bluuebunny/crossref_metadata_2025 huggingface dataset:
Design and Rate Control of Large Titanium Alloy Springs for Aerospace Applications
Authors: Li Lei , Xu Qiufa , Yang Haiying , Ying Yang , Cao Zuhan , Guo Dizi , Ji Vincent
Published: June 2024

During the separation between satellite and launch vehicles, large steel springs are often used as compression separation spring sets in a catapult separation system. Replacing the steel springs with titanium alloy springs could reduce weight by about 50%. Although titanium alloy springs have been widely used in the aerospace field due to their excellent performance, there are few reports on the design of high-precision titanium alloy springs. The current spring design standards mainly focus on steel springs with helix angles between 5° and 9°, which are not applicable to titanium springs. Moreover, the change in spring rate with ambient temperature should also be considered. In this paper, β-C titanium alloy was used to design and prepare large compression separation springs, replacing steel springs in the catapult separation system. The design of titanium alloy springs took into account the big helix angle. The relationship between helix angle and the number of active coils was calculated. The parameters of titanium alloy springs were determined by the shear stress of the spring at working length. The effects of aging temperature and aging duration on the mechanical properties and modulus of β-C alloy were studied. By adjusting the aging process, the β-C alloy spring rate was controlled to meet the design requirements. The effect of ambient temperature on the mechanical properties and modulus of β-C titanium alloy were also investigated. It was found that as the ambient temperature increased, the rate of the β-C alloy spring gradually decreased.
Experimental and Numerical Analysis of Straw Motion under the Action of an Anti-Blocking Mechanism for a No-Till Maize Planter
Authors: Zhang Qingyi , Fang Huimin , Xu Gaowei , Niu Mengmeng , Li Jinyu
Published: June 2024

To address the low clearance rate issue of the anti-blocking mechanism for maize no-till planters in the Huang-Huai-Hai Plain of China, experiments and simulations were conducted to analyze the individual and collective movements of straw under the action of the round roller-claw anti-blocking mechanism. A tracer-based measurement method for straw displacement was applied firstly. Experimental results showed that the straw forward displacement could be characterized by the average horizontal displacements of longitudinal and lateral tracers, while the straw side displacement could be characterized by the lateral displacement of the longitudinal tracer. The straw forward displacement was 58.95% greater than the side displacement. Forward, side, and total displacements of straw increased as the mechanism’s forward speed increased from 3 km/h to 7 km/h, with corresponding rates of increase at 233.98%, 43.20%, and 162.47%, respectively. Furthermore, a model of straw–soil–mechanism interaction was constructed in EDEM 2022 software. The relative error between experimental and simulated straw clearance rates was 11.20%, confirming the applicability of the simulation model for studying straw–soil–mechanism interaction. Based on the simulation model, three straw tracers of different lengths were selected to study the motion behavior of straw. It was inferred that despite differences in straw length, the movement behaviors of the three straw tracers under the influence of the anti-blocking mechanism were similar. Additionally, longer straws exhibited greater displacements in all directions. This paper serves as a reference for studying straw motion behavior influenced by anti-blocking mechanisms.
Improved Lightweight Zero-Reference Deep Curve Estimation Low-Light Enhancement Algorithm for Night-Time Cow Detection
Authors: Yu Zijia , Guo Yangyang , Zhang Liyuan , Ding Yi , Zhang Gan , Zhang Dongyan
Published: June 2024

With the advancement of agricultural intelligence, dairy-cow farming has become a significant industry, and the application of computer vision technology in the automated monitoring of dairy cows has also attracted much attention. However, most of the images in the conventional detection dataset are high-quality images under normal lighting, which makes object detection very challenging in low-light environments at night. Therefore, this study proposed a night-time detection framework for cows based on an improved lightweight Zero-DCE (Zero-Reference Deep Curve Estimation) image enhancement network for low-light images. Firstly, the original feature extraction network of Zero-DCE was redesigned with an upsampling structure to reduce the influence of noise. Secondly, a self-attention gating mechanism was introduced in the skip connections of the Zero-DCE to enhance the network’s attention to the cow area. Then, an improved kernel selection module was introduced in the feature fusion stage to adaptively adjust the size of the receptive field. Finally, a depthwise separable convolution was used to replace the standard convolution of Zero-DCE, and an Attentive Convolutional Transformer (ACT) module was used to replace the iterative approach in Zero-DCE, which further reduced the computational complexity of the network and speeded up the inference. Four different object-detection models, YOLOv5, CenterNet, EfficientDet, and YOLOv7-tiny, were selected to evaluate the performance of the improved network and were tested on the night-time dataset before and after enhancement. Experimental results demonstrate that the detection performance of all models is significantly improved when processing night-time image samples through the enhanced Zero-DCE model. In summary, the improved lightweight Zero-DCE low-light enhancement network proposed in this study shows excellent performance, which can ensure that various object-detection models can quickly and accurately identify targets in low-light environments at night and are suitable for real-time monitoring in actual production environments.
The Effects of Flax and Mustard Seed Inclusion in Dairy Goats’ Diet on Milk Nutritional Quality
Authors: Oancea Alexandra-Gabriela , Dragomir Catalin , Untea Arabela Elena , Saracila Mihaela , Cismileanu Ana Elena , Vlaicu Petru Alexandru , Varzaru Iulia
Published: June 2024

