefly from $^{90}$Sr decays. We compare these results with what can be achieved via Inverse Beta Decay (IBD). We demonstrate that at low nuclear recoil energies CEvNS events rates exceed the IBD event rates by 2--3 orders of magnitude for a given detector mass. We find that a 10\,kg argon or germanium detector 3 meters from a fuel cask can detect over 100 events per year if a nuclear recoil threshold under 100 eV can be achieved.