bluuebunny/crossref_metadata_2025_split huggingface dataset:
Structure of the human RNA polymerase I elongation complex
Authors: Zhao Dan , Liu Weida , Chen Ke , Wu Zihan , Yang Huirong , Xu Yanhui 
Published: October 2021

AbstractEukaryotic RNA polymerase I (Pol I) transcribes ribosomal DNA and generates RNA for ribosome synthesis. Pol I accounts for the majority of cellular transcription activity and dysregulation of Pol I transcription leads to cancers and ribosomopathies. Despite extensive structural studies of yeast Pol I, structure of human Pol I remains unsolved. Here we determined the structures of the human Pol I in the pre-translocation, post-translocation, and backtracked states at near-atomic resolution. The single-subunit peripheral stalk lacks contacts with the DNA-binding clamp and is more flexible than the two-subunit stalk in yeast Pol I. Compared to yeast Pol I, human Pol I possesses a more closed clamp, which makes more contacts with DNA. The Pol I structure in the post-cleavage backtracked state shows that the C-terminal zinc ribbon of RPA12 inserts into an open funnel and facilitates “dinucleotide cleavage” on mismatched DNA–RNA hybrid. Critical disease-associated mutations are mapped on Pol I regions that are involved in catalysis and complex organization. In summary, the structures provide new sights into human Pol I complex organization and efficient proofreading.
GAK and PRKCD are positive regulators of PRKN-independent mitophagy
Authors: Munson Michael J. , Mathai Benan J. , Ng Matthew Yoke Wui , Trachsel-Moncho Laura , de la Ballina Laura R. , Schultz Sebastian W. , Aman Yahyah , Lystad Alf H. , Singh Sakshi , Singh Sachin , Wesche Jørgen , Fang Evandro F. , Simonsen Anne 
Published: October 2021

AbstractThe mechanisms involved in programmed or damage-induced removal of mitochondria by mitophagy remains elusive. Here, we have screened for regulators of PRKN-independent mitophagy using an siRNA library targeting 197 proteins containing lipid interacting domains. We identify Cyclin G-associated kinase (GAK) and Protein Kinase C Delta (PRKCD) as regulators of PRKN-independent mitophagy, with both being dispensable for PRKN-dependent mitophagy and starvation-induced autophagy. We demonstrate that the kinase activity of both GAK and PRKCD are required for efficient mitophagy in vitro, that PRKCD is present on mitochondria, and that PRKCD facilitates recruitment of ULK1/ATG13 to early autophagic structures. Importantly, we demonstrate in vivo relevance for both kinases in the regulation of basal mitophagy. Knockdown of GAK homologue (gakh-1) in C. elegans or knockout of PRKCD homologues in zebrafish led to significant inhibition of basal mitophagy, highlighting the evolutionary relevance of these kinases in mitophagy regulation.
Differential activation of Ca2+ influx channels modulate stem cell potency, their proliferation/viability and tissue regeneration
Authors: Ahamad Naseem , Sun Yuyang , Nascimento Da Conceicao Viviane , Xavier Paul Ezhilan Caroline R. D. , Natarajan Mohan , Singh Brij B. 
Published: October 2021

AbstractStem cells have indefinite self-renewable capability; however, factors that modulate their pluripotency/function are not fully identified. Here we show that store-dependent Ca2+ entry is essential for modulating the function of bone marrow-derived mesenchymal stem cells (MSCs). Increasing external Ca2+ modulated cell cycle progression that was critical for MSCs survival. Additionally, Ca2+ was critical for stem proliferation, its differentiation, and maintaining stem cell potential. Ca2+ channel characterization, including gene silencing, showed two distinct Ca2+ entry channels (through Orai1/TRPC1 or via Orai3) that differentially regulate the proliferation and viability of MSCs. Importantly, NFκB translocation, but not JNK/ERK into the nucleus, was observed upon store depletion, which was blocked by the addition of Ca2+ channel inhibitors. Radiation lead to a decrease in saliva secretion, decrease in acinar cell number, and enlarged ducts were observed, which were restored by the transplantation of stem cells that were propagated in higher Ca2+. Finally radiation showed a decrese in TRPC1 expression along with a decrese in AQP5, which was again restored upon MSC tranplantation. Together these results suggest that Ca2+ entry is essential for stem cell function that could be critical for regenerative medicine.
Molecular characterization and antifungal activity against non-dermatophyte molds causing onychomycosis
Authors: Pakshir Keyvan , Kamali Mandana , Nouraei Hasti , Zomorodian Kamiar , Motamedi Marjan , Mahmoodi Mozhgan 
Published: October 2021