Our study evaluated the impact of incorporating flax seeds or a flax and mustard seeds’ mixture into goats’ diets to enhance milk polyunsaturated fatty acids (PUFAs). The incorporation of mustard seeds also aimed to slow the lipid oxidation process. A three-week feeding trial was conducted on 18 lactating goats, randomly distributed in three groups: control (C), FS (replacing 12% of the oil source with flax seeds), and FMS (replacing a quarter of flax seeds with mustard seeds). Flax seed inclusion improved the quality of milk fat by reducing saturated fatty acid concentration (p = 0.004) and increasing PUFA levels (p = 0.001). Both experimental groups significantly lowered the omega 6/omega 3 fatty acid ratio (p < 0.001). The FMS group showed a significantly higher total vitamin E concentration (p = 0.007). The fat oxidation parameters revealed that after 24 h of storage at room temperature, the p-anisidine value increased for the FS group compared to the C group, while the FSM group showed no significant difference, suggesting that the combined inclusion of flax and mustard seeds may prolong milk storage time by mitigating secondary oxidation products. This highlights the potential benefits of incorporating the studied seeds into goats’ diets for improving milk quality and extending its shelf life.
Quantitative Trait Loci Mapping and Association Analysis of Solanesol Content in Tobacco (Nicotiana tabacum L.)
Authors: Liu Jing , Xiang Dehu , Du Yongmei , Zhang Zhongfeng , Zhang Hongbo , Cheng Lirui , Fu Qiujuan , Yan Ning , Ju Fuzhu , Qi Chaofan , Lei Yunkang , Wang Jun , Liu Yanhua
Published: June 2024

Solanesol, which accumulates predominantly in the leaves of tobacco plants, has medically important bioactive properties. To investigate the genetic basis of solanesol in tobacco (Nicotiana tabacum), the solanesol contents of 222 accessions, 206 individuals from an N. tabacum Maryland609 (low-solanesol) × K326 (high-solanesol) F2 population and their corresponding F1 self-pollinations, were determined using ultra-performance liquid chromatography. Genome-wide quantitative trait locus (QTL) and association analysis were performed to identify QTLs and markers associated with solanesol content based on simple sequence repeat molecular markers. A total of 12 QTLs underlying solanesol content were mapped to seven linkage groups (LGs), with three of the QTLs (QTL3-1, QTL21-6, and QTL23-3) explaining 5.19–10.05% of the phenotypic variation. Association analysis revealed 38 significant marker-trait associations in at least one environment. The associations confirmed the QTLs located on LG3, LG10, LG14, LG21, and LG23, while new elite makers were located on 11 additional LGs, each explaining, respectively, 5.16–20.07% of the phenotypic variation. The markers LG14-PT54448, LG10-PT60114-2, LG10-PT60510, LG10-PT61061, and LG-21PT20388 may be useful for molecular-assisted selection of solanesol content in tobacco leaves. These results increase our understanding of the inheritance of solanesol-associated genes and will contribute to molecular-assisted breeding and further isolation of regulatory genes involved in solanesol biosynthesis in tobacco leaves.
Optimal Nitrogen Fertilizer Rates for Soybean Cultivation
Authors: Wysokinski Andrzej , Wysokińska Aleksandra , Noulas Christos , Wysokińska Anna
Published: June 2024

The soybean (Glycine max. L. Merr) can satisfy a large portion of its requirement for nitrogen (N) by living in symbiosis with symbiotic bacteria. However, this source of N may be inadequate in varieties with high yield potential. To fully exploit this potential, soybeans should additionally utilize mineral forms of nitrogen present in the soil. The aim of this study was to determine the effect of varied nitrogen fertilizer application rates on the dry weight of the separated parts of soybean plants and the whole plant, including the number and weight of root nodules, the potential to reduce atmospheric nitrogen (N2), and the content and uptake of nitrogen. Four levels of pre-sowing nitrogen fertilizer supply were tested: 0, 60, 120, and 180 kg N·ha−1. Measurements of the tested parameters were taken during the flowering stage and the fully ripe stage. During the flowering stage, a reduction in the number of root nodules was observed following the application of 120 and 180 kg N·ha−1. In the fully ripe stage, each increase in nitrogen application caused a systematic decrease in the number of nodules on the roots. Increasing the level of nitrogen application therefore reduced the N2 fixation potential of soybeans, regardless of the developmental stage. The use of high doses of nitrogen in soybean cultivation did not increase seed yield or the weight of the entire plant. With high doses of nitrogen, the content and accumulation of nitrogen in soybean seeds and total mass did not increase. Therefore, the content and yield of crude protein did not increase. The main organ of nitrogen accumulation in the soybean flowering stage was the leaves (58.6–64.8% of total N uptake), however, in the fully ripe stage, it was the seeds (66.8–74.2% of total N uptake).
Levels of Different Microbial Groups on Inert Surfaces of Poultry Slaughterhouses: Identification Using Matrix-Assisted Laser Desorption Ionization Time-of-Flight and Detection of Extended-Spectrum Beta-Lactamase- and Carbapenemase-Producing Enterobacteria
Authors: Panera-Martínez Sarah , Rodríguez-Melcón Cristina , Rodríguez-Campos Daniel , Pérez-Estébanez Nuria , Capita Rosa , Alonso-Calleja Carlos
Published: June 2024

