bluuebunny/crossref_metadata_2025_split huggingface dataset:
NICU discharge preparation and transition planning: introduction
Authors: Smith Vincent C. 
Published: February 2022

AbstractThe National Perinatal Association (NPA) coordinated a multidisciplinary work group to develop guidelines and recommendations for Neonatal Intensive Care Unit (NICU) discharge preparation and thus the transition from NICU to home for infants admitted to the NICU and their families. In this section, we explore the concepts of NICU discharge readiness as well as transition planning and preparation. We describe the process that was used to develop the guidelines and recommendations as well as the timeline for the work. NPA hopes that the readers will find the Interdisciplinary Guidelines and Recommendations for NICU Discharge Preparation and Transition Planning to be beneficial, useful, and pertinent.
Whole-brain opto-fMRI map of mouse VTA dopaminergic activation reflects structural projections with small but significant deviations
Authors: Ioanas Horea-Ioan , Saab Bechara John , Rudin Markus 
Published: February 2022

AbstractAscending dopaminergic projections from neurons located in the Ventral Tegmental Area (VTA) are key to the etiology, dysfunction, and control of motivation, learning, and addiction. Due to the evolutionary conservation of this nucleus and the extensive use of mice as disease models, establishing an assay for VTA dopaminergic signaling in the mouse brain is crucial for the translational investigation of motivational control as well as of neuronal function phenotypes for diseases and interventions. In this article we use optogenetic stimulation directed at VTA dopaminergic neurons in combination with functional Magnetic Resonance Imaging (fMRI), a method widely used in human deep brain imaging. We present a comprehensive assay producing the first whole-brain opto-fMRI map of dopaminergic activation in the mouse, and show that VTA dopaminergic system function is consistent with its structural VTA projections, diverging only in a few key aspects. While the activation map predominantly highlights target areas according to their relative projection densities (e.g., strong activation of the nucleus accumbens and low activation of the hippocampus), it also includes areas for which a structural connection is not well established (such as the dorsomedial striatum). We further detail the variability of the assay with regard to multiple experimental parameters, including stimulation protocol and implant position, and provide evidence-based recommendations for assay reuse, publishing both reference results and a reference analysis workflow implementation.
Sirt5-mediated desuccinylation of OPTN protects retinal ganglion cells from autophagic flux blockade in diabetic retinopathy
Authors: Zhang Ye , Li Tingting , Cai Xuan , Long Da , Wang Xiangning , Liu Chang , Wu Qiang 
Published: February 2022

AbstractRetinal neurodegeneration develops early in the course of diabetic retinopathy (DR), and our previous research showed that succinate accumulation results in retinal ganglion cells (RGCs) dysfunction in the retinas of rats with DR. Succinate can enhance lysine succinylation, but the succinylation of DR is not well understood. In this study, we investigated the role of the succinylome in DR and identified the key factor in this process. TMT labeling and LC–MS/MS analysis were combined to quantify the differentially succinylated proteins between vitreous humor (VH) samples from DR and non-DR patients. A total of 74 sites in 35 proteins were differentially succinylated between DR and non-DR vitreous humor samples, among which succinylation of the K108 site of optineurin (OPTN K108su) in the defense response was enriched by GO analysis based on the biological process category. Then, using a streptozotocin (STZ)-induced diabetic rat model, R28 cells and primary rat RGCs (rRGCs), we demonstrated that OPTN underwent lysine succinylation in the retinas of rats with DR and that OPTN K108su mediated autophagic flux blockade under high-glucose (HG) conditions. Sirt5 can desuccinylate OPTN K108su, thus protecting RGCs function from high glucose-induced RGCs autophagic flux blockade in the diabetic retina. Overall, desuccinylation of OPTN is an essential adaptive mechanism for ameliorating autophagic flux blockade in RGCs under DR conditions, and targeting the Sirt5-desuccK108-OPTN axis may thus open an avenue for therapeutic intervention in RCGs dysfunction.
Mechanism of the insulator-to-metal transition and superconductivity in the spin liquid candidate NaYbSe2 under pressure
Authors: Xu Yuanji , Sheng Yutao , Yang Yi-feng 
Published: February 2022

