Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcriptional Landscape of 3D vs. 2D Ovarian Cancer Cell Models

Simple Summary Ovarian cancer is one of the most lethal female cancers. Numerous investigations into the development and progression of this disease have resulted in the creation of numerous three-dimensional culture models to better reflect the natural microenvironment of these tumours. In this study, we leverage the available transcriptomics and clinical and novel experimental data to evaluate the impact of the growth conditions on various cancer cells and examine whether they better approximate the behaviour of tumour cells compared to the classical two-dimensional models. Our results show that variability in the growth conditions can impact key genes and biological processes that are hallmarks of cancer, highlighting the need for future studies to identify which is the most appropriate in vitro/preclinical model to study tumour microenvironments. Abstract Three-dimensional (3D) cancer models are revolutionising research, allowing for the recapitulation of an in vivo-like response through the use of an in vitro system, which is more complex and physiologically relevant than traditional monolayer cultures. Cancers such as ovarian (OvCa) are prone to developing resistance, are often lethal, and stand to benefit greatly from the enhanced modelling emulated by 3D cultures. However, the current models often fall short of the predicted response, where reproducibility is limited owing to the lack of standardised methodology and established protocols. This meta-analysis aims to assess the current scope of 3D OvCa models and the differences in the genetic profiles presented by a vast array of 3D cultures. An analysis of the literature (Pubmed.gov) spanning 2012–2022 was used to identify studies with paired data of 3D and 2D monolayer counterparts in addition to RNA sequencing and microarray data. From the data, 19 cell lines were found to show differential regulation in their gene expression profiles depending on the bio-scaffold (i.e., agarose, collagen, or Matrigel) compared to 2D cell cultures. The top genes differentially expressed in 2D vs. 3D included C3, CXCL1, 2, and 8, IL1B, SLP1, FN1, IL6, DDIT4, PI3, LAMC2, CCL20, MMP1, IFI27, CFB, and ANGPTL4. The top enriched gene sets for 2D vs. 3D included IFN-α and IFN-γ response, TNF-α signalling, IL-6-JAK-STAT3 signalling, angiogenesis, hedgehog signalling, apoptosis, epithelial–mesenchymal transition, hypoxia, and inflammatory response. Our transversal comparison of numerous scaffolds allowed us to highlight the variability that can be induced by these scaffolds in the transcriptional landscape and identify key genes and biological processes that are hallmarks of cancer cells grown in 3D cultures. Future studies are needed to identify which is the most appropriate in vitro/preclinical model to study tumour microenvironments.
Determinan repurchase intention dalam belanja online

The purpose of this study is to identify online repurchase intentions among Indonesian consumers, especially student consumers, with a special focus on the effects of e-satisfaction, e-trust and e-loyalty. As there is limited research explaining online repurchase intentions in developing countries, this study was conducted in Indonesia. An empirical study was conducted using a questionnaire survey and a total of 290 respondents were used to test the proposed model. The results showed that e-satisfaction, e-trust and e-loyalty had a positive effect on repurchase intentions at online stores. In addition, it was found that trust in online stores is the main determinant of online repurchase intentions followed by satisfied online customers and loyal to online store websites.
A kernel approach to implementation of local linear discriminant analysis for face recognition

The multiple linear model is used successfully to extend the linear model to nonlinear problems. However, the conventional multilinear models fail to learn the global structure of a training data set because the local linear models are independent of each other. Furthermore, the local linear transformations are learned in the original space. Therefore, the performance of multilinear methods is strongly dependent on the results of partition. This paper presents a kernel approach for the implementation of the local linear discriminant analysis for face recognition problems. In the original space, we utilize a set of local linear transformations with interpolation to approximate an optimal global nonlinear transformation. Based on the local linear models in the original space, we derive an explicit kernel mapping to map the training data into a high‐dimensional transformed space. The optimal transformation is learned globally in the transformed space. Experimental results show that the proposed method is more robust to the partition results than the conventional multilinear methods. Compared with the general nonlinear kernels that utilize a black‐box mapping, our proposed method can reduce the negative effects caused by the potential overfitting problem. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Corrosive Dibenzyl Disulfide Concentration Prediction in Transformer Oil Using Deep Neural Network

Dibenzyl disulfide (DBDS) is the most prevalent corrosive sulfur in transformer oil. It reacts with the transformer windings to produce copper sulfide (Cu2S) and gets deposited on the insulating paper’s surface, leading to interturn faults within the transformer windings. Hence, this article proposes a deep neural network (DNN) to predict the DBDS content in transformer oil. The parameters like interfacial tension (IFT), breakdown voltage (BDV), water content (WC), oxygen, neutralization number (NN), color, furan content, and specific gravity (SG) were used as features to train and test the DNN model. The performance of the regression model was evaluated using mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination ( ${R}^{{2}}{)}$ . Moreover, extensive analysis is carried out by varying feature combinations and test-train ratios to obtain the best prediction model. The values of DBDS predicted by DNN were further used to determine the corrosive sulfur concentration in transformer oil. The proposed method is validated on real-life transformer data obtained from the online dataset and on data obtained from the local power utilities. A comparative study showed better efficacy of the proposed DNN model than other prediction models for accurate DBDS prediction.
Persistence of local anisotropy of passive scalars in wall-bounded flows