AbstractOnychomycosis is a fungal disease that caused by different types of fungi. Non-dermatophyte molds are a large saprophytic fungi group that live in nature and could affect traumatic nails. The aim of this study was to identify non-dermatophyte molds causing onychomycosis and evaluation of several antifungal activities against the isolates. The samples consisted of 50 non-dermatophyte molds isolated from patients with onychomycosis confirmed by direct and culture examination fungal. DNA was extracted, amplified, and sequenced. Disk diffusion method was used to evaluate itraconazole, fluconazole, ketoconazole, terbinafine, posaconazole, and econazole activity against the isolates. The species identified as: Aspergillus flavus 22 (44%), A. niger 12 (24%), A. fumigates, 3 (6%), A. sydowii 3 (6%), A. terreus 1 (2%), Penicillium commune 2 (4%), P. glabrum 2 (4%), P. chrysogenum, 1 (2%), Fusarium solani 3 (6%) and F. thapsinum 1 (2%). Most of the samples were sensitive to terbinafine, itraconazole, and econazole and 94% of the isolates were resistant to fluconazole. This study showed that Aspergillus species were the most common cause of non-dermatophyte mold onychomycosis and fluconazole was the most resistant antifungals. Care must be taken to choose the appropriate antifungal drug for a better cure.
Long-term evaluation on urban intensive land use in five fast-growing cities of northern China with GEE support
Authors: Shang Yiqun , Zheng Xinqi , Han Rongqing , Liu Wenchao , Xiao Fei 
Published: October 2021

AbstractIntensive land use (ILU) is a multi-objective optimization process that aims to simultaneously improve the economic, social, and ecological benefits, as well as the carrying capacity of the land, without increasing additional land, and evaluation of the ILU over long time series has a guiding significance for rational land use. To tackle inefficient extraction of information, subjective selection of dominant factor, and lack of prediction in previous evaluation studies, this paper proposes a novel framework for evaluation and analysis of ILU by, first, using Google Earth Engine (GEE) to extract cities’ built-up land information, second, by constructing an index system that links economic, social and ecological aspects to evaluate the ILU degree, third, by applying Geodetector to identify the dominant factor on the ILU, finally, by using the S-curve to predict the degree. Based on the case study data from northern China’s five fast-growing cities (i.e., Beijing, Tianjin, Shijiazhuang, Jinan, Zhengzhou), the findings show that the ILU degree for all cities has increased over the past 30 years, with the highest growth rate between 2000 and 2010. Beijing had the highest degree in 2018, followed by Tianjin, Zhengzhou, Jinan, and Shijiazhuang. In terms of the time dimension, the dominant factor for all cities shifted from the output-value proportion of secondary and tertiary industries in the early stage to the economic density in the late stage. In terms of the space dimension, the dominant factor varied from cities. It is worth noting that economic density was the dominant factor in the two high-level ILU cities, Beijing and Tianjin, indicating that economic strength is the main driver of the ILU. Moreover, cities with high-level ILU at the current stage will grow slowly in the ILU degree from 2020 to 2035, while Zhengzhou and Jinan, whose ILU has been in the midstream recently, will grow the most among the cities.
Discovery and characterization of high-affinity, potent SARS-CoV-2 neutralizing antibodies via single B cell screening
Authors: Schardt John S. , Pornnoppadol Ghasidit , Desai Alec A. , Park Kyung Soo , Zupancic Jennifer M. , Makowski Emily K. , Smith Matthew D. , Chen Hongwei , Garcia de Mattos Barbosa Mayara , Cascalho Marilia , Lanigan Thomas M. , Moon James J. , Tessier Peter M. 
Published: October 2021