Knowledge of the microbiota present in food processing environments is a significant advance that will allow for better evaluation of the risk of food contamination and a better design of the procedures for sanitization. The levels of microbial group indicators of hygienic quality were determined in different areas of the slaughter lines of two poultry slaughterhouses in the northwest of Spain (22 surfaces in each slaughterhouse were studied). The average microbial levels (cfu/cm2) were 2.15 × 102 ± 4.26 × 102 (total aerobic counts, TAC), 1.99 × 102 ± 5.00 × 102 (psychrotrophic microorganisms), 3.10 × 100 ± 1.37 × 101 (enterobacteria), 3.96 × 100 ± 2.55 × 101 (coliforms), 1.80 × 10−1 ± 7.79 × 10−1 (enterococci), and 1.12 × 10−1 ± 3.35 × 10−1 (vancomycin-resistant enterococci, VRE). TAC and psychrotrophic microorganisms were the most abundant groups in all samples (p < 0.05). The counts of both microbial groups were higher (p < 0.05) in samples of Slaughterhouse A than in those of Slaughterhouse B. Microbial loads for the rest of the bacteria were not influenced by the slaughterhouse sampled (p > 0.05). All 44 samples showed TAC and psychrotrophic microorganisms. Colonies of the rest of the microbial groups were only found in 26 samples (59.1% of the total). The isolates (one from each sample) were identified with MALDI-TOF and PCR. Gram-negative bacteria (all Enterobacteriaceae) were isolated in 23 samples, and Gram-positive bacteria were isolated in 16 (9 Enterococcus spp., 2 Enterococcus spp. and VRE, 3 VRE, 1 Enterococcus spp. and Listeria spp., and 1 Listeria spp.). The resistance of the strains to 11 (Enterococcus spp.) or 17 (Enterobacteriaceae) antibiotics was determined (disk diffusion, CLSI), finding an average of 2.05 ± 2.06 resistances per strain (3.46 ± 2.27 if reduced susceptibility reactions are included). A total of 37.3% of the Enterobacteriaceae isolates had a gene for resistance to beta-lactam antibiotics (blaTEM, blaCTX-M-15, blaKPC, blaCMY-2 or blaNDM). The high prevalence of resistant bacteria and resistance genes highlights the need to establish measures to control the spread of antibiotic resistance in poultry slaughterhouses. The findings of this work could contribute to the design of more effective sanitation procedures.
Methicillin-Resistant Staphylococcus aureus (MRSA) in Dairy Products and Bulk-Tank Milk (BTM)
Authors: González-Machado Camino , Capita Rosa , Alonso-Calleja Carlos
Published: June 2024

In order to contribute to an assessment of the role of food in the risks of transmission of methicillin-resistant Staphylococcus aureus (MRSA), a review was undertaken of research on this microorganism in milk and dairy products published from January 2001 to February 2024. A total of 186 publications were selected, 125 for dairy products and 61 for bulk-tank milk (BTM). MRSA was detected in 68.8% of the research into dairy products and 73.8% of investigations relating to BTM, although in most studies the prevalence was less than 5%. Of the set of S. aureus strains isolated, approximately 30% corresponded to MRSA. The foods most extensively contaminated with this microorganism were raw milk and some types of soft cheese. Determination of the mecA gene on its own is known not to suffice for the detection of all MRSA strains. The great diversity of techniques used to study MRSA in milk and dairy products made it difficult to draw comparisons between studies. It would thus be advisable to develop a standardized protocol for the study of this microorganism in foods.
Industry Foundation Class-Based Building Information Modeling Lightweight Visualization Method for Steel Structures
Authors: Sun Zhiguo , Wang Chen , Wu Jie
Published: June 2024

The efficient extraction, storage, and visualization of geometric and semantic information is a key foundation for the operation of the building information modeling (BIM) platform. This study aims to develop a lightweight BIM system and optimize the system’s performance according to the specific characteristics of steel structures. This study proposes several novel techniques for extracting and decoupling the geometric and semantic information of components from industry foundation class (IFC) files. A redundancy removal approach combining the principal content analysis (PCA) algorithm and the Hausdorff-based comparison algorithm is proposed to identify standardized steel components, and a lightweight visualization method on Web3D for redundant instances is also presented. A loading mechanism of the level of detail (LOD) model based on a mesh simplification algorithm is presented to optimize the display efficiency. The developed system is evaluated by three steel structural models. Using the redundancy removal approach, the number of instances is decreased by 96.46% in less than 30 s and over 30 FPS (frame per second) is kept when rendering. Using the LOD loading mechanism, 95.38% of vertices and 98.46% of patches are eliminated under 50 mm precision. The experiment results indicate that users can quickly load large BIM models and fetch sufficient information from the website.
Collaborative Analysis of Learners’ Emotional States Based on Cross-Modal Higher-Order Reasoning
Authors: Wu Wenyan , Zhao Jingtao , Shen Xingbo , Feng Guang
Published: June 2024

Emotion is a significant factor influencing education and teaching, closely intertwined with learners’ cognitive processing. Conducting analysis of learners’ emotions based on cross-modal data is beneficial for achieving personalized guidance in intelligent educational environments. Currently, due to factors such as data scarcity and environmental noise, data imbalances have led to incomplete or missing emotional information. Therefore, this study proposes a collaborative analysis model based on attention mechanisms. The model extracts features from various types of data using different tools and employs multi-head attention mechanisms for parallel processing of feature vectors. Subsequently, through a cross-modal attention collaborative interaction module, effective interaction among visual, auditory, and textual information is facilitated, significantly enhancing comprehensive understanding and the analytical capabilities of cross-modal data. Finally, empirical evidence demonstrates that the model can effectively improve the accuracy and robustness of emotion recognition in cross-modal data.
Advances in the Diagnosis, Treatment, and Prognosis of Chronic Kidney Disease: A Reflection on Recent Developments
Authors: George Cindy , Kengne Andre Pascal
Published: June 2024

