Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of tumor and epithelial phenotypes on metabolism in breast cancer

, Abstract Purpose— Cancer cells have altered metabolism, with increased glucose uptake, glycolysis, and biomass production. This study performed genomic and metabolomic analyses to elucidate how tumor and stromal genomic characteristics influence tumor metabolism. Experimental Design— Thirty-three breast tumors and six normal breast tissues were analyzed by gene expression microarray and by mass spectrometry for metabolites. Gene expression data and clinical characteristics were evaluated in association with metabolic phenotype. To evaluate the role of stromal interactions in altered metabolism, cocultures were performed using breast cancer cells and primary cancer-associated fibroblasts (CAFs). Results— Across all metabolites, unsupervised clustering resulted in two main sample clusters. Normal breast tissue and a subset of tumors with less aggressive clinical characteristics had lower levels of nucleic and amino acids and glycolysis byproducts, while more aggressive tumors had higher levels of these Warburg-associated metabolites. While tumor intrinsic subtype did not predict metabolic phenotype, metabolic cluster was significantly associated with expression of a wound response signature. In cocultures, CAFs from basal-like breast cancers increased glucose up take and basal-like epithelial cells increased glucose oxidation and glycogen synthesis, suggesting interplay of stromal and epithelial phenotypes on metabolism. Cytokine arrays identified hepatocyte growth factor (HGF) as a potential mediator of stromal-epithelial interaction, and antibody neutralization of HGF resulted in reduced expression of glucose transporter 1 (GLUT1) and decreased glucose uptake by epithelium. Conclusions— Both tumor/epithelial and stromal characteristics play important roles in metabolism. Warburg-like metabolism is influenced by changes in stromal-epithelial interactions, including altered expression of HGF/Met pathway and GLUT1 expression. with specific targetable gene expression changes (HGF/GLUT1). This study demonstrates that integration of multiple data types in human tumor studies, together with in vitro experimental studies that dissect heterotypic interactions, can yield important advances in understanding the complex metabolic and genomic interactions during tumor evolution.
Admissibility in Strength-based Argumentation: Complexity and Algorithms (Extended Version with Proofs)

Recently, Strength-based Argumentation Frameworks (StrAFs) have been proposed to model situations where some quantitative strength is associated with arguments. In this setting, the notion of accrual corresponds to sets of arguments that collectively attack an argument. Some semantics have already been defined, which are sensitive to the existence of accruals that collectively defeat their target, while their individual elements cannot. However, until now, only the surface of this framework and semantics have been studied. Indeed, the existing literature focuses on the adaptation of the stable semantics to StrAFs. In this paper, we push forward the study and investigate the adaptation of admissibility-based semantics. Especially, we show that the strong admissibility defined in the literature does not satisfy a desirable property, namely Dung's fundamental lemma. We therefore propose an alternative definition that induces semantics that behave as expected. We then study computational issues for these new semantics, in particular we show that complexity of reasoning is similar to the complexity of the corresponding decision problems for standard argumentation frameworks in almost all cases. We then propose a translation in pseudo-Boolean constraints for computing (strong and weak) extensions. We conclude with an experimental evaluation of our approach which shows in particular that it scales up well for solving the problem of providing one extension as well as enumerating them all.
SBP Working Paper Series

Weak governance adversely affects firm’s net worth and consequently the value of its collateral. This negative impact on the collateral reduces the external credit available for importing inputs constraining potential output. As a result, a stronger procyclical monetary policy stance is adopted for protecting the exchange rate and hence arresting the degradation in the collateral constraint. JEL Codes: E5; F4; O1
E NVIRONMENT -A DVERSARIAL SUB -T ASK C URRICU - LUM FOR E FFICIENT R EINFORCEMENT L EARNING A

Reinforcement learning (RL)’s efficiency can drastically degrade on long-horizon tasks due to sparse rewards and the learned policy can be fragile to small changes in deployed environments. We address these two challenges by automatically generating a curriculum of tasks with coupled environments. To this end, we train two curriculum policies together with RL: (1) a co-operative planning policy recursively decomposing a hard task into coarse-to-fine sub-task sequences as a tree; and (2) an adversarial policy modifying the environment (e.g., position/size of obstacles) in each sub-task. They are complementary in acquiring more informative feedback for RL: the planning policy provides dense reward of finishing easier sub-tasks while the environment policy modifies these sub-tasks to be adequately challenging and diverse so the RL agent can quickly adapt to different tasks/environments. On the other hand, they are trained using the RL agent’s dense feedback on sub-tasks so the sub-task curriculum keeps adaptive to the agent’s progress via this “iterative mutual-boosting” scheme. Moreover, the sub-task tree naturally enables an easyto-hard curriculum for every policy: its top-down construction gradually increases sub-tasks the planning policy needs to generate, while the adversarial training between the environment policy and the RL policy follows a bottom-up traversal that starts from a dense sequence of easier sub-tasks allowing more frequent modifications to the environment. Therefore, jointly training the three policies leads to efficient RL guided by a curriculum progressively improving the sparse reward and generalization. We compare our method with popular RL/planning approaches targeting similar problems and the ones with environment generators or adversarial agents. Thorough experiments on diverse benchmark tasks demonstrate significant advantages of our method on improving RL’s efficiency and generalization.
Comparative Study of Some Mechanical Properties of Hybrid Polymeric Composites Prepared by using Friction Stir Processing

