bluuebunny/crossref_metadata_2025_split huggingface dataset:
Targeting CCR2+ macrophages with BET inhibitor overcomes adaptive resistance to anti-VEGF therapy in ovarian cancer
Authors: Wu Yutuan , Jennings Nicholas B. , Sun Yunjie , Dasari Santosh K. , Bayraktar Emine , Corvigno Sara , Stur Elaine , Glassman Deanna , Mangala Lingegowda S. , Lankenau Ahumada Adrian , Westin Shannon N. , Sood Anil K. , Hu Wei 
Published: January 2022

Abstract
Purpose
Tumor-associated macrophages (TAMs) are known to contribute to adaptive resistance to anti-vascular endothelial growth factor (VEGF) antibody (AVA) therapy in ovarian cancer. BET (bromodomain and extra-terminal domain) inhibitors (BETi) may have unique roles in targeting TAMs. Our objective was to examine the effects of BETi on TAMs, especially in the context of enhancing the efficacy of AVA therapy.

Methods
We conducted a series of in vitro (MTT assay, apoptosis, flow cytometry, and RNA sequencing) and in vivo (xenograft ovarian cancer model) experiments to determine the biological effects of BETi combined with AVA in ovarian cancer. For statistical analysis, a two-tailed Student’s t test (equal variance) or ANOVA was used for multiple groups’ comparison, and p < 0.05 was considered significant.

Results
BETi resulted in a dose-dependent decrease in cell viability and induced apoptosis (p < 0.01) in ovarian cancer cells (SKOV3ip1, OVCAR5, and OVCAR8). Treatment with BETi significantly increased apoptosis in THP-1 monocytes and macrophages (PMA-differentiated THP-1; p < 0.01). Furthermore, BETi selectively induced greater apoptosis in M2-like macrophages (PMA and IL-4, IL-13-differentiated THP-1) (31.3%-36.1%) than in M1-like macrophages (PMA and LPS-differentiated THP-1) (12.4%-18.5%) (p < 0.01). Flow cytometry revealed that the percentage of M1-like macrophages (CD68+/CD80+) was significantly increased after treatment with low-dose BETi (ABBV-075 0.1 µM; p < 0.05), whereas the percentage of CD68+/CCR2+ macrophages was significantly decreased (p < 0.001); these findings suggest that BETi may selectively inhibit the survival of CCR2+ macrophages and re-polarize the macrophages into an M1-like phenotype. RNA-seq analysis revealed that BETi selectively targeted macrophage infiltration-related cytokines/chemokines in ovarian cancer (adjusted p < 0.05 and Log2 fold change ≥ 1.5). Finally, using in vivo ovarian cancer models, compared with control or monotherapy, the combination of BETi (ABBV-075) and bevacizumab resulted in greater inhibition of tumor growth and macrophage infiltration (p < 0.05) and longer survival of tumor-bearing mice (p < 0.001).

Conclusions
Our findings indicate a previously unrecognized role for BETi in selectively targeting CCR2+ TAMs and enhancing the efficacy of AVA therapy in ovarian cancer.
Node Multiway Cut and Subset Feedback Vertex Set on Graphs of Bounded Mim-Width
Authors: Bergougnoux Benjamin , Papadopoulos Charis , Telle Jan Arne 
Published: January 2022

AbstractThe two weighted graph problems Node Multiway Cut (NMC) and Subset Feedback Vertex Set (SFVS) both ask for a vertex set of minimum total weight, that for NMC disconnects a given set of terminals, and for SFVS intersects all cycles containing a vertex of a given set. We design a meta-algorithm that allows to solve both problems in time $$2^{O(rw^3)}\cdot n^{4}$$

2

O
(
r

w
3

)

·

n
4

, $$2^{O(q^2\log (q))}\cdot n^{4}$$

2

O
(

q
2

log

(
q
)

)

