bluuebunny/crossref_metadata_2025_split huggingface dataset:
RNA m6A methylation modulates airway inflammation in allergic asthma via PTX3-dependent macrophage homeostasis
Authors: Han Xiao , Liu Lijuan , Huang Saihua , Xiao Wenfeng , Gao Yajing , Zhou Weitao , Zhang Caiyan , Zheng Hongmei , Yang Lan , Xie Xueru , Liang Qiuyan , Tu Zikun , Yu Hongmiao , Fu Jinrong , Wang Libo , Zhang Xiaobo , Qian Liling , Zhou Yufeng 
Published: November 2023

AbstractN6-methyladenosine (m6A), the most prevalent mRNA modification, has an important function in diverse biological processes. However, the involvement of m6A in allergic asthma and macrophage homeostasis remains largely unknown. Here we show that m6A methyltransferases METTL3 is expressed at a low level in monocyte-derived macrophages from childhood allergic asthma patients. Conditional knockout of Mettl3 in myeloid cells enhances Th2 cell response and aggravates allergic airway inflammation by facilitating M2 macrophage activation. Loss and gain functional studies confirm that METTL3 suppresses M2 macrophage activation partly through PI3K/AKT and JAK/STAT6 signaling. Mechanistically, m6A-sequencing shows that loss of METTL3 impairs the m6A-YTHDF3-dependent degradation of PTX3 mRNA, while higher PTX3 expression positively correlates with asthma severity through promoting M2 macrophage activation. Furthermore, the METTL3/YTHDF3-m6A/PTX3 interactions contribute to autophagy maturation in macrophages by modulating STX17 expression. Collectively, this study highlights the function of m6A in regulating macrophage homeostasis and identifies potential targets in controlling allergic asthma.
IRAK4 degrader in hidradenitis suppurativa and atopic dermatitis: a phase 1 trial
Authors: Ackerman Lindsay , Acloque Gerard , Bacchelli Sandro , Schwartz Howard , Feinstein Brian J. , La Stella Phillip , Alavi Afsaneh , Gollerkeri Ashwin , Davis Jeffrey , Campbell Veronica , McDonald Alice , Agarwal Sagar , Karnik Rahul , Shi Kelvin , Mishkin Aimee , Culbertson Jennifer , Klaus Christine , Enerson Bradley , Massa Virginia , Kuhn Eric , Sharma Kirti , Keaney Erin , Barnes Randy , Chen Dapeng , Zheng Xiaozhang , Rong Haojing , Sabesan Vijay , Ho Chris , Mainolfi Nello , Slavin Anthony , Gollob Jared A. 
Published: November 2023

AbstractToll-like receptor–driven and interleukin-1 (IL-1) receptor–driven inflammation mediated by IL-1 receptor–associated kinase 4 (IRAK4) is involved in the pathophysiology of hidradenitis suppurativa (HS) and atopic dermatitis (AD). KT-474 (SAR444656), an IRAK4 degrader, was studied in a randomized, double-blind, placebo-controlled phase 1 trial where the primary objective was safety and tolerability. Secondary objectives included pharmacokinetics, pharmacodynamics and clinical activity in patients with moderate to severe HS and in patients with moderate to severe AD. KT-474 was administered as a single dose and then daily for 14 d in 105 healthy volunteers (HVs), followed by dosing for 28 d in an open-label cohort of 21 patients. Degradation of IRAK4 was observed in HV blood, with mean reductions after a single dose of ≥93% at 600–1,600 mg and after 14 daily doses of ≥95% at 50–200 mg. In patients, similar IRAK4 degradation was achieved in blood, and IRAK4 was normalized in skin lesions where it was overexpressed relative to HVs. Reduction of disease-relevant inflammatory biomarkers was demonstrated in the blood and skin of patients with HS and patients with AD and was associated with improvement in skin lesions and symptoms. There were no drug-related infections. These results, from what, to our knowledge, is the first published clinical trial using a heterobifunctional degrader, provide initial proof of concept for KT-474 in HS and AD to be further confirmed in larger trials. ClinicalTrials.gov identifier: NCT04772885.
Integrated chemical and genetic screens unveil FSP1 mechanisms of ferroptosis regulation
Authors: Nakamura Toshitaka , Mishima Eikan , Yamada Naoya , Mourão André Santos Dias , Trümbach Dietrich , Doll Sebastian , Wanninger Jonas , Lytton Elena , Sennhenn Peter , Nishida Xavier da Silva Thamara , Angeli José Pedro Friedmann , Sattler Michael , Proneth Bettina , Conrad Marcus 
Published: November 2023

AbstractFerroptosis, marked by iron-dependent lipid peroxidation, may present an Achilles heel for the treatment of cancers. Ferroptosis suppressor protein-1 (FSP1), as the second ferroptosis mainstay, efficiently prevents lipid peroxidation via NAD(P)H-dependent reduction of quinones. Because its molecular mechanisms have remained obscure, we studied numerous FSP1 mutations present in cancer or identified by untargeted random mutagenesis. This mutational analysis elucidates the FAD/NAD(P)H-binding site and proton-transfer function of FSP1, which emerged to be evolutionarily conserved among different NADH quinone reductases. Using random mutagenesis screens, we uncover the mechanism of action of next-generation FSP1 inhibitors. Our studies identify the binding pocket of the first FSP1 inhibitor, iFSP1, and introduce the first species-independent FSP1 inhibitor, targeting the NAD(P)H-binding pocket. Conclusively, our study provides new insights into the molecular functions of FSP1 and enables the rational design of FSP1 inhibitors targeting cancer cells.
Memorability-based multimedia analytics for robotic interestingness prediction system using trimmed Q-learning algorithm
Authors: Ali Hasnain , Gilani Syed Omer , Waris Asim , Shah Umer Hameed , Khattak Muazzam A. Khan , Khan Muhammad Jawad , Afzal Namra 
Published: November 2023

AbstractMobile robots are increasingly employed in today’s environment. Perceiving the environment to perform a task plays a major role in the robots. The service robots are wisely employed in the fully (or) partially known user’s environment. The exploration and exploitation of the unknown environment is a tedious task. This paper introduces a novel Trimmed Q-learning algorithm to predict interesting scenes via efficient memorability-oriented robotic behavioral scene activity training. The training process involves three stages: online learning and short-term and long-term learning modules. It is helpful for autonomous exploration and making wiser decisions about the environment. A simplified three-stage learning framework is introduced to train and predict interesting scenes using memorability. A proficient visual memory schema (VMS) is designed to tune the learning parameters. A role-based profile arrangement is made to explore the unknown environment for a long-term learning process. The online and short-term learning frameworks are designed using a novel Trimmed Q-learning algorithm. The underestimated bias in robotic actions must be minimized by introducing a refined set of practical candidate actions. Finally, the recalling ability of each learning module is estimated to predict the interesting scenes. Experiments conducted on public datasets, SubT, and SUN databases demonstrate the proposed technique’s efficacy. The proposed framework has yielded better memorability scores in short-term and online learning at 72.84% and in long-term learning at 68.63%.
Leveraging a novel NFT-enabled blockchain architecture for the authentication of IoT assets in smart cities
Authors: Khalil Usman , Malik Owais Ahmed , Hong Ong Wee , Uddin Mueen 
Published: November 2023

AbstractThe concept of smart city architecture requires a comprehensive solution that can combine real-time response applications for cyber-physical systems. However, the architecture faces challenges that can obstruct the operations in terms of systems, processes, and data flow as far as the breach risk is concerned. Though the field has been researched with the existence of centralized and distributed architectures to support smart cities. Research gaps regarding security concerns, platform assistance, and resource management continue to persist. This research article presents a novel blockchain-based architecture that proposes expansion in the non-fungible tokens (NFTs) to cater to the expansion of IoT-enabled smart assets. It enables NFTs to employ fog computing for all users and smart devices connected to a fog node in a cyber-physical system. The proposed expansion suggested in Non-Fungible Tokens (NFTs) for IoT assets representation in a cyber-physical system, provides devices and user identification and authentication functionality. The proposed NFT architecture has been designed to provide a smart city solution for cyber-physical systems that ensures robust security features (such as CIA) by introducing new attributes and functions for Owner, User, Fog, and IoT device/s authentication. The validation and rigor of the security services, efficiency, and latency have been achieved by deployments on private and public ledgers. The efficiency, and cost-effectiveness of the suggested functions and components have been evaluated in terms of evaluation cost and time complexity which resulted in promising results, obtained and validated on a testnet. The evaluation cost for the devised mint component was approximately 81%, and devisedapprove()was approximately 23% more efficient than other solutions.
Coalescence and mixing dynamics of droplets in acoustic levitation by selective colour imaging and measurement
Authors: Honda Kota , Fujiwara Kota , Hasegawa Koji , Kaneko Akiko , Abe Yutaka 
Published: November 2023