AbstractMonoclonal antibodies that target SARS-CoV-2 with high affinity are valuable for a wide range of biomedical applications involving novel coronavirus disease (COVID-19) diagnosis, treatment, and prophylactic intervention. Strategies for the rapid and reliable isolation of these antibodies, especially potent neutralizing antibodies, are critical toward improved COVID-19 response and informed future response to emergent infectious diseases. In this study, single B cell screening was used to interrogate antibody repertoires of immunized mice and isolate antigen-specific IgG1+ memory B cells. Using these methods, high-affinity, potent neutralizing antibodies were identified that target the receptor-binding domain of SARS-CoV-2. Further engineering of the identified molecules to increase valency resulted in enhanced neutralizing activity. Mechanistic investigation revealed that these antibodies compete with ACE2 for binding to the receptor-binding domain of SARS-CoV-2. These antibodies may warrant further development for urgent COVID-19 applications. Overall, these results highlight the potential of single B cell screening for the rapid and reliable identification of high-affinity, potent neutralizing antibodies for infectious disease applications.
Genetic landscape of T cells identifies synthetic lethality for T-ALL
Authors: O’Meara Connor P. , Guerri Lucia , Lawir Divine-Fondzenyuy , Mateos Fernando , Iconomou Mary , Iwanami Norimasa , Soza-Ried Cristian , Sikora Katarzyna , Siamishi Iliana , Giorgetti Orlando , Peter Sarah , Schorpp Michael , Boehm Thomas 
Published: October 2021

AbstractTo capture the global gene network regulating the differentiation of immature T cells in an unbiased manner, large-scale forward genetic screens in zebrafish were conducted and combined with genetic interaction analysis. After ENU mutagenesis, genetic lesions associated with failure of T cell development were identified by meiotic recombination mapping, positional cloning, and whole genome sequencing. Recessive genetic variants in 33 genes were identified and confirmed as causative by additional experiments. The mutations affected T cell development but did not perturb the development of an unrelated cell type, growth hormone-expressing somatotrophs, providing an important measure of cell-type specificity of the genetic variants. The structure of the genetic network encompassing the identified components was established by a subsequent genetic interaction analysis, which identified many instances of positive (alleviating) and negative (synthetic) genetic interactions. Several examples of synthetic lethality were subsequently phenocopied using combinations of small molecule inhibitors. These drugs not only interfered with normal T cell development, but also elicited remission in a model of T cell acute lymphoblastic leukaemia. Our findings illustrate how genetic interaction data obtained in the context of entire organisms can be exploited for targeted interference with specific cell types and their malignant derivatives.
Strong nutrient-plant interactions enhance the stability of ecosystems
Authors: Schonberger Zachariah G. , McCann Kevin , Gellner Gabriel 
Published: October 2021

AbstractModular food web theory shows how weak energetic fluxes resulting from consumptive interactions plays a major role in stabilizing food webs in space and time. Despite the reliance on energetic fluxes, food web theory surprisingly remains poorly understood within an ecosystem context that naturally focuses on material fluxes. At the same time, while ecosystem theory has employed modular nutrient-limited ecosystem models to understand how limiting nutrients alter the structure and dynamics of food webs, ecosystem theory has overlooked the role of key ecosystem interactions and their strengths (e.g., plant-nutrient; R-N) in mediating the stability of nutrient-limited ecosystems. Here, towards integrating food web theory and ecosystem theory, we first briefly review consumer-resource interactions (C-R) highlighting the relationship between the structure of C-R interactions and the stability of food web modules. We then translate this framework to nutrient-based systems, showing that the nutrient-plant interaction behaves as a coherent extension of current modular food web theory; however, in contrast to the rule that weak C-R interactions tend to be stabilizing we show that strong nutrient-plant interactions are potent stabilizers in nutrient-limited ecosystem models.
A new family of “megaphages” abundant in the marine environment
Authors: Michniewski Slawomir , Rihtman Branko , Cook Ryan , Jones Michael A , Wilson William H , Scanlan David J , Millard Andrew 
Published: October 2021

Abstract
Megaphages, bacteriophages harbouring extremely large genomes, have recently been found to be ubiquitous, being described from a variety of microbiomes ranging from the animal gut to soil and freshwater systems. However, no complete marine megaphage has been identified to date. Here, using both short and long read sequencing, we assembled &gt;900 high-quality draft viral genomes from water in the English Channel. One of these genomes included a novel megaphage, Mar_Mega_1 at &gt;650 Kb, making it one of the largest phage genomes assembled to date. Utilising phylogenetic and network approaches, we found this phage represents a new family of megaphages. Genomic analysis showed Mar_Mega_1 shares relatively few homologues with its closest relatives, but, as with other megaphages Mar_Mega_1 contained a variety of auxiliary metabolic genes responsible for carbon metabolism and nucleotide biosynthesis, including a NADP-dependent isocitrate dehydrogenase [Idh] and nicotinamide-nucleotide amidohydrolase [PncC], which have not previously been identified in megaphages. Mar_Mega_1 was abundant in a marine virome sample and related phages are widely prevalent in the oceans.
Gender Spectrum Voice Inventory: A Pilot Study
Authors: Van Hook Emily , Duffy Sarah 
Published: October 2021

