Miracle-dz/UniFAR-FTUs huggingface dataset:
Claudin-20 promotes an aggressive phenotype in human breast cancer cells

Claudin-20 is a member of the Claudin family of transmembrane proteins located in the tight junction (TJ) of cells of epithelial origin. Due to the increasing evidence supporting the role of TJ proteins in preventing tumor cell metastatic behavior, this study sought to evaluate the distribution of Claudin-20 in human breