Miracle-dz/UniFAR-FTUs huggingface dataset:
Targeted mutagenesis with sequence‐specific nucleases for accelerated improvement of polyploid crops: Progress, challenges, and prospects

Many of the world's most important crops are polyploid. The presence of more than two sets of chromosomes within their nuclei and frequently aberrant reproductive biology in polyploids present obstacles to conventional breeding. The presence of a larger number of homoeologous copies of each gene makes random mutation breeding a daunting task for polyploids. Genome editing has revolutionized improvement of polyploid crops as multiple gene copies and/or alleles can be edited simultaneously while preserving the key attributes of elite cultivars. Most genome‐editing platforms employ sequence‐specific nucleases (SSNs) to generate DNA double‐stranded breaks at their target gene. Such DNA breaks are typically repaired via the error‐prone nonhomologous end‐joining process, which often leads to frame shift mutations, causing loss of gene function. Genome editing has enhanced the disease resistance, yield components, and end‐use quality of polyploid crops. However, identification of candidate targets, genotyping, and requirement of high mutagenesis efficiency remain bottlenecks for targeted mutagenesis in polyploids. In this review, we will survey the tremendous progress of SSN‐mediated targeted mutagenesis in polyploid crop improvement, discuss its challenges, and identify optimizations needed to sustain further progress.
Cell culture: in vitro model system and a promising path to in vivo applications

In the 1800s, scientists discovered a useful alternative to in vivo research, the in vitro cell culture system. Cell culture has been widely used in academia and industry for diverse issues such as cell biology research to vaccine production and cancer drug discoveries. This article will provide a brief introduction and summary of cell culture history with some key milestones and discuss the future of cell culture from the author’s perspective. When talking about cells, what comes to mind? For me, it is the most well-known cell line, HeLa. HeLa cells were the first immortalized human cell line and may be one of the most important scientific discoveries of the last century. In 1951, HeLa cells were isolated and propagated from cervical cancer tissue from a patient named Henrietta Lacks (HeLa) by Johns Hopkins researcher, Dr George Gey. Since then, this cell line has played an important role in many scientific discoveries, from outer space research to the COVID-19 vaccines, and of course, cancer research all over the world. Other important cell lines include the hybridomas established in 1975 (hybrid cell lines that produce antibodies), mouse embryonic stem cells (ESCs) established in 1981, and human ESCs in 1998. Most recently, in 2006, the world was amazed by the exciting discovery of induced pluripotent stem cells (iPSCs) which were reprogrammed from adult cells back to ESC-like states as a promising alternative to ESCs. In both basic and translational research, cell culture is an indispensable tool. It is becoming more important with the rapid development of reprogramming, gene editing, and in vitro differentiation as well as threedimensional (3D) culture and bioprinting technologies.
IMAGE RESTORATION: METHOD- INDEPENDENT LIMIT OF EFFICIENCY AND ITS REALIZATION

Abstract Due to radiational and external noise, any image forming system can be described only in a frame of a stochastic model, with probability f(N, 5) of occurrence of the observed image N, for any searched object S. Thus, restoration problem must inevitably be considered as a statistical estimation of unknown parameters S. Proceeding from the appropriate definition of restoration efficiency, it is possible to show that the method-independent limit of efficiency and accuracy is set by the Rao-Cramer theorem. The most promising way to achieve the theoretical limit is based on a maximum likelihood image restoration (MLIR) method. Indeed, MLIR gives limiting accuracy when light intensity begins to exceed the mean level of external noise. The maximum entropy method includes non-necessary, and logically inconsistent, assumptions. The concrete formulae for the calculation of an object estimate, and its accuracy, are given. Test cases are given, and examples, of using the proposed approach to different invers.
Can COVID-19 have a clinically significant effect on drug metabolism?