AbstractThe quantum spin liquid candidate NaYbSe2 was recently reported to exhibit a Mott transition under pressure. Superconductivity was observed in the high-pressure metallic phase, raising the question concerning its relation with the low-pressure quantum spin liquid ground state. Here we combine the density functional theory and the dynamical mean-field theory to explore the underlying mechanism of the insulator-to-metal transition and superconductivity and establish an overall picture of its electronic phases under pressure. Our results suggest that NaYbSe2 is a charge-transfer insulator at ambient pressure. Upon increasing pressure, however, the system first enters a semi-metallic state with incoherent Kondo scattering against coexisting localized Yb-4f moments, and then turns into a heavy-fermion metal. In between, there may exist a delocalization quantum critical point responsible for the observed non-Fermi liquid region with linear-in-T resistivity. The insulator-to-metal transition is therefore a two-stage process. Superconductivity emerges in the heavy-fermion phase with well-nested Yb-4f Fermi surfaces, suggesting that spin fluctuations may play a role in the Cooper pairing. NaYbSe2 might therefore be the 3rd Yb-based heavy-fermion superconductor with a very “high” Tc than most heavy-fermion superconductors.
Identification and structure of an extracellular contractile injection system from the marine bacterium Algoriphagus machipongonensis
Authors: Xu Jingwei , Ericson Charles F. , Lien Yun-Wei , Rutaganira Florentine U. N. , Eisenstein Fabian , Feldmüller Miki , King Nicole , Pilhofer Martin 
Published: February 2022

AbstractContractile injection systems (CISs) are phage tail-like nanomachines, mediating bacterial cell–cell interactions as either type VI secretion systems (T6SSs) or extracellular CISs (eCISs). Bioinformatic studies uncovered a phylogenetic group of hundreds of putative CIS gene clusters that are highly diverse and widespread; however, only four systems have been characterized. Here we studied a putative CIS gene cluster in the marine bacterium Algoriphagus machipongonensis. Using an integrative approach, we show that the system is compatible with an eCIS mode of action. Our cryo-electron microscopy structure revealed several features that differ from those seen in other CISs: a ‘cap adaptor’ located at the distal end, a ‘plug’ exposed to the tube lumen, and a ‘cage’ formed by massive extensions of the baseplate. These elements are conserved in other CISs, and our genetic tools identified that they are required for assembly, cargo loading and function. Furthermore, our atomic model highlights specific evolutionary hotspots and will serve as a framework for understanding and re−engineering CISs.
Direct neutrino-mass measurement with sub-electronvolt sensitivity
Authors:   The KATRIN Collaboration, Aker M. , Beglarian A. , Behrens J. , Berlev A. , Besserer U. , Bieringer B. , Block F. , Bobien S. , Böttcher M. , Bornschein B. , Bornschein L. , Brunst T. , Caldwell T. S. , Carney R. M. D. , La Cascio L. , Chilingaryan S. , Choi W. , Debowski K. , Deffert M. , Descher M. , Díaz Barrero D. , Doe P. J. , Dragoun O. , Drexlin G. , Eitel K. , Ellinger E. , Engel R. , Enomoto S. , Felden A. , Formaggio J. A. , Fränkle F. M. , Franklin G. B. , Friedel F. , Fulst A. , Gauda K. , Gil W. , Glück F. , Grössle R. , Gumbsheimer R. , Gupta V. , Höhn T. , Hannen V. , Haußmann N. , Helbing K. , Hickford S. , Hiller R. , Hillesheimer D. , Hinz D. , Houdy T. , Huber A. , Jansen A. , Karl C. , Kellerer F. , Kellerer J. , Kleifges M. , Klein M. , Köhler C. , Köllenberger L. , Kopmann A. , Korzeczek M. , Kovalík A. , Krasch B. , Krause H. , Kunka N. , Lasserre T. , Le T. L. , Lebeda O. , Lehnert B. , Lokhov A. , Machatschek M. , Malcherek E. , Mark M. , Marsteller A. , Martin E. L. , Melzer C. , Menshikov A. , Mertens S. , Mostafa J. , Müller K. , Neumann H. , Niemes S. , Oelpmann P. , Parno D. S. , Poon A. W. P. , Poyato J. M. L. , Priester F. , Ramachandran S. , Robertson R. G. H. , Rodejohann W. , Röllig M. , Röttele C. , Rodenbeck C. , Ryšavý M. , Sack R. , Saenz A. , Schäfer P. , Schaller née Pollithy A. , Schimpf L. , Schlösser K. , Schlösser M. , Schlüter L. , Schneidewind S. , Schrank M. , Schulz B. , Schwemmer A. , Šefčík M. , Sibille V. , Siegmann D. , Slezák M. , Spanier F. , Steidl M. , Sturm M. , Sun M. , Tcherniakhovski D. , Telle H. H. , Thorne L. A. , Thümmler T. , Titov N. , Tkachev I. , Urban K. , Valerius K. , Vénos D. , Vizcaya Hernández A. P. , Weinheimer C. , Welte S. , Wendel J. , Wilkerson J. F. , Wolf J. , Wüstling S. , Wydra J. , Xu W. , Yen Y.-R. , Zadoroghny S. , Zeller G. 
Published: February 2022