Chronic kidney disease (CKD) remains a critical global health issue, affecting more than 840 million individuals globally [...]
Effects of La-Co Co-Substitution and Magnetic Field Pressing on the Structural and Magnetic Properties of SrM Hexaferrites
Authors: Lee Kanghyuk , Lee Sunwoo , Kang Young-Min , Yoo Sang-Im
Published: June 2024

We carefully investigated the effects of La-Co co-substitution and magnetic field pressing (MFP) on the structural and magnetic properties of SrM hexaferrites. Samples composed of Sr1−xLaxFe12−xCoxO19 were sintered at 1230 °C for 2 h in air with sintering additives composed of 0.7 wt% CaCO3 and 0.7 wt% SiO2. A single M-type phase was confirmed to exist up to x = 0.3. Rietveld refinement revealed a slight decrease in lattice parameter a and the unit cell volume (Vcell) with an increasing x, while parameter c showed a significant decrease. The saturation magnetization (Ms) values increased from 70.90 to 72.40 emu/g with an increasing x from 0.0 to 0.15 and then decreased to 71.38 emu/g with further increasing of x to 0.3, while the anisotropy field (Ha) increased from 17.7 to 25.9 kOe, leading to a continuous increase in the intrinsic coercivity (Hci), from 3.52 to 5.00 kOe, respectively. Using the MFP process, the c-axis of M-type hexaferrite grains could be effectively aligned to the applied field direction, which significantly affected the microstructures and, thus, magnetic properties of samples. Unlike non-MFP samples, exhibiting a significant increase in the average grain size (davg) but almost unaltered average thickness (tavg) with an increasing x from 0.0 to 0.3, MFP-processed samples exhibited almost unaltered davg values but a continuous decrease in tavg. Consequently, the variation in remanent flux density (Br) versus x followed that of Ms versus x and thus exhibited the highest Br of 4.05 kG for x = 0.15, leading to the highest maximum energy product {(BH)max} of 3.62 MGOe. With an increasing x from 0.0 to 0.3, the Hci values continuously increased from 3.14 to 3.84 kOe mainly due to a continuous increase in Ha, although they were significantly lowered in comparison with those of non-MFP samples because of a large increase in Br for a given composition x. A higher Mr/Ms ratio always resulted in a larger (BH)max in our samples regardless of x. A careful comparison of the microstructures and magnetic properties between MFP and non-MFP samples provided valuable insights into a broad area of permanent magnet optimization.
Parameter Sensitivity Analysis for Machining Operation of Autofrettaged Cylinder Using Taguchi Method
Authors: Oymak Murat , Yıldırım Halil , Yıldız Mustafa , Özcan Burak , Çelebi Yasin , Çelebi Muhammet Abdullah Enes , Çelik Veli
Published: June 2024

This study investigates the impact of material parameters such as yield strength (Sy), Young’s modulus (E), and tangent modulus (T) on the safety factor (SF) of autofrettaged cylinders under 400 MPa working pressure, considering the three scenarios: no machining, internal machining, and external machining. Finite element (FE) simulations were conducted based on the Taguchi experimental design and converted into signal-to-noise (S/N) ratios to determine the optimal settings. ANOVA was utilized to evaluate the significance and percentage contributions of each factor. The analysis indicated that Sy is the most influential parameter on SF, contributing approximately 98.20% across all scenarios, including no machining, internal machining, and external machining. The contributions of E and T were minimal, but T had a slightly greater effect than E. The analytical validation of the FE model showed good agreement, with a maximum deviation of 4.37% for no machining, 4.75% for internal machining, and 5.20% for external machining. Regression analysis further confirmed the high prediction capability of the model, validated using AISI 4340 steel. The study concludes that internal machining results in higher residual stress loss compared to external machining. Overall, the analytical method tends to provide lower SF values than the numerical method, highlighting its conservative nature.
Image Deraining Algorithm Based on Multi-Scale Features
Authors: Yang Jingkai , Wang Jingyuan , Li Yanbo , Yao Bobin , Xu Tangwen , Lu Ting , Gao Xiaoxuan , Chen Junshuo , Liu Weiyu
Published: June 2024

In target detection, tracking, and recognition tasks, high-quality images can achieve better results. However, in actual scenarios, the visual effects and data quality of images are greatly reduced due to the influence of environmental factors, which affect subsequent detection, recognition, and other tasks. Therefore, this paper proposes an image rain removal algorithm based on multi-scale features, which can effectively remove rain streaks. First of all, this paper proposes a deraining algorithm that combines spatial information to improve the network’s generalization ability on real images, aiming at the problem of synthetic datasets used by previous deraining algorithms. Then, by proposing a multi-scale rain removal algorithm, it improves the feature extraction capabilities of existing deraining algorithms. Before extracting deep rain features, a preliminary fusion of multi-scale shallow features can be performed, which can show better performance in images of different sizes. In addition, a spatial attention module and channel are introduced. The attention module increases the ability to extract rain information at each scale; the resulting multi-scale feature image rain removal algorithm is called MFD. Finally, the rain removal algorithm is validated on the rain removal dataset, and the proposed method can effectively remove rain patterns, provide strong performance improvement on several datasets in the image rain removal task, and provide high-quality images for subsequent detection and recognition tasks.
Reversal of the Word Sense Disambiguation Task Using a Deep Learning Model
Authors: Laukaitis Algirdas
Published: June 2024