Local isotropy of passive scalars in fully developed turbulent channel flow is studied by way of direct numerical simulations. We observe a persistent small-scale anisotropy that (i) persists after the flow has undergone substantial mixing and (ii) is independent of the original large-scale anisotropic initial conditions of the scalar field. This latter observation is in sharp contrast with the persistent local anisotropy observed in homogeneous, isotropic turbulence with imposed mean scalar gradients, for which the small-scale anisotropy is directly correlated to the imposed large-scale anisotropy by way of ramp-cliff structures. The anisotropy observed in the present work is linked to the production of 〈εθ 〉 due to the mean velocity gradient. A major implication of the work is that local isotropy of passive scalar fields may never hold in flows in which mean velocity gradients exist, even after mean scalar gradients have been eliminated by the turbulence.
Management of asymptomatic hyperuricemia at primary care

Asymptomatic Hyperuricemia (AHU) is a condition with abnormal serum uric acid levels, without clinical signs or symptoms. AHU is a risk factor of various clinical conditions as hypertension, renal diseases, metabolic syndrome, stroke etc. It is controversial that AHU should be treated with Uric acid lowering therapy (ULT), and most of the latest guidelines do not suggest the use of ULT. However, non-pharmaceutical measures can help in management of this condition
Specific features of the financial system of Ukraine in the conditions of war

The impact of a range of factors on the country’s financial system, primarily related to the war, determines the relevance and importance of conducting this study. In modern conditions, the sphere of finance is transformed and adapted, therefore the purpose of this study was to investigate the current state of the country’s financial system in the context of the functioning of its main structural elements. The methodological basis of this study included the use of both general scientific methods, such as analysis, synthesis, comparison, comparison, and specific methods. To detect existing trends and processes in the financial system of Ukraine, statistical methods of dynamic and structural analysis were used. The study used a complex approach to the investigation of the financial system, specifically, the budget sphere, the banking system, the securities market, and the currency sphere were analysed. The main processes that affect the formation and implementation of the revenue and expenditure part of the state budget have been identified. The volume of tax revenues and their share in the formation of state budget revenues in the pre-war and wartime periods were compared. The main reasons for the growth of the budget deficit have been identified. The key sources used to cover the deficit were highlighted. The state of the national securities market was described based on the analysis of the structure of trade transactions in terms of financial instruments. The study found that the main share of trade transactions takes place with government debt securities. The banking sector, the financial system of the country, based on indicators of liquidity and profitability, was studied. Using the stability rating of banking institutions, it was found that leading positions in it are occupied by financial institutions that have foreign sources of financing, as well as state banks. The dynamics of devaluation of the hryvnia with the dynamics of inflationary processes were investigated. The results of the study allow for a comprehensive assessment of the current state of the country’s financial system and will be useful both to actors in the country’s financial sector and to managers who determine the basis of its functioning
Large quantum systems: a mathematical and numerical perspective

This thesis is devoted to the mathematical study of variational models for large quantum systems. The mathematical methods are that of nonlinear analysis, calculus of variations, partial differential equations, spectral theory, and numerical analysis. The first part contains some results on finite systems. We study several approximations of the N-body Schrodinger for electrons in an atom or a molecule, and then the so-called Hartree-Fock-Bogoliubov model for a system of fermions interacting via the gravitational force. In a second part, we propose a new method allowing to prove the existence of the thermodynamic limit of Coulomb quantum systems. Then, we construct two Hartree-Fock-type models for infinite systems. The first is a relativistic theory deduced from Quantum Electrodynamics, allowing to describe the behavior of electrons, coupled to that of Dirac's vacuum which can become polarized. The second model describes a nonrelativistic quantum crystal in the presence of a charged defect. A new numerical method is also proposed. The last part of the thesis is devoted to spectral pollution, a phenomenon which is observed when trying to approximate eigenvalues in a gap of the essential spectrum of a self-adjoint operator, for instance for periodic Schrodinger or Dirac operators.
Molecular Network for Regulation of Seed Size in Plants