AbstractSince the discovery of neutrino oscillations, we know that neutrinos have non-zero mass. However, the absolute neutrino-mass scale remains unknown. Here we report the upper limits on effective electron anti-neutrino mass, mν, from the second physics run of the Karlsruhe Tritium Neutrino experiment. In this experiment, mν is probed via a high-precision measurement of the tritium β-decay spectrum close to its endpoint. This method is independent of any cosmological model and does not rely on assumptions whether the neutrino is a Dirac or Majorana particle. By increasing the source activity and reducing the background with respect to the first physics campaign, we reached a sensitivity on mν of 0.7 eV c–2 at a 90% confidence level (CL). The best fit to the spectral data yields $${{\mbox{}}}{m}_{\nu }^{2}{{\mbox{}}}$$

m

ν

2

= (0.26 ± 0.34) eV2 c–4, resulting in an upper limit of mν < 0.9 eV c–2 at 90% CL. By combining this result with the first neutrino-mass campaign, we find an upper limit of mν < 0.8 eV c–2 at 90% CL.
Mass Screening of SARS-CoV-2 Variants using Sanger Sequencing Strategy in Hiroshima, Japan
Authors: Ko Ko , Takahashi Kazuaki , Nagashima Shintaro , E Bunthen , Ouoba Serge , Hussain Md Razeen Ashraf , Akita Tomoyuki , Sugiyama Aya , Sakaguchi Takemasa , Tahara Hidetoshi , Ohge Hiroki , Ohdan Hideki , Kubo Tatsuhiko , Ishikawa Nobuhisa , Takafuta Toshiro , Fujii Yoshiki , Mimori Michi , Okada Fumie , Kishita Eisaku , Ariyoshi Kunie , Kuwabara Masao , Tanaka Junko 
Published: February 2022

AbstractThis study aimed to develop the feasible and effective universal screening strategy of the notable SARS-CoV-2 variants by Sanger Sequencing Strategy and then practically applied it for mass screening in Hiroshima, Japan. A total of 734 samples from COVID-19 confirmed cases in Hiroshima were screened for the notable SARS-CoV-2 variants (B.1.1.7, B.1.351, P.1, B.1.617.2, B.1.617.1, C.37, B.1.1.529, etc.). The targeted spike region is amplified by nested RT-PCR using in-house designed primer set hCoV-Spike-A and standard amplification protocol. Additionally, randomly selected 96 samples were also amplified using primer sets hCoV-Spike-B and hCoV-Spike-C. The negative amplified samples were repeated for second attempt of amplification by volume-up protocol. Thereafter, the amplified products were assigned for Sanger sequencing using corresponding primers. The positive amplification rate of primer set hCoV-Spike-A, hCoV-Spike-B and hCoV-Spike-C were 87.3%, 83.3% and 93.8% respectively for standard protocol and increased to 99.6%, 95.8% and 96.9% after second attempt by volume-up protocol. The readiness of genome sequences was 96.9%, 100% and 100% respectively. Among 48 mutant isolates, 26 were B.1.1.7 (Alpha), 7 were E484K single mutation and the rest were other types of mutation. Moreover, 5 cluster cases with single mutation at N501S were firstly reported in Hiroshima. This study indicates the reliability and effectiveness of Sanger sequencing to screen large number of samples for the notable SARS-CoV-2 variants. Compared to the Next Generation Sequencing (NGS), our method introduces the feasible, universally applicable, and practically useful tool for identification of the emerging variants with less expensive and time consuming especially in those countries where the NGS is not practically available. Our method allows not only to identify the pre-existing variants but also to examine other rare type of mutation or newly emerged variants and is crucial for prevention and control of pandemic.
Stain-free detection of embryo polarization using deep learning
Authors: Shen Cheng , Lamba Adiyant , Zhu Meng , Zhang Ray , Zernicka-Goetz Magdalena , Yang Changhuei 
Published: February 2022