Purpose:
Gender affirming voice therapy (GAVT) for transgender and nonbinary people, for some, is a vital step to gaining confidence and satisfaction with one's gender presentation. Speech-language pathologists (SLPs) guide these individuals through this process through skilled voice evaluation and therapy. There are currently scarce validated clinical assessments for transgender men or nonbinary people seeking GAVT. This was a pilot study to trial the Gender Spectrum Voice Inventory (GSVI), a comprehensive gender affirming voice assessment created by the researchers to fill this gap in available clinical tools. A comparable assessment does not exist currently in the field.

Method:
This study used a mixed-methods design. SLP participants administered the GSVI to four transgender feminine clients and completed a survey afterwards. It is a future goal to expand the researched population for the GSVI to people across the gender spectrum. The deidentified test data were analyzed by the researchers for potential trends. The desired outcomes were to determine if the GSVI had possible indications of face validity and if SLPs administering the test perceived it to be a comprehensive and effective assessment.

Conclusions:
Findings suggest the developed measure may have good face validity. Survey results were generally positive in the areas of the test's utility, effectiveness, and comprehensiveness, and areas were identified for improvement in the test's continued development. These results call for future trials of the GSVI, investigating its validity and establishing norms in the future, with clients across the gender spectrum and a larger sample size.
FANET: Smart city mobility off to a flying start with self‐organized drone‐based networks
Authors: Siddiqi Muhammad Hameed , Draz Umar , Ali Amjad , Iqbal Muddesar , Alruwaili Madallah , Alhwaiti Yousef , Alanazi Saad 
Published: October 2021

AbstractDue to recent advancements in smart city traffic and transport monitoring industry 4.0 applications. Flying Ad‐Hoc Networks (FANETs) ability to cover geographically large areas, makes it a suitable technology to address the challenges faced during remote areas traffic monitoring. The implementation of drone based FANETs have several advantages in remote traffic monitoring, including free air‐to‐air drone assisted communication zone and smart surveillance and security. The drone‐based FANETs can be deployed within minutes without requiring physical infrastructure, making it suitable for mission critical applications in several areas of interests. Here a drone‐based FANETs application for smart city remote traffic monitoring is presented while addressing several challenges including coverage of larger geographical area and data communication links between FANETs nodes. A FANET‐inspired enhanced ACO algorithm that easily coped with drone assisted technology of FANETs is proposed to cover the large areas. Simulation results are presented to compare the proposed technique against different network lifetime and number of received packets. The presented results show that the proposed technique perform better compared to other state‐of‐the‐art techniques.
Energy‐efficiency hybrid beamforming architecture design for MIMO network
Authors: Cao Kuo , Zhang Jiao , Xiong Jun 
Published: October 2021

AbstractThis letter investigates the hybrid beamforming architecture design for MIMO network, where the connection between each antenna and each radio frequency chain is controlled by a switch. The optimization of the switch‐controlled connection state vector is studied for maximizing the energy efficiency of the system. The problem of connection‐state vector design is transformed into a single‐variable optimization problem, and the optimal hybrid beamforming architecture is derived. Numerical examples validate the effectiveness of the proposed scheme.
A decomposition‐based multi‐time dimension long short‐term memory model for short‐term electric load forecasting
Authors: Huang Jiehui , Zhou Zhiwang , Li Chunquan , Liao Zhiyuan , Liu Peter X. 
Published: October 2021

