bluuebunny/crossref_metadata_2025_split huggingface dataset:
Wave Equation Dispersion Inversion of Distributed Acoustic Sensing Data
Authors: Liu Hui , Li Jing , Wang Yibo 
Published: November 2022

AbstractDistributed acoustic sensing (DAS) is a new dense sampling seismic observation technique rapidly developed to image near‐surface velocity structures. Compared with traditional seismometer surveys, DAS can record the strain rate data with a high spatial and temporal resolution and low maintenance cost observation. However, the strain variation recorded by the DAS system is the horizontal component with a low signal‐to‐noise ratio because the coupling between the fiber and the earth is poor. In addition, conventional velocity inversion algorithms such as 1D dispersion curve inversion and 2D/3D full‐waveform inversion (FWI) are developed for particle velocity components. Hence, their application in DAS strain‐rate data requires conversion to particle velocity data, which would bring some numerical error. In this work, we propose the DAS wave‐equation dispersion inversion (DAS‐WD) method to invert the S‐wave velocity model directly from DAS strain rate data. The WD method does not require 1D dispersion curve inversion layered assumptions and overcomes the problem that the FWI is challenging to converge because the recorded horizontal DAS component data is noisy. Meanwhile, the strain rate DAS‐WD theory avoids the error caused by velocity and strain rate conversion by scaling of apparent velocity followed by time integrations. The typical synthetic and offshore DAS field data demonstrate that the proposed DAS‐WD method has great potential for a high‐resolution velocity tomogram.
Advanced Potential Field Analysis Applied to Mise‐à‐la‐Masse Surveys for Leakage Detection
Authors: Mary Benjamin , Peruzzo Luca , Wu Yuxin , Cassiani Giorgio 
Published: November 2022

AbstractTraditionally interpretation of Mise‐à‐la‐masse (MALM) is limited to the visualization of equipotential contours in order to infer qualitatively the extent of the anomaly. MALM inversion algorithms rely on having a good knowledge of the electrical resistivity distribution in the subsoil. Conversely, potential imaging methods have shown their strength for several applications to quickly estimate the depth of sources even in highly heterogeneous media. In the case of the MALM method, the physics may be described by Poisson's equation. As the conductivity term is modulating the flux of current, MALM is generally referred to as a pseudo‐potential method. In this work, we have tested, for the first time, the application of the potential field theory to MALM in order to identify the current source depth. Synthetic modeling shows that the proposed algorithm is effective and efficient, using surface voltage measurements for different resistivity contrasts, anomaly depths and noise levels. We then applied the method to the real field case of a landfill leakage and showed how very different source depth estimates result from an intact or a damaged landfill liner.
Identifying Spatiotemporal Patterns of Hillslope Subsurface Flow in an Alpine Critical Zone on the Qinghai‐Tibetan Plateau Based on Three‐Year, High‐Resolution Field Observations
Authors: Hu Guangrong , Li Xiaoyan , Yang Xiaofan , Shi Fangzhong , Sun Haoran , Cui Baoshan 
Published: November 2022

AbstractThe spatial heterogeneity and temporal variability of hillslope subsurface flow (SSF) generation are poorly understood in the alpine critical zone (CZ). Owing to the limitations of observation technology bottlenecks and harsh environments, existing studies lack a holistic and process‐based understanding of SSF generation, particularly for the deep CZ and annual freeze‐thaw (FT) cycle. This study improves the conventional trenching observation scheme and is the first to have successfully monitored SSF processes from the soil to the bedrock layer with high‐resolution from 2017 to 2020 in a headwater catchment of the Qinghai‐Tibetan Plateau. The results show that hillslope runoff generation exhibited similar trends over the years while exhibiting distinct patterns across seasons. Spatially, slope aspects significantly affect the runoff amount and ratio. The south‐facing slope is dominated by overland flow (OF, 84.2%–97.0%), while the north‐facing slope is dominated by SSF (88.9%–97.3%). The runoff depth of the north‐facing slope is 11.0 times that of the south‐facing slope. The SSF in the deep CZ accounts for 20.8%–37.8% of the total runoff along the north‐facing slope. Temporally, runoff generation exhibits distinct patterns across seasons. OF exhibits two different patterns, while SSF exhibits five across seasons. Our results indicate that runoff generation in different FT stages should be studied by the time phase. Therefore, we propose seasonal or FT period‐based classification criteria and divide OF and SSF into two and three consecutive periods throughout the calendar year. We suggest that these inferences and observations should be considered in the framework of hydrological models in cold regions.
Are the antidepressant effects of insulin-sensitizing medications related to improvements in metabolic markers?
Authors: Toba-Oluboka Temi , Vochosková Kristýna , Hajek Tomas 
Published: November 2022