Word sense disambiguation (WSD) remains a persistent challenge in the natural language processing (NLP) community. While various NLP packages exist, the Lesk algorithm in the NLTK library demonstrates suboptimal accuracy. In this research article, we propose an innovative methodology and an open-source framework that effectively addresses the challenges of WSD by optimizing memory usage without compromising accuracy. Our system seamlessly integrates WSD into NLP tasks, offering functionality similar to that provided by the NLTK library. However, we go beyond the existing approaches by introducing a novel idea related to WSD. Specifically, we leverage deep neural networks and consider the language patterns learned by these models as the new gold standard. This approach suggests modifying existing semantic dictionaries, such as WordNet, to align with these patterns. Empirical validation through a series of experiments confirmed the effectiveness of our proposed method, achieving state-of-the-art performance across multiple WSD datasets. Notably, our system does not require the installation of additional software beyond the well-known Python libraries. The classification model is saved in a readily usable text format, and the entire framework (model and data) is publicly available on GitHub for the NLP research community.
Investigation of Lateral and Longitudinal Deformation of Submarine Nuclear Power Plant Water-Intake Tunnel on Non-Uniform Soft Soil during Earthquake
Authors: Zhao Jie , Qian Bo , Gan Changjiang , Wang Jianshan , Peng Yanli
Published: June 2024

The safety-grade water-intake immersed tunnel plays a vital role in the nuclear power cooling system, and its seismic safety is crucial. This paper employs the response displacement method and dynamic time-history analysis using the finite element software ANSYS to construct a beam–spring model and a 3D finite element model of a shield tunnel and foundation. It also develops equivalent linear dynamic constitutive and viscoelastic boundary element subprograms. This study focuses on the weak joint sections of immersed tunnels, conducting a seismic performance analysis under extreme safety earthquake conditions (SL-2). The results indicate that the joint stiffness of immersed tunnels and the increase in seismic peak values do not affect the trend of joint opening variation with longitudinal position. The change in joint opening is primarily located where the thickness of the cover layer changes abruptly or where the soil hardness is unevenly distributed. The joint opening is mainly influenced by seismic forces when considering static and dynamic superposition. When the stiffness of the joint GINA water stop exceeds a certain value, the correlation between stiffness change and joint compression–tension variation gradually weakens. This research can provide a reference for the seismic design of similar projects.
Enhancing Seed Germination Test Classification for Pole Sitao (Vigna unguiculata (L.) Walp.) Using SSD MobileNet and Faster R-CNN Models
Authors: Brutas Mariel John B. , Fajardo Arthur L. , Quilloy Erwin P. , Manuel Luther John R. , Borja Adrian A.
Published: June 2024

The classification of germinated pole sitao (Vigna unguiculata (L.) Walp.) seeds is important in seed germination tests. The automation of this process has been explored for different grain and legume seeds but is only limited to binary classification. This study aimed to develop a classifier system that can recognize three classes: normal, abnormal, and ungerminated. SSD MobileNet and Faster R-CNN models were trained to perform the classification. Both were trained using 1500 images of germinated seeds at fifth- and eighth-day observations. Each class had 500 images. The trained models were evaluated using 150 images per class. The SSD MobileNet model had an accuracy of 0.79 while the Faster R-CNN model had an accuracy of 0.75. The results showed that the average accuracies for the classes were significantly different from one another based on one-way ANOVA at a 95% confidence level with an F-critical value of 3.0159. The SSD MobileNet model outperformed the Faster R-CNN model in classifying pole sitao seeds, with improved precision in identifying abnormal and ungerminated seeds on the fifth day and normal and ungerminated seeds on the eighth day. The results confirm the potential of the SSD MobileNet model as a more reliable classifier in germination tests.
Witbier Fermented by Sequential Inoculation of Schizosaccharomyces pombe and Saccharomyces cerevisiae: Influence of Starchy Ingredients and S. cerevisiae Strain Used for In-Bottle Refermentation
Authors: Baiano Antonietta , Grieco Francesco , Fiore Anna
Published: June 2024

Great attention has recently been dedicated to the use of non-Saccharomyces yeast strains for the development of new beer formulations. However, the effect of the Saccharomyces strain used in the refermentation of this type of beer has never been investigated. The research described aimed to optimize the quality of beers fermented by an oenological Schizosaccharomyces pombe strain alternately combined with two S. cerevisiae strains (WB06, commercial; 9502, of an oenological origin). The influence of both in-bottle refermentation (alternately carried out by one of the two Saccharomyces cerevisiae strains used in the sequential first fermentation) and starchy ingredients (three mixtures of 65% of malted barley alternately combined with 35% of unmalted common, durum, or emmer wheat) was studied. The beer formulation was optimized through a two-factor mixed three- and two-level design, where the two factors were the starchy ingredients and the refermenting S. cerevisiae. Beers from durum wheat beers refermented by WB06 had the highest alcohol contents. Common wheat beers refermented by 9502 showed the highest antioxidant activity values. The highest overall sensory score was assigned to the beers refermented by 9502. The fitted quadratic model had a good predictive ability for five physicochemical and fourteen sensory characteristics, with an R2 often higher than 0.9.
HEAL: High-Frequency Enhanced and Attention-Guided Learning Network for Sparse-View CT Reconstruction
Authors: Li Guang , Deng Zhenhao , Ge Yongshuai , Luo Shouhua
Published: June 2024