Hepatic metabolism is one of three major routes of drug elimination. Drug metabolism generally involves a sequence of two reactions that allow for efficient excretion by the kidneys. Phase I reactions are catabolic and typically involve reduction, oxidation, or hydrolysis to convert lipophilic agents into more polar and reactive products. Phase II reactions are anabolic and consist of conjugation reactions with endogenous hydrophilic substances to further polarize and usually inactivate the parent drug [1]. The Cytochrome P450 (CYP450) enzymes, a set of heme proteins, frequently catalyze reactions involved in phase I metabolism. Although there exist 57 CYP isoforms in humans, not all are involved in drug metabolism, and CYP3A4/5, CYP2D6, CYP2C8/9, and CYP1A2 are responsible for the majority of reactions catalyzed by CYP450 [2]. Herein, we aim to explore the possibility of whether COVID-19 infection and associated inflammation can affect drug metabolism.
Evaluation of silicone rubber-lead shield's effectiveness in protecting the breast during thoracic CT

Radiation of thoracic computed tomography (CT) involves the breast although it is not considered an organ of interest. According to the International Commission on Radiological Protection (ICRP) No. 103, the breast is an organ with a high level of sensitivity when interacting with x-rays, increasing the potential risk of breast cancer. Therefore, the radiation dose must be optimized while maintaining image quality. The dose optimization can be accomplished using a radiation shield. This study aims to determine the effect of silicone rubber (SR)-lead (Pb) in various thicknesses as an alternative protective material limiting dose and preserving the image quality of the breast in thoracic CT. SR-Pb was made from SR and Pb by a simple method. The SR-Pb had thicknesses of 3, 6, 9, and 12 mm. The breast dose was measured using a CT dose profiler on the surface of the breast phantom. The CT number and the noise level of the resulting image were determined quantitatively. The dose without the radiation shield was 5.4 mGy. The doses measured using shielding with thicknesses of 3, 6, 9, and 12 mm were 5.2, 4.5, 4.3, and 3.3 mGy, respectively. Radiation shielding with a thickness of 12 mm reduced breast surface dose by up to 38%. The CT numbers and noise levels for the left and right breast phantom images were almost the same as those without radiation shields indicating there were only slight artifacts in the image. Therefore, SR-Pb is considered a good shielding material which can be pplied in a clinical setting by placing it directly on the breast surface for dose optimization.
On some noncoercive variational inequalities containing degenerate elliptic operators

Abstract We are concerned with the solvability of variational inequalities that contain degenerate elliptic operators. By using a recession approach, we find conditions on the boundary conditions such that the inequality has at least one solution. Existence results of Landesman-Lazer type for a nonsmooth inequality and a resonance problem for a weighted p-Laplacian are discussed in detail.
Genotypic investigation of a rotavirus cluster at a quaternary-care pediatric hospital

Abstract Rotavirus (RV) was a common healthcare-associated infection prior to the introduction of the RV vaccine. Following widespread RV vaccination, healthcare-associated rotavirus cases are rare. We describe an investigation of a cluster of rotavirus infections in a pediatric hospital in which an uncommon genotype not typically circulating in the United States was detected.
A utilization distribution for the global population of Cape Vultures (Gyps coprotheres) to guide wind energy development.

The rapid development of wind energy in southern Africa represents an additional threat to the already fragile populations of African vultures. The distribution of the Vulnerable Cape Vulture Gyps coprotheres overlaps considerably with wind energy development areas in South Africa, creating conflicts that can hinder both vulture conservation and sustainable energy development. To help address this conflict and aid in the safe placement of wind energy facilities, we map the utilization distribution (UD) of this species across its distributional range. Using tracking data from 68 Cape Vultures, collected over the last 20 years, we develop a spatially-explicit habitat use model to estimate the expected UDs around known colonies. Scaling the UDs by the number of vultures expected to use each of the colonies, we estimate the Cape Vulture population utilization distribution (PUD), and determine its exposure to wind farm impacts. To complement our results, we model the probability of a vulture flying within the rotor sweep area of a wind turbine throughout the species range, and use it to identify areas that are particularly prone to collisions. Overall, our estimated PUD correlates well with reporting rates of the species from the Southern African Bird Atlas Project, currently used to assess potential overlap between Cape Vultures and wind energy developments, but it adds important benefits, such as providing a spatial gradient of activity estimates over the entire species range. We illustrate the application of our maps by analysing the exposure of Cape Vultures in the Renewable Energy Development Zones (REDZs) in South Africa. This application is a scalable procedure that can be applied at different planning phases, from strategic, nation-wide planning to project-level assessments.
Design and Fabrication of PERC-Like CdTe Solar Cells Using Micropatterned Al2O3 Layer

