bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of Deep Brain Stimulation therapy on the vertebral sagittal balance in Parkinson’s disease patients
Authors: Somma Teresa , Fellico Fabrizio , De Rosa Andrea , Bocchino Andrea , Corvino Sergio , Milone Antonio , Cappabianca Paolo , Esposito Felice 
Published: August 2023

Abstract
Background
Parkinson’s Disease (PD) is characterized by cardinal motor signs: 4-6 Hz resting tremor, rigidity and bradykinesia. In addition, 3-18% of PD patients have camptocormia, an abnormal forward flexion of the thoracolumbar spine, which may have a negative impact on patients' quality of life. Different possible treatments have been suggested for such a condition, but no one is resolutive.
This study aims to define the possible impact of DBS, with selective targeting on the dorso-lateral region of STN, on the sagittal balance of patients affected by PD.
Patients and Methods
Among all patients undergone DBS procedures in our Institution, we selected 8 subjects, 4 female and 4 males, with selective targeting on the dorso-lateral region of the Subthalamic Nucleus (STN) because of camptocormia and other severe postural changes. Radiological assessments of spinal balance parameters before surgery and at 6 and 12 months post-surgery were carried out.
Results
Comparison of preoperative and postoperative spine X-ray data showed that patients showed a statistically significant improvement in dorsal kyphosis angle (D-Cobb) 12 months after the operation.
Conclusions
Deep Brain Stimulation with selective targeting of the dorsal lateral part of the STN may induce changes of the posture in patients with Parkinson’s Disease 12 months after the operation, which appears to improve in this small sample size, but larger observational and controlled trials would be required to confirm this observation.
A single-cell transcriptomic study reveals immune suppressive cancer cell-immune cell interactions in the triple negative canine breast cancers
Authors: Kim Myung-Chul , Borcherding Nick , Song Woo-Jin , Kolb Ryan , Zhang Weizhou 
Published: August 2023

Abstract
Clinical trials show promising outcomes for dogs with advanced solid tumors following treatment with immune checkpoint inhibitors (ICIs). Triple-negative breast cancer (TNBC) is very aggressive with very low response rates to ICIs. No study defines how canine TNBC interacts with the immune system within the tumor microenvironment, which is investigated in this study at the single cell level. Single cell RNA sequencing (scRNA-seq) datasets, including 6 groups of 30 dogs, were subject to integrated bioinformatic analysis. Immune modulatory TNBC subsets were identified by functional enrichment with immune-suppressive gene sets, including anti-inflammatory and M2-like macrophages. Key genes and immune-suppressive signaling pathways for TNBC included angiogenesis and leukocyte chemotaxis. Interactome analysis identified significant interactions between distinct subsets of cancer cells and effector T cells, suggesting T cell suppression. This is the first study to define immune-suppressive cancer cell subsets at the single-cell level, revealing potential mechanisms by which TNBC induces immune evasion in dogs.
WITHDRAWN: Breast Cancer Detection by Using VGG19 Deep Learning Network with Image Filtering and Histogram Equalization Methods
Authors: ESMERAY Furkan , GÜLTEN Arif 
Published: August 2023

Abstract
Deep learning techniques, one of these machine learning techniques, are also at a very important point. Thanks to the classification made with deep learning techniques, high accuracy rates can be obtained for cancer diagnosis and faster results can be obtained. In this study, VGG19 network architecture, one of the deep learning methods, was used to classify mammogram images.
In addition, image equalization and image filtering methods were applied to the images used. In this way, it was also determined which method achieved higher accuracy when the image filtering and image synchronization methods used were run together with the VGG19 deep learning network architecture. The combination of CLAHE histogram equalization and VGG19 deep learning network gave the highest accuracy. The accuracy rate of the training data in the created network is 99.82%. In addition, the loss rate of the training data in the network is 0.76% and the validation rate of the test data in the network is 99.63%. The number of correct positive images is 796, the number of false positive images is 0, the number of correct negative images is 798, and the number of false negative images is 6. These image numbers belong to the test data. The number of correctly classified images in the test data is 1594. These values ​​are very good values ​​for classification of mammogram images.
Simulation of ground motion emissions from wind turbines in low mountain ranges: implications for amplitude decay prediction
Authors: Gaßner Laura , Gärtner Marie A. , Ritter Joachim 
Published: August 2023