X-ray computed tomography (CT) imaging technology has become an indispensable diagnostic tool in clinical examination. However, it poses a risk of ionizing radiation, making the reduction of radiation dose one of the current research hotspots in CT imaging. Sparse-view imaging, as one of the main methods for reducing radiation dose, has made significant progress in recent years. In particular, sparse-view reconstruction methods based on deep learning have shown promising results. Nevertheless, efficiently recovering image details under ultra-sparse conditions remains a challenge. To address this challenge, this paper proposes a high-frequency enhanced and attention-guided learning Network (HEAL). HEAL includes three optimization strategies to achieve detail enhancement: Firstly, we introduce a dual-domain progressive enhancement module, which leverages fidelity constraints within each domain and consistency constraints across domains to effectively narrow the solution space. Secondly, we incorporate both channel and spatial attention mechanisms to improve the network’s feature-scaling process. Finally, we propose a high-frequency component enhancement regularization term that integrates residual learning with direction-weighted total variation, utilizing directional cues to effectively distinguish between noise and textures. The HEAL network is trained, validated and tested under different ultra-sparse configurations of 60 views and 30 views, demonstrating its advantages in reconstruction accuracy and detail enhancement.
Understanding the Temporal Dynamics of Accelerated Brain Aging and Resilient Brain Aging: Insights from Discriminative Event-Based Analysis of UK Biobank Data
Authors: Lin Lan , Wu Yutong , Liu Lingyu , Sun Shen , Wu Shuicai
Published: June 2024

The intricate dynamics of brain aging, especially the neurodegenerative mechanisms driving accelerated (ABA) and resilient brain aging (RBA), are pivotal in neuroscience. Understanding the temporal dynamics of these phenotypes is crucial for identifying vulnerabilities to cognitive decline and neurodegenerative diseases. Currently, there is a lack of comprehensive understanding of the temporal dynamics and neuroimaging biomarkers linked to ABA and RBA. This study addressed this gap by utilizing a large-scale UK Biobank (UKB) cohort, with the aim to elucidate brain aging heterogeneity and establish the foundation for targeted interventions. Employing Lasso regression on multimodal neuroimaging data, structural MRI (sMRI), diffusion MRI (dMRI), and resting-state functional MRI (rsfMRI), we predicted the brain age and classified individuals into ABA and RBA cohorts. Our findings identified 1949 subjects (6.2%) as representative of the ABA subpopulation and 3203 subjects (10.1%) as representative of the RBA subpopulation. Additionally, the Discriminative Event-Based Model (DEBM) was applied to estimate the sequence of biomarker changes across aging trajectories. Our analysis unveiled distinct central ordering patterns between the ABA and RBA cohorts, with profound implications for understanding cognitive decline and vulnerability to neurodegenerative disorders. Specifically, the ABA cohort exhibited early degeneration in four functional networks and two cognitive domains, with cortical thinning initially observed in the right hemisphere, followed by the temporal lobe. In contrast, the RBA cohort demonstrated initial degeneration in the three functional networks, with cortical thinning predominantly in the left hemisphere and white matter microstructural degeneration occurring at more advanced stages. The detailed aging progression timeline constructed through our DEBM analysis positioned subjects according to their estimated stage of aging, offering a nuanced view of the aging brain’s alterations. This study holds promise for the development of targeted interventions aimed at mitigating age-related cognitive decline.
A Multi-View Deep Learning Model for Thyroid Nodules Detection and Characterization in Ultrasound Imaging
Authors: Vahdati Sanaz , Khosravi Bardia , Robinson Kathryn A. , Rouzrokh Pouria , Moassefi Mana , Akkus Zeynettin , Erickson Bradley J.
Published: June 2024

Thyroid Ultrasound (US) is the primary method to evaluate thyroid nodules. Deep learning (DL) has been playing a significant role in evaluating thyroid cancer. We propose a DL-based pipeline to detect and classify thyroid nodules into benign or malignant groups relying on two views of US imaging. Transverse and longitudinal US images of thyroid nodules from 983 patients were collected retrospectively. Eighty-one cases were held out as a testing set, and the rest of the data were used in five-fold cross-validation (CV). Two You Look Only Once (YOLO) v5 models were trained to detect nodules and classify them. For each view, five models were developed during the CV, which was ensembled by using non-max suppression (NMS) to boost their collective generalizability. An extreme gradient boosting (XGBoost) model was trained on the outputs of the ensembled models for both views to yield a final prediction of malignancy for each nodule. The test set was evaluated by an expert radiologist using the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS). The ensemble models for each view achieved a mAP0.5 of 0.797 (transverse) and 0.716 (longitudinal). The whole pipeline reached an AUROC of 0.84 (CI 95%: 0.75–0.91) with sensitivity and specificity of 84% and 63%, respectively, while the ACR-TIRADS evaluation of the same set had a sensitivity of 76% and specificity of 34% (p-value = 0.003). Our proposed work demonstrated the potential possibility of a deep learning model to achieve diagnostic performance for thyroid nodule evaluation.
Chromophore-Assisted Light Inactivation for Protein Degradation and Its Application in Biomedicine
Authors: Zhou Lvjia , Na Jintong , Liu Xiyu , Wu Pan
Published: June 2024