Recent studies have investigated novel strategies to further improve the limited <tex>$V_{oc}$</tex> of CdTe solar cells via increased carrier lifetime and doping density of CdTe thin films. Among various metal oxides, aluminum oxide (Al<inf>2</inf>O<inf>3</inf>) is a promising passivation candidate, where the negatively charged Al<inf>2</inf>O<inf>3</inf> layer repels the minority carrier in CdTe and Al<inf>2</inf>O<inf>3</inf> provides a chemically passivating interface, increasing the carrier lifetime. Despite the continuing efforts, an optimized back-contact architecture to improve the <tex>$V_{oc}$</tex> while maintaining high <tex>$J_{sc}$</tex> and <tex>$FF$</tex> is still under development. In this work, we report the design, fabrication, and characterization of PERC-like CdTe solar cells, where an Al<inf>2</inf>O<inf>3</inf> passivation layer is patterned using laser-beam lithography. Our process enables reproducible patterning on a rough surface CdTe while maintaining the size of the array in the design. Analysis of CdTe PERC devices (As-doped) shows a notably different <tex>$V_{oc}$</tex> trend compared to <tex>$FF$</tex> and <tex>$J_{sc}$</tex>, independent of the patterned array structures used in this study. The subsurface electronic structure of CdTe and the interplay between carrier selectivity and collection of the patterned Al<inf>2</inf>O<inf>3</inf> could be responsible for the observed PV characteristics.
Poisson Limit Theorems for Systems with Product Structure

We obtain a Poisson Limit for return times to small sets for product systems. Only one factor is required to be hyperbolic while the second factor is only required to satisfy polynomial deviation bounds for ergodic sums. In particular, the second fact can be either elliptic or parabolic. As an application of our main result, several maps of the form Anosov map $\times$ another map are shown to satisfy a Poisson Limit Theorem at typical points, some even at all points. The methods can be extended to certain types of skew products, including $T,T^{-1}$-maps of high rank.
Low-Resource Compositional Semantic Parsing with Concept Pretraining

Semantic parsing plays a key role in digital voice assistants such as Alexa, Siri, and Google Assistant by mapping natural language to structured meaning representations. When we want to improve the capabilities of a voice assistant by adding a new domain, the underlying semantic parsing model needs to be retrained using thousands of annotated examples from the new domain, which is time-consuming and expensive. In this work, we present an architecture to perform such domain adaptation automatically, with only a small amount of metadata about the new domain and without any new training data (zero-shot) or with very few examples (few-shot). We use a base seq2seq (sequence-to-sequence) architecture and augment it with a concept encoder that encodes intent and slot tags from the new domain. We also introduce a novel decoder-focused approach to pretrain seq2seq models to be concept aware using Wikidata and use it to help our model learn important concepts and perform well in low-resource settings. We report few-shot and zero-shot results for compositional semantic parsing on the TOPv2 dataset and show that our model outperforms prior approaches in few-shot settings for the TOPv2 and SNIPS datasets.
Galactic population synthesis of radioactive nucleosynthesis ejecta