Abstract
The characterization and prediction of wind turbine (WT) emissions are important steps in reducing their impact on humans or sensitive technologies such as seismic stations or physics experiments. Here, WT ground motion emissions are studied along two measurement lines set up at two wind farms on the Eastern Swabian Alb, southwest Germany. The main purpose of the data analysis is to estimate amplitude decay rates from vertical component data and surface wave phase velocities excited by the permanent motion of the WT towers. Phase velocities as well as geological information serve as input to build realistic subsurface models for numerical wave field simulations. Amplitude A decay rates are characterized by b-values through A~1/rb depending on distance r and are derived from peaks in power spectral density (PSD). We find an increase of bPSD with frequency from 0.5 to 3.2. For low frequencies (1.2 Hz and 3.6 Hz), bPSD~0.5-1.1, hence close to the geometrical spreading factor of surface waves (bPSD=1). Anelastic damping and scattering seem not to be significant at these frequencies which also shows in numerical simulations for quality factors Q=50-200. We also find that the emitted wavefields from several WTs interfere, especially in the near-field, and produce strong local ground motion amplitudes. The inclusion of a steep topography present in low mountain ranges adds more wave field distortions which can further increase the amplitudes. This needs to be considered when predicting WT induced ground motions.
Bioindication for Ecosystem Regeneration towards Natural conditions – the BERN data base and BERN model
Authors: Schlutow Angela , Kraft Philipp , Scheuschner Thomas , Schröder Winfried 
Published: August 2023

Abstract
The BERN model (Bioindication for Ecosystem Regeneration towards Natural conditions) was developed to integrate ecological cause-effect relationships into studies on environmental status assessment and forecasting. Common approaches such as ordination (Roberts 1986) or bioindication based on individual species like PROPS or MultiMOVE model are of limited use because it is not possible to predict the occurrence of a plant species on the basis of site factors, since the competitive influences cannot be determined in advance according to current knowledge. Therefore, the BERN model takes into account the structure of plant communities with the abundance and cover rates of species in the competitive equilibrium of pristine plant communities as a reference for determining anthropogenically induced changes. Qualitative knowledge on the relationship between site types and vegetation communities is widely available, as can be seen from the extensive phytosociological publications. The aim of the BERN model is to make part of this knowledge available in numerical form for computer-based ecosystem modelling. The task of the BERN database is to document reference data for the occurrence of plant species in competitive equilibrium with each other and in equilibrium with the site factors. The BERN model combines the fundamental species niches that mainly form the competitively stable structure of a pristine plant community in order to determine the realised multifactorial niche of this community. The community database includes 887 central European plant communities and links to their diagnostically defining species composition. The database defines the niche of 2210 central European plant species for the soil properties pH, base saturation, carbon to nitrogen ratio and wetness index and the climatic properties continentality, length of vegetation period and climatic water balance. Another difference to all the other models mentioned above is the BERN database with historical recordings at more or less undisturbed sites. The BERN database now contains so many vegetation relevés that the database can be considered representative at least for Central Europe. The methodology of creating the BERN database and the BERN model is documented and applications are demonstrated with examples.
Exosomes from adipose-derived stem cells alleviate inflammation and oxidative stress induced by psoriasis serum exosomes via autophagy regulation in keratinocytes
Authors: Kim Hye Ran , Lee So Yeon , You Ga Eun , Kim Hye One , Park Chun Wook , Chung Bo Young 
Published: August 2023