- In this study, it was investigated the effect of adding (graphite, Alumina (Al 2 O 3 ) and Copper (Cu)) micro particles with different volumetric fractions (0, 2, 4 and 6%) as reinforcement materials into the binary polymeric blend consist of (85% high density polyethylene (HDPE):15% polyvinyl chloride (PVC)) as a matrix material to produce hybrid composite of polymeric materials through friction stir processing (FSP) technique. The results of mechanical tests showed that as the reinforcement powders were introduced to the matrix material, led to enhancements in each of the flexural strength, flexural modulus, and maximum shear stress. where it was increased by (38, 43 and 48 %) respectively. While impact strength and fracture toughness decrease by the ratio (6 and 20%) respectively. So, in this work, the volumetric fractions of powders added to the polymer surface was determined by using Response Surface Methodology (RSM). As well as, the results of the prepared samples, it was confirmed by Fourier Transform Infrared Spectrometers (FTIR) test and Morphology test.
Classical mutagenesis in higher plants

For a long time, mutagenesis research in plants focused on crop improvement and, especially for crop plants, opimised protocols were developed with barley being one of the favourite species. However, the interest in mutagenesis has shifted to basic plant research in the last 20 years, when the power of mutant approaches in combination with molecular techniques to investigate the molecular nature of the genes became fully appreciated
The role of organizational competence on information security job The role of organizational competence on information security job performance performance

Organizations invest manpower and resources to ensure that sensitive corporate data is secure in the hands of its employees. Information security policies of these organizations explicitly state responsibilities for employees. But there is a massive gap between employee security performance and understanding of their information security requirements. In this study, we explore the factors that can enhance information security job performance of employees within organizations. We argue that employee information security performance can be enhanced by developing organizational security competencies. We conduct the argument through a case study of a public sector bank in India. The in-depth case study allows us to develop a theoretical understanding of how different aspects of organizational competence allow organizations to come together to enhance information security performance.
Modifications of PI and EI under Gaussian Noise Assumption in Current Optima

Bayesian optimisation is a widely used tool for the hyper-parameter optimisations of black box functions. It implements a cheaper surrogate model such as Gaussian processes (GPs) to model search space. Acquisition functions on the top of GPs such as Probability Improvement (PI) and Expected Improvement (EI) are used to query the distribution of loss at all unevaluated positions in order to find the best one in theory. Traditionally, both acquisition functions use current optima in computations directly, but GPs assume that observations are noise corrupted. In this work, we mathematically derive modify PI and EI under Gaussian noise assumption. Modified PI and EI are compared with original versions on benchmark functions. We show that modified versions converge faster in same number of iterations and can achieve better performance in complex loss functions with reduced iterations.
Construction and Application of Automatic Attendance Prediction System for Classroom Education Teaching Based on Random Matrix Theory

Random matrix theory, as a theoretical tool for statistical processing of complex systems and high-dimensional data, has been more and more combined with big data technology and applied to automatic attendance prediction analysis of classroom education. Firstly, the basic definition of the random matrix is introduced. Secondly, the single ring theorem of the random matrix is introduced. Based on this, the random matrix model of data is established. However, few undergraduate or graduate students in physics have been exposed to this theory. By introducing the basic concept of random matrix theory, this paper analyzes the necessity and feasibility of introducing random matrix theory into automatic attendance prediction teaching in classroom education. As a biometrics technology, random matrix theory technology has been widely used in various fields of the identity verification system, and classroom attendance system is an important field of random matrix theory technology application. The traditional random matrix theory class attendance has the problem of slow recognition speed and low accuracy. With the popularity of deep learning, the random matrix theory based on deep learning has gradually replaced the traditional random matrix theory. In this paper, the SSD target detection algorithm, NB algorithm, and RF algorithm based on deep learning are used to improve and optimize the traditional random matrix theory class attendance system, which effectively improves the efficiency and accuracy of random matrix theory attendance. Then, aiming at the low recall rate and recognition rate of random matrix theory in low-pixel images, we try to apply the random matrix theory of single image resolution reconstruction method to face detection and random matrix theory and give a calculation method to deal with outliers and noise.
LamBERTa: Law Article Mining Based on Bert Architecture for the Italian Civil Code

