Miracle-dz/UniFAR-FTUs huggingface dataset:
Transitions between hyperfine structure states of antiprotonic $^4 \mathrm{He}$ at collisions with medium atoms: interaction \emph{ab initio}

Collisions of metastable antiprotonic helium atoms with atoms of the medium induce, among other processes, transitions between hyperfine structure (HFS) states, as well as shifts and broadening of microwave M1 spectral lines. In order to obtain matrix potential of interaction between $(\mathrm{\bar{p} He} ^+)$ and $\mathrm{He}$, we have calculated the potential energy surface (PES) in the framework of unrestricted Hartree-Fock method taking into account electron correlations in the second-order perturbation theory (MP2). With this potential, the system of close-coupling equations for HFS channels is solved numerically. Cross sections and transition rates, shifts and broadening of M1 spectral lines are calculated. They are used to obtain a numerical solution of the master equation that determines the time evolution of the HFS-states density matrix. The results are compared with the experimental data and with the results of model calculation.
Cascaded Detail-Preserving Networks for Super-Resolution of Document Images

The accuracy of OCR is usually affected by the quality of the input document image and different kinds of marred document images hamper the OCR results. Among these scenarios, the low-resolution image is a common and challenging case. In this paper, we propose the cascaded networks for document image super-resolution. Our model is composed by the Detail-Preserving Networks with small magnification. The loss function with perceptual terms is designed to simultaneously preserve the original patterns and enhance the edge of the characters. These networks are trained with the same architecture and different parameters and then assembled into a pipeline model with a larger magnification. The low-resolution images can upscale gradually by passing through each Detail-Preserving Network until the final high-resolution images. Through extensive experiments on two scanning document image datasets, we demonstrate that the proposed approach outperforms recent state-of-the-art image super-resolution methods, and combining it with standard OCR system lead to signification improvements on the recognition results.
Towards robust word embeddings for noisy texts

Research on word embeddings has mainly focused on improving their performance on standard corpora, disregarding the difficulties posed by noisy texts in the form of tweets and other types of non-standard writing from social media. In this work, we propose a simple extension to the skipgram model in which we introduce the concept of bridge-words, which are artificial words added to the model to strengthen the similarity between standard words and their noisy variants. Our new embeddings outperform baseline models on noisy texts on a wide range of evaluation tasks, both intrinsic and extrinsic, while retaining a good performance on standard texts. To the best of our knowledge, this is the first explicit approach at dealing with these types of noisy texts at the word embedding level that goes beyond the support for out-of-vocabulary words.
Medium-band photometric reverberation mapping of AGNs at 0.1 < z < 0.8. Techniques and sample

The most popular method of the broad-line region size estimation in active galactic nuclei (AGN) is the reverberation mapping based on measuring the time delay between the continuum flux and the flux in the emission lines. In our work, we apply the method of photometric reverberation mapping in mid-band filters, adapted for observations on the 1-m Zeiss-1000 telescope of Special Astrophysical Observatory of Russian Academy of Sciences, for the study of AGN with broad lines in the range of redshifts $0.1 < z < 0.8$. This paper provides a sample of 8 objects, describes the technique of observations and data processing for 2 studied objects to demonstrate the stability of the used method.
Detection of a Frameshift Deletion in the SPTBN4 Gene Leads to Prevention of Severe Myopathy and Postnatal Mortality in Pigs

Piglet mortality is a complex phenotype that depends on the environment, selection on piglet health, but also on the interaction between the piglet and sow. However, also monogenic recessive defects contribute to piglet mortality. Selective breeding has decreased overall piglet mortality by improving both mothering abilities and piglet viability. However, variants underlying recessive monogenic defects are usually not well captured within the breeding values, potentially drifting to higher frequency as a result of intense selection or genetic drift. This study describes the identification by whole-genome sequencing of a recessive 16-bp deletion in the SPTBN4 gene causing postnatal mortality in a pig breeding line. The deletion induces a frameshift and a premature stop codon, producing an impaired and truncated spectrin beta non-erythrocytic 4 protein (SPTBN4). Applying medium density single nucleotide polymorphism (SNP) data available for all breeding animals, a pregnant carrier sow sired by a carrier boar was identified. Of the resulting piglets, two confirmed homozygous piglets suffered from severe myopathy, hind-limb paralysis, and tremors. Histopathological examination showed dispersed degeneration and decrease of cross-striations in the dorsal and hind-limb muscle fibers of the affected piglets. Hence, the affected piglets are unable to walk or drink, usually resulting in death within a few hours after birth. This study demonstrates how growing genomic resources in pig breeding can be applied to identify rare syndromes in breeding populations, that are usually poorly documented and often are not even known to have a genetic basis. The study allows to prevent carrier-by-carrier matings, thereby gradually decreasing the frequency of the detrimental allele and avoiding the birth of affected piglets, improving animal welfare. Finally, these “natural knockouts” increase our understanding of gene function within the mammalian clade, and provide a potential model for human disease.
Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns

Computational Chemistry is currently a synergistic assembly between ab initio calculations, simulation, machine learning (ML) and optimization strategies for describing, solving and predicting chemical data and related phenomena. These include accelerated literature searches, analysis and prediction of physical and quantum chemical properties, transition states, chemical structures, chemical reactions, and also new catalysts and drug candidates. The generalization of scalability to larger chemical problems, rather than specialization, is now the main principle for transforming chemical tasks in multiple fronts, for which systematic and cost-effective solutions have benefited from ML approaches, including those based on deep learning (e.g. quantum chemistry, molecular screening, synthetic route design, catalysis, drug discovery). The latter class of ML algorithms is capable of combining raw input into layers of intermediate features, enabling bench-to-bytes designs with the potential to transform several chemical domains. In this review, the most exciting developments concerning the use of ML in a range of different chemical scenarios are described. A range of different chemical problems and respective rationalization, that have hitherto been inaccessible due to the lack of suitable analysis tools, is thus detailed, evidencing the breadth of potential applications of these emerging multidimensional approaches. Focus is given to the models, algorithms and methods proposed to facilitate research on compound design and synthesis, materials design, prediction of binding, molecular activity, and soft matter behavior. The information produced by pairing Chemistry and ML, through data-driven analyses, neural network predictions and monitoring of chemical systems, allows (i) prompting the ability to understand the complexity of chemical data, (ii) streamlining and designing experiments, (ii) discovering new molecular targets and materials, and also (iv) planning or rethinking forthcoming chemical challenges. In fact, optimization engulfs all these tasks directly.
Methemoglobin Modulation as an Intravascular Contrast Agent for Magnetic Resonance Imaging: Proof of Concept

Objective: The aim of this feasibility study was to investigate methemoglobin modulation in vivo as a potential magnetic resonance imaging (MRI) gadolinium based contrast agent (GBCA) alternative. Recently, gadolinium tissue deposition was identified and safety concerns were raised after adverse effects were discovered in canines and humans. Because of this, alternative contrast agents are warranted. One potential alternative is methemoglobinemia induction, which can create T1-weighted signal in vitro. Canines with hereditary methemoglobinemia represent a unique opportunity to investigate methemoglobin modulation. Our objective was to determine if methemoglobinemia could create high intravascular T1-signal in vivo with reversal using methylene blue. Methods: To accomplish this study, a 1.5-year-old male-castrated mixed breed canine with hereditary methemoglobinemia underwent 3T-MRI/MRA with T1-weighted sequences including 3D-T1-weighted Magnetization Prepared Rapid Acquisition Gradient Echo (MPRAGE) and 3D-Time-Of-Flight (TOF). Images were acquired during baseline methemoglobinemia and rescued using intravenous methylene blue (1 mg/kg). Intravascular T1-signal was compared between baseline methemoglobinemia and post-methylene blue. N = 10 separate T1-signal measurements were acquired for each vascular structure, normalized to muscle. Significance was determined using paired two-tailed t-tests and threshold alpha = 0.05. Fold-change was also calculated using the ratio of T1-signal between methemoglobinemia and post-methylene blue states. Results: At baseline, methemoglobin levels measured 19.5% and decreased to 4.9% after methylene blue. On 3D-T1-weighted MPRAGE, visible signal change was present in internal vertebral venous plexus (IVVP, 1.34 ± 0.09 vs. 0.83 ± 0.05, p < 0.001, 1.62 ± 0.06-fold) and external jugular veins (1.54 ± 0.07 vs. 0.87 ± 0.06, p < 0.001, 1.78 ± 0.10-fold). There was also significant change in ventral spinal arterial signal (1.21 ± 0.11 vs. 0.79 ± 0.07, p < 0.001, 1.54 ± 0.16-fold) but not in carotid arteries (2.12 ± 0.10 vs. 2.16 ± 0.11, p = 0.07, 0.98 ± 0.03-fold). On 3D-TOF, visible signal change was in IVVP (1.64 ± 0.14 vs. 1.09 ± 0.11, p < 0.001, 1.50 ± 0.11-fold) and there was moderate change in external jugular vein signal (1.51 ± 0.13 vs. 1.19 ± 0.08, p < 0.001, 1.27 ± 0.07-fold). There were also small but significant differences in ventral spinal arterial signal (2.00 ± 0.12 vs. 1.78 ± 0.10, p = 0.002, 1.13 ± 0.10-fold) but not carotid arteries (2.03 ± 0.17 vs. 1.99 ± 0.17, p = 0.15, 1.02 ± 0.04-fold). Conclusion: Methemoglobin modulation produces intravascular contrast on T1-weighted MRI in vivo. Additional studies are warranted to optimize methemoglobinemia induction, sequence parameters for maximal tissue contrast, and safety parameters prior to clinical implementation.
Monoamines as Drug Targets in Chronic Pain: Focusing on Neuropathic Pain