AbstractAcoustic levitation is well-suited to ‘lab-on-a-drop’ contactless chemical analysis of droplets. Rapid mixing is of fundamental importance in lab-on-a-drop platforms and many other applications involving droplet manipulation. Small droplets, however, have low Reynolds numbers; thus, mixing via turbulence is not possible. Inducing surface oscillation is effective in this regard, however, the relationship between internal flow and mixing dynamics of droplets remains unclear. In this study, we conducted a set of simultaneous optical measurements to assess both the flow field and the distribution of fluid components within acoustically levitated droplets. To achieve this, we developed a technique to selectively separate fluorescent particles within each fluid, permitting the measurement of the concentration field based on the data from the discrete particle distribution. This approach revealed a relationship between the mixing process and the internal flow caused by surface oscillation. Thus, the internal flow induced by surface oscillation could enhance droplet mixing. Our findings will be conducive to the application and further development of lab-on-a-drop devices.
Inhibitory efficiency of Andrographis paniculata extract on viral multiplication and nitric oxide production
Authors: Siridechakorn Ittipon , Bhattarakosol Parvapan , Sasivimolrattana Thanayod , Anoma Sasiprapa , Wongwad Eakkaluk , Nuengchamnong Nitra , Kowitdamrong Ekasit , Boonyasuppayakorn Siwaporn , Waranuch Neti 
Published: November 2023

AbstractAndrographis paniculata (Burm. F.) Nees is a medicinal plant previously reported with broad-spectrum antivirals but the mode of inhibition remains elusive. The objective of this study was to identify the most active fraction from A. paniculata ethanol extract (APE, APE-2A, APE-2B and APE-2C) and dry powder extract (APSP) against influenza A (H3N2), representing RNA viruses, and herpes simplex virus-1 (HSV-1), representing DNA viruses. The results showed that the fractions APSP, APE, APE-2B, and APE-2C directly neutralized the HSV-1 and influenza A (H3N2) when incubated at room temperature for 60 min before infecting the cells. The results also showed that the additional APE-2A fraction also directly neutralized the influenza A (H3N2), but not the HSV-1. The APE, APE-2B and APE-2C inhibited the HSV-1 by more than 0.5 log when the fractions were introduced after infection. Similarly, the APSP and APE inhibited the influenza A (H3N2) more than 0.5 log after infection. Only 50 μg/mL APE-2C inhibited the viruses greater than 0.5 log. In addition, A. paniculata extracts were also evaluated for their interfering capacities against nitric oxide (NO) production in LPS-activated RAW 264.7 macrophages. As well, APE-2C potently inhibited NO production at the IC50 of 6.08 μg/mL. HPLC and LC–MS analysis indicated that the most actively antiviral fractions did not contain any andrographolide derivatives, whereas the andrographolide-rich fractions showed moderate activity.
Harmonization of multi-site diffusion tensor imaging data for cervical and thoracic spinal cord at 1.5 T and 3 T using longitudinal ComBat
Authors: Middleton Devon M. , Li Yutong , Chen Andrew , Shinohara Russell , Fisher Joshua , Krisa Laura , Elliot Mark , Faro Scott H. , Woo John H. , Flanders Adam E. , Mohamed Feroze B. 
Published: November 2023

AbstractMRI scanner hardware, field strengths, and sequence parameters are major variables in diffusion studies of the spinal cord. Reliability between scanners is not well known, particularly for the thoracic cord. DTI data was collected for the entire cervical and thoracic spinal cord in thirty healthy adult subjects with different MR vendors and field strengths. DTI metrics were extracted and averaged for all slices within each vertebral level. Metrics were examined for variability and then harmonized using longitudinal ComBat (longComBat). Four scanners were used: Siemens 3 T Prisma, Siemens 1.5 T Avanto, Philips 3 T Ingenia, Philips 1.5 T Achieva. Average full cord diffusion values/standard deviation for all subjects and scanners were FA: 0.63, σ = 0.10, MD: 1.11, σ = 0.12 × 10−3 mm2/s, AD: 1.98, σ = 0.55 × 10−3 mm2/s, RD: 0.67, σ = 0.31 × 10−3 mm2/s. FA metrics averaged for all subjects by level were relatively consistent across scanners, but large variability was found in diffusivity measures. Coefficients of variation were lowest in the cervical region, and relatively lower for FA than diffusivity measures. Harmonized metrics showed greatly improved agreement between scanners. Variability in DTI of the spinal cord arises from scanner hardware differences, pulse sequence differences, physiological motion, and subject compliance. The use of longComBat resulted in large improvement in agreement of all DTI metrics between scanners. This study shows the importance of harmonization of diffusion data in the spinal cord and potential for longitudinal and multisite clinical research and clinical trials.
A deep-learning automated image recognition method for measuring pore patterns in closely related bolivinids and calibration for quantitative nitrate paleo-reconstructions
Authors: Govindankutty Menon Anjaly , Davis Catherine V. , Nürnberg Dirk , Nomaki Hidetaka , Salonen Iines , Schmiedl Gerhard , Glock Nicolaas 
Published: November 2023

AbstractEutrophication is accelerating the recent expansion of oxygen-depleted coastal marine environments. Several bolivinid foraminifera are abundant in these oxygen-depleted settings, and take up nitrate through the pores in their shells for denitrification. This makes their pore density a possible nitrate proxy. This study documents three aspects related to the porosity of bolivinids. 1. A new automated image analysis technique to determine the number of pores in bolivinids is tested. 2. The pore patterns ofBolivina spissafrom five different ocean settings are analysed. The relationship between porosity, pore density and mean pore size significantly differs between the studied locations. Their porosity is mainly controlled by the size of the pores at the Gulf of Guayaquil (Peru), but by the number of pores at other studied locations. This might be related to the presence of a different crypticBolivinaspecies in the Gulf of Guayaquil. 3. The pore densities of closely related bolivinids in core-top samples are calibrated as a bottom-water nitrate proxy.Bolivina spissaandBolivina subadvenashowed the same correlation between pore density and bottom-water nitrate concentrations, while the pore density ofBolivina argenteaandBolivina subadvena accumeatais much higher.
Neural correlates of weight-shift training in older adults: a randomized controlled study
Authors: de Rond Veerle , D’Cruz Nicholas , Hulzinga Femke , McCrum Christopher , Verschueren Sabine , de Xivry Jean-Jacques Orban , Nieuwboer Alice 
Published: November 2023

AbstractMediolateral weight-shifting is an important aspect of postural control. As it is currently unknown whether a short training session of mediolateral weight-shifting in a virtual reality (VR) environment can improve weight-shifting, we investigated this question and also probed the impact of practice on brain activity. Forty healthy older adults were randomly allocated to a training (EXP, n = 20, age = 70.80 (65–77), 9 females) or a control group (CTR, n = 20, age = 71.65 (65–82), 10 females). The EXP performed a 25-min weight-shift training in a VR-game, whereas the CTR rested for the same period. Weight-shifting speed in both single- (ST) and dual-task (DT) conditions was determined before, directly after, and 24 h after intervention. Functional Near-Infrared Spectroscopy (fNIRS) assessed the oxygenated hemoglobin (HbO2) levels in five cortical regions of interest. Weight-shifting in both ST and DT conditions improved in EXP but not in CTR, and these gains were retained after 24 h. Effects transferred to wider limits of stability post-training in EXP versus CTR. HbO2 levels in the left supplementary motor area were significantly increased directly after training in EXP during ST (change < SEM), and in the left somatosensory cortex during DT (change > SEM). We interpret these changes in the motor coordination and sensorimotor integration areas of the cortex as possibly learning-related.
Chiral domain dynamics and transient interferences of mirrored superlattices in nonequilibrium electronic crystals
Authors: Ravnik J. , Vaskivskyi Ye. , Vodeb J. , Diego M. , Venturini R. , Gerasimenko Ya. , Kabanov V. , Kranjec A. , Mihailovic D. 
Published: November 2023

AbstractMirror symmetry plays a major role in determining the properties of matter and is of particular interest in condensed many-body systems undergoing symmetry breaking transitions under non-equilibrium conditions. Typically, in the aftermath of such transitions, one of the two possible broken symmetry states is emergent. However, synthetic systems and those formed under non-equilibrium conditions may exhibit metastable states comprising of both left (L) and right (R) handed symmetry. Here we explore the formation of chiral charge-density wave (CDW) domains after a laser quench in 1T-TaS2 with scanning tunneling microscopy. Typically, we observed transient domains of both chiralities, separated spatially from each other by domain walls with different structure. In addition, we observe transient density of states modulations consistent with interference of L and R-handed charge density waves within the surface monolayer. Theoretical modeling of the intertwined domain structures using a classical charged lattice gas model reproduces the experimental domain wall structures. The superposition (S) state cannot be understood classically within the correlated electron model but is found to be consistent with interferences of L and R-handed charge-density waves within domains, confined by surrounding domain walls, vividly revealing an interference of Fermi electrons with opposite chirality, which is not a result of inter-layer interference, but due to the interaction between electrons within a single layer, confined by domain wall boundaries.
Experimental and numerical investigation on failure mechanism of expansive soil subgrade slope
Authors: Zhang Hongri , Yang Jiming , Wang Lei , Xu Yongfu , Lan Sulian , Luo Junhui , Chang Zhenchao 
Published: November 2023

AbstractShallow landslides of expensive soil slopes occur from time to time, and most engineering geological problems are directly or indirectly caused by soil structure cracks. The existence of tensile cracks can significantly affect the hydro-mechanical properties of soils. In this paper, the mechanism of expansive soil landslide formation is explored, and swelling pressures, and drying and wetting cycles are introduced into the discrete element method (DEM), and the landslide process of expansive soils is studied by the numerical simulation mothed. The relationship between the crack development and the instability of expansive soil slopes was investigated. The results show that under the condition of seasonal dry and wet alternation, the cracks of the expansive soil slope gradually develop, the rainwater infiltrates rapidly, the mechanical properties gradually deteriorate, and under the effect of such adverse cycle, the soil gradually softens and the stability decreases. Under the influence of human activities, vehicle loads and other factors, the slope body slides. These findings are helpful for the design and construction of expansive soil slopes and foundations.
Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU
Authors: Sikora Andrea , Zhang Tianyi , Murphy David J. , Smith Susan E. , Murray Brian , Kamaleswaran Rishikesan , Chen Xianyan , Buckley Mitchell S. , Rowe Sandra , Devlin John W. 
Published: November 2023

AbstractFluid overload, while common in the ICU and associated with serious sequelae, is hard to predict and may be influenced by ICU medication use. Machine learning (ML) approaches may offer advantages over traditional regression techniques to predict it. We compared the ability of traditional regression techniques and different ML-based modeling approaches to identify clinically meaningful fluid overload predictors. This was a retrospective, observational cohort study of adult patients admitted to an ICU ≥ 72 h between 10/1/2015 and 10/31/2020 with available fluid balance data. Models to predict fluid overload (a positive fluid balance ≥ 10% of the admission body weight) in the 48–72 h after ICU admission were created. Potential patient and medication fluid overload predictor variables (n = 28) were collected at either baseline or 24 h after ICU admission. The optimal traditional logistic regression model was created using backward selection. Supervised, classification-based ML models were trained and optimized, including a meta-modeling approach. Area under the receiver operating characteristic (AUROC), positive predictive value (PPV), and negative predictive value (NPV) were compared between the traditional and ML fluid prediction models. A total of 49 of the 391 (12.5%) patients developed fluid overload. Among the ML models, the XGBoost model had the highest performance (AUROC 0.78, PPV 0.27, NPV 0.94) for fluid overload prediction. The XGBoost model performed similarly to the final traditional logistic regression model (AUROC 0.70; PPV 0.20, NPV 0.94). Feature importance analysis revealed severity of illness scores and medication-related data were the most important predictors of fluid overload. In the context of our study, ML and traditional models appear to perform similarly to predict fluid overload in the ICU. Baseline severity of illness and ICU medication regimen complexity are important predictors of fluid overload.