·

n
4

, and $$n^{O(k^2)}$$

n

O
(

k
2

)

where rw is the rank-width, q the $${\mathbb {Q}}$$
Q
-rank-width, and k the mim-width of a given decomposition. This answers in the affirmative an open question raised by Jaffke et al. (Algorithmica 82(1):118–145, 2020) concerning an algorithm for SFVS parameterized by mim-width. By a unified algorithm, this solves both problems in polynomial-time on the following graph classes: Interval, Permutation, and Bi-Interval graphs, Circular Arc and Circular Permutation graphs, Convex graphs, k-Polygon, Dilworth-k and Co-k-Degenerate graphs for fixed k; and also on Leaf Power graphs if a leaf root is given as input, on H-Graphs for fixed H if an H-representation is given as input, and on arbitrary powers of graphs in all the above classes. Prior to our results, only SFVS was known to be tractable restricted only on Interval and Permutation graphs, whereas all other results are new.
Association between El Niño-Southern Oscillation events and stroke: a case-crossover study in Kaunas city, Lithuania, 2000–2015
Authors: Vaičiulis Vidmantas , Venclovienė Jonė , Kačienė Giedrė , Tamošiūnas Abdonas , Kiznys Deividas , Lukšienė Dalia , Radišauskas Ričardas 
Published: January 2022

AbstractThe aim of this study was to determine the association between the daily number of cases of ischemic stroke (IS) and hemorrhagic stroke (HS) in patients aged 25–64 years and the El Niño-Southern Oscillation (ENSO) events during 2000–2015. As an indicator of the effect of the ENSO, the monthly NIÑO 3.4 index (Equatorial Pacific Sea Surface Temperature) was used. During the 5844-day study period, 5600 cases of stroke (3170 (56.61%) in men and 2430 (43.39%) in women) were analyzed. Of these, 4354 (77.8%) cases were IS, and 1041 (18.6%) cases were HS. In 3496 (62.2%) cases, stroke occurred in the age group of 55–64 years. In the analysis, we used the following categories of the ENSO events: strong La Niña, moderate La Niña, moderate El Niño, and strong El Niño. The effect of the ENSO was examined by using the multivariate Poisson regression adjusting for weather variables. The highest risk of both strokes (BS) was observed on days of strong and moderate La Niña (rate ratio (RR) 1.27, 95% CI 1.13–1.42) and RR = 1.15 (1.07–1.23), respectively), while the risk for IS was the highest on days of moderate El Niño (RR = 1.11(1.02–1.20)). A lower risk for BS was found on days of strong El Niño (RR = 0.77(0.62–0.97)). We found that ENSO events affected the occurrence of BS and IS in all age groups, and the strongest effect was observed among females. The results of this study provide new evidence that ENSO events may affect the risk of stroke, especially the risk of IS.
A Bayesian finite-element trained machine learning approach for predicting post-burn contraction
Authors: Egberts Ginger , Schaaphok Marianne , Vermolen Fred , Zuijlen Paul van 
Published: January 2022

AbstractBurn injuries can decrease the quality of life of a patient tremendously, because of esthetic reasons and because of contractions that result from them. In severe case, skin contraction takes place at such a large extent that joint mobility of a patient is significantly inhibited. In these cases, one refers to a contracture. In order to predict the evolution of post-wounding skin, several mathematical model frameworks have been set up. These frameworks are based on complicated systems of partial differential equations that need finite element-like discretizations for the approximation of the solution. Since these computational frameworks can be expensive in terms of computation time and resources, we study the applicability of neural networks to reproduce the finite element results. Our neural network is able to simulate the evolution of skin in terms of contraction for over one year. The simulations are based on 25 input parameters that are characteristic for the patient and the injury. One of such input parameters is the stiffness of the skin. The neural network results have yielded an average goodness of fit ($$R^2$$

R
2

) of 0.9928 (± 0.0013). Further, a tremendous speed-up of 19354X was obtained with the neural network. We illustrate the applicability by an online medical App that takes into account the age of the patient and the length of the burn.
Social ski driver conditional autoregressive-based deep learning classifier for flight delay prediction
Authors: Bisandu Desmond Bala , Moulitsas Irene , Filippone Salvatore 
Published: January 2022