AbstractInsulin-sensitizing medications were originally used in psychiatric practice to treat weight gain and other metabolic side effects that accompany the use of mood stabilizers, antipsychotics, and some antidepressants. However, in recent studies these medications have been shown to cause improvement in depressive symptoms, creating a potential new indication outside of metabolic regulation. However, it is still unclear whether the antidepressant properties of these medications are associated with improvements in metabolic markers. We performed a systematic search of the literature following PRISMA guidelines of studies investigating antidepressant effects of insulin-sensitizing medications. We specifically focused on whether any improvements in depressive symptoms were connected to the improvement of metabolic dysfunction. Majority of the studies included in this review reported significant improvement in depressive symptoms following treatment with insulin-sensitizing medications. Nine out of the fifteen included studies assessed for a correlation between improvement in symptoms and changes in metabolic markers and only two of the nine studies found such association, with effect sizes ranging from R2 = 0.26–0.38. The metabolic variables, which correlated with improvements in depressive symptoms included oral glucose tolerance test, fasting plasma glucose and glycosylated hemoglobin following treatment with pioglitazone or metformin. The use of insulin-sensitizing medications has a clear positive impact on depressive symptoms. However, it seems that the symptom improvement may be unrelated to improvement in metabolic markers or weight. It is unclear which additional mechanisms play a role in the observed clinical improvement. Some alternative options include inflammatory, neuroinflammatory changes, improvements in cognitive functioning or brain structure. Future studies of insulin-sensitizing medications should measure metabolic markers and study the links between changes in metabolic markers and changes in depression. Additionally, it is important to use novel outcomes in these studies, such as changes in cognitive functioning and to investigate not only acute, but also prophylactic treatment effects.
The utility of ctDNA in detecting minimal residual disease following curative surgery in colorectal cancer: a systematic review and meta-analysis
Authors: Faulkner Lucy G. , Howells Lynne M. , Pepper Coral , Shaw Jacqueline A. , Thomas Anne L. 
Published: November 2022

Abstract
Introduction
Colorectal cancer is the fourth most common cancer in the UK. There remains a need for improved risk stratification following curative resection. Circulating-tumour DNA (ctDNA) has gained particular interest as a cancer biomarker in recent years. We performed a systematic review to assess the utility of ctDNA in identifying minimal residual disease in colorectal cancer.

Methods
Studies were included if ctDNA was measured following curative surgery and long-term outcomes were assessed. Studies were excluded if the manuscript could not be obtained from the British Library or were not available in English.

Results
Thirty-seven studies met the inclusion criteria, involving 3002 patients. Hazard ratios (HRs) for progression-free survival (PFS) were available in 21 studies. A meta-analysis using a random effects model demonstrated poorer PFS associated with ctDNA detection at the first liquid biopsy post-surgery [HR: 6.92 CI: 4.49–10.64 p < 0.00001]. This effect was also seen in subgroup analysis by disease extent, adjuvant chemotherapy and assay type.

Discussion
Here we demonstrate that ctDNA detection post-surgery is associated with a greater propensity to disease relapse and is an independent indicator of poor prognosis. Prior to incorporation into clinical practice, consensus around timing of measurements and assay methodology are critical.

Protocol registration
The protocol for this review is registered on PROSPERO (CRD42021261569).
How the histological structure of some lung cancers shaped almost 70 years of radiobiology
Authors: Worth Katja R. , Papandreou Ioanna , Hammond Ester M. 
Published: November 2022

