bluuebunny/crossref_metadata_2025_split huggingface dataset:
Function-specific IL-17A and dexamethasone interactions in primary human airway epithelial cells
Authors: Rahmawati Siti Farah , Vos Rémon , Bos I. Sophie T. , Kerstjens Huib A. M. , Kistemaker Loes E. M. , Gosens Reinoud 
Published: June 2022

AbstractAsthmatics have elevated levels of IL-17A compared to healthy controls. IL-17A is likely to contribute to reduced corticosteroid sensitivity of human airway epithelium. Here, we aimed to investigate the mechanistic underpinnings of this reduced sensitivity in more detail. Differentiated primary human airway epithelial cells (hAECs) were exposed to IL-17A in the absence or presence of dexamethasone. Cells were then collected for RNA sequencing analysis or used for barrier function experiments. Mucus was collected for volume measurement and basal medium for cytokine analysis. 2861 genes were differentially expressed by IL-17A (Padj < 0.05), of which the majority was not sensitive to dexamethasone (< 50% inhibition). IL-17A did inhibit canonical corticosteroid genes, such as HSD11B2 and FKBP5 (p < 0.05). Inflammatory and goblet cell metaplasia markers, cytokine secretion and mucus production were all induced by IL-17A, and these effects were not prevented by dexamethasone. Dexamethasone did reverse IL-17A-stimulated epithelial barrier disruption, and this was associated with gene expression changes related to cilia function and development. We conclude that IL-17A induces function-specific corticosteroid-insensitivity. Whereas inflammatory response genes and mucus production in primary hAECs in response to IL-17A were corticosteroid-insensitive, corticosteroids were able to reverse IL-17A-induced epithelial barrier disruption.
The alcohol dehydrogenase isoenzyme (ADH I) as a marker of intrahepatic cholestasis of pregnancy
Authors: Piechota Joanna , Jelski Wojciech , Orywal Karolina , Mroczko Barbara 
Published: June 2022

AbstractIntrahepatic cholestasis of pregnancy (ICP) is an important pregnancy liver disorder. The alterations of different enzymes activity in the hepatocytes in the course of liver diseases are reflected in an increase in the activity of the corresponding enzymes in the blood. In present study we assayed the activity of alcohol dehydrogenase (ADH) and its isoenzyme in the serum of patients with ICP. Serum were collected from 100 pregnancies with ICP in the second or third trimester of pregnancy. Serum samples were also taken from 100 healthy pregnant women. The activity of ADH I was measured by spectrofluorometric method, ADH total was measured by photometric method. There was significant increase in the activity of ADH I (2.79 mU/l vs. 1.72 mU/l) and total ADH activity (1103 mU/l vs. 682 mU/l) in the sera of women with ICP compared to the healthy pregnant women. Importantly, the sensitivity and specificity of ADH I for diagnosis of ICP were 85% and 91%, respectively. Area under the Receiver Operating Curve for ADH I in ICP was 0.81. The activity of ADH I in the sera of women with ICP is statistically significantly increased, which may have a diagnostic significance for ICP patients.
Resolution of impaired multisensory processing in autism and the cost of switching sensory modality
Authors: Crosse Michael J. , Foxe John J. , Tarrit Katy , Freedman Edward G. , Molholm Sophie 
Published: June 2022

AbstractChildren with autism spectrum disorders (ASD) exhibit alterations in multisensory processing, which may contribute to the prevalence of social and communicative deficits in this population. Resolution of multisensory deficits has been observed in teenagers with ASD for complex, social speech stimuli; however, whether this resolution extends to more basic multisensory processing deficits remains unclear. Here, in a cohort of 364 participants we show using simple, non-social audiovisual stimuli that deficits in multisensory processing observed in high-functioning children and teenagers with ASD are not evident in adults with the disorder. Computational modelling indicated that multisensory processing transitions from a default state of competition to one of facilitation, and that this transition is delayed in ASD. Further analysis revealed group differences in how sensory channels are weighted, and how this is impacted by preceding cross-sensory inputs. Our findings indicate that there is a complex and dynamic interplay among the sensory systems that differs considerably in individuals with ASD.
C. elegans monitor energy status via the AMPK pathway to trigger innate immune responses against bacterial pathogens
Authors: Ju Shouyong , Chen Hanqiao , Wang Shaoying , Lin Jian , Ma Yanli , Aroian Raffi V. , Peng Donghai , Sun Ming 
Published: June 2022

AbstractPathogen recognition and the triggering of host innate immune system are critical to understanding pathogen-host interaction. Cellular surveillance systems have been identified as an important strategy for the identification of microbial infection. In the present study, using Bacillus thuringiensis-Caenorhabditis elegans as a model, we found an approach for surveillance systems to sense pathogens. We report that Bacillus thuringiensis Cry5Ba, a typical pore-forming toxin, caused mitochondrial damage and energy imbalance by triggering potassium ion leakage, instead of directly targeting mitochondria. Interestingly, we find C. elegans can monitor intracellular energy status to trigger innate immune responses via AMP-activated protein kinase (AMPK), secreting multiple effectors to defend against pathogenic attacks. Our study indicates that the imbalance of energy status is a prevalent side effect of pathogen infection. Furthermore, the AMPK-dependent surveillance system may serve as a practicable strategy for the host to recognize and defense against pathogens.
The seasonal evolution of subglacial drainage pathways beneath a soft-bedded glacier
Authors: Hart Jane K. , Young David S. , Baurley Nathaniel R. , Robson Benjamin A. , Martinez Kirk 
Published: June 2022

AbstractSubglacial hydrology is a key element in glacier response to climate change, but investigations of this environment are logistically difficult. Most models are based on summer data from glaciers resting on rigid bedrocks. However a significant number of glaciers rest on soft (unconsolidated sedimentary) beds. Here we present a unique multi-year instrumented record of the development of seasonal subglacial behavior associated with an Icelandic temperate glacier resting on a deformable sediment layer. We observe a distinct annual pattern in the subglacial hydrology based on self-organizing anastomosing braided channels. Water is stored within the subglacial system itself (till, braided system and ‘ponds’), allowing the rapid access of water to enable glacier speed-up events to occur throughout the year, particularly in winter.
Gene expression profile associated with Asmt knockout-induced depression-like behaviors and exercise effects in mouse hypothalamus
Authors: Liu Wenbin , Huang Zhuochun , Xia Jie , Cui Zhiming , Li Lingxia , Qi Zhengtang , Liu Weina 
Published: June 2022

Abstract
Sleep disorder caused by abnormal circadian rhythm is one of the main symptoms and risk factors of depression. As a known hormone regulating circadian rhythms, melatonin (MT) is also namely N-acetyl-5-methoxytryptamine. N-acetylserotonin methyltransferase (Asmt) is the key rate-limiting enzyme of MT synthesis and has been reportedly associated with depression. Although 50–90% of patients with depression have sleep disorders, there are no effective treatment ways in the clinic. Exercise can regulate circadian rhythm and play an important role in depression treatment. In the present study, we showed that Asmt knockout induced depression-like behaviors, which were ameliorated by swimming exercise. Moreover, swimming exercise increased serum levels of MT and 5-hydroxytryptamine (5-HT) in Asmt knockout mice. In addition, the microarray data identified 10 differentially expressed genes (DEGs) in KO mice compared with WT mice and 29 DEGs in KO mice after swimming exercise. Among the DEGs, the direction and magnitude of change in epidermal growth factor receptor pathway substrate 8-like 1 (Eps8l1) and phospholipase C-β 2 (Plcb2) were confirmed by qRT-PCR partly. Subsequent bioinformatic analysis showed that these DEGs were enriched significantly in the p53 signaling pathway, long-term depression and estrogen signaling pathway. In the protein–protein interaction (PPI) networks, membrane palmitoylated protein 1 (Mpp1) and p53-induced death domain protein 1 (Pidd1) were hub genes to participate in the pathological mechanisms of depression and exercise intervention. These findings may provide new targets for the treatment of depression.
INK4 cyclin-dependent kinase inhibitors as potential prognostic biomarkers and therapeutic targets in hepatocellular carcinoma
Authors: Liu Hui , Jia Songhao , Guo Kun , Li Rongkuan 
Published: June 2022

Abstract
The INK4 family is an important family of cyclin-dependent kinase inhibitors (CDKIs) and consists of CDKN2A, CDKN2B, CDKN2, and CDKN2D. Abnormal expression of CDKN2A has been reported in hepatocellular carcinoma (HCC) and is associated with the prognosis of patients and infiltration of immune cells. However, there is a lack of systematic research on the roles of the other INK4 family members in the diagnosis, prognosis, and immune regulation of HCC. Using online public databases and clinical samples, we comprehensively analyzed the INK4 family in HCC. All four INK4 proteins were overexpressed in HCC and correlated with advanced cancer stage and poor prognosis. INK4 expression accurately distinguished tumor from normal tissue, particularly CDKN2A and CDKN2C. The INK4 family participated in cell-cycle regulation and the DNA damage repair pathway, which inhibited genotoxic-induced apoptosis in tumorigenesis. INK4 proteins were positively correlated with the infiltration of immune cells (B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells) and immune checkpoints (CTLA-4, PD1, and PD-L1). CDKN2D had the highest correlation (correlation coefficient &gt;0.3) with all the above-mentioned infiltrating immune cells and immune checkpoints, indicating that it may be useful as an immunotherapy target. The INK4 family was valuable for diagnosis and predicting the prognosis of HCC and participated in the occurrence, progression, and immune regulation of HCC, demonstrating its potential as a diagnostic and prognostic biomarker and therapeutic target in HCC.
Dynamic Assessment for Children With Communication Disorders: A Systematic Scoping Review and Framework
Authors: Bamford Claire King , Masso Sarah , Baker Elise , Ballard Kirrie J. 
Published: June 2022

Purpose:
The aim of this systematic scoping review was to identify the elements that exist in dynamic assessments of communication in children, synthesize and arrange them into a framework, and investigate how these elements have been used in published literature.

Method:
Seven databases were searched using clusters of keywords themed around “dynamic assessment,” “communication,” and “children.” Papers were reviewed against eligibility criteria by two independent reviewers at both title/abstract and full-text screening stages. Data charting included information about study design and the methodological characteristics of identified dynamic assessments.

Results:
Sixty-five papers met inclusion criteria. Dynamic assessments were used in a range of areas of communication and used diverse methodologies. A total of 23 elements were identified and formulated into a Dynamic Assessment Framework, arranged into eight categories within two broad domains. A majority of assessments utilized prompting or cueing as instruction (54%), provided a predetermined amount of instruction (55%), measured child performance (63%), and were prescripted (60%).

Conclusions:
Dynamic assessments are complex. Utilizing a dynamic assessment in clinical practice or research requires a deep understanding of the purpose, clinical population, implementation, and data collection and measurement requirements. From our review of research involving dynamic assessments of communication in children, there is a need for greater transparency of reporting of the elements comprising dynamic assessments. The Dynamic Assessment Framework presented in this review article offers researchers and clinicians a way to have transparent discussions and extend our collective insights into the value of dynamic assessment of children's communication skills.

Supplemental Material:

https://doi.org/10.23641/asha.20151830
Scope of machine learning applications for addressing the challenges in next‐generation wireless networks
Authors: Samanta Raj Kumar , Sadhukhan Bikash , Samaddar Hiranmay , Sarkar Suvobrata , Koner Chandan , Ghosh Monidepa 
Published: June 2022