AbstractThe importance of robust flight delay prediction has recently increased in the air transportation industry. This industry seeks alternative methods and technologies for more robust flight delay prediction because of its significance for all stakeholders. The most affected are airlines that suffer from monetary and passenger loyalty losses. Several studies have attempted to analysed and solve flight delay prediction problems using machine learning methods. This research proposes a novel alternative method, namely social ski driver conditional autoregressive-based (SSDCA-based) deep learning. Our proposed method combines the Social Ski Driver algorithm with Conditional Autoregressive Value at Risk by Regression Quantiles. We consider the most relevant instances from the training dataset, which are the delayed flights. We applied data transformation to stabilise the data variance using Yeo-Johnson. We then perform the training and testing of our data using deep recurrent neural network (DRNN) and SSDCA-based algorithms. The SSDCA-based optimisation algorithm helped us choose the right network architecture with better accuracy and less error than the existing literature. The results of our proposed SSDCA-based method and existing benchmark methods were compared. The efficiency and computational time of our proposed method are compared against the existing benchmark methods. The SSDCA-based DRNN provides a more accurate flight delay prediction with 0.9361 and 0.9252 accuracy rates on both dataset-1 and dataset-2, respectively. To show the reliability of our method, we compared it with other meta-heuristic approaches. The result is that the SSDCA-based DRNN outperformed all existing benchmark methods tested in our experiment.
An evolving ensemble model of multi-stream convolutional neural networks for human action recognition in still images
Authors: Slade Sam , Zhang Li , Yu Yonghong , Lim Chee Peng 
Published: January 2022

AbstractStill image human action recognition (HAR) is a challenging problem owing to limited sources of information and large intra-class and small inter-class variations which requires highly discriminative features. Transfer learning offers the necessary capabilities in producing such features by preserving prior knowledge while learning new representations. However, optimally identifying dynamic numbers of re-trainable layers in the transfer learning process poses a challenge. In this study, we aim to automate the process of optimal configuration identification. Specifically, we propose a novel particle swarm optimisation (PSO) variant, denoted as EnvPSO, for optimal hyper-parameter selection in the transfer learning process with respect to HAR tasks with still images. It incorporates Gaussian fitness surface prediction and exponential search coefficients to overcome stagnation. It optimises the learning rate, batch size, and number of re-trained layers of a pre-trained convolutional neural network (CNN). To overcome bias of single optimised networks, an ensemble model with three optimised CNN streams is introduced. The first and second streams employ raw images and segmentation masks yielded by mask R-CNN as inputs, while the third stream fuses a pair of networks with raw image and saliency maps as inputs, respectively. The final prediction results are obtained by computing the average of class predictions from all three streams. By leveraging differences between learned representations within optimised streams, our ensemble model outperforms counterparts devised by PSO and other state-of-the-art methods for HAR. In addition, evaluated using diverse artificial landscape functions, EnvPSO performs better than other search methods with statistically significant difference in performance.
Design Considerations of a Dual Mode X-Band EPR Resonator for Rapid In-Situ Microwave Heating
Authors: Barter Michael , Magri Giuseppina , Harari Jaafar , Choi Heungjae , Folli Andrea , Slocombe Daniel R. , Richards Emma , Murphy Damien M. , Porch Adrian 
Published: January 2022

AbstractThis paper describes the design considerations for a dual mode X-band continuous wave (CW) Electron Paramagnetic Resonance (EPR) cavity, for simultaneous EPR measurement and microwave heating of the same sample. An elliptical cavity geometry is chosen to split the degeneracy of the TM110 mode, allowing for a well resolved EPR signal with the TM110,a and TM110,b modes resonating at around 10 GHz and 9.5 GHz, respectively, the latter of which is used for EPR measurements. This geometry has the benefit that the TM010 mode used for microwave heating resonates at 6.1 GHz, below the cut off frequency of the X-band waveguide used for the EPR channel, providing effective isolation between the heating and EPR channels. The use of a pair of 9 µm thick copper clad laminates as the flat cavity walls allows for sufficient penetration of the modulation field (Bmod) into the cavity, as well as maintaining a high cavity Q factor (> 5700) for sensitive EPR measurements. Locating the heating port at an angle of 135° to the EPR port provides additional space for easier coupling adjustment and for larger sample access to be accommodated. The associated decrease of EPR signal strength is fully compensated for by using a 7.2 GHz low pass filter on the heating port. EPR spectra using 1.6 mm and 4.0 mm sample tubes are shown at room temperature (298 K) and 318 K for a standard Cu(acac)2 solution, demonstrating the effectiveness of this dual-mode EPR cavity for microwave heating during EPR detection.
Interorgan amino acid interchange in propionic acidemia: the missing key to understanding its physiopathology
Authors: Stanescu Sinziana , Belanger-Quintana Amaya , Fernandez-Felix Borja Manuel , Ruiz-Sala Pedro , del Valle Mercedes , Garcia Fernando , Arrieta Francisco , Martinez-Pardo Mercedes 
Published: January 2022