The functional investigation of proteins holds immense significance in unraveling physiological and pathological mechanisms of organisms as well as advancing the development of novel pharmaceuticals in biomedicine. However, the study of cellular protein function using conventional genetic manipulation methods may yield unpredictable outcomes and erroneous conclusions. Therefore, precise modulation of protein activity within cells holds immense significance in the realm of biomedical research. Chromophore-assisted light inactivation (CALI) is a technique that labels photosensitizers onto target proteins and induces the production of reactive oxygen species through light control to achieve precise inactivation of target proteins. Based on the type and characteristics of photosensitizers, different excitation light sources and labeling methods are selected. For instance, KillerRed forms a fusion protein with the target protein through genetic engineering for labeling and inactivates the target protein via light activation. CALI is presently predominantly employed in diverse biomedical domains encompassing investigations into protein functionality and interaction, intercellular signal transduction research, as well as cancer exploration and therapy. With the continuous advancement of CALI technology, it is anticipated to emerge as a formidable instrument in the realm of life sciences, yielding more captivating outcomes for fundamental life sciences and precise disease diagnosis and treatment.
Bone Incorporation of a Poly (L-Lactide-Co-D, L-Lactide) Internal Fixation Device in a Rat’s Tibia: Microtomographic, Confocal LASER, and Histomorphometric Analysis
Authors: Mamani-Valeriano Harrisson Lucho , Silva Nelson Padilha , Nímia Heloisa Helena , Pereira-Silva Maísa , Oliveira Maria Eduarda de Freitas Santana , Rodrigues Letícia Gabriella de Souza , Tavares Paulo Matheus Honda , Hadad Henrique , de Jesus Laís Kawamata , Santos Ana Flávia Piquera , Barbosa Débora de Barros , Poli Pier Paolo , Maiorana Carlo , Carvalho Paulo Sergio Perri de , Okamoto Roberta , Souza Francisley Ávila
Published: June 2024

This study evaluated the bone incorporation process of a screw-shaped internal fixation device made of poly (L-lactide-co-D, L-lactide) (PLDLLA). Thirty-two male Wistar rats received 32 fixation devices (2 mm × 6 mm) randomly assigned to either the right or left tibia and one implant in each animal. After 7, 14, 28, and 42 days, the rats were euthanized and the specimens were subjected to microtomographic computed tomography (microCT) and histomorphometric analyses to evaluate bone interface contact (BIC%) and new bone formation (NBF%) in cortical and cancellous bone areas. The animals euthanized on days 28 and 42 were treated with calcein and alizarin red, and confocal LASER microscopy was performed to determine the mineral apposition rate (MAR). Micro-CT revealed a higher percentage of bone volume (p < 0.006), trabecular separation (p < 0.001), and BIC in the cortical (p < 0.001) and cancellous (p = 0.003) areas at 28 and 42 days than at 7 and 14 days. The cortical NBF at 42 days was greater than that at 7 and 14 days (p = 0.022). No statistically significant differences were observed in cancellous NBF or MAR at 28 and 42 days. Based on these results, it can be seen that the PLDLLA internal fixation device is biocompatible and allows new bone formation around the screw thread.
Determination of Atmospheric Pollen Grains by Volumetric Method in Sarıkamış District (Kars-Türkiye)
Authors: Akpınar Salih , Altunoğlu Mustafa Kemal
Published: June 2024

Atmospheric pollen was investigated using the Lanzoni VPSS 2000 (Lanzoni, Bologna- Italy) device for 2 years between 2012 and 2013 in the Sarıkamış district of Kars province, one of the highest regions of Türkiye. A total of 37,909 pollen grains were collected: 15,298 pollen grains in 2012 and 22,611 pollen grains in 2013. Out of 43 identified taxa, 21 were arboreal, and 22 were non-arboreal. Pollen from arboreal plants accounted for 36.34% (13,778 pollen grains) of the total, while pollen from non-arboreal plants accounted for 63.56% (24,095 pollen grains). Additionally, 0.10% (36 pollen grains) belonged to unidentified pollen. The most frequent arboreal plant pollen in the Sarıkamış atmosphere were Pinaceae (29.79%), Cupressaceae/Taxaceae (2.54%), and Morus (1.30%). The main non-arboreal plant pollen in the atmosphere of Sarıkamış were Poaceae (44.60%), Artemisia (2.98%), Amaranthaceae (2.79%), Rumex (2.41%), Urticaceae (2.33%), Plantago (2.19%), and Boraginaceae (1.40%). The maximum pollen concentration was observed in June and was associated with the pollination of Pinaceae and Poaceae.
Clinical and Metabolic Particularities of a Roma Population with Diabetes—Considering Ethnic Disparities in Approaching Healthcare Management
Authors: Cosoreanu Andrada , Rusu Emilia , Rusu Florin , Stanciu Silviu , Ungureanu Ioana , Donici Marius , Visinescu Alexandra , Enache Georgiana , Radulian Gabriela
Published: June 2024