We present LamBERTa, a BERT-based framework for law article retrieval as a prediction problem, focusing on civil-law codes, and specifically trained on the Italian civil code. To the best of our knowledge, LamBERTa is the first advanced, deep learning approach to law article prediction for the Italian legal system. This paper is an extended abstract from our recent research work in [1].
The D F508 mutation shortens the biochemical half-life of plasma membrane CFTR in polarized epithelial cells

Marino. The D F508 mutation shortens the biochemical half-life of plasma membrane CFTR in polarized epithelial cells. Am J Physiol Cell Physiol 280: C166–C174, 2001. —Although the biosynthetic arrest of the D F508 mutant of cystic ﬁbrosis transmembrane conductance regulator (CFTR) can be partially reversed by physical and chemical means, recent evidence suggests that the functional stability of the mutant protein after reaching the cell surface is com-promised. To understand the molecular basis for this observation, the current study directly measured the half-life of D F508 and wild-type CFTR at the cell surface of transfected LLC-PK 1 cells. Plasma membrane CFTR expression over time was characterized biochemically and functionally in these polarized epithelial cells. Surface biotinylation, streptavidin extraction, and quantitative immunoblot analysis determined the biochemical half-life of plasma membrane D F508 CFTR to be ; 4 h, whereas the plasma membrane half-life of wild-type CFTR exceeded 48 h. This difference in biochemical stability correlated with CFTR-mediated transport function. These ﬁndings indicate that the D F508 mutation decreases the biochemical stability of CFTR at the cell surface. We conclude that the D F508 mutation triggers more rapid internalization of CFTR and/or its preferential sorting to a pathway of rapid degradation. at 4°C with R3194 (polyclonal anti-CFTR) and/or monoclonal antibody 6H (monoclonal against the b -subunit of rat Na 1 K 1 -ATPase, generously provided by Michael J. Caplan, Yale School of Medicine, New Haven, CT). Anti-CFTR labeling was detected with FITC-conjugated goat anti-rabbit F(ab 9 ) 2 fragments, and anti-Na 1 -K 1 -ATPase labeling was detected with tetramethylrhodamine isothiocyanate-conjugated goat anti-mouse F(ab 9 ) 2 fragments (Jackson ImmunoResearch Labs, West Grove, PA). For double-label experiments, pri- mary antibodies were applied simultaneously as were the ﬂuorescent-conjugated secondary antibodies. Fluorescent signals were visualized on an Axiophot ﬂuorescent micro-scope (Zeiss) and digitally stored using Photoshop 4.01 soft- ware (Adobe Systems, Mountain View, CA). Photoshop was not used to modify images other than to adjust contrast for improved signal deﬁnition. No signal was detected in the absence of primary antibody, indicating that background labeling was low under the experimental conditions em-ployed (data not shown).
Real-Time Locational Detection of Stealthy False Data Injection Attack in Smart Grid: Using Multivariate-Based Multi-Label Classification Approach

Recently, false data injection attacks (FDIAs) have been identified as a significant category of cyber-attacks targeting smart grids’ state estimation and monitoring systems. These cyber-attacks aim to mislead control system operations by compromising the readings of various smart grid meters. The real-time and precise locational identification of FDIAs is crucial for smart grid security and reliability. This paper proposes a multivariate-based multi-label locational detection (MMLD) mechanism to detect the presence and locations of FDIAs in real-time measurements with precise locational detection accuracy. The proposed architecture is a parallel structure that concatenates Long Short-Term Memory (LSTM) with Temporal Convolutional Neural Network (TCN). The proposed architecture is trained using Keras with Tensorflow libraries, and its performance is verified using an IEEE standard bus system in the MATPOWER package. Extensive testing has shown that the proposed approach effectively improves the presence-detection accuracy for locating stealthy FDIAs in small and large systems under various attack conditions. In addition, this work provides a customized loss function for handling the class imbalance problem. Simulation results reveal that our MMLD technique has a modest advantage in some aspects. First, our mechanism outperforms benchmark models because the problem is formulated as a multivariate-based multi-label classification problem. Second, it needs fewer iterations for training and reaching the optimal model. More specifically, our approach is less complex and more scalable than benchmark algorithms.
Melanoma Classification Using Feature Extraction Methods and Machine Learning Approaches