AbstractPivotal research led by Louis Harold Gray in the 1950s suggested that oxygen plays a vital role during radiotherapy. By proving that tumours have large necrotic cores due to hypoxia and that hypoxic cells require significantly larger doses of ionising radiation to achieve the same cell kill, Thomlinson and Gray inspired the subsequent decades of research into better defining the mechanistic role of molecular oxygen at the time of radiation. Ultimately, the work pioneered by Thomlinson and Gray led to numerous elegant studies which demonstrated that tumour hypoxia predicts for poor patient outcomes. Furthermore, this subsequently resulted in investigations into markers and measurement of hypoxia, as well as modification strategies. However, despite an abundance of pre-clinical data supporting hypoxia-targeted treatments, there is limited widespread application of hypoxia-targeted therapies routinely used in clinical practice. Significant contributing factors underpinning disappointing clinical trial results include the use of model systems which are more hypoxic than human tumours and a failure to stratify patients based on levels of hypoxia. However, translating the original findings of Thomlinson and Gray remains a research priority with the potential to significantly improve patient outcomes and specifically those receiving radiotherapy.
FMR1 promotes the progression of colorectal cancer cell by stabilizing EGFR mRNA in an m6A-dependent manner
Authors: Hu Yuhan , Gao Qingzu , Ma Shuai , Yu Pei , Ding Shuang , Yao Xiaofei , Zhang Zheying , Lu Shuya , Lu Manman , Zhang Jinghang , Wang Yanling , Qian Xinlai , Zhong Jiateng 
Published: November 2022

AbstractFMR1, a new m6A reader, is known to be involved in the regulation of cancer progression. However, its role, regulatory mechanism, and clinical significance in colorectal cancer (CRC) are elusive. Here, we showed that FMR1 was upregulated in CRC, and it promoted proliferation and metastasis of CRC cells in vitro and in vivo. Mechanically, FMR1 recognized the m6A-modification site in EGFR mRNA, a key molecule in cancer occurrence and targeted therapy, sustained its stability and maintained its expression in an m6A-dependent manner, thereby promoting the tumorigenesis and metastasis of CRC. And the effect of FMR1 knockdown in CRC cells could be abolished by METTL3. Furthermore, FMR1 shRNA plasmid carried by attenuated Salmonella has an effective anti-tumor effect in vivo. Collectively, we identified the METTL3/FMR1/EGFR axis in the progression of CRC. This novel mechanism indicated that the METTL3/FMR1/EGFR axis is a potential target for early therapeutic intervention in CRC progression.
Myo1b promotes tumor progression and angiogenesis by inhibiting autophagic degradation of HIF-1α in colorectal cancer
Authors: Chen Yi-Hong , Xu Nan-Zhu , Hong Chang , Li Wen-Qi , Zhang Yi-Qiong , Yu Xin-Yi , Huang Yue-Le , Zhou Jue-Yu 
Published: November 2022

AbstractMyosin 1b (Myo1b) is an important single-headed membrane-associated motor of class I myosins that participate in many critical physiological and pathological processes. Mounting evidence suggests that the dysregulation of Myo1b expression has been extensively investigated in the development and progression of several tumors. However, the functional mechanism of Myo1b in CRC angiogenesis and autophagy progression remains unclear. Herein, we found that the expression of Myo1b was upregulated in CRC tissues and its high expression was correlated with worse survival. The overexpression of Myo1b promoted the proliferation, migration and invasion of CRC cells. Conversely, silencing of Myo1b suppressed tumor progression both in vitro and in vivo. Further studies indicated that Myo1b inhibited the autophagosome-lysosome fusion and potentiated the VEGF secretion of CRC cells to promote angiogenesis. Mechanistically, Myo1b blocked the autophagic degradation of HIF-1α and then led to the accumulation of HIF-1α, thus enhancing VEGF secretion and then promoting tumor angiogenesis in CRC. Together, our study provided novel insights into the role of Myo1b in CRC progression and revealed that it might be a feasible predictive biomarker and promising therapeutic target for CRC patients.
Integrated proteogenomic characterization of medullary thyroid carcinoma
Authors: Shi Xiao , Sun Yaoting , Shen Cenkai , Zhang Yan , Shi Rongliang , Zhang Fan , Liao Tian , Lv Guojun , Zhu Zhengcai , Jiao Lianghe , Li Peng , Xu Tiansheng , Qu Ning , Huang Naisi , Hu Jiaqian , Zhang Tingting , Gu Yanzi , Qin Guangqi , Guan Haixia , Pu Weilin , Li Yuan , Geng Xiang , Zhang Yan , Chen Tongzhen , Huang Shenglin , Zhang Zhikang , Ge Shuting , Wang Wu , Xu Weibo , Yu Pengcheng , Lu Zhongwu , Wang Yulong , Guo Liang , Wang Yu , Guo Tiannan , Ji Qinghai , Wei Wenjun 
Published: November 2022