Diffuse gamma-ray line emission traces freshly produced radioisotopes in the interstellar gas, providing a unique perspective on the entire Galactic cycle of matter from nucleosynthesis in massive stars to their ejection and mixing in the interstellar medium. We aim at constructing a model of nucleosynthesis ejecta on galactic scale which is specifically tailored to complement the physically most important and empirically accessible features of gamma-ray measurements in the MeV range, in particular for decay gamma-rays such as $^{26}$Al, $^{60}$Fe or $^{44}$Ti. Based on properties of massive star groups, we developed a Population Synthesis Code which can instantiate galaxy models quickly and based on many different parameter configurations, such as the star formation rate, density profiles, or stellar evolution models. As a result, we obtain model maps of nucleosynthesis ejecta in the Galaxy which incorporate the population synthesis calculations of individual massive star groups. Based on a variety of stellar evolution models, supernova explodabilities, and density distributions, we find that the measured $^{26}$Al distribution from INTEGRAL/SPI can be explained by a Galaxy-wide population synthesis model with a star formation rate of $4$-$8\,\mathrm{M_{\odot}\,yr^{-1}}$ and a spiral-arm dominated density profile with a scale height of at least 700 pc. Our model requires that most massive stars indeed undergo a supernova explosion. This corresponds to a supernova rate in the Milky Way of $1.8$-$2.8$ per century, with quasi-persistent $^{26}$Al and $^{60}$Fe masses of $1.2$-$2.4\,\mathrm{M_{\odot}}$ and $1$-$6\,\mathrm{M_{\odot}}$, respectively. Comparing the simulated morphologies to SPI data suggests that a frequent merging of superbubbles may take place in the Galaxy, and that an unknown but strong foreground emission at 1.8 MeV could be present.
Solitary kinetic Alfvén waves: A study of the Poynting flux

Using a particular mode of the FREJA wave experiment (three magnetic and one electric simultaneous measurements), we have investigated the electromagnetic structure of the solitary kinetic Alfven waves observed in the topside ionosphere. It is shown that these strong electromagnetic spikes (ΔE ≈ 100 mV/m and ΔB ≈ 10 nT) have mainly a rotational character with, nevertheless, a tiny compressional component (ΔB‖/ΔB ≈ 10 %). They seem to be associated with small‐scale (100 m) tubular current structures. On the assumption that their electric component is perpendicular to the magnetic one their Poynting flux is estimated. Values of the order of 10−3 W/m2 are measured.
Macroeconomic forecasting and sovereign risk assessment using deep learning techniques

In this study, we propose a novel approach of nowcasting and forecasting the macroeconomic status of a country using deep learning techniques. We focus particularly on the US economy but the methodology can be applied also to other economies. Specifically US economy has suffered a severe recession from 2008 to 2010 which practically breaks out conventional econometrics model attempts. Deep learning has the advantage that it models all macro variables simultaneously taking into account all interdependencies among them and detecting non-linear patterns which cannot be easily addressed under a univariate modelling framework. Our empirical results indicate that the deep learning methods have a superior out-of-sample performance when compared to traditional econometric techniques such as Bayesian Model Averaging (BMA). Therefore our results provide a concise view of a more robust method for assessing sovereign risk which is a crucial component in investment and monetary decisions.
Influence of freestream turbulence on the near-field growth of a turbulent cylinder wake: Turbulent entrainment and wake meandering

The effect of freestream turbulence on the spreading of the near wake of a circular cylinder is investigated through particle image velocimetry (PIV) data. Different ``flavours'' of freestream turbulence, in which the turbulence intensity and integral length scale are independently varied, are subjected to the cylinder. The time-averaged spreading of the near wake is decoupled into the growth through entrainment and the envelope of the spatial extent of the instantaneous wake due to wake ``meandering'', induced by the presence of large-scale coherent structures, i.e. the von Karman vortex street. Unlike for the far field of a turbulent wake, examined by Kankanwadi and Buxton (2020), it is shown that freestream turbulence enhances the entrainment rate into the wake in comparison to a non-turbulent background. Both the turbulence intensity and the integral length scale of the background turbulence are important in this regard, contrasting to the far wake where only the turbulence intensity is important. Additionally, wake meandering is enhanced by the presence of background turbulence, and here the integral length scale is the dominant parameter. Combining these findings yields the oft-reported result that background turbulence enhances the time-averaged near-wake growth rate. The influence of wake meandering is isolated by conducting similar experiments in which a splitter plate is mounted to the rear face of the cylinder, thereby eliminating the von Karman vortex street. These results show that when large-scale vortices aren't present within the turbulent wake then freestream turbulence actually suppresses the entrainment rate, relative to a non-turbulent background, in results that mirror Kankanwadi and Buxton (2020). We therefore postulate that freestream turbulence has the effect to enhance large-scale entrainment via engulfment, but suppress entrainment via small-scale nibbling.
Mesostructures: Beyond Spectrogram Loss in Differentiable Time-Frequency Analysis