Monoamines are involved in regulating the endogenous pain system and indeed, peripheral and central monoaminergic dysfunction has been demonstrated in certain types of pain, particularly in neuropathic pain. Accordingly, drugs that modulate the monaminergic system and that were originally designed to treat depression are now considered to be first line treatments for certain types of neuropathic pain (e.g., serotonin and noradrenaline (and also dopamine) reuptake inhibitors). The analgesia induced by these drugs seems to be mediated by inhibiting the reuptake of these monoamines, thereby reinforcing the descending inhibitory pain pathways. Hence, it is of particular interest to study the monoaminergic mechanisms involved in the development and maintenance of chronic pain. Other analgesic drugs may also be used in combination with monoamines to facilitate descending pain inhibition (e.g., gabapentinoids and opioids) and such combinations are often also used to alleviate certain types of chronic pain. By contrast, while NSAIDs are thought to influence the monoaminergic system, they just produce consistent analgesia in inflammatory pain. Thus, in this review we will provide preclinical and clinical evidence of the role of monoamines in the modulation of chronic pain, reviewing how this system is implicated in the analgesic mechanism of action of antidepressants, gabapentinoids, atypical opioids, NSAIDs and histaminergic drugs.
TcellSubC: An Atlas of the Subcellular Proteome of Human T Cells

We have curated an in-depth subcellular proteomic map of primary human CD4+ T cells, divided into cytosolic, nuclear and membrane fractions generated by an optimized fractionation and HiRIEF-LC-MS/MS workflow for limited amounts of primary cells. The subcellular proteome of T cells was mapped under steady state conditions, as well as upon 15 min and 1 h of T cell receptor (TCR) stimulation, respectively. We quantified the subcellular distribution of 6,572 proteins and identified a subset of 237 potentially translocating proteins, including both well-known examples and novel ones. Microscopic validation confirmed the localization of selected proteins with previously known and unknown localization, respectively. We further provide the data in an easy-to-use web platform to facilitate re-use, as the data can be relevant for basic research as well as for clinical exploitation of T cells as therapeutic targets.
Long Noncoding Ribonucleic Acid MSTRG.59589 Promotes Porcine Skeletal Muscle Satellite Cells Differentiation by Enhancing the Function of PALLD

Skeletal muscle satellite cells are a class of undifferentiated mononuclear myogenic stem cells distributed between the myofibroblast and membrane basement. Since their development determines the development of skeletal muscles, knowledge of their proliferation, differentiation, and fate is vital for understanding skeletal muscle development. Increasing evidence have shown that long noncoding RNA (lncRNA) plays an important role in regulating the development process of satellite cells. Based on the results of our previous studies, we screened lncRNA MSTRG.59589, which is highly expressed in skeletal muscle tissue. In the present study, knockdown of MSTRG.59589 significantly inhibited satellite cell differentiation at various time points, whereas overexpression of MSTRG.59589 demonstrated opposite effects. An MSTRG.59589 knockdown cell model was constructed for transcriptome sequencing, and RNA sequencing analysis screened out a large number of differentially expressed genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of these differentially expressed genes revealed that they are mainly enriched in actin cytoskeleton, muscle contraction, and other pathways related to muscle development. Mechanistic analyses showed that MSTRG.59589 could promote the differentiation process of skeletal muscle satellite cells by positively regulating the expression level of the target gene PALLD. This experiment lays a theoretical foundation for deeper studies on the mechanism of MSTRG.59589 in the differentiation of porcine skeletal muscle satellite cells.
Migration Paths for Smart Grids and Their Evaluation Using a Case Study

This contribution will deal with the topic of migration paths in the Smart Grid domain. Migration paths - defined as development steps from one development stage to the next - will be investigated to support network operators in planning the processes and technologies necessary to meet the future requirements of the energy system transformation. For this purpose, a process model was developed which is presented and applied on the basis of a case study. The dependencies and influences between the dimensions considered are investigated and an algorithm is developed and used for their qualitative evaluation. By analyzing and evaluating the migration paths, particularly critical development steps on the entire roadmap that require special control can be shown to the distribution grid.
The Impact of Spatial Normalization Strategies on the Temporal Features of the Resting-State Functional MRI: Spatial Normalization Before rs-fMRI Features Calculation May Reduce the Reliability

Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies frequently applied the spatial normalization on fMRI time series before the calculation of temporal features (here referred to as “Prenorm”). We hypothesized that calculating the rs-fMRI features, for example, functional connectivity (FC), regional homogeneity (ReHo), or amplitude of low-frequency fluctuation (ALFF) in individual space, before the spatial normalization (referred to as “Postnorm”) can be an improvement to avoid artifacts and increase the results’ reliability. We utilized two datasets: (1) simulated images where temporal signal-to-noise ratio (tSNR) is kept a constant and (2) an empirical fMRI dataset with 50 healthy young subjects. For simulated images, the tSNR is constant as generated in individual space but increased after Prenorm and intersubject variability of tSNR was induced. In contrast, tSNR was kept constant after Postnorm. Consistently, for empirical images, higher tSNR, ReHo, and FC (default mode network, seed in precuneus) and lower ALFF were found after Prenorm compared to those of Postnorm. Coefficient of variability of tSNR and ALFF was higher after Prenorm compared to those of Postnorm. Moreover, the significant correlation was found between simulated tSNR after Prenorm and empirical tSNR, ALFF, and ReHo after Prenorm, indicating algorithmic variation in empirical rs-fMRI features. Furthermore, comparing to Prenorm, ALFF and ReHo showed higher intraclass correlation coefficients between two serial scans after Postnorm. Our results indicated that Prenorm may induce algorithmic intersubject variability on tSNR and reduce its reliability, which also significantly affected ALFF and ReHo. We suggest using Postnorm instead of Prenorm for future rs-fMRI studies using ALFF/ReHo.
Convolutional Neural Net-Based Cassava Storage Root Counting Using Real and Synthetic Images

Cassava roots are complex structures comprising several distinct types of root. The number and size of the storage roots are two potential phenotypic traits reflecting crop yield and quality. Counting and measuring the size of cassava storage roots are usually done manually, or semi-automatically by first segmenting cassava root images. However, occlusion of both storage and fibrous roots makes the process both time-consuming and error-prone. While Convolutional Neural Nets have shown performance above the state-of-the-art in many image processing and analysis tasks, there are currently a limited number of Convolutional Neural Net-based methods for counting plant features. This is due to the limited availability of data, annotated by expert plant biologists, which represents all possible measurement outcomes. Existing works in this area either learn a direct image-to-count regressor model by regressing to a count value, or perform a count after segmenting the image. We, however, address the problem using a direct image-to-count prediction model. This is made possible by generating synthetic images, using a conditional Generative Adversarial Network (GAN), to provide training data for missing classes. We automatically form cassava storage root masks for any missing classes using existing ground-truth masks, and input them as a condition to our GAN model to generate synthetic root images. We combine the resulting synthetic images with real images to learn a direct image-to-count prediction model capable of counting the number of storage roots in real cassava images taken from a low cost aeroponic growth system. These models are used to develop a system that counts cassava storage roots in real images. Our system first predicts age group ('young' and 'old' roots; pertinent to our image capture regime) in a given image, and then, based on this prediction, selects an appropriate model to predict the number of storage roots. We achieve 91% accuracy on predicting ages of storage roots, and 86% and 71% overall percentage agreement on counting 'old' and 'young' storage roots respectively. Thus we are able to demonstrate that synthetically generated cassava root images can be used to supplement missing root classes, turning the counting problem into a direct image-to-count prediction task.
Regrowth of Microcosm Biofilms on Titanium Surfaces After Various Antimicrobial Treatments

Objectives: Our aim of this work was to investigate the regrowth of implant-related biofilms after various antimicrobial treatments in vitro. Methods: Saliva-derived microcosm biofilms were grown on titanium discs in an active attachment model. Treatments including hydrogen peroxide (HP), citric acid (CA), chlorhexidine (CHX), and distilled water (control), at different concentrations, were applied to 2-day biofilms for 1 or 5 min. The viability, lactic acid production, and composition of the biofilms were followed for 3 days. The biofilm composition was analyzed by 16S rDNA amplicon sequencing. Results: The short treatments of CA, CHX, and HP resulted in a 2–3 log reduction in biofilm viability and lactic acid production immediately. However, both parameters returned to the pre-treatment level within 2 days due to biofilm regrowth. The alpha diversity of the regrown biofilms in antimicrobial-treated groups tended to decrease, whereas the diversity of those in water-treated group increased. The composition of the regrown biofilms altered compared to those before treatments. Streptococcus and Enterobacteriaceae were enriched in the regrown biofilms. Conclusions: Although the antimicrobial treatments were efficient, the multi-species biofilms were indeed able to regrow within 2 days. The regrown biofilms display an altered microbial diversity and composition, which in the oral cavity may lead to an aggressive infection.