The size of seeds is particularly important for agricultural development, as it is a key trait that determines yield. It is controlled by the coordinated development of the integument, endosperm, and embryo. Large seeds are an important way of improving the ultimate “sink strength” of crops, providing more nutrients for early plant growth and showing certain tolerance to abiotic stresses. There are several pathways for regulating plant seed size, including the HAIKU (IKU) pathway, ubiquitin–proteasome pathway, G (Guanosine triphosphate) protein regulatory pathway, mitogen-activated protein kinase (MAPK) pathway, transcriptional regulators pathway, and phytohormone regulatory pathways including the auxin, brassinosteroid (BR), gibberellin (GA), jasmonic acid (JA), cytokinin (CK), Abscisic acid (ABA), and microRNA (miRNA) regulatory pathways. This article summarizes the seed size regulatory network and prospective ways of improving yield. We expect that it will provide a valuable reference to researchers in related fields.
Impacts on sea ice analyses from the assumption of uncorrelated ice thickness observation errors: Experiments using a 1D toy model

Abstract Sea ice prediction centres are moving toward the assimilation of ice thickness observations under the simplifying assumption that the observation errors are uncorrelated. The assumption of uncorrelated observation errors is attractive because the errors can be represented by a diagonal observation error covariance matrix, which is inexpensive to invert. In this paper a set of idealized experiments are carried out to investigate the impact of this assumption on sea ice analyses. A background error covariance matrix is generated using a 1D toy model for sea, i.e. forced with idealized models of the ocean and atmosphere. Analysis error covariance matrices are then calculated using this matrix for both correlated and uncorrelated observation error covariance matrices, . The results indicate when the true is correlated, using a diagonal approximation results in an analysis that is overconfident at the large scales, in that the analysis error standard deviation at the large scales is underestimated. It is also shown that for the largest observation error correlation length scale tested, 150 km, the analysis error standard deviation for ice thickness is reduced by 10.8% relative to the background error standard deviation when has the correct correlation length scale of 150 km, whereas when a diagonal approximation to is used in combination with an inflation factor, the reduction is to 6.3%.
Contrastive Learning Network Based on Causal Attention for Fine-Grained Ship Classification in Remote Sensing Scenarios

Fine-grained classification of ship targets is an important task in remote sensing, having numerous applications in military reconnaissance and sea surveillance. Due to the influence of various imaging factors, ship targets in remote sensing images have considerable inter-class similarity and intra-class difference, which brings significant challenges to fine-grained classification. In response, we developed a contrastive learning network based on causal attention (C2Net) to improve the model’s fine-grained identification ability from local details. The asynchronous feature learning mode of “decoupling + aggregation” is adopted to reduce the mutual influence between local features and improve the quality of local features. In the decoupling stage, the feature vectors of each part of the ship targets are de-correlated using a decoupling function to prevent feature adhesion. Considering the possibility of false associations between results and features, the decoupled part is designed based on the counterfactual causal attention network to enhance the model’s predictive logic. In the aggregation stage, the local attention weight learned in the decoupling stage is used to carry out feature fusion on the trunk feature weight. Then, the proposed feature re-association module is used to re-associate and integrate the target local information contained in the fusion feature to obtain the target feature vector. Finally, the aggregation function is used to complete the clustering process of the target feature vectors and fine-grained classification is realized. Using two large-scale datasets, the experimental results show that the proposed C2Net method had better fine-grained classification than other methods.
Computational wind analysis of an open air-inflated membrane structure

The current research aimed to obtain mean pressure distribution over an air-inflated membrane structure using Computational Wind Engineering tools. The steady-state analysis applied the Reynolds-Averaged Navier-Stokes equations with the standard turbulence model. The pressure coefficients were compared with former experimental results to validate the numerical solution. Significant errors were detected close to the critical flow separation points when comparing the numerical results with the wind tunnel tests. However, these errors are local, and the numerical methodology provides accurate results in those areas with minor turbulence motion influence. In general, the numerical solution provided good approximation of the pressure coefficient fields.
Recursive relation for zero inflated Poisson mixture distributions

The paper extends the work of Sarguta who derived recursive relations for univariate distributions by considering the ZIP continuous mixtures. The paper gives a recursive formular which can be used to evaluate the mixed distributions which can be used when the probability distribution functions cannot be evaluated explicitly. Integration by parts is often employed when deriving the recursive formulas. From section two up to section seven, we derived the recursive formulas for ZIP mixture distributions using Rectangular, Exponential, Gamma with two parameters, PoissonBeta and Inverted Beta as mixing distributions.
Fairness-Guaranteed DER Coordination Under False Data Injection Attacks