AbstractPolarization of the mammalian embryo at the right developmental time is critical for its development to term and would be valuable in assessing the potential of human embryos. However, tracking polarization requires invasive fluorescence staining, impermissible in the in vitro fertilization clinic. Here, we report the use of artificial intelligence to detect polarization from unstained time-lapse movies of mouse embryos. We assembled a dataset of bright-field movie frames from 8-cell-stage embryos, side-by-side with corresponding images of fluorescent markers of cell polarization. We then used an ensemble learning model to detect whether any bright-field frame showed an embryo before or after onset of polarization. Our resulting model has an accuracy of 85% for detecting polarization, significantly outperforming human volunteers trained on the same data (61% accuracy). We discovered that our self-learning model focuses upon the angle between cells as one known cue for compaction, which precedes polarization, but it outperforms the use of this cue alone. By compressing three-dimensional time-lapsed image data into two-dimensions, we are able to reduce data to an easily manageable size for deep learning processing. In conclusion, we describe a method for detecting a key developmental feature of embryo development that avoids clinically impermissible fluorescence staining.
A simple yet efficient approach for electrokinetic mixing of viscoelastic fluids in a straight microchannel
Authors: Sasmal C. 
Published: February 2022

AbstractMany complex fluids such as emulsions, suspensions, biofluids, etc., are routinely encountered in many micro and nanoscale systems. These fluids exhibit non-Newtonian viscoelastic behaviour instead of showing simple Newtonian one. It is often needed to mix such viscoelastic fluids in small-scale micro-systems for further processing and analysis which is often achieved by the application of an external electric field and/or using the electroosmotic flow phenomena. This study proposes a very simple yet efficient strategy to mix such viscoelastic fluids based on extensive numerical simulations. Our proposed setup consists of a straight microchannel with small patches of constant wall zeta potential, which are present on both the top and bottom walls of the microchannel. This heterogeneous zeta potential on the microchannel wall generates local electro-elastic instability and electro-elastic turbulence once the Weissenberg number exceeds a critical value. These instabilities and turbulence, driven by the interaction between the elastic stresses and the streamline curvature present in the system, ultimately lead to a chaotic and unstable flow field, thereby facilitating the mixing of such viscoelastic fluids. In particular, based on our proposed approach, we show how one can use the rheological properties of fluids and associated fluid-mechanical phenomena for their efficient mixing even in a straight microchannel.
Humans represent the precision and utility of information acquired across fixations
Authors: Stewart Emma E. M. , Ludwig Casimir J. H. , Schütz Alexander C. 
Published: February 2022

AbstractOur environment contains an abundance of objects which humans interact with daily, gathering visual information using sequences of eye-movements to choose which object is best-suited for a particular task. This process is not trivial, and requires a complex strategy where task affordance defines the search strategy, and the estimated precision of the visual information gathered from each object may be used to track perceptual confidence for object selection. This study addresses the fundamental problem of how such visual information is metacognitively represented and used for subsequent behaviour, and reveals a complex interplay between task affordance, visual information gathering, and metacogntive decision making. People fixate higher-utility objects, and most importantly retain metaknowledge abouthow muchinformation they have gathered about these objects, which is used to guide perceptual report choices. These findings suggest that such metacognitive knowledge is important in situations where decisions are based on information acquired in a temporal sequence.
Combining deep learning and fluorescence imaging to automatically identify fecal contamination on meat carcasses
Authors: Gorji Hamed Taheri , Shahabi Seyed Mojtaba , Sharma Akshay , Tande Lucas Q. , Husarik Kaylee , Qin Jianwei , Chan Diane E. , Baek Insuck , Kim Moon S. , MacKinnon Nicholas , Morrow Jeffrey , Sokolov Stanislav , Akhbardeh Alireza , Vasefi Fartash , Tavakolian Kouhyar 
Published: February 2022

