bluuebunny/crossref_metadata_2025_split huggingface dataset:
Investigating the stability of aromatic carboxylic acids in hydrated magnesium sulfate under UV irradiation to assist detection of organics on Mars
Authors: Alberini Andrew , Fornaro Teresa , García-Florentino Cristina , Biczysko Malgorzata , Poblacion Iratxe , Aramendia Julene , Madariaga Juan Manuel , Poggiali Giovanni , Vicente-Retortillo Álvaro , Benison Kathleen C. , Siljeström Sandra , Biancalani Sole , Lorenz Christian , Cloutis Edward A. , Applin Dan M. , Gómez Felipe , Steele Andrew , Wiens Roger C. , Hand Kevin P. , Brucato John R. 
Published: July 2024

AbstractThe Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) instrument onboard the Mars 2020 Perseverance rover detected so far some of the most intense fluorescence signals in association with sulfates analyzing abraded patches of rocks at Jezero crater, Mars. To assess the plausibility of an organic origin of these signals, it is key to understand if organics can survive exposure to ambient Martian UV after exposure by the Perseverance abrasion tool and prior to analysis by SHERLOC. In this work, we investigated the stability of organo-sulfate assemblages under Martian-like UV irradiation and we observed that the spectroscopic features of phthalic and mellitic acid embedded into hydrated magnesium sulfate do not change for UV exposures corresponding to at least 48 Martian sols and, thus, should still be detectable in fluorescence when the SHERLOC analysis takes place, thanks to the photoprotective properties of magnesium sulfate. In addition, different photoproduct bands diagnostic of the parent carboxylic acid molecules could be observed. The photoprotective behavior of hydrated magnesium sulfate corroborates the hypothesis that sulfates might have played a key role in the preservation of organics on Mars, and that the fluorescence signals detected by SHERLOC in association with sulfates could potentially arise from organic compounds.
Transcriptome sequencing of garlic reveals key genes related to the heat stress response
Authors: Yang Qing-Qing , Yang Feng , Liu Can-Yu , Zhao Yong-Qiang , Li Meng-Yao , Lu Xin-Juan , Ge Jie , Zhang Bi-Wei , Li Meng-Qian , Yang Yan , Fan Ji-De 
Published: July 2024

AbstractWith global warming, heat stress has become an important factor that seriously affects crop yield and quality. Therefore, understanding plant responses to heat stress is important for agricultural practice, but the molecular mechanism of high-temperature tolerance in garlic remains unclear. In this study, ‘Xusuan No. 6’ was used as the experimental material. After heat stress for 0 (CK), 2 and 24 h, transcriptome sequencing was used to screen metabolic pathways and differentially expressed genes (DEGs) closely related to heat stress and was further verified by quantitative real-time polymerase chain reaction (qRT-PCR). A total of 86,110 unigenes obtained from the raw transcriptome sequencing data were spliced. After 2 h of heat treatment, the expression levels of 8898 genes increased, and 3829 genes were decreased in leaves. After 24 h, the expression levels of 7167 genes were upregulated, and 3176 genes were downregulated. Gene Ontology enrichment analysis showed that DEGs were mainly enriched in seven categories: cellular processes, metabolic processes, binging, catalytic activity, cellular anatomical entity and protein-containing complex response to stimulus. Kyoto Encyclopedia of Genes and Genomes pathway enrichment showed that DEGs are involved in protein processing in the endoplasmic reticulum, plant hormone signal transduction, phenylpropanoid biosynthesis, and photosynthetic antenna proteins. Six genes were selected and further verified by qRT-PCR. In this study, the full-length transcriptome of garlic was constructed, and the regulatory genes related to the heat resistance of garlic were studied. Taken together, these findings can provide a theoretical basis for the cloning of heat resistance genes in garlic and for the analysis of heat resistance mechanisms.
Automatic wild bird repellent system that is based on deep-learning-based wild bird detection and integrated with a laser rotation mechanism
Authors: Chen Yu-Chieh , Chu Jing-Fang , Hsieh Kuang-Wen , Lin Tzung-Han , Chang Pei-Zen , Tsai Yao-Chuan 
Published: July 2024