Abstract
Background
Propionic acidemia is an inborn error of metabolism caused by a deficiency in the mitochondrial enzyme propionyl-CoA carboxylase that converts the propionyl CoA to methyl malonyl CoA. This leads to profound changes in distinct metabolic pathways, including the urea cycle, with consequences in ammonia detoxification. The implication of the tricarboxylic acid cycle is less well known, but its repercussions could explain both some of the acute and long-term symptoms of this disease.

Materials and methods
The present observational study investigates the amino acid profiles of patients with propionic acidemia being monitored at the Hospital Ramón y Cajal (Madrid, Spain), between January 2015 and September 2017, comparing periods of metabolic stability with those of decompensation with ketosis and/or hyperammonemia.

Results
The concentrations of 19 amino acids were determined in 188 samples provided by 10 patients. We identified 40 metabolic decompensation episodes (22 only with ketosis and 18 with hyperammonemia). Plasma glutamine and alanine levels were reduced during these metabolic crises, probably indicating deficiency of anaplerosis (p < 0.001 for both alanine and glutamine). Hypocitrulllinemia and hypoprolinemia were also detected during hyperammonemia (p < 0.001 and 0.03, respectively).

Conclusions
The amino acid profile detected during decompensation episodes suggests deficient anaplerosis from propionyl-CoA and its precursors, with implications in other metabolic pathways like synthesis of urea cycle amino acids and ammonia detoxification.
Error bounds and a condition number for the absolute value equations
Authors: Zamani Moslem , Hladík Milan 
Published: January 2022

AbstractDue to their relation to the linear complementarity problem, absolute value equations have been intensively studied recently. In this paper, we present error bound conditions for absolute value equations. Along with the error bounds, we introduce a condition number. We consider general scaled matrix p-norms, as well as particular p-norms. We discuss basic properties of the condition number, including its computational complexity. We present various bounds on the condition number, and we give exact formulae for special classes of matrices. Moreover, we consider matrices that appear based on the transformation from the linear complementarity problem. Finally, we apply the error bound to convergence analysis of two methods for solving absolute value equations.
A Learning Theory Approach to Attachment Theory: Exploring Clinical Applications
Authors: Bosmans Guy , Van Vlierberghe Leen , Bakermans-Kranenburg Marian J. , Kobak Roger , Hermans Dirk , van IJzendoorn Marinus H. 
Published: January 2022

AbstractAlthough clinicians typically acknowledge the importance of insecure attachment as one factor that can contribute to children’s psychopathology, translating attachment theory into clinical practice has proved a challenge. By specifying some of the mechanisms through which the child’s attachment develops and changes, learning theory can enhance attachment based approaches to therapy. Specifically, interventions building on operant (parent management training) and classical (exposure therapy) learning can be used to stimulate new learning that increases the child’s security and confidence in the parent’s availability and responsiveness. To explore the clinical application and utility of a Learning Theory of Attachment (LTA), we focus on two attachment-focused interventions: Video-feedback Intervention to promote Positive Parenting and Sensitive Discipline (VIPP-SD) and Middle Childhood Attachment-based Family Therapy (MCAT). VIPP-SD is an evidence-based parent management training designed to promote sensitive parenting and secure attachment in early childhood. MCAT is a recently developed intervention that uses exposure to stimulate secure attachment in middle childhood. LTA sheds light on the mechanisms set in train by VIPP-SD and MCAT facilitating the induction of professionals in clinical applications.
The Role of Comorbid Symptoms in Perceived Stress and Sleep Problems in Adolescent ADHD
Authors: Frick Matilda A. , Meyer Jenny , Isaksson Johan 
Published: January 2022

