bluuebunny/crossref_metadata_2025_split huggingface dataset:
LDCT image denoising algorithm based on two-dimensional variational mode decomposition and dictionary learning
Authors: HAN YU , Zhang Nan , Ju Mingchi , Wang Yingzhi , Ding YAN 
Published: January 2024

Abstract

Low-dose CT images result in lower image reconstruction quality due to reduced radiation dose, producing noise and artifacts that affect the accuracy of clinical medical diagnosis. To address this problem, we combined 2D variational modal decomposition and dictionary learning. We proposed a low-dose CT (LDCT) image denoising algorithm based on an improved K-SVD algorithm with image decomposition. The traditional K-SVD algorithm lacks consideration for the differences in structural information between images. To address this problem, we employ the two-dimensional variational mode decomposition (2D-VMD) method to decompose the image into distinct modal components. Through the adaptive learning of dictionaries based on the characteristics of each modal component, independent denoising processing is applied to each component, avoiding the loss of structural and detailed information in the image. In addition, we introduce the regularized orthogonal matching pursuit algorithm (ROMP) and dictionary atom optimization method to improve the sparse representation ability of the dictionary and reduce the impact of noise atoms on denoising performance. The experiments show that the proposed method outperforms other denoising methods regarding peak signal-to-noise ratio and structural similarity. The proposed method maintains the denoised image details and structural information while removing LDCT image noise and artifacts. The image quality after denoising is significantly improved and facilitates more accurate detection and analysis of lesion areas.
NO-Classifier: Prediction of Anti- Neuroinflammatory Agents Using Text Mining of 3D Molecular Fingerprints
Authors: Lee Si Eun , Ahn Sangjin , Kumar Surendra , Kim Mi-hyun 
Published: January 2024

Abstract

CNS Drug discovery has been challenging due to the lack of clarity on CNS diseases' basic biological and pathological mechanisms. Despite the difficulty, some CNS drugs have been developed based on phenotypic effects. Herein, we propose a phenotype-structure relationship model, which predicts an anti-neuroinflammatory potency based on 3D molecular structures of the phenotype-active or inactive compounds without specifying targets. For this chemo-centric study, a predictive model of the nitric oxide (NO) inhibitory potency in hyper-activated microglia is built from the 548 agents, which were collected from 95 research articles (28 substructures consisting of natural products and synthetic scaffolds) and doubly externally validated by the agents of 9 research articles as third set. 3D Structures (multi-conformer ensemble) of every agent were encoded into the E3FP molecular fingerprint of the Keiser group as a 3D molecular representation. The location information of the molecular fingerprints could be learned and validated to classify the inhibitory potency of compounds (IC50 cut-off between bi-classes: 37.1 μM): (1) multi-layer perceptron (MLP) (accuracy: 0.962, AUC: 0.994), (2) recurrent neural network (RNN) (accuracy: 0.966, AUC: 0.994), and (3) convolutional neural network (CNN) (accuracy: 0.969, AUC: 0.996). The high performance of these models was compared with that of four classical machine classification models (Logistic, Ridge, Lasso, and Naïve Bayes). We named the bi-class models NO-Classifier. Out-of-set validation and decision region analysis of the out-of-set doubly demonstrated NO-Classifier effectively discerned the anti-inflammatory potency of testing compounds in inflammatory cell phenotype with the webserver in https://no-classifier.onrender.com.
Learning Agility and Adaptive Legged Locomotion via Curricular Hindsight Reinforcement Learning
Authors: Li Sicen , Pang Yiming , Bai Panju , Liu Zhaojin , Wang Gang , Hu Shihao , Wang Liquan , Li Jiawei 
Published: January 2024

Abstract

Agile and adaptive maneuvers such as fall recovery, high-speed turning, and sprinting in the wild are challenging for legged systems. We propose a Curricular Hindsight Reinforcement Learning (CHRL) that learns an end-to-end tracking controller that achieves powerful agility and adaptation for the legged robot. The two key components are (i) a novel automatic curriculum strategy on task difficulty and (ii) a Hindsight Experience Replay strategy adapted to legged locomotion tasks. We demonstrated successful agile and adaptive locomotion on a real quadruped robot that performed fall recovery autonomously, coherent trotting, sustained outdoor running speeds up to 3.45 m/s, and tuning speeds up to 3.2 rad/s. This system produces adaptive behaviors responding to changing situations and unexpected disturbances on natural terrains like grass and dirt.
Nrn1 Epistasis With Bdnf and Cacna1c: Mediation Effects on Symptom Severity Through Neuroanatomical Changes in Schizophrenia
Authors: Almodóvar-Payá Carmen , Guardiola-Ripoll Maria , Giralt-López Maria , Oscoz-Irurozqui Maitane , Canales-Rodríguez Erick Jorge , Madre Mercè , Soler-Vidal Joan , Ramiro Núria , Callado Luis Felipe , Arias Bárbara , Gallego Carme , Pomarol-Clotet Edith , Fatjó-Vilas Mar 
Published: January 2024