AbstractWild bird repulsion is critical in agriculture because it helps avoid agricultural food losses and mitigates the risk of avian influenza. Wild birds transmit avian influenza in poultry farms and thus cause large economic losses. In this study, we developed an automatic wild bird repellent system that is based on deep-learning-based wild bird detection and integrated with a laser rotation mechanism. When a wild bird appears at a farm, the proposed system detects the bird’s position in an image captured by its detection unit and then uses a laser beam to repel the bird. The wild bird detection model of the proposed system was optimized for detecting small pixel targets, and trained through a deep learning method by using wild bird images captured at different farms. Various wild bird repulsion experiments were conducted using the proposed system at an outdoor duck farm in Yunlin, Taiwan. The statistical test results of our experimental data indicated that the proposed automatic wild bird repellent system effectively reduced the number of wild birds in the farm. The experimental results indicated that the developed system effectively repelled wild birds, with a high repulsion rate of 40.3% each day.
Selection of pre-trained weights for transfer learning in automated cytomegalovirus retinitis classification
Authors: Choopong Pitipol , Kusakunniran Worapan 
Published: July 2024

AbstractCytomegalovirus retinitis (CMVR) is a significant cause of vision loss. Regular screening is crucial but challenging in resource-limited settings. A convolutional neural network is a state-of-the-art deep learning technique to generate automatic diagnoses from retinal images. However, there are limited numbers of CMVR images to train the model properly. Transfer learning (TL) is a strategy to train a model with a scarce dataset. This study explores the efficacy of TL with different pre-trained weights for automated CMVR classification using retinal images. We utilised a dataset of 955 retinal images (524 CMVR and 431 normal) from Siriraj Hospital, Mahidol University, collected between 2005 and 2015. Images were processed using Kowa VX-10i or VX-20 fundus cameras and augmented for training. We employed DenseNet121 as a backbone model, comparing the performance of TL with weights pre-trained on ImageNet, APTOS2019, and CheXNet datasets. The models were evaluated based on accuracy, loss, and other performance metrics, with the depth of fine-tuning varied across different pre-trained weights. The study found that TL significantly enhances model performance in CMVR classification. The best results were achieved with weights sequentially transferred from ImageNet to APTOS2019 dataset before application to our CMVR dataset. This approach yielded the highest mean accuracy (0.99) and lowest mean loss (0.04), outperforming other methods. The class activation heatmaps provided insights into the model's decision-making process. The model with APTOS2019 pre-trained weights offered the best explanation and highlighted the pathologic lesions resembling human interpretation. Our findings demonstrate the potential of sequential TL in improving the accuracy and efficiency of CMVR diagnosis, particularly in settings with limited data availability. They highlight the importance of domain-specific pre-training in medical image classification. This approach streamlines the diagnostic process and paves the way for broader applications in automated medical image analysis, offering a scalable solution for early disease detection.
Efficient generation of a stable CHO-K1 cell line overexpressing the human water channel aquaporin-5 as tool to generate therapeutic antibodies
Authors: Jagemann Lucas , Sciucca Nia , Bombardieri Michele , Corsiero Elisa 
Published: July 2024

AbstractAquaporins (AQPs) are a family of water permeable channels expressed on the plasma membrane with AQP5 being the major channel expressed in several human tissues including salivary and lacrimal glands. Anti-AQP5 autoantibodies have been observed in patients with Sjögren’s syndrome who are characterised by dryness of both salivary and lacrimal glands, and they have been implicated in the underlying mechanisms of glandular dysfunction. AQP5 is formed by six transmembrane helices linked with three extracellular and two intracellular loops. Develop antibodies against membrane protein extracellular loops can be a challenge due to the difficulty in maintaining these proteins as recombinant in their native form. Therefore, in this work we aimed to generate an efficient stable-transfected cell line overexpressing human AQP5 (CHO-K1/AQP5) to perform primarily cell-based phage display biopanning experiments to develop new potential recombinant antibodies targeting AQP5. We also showed that the new CHO-K1/AQP5 cell line can be used to study molecular mechanisms of AQP5 sub-cellular trafficking making these cells a useful tool for functional studies.
Composite branched and linear F-actin maximize myosin-induced membrane shape changes in a biomimetic cell model
Authors: Sakamoto Ryota , Murrell Michael P. 
Published: July 2024