Abstract

Exosomes play a key role in cell communication and are involved in both pathological and physiological processes. Autophagy dysfunction and oxidative stress are linked to immune-mediated inflammatory diseases such as psoriasis. Stem cell-derived exosomes exhibit immunomodulatory and antioxidant efficacy. We aimed to investigate the impact of psoriasis serum-derived exosomes on inflammation, oxidative stress, and autophagy in keratinocytes. Additionally, we explored the therapeutic potential of adipose-derived stem cell (ADSC) exosomes against inflammation induced by psoriasis serum exosomes. To validate psoriasis patient serum-derived exosomes and ADSC exosomes, we used nanoparticle tracking analysis, western blotting, flow cytometry, and immunofluorescence. qPCR was used to study changes in the gene expression of proinflammatory cytokines and oxidative stress markers in HaCaT cells treated with psoriasis serum-derived exosomes or ADSC exosomes. The effects of these exosomes on autophagy in HaCaT cells were evaluated by western blotting and immunofluorescence. The treatment of HaCaT cells with psoriasis serum-derived exosomes increased proinflammatory cytokine production and oxidative stress-related factor (Nox2 and Nox4) expression and decreased Nrf2 expression via P65/NF-κB and P38/MAPK activation. Compared with healthy control serum-derived exosomes, psoriasis serum-derived exosomes decreased ATG5, P62, Beclin1, and LC3 expression and autophagosome production in HaCaT cells. Conversely, ADSC exosomes suppressed proinflammatory cytokine and oxidative stress production, and restored autophagy in HaCaT cells treated with psoriasis serum-derived exosomes. These findings suggest that ADSC exosomes exhibit a suppressive effect on psoriasis serum exosome-induced inflammation and oxidative stress by regulating autophagy in keratinocytes.
3D high-efficiency property inversion method for potential field data in ellipsoidal coordinate system and its application to South China Sea
Authors: Jiang Zhexin , Ma Guoqing , Wang Taihan , Meng Qingfa , Li Lili 
Published: August 2023

Abstract

The method of property inversion for gravity and magnetic data in ellipsoidal coordinate system is more suitable for obtaining large-area physical structure of the Earth. We built a method of high-efficiency property inversion for gravity and magnetic data in ellipsoidal coordinate system to obtain the density and magnetic susceptibility structure and used the exchangeability and equivalence of the grids at the same latitude to improve the inversion efficiency; in addition, a volume weight function was introduced to suppress the influence of the difference in grid size caused by latitudinal variation to improve the precision. We also first derived the forward formula of magnetic anomaly in ellipsoidal coordinate system according to the Gauss–Legendre quadrature (GLQ) and Poisson's formula for inversion. The high-efficiency inversion method for gravity and magnetic data in ellipsoidal coordinate system was tested on theoretical models, which can better restore the property distribution of the source, improve the computing efficiency and reduce memory requirements. The method was applied to the gravity data of South China Sea to obtain the 3D density structure, and the boundaries of ocean-continent transition were obtained based on the density and magnetic susceptibility variation characteristics. The results can better reveal the process of South China Sea expansion.
Environmental Assessment of Woven Hemp Fibre Reinforced Epoxy Composites and Potential Applications in Aerospace and Electric Scooter Industries
Authors: Zarafshani Hanie , Watjanatepin Ponnapat , Lepelaar Mark , Verbruggen Jens , Ouagne Pierre , Luca Rosario , Li Qinyu , Scarpa Fabrizio , Placet Vincent , Acker Karel 
Published: August 2023

Abstract

This paper aims to present a hemp-based natural fibres composite value chain that could be implemented in biocomposite structures and replace synthetic fibres. Life cycle assessment (LCA) allows the assessment of the environmental footprint of the hemp fibre reinforcement value chain. This general case is complemented with two hemp fibre reinforcement applications, i.e. (1) a hemp fibre reinforced epoxy-based aircraft dashboard panel and (2) a hemp fibre reinforced epoxy-based biomonocoque for an electric scooter. LCA is also used to assess the environmental footprint of the application cases. All the hemp fibre-based value chain applications shown here demonstrate a lower environmental footprint than the fossil-based reinforcement value chain, particularly regarding the global warming potential, among other impact categories except for the case where the specific tensile strength was used as the functional unit for comparison. Impact categories related to agricultural activities, such as the eutrophication potential, land use, and freshwater consumption, are higher for the hemp-based value chains than for conventional synthetic fibre-based solutions. This study shows how conventional synthetic fibre products have the potential to be replaced by hemp-based natural fibre solutions depending on the required applications, which could lower the environmental footprint and help contribute to the ongoing climate mitigation efforts.
Emerging role and clinical implication of mRNA scavenger decapping enzyme in colorectal cancer
Authors: Dimberg Jan , Shamoun Levar , Johansson Gustaf , Landerholm Kalle , Wågsäter Dick 
Published: August 2023