Computer musicians refer to mesostructures as the intermediate levels of articulation between the microstructure of waveshapes and the macrostructure of musical forms. Examples of mesostructures include melody, arpeggios, syncopation, polyphonic grouping, and textural contrast. Despite their central role in musical expression, they have received limited attention in deep learning. Currently, autoencoders and neural audio synthesizers are only trained and evaluated at the scale of microstructure: i.e., local amplitude variations up to 100 milliseconds or so. In this paper, we formulate and address the problem of mesostructural audio modeling via a composition of a differentiable arpeggiator and time-frequency scattering. We empirically demonstrate that time--frequency scattering serves as a differentiable model of similarity between synthesis parameters that govern mesostructure. By exposing the sensitivity of short-time spectral distances to time alignment, we motivate the need for a time-invariant and multiscale differentiable time--frequency model of similarity at the level of both local spectra and spectrotemporal modulations.
The Distribution of Metallicities in the Local Galactic Interstellar Medium

In this investigation, we present an analysis of the metallicity distribution that pertains to neutral gas in the local Galactic interstellar medium (ISM). We derive relative ISM metallicities for a sample of 84 sight lines probing diffuse atomic and molecular gas within 4 kpc of the Sun. Our analysis is based in large part on column density measurements reported in the literature for 22 different elements that are commonly studied in interstellar clouds. We supplement the literature data with new column density determinations for certain key elements and several individual sight lines important to our analysis. Our methodology involves comparing the relative gas-phase abundances of many different elements for a given sight line to simultaneously determine the strength of dust depletion in that direction and the overall metallicity offset. We find that many sight lines probe multiple distinct gas regions with different depletion properties, which complicates the metallicity analysis. Nevertheless, our results provide clear evidence that the dispersion in the metallicities of neutral interstellar clouds in the solar neighborhood is small (∼0.10 dex) and only slightly larger than the typical measurement uncertainties. We find no evidence for the existence of very low metallicity gas (as has recently been reported by De Cia et al.) along any of the 84 sight lines in our sample. Our results are consistent with a local Galactic ISM that is well mixed and chemically homogeneous.
YOLO-CL: Galaxy cluster detection in the SDSS with deep machine learning

(Abridged) Galaxy clusters are a powerful probe of cosmological models. Next generation large-scale optical and infrared surveys will reach unprecedented depths over large areas and require highly complete and pure cluster catalogs, with a well defined selection function. We have developed a new cluster detection algorithm YOLO-CL, which is a modified version of the state-of-the-art object detection deep convolutional network YOLO, optimized for the detection of galaxy clusters. We trained YOLO-CL on color images of the redMaPPer cluster detections in the SDSS. We find that YOLO-CL detects $95-98\%$ of the redMaPPer clusters, with a purity of $95-98\%$ calculated by applying the network to SDSS blank fields. When compared to the MCXC2021 X-ray catalog in the SDSS footprint,YOLO-CL is more complete then redMaPPer, which means that the neural network improved the cluster detection efficiency of its training sample. The YOLO-CL selection function is approximately constant with redshift, with respect to the MCXC2021 cluster mean X-ray surface brightness. YOLO-CL shows high performance when compared to traditional detection algorithms applied to SDSS. Deep learning networks benefit from a strong advantage over traditional galaxy cluster detection techniques because they do not need galaxy photometric and photometric redshift catalogs. This eliminates systematic uncertainties that can be introduced during source detection, and photometry and photometric redshift measurements. Our results show that YOLO-CL is an efficient alternative to traditional cluster detection methods. In general, this work shows that it is worth exploring the performance of deep convolution networks for future cosmological cluster surveys, such as the Rubin/LSST, Euclid or the Roman Space Telescope surveys.