AbstractFood safety and foodborne diseases are significant global public health concerns. Meat and poultry carcasses can be contaminated by pathogens like E. coli and salmonella, by contact with animal fecal matter and ingesta during slaughter and processing. Since fecal matter and ingesta can host these pathogens, detection, and excision of contaminated regions on meat surfaces is crucial. Fluorescence imaging has proven its potential for the detection of fecal residue but requires expertise to interpret. In order to be used by meat cutters without special training, automated detection is needed. This study used fluorescence imaging and deep learning algorithms to automatically detect and segment areas of fecal matter in carcass images using EfficientNet-B0 to determine which meat surface images showed fecal contamination and then U-Net to precisely segment the areas of contamination. The EfficientNet-B0 model achieved a 97.32% accuracy (precision 97.66%, recall 97.06%, specificity 97.59%, F-score 97.35%) for discriminating clean and contaminated areas on carcasses. U-Net segmented areas with fecal residue with an intersection over union (IoU) score of 89.34% (precision 92.95%, recall 95.84%, specificity 99.79%, F-score 94.37%, and AUC 99.54%). These results demonstrate that the combination of deep learning and fluorescence imaging techniques can improve food safety assurance by allowing the industry to use CSI-D fluorescence imaging to train employees in trimming carcasses as part of their Hazard Analysis Critical Control Point zero-tolerance plan.
Brachytic2 mutation is able to counteract the main pleiotropic effects of brown midrib3 mutant in maize
Authors: Landoni Michela , Cassani Elena , Ghidoli Martina , Colombo Federico , Sangiorgio Stefano , Papa Gabriella , Adani Fabrizio , Pilu Roberto 
Published: February 2022

AbstractMaize is the basis of nutrition of domesticated herbivores and one of the most promising energy crops. The presence of lignin in the cell wall, tightly associated to carbohydrates, prevents the physical access of enzymes such as cellulase, limiting the carbohydrate degradability and consequently the energy value. To increase the utilization of the biomass cellulose content, the challenge of breeding programs is to lower or modify the lignin components. In maize several mutations are able to modify the lignin content and in particular the mutation in brown midrib3 (bm3) gene appeared as one of the most promising in breeding programs. Unfortunately this mutation has several negative pleiotropic effects on various important agronomic traits such as stay green, lodging and susceptibility to several infections.The maize Brachyitic 2 (br2) gene encodes for a putative protein involved in polar movement of auxins. br2 mutant plants are characterized by shortening of lower stalk internodes, unusual stalk strength and tolerance to wind lodging, darker leaves persisting longer in the active green state in comparison to wild type plants, suggesting a possible utilization of br2 plants to counteract the negative effects of the bm3 mutation. In this work, we report the generation and a preliminary characterization of the double mutant bm3 br2, suggesting the potential use of this new genetic material to increase biomass cellulose utilization.
Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN
Authors: Kuang Xingyan , Wang Fan , Hernandez Kyle M. , Zhang Zhenyu , Grossman Robert L. 
Published: February 2022

AbstractEffective and timely antibiotic treatment depends on accurate and rapid in silico antimicrobial-resistant (AMR) predictions. Existing statistical rule-based Mycobacterium tuberculosis (MTB) drug resistance prediction methods using bacterial genomic sequencing data often achieve varying results: high accuracy on some antibiotics but relatively low accuracy on others. Traditional machine learning (ML) approaches have been applied to classify drug resistance for MTB and have shown more stable performance. However, there is no study that uses deep learning architecture like Convolutional Neural Network (CNN) on a large and diverse cohort of MTB samples for AMR prediction. We developed 24 binary classifiers of MTB drug resistance status across eight anti-MTB drugs and three different ML algorithms: logistic regression, random forest and 1D CNN using a training dataset of 10,575 MTB isolates collected from 16 countries across six continents, where an extended pan-genome reference was used for detecting genetic features. Our 1D CNN architecture was designed to integrate both sequential and non-sequential features. In terms of F1-scores, 1D CNN models are our best classifiers that are also more accurate and stable than the state-of-the-art rule-based tool Mykrobe predictor (81.1 to 93.8%, 93.7 to 96.2%, 93.1 to 94.8%, 95.9 to 97.2% and 97.1 to 98.2% for ethambutol, rifampicin, pyrazinamide, isoniazid and ofloxacin respectively). We applied filter-based feature selection to find AMR relevant features. All selected variant features are AMR-related ones in CARD database. 78.8% of them are also in the catalogue of MTB mutations that were recently identified as drug resistance-associated ones by WHO. To facilitate ML model development for AMR prediction, we packaged every step into an automated pipeline and shared the source code at https://github.com/KuangXY3/MTB-AMR-classification-CNN.