AbstractThe architecture of the actin cortex determines the generation and transmission of stresses, during key events from cell division to migration. However, its impact on myosin-induced cell shape changes remains unclear. Here, we reconstitute a minimal model of the actomyosin cortex with branched or linear F-actin architecture within giant unilamellar vesicles (GUVs, liposomes). Upon light activation of myosin, neither the branched nor linear F-actin architecture alone induces significant liposome shape changes. The branched F-actin network forms an integrated, membrane-bound “no-slip boundary” -like cortex that attenuates actomyosin contractility. By contrast, the linear F-actin network forms an unintegrated “slip boundary“ -like cortex, where actin asters form without inducing membrane deformations. Notably, liposomes undergo significant deformations at an optimized balance of branched and linear F-actin networks. Our findings highlight the pivotal roles of branched F-actin in force transmission and linear F-actin in force generation to yield membrane shape changes.
Reducing chemical inputs in agriculture requires a system change
Authors: Brunelle Thierry , Chakir Raja , Carpentier Alain , Dorin Bruno , Goll Daniel , Guilpart Nicolas , Maggi Federico , Makowski David , Nesme Thomas , Roosen Jutta , Tang Fiona H. M. 
Published: July 2024

AbstractMany countries have implemented policies to reduce the use of chemical inputs in agriculture. However, these policies face many obstacles that limit their effectiveness. The purpose of this paper is to review the main challenges associated with reducing chemical inputs in agriculture and to propose potential solutions. Our analysis, based on a literature review linking agronomy and economics, shows that several agronomic options have proven effective in reducing chemical inputs or mitigating their negative impacts. We argue that the organization of the agri-food system itself is a major barrier to their implementation. Involving all stakeholders, from the chemical input industry to consumers, and designing appropriate policy frameworks are key to address this issue. We recommend combining different policy instruments, such as standards, taxes and subsidies, in a simplified and coherent way to increase effectiveness and ensure better coordination in the adoption of sustainable practices.
Achieving multi-modal brain disease diagnosis performance using only single-modal images through generative AI
Authors: Sun Kaicong , Zhang Yuanwang , Liu Jiameng , Yu Ling , Zhou Yan , Xie Fang , Guo Qihao , Zhang Han , Wang Qian , Shen Dinggang 
Published: July 2024

AbstractBrain disease diagnosis using multiple imaging modalities has shown superior performance compared to using single modality, yet multi-modal data is not easily available in clinical routine due to cost or radiation risk. Here we propose a synthesis-empowered uncertainty-aware classification framework for brain disease diagnosis. To synthesize disease-relevant features effectively, a two-stage framework is proposed including multi-modal feature representation learning and representation transfer based on hierarchical similarity matching. Besides, the synthesized and acquired modality features are integrated based on evidential learning, which provides diagnosis decision and also diagnosis uncertainty. Our framework is extensively evaluated on five datasets containing 3758 subjects for three brain diseases including Alzheimer’s disease (AD), subcortical vascular mild cognitive impairment (MCI), and O[6]-methylguanine-DNA methyltransferase promoter methylation status for glioblastoma, achieving 0.950 and 0.806 in area under the ROC curve on ADNI dataset for discriminating AD patients from normal controls and progressive MCI from static MCI, respectively. Our framework not only achieves quasi-multimodal performance although using single-modal input, but also provides reliable diagnosis uncertainty.
Narrative review of recent developments and the future of penicillin allergy de-labelling by non-allergists
Authors: Powell Neil , Blank Michael , Luintel Akish , Elkhalifa Shuayb , Bhogal Rashmeet , Wilcock Michael , Wakefield Michael , Sandoe Jonathan 
Published: July 2024