AbstractThe convenience of availing quality services at affordable costs anytime and anywhere makes mobile technology very popular among users. Due to this popularity, there has been a huge rise in mobile data volume, applications, types of services, and number of customers. Furthermore, due to the COVID‐19 pandemic, the worldwide lockdown has added fuel to this increase as most of our professional and commercial activities are being done online from home. This massive increase in demand for multi‐class services has posed numerous challenges to wireless network frameworks. The services offered through wireless networks are required to support this huge volume of data and multiple types of traffic, such as real‐time live streaming of videos, audios, text, images etc., at a very high bit rate with a negligible delay in transmission and permissible vehicular speed of the customers. Next‐generation wireless networks (NGWNs, i.e. 5G networks and beyond) are being developed to accommodate the service qualities mentioned above and many more. However, achieving all the desired service qualities to be incorporated into the design of the 5G network infrastructure imposes large challenges for designers and engineers. It requires the analysis of a huge volume of network data (structured and unstructured) received or collected from heterogeneous devices, applications, services, and customers and the effective and dynamic management of network parameters based on this analysis in real time. In the ever‐increasing network heterogeneity and complexity, machine learning (ML) techniques may become an efficient tool for effectively managing these issues. In recent days, the progress of artificial intelligence and ML techniques has grown interest in their application in the networking domain. This study discusses current wireless network research, brief discussions on ML methods that can be effectively applied to the wireless networking domain, some tools available to support and customise efficient mobile system design, and some unresolved issues for future research directions.
A combinatorial precoding scheme of cell‐free massive MIMO with channel aging
Authors: Fang Hao , Hu Han , Yang Longxiang , Zhang Yao , Qiao Xu 
Published: June 2022

AbstractHere, we investigate the precoding schemes for the downlink data transmission of a time‐division duplex cell‐free massive multiple‐input multiple‐output (MIMO) system with channel aging, which arises from the user mobility. Closed‐form spectral efficiency (SE) expressions of the downlink with the normalized conjugate beamforming (NCB), and the full‐pilot zero‐forcing (FZF), are derived, which are used for the analytical system performance evaluation. Then, a novel combinatorial precoding scheme with enhanced system SE performance, which adopts either NCB or FZF according to each user channel aging condition, is proposed. Moreover, a pilot allocation strategy is proposed to alleviate the extra interference brought by the combinatorial precoding scheme. Also, a statistical channel cooperative power control is employed to further improve the performance for all the above precoding schemes. Numerical results show that the proposed precoding scheme can substantially improve the average downlink SE.
Boundary tracking control of an unstable cascaded heat system with a non‐collocated feedback
Authors: Zhang Yu‐Long , Wang Jun‐Min 
Published: June 2022

AbstractHere, we consider the output tracking problem of two cascaded heat equations through boundary interconnection, where one heat equation is unstable and the control input is placed on the boundary of the other heat equation. A feedforward control is designed to realize the output tracking to the desired reference signal. A non‐collocated static feedback control is proposed to stabilize the tracking error system. The admissible feedback gain is obtained by applying the Nyquist criterion. Then the closed‐loop tracking error system is showed to be exponentially stable. Finally, the tracking performance is illustrated by the numerical simulation.
MobileTrack: Siamese efficient mobile network for high‐speed UAV tracking
Authors: Xue Yuanliang , Jin Guodong , Shen Tao , Tan Lining , Yang Jing , Hou Xiaohan 
Published: June 2022

AbstractRecently, Siamese‐based trackers have drawn amounts of attention in visual tracking field because of their excellent performance. However, visual object tracking on Unmanned Aerial Vehicles platform encounters difficulties under circumstances such as small objects and similar objects interference. Most existing tracking methods for aerial tracking adopt deeper networks or inefficient policies to promote performance, but most trackers can hardly meet real‐time requirements on mobile platforms with limited computing resources. Thus, in this work, an efficient and lightweight siamese tracker (MobileTrack) is proposed for high‐time Unmanned Aerial Vehicles tracking, realising the balance between performance and speed. Firstly, a lightweight convolutional network (D‐MobileNet) is designed to enhance the characterisation ability of small objects. Secondly, an efficient object‐aware module is proposed for local cross‐channel information exchange, enhancing the feature information of the tracking object. Besides, an anchor‐free region proposal network is introduced to predict the object pixel by pixel. Finally, deep and shallow feature information is fully utilised by cascading multiple anchor‐free region proposal networks for accurate locating and robust tracking. Extensive experiments on the three Unmanned Aerial Vehicles benchmarks show that the proposed tracker achieves outstanding performance while keeping a beyond‐real‐time speed.
Spaceborne pose determination based on image to 3D digital surface model matching
Authors: Chang Xing , Yang Guiqin , Chen Jiayu , Wang Xiaopeng , Jiang Zhanjun 
Published: June 2022

