bluuebunny/crossref_metadata_2025_split huggingface dataset:
Focused CRISPR-Cas9 genetic screening reveals USO1 as a vulnerability in B-cell acute lymphoblastic leukemia
Authors: Jaiswal Amit Kumar , Truong Hellen , Tran Tiffany M. , Lin Tasha L. , Casero David , Alberti Michael O. , Rao Dinesh S. 
Published: June 2021

AbstractPost-transcriptional gene regulation, including that by RNA binding proteins (RBPs), has recently been described as an important mechanism in cancer. We had previously identified a set of RBPs that were highly dysregulated in B-cell acute lymphoblastic leukemia (B-ALL) with MLL translocations, which carry a poor prognosis. Here, we sought to functionally characterize these dysregulated RBP genes by performing a focused CRISPR dropout screen in B-ALL cell lines, finding dependencies on several genes including EIF3E, EPRS and USO1. Validating our findings, CRISPR/Cas9-mediated disruption of USO1 in MLL-translocated B-ALL cells reduced cell growth, promoted cell death, and altered the cell cycle. Transcriptomic analysis of USO1-deficient cells revealed alterations in pathways related to mTOR signaling, RNA metabolism, and targets of MYC. In addition, USO1-regulated genes from these experimental samples were significantly and concordantly correlated with USO1 expression in primary samples collected from B-ALL patients. Lastly, we found that loss of Uso1 inhibited colony formation of MLL-transformed in primary bone marrow cells from Cas9-EGFP mice. Together, our findings demonstrate an approach to performing focused sub-genomic CRISPR screens and highlight a putative RBP vulnerability in MLL-translocated B-ALL, thus identifying potential therapeutic targets in this disease.
Correlative microscopy and block-face imaging (CoMBI) method for both paraffin-embedded and frozen specimens
Authors: Ishii Nobukazu , Tajika Yuki , Murakami Tohru , Galipon Josephine , Shirahata Hiroyoshi , Mukai Ryo , Uehara Daisuke , Kaneko Ryosuke , Yamazaki Yuichi , Yoshimoto Yuhei , Iwasaki Hirohide 
Published: June 2021

AbstractCorrelative microscopy and block-face imaging (CoMBI), a method that we previously developed, is characterized by the ability to correlate between serial block-face images as 3-dimensional (3D) datasets and sections as 2-dimensional (2D) microscopic images. CoMBI has been performed for the morphological analyses of various biological specimens, and its use is expanding. However, the conventional CoMBI system utilizes a cryostat, which limits its compatibility to only frozen blocks and the resolution of the block-face image. We developed a new CoMBI system that can be applied to not only frozen blocks but also paraffin blocks, and it has an improved magnification for block-face imaging. The new system, called CoMBI-S, comprises sliding-type sectioning devices and imaging devices, and it conducts block slicing and block-face imaging automatically. Sections can also be collected and processed for microscopy as required. We also developed sample preparation methods for improving the qualities of the block-face images and 3D rendered volumes. We successfully obtained correlative 3D datasets and 2D microscopic images of zebrafish, mice, and fruit flies, which were paraffin-embedded or frozen. In addition, the 3D datasets at the highest magnification could depict a single neuron and bile canaliculus.
Establishment and characterization of a new human colon cancer cell line, PUMC-CRC1
Authors: Bian Xiaocui , Cao Fang , Wang Xiaowan , Hou Yuhong , Zhao Haitao , Liu Yuqin 
Published: June 2021

AbstractColorectal cancer (CRC) is one of the most common and fatal gastrointestinal cancers worldwide. Considering their diversity, the establishment of new continuous CRC cell lines with clear genetic backgrounds will provide useful tools for exploring molecular mechanisms, screening and evaluating antitumor drugs in CRC studies. Our de novo CRC cell line, PUMC-CRC1 (Peking Union Medical College Colorectal Cancer 1) was derived from a 47-year-old Chinese female patient diagnosed with moderately to poorly differentiated colon adenocarcinoma. Multiple experiments were used for full characterization. The new cell line was epithelial-like and was passaged for more than 40 times, with a population doubling time of 44 h in vitro, detected by cell counts. The cells exhibited complicated chromosomal abnormalities. The tumor formation rate in SCID mice was 100%. The xenograft tumor was adenocarcinoma with poor to moderate differentiation by Haematoxylin and Eosin staining (H&E) sections. Immunohistochemistry (IHC) analysis and next-generation sequencing (NGS) revealed microsatellite stable (MSS), APC (p.T1493fs) inactivation, KRAS (p.G12V) activation, and SMAD4 (p.V506A) mutation. Quality control of the cell line proved mycoplasma negative and identical STR profile with that of the original tissue, and no interspecific or intraspecific cross contamination was detected. In conclusion, PUMC-CRC1 was a newly established and well characterized human colon cancer cell line, which might be a good model for both in vitro and in vivo studies of the mechanism of colon cancer progression and the treatment strategies for MSS CRC.
Psychophysical profiles in super-recognizers
Authors: Nador Jeffrey D. , Zoia Matteo , Pachai Matthew V. , Ramon Meike 
Published: June 2021