Abstract
Turnover of RNA is a regulated process that in part controls gene expression. This process is partly controlled by the scavenger decapping enzyme (DcpS). This study aimed to investigate the expression of DcpS in colorectal cancer (CRC) tissue, to evaluate its prognostic significance in patients with CRC and to investigate potentially targeted genes by DcpS. DcpS expression was localized to the epithelial cells of both control and cancer tissue. Tumor and paired control tissue samples from 100 patients who underwent surgical resection for primary colorectal adenocarcinomas were utilized. mRNA and protein of DcpS was significantly up-regulated in the patients with CRC and the mRNA level was higher in rectal cancer tissue compared to colon cancer tissue (p < 0.05). Lowest tertile levels of DcpS mRNA in cancer tissue was associated with a decreased cancer-specific survival rate with a hazard ratio (HR) of 4.7 (95% CI = 1.02–12.3), independent of disease stage. The low level of DcpS mRNA was a predictor of poorer survival in patients with rectal and disseminated cancer and in patients receiving adjuvant treatment (p < 0.05). After silencing DcpS in Caco-2 cancer altered expression of several genes associated with RNA, cell cycle regulation, alternative splicing and microRNA was observed and resulted in 23% decrease of proliferation. These results indicate that DcpS has potential as a prognostic factor for CRC but further studies in a broader cohort are warranted to evaluate the significance of the findings in the clinic.
Caproate production from Enset fiber in one-pot two-step fermentation using anaerobic fungi (Neocallimastix cameroonii strain G341) and Clostridium kluyveri DSM 555
Authors: Seid Nebyat , Ochsenreither Katrin , Neumann Anke 
Published: August 2023

Abstract
Background
Lignocellulosic biomass plays a crucial role in creating a circular bioeconomy and minimizing environmental impact. Enset biomass is a byproduct of traditional Ethiopian Enset food processing that is thrown away in huge quantities. This study aimed to produce caproate from Enset fiber using Neocallimastix cameroonii strain G341 and Clostridium kluyveri DSM 555 in one-pot two-step fermentation.
Results
The process started by growing N. cameroonii on Enset fiber as a carbon source for 7 days. Subsequently, the fungal culture was inoculated with active C. kluyveri preculture and further incubated. The results showed that N. cameroonii grew on 0.25 g untreated Enset fiber as the sole carbon source and produced 1.16 mmol acetate, 0.51 mmol hydrogen, and 1.34 mmol formate. In addition, lactate, succinate, and ethanol were detected in small amounts, 0.17 mmol, 0.08 mmol, and 0.7 mmol, respectively. After inoculating with C. kluyveri, 0.3 mmol of caproate and 0.48 mmol of butyrate were produced, and hydrogen production also increased to 0.95 mmol compared to sole N. cameroonii fermentation. Moreover, after the culture was supplemented with 2.18 mmol of ethanol during C. kluyveri inoculation, caproate, and hydrogen production was further increased to 1.2 and 1.36 mmol, respectively, and the consumption of acetate also increased.
Conclusion
A novel microbial cell factory was developed to convert untreated lignocellulosic Enset fibers into the medium chain carboxylic acid caproate and H2 by a co-culture of the anaerobic fungi N. cameroonii and C. kluyveri. This opens a new value chain for Enset farmers, as the process requires only locally available raw materials and low-price fermenters. As the caproate production was mainly limited by the available ethanol, the addition of locally produced ethanol-containing fermentation broth would further increase the titer.
Enhancing Breast Cancer Histopathological Image Classification using Attention-Based High Order Covariance Pooling
Authors: Waqas Muhammad , Ahmed Amr , Maul Tomas , Liao Iman Yi 
Published: August 2023