Due the unbalanced melanoma data and the complexity and resolution of the melanoma image backgrounds, classification of the melanoma regions is very challenging. In this paper, EffNet B5 models with different augmentation methods and machine learning models that use feature vectors from feature extraction method are proposed to solve the classification task. GLCM, LBP, SFTA, and ResNet18 are chosen as the feature extraction methods in this study, while SVM, Random Forest, and XGBoost are chosen to be the machine learning classification models. In this work, the most effective feature extraction algorithm and the classification algorithm with the best AUC scores are determined.
Elements for Developing a Value-Added Digital Services Model for Elements for Developing a Value-Added Digital Services Model for Rural Entrepreneurs in Namibia: An Exploratory Study Rural Entrepreneurs in Namibia: An Exploratory Study

Supporting digitally enabled entrepreneurship in rural areas requires a holistic approach to ensure rural entrepreneurs take advantage of digital services finding innovative solutions that allow businesses to thrive in competitive markets. In the absence of a model for value-added digital services for rural entrepreneurs, it is uncertain what type of digital services should be offered for rural entrepreneurs once the challenges of connectivity, electricity and digital skills have been addressed and whether those digital rural services are responsive to rural communities' needs and aspirations. The purpose of this study was to explore elements to be considered when developing a Valued Added Digital Services Model for Rural Entrepreneurs in Namibia. The study applied a cross-sectional survey using a mixed-method to collect data from 134 respondents comprising 14 rural entrepreneurs and 63 members from four rural communities and 57 ICT sector key informants. The results elucidated that, Digital Infrastructure, Digital Skills, Digital Inclusion, Digital Services and Digital Actors are the five key elements for the model. The study proposed a conceptual model that facilitates the understanding and underscore the effectiveness of an ecosystem approach that is embedded in drivers and pillars of a thriving entrepreneur and proposes interventions to mitigate the barriers for effective adoption of digital services by rural entrepreneurs.
R EVISITING THE L OTTERY T ICKET H YPOTHESIS : A R AMANUJAN G RAPH P ERSPECTIVE

Neural networks for machine learning applications often yield to weight pruning resulting in a sparse subnetwork that is adequate for a given task. Retraining these ‘lottery ticket’ subnetworks from their initialization minimizes the computational burden while preserving the test set accuracy of the original network. The existing literature only confirms that pruning is needed and it can be achieved up to a certain sparsity. We analyze the pruned network in the context of the properties of Ramanujan expander graphs. We consider the feed-forward network (both multi-layer perceptron and convolutional network) as a series of bipartite graphs which establish the connection from input to output. Now, as the fraction of remaining weights reduce with increasingly aggressive pruning, distinct regimes are observed: initially, no significant decrease in accuracy is demonstrated, and then the accuracy starts dropping rapidly. We empirically show that in the first regime, the pruned lottery ticket sub-network remains a Ramanujan graph. Subsequently, with the loss of Ramanujan graph property, accuracy begins to reduce sharply. This characterizes an absence of resilient connectivity in the pruned sub-network. We also propose a modified iterative pruning algorithm which removes edges in only the layers that are Ramanujan graphs thus preserving global connectivity even for heavily pruned networks. We perform experiments on MNIST and CIFAR10 datasets using different established feed-forward architectures to support the criteria for obtaining the winning ticket using the proposed algorithm.
scientific and operational hydrology

The present needs for international collaboration in ths field of hydrology are discussed, primarily as they relate to water problems in international river basins. The scope and intensity of such collaboration have under-gone considerable change during the past two decades. The responsibilities of the international inter-govern-mental organizations have also been in a state of flux, and the developments leading to the present organizational arrangements are described. The role of the United Nations and the specialized agencies in water resources development are discussed as they pertain to the applications of hydrology. Several of the international scientific associations and unions are active in the field of hydrology and these also are considered.
The Influence Mechanism of Education on Health from the Sustainable Development Perspective

Good health and quality education are two important goals of the Sustainable Development Goals (SDGs). More and more people pay attention to physical and mental health in a pandemic age. Previous studies have paid more attention to the relationship between socioeconomic status and health, and also scholars at home and abroad have not reached a consistent conclusion on how education aects health. In this study, we try to explore the relationship between education and health from the sustainable development perspective and its internal inuence mechanism through the data of China General Social Survey (CGSS) in 2015 and 2017, according to Grossman’s health demand model. e results indicated that from the junior high school education, physical health improved with the increase of the education level, but this eect was gradually weakened. e relationship between education and mental health is not a simple linear relationship but an inverted U-shaped change. After 16 years of education, mental health gradually decreases. Compared with women and rural population, men and urban population have better health. Education leads to labor market segmentation, which makes people with dierent education levels in dierent social structure positions, resulting in dierentiation of lifestyles, psychology, and social interaction, and this ultimately leads to health inequality. Education indirectly improves people’s health by changing healthy behaviors, cultivating healthy psychology, and strengthening social interactions.