AbstractWe examined perceived stress and sleep problems in adolescent ADHD and whether this varies as a function of ADHD presentation and sex. Further, we mapped structural associations between ADHD symptoms, comorbid symptoms, perceived stress, and sleep problems. Participants were 306 adolescents aged 13–19 years (66.8% females, 193 had an ADHD diagnosis, 113 were controls). Parents rated ADHD symptoms, all other constructs were self-rated. Adolescents with ADHD had elevated levels of perceived stress and sleep problems. Girls with ADHD reported the highest levels of perceived stress. Emotional symptoms mediated the effect of inattention whereas conduct problems mediated the effect of hyperactivity/impulsivity on stress and sleep. Perceived stress and sleep problems should be considered when mapping ADHD-related problems. Comorbid symptoms are potential intervention targets that may increase treatment response.
Endocrine secretory granule production is caused by a lack of REST and intragranular secretory content and accelerated by PROX1
Authors: Ishii Jun , Sato-Yazawa Hanako , Kashiwagi Korehito , Nakadate Kazuhiko , Iwamoto Masami , Kohno Kakeru , Miyata-Hiramatsu Chie , Masawa Meitetsu , Onozaki Masato , Noda Shuhei , Miyazawa Tadasuke , Takagi Megumi , Yazawa Takuya 
Published: January 2022

AbstractEndocrine secretory granules (ESGs) are morphological characteristics of endocrine/neuroendocrine cells and store peptide hormones/neurotransmitters. ESGs contain prohormones and ESG-related molecules, mainly chromogranin/secretogranin family proteins. However, the precise mechanism of ESG formation has not been elucidated. In this study, we experimentally induced ESGs in the non-neuroendocrine lung cancer cell line H1299. Since repressive element 1 silencing transcription factor (REST) and prospero homeobox 1 (PROX1) are closely associated with the expression of ESG-related molecules, we edited the REST gene and/or transfected PROX1 and then performed molecular biology, immunocytochemistry, and electron and immunoelectron microscopy assays to determine whether ESG-related molecules and ESGs were induced in H1299 cells. Although chromogranin/secretogranin family proteins were induced in H1299 cells by knockout of REST and the induction was accelerated by the PROX1 transgene, the ESGs could not be defined by electron microscopy. However, a small number of ESGs were detected in the H1299 cells lacking REST and expressing pro-opiomelanocortin (POMC) by electron microscopy. Furthermore, many ESGs were produced in the REST-lacking and PROX1- and POMC-expressing H1299 cells. These findings suggest that a lack of REST and the expression of genes related to ESG content are indispensable for ESG production and that PROX1 accelerates ESG production.Trial registration: Not applicable.
Financial Literacy and Portfolio Diversity in China
Authors: Peng Congmin , She Po-Wen , Lin Ming-Kun 
Published: January 2022

AbstractThis study documents the effect of financial literacy on the portfolio diversity of family wealth in China with a two-way, fixed-effect model. The results show that most people in China do not have sufficient financial literacy to understand financial assets, which implies a lack of financial knowledge in China. It also looks into the determinants of holding each financial asset, no matter how high- or low-risk, which has not been fully explored in the existing literature. The empirical results found that the knowledge of portfolio diversity increases according to their levels of education and financial literacy. In addition, people living in urban areas diversify their portfolios more than in rural areas. People with high financial literacy understand the value of time (interest); therefore, they prefer to have portfolios that evaluate time and avoid options that do not (cash). We also found that people’s characteristics in risk significantly affect whether they hold stock, mutual funds, or other financial assets.
On love, becomings, and true generosity for science education: honoring Paulo Freire
Authors: Frausto Aceves Alejandra , Torres-Olave Betzabé , Tolbert Sara 
Published: January 2022

AbstractIn this editorial article, we draw on our experiences to create an opening for critical reflection in honor of Paulo Freire's centenary. We start by addressing the need to overcome the productivity logic of the Academy and instead prioritize thinking and being in communion with others. We orient toward Freirean critical hope as an impetus to move beyond the limits of commodified time and engage in the process of collective becoming. We share our struggles and tensions through stories of Freirean becomings, reflecting on those "limited situations" in which our critical hope acted as catalysts for us to embrace our unfinishedness. We draw from our collective experiences and becomings to think more deeply about “the heart” of science education. We believe a heart-centered science education is one that forges solidarity with human and more-than-human others, and exploits pockets of resistance in the name of more socially and ecologically just present futures.