AbstractThis article outlines recent developments in non-allergist delivered penicillin allergy de-labelling (PADL), discusses remaining controversies and uncertainties and explores the future for non-allergist delivered PADL. Recent developments include national guidelines for non-allergist delivered PADL and validation of penicillin allergy risk assessment tools. Controversies remain on which penicillin allergy features are low risk of genuine allergy. In the future genetic or immunological tests may facilitate PADL.
Mitigation of Subsequent Ovariectomy Responses through Prior Exercise
Training in Rats
Authors: da Palma Renata Kelly , Angelis Kátia De , Shimojo Guilherme Lemos , Dutra Marina Rascio Henriques , dos Santos Camila Paixão , Bernardes Nathalia , Bolsoni-Lopes Andressa , Curi Rui , Gomes João Lucas , Oliveira Edilamar Menezes , Irigoyen Maria Claudia , Malfitano Christiane 
Published: July 2024

AbstractIt is well known that cardiometabolic dysfunction gradually increases after
menopause, and the sedentary lifestyle can aggravate this condition. Therefore,
we compared the effects of aerobic exercise training during the premenopausal
period and after ovariectomy (OVX) on metabolic, hemodynamic, and autonomic
parameters in an experimental rat model of menopause. The female rats were
divided into four groups: control (C), sedentary OVX (SO), trained OVX (TO), and
previously trained OVX (PTO). The PTO group was trained for 4 weeks prior to+8
weeks after OVX, and the TO group trained only after OVX on a motor treadmill.
Autonomic modulation was evaluated, white adipose tissue (WAT) was removed and
weighed, and lipolysis was assessed. The citrate synthase activity in the soleus
muscle was analyzed. The trained groups prevented the impairment of baroreceptor
reflex sensitivity in relation to SO; however, only PTO reduced the
low-frequency band of the pulse interval compared to SO. PTO reduced the weight
of WAT compared to the other groups; lipolysis in PTO was similar to that in C.
PTO preserved muscle metabolic injury in all types of fibers analyzed. In
conclusion, this study suggests that exercise training should be recommended in
a premenopausal model to prevent cardiometabolic and autonomic menopause-induced
deleterious effects.
A deep learning-based method for the detection and segmentation of breast masses in ultrasound images
Authors: Li Wanqing , Ye Xianjun , Chen Xuemin , Jiang Xianxian , Yang Yidong 
Published: July 2024

Abstract

Objective. Automated detection and segmentation of breast masses in ultrasound images are critical for breast cancer diagnosis, but remain challenging due to limited image quality and complex breast tissues. This study aims to develop a deep learning-based method that enables accurate breast mass detection and segmentation in ultrasound images. Approach. A novel convolutional neural network-based framework that combines the You Only Look Once (YOLO) v5 network and the Global-Local (GOLO) strategy was developed. First, YOLOv5 was applied to locate the mass regions of interest (ROIs). Second, a Global Local-Connected Multi-Scale Selection (GOLO-CMSS) network was developed to segment the masses. The GOLO-CMSS operated on both the entire images globally and mass ROIs locally, and then integrated the two branches for a final segmentation output. Particularly, in global branch, CMSS applied Multi-Scale Selection (MSS) modules to automatically adjust the receptive fields, and Multi-Input (MLI) modules to enable fusion of shallow and deep features at different resolutions. The USTC dataset containing 28 477 breast ultrasound images was collected for training and test. The proposed method was also tested on three public datasets, UDIAT, BUSI and TUH. The segmentation performance of GOLO-CMSS was compared with other networks and three experienced radiologists. Main results. YOLOv5 outperformed other detection models with average precisions of 99.41%, 95.15%, 93.69% and 96.42% on the USTC, UDIAT, BUSI and TUH datasets, respectively. The proposed GOLO-CMSS showed superior segmentation performance over other state-of-the-art networks, with Dice similarity coefficients (DSCs) of 93.19%, 88.56%, 87.58% and 90.37% on the USTC, UDIAT, BUSI and TUH datasets, respectively. The mean DSC between GOLO-CMSS and each radiologist was significantly better than that between radiologists (p < 0.001). Significance. Our proposed method can accurately detect and segment breast masses with a decent performance comparable to radiologists, highlighting its great potential for clinical implementation in breast ultrasound examination.
Particle tracking, recognition and LET evaluation of out-of-field proton therapy delivered to a phantom with implants
Authors: Bălan Cristina , Granja Carlos , Mytsin Gennady , Shvidky Sergey , Molokanov Alexander , Marek Lukas , Chiș Vasile , Oancea Cristina 
Published: July 2024