AbstractMedullary thyroid carcinoma (MTC) is a rare neuroendocrine malignancy derived from parafollicular cells (C cells) of the thyroid. Here we presented a comprehensive multi-omics landscape of 102 MTCs through whole-exome sequencing, RNA sequencing, DNA methylation array, proteomic and phosphoproteomic profiling. Integrated analyses identified BRAF and NF1 as novel driver genes in addition to the well-characterized RET and RAS proto-oncogenes. Proteome-based stratification of MTCs revealed three molecularly heterogeneous subtypes named as: (1) Metabolic, (2) Basal and (3) Mesenchymal, which are distinct in genetic drivers, epigenetic modification profiles, clinicopathologic factors and clinical outcomes. Furthermore, we explored putative therapeutic targets of each proteomic subtype, and found that two tenascin family members TNC/TNXB might serve as potential prognostic biomarkers for MTC. Collectively, our study expands the knowledge of MTC biology and therapeutic vulnerabilities, which may serve as an important resource for future investigation on this malignancy.
Flexomagnetism and vertically graded Néel temperature of antiferromagnetic Cr2O3 thin films
Authors: Makushko Pavlo , Kosub Tobias , Pylypovskyi Oleksandr V. , Hedrich Natascha , Li Jiang , Pashkin Alexej , Avdoshenko Stanislav , Hübner René , Ganss Fabian , Wolf Daniel , Lubk Axel , Liedke Maciej Oskar , Butterling Maik , Wagner Andreas , Wagner Kai , Shields Brendan J. , Lehmann Paul , Veremchuk Igor , Fassbender Jürgen , Maletinsky Patrick , Makarov Denys 
Published: November 2022

AbstractAntiferromagnetic insulators are a prospective materials platform for magnonics, spin superfluidity, THz spintronics, and non-volatile data storage. A magnetomechanical coupling in antiferromagnets offers vast advantages in the control and manipulation of the primary order parameter yet remains largely unexplored. Here, we discover a new member in the family of flexoeffects in thin films of Cr2O3. We demonstrate that a gradient of mechanical strain can impact the magnetic phase transition resulting in the distribution of the Néel temperature along the thickness of a 50-nm-thick film. The inhomogeneous reduction of the antiferromagnetic order parameter induces a flexomagnetic coefficient of about 15 μB nm−2. The antiferromagnetic ordering in the inhomogeneously strained films can persist up to 100 °C, rendering Cr2O3 relevant for industrial electronics applications. Strain gradient in Cr2O3 thin films enables fundamental research on magnetomechanics and thermodynamics of antiferromagnetic solitons, spin waves and artificial spin ice systems in magnetic materials with continuously graded parameters.
Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer
Authors: Shamai Gil , Livne Amir , Polónia António , Sabo Edmond , Cretu Alexandra , Bar-Sela Gil , Kimmel Ron 
Published: November 2022

AbstractProgrammed death ligand-1 (PD-L1) has been recently adopted for breast cancer as a predictive biomarker for immunotherapies. The cost, time, and variability of PD-L1 quantification by immunohistochemistry (IHC) are a challenge. In contrast, hematoxylin and eosin (H&E) is a robust staining used routinely for cancer diagnosis. Here, we show that PD-L1 expression can be predicted from H&E-stained images by employing state-of-the-art deep learning techniques. With the help of two expert pathologists and a designed annotation software, we construct a dataset to assess the feasibility of PD-L1 prediction from H&E in breast cancer. In a cohort of 3,376 patients, our system predicts the PD-L1 status in a high area under the curve (AUC) of 0.91 – 0.93. Our system is validated on two external datasets, including an independent clinical trial cohort, showing consistent prediction performance. Furthermore, the proposed system predicts which cases are prone to pathologists miss-interpretation, showing it can serve as a decision support and quality assurance system in clinical practice.
T cell responses at diagnosis of amyotrophic lateral sclerosis predict disease progression
Authors: Yazdani Solmaz , Seitz Christina , Cui Can , Lovik Anikó , Pan Lu , Piehl Fredrik , Pawitan Yudi , Kläppe Ulf , Press Rayomand , Samuelsson Kristin , Yin Li , Vu Trung Nghia , Joly Anne-Laure , Westerberg Lisa S. , Evertsson Björn , Ingre Caroline , Andersson John , Fang Fang 
Published: November 2022

AbstractAmyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease, involving neuroinflammation and T cell infiltration in the central nervous system. However, the contribution of T cell responses to the pathology of the disease is not fully understood. Here we show, by flow cytometric analysis of blood and cerebrospinal fluid (CSF) samples of a cohort of 89 newly diagnosed ALS patients in Stockholm, Sweden, that T cell phenotypes at the time of diagnosis are good predictors of disease outcome. High frequency of CD4+FOXP3− effector T cells in blood and CSF is associated with poor survival, whereas high frequency of activated regulatory T (Treg) cells and high ratio between activated and resting Treg cells in blood are associated with better survival. Besides survival, phenotypic profiling of T cells could also predict disease progression rate. Single cell transcriptomics analysis of CSF samples shows clonally expanded CD4+ and CD8+ T cells in CSF, with characteristic gene expression patterns. In summary, T cell responses associate with and likely contribute to disease progression in ALS, supporting modulation of adaptive immunity as a viable therapeutic option.
Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis
Authors: Chen Yanshuo , Wang Yixuan , Chen Yuelong , Cheng Yuqi , Wei Yumeng , Li Yunxiang , Wang Jiuming , Wei Yingying , Chan Ting-Fung , Li Yu 
Published: November 2022

AbstractSingle-cell RNA-sequencing has become a powerful tool to study biologically significant characteristics at explicitly high resolution. However, its application on emerging data is currently limited by its intrinsic techniques. Here, we introduce Tissue-AdaPtive autoEncoder (TAPE), a deep learning method connecting bulk RNA-seq and single-cell RNA-seq to achieve precise deconvolution in a short time. By constructing an interpretable decoder and training under a unique scheme, TAPE can predict cell-type fractions and cell-type-specific gene expression tissue-adaptively. Compared with popular methods on several datasets, TAPE has a better overall performance and comparable accuracy at cell type level. Additionally, it is more robust among different cell types, faster, and sensitive to provide biologically meaningful predictions. Moreover, through the analysis of clinical data, TAPE shows its ability to predict cell-type-specific gene expression profiles with biological significance. We believe that TAPE will enable and accelerate the precise analysis of high-throughput clinical data in a wide range.
Deep learning for fully-automated nuclear pleomorphism scoring in breast cancer
Authors: Mercan Caner , Balkenhol Maschenka , Salgado Roberto , Sherman Mark , Vielh Philippe , Vreuls Willem , Polónia António , Horlings Hugo M. , Weichert Wilko , Carter Jodi M. , Bult Peter , Christgen Matthias , Denkert Carsten , van de Vijver Koen , Bokhorst John-Melle , van der Laak Jeroen , Ciompi Francesco 
Published: November 2022

AbstractTo guide the choice of treatment, every new breast cancer is assessed for aggressiveness (i.e., graded) by an experienced histopathologist. Typically, this tumor grade consists of three components, one of which is the nuclear pleomorphism score (the extent of abnormalities in the overall appearance of tumor nuclei). The degree of nuclear pleomorphism is subjectively classified from 1 to 3, where a score of 1 most closely resembles epithelial cells of normal breast epithelium and 3 shows the greatest abnormalities. Establishing numerical criteria for grading nuclear pleomorphism is challenging, and inter-observer agreement is poor. Therefore, we studied the use of deep learning to develop fully automated nuclear pleomorphism scoring in breast cancer. The reference standard used for training the algorithm consisted of the collective knowledge of an international panel of 10 pathologists on a curated set of regions of interest covering the entire spectrum of tumor morphology in breast cancer. To fully exploit the information provided by the pathologists, a first-of-its-kind deep regression model was trained to yield a continuous scoring rather than limiting the pleomorphism scoring to the standard three-tiered system. Our approach preserves the continuum of nuclear pleomorphism without necessitating a large data set with explicit annotations of tumor nuclei. Once translated to the traditional system, our approach achieves top pathologist-level performance in multiple experiments on regions of interest and whole-slide images, compared to a panel of 10 and 4 pathologists, respectively.