Abstract
The type of cancer that affects female patients most frequently is breast cancer. Computer-aided diagnosis, these days, proves to be helpful for many diseases including breast cancer. Deep learning based approaches have yielded encouraging results in many computer vision applications. In the case of breast cancer histopathological image classification, many methods have been proposed but the accuracy is still insufficient for full deployment. Due to this reason, we propose a deep learning based approach, DHA-Net which consists of an attention module and a high-order pooling layer incorporated into a ResNet-18 architecture. DHA-Net was applied to three well-known datasets in the field of breast cancer histopathological image classification, i.e. BreakHis, BACH2018 and a closely related Kaggle-Breast cancer histopathology dataset. In this area of medical imaging, experiments have shown that DHA-Net works better than other existing techniques.
Viral Genome Sequencing to Decipher In-Hospital SARS-CoV-2 Transmission Events
Authors: Esser Elisabeth , Schulte Eva C. , Graf Alexander , Karollus Alexander , Smith Nicholas H. , Michler Thomas , Dvoretskii Stefan , Angelov Angel , Sonnabend Michael , Peter Silke , Engesser Christina , Radonic Aleksandar , Thürmer Andrea , von Kleist Max , Gebhardt Friedemann , Costa Clarissa Prazeres da , Busch Dirk H. , Muenchhoff Maximilian , Blum Helmut , Keppler Oliver T. , Gagneur Julien , Protzer Ulrike 
Published: August 2023

Abstract
Background: The SARS-CoV-2 pandemic has highlighted the need to better define in-hospital transmissions, a need that extends to all other common infectious diseases encountered in clinical settings.
Objectives: To evaluate how whole viral genome sequencing can contribute to deciphering nosocomial SARS-CoV-2 transmission
Methods: 926 SARS-CoV-2 viral genomes from 622 staff members and patients were collected between February 2020 and January 2021 at a university hospital in Munich, Germany, and analysed along with the place of work, duration of hospital stay and ward transfers. Bioinformatically defined transmission clusters inferred from viral genome sequencing were compared to those inferred from interview-based contact tracing. An additional dataset collected at the same time at another university hospital in the same city was used to account for multiple independent introductions.
Results: Clustering analysis of 619 viral genomes generated 18 clusters ranging from 3 to 29 individuals. Sequencing-based transmission clusters showed little overlap to those based on contact tracing data. The viral genomes were significantly more closely related to each other than comparable genomes collected simultaneously at other hospitals in the same city (n=681), suggesting nosocomial transmission. Longitudinal sampling from individual patients suggested possible cross-infection events during the hospital stay in 19.2% of individuals (14 of 73 individuals).
Conclusion: Clustering analysis of SARS-CoV-2 whole genome sequences can reveal cryptic transmission events missed by classical, interview-based contact tracing, helping to decipher in-hospital transmissions. These results, in line with other studies across the world, advocate for viral genome sequencing as pathogen transmission surveillance tools in hospitals.
Neural Network-Based Cross-Channel Chroma Prediction for Versatile Video Coding
Authors: Liang Fang , Zhang Jingde 
Published: August 2023

Abstract

Despite linear models being introduced in the latest Versatile Video Coding (VVC) standard to exploit the correlation among luma and chroma channels for removing redundancy, these models cannot take into account the nonlinearity of components, resulting in degraded intra prediction precision. In this paper, a neural network-based method is proposed for cross-channel chroma intra prediction to enhance the coding efficiency. Specifically, the neighboring reference and co-located samples are separately input into the proposed network to exploit spatial and cross-channel correlations fully. Furthermore, in order to acquire a more compact representation of residual signals, a transform-based loss is employed to enhance the effectiveness of the compression. The proposed method is integrated into VVC, competing with the intrinsic chroma prediction regarding rate-distortion optimization to enhance coding performance further. The extensive experimental results demonstrate the superiority of the proposed method over the VVC test model (VTM) 7.0, achieving average bitrate savings of 2.68% and 2.11% for U and V chroma components, respectively.