Abstract

Objective. This study aims to assess the composition of scattered particles generated in proton therapy for tumors situated proximal to some titanium (Ti) dental implants. The investigation involves decomposing the mixed field and recording Linear Energy Transfer (LET) spectra to quantify the influence of metallic dental inserts located behind the tumor. Approach. A therapeutic conformal proton beam was used to deliver the treatment plan to an anthropomorphic head phantom with two types of implants inserted in the target volume (made of Ti and plastic, respectively). The scattered radiation resulted during the irradiation was detected by a hybrid semiconductor pixel detector MiniPIX Timepix3 that was placed distal to the Spread-out Bragg peak. Visualization and field decomposition of stray radiation were generated using algorithms trained in particle recognition based on artificial intelligence neural networks (AI NN). Spectral sensitive aspects of the scattered radiation were collected using two angular positions of the detector relative to the beam direction: 0° and 60°. Results. Using AI NN, 3 classes of particles were identified: protons, electrons & photons, and ions & fast neutrons. Placing a Ti implant in the beam’s path resulted in predominantly electrons and photons, contributing 52.2% of the total number of detected particles, whereas for plastic implants, the contribution was 65.4%. Scattered protons comprised 45.5% and 31.9% with and without metal inserts, respectively. The LET spectra were derived for each group of particles identified, with values ranging from 0.01 to 7.5 keV μm−1 for Ti implants/plastic implants. The low-LET component was primarily composed of electrons and photons, while the high-LET component corresponded to protons and ions. Significance. This method, complemented by directional maps, holds the potential for evaluating and validating treatment plans involving stray radiation near organs at risk, offering precise discrimination of the mixed field, and enhancing in this way the LET calculation.
Asymmetric nanofracture in WS2 for its local toughness anisotropy
Authors: Yang Xing , Zhang Bin 
Published: July 2024

Abstract
Fracture in crystal lattices usually occurs with discrete atomic bond breakages around the crack tip. WS2 involves three-layer atomic structures, where the atomic stress near the crack front exhibits thickness dependence and significantly relies on the local distortion of lattice geometry. We show that the T-stress obtained by over-deterministic methods, and the continuum circumferential stress, are limited in predicting the nanocrack kinking of WS2 strips by molecular dynamics simulations. As the far-field displacement loads, the T-stress initially increases in negative, followed by a slight jump at the initiation of kinking, and the continuum circumferential stress cannot accurately capture the variation of atomic stresses at the crack tip. This can be attributed to the local anisotropy in atomic lattices, and the crack preferentially extends in the zigzag direction of the local maximum energy release rate. Our work might provide insights into the fabrication and assembly of WS2 nanodevices.
Theoretical predictions of nuclear binding energy for the observed nuclei: the influence of coefficients and terms in a semi-empirical mass formula
Authors: Saad A F , Elghobary Zeyad I 
Published: July 2024

Abstract
The aim of this study is to calculate the nuclear binding energy for the 2457 nuclei which experimentally observed, as tabulated in the atomic mass evaluation AME2020, by using the semi-empirical mass formula based on the renowned liquid drop model (LDM). The Bethe-Weizsäcker (BW) mass formula-four terms (BWMF-4T) is extended to incorporate an additional six terms, resulting in a formula of ten terms. Our findings demonstrate commendable accuracy and reliability, as substantiated by the acceptable level of uncertainty observed when compared them to experimental data and existing models. The influence of coefficients and terms in the semi-empirical mass formula has been determined. All possible combinations of the terms are fitted in turn to the measured nuclear masses, and the outcomes are analyzed in order to reveal correlations and mutual influences between the various terms in all observed mass regions of the periodic table. The fitted surface energy and symmetry coefficients in the semi-empirical mass formula-ten terms (SEMF-10T) are remaining 26.4 and 33.3 MeV respectively. In contrast, coefficients fitted in the classic BW formula with and without one or two additional terms are around 17 and 23 MeV, respectively. The interplay between different terms in this mass formula is found to be significant and interesting.