AbstractDeveloping an accurate onboard‐camera pose estimation is one major challenge of satellite systems, and the attempt of improving remote sensing camera pose accuracy never ceases. The camera pose can be recovered by aligning a captured 2D image and a 3D digital surface model of the corresponding scene. In this paper, a novel camera pose estimation method from captured images with the over known real scene 3D products is proposed to enhance remote sensing camera attitude accuracy. The purpose of this estimation is to determine the pose of a camera purely from an image based on a known 3D model, where 3D products of very high spatial resolution are projected onto image space by virtual camera system with error‐contained initial exterior orientation parameters, and whether the pose of the camera can be determined precisely depends on the 2D–3D registration result. The process consists of two steps: (1) feature extraction and (2) similarity measure and registration. Furthermore, the proposed method revises the rotation matrix and translation vector by utilizing formulation based on quaternion representation of rotation, respectively. We evaluate our method on challenging simulation data and results show that acceptable accuracy of camera pose can be achieved.
Advanced post‐impact safety and stability control for electric vehicles
Authors: Ao Di , Li Jialin , Zhang Lin , Hong Jinlong 
Published: June 2022

AbstractIn terms of the possible secondary or multiple events accidents (MEA) after an initial vehicle to vehicle (V2V) impact, this research proposes an active safety & stability controller for independent‐driven electric vehicles towards recovering the vehicle to safety states after an impact. The controller aims to regulate the course angle and lateral deviation that enforce the vehicle back to its original driving path. The upper‐level controller is derived based on sliding mode technique. And the low‐level controller aims to solve a convex quadratic allocation problem, which inherently incorporates fault‐tolerance property. The independent in‐wheel‐motor (IWM) fault is very likely to happen under a real V2V impact and it contributes much to the driving safety & stability. Two typical real‐end‐collision scenarios under low‐ and high longitudinal velocities are designed to verify the proposed controller performance. The low‐ and high velocities collisions aim to simulate the urban and highway accidents, respectively. Compared to other control methods, i.e. pure braking and static allocation, the RMSEs of safety (lateral deviation, course angle) and stability indexes (yaw rate, sideslip angle) under proposed controller reduce significantly both in urban and highway accidents. Moreover, the controller performs robust enough against the impact‐induced IWM fault. It further supports the effectiveness at dealing with the safety and stability of the ego vehicle after an initial impact and prevent possible MEA.
A multi‐channel geometric algebra residual network for traffic data prediction
Authors: Zang Di , Chen Xihao , Lei Juntao , Wang Zengqiang , Zhang Junqi , Cheng Jiujun , Tang Keshuang 
Published: June 2022

AbstractTraffic data prediction offers a significant way to evaluate the future traffic congestion status; many deep learning based approaches have been widely applied in this field. Most current methods only consider short‐term traffic data forecasting; however, long‐term prediction, which supports the optimized distribution of traffic resources, is not well studied. Besides, multiple traffic parameters enable stronger constraints for the data estimation, but the correlation between them in both spatial and temporal domains has not been efficiently learned. Geometric algebra, as a generalization of linear algebra, provides a framework to encode multidimensional data and analyze the correlation. By combining the advantages of the deep neural network and geometric algebra, a multi‐channel geometric algebra residual network (MGAResNet) is proposed to address the problem of long‐term traffic data prediction. Traffic data obtained from two urban expressways are employed and experimental results demonstrate that the approach outperforms the state‐of‐the‐art work.