AbstractFacial identity matching ability varies widely, ranging from prosopagnosic individuals (who exhibit profound impairments in face cognition/processing) to so-called super-recognizers (SRs), possessing exceptional capacities. Yet, despite the often consequential nature of face matching decisions—such as identity verification in security critical settings—ability assessments tendentially rely on simple performance metrics on a handful of heterogeneously related subprocesses, or in some cases only a single measured subprocess. Unfortunately, methodologies of this ilk leave contributions of stimulus information to observed variations in ability largely un(der)specified. Moreover, they are inadequate for addressing the qualitative or quantitative nature of differences between SRs’ abilities and those of the general population. Here, therefore, we sought to investigate individual differences—among SRs identified using a novel conservative diagnostic framework, and neurotypical controls—by systematically varying retinal availability, bandwidth, and orientation of faces’ spatial frequency content in two face matching experiments. Psychophysical evaluations of these parameters’ contributions to ability reveal that SRs more consistently exploit the same spatial frequency information, rather than suggesting qualitatively different profiles between control observers and SRs. These findings stress the importance of optimizing procedures for SR identification, for example by including measures quantifying the consistency of individuals’ behavior.
Initial radiological signs of dentofacial deformity in juvenile idiopathic arthritis
Authors: Stoustrup Peter , Traberg Michelle Sys , Matzen Louise Hauge , Glerup Mia , Küseler Annelise , Herlin Troels , Pedersen Thomas Klit 
Published: June 2021

AbstractJuvenile idiopathic arthritis (JIA) is the most common rheumatic disease in childhood and the temporomandibular joint (TMJ) is often involved. TMJ arthritis in growing individuals can cause deformation of facial skeleton (dentofacial deformity) and TMJ components (TMJ deformity). Treatment outcome hinges on early initiation of anti-inflammatory treatment and orthopaedic treatment with dental splints. The aim of the present study was to characterize the radiological signs of dentofacial deformity in patients with a JIA-induced need for orthopaedic treatment. We retrospectively studied 96 patients with JIA and 20 non-JIA controls to identify the initial radiological signs of JIA-induced dentofacial deformity leading to initiation of orthopaedic treatment. We found that initial radiological signs of dentofacial deformities were subtle and characterized by minor mandibular asymmetry and occlusal plane steepening. Radiological findings of TMJ deformity associated with initial dentofacial deformity were frequent and characterized by condylar articular surface flattening (OR 8.42), condylar subcortical cyst (OR 5.94), condylar surface erosion (OR 5.38) and condylar deviation in form (OR 25.39). Radiological signs of TMJ deformity were also documented in TMJs considered “healthy” during initial clinical and radiological examination. This study presents new knowledge of importance for early diagnosis of dentofacial deformity in JIA. Early diagnosis of dentofacial deformity is important as treatment outcome is greatly influenced by timely initiation.
Quantitative not qualitative histology differentiates aneurysmal from nondilated ascending aortas and reveals a net gain of medial components
Authors: Yousef Sameh , Matsumoto Nana , Dabe Issam , Mori Makoto , Landry Alden B. , Lee Shin-Rong , Kawamura Yuki , Yang Chen , Li Guangxin , Assi Roland , Vallabhajosyula Prashanth , Geirsson Arnar , Moeckel Gilbert , Humphrey Jay D. , Tellides George 
Published: June 2021