The Roma population is Europe’s largest ethnic minority, yet data on the prevalence of non-communicable diseases remain scarce in medical literature. This study aimed to compare the clinical and metabolic particularities of a Roma population with diabetes with a group of non-Roma. We conducted an observational, transversal study and evaluated 808 adult patients with diabetes mellitus, from a tertiary diabetes care hospital. The prevalence of metabolic syndrome was high among both groups, 94.3% in the Roma patients and 89.1% in the non-Roma. A slightly higher mean value of the triglyceride–glucose (TyG) index was observed among the Roma group (10.07 ± 0.71 versus 9.71 ± 0.82). Among the non-Roma, variables that were significantly associated with the TyG index were glycated hemoglobin (HbA1c), total cholesterol (TC), high density lipoprotein–cholesterol (HDL-c), and low-density lipoprotein-cholesterol (LDL-c), while among the Roma, HbA1c and HDL-c were correlated with this index. There were no differences concerning myocardial infarction; however, the number of patients with a history of stroke was 2.1 times higher in the Roma group compared to the non-Roma group. The prevalence of cardiovascular risk factors, cardiovascular disease, and microvascular complications among the study’s Roma population are quite significant, underscoring the importance of ethnic disparities in approaching healthcare management strategies.
The Impact of Nano-Hydroxyapatite Scaffold Enrichment on Bone Regeneration In Vivo—A Systematic Review
Authors: Mitić Dijana , Čarkić Jelena , Jaćimović Jelena , Lazarević Miloš , Jakšić Karišik Milica , Toljić Boško , Milašin Jelena
Published: June 2024

Objectives: In order to ensure improved and accelerated bone regeneration, nano-hydroxyapatite scaffolds are often enriched with different bioactive components to further accelerate and improve bone healing. In this review, we critically examined whether the enrichment of nHAp/polymer scaffolds with growth factors, hormones, polypeptides, microRNAs and exosomes improved new bone formation in vivo. Materials and Methods: Out of 2989 articles obtained from the literature search, 106 papers were read in full, and only 12 articles met the inclusion criteria for this review. Results: Several bioactive components were reported to stimulate accelerated bone regeneration in a variety of bone defect models, showing better results than bone grafting with nHAp scaffolds alone. Conclusions: The results indicated that composite materials based on nHAp are excellent candidates as bone substitutes, while nHAp scaffold enrichment further accelerates bone regeneration. The standardization of animal models should be provided in order to clearly define the most significant parameters of in vivo studies. Only in this way can the adequate comparison of findings from different in vivo studies be possible, further advancing our knowledge on bone regeneration and enabling its translation to clinical settings.
Enhancing Cooperation in 5–6-Year-Old Rural Chinese Children through Cooperative Constructive Play Based on Anji Play: A Quasi-Experimental Study
Authors: Lin Xinya , Wu Yunpeng , Wu Jianfen , Qin Liping
Published: June 2024

Cooperation plays a crucial role in children’s social development and adaptation. This study designed a cooperative constructive play (CCP) intervention based on the Anji Play teaching model and evaluated its effectiveness in a quasi-experimental design involving 60 rural Chinese children aged 5–6 years. Participants were divided into an experimental group, which engaged in 12 weeks of CCP, and a control group, which continued with regular teaching activities. The cooperation data were collected through a truck racing task using pre-, mid-, post-, and follow-up tests, while the cooperation levels and strategies were evaluated by video observations of role-playing games before and after the tests. The results indicated significant improvements in cooperation scores in both the truck racing task and role-playing games in the experimental group compared to the baseline, with no similar enhancements observed in the control group. Furthermore, cooperation scores in the follow-up were higher than pre- and post-test scores, demonstrating the CCP’s effectiveness in fostering child cooperation, transferability to other contexts, and sustainability. These findings suggest that CCP intervention based on Anji Play can significantly enhance cooperation in children, offering a valuable tool for educational practices.
The Impact of Parental Autonomy Support on Family Adaptation in the Context of “Double Reduction”: The Mediating Role of Parent–Child Communication and Cohesion
Authors: Xie Ruibo , Wang Xuan , Ding Yangguang , Chen Yanling , Ding Wan
Published: June 2024

The implementation of the “Double Reduction” policy indicates a significant change in the way households operate, such as through parental education conception and parenting form, in which family adaptation needs particular attention. Parental autonomy support has been evidenced to be related to family adaptation in prior studies. However, the mechanism underlying the relationship between parental autonomy support and family adaptation in the context of “Double Reduction” are not clear enough but remain fascinating. This study aims to explore the process through which parental autonomy support affects the whole family’s adaptation in the context of “Double Reduction” from the perspectives of parent–child behavior and emotions (i.e., parent–child communication and parent–child cohesion). A cross-sectional design based on the questionnaire method was used to collect the characteristics of 4239 adolescent parents (1493 fathers and 3427 mothers; Mage = 43.20, SDage = 22.39) one year after the implementation of the “Double Reduction” policy. In addition, this study also used the retrospective method to obtain the characteristics of parental autonomy support before the “Double Reduction” policy. In the context of “Double Reduction”, the research results found that parental autonomy support can predict family adaptation; parental autonomy support can also influence the whole family’s adaptation through the quality of parent–child interaction. This study reveals the impact mechanism of parental autonomy support on family adaptation under the background of “Double Reduction” in China and provides insights on how to improve the adaptation of the entire family from the perspective of parent–child interaction.