Abstract

The expression of Neuritin-1 (NRN1), a neurotrophic factor crucial for neurodevelopment and synaptic plasticity, is enhanced by the Brain Derived Neurotrophic Factor (BDNF). Although the receptor of NRN1 remains unclear, it is suggested that NRN1's activation of the insulin receptor (IR) pathway promotes the transcription of the calcium voltage-gated channel subunit alpha1 C (CACNA1C). These three genes have been independently associated with schizophrenia risk, symptomatology, and brain differences. However, research on how they synergistically modulate these phenotypes is scarce. We aimed to study whether the genetic epistasis between these genes affects the risk and clinical presentation of the disorder via its effect on brain structure. First, we tested the epistatic effect of NRN1 and BDNF or CACNA1C on i) the risk for SZ, ii) clinical symptoms severity and functionality (onset, PANSS, CGI and GAF), and iii) brain cortical structure (thickness, surface area and volume measures estimated using FreeSurfer) in a sample of 86 SZ patients and 89 healthy subjects. Second, we explored whether those brain clusters influenced by epistatic effects mediate the clinical profiles. Although we did not find a direct epistatic impact on the risk, our data unveiled significant effects on the disorder's clinical presentation. Specifically, the NRN1-rs10484320 x BDNF-rs6265 interplay influenced PANSS general psychopathology, and the NRN1-rs4960155 × CACNA1C-rs1006737 interaction affected GAF scores. Moreover, several interactions between NRN1 SNPs and BDNF-rs6265 significantly influenced the surface area and cortical volume of the frontal, parietal, and temporal brain regions within patients. The NRN1-rs10484320 × BDNF-rs6265 epistasis in the left lateral orbitofrontal cortex fully mediated the effect on PANSS general psychopathology. Our study not only adds clinical significance to the well-described molecular relationship between NRN1 and BDNF but also underscores the utility of deconstructing SZ into biologically validated brain-imaging markers to explore their meditation role in the path from genetics to complex clinical manifestation.
Effect of carboxylic acids in promoting the radical decomposition of H 2 O 2 in Fenton processes for the treatment of agrochemicals
Authors: Zin Lisandra Eda Fusinato , Mulinari Jéssica , Oro Carolina Elisa Demaman , Luccio Marco Di , Dallago Rogério Marcos 
Published: January 2024

Abstract
Agrochemicals have become essential to meet the increasing demand for food and other commodities, but they can contaminate the environment, especially water resources, if not properly managed. Advanced Oxidation Processes (AOP), such as Fenton’s process, are a quick alternative to remove these toxic compounds from water and wastewater. Previous studies suggest that carboxylic acids can promote the Fenton reaction by accelerating the degradation rate of H2O2 and the formation of hydroxyl radicals. In this study, formic and acetic acids were applied in a heterogeneous Fenton system to degrade imidacloprid (C9H10N5ClO2), a model agrochemical molecule. Activated limonite and steel wool were used as low-cost heterogeneous iron precursors. The activated limonite was produced by reducing limonite’s iron under H2 flow at 200 and 300°C. The Fenton process with 300°C-activated limonite showed a reaction rate approximately 8-fold higher than the test using natural limonite and 2-fold higher than the one with limonite activated at 200°C. Adding acetic acid to the Fenton process using the 300°C-activated limonite increased the reaction rate by more than 2-fold. When steel wool was used as the iron precursor, the addition of acetic acid resulted in the complete degradation of imidacloprid within one minute of reaction. Acetic acid exhibited a higher promoting activity than formic acid, and the degradation rate increased with increasing concentrations of both carboxylic acids. This study indicates that carboxylic acids can serve as Fenton promoters to increase the degradation rate of agrochemicals, such as imidacloprid, present in water and wastewater.
Frequency Independent Circular Polarized Featured Multiband Rectenna for RF Ambient Energy Harvesting System
Authors: Roy Sunanda , Tiang Jun-Jiat , Roslee Mardeni Bin , Murshed Abu Hena , Sah Pallav Kumar 
Published: January 2024

Abstract

This research presents a proposed quad-band antenna design aimed at fulfilling the requirements of antenna rectifiers operatingwithin the operating range of 0.5 - 3 GHz. In order to enhance the functionality of Internet of Things (IoT) nodes in real-worldambient settings, a hybrid methodology is used that integrates power from both radio frequency (RF) and direct current (DC)sources. This strategy utilizes certain frequency bands, including GSM 900, GSM 1800, Wi-Fi 2.4 and LTE 2600 GHz bands, toprovide the desired power supply. A circularly polarized bowtie-log periodic antenna with a ring-like central framework andasymmetric slots facilitates more accurate matching in the top four frequency bands and multiband consequence, whereascircular shapes loaded within an open rectangular antenna are used in the lower frequency bands. The proposed approachfor achieving consistent rectenna output under numerous levels of input power and resistances to loads involves the use of amodified π- network with microstrip matching elements. The dc rectification efficiency is highest at 73.30% (at 0.9, 1.8, 2.1, and2.44 GHz for - 17.5 dBm input power level), and 61.8% (at 0.9, and 1.84 GHz for - 14 dBm input power level), individually for thevalue of load resistance at 6.1 kΩ. The DC output voltage that is generated in the system is 464.5 mV to 492.2 mV for indoorand ambient environments, respectively. The encouraging outcomes in both indoor and outdoor environments are great forpowering IoT devices that consume low-power.
Research on Front-Vehicle taillight recognition based on image processing and matching
Authors: Zhang Huayue , Zhang Junyou , Wang Shufeng 
Published: January 2024

Abstract

Taillight recognition is the key to predicting the intention of the vehicle ahead, but the current detection algorithms still have problems in small target and taillight matching. In order to solve the above problems, this paper analyzes the characteristics of automobile taillight signal, combines the image processing algorithm with the object detection technology, and puts forward a taillight signal recognition model based on image processing. Firstly, the characteristics of automobile taillight signal are analyzed, and the taillight is extracted by using HSV color space and corrosion expansion algorithm, and then the taillight is matched by artificial preset experience value to solve the problem of matching headlight. Secondly, P2 small target detection model is added to the YOLOv8s model to improve the recognition ability of small targets, and CA(coordinate attention) is inserted to reduce the interference of other light sources. Finally, EIOU Loss was added to solve the problem of sample imbalance caused by a small number of motorcycles. In this paper, the actual scene video is used for ablation experiments to verify the effectiveness of the improved algorithm. The experimental results show that the mAP value of the model is 9.3% higher than that of the YOLOv8s model.
Evaluating the effects of Curcumin Nano-chitosan on miR-221 and miR-222 expression and Wnt/β-catenin pathways in MCF- 7, MDA-MB-231 and SKBR3 cell lines
Authors: Eslaminejad Touba , Nematollahi-Mahani Seyed Noureddin , Sargazi Marzieh Lotfian , Ansari Mehdi , Mirzaie Vida 
Published: January 2024

Abstract

Breast cancer is one of the most common diseases worldwide. miR-221 and miR-222 are two microRNAs with pivotal roles in many cellular processes which regulate the Wnt/β-catenin pathway. Curcumin (CUR), a yellow polyphenolic compound, targets numerous pathways relevant to cancer therapy. The main aim of this study was to compare the ability of chitosan curcumin nanoparticle (CC-CUR) with the curcumin in modulatingmiR-221andmiR-222expression through Wnt/β-catenin pathway in MCF-7, MDA-MB-231 and SK-BR-3 breast cancer cells. Chitosan-cyclodextrin-tripolyphosphate containing curcumin nanoparticles (CC-CUR) were prepared. Experimental groups including CC-CUR, CUR and negative control were designed. The expression ofmiR-221andmiR-222and Wnt/β-catenin pathway genes was measured. The level ofmiR-221andmiR-222andβ-cateningenes decreased in MCF-7 and MDA-MB-231 cells andWIF1gene increased in all cells in CC-CUR group. However, in SK-BR-3 cells miRs andWIF1gene expressions were increased following CC-CUR administration andβ-catenindecreased by administration of CUR. Significant decreasing ofβ-cateninand increasing ofWIF1gene in almost all three cell lines, indicates that this formulation exerts its effect mainly through the Wnt/β-catenin pathway. These preliminary findings may pave the way for the use of curcumin nanoparticles in the treatment of some known cancers.
Study on the spatial accessibility and influencing factors of traditional villages in the Central Plains Urban Agglomeration of China
Authors: Zhang Yunxing , Li Ziyang , Zhao Cunwei , Li Zhigang , Zhai Feifei , Li Haidong 
Published: January 2024