AbstractMedial degeneration is a common histopathological finding in aortopathy and is considered a mechanism for dilatation. We investigated if medial degeneration is specific for sporadic thoracic aortic aneurysms versus nondilated aortas. Specimens were graded by pathologists, blinded to the clinical diagnosis, according to consensus histopathological criteria. The extent of medial degeneration by qualitative (semi-quantitative) assessment was not specific for aneurysmal compared to nondilated aortas. In contrast, blinded quantitative assessment of elastin amount and medial cell number distinguished aortic aneurysms and referent specimens, albeit with marked overlap in results. Specifically, the medial fraction of elastin decreased from dilution rather than loss of protein as cross-sectional amount was maintained while the cross-sectional number, though not density, of smooth muscle cells increased in proportion to expansion of the media. Furthermore, elastic lamellae did not thin and interlamellar distance did not diminish as expected for lumen dilatation, implying a net gain of lamellar elastin and intralamellar cells or extracellular matrix during aneurysmal wall remodeling. These findings support the concepts that: (1) medial degeneration need not induce aortic aneurysms, (2) adaptive responses to altered mechanical stresses increase medial tissue, and (3) greater turnover, not loss, of mural cells and extracellular matrix associates with aortic dilatation.
A simple high-speed random number generator with minimal post-processing using a random Raman fiber laser
Authors: Monet Frédéric , Boisvert Jean-Sébastien , Kashyap Raman 
Published: June 2021

AbstractA simple novel method for random number generation is presented, based on a random Raman fiber laser. This laser is built in a half-open cavity scheme, closed on one side by a narrow-linewidth 100 mm fiber Bragg grating. The interaction between the randomly excited lasing modes of this laser, in addition to nonlinear effects such as modulation instability, allow the generation of random bits at rates of up to 540 Gbps with minimal post processing. Evaluation of the resulting bit streams’ randomness by the NIST statistical test suite highlights the importance of evaluating the physical entropy content, as bit sequences generated by this random laser pass all the statistical tests with a significance level of 0.01, despite being generated at more than twice the theoretical entropy generation speed.
Silage quality and biogas production from Spartina pectinata L. fermented with a novel xylan-degrading strain of Lactobacillus buchneri M B/00077
Authors: Kupryś-Caruk Marta , Choińska Renata , Dekowska Agnieszka , Piasecka-Jóźwiak Katarzyna 
Published: June 2021

AbstractThe aim of the current study was to determine the ability of the Lactobacillus buchneri M B/00077 strain to degrade xylan, its impact on the quality of silage made from the lignocellulosic biomass of Spartina pectinata L., as well as the efficiency of biogas production. In the model in vitro conditions the L. buchneri M B/00077 strain was able to grow in a medium using xylan as the sole source of carbon, and xylanolytic activity was detected in the post-culture medium. In the L. buchneri M B/00077 genome, genes encoding endo-1,4-xylanase and β-xylosidase were identified. The silages prepared using L. buchneri M B/00077 were characterized by a higher concentration of acetic and propionic acids compared to the controls or the silages prepared with the addition of commercial xylanase. The addition of bacteria increased the efficiency of biogas production. From the silages treated with L. buchneri M B/00077, 10% and 20% more biogas was obtained than from the controls and the silages treated with commercial xylanase, respectively. The results of the current study indicated the strain L. buchneri M B/00077 as being a promising candidate for further application in the field of pretreatment of lignocellulosic biomass.
Precise CAG repeat contraction in a Huntington’s Disease mouse model is enabled by gene editing with SpCas9-NG
Authors: Oura Seiya , Noda Taichi , Morimura Naoko , Hitoshi Seiji , Nishimasu Hiroshi , Nagai Yoshitaka , Nureki Osamu , Ikawa Masahito 
Published: June 2021

AbstractThe clustered regularly interspaced palindromic repeats (CRISPR)/Cas9 system is a research hotspot in gene therapy. However, the widely used Streptococcus pyogenes Cas9 (WT-SpCas9) requires an NGG protospacer adjacent motif (PAM) for target recognition, thereby restricting targetable disease mutations. To address this issue, we recently reported an engineered SpCas9 nuclease variant (SpCas9-NG) recognizing NGN PAMs. Here, as a feasibility study, we report SpCas9-NG-mediated repair of the abnormally expanded CAG repeat tract in Huntington’s disease (HD). By targeting the boundary of CAG repeats with SpCas9-NG, we precisely contracted the repeat tracts in HD-mouse-derived embryonic stem (ES) cells. Further, we confirmed the recovery of phenotypic abnormalities in differentiated neurons and animals produced from repaired ES cells. Our study shows that SpCas9-NG can be a powerful tool for repairing abnormally expanded CAG repeats as well as other disease mutations that are difficult to access with WT-SpCas9.
Multiomic analysis of the Arabian camel (Camelus dromedarius) kidney reveals a role for cholesterol in water conservation
Authors: Alvira-Iraizoz Fernando , Gillard Benjamin T. , Lin Panjiao , Paterson Alex , Pauža Audrys G. , Ali Mahmoud A. , Alabsi Ammar H. , Burger Pamela A. , Hamadi Naserddine , Adem Abdu , Murphy David , Greenwood Michael P. 
Published: June 2021

AbstractThe Arabian camel (Camelus dromedarius) is the most important livestock animal in arid and semi-arid regions and provides basic necessities to millions of people. In the current context of climate change, there is renewed interest in the mechanisms that enable camelids to survive in arid conditions. Recent investigations described genomic signatures revealing evolutionary adaptations to desert environments. We now present a comprehensive catalogue of the transcriptomes and proteomes of the dromedary kidney and describe how gene expression is modulated as a consequence of chronic dehydration and acute rehydration. Our analyses suggested an enrichment of the cholesterol biosynthetic process and an overrepresentation of categories related to ion transport. Thus, we further validated differentially expressed genes with known roles in water conservation which are affected by changes in cholesterol levels. Our datasets suggest that suppression of cholesterol biosynthesis may facilitate water retention in the kidney by indirectly facilitating the AQP2-mediated water reabsorption.
A low-cost post-processing technique improves weather forecasts around the world
Authors: Hewson Timothy David , Pillosu Fatima Maria 
Published: June 2021

AbstractComputer-generated weather forecasts divide the Earth’s surface into gridboxes, each currently spanning about 400 km2, and predict one value per gridbox. If weather varies markedly within a gridbox, forecasts for specific sites inevitably fail. Here we present a statistical post-processing method for ensemble forecasts that accounts for the degree of variation within each gridbox, bias on the gridbox scale, and the weather dependence of each. When applying this post-processing, skill improves substantially across the globe; for extreme rainfall, for example, useful forecasts extend 5 days ahead, compared to less than 1 day without post-processing. Skill improvements are attributed to creation of huge calibration datasets by aggregating, globally rather than locally, forecast-observation differences wherever and whenever the observed “weather type” was similar. A strong focus on meteorological understanding also contributes. We suggest that applications for our methodology include improved flash flood warnings, physics-related insights into model weaknesses and global pointwise re-analyses.
Chiral supramolecular polymerization of dicyanostilbenes with emergent circularly polarized luminescence behavior
Authors: Yin Yueru , Chen Ze , Han Yifei , Liao Rui , Wang Feng 
Published: June 2021

A novel type of circularly polarized luminescence (CPL) active systems have been constructed via chiral supramolecular polymerization of dicyanostilbene-based monomers.
Network intrusion detection using machine learning approaches: Addressing data imbalance
Authors: Ahsan Rahbar , Shi Wei , Corriveau Jean‐Pierre 
Published: June 2021

AbstractCybersecurity has become a significant issue. Machine learning algorithms are known to help identify cyberattacks such as network intrusion. However, common network intrusion datasets are negatively affected by class imbalance: the normal traffic behaviour constitutes most of the dataset, whereas intrusion traffic behaviour forms a significantly smaller portion. A comparative evaluation of the performance is conducted of several classical machine learning algorithms, as well as deep learning algorithms, on the well‐known National Security Lab Knowledge Discovery and Data Mining dataset for intrusion detection. More specifically, two variants of a fully connected neural network, one with an autoencoder and one without, have been implemented to compare their performance against seven classical machine learning algorithms. A voting classifier is also proposed to combine the decisions of these nine machine learning algorithms. All of the models are tested in combination with three different resampling techniques: oversampling, undersampling, and hybrid sampling. The details of the experiments conducted and an analysis of their results are then discussed.
Distribution line parameter estimation driven by probabilistic data fusion of D‐PMU and AMI
Authors: Xiao Mengmeng , Xie Wei , Fang Chen , Wang Shaorong , Li Yan , Liu Shu , Ullah Zia , Zheng Xuejun , Arghandeh Reza 
Published: June 2021

AbstractThis paper proposes a novel distribution line parameter estimation method, driven by the probabilistic data fusion of the distributed phasor measurement unit (D‐PMU) and the advanced measurement infrastructure. The synchronized and high‐precision D‐PMU is utilized to tackle the challenge risen by the a‐synchronization of smart meters. Correspondingly, a time‐alignment algorithm is proposed to obtain the time‐synchronous error (TSE) dataset for the up‐stream smart meter. The non‐parametric estimation method is performed then to evaluate the probabilistic density curve of TSE. Furthermore, TSE data of down‐stream smart meters are generated by implementing the acceptance‐rejection process based on the obtained probabilistic density curve. Leveraging the generated TSE dataset, a new time‐shifted D‐PMU curve is probabilistically aligned or fused with the down‐stream advanced measurement infrastructure curves. According to the complete voltage drop model, the line parameter estimation of resistance and reactance is formulated as a quadratic programming problem and solved by Optimal Toolbox in MATLAB by conducting multi‐run Monte‐Carlo simulations under various scenarios. Simulation results demonstrate the effectiveness and robustness of the proposed methodology.
Road extraction from high resolution remote sensing image via a deep residual and pyramid pooling network
Authors: Han Yibo , Han Pu , Jia Manlei 
Published: June 2021

AbstractThe road extraction from high resolution remote sensing image is of great importance in a variety of applications. Recently, the abundant deep convolutional neural networks are proposed for road extraction task. However, the existing approaches lack suitable strategy to utilize multiple views road features for road extraction, which fails to extract road with smooth appearance and accurate boundary under complex scenes. To address this problem, the authors propose a novel deep residual and pyramid pooling network (DRPPNet) for extracting road regions from high resolution remote sensing image. The DRPPNet consists of three parts: deep residual network (DResNet), pyramid pooling module (PPM) and deep decoder (DD). Specially, the DResNet uses several residual blocks to extract deep road features from input images, which can enhance learning ability of DRPPNet and avoid gradient vanish. Then, PPM is proposed to fuse road features from multiple views and it aims to address disadvantage of single view feature. Finally, the DD is used to recover size of feature maps to input size. Extensive experiments on two challenging road datasets demonstrate that proposed method outperforms the state‐of‐the‐art methods greatly on performance of road extraction task.
Roadside pedestrian motion prediction using Bayesian methods and particle filter
Authors: Xu Qing , Wu Haoran , Wang Jianqiang , Xiong Hui , Liu Jinxin , Li Keqiang 
Published: June 2021

AbstractAccidents between vehicles and pedestrians account for a large partition of severe traffic accidents. So, pedestrian motion prediction becomes a major concern for intelligent vehicles. However, current researches often neglect pedestrian behaviour and/or intention in motion prediction. Meanwhile, related works are scattered and divided into many small fields. No integrated system is proposed to connect the task of perception and decision. To solve these problems, a pedestrian motion prediction model is proposed in this paper. The proposed method predicts pedestrian motion based on the combination of pedestrian crossing behaviour and intention. Pedestrian behaviour is recognized using the Bayesian posterior model, and pedestrian intention is recognized by the dynamic Bayesian network. A modified particle filter and a behavioural motion model are used to integrate the behaviour and intention into motion prediction. The effectiveness of the proposed method is verified in our provided BPI dataset with eight typical scenarios defined by road type, vehicle velocity etc. The results show that this method can give an accurate distribution of pedestrians’ future trajectories.
Joint phase error estimation and sparse scene restoration algorithms for frequency agile radar
Authors: Fei Zhiting , Zhang Jindong , Zhu Xiaohua , Ding Xun 
Published: June 2021

AbstractFrequency agile radar (FAR) systems have been demonstrated to have outstanding performance in electronic counter‐countermeasures (ECCM). With increased bandwidth in which FAR can switch carrier frequency, the phase errorswill appear at the corresponding frequency point, resulting in the decrease of coherence of the system. To accomplish phase error correction and range and velocity information extraction of the target simultaneously, a joint phase error estimation and sparse scene restoration algorithm based on sparse recovery (JPSA‐SR) is proposed here. To further reduce the computational complexity of the algorithm, a joint phase error estimation and sparse scene restoration algorithm based on alternating direction method of multipliers (JPSA‐ADMM) is also proposed. Numerical examples show the effectiveness of the proposed algorithms.