The development of the Internet of Energy (IoE) is facilitated by the integration of information technology and the growing utilization of distributed energy resources (DERs). The usage of DERs, particularly photovoltaic systems and battery energy storage systems, in IoE has revealed the potential for DERs to be leveraged for grid control. To encourage DER owners to participate in grid management, grid operators must coordinate DERs with guaranteed fairness. However, the fairness of DER coordination is now endangered due to the growing concerns about cyber attacks on DERs. This article considers false data injection attacks (FDIAs), where attackers can tamper with measurements sent to the grid operator. We study the impact of FDIAs on the fairness of the DER coordination and develop an algorithm that guarantees fairness in the presence of FDIAs. DER coordination is formulated as an optimal power flow problem that reduces voltage fluctuations and attack impacts, increases DER revenues, and ensures system-wide fairness. To achieve fair DER coordination, we propose an analog definition of fairness for different DER types and incorporate the fairness measures into DER coordination. Additionally, a robust least absolute shrinkage and selection operator regularizer is designed to forecast the actual values of fraudulent measurements and mitigate the attack’s impacts. Using a distribution feeder from the Southern California Edison system, we demonstrate the effectiveness of the proposed approach: fairness is assured both with and without attacks. Additionally, the proposed algorithm’s efficiency is justified by an average execution time of 2.56 s.
Anatomical variations in the insular cortex in individuals at a clinical high-risk state for psychosis and patients with schizophrenia

Introduction Since the number of insular gyri is higher in schizophrenia patients, it has potential as a marker of early neurodevelopmental deviations. However, it currently remains unknown whether the features of the insular gross anatomy are similar between schizophrenia patients and individuals at risk of psychosis. Furthermore, the relationship between anatomical variations in the insular cortex and cognitive function has not yet been clarified. Methods The gross anatomical features (i.e., the number of gyri and development pattern of each gyrus) of the insular cortex were examined using magnetic resonance imaging, and their relationships with clinical characteristics were investigated in 57 subjects with an at-risk mental state (ARMS) and 63 schizophrenia patients in comparison with 61 healthy controls. Results The number of insular gyri bilaterally in the anterior subdivision was higher in the ARMS and schizophrenia groups than in the control group. The schizophrenia group was also characterized by a higher number of insular gyri in the left posterior subdivision. A well-developed right middle short insular gyrus was associated with symptom severity in first-episode schizophrenia patients, whereas chronic schizophrenia patients with a well-developed left accessory gyrus were characterized by less severe cognitive impairments in motor and executive functions. The features of the insular gross anatomy were not associated with clinical characteristics in the ARMS group. Discussion The features of the insular gross anatomy that were shared in the ARMS and schizophrenia groups may reflect a vulnerability to psychosis that may be attributed to anomalies in the early stages of neurodevelopment. However, the contribution of the insular gross anatomy to the clinical characteristics of schizophrenia may differ according to illness stages.
Efeito do laser de baixa potência sobre células musculares C2C12 submetidas à lesão por veneno da serpente Bothrops jararacussu.

O veneno das serpentes do genero Bothrops, induz uma intensa reacao inflamatoria local podendo evoluir para necrose tecidual. A soroterapia apresenta eficacia em neutralizar os efeitos sistemicos, porem sua acao nao se estende as manifestacoes locais. O laser de baixa potencia (LBP) e usado em situacoes de lesao muscular, pois apresenta efeitos biologicos, tais como analgesicos, antiinflamatorios e cicatrizantes. O objetivo deste trabalho foi analisar o efeito do LBP em celulas musculares C2C12 submetidas a lesao por veneno da serpente Bothrops jararacussu quanto a: viabilidade, descolamento celular e liberacao das enzimas CK e LDH. As celulas receberam o veneno (12.5 µg/mL) e foram imediatamente irradiadas com LBP Indio Galio Aluminio Fosforo e Arseneto de Galio Aluminio, nos comprimentos de onda (λ) 685nm e 830nm, densidade de energia 4J/cm2, potencia de 100mW, energia total 1.3J, tempo de aplicacao de 13 segundos por ponto e as celulas foram incubadas por 15, 30 e 60 minutos.Os resultados demonstraram que o veneno afetou a viabilidade e a integridade das celulas de forma dose-dependente, sendo escolhida a concentracao 12.5 µg/mL para a realizacao dos experimentos com o LBP. O LBP causou aumento significativo na viabilidade celular, nos tempos analisados no λ 830 nm, entretanto o LBP no λ 685 nm foi efetivo somente nos tempos de 15 e 30 min. O LBP diminuiu a liberacao de CK e LDH em todos os tempos analisados e com os dois λ utilizados. No entanto, o LBP nao foi capaz de proteger as celulas contra o descolamento celular causado pelo veneno. Desta forma, verificou-se que o LBP foi capaz de proteger as celulas musculares C2C12 contra o potente efeito miotoxico do veneno B. jararacussu e que esta protecao esta relacionada ao efeito protetor da membrana celular. Ainda, os resultados obtidos sugerem que o LBP pode ser considerado uma ferramenta terapeutica eficaz em pacientes picados por serpentes.