Abstract
Traditional villages, as important carriers of rural tangible and intangible cultural heritage, have immeasurable economic and cultural value. However, complex geographical characteristics cause the spatial connection between traditional villages and the outside world to be inconvenient, and rapid urbanization ignores their development. Thus, the activation and utilization of traditional villages was difficult. Studying the spatial accessibility of traditional villages is important for development and utilization because of its theoretical and practical significance. A total of 696 traditional villages in the Central Plains Urban Agglomeration (CPUA) of China were taken as the research object, and kernel density estimation and the nearest neighbor index based on ArcGIS were used to analyze their spatial distribution characteristics. Then, the spatial accessibility time costs were calculated via the Amap API. Finally, the influence of each influencing factor was analyzed quantitatively via PCCs, OLS regression and GWR. The results showed that (1) the spatial distribution of traditional villages was clustered, and there was a high-value core-cluster area in the north and two subhigh-value areas in the central and southern parts. (2) The spatial accessibility level of traditional villages exhibited a “core-edge” pattern, decreasing from the core area to the edge area. (3) Factors that exhibited spatial heterogeneity. Influence from the largest to the smallest: slope, density of river, density of road, GDP, density of population, elevation. In addition, the elevation and density of rivers had the greatest impact intensity in the northeast, the slope had the greatest impact in the north, and the density of roads, GDP, and population had the greatest impact at the edge far from the core area.
Endophytic bacteria discovered in oil body organelle of liverworts (Marchantiophyta)
Authors: Young Blair , Thiers Barbara , White James , Struwe Lena 
Published: January 2024

Abstract
Oil bodies are unique organelles with unknown physiological function only present in liverworts (Marchantiophyta). We show that oil bodies host masses of endophytic bacteria derived through cell division in apical meristems, leading to oil body formation in maturing cells. The early divergence of liverworts in land plant evolution indicates that this is one of the oldest symbioses in terrestrial plants with implications for early land plant evolution.
Keystrokes: A practical exploration of semantic drift in timed word association tasks
Authors: MacNiven Sean , MacNiven Maxime , Tench Ralph 
Published: January 2024

Abstract
This study investigates the phenomena of semantic drift through the lenses of language and situated simulation (LASS) and the word frequency effect (WFE) within a timed word association task. Our primary objectives were to determine whether semantic drift can be identified over the short time (25 seconds) of a free word association task (a predicted corollary of LASS), and whether more frequent terms are generated earlier in the process (as expected due to the WFE). We hypothesized that terms generated later in the task (fourth time quartile) would be semantically more distant (cosine similarity) from the cue word than those generated earlier (first quartile), indicating semantic drift. Additionally, we explored the WFE by hypothesizing that earlier generated words would be more frequent and less diverse.
Utilizing a dataset matched with GloVe 300B word embeddings, we analysed semantic distances among 1569 unique term pairs for five cues words (tree, dog, quality, plastic and love) across different time quartiles. Our results supported the presence of semantic drift, with significant evidence of within-participant, semantic drift from the first to fourth quartile. Slightly greater sparsity was also found in later stages, suggesting further evidence of semantic drift, though the effects for sparsity were small. In terms of the WFE, we observed a notable decrease in the diversity of terms generated earlier in the task, while more unique terms (greater diversity and relative uniqueness) were generated in the 4th time quartile, aligning with our hypothesis that more frequently used words dominate early stages of a word association task. We also found the most frequent terms were semantically closer on average than the least frequent terms generated.
Theoretically, our study contributes to the understanding of LASS and the WFE. It suggests that semantic drift might serve as a scalable indicator of the invocation of language versus simulation systems in LASS and might also be used to explore cognition within word association tasks more generally. The findings also add a temporal and relational dimension to the WFE.
Practically, our research highlights the utility of word association tasks in understanding semantic drift and the diffusion of word usage over a sub-minute task, arguably the shortest practically feasible timeframe, offering a scalable method to explore group and individual changes in semantic relationships, whether via the targeted diffusion of influence in a marketing campaign, or seeking to understand differences in cognition more generally. Possible practical uses and opportunities for future research are discussed.
Association of metabolic syndrome, its components, and obesity with bone mineral density in the female population: evidence from NHANES
Authors: Xiong Rui , Zhang Ning , Han Min 
Published: January 2024

Abstract
Objective
We investigated the association of metabolic syndrome (MetS) and obesity with low bone mineral density (LBMD) in women and different menopausal status groups.
Methods
Based on the five NHANES 2-year cycles data, 1662 female participants aged 40 to 79 with BMD data of the lumbar spine and femoral neck were enrolled. Multivariate logistic regression analysis was used to estimate the association between MetS, its components, and obesity with the risk of LBMD in overall participants and premenopausal and postmenopausal subgroups.
Results
MetS was negatively associated with the risk of LBMD in the femoral neck but with no significant association in the lumbar spine. Among the components of MetS, waist circumference, and blood glucose showed negative associations with the LBMD risk. Overweight and obese were negatively associated with the LBMD risk in both femoral necks and lumbar spine. In contrast to the premenopausal participants, MetS was negatively associated with the femoral neck LBMD risk in the postmenopausal population.
Conclusion
Our study found no association or negative association between MetS and the risk of LBMD in the female population. Overweight/obese were negatively associated with the risk of LBMD. These associations may be more pronounced in the postmenopausal population than in the premenopausal population.