AbstractShort‐term load forecasting is essential to power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate the intrinsic time‐related dimensions of electric load data and the decomposition methods into machine learning models so that their prediction accuracy and robustness still have much room for improvement. To solve this problem, this paper proposes a decomposition‐based multi‐time dimension long short‐term memory (DB‐MTD‐LSTM) model for short‐term electric load forecasting (STELF). In DB‐MTD‐LSTM, empirical mode decomposition with adaptive noise (CEEMDAN) is first introduced to smooth non‐linear non‐stationary electric load data and constrain the modal aliasing or noise of decomposed electric load data in the traditional decomposed method. A joint relevant time dimensions method (JRTDM) is then developed using autocorrelation analysis to rationally extract the temporal characteristics of decomposed data in multiple time dimensions. An improved LSTM called MTD‐LSTM is developed by combining JRTDM with LSTM, which can effectively apply multi‐dimensional time characteristics of the decomposed load to improve the predictive accuracy and robustness. Several datasets from Australia and China are performed to check the predictive performance of DB‐MTD‐LSTM. Experimental results verify that DB‐MTD‐LSTM has better predictive accuracy and satisfactory robustness compared with state‐of‐the‐art and conventional predictive models.
Deep learning‐based SCUC decision‐making: An intelligent data‐driven approach with self‐learning capabilities
Authors: Yang Nan , Yang Cong , Xing Chao , Ye Di , Jia Junjie , Chen Daojun , Shen Xun , Huang Yuehua , Zhang Lei , Zhu Binxin 
Published: October 2021

AbstractThis paper proposes an intelligent Deep Learning (DL) based approach for Data‐Driven Security‐Constrained Unit Commitment (DD‐SCUC) decision‐making. The proposed approach includes data pre‐processing and a two‐stage decision‐making process. Firstly, historical data is accumulated and pre‐processed. Then, the DD‐SCUC model is created based on the Gated Recurrent Unit‐Neural Network (GRU‐NN). The mapping model between system daily load and decision results is created by training the DL model with historical data and then is utilized to make SCUC decisions. The two‐stage decision‐making process outputs the decision results based on various applications and scenarios. This approach has self‐learning capabilities because the accumulation of historical data sets can revise the mapping model and therefore improve its accuracy. Simulation results from the IEEE 118‐bus test system and a real power system from China showed that compared with deterministic Physical‐Model‐Driven (PMD)‐SCUC methods, the approach has higher accuracy, better efficiency in the practical use case, and better adaptability to different types of SCUC problems.
Probabilistic optimal planning in active distribution networks considering non‐linear loads based on data clustering method
Authors: Ebrahimi Hasan , Rezaeian‐Marjani Saeed , Galvani Sadjad , Talavat Vahid 
Published: October 2021

AbstractRenewable energies have a significant portion in supplying energy demands in modern distribution networks. Due to the wide use of power electronic devices, these networks may have power quality problems. The unpredictable nature of renewable energies, besides the effect of non‐linear loads brings out serious planning and operating challenges for distribution systems. Basicly, harmonic distortion is a severe problem for both electric efficiency and power energy customers. This study proposes an optimal scheduling strategy for wind turbine's integrated distribution networks with non‐linear loads using a multi‐objective individualized instruction mechanism teaching‐learning‐based optimization algorithm and the best solution is selected via the TOPSIS technique. In the proposed strategy, energy storage systems are optimally scheduled besides wind turbines, and reactive power compensators. Also, to use the distribution network more efficiently, an optimal network reconfiguration is applied. The wind turbine's output and load demands have probabilistic nature. The proposed scheme reduces the total harmonic distortion as well as total costs. The efficacy of the proposed management scheme is investigated using the IEEE standard 33 bus distribution network. Also, the performance of the multi‐objective individualized instruction mechanism teaching‐learning‐based optimization algorithm is compared with the multi‐objective particle swarm optimization algorithm.
Image enhancement via texture protection Retinex
Authors: Dong Linlu , Zhao Liangjun , Wang Jun 
Published: October 2021

AbstractImages obtained in dim light do not clearly represent the target scene, limiting information transmission on the image carrier. This study proposes a texture‐preserving image enhancement method, i.e. ETPR. The proposed method draws the illumination map of a low light image by Max‐RGB, and then applies the weighted median filter algorithm and Retinex to improve the illumination map further. Then, the enhanced image is changed from RGB mode to YCbCr mode and the texture of the luminance component Y is described. It is achieved by denoising the texture of the image to be enhanced, and then the final enhanced image is obtained by the method of sub‐region image fusion denoising. The effectiveness of ETPR is established by comparing it with existing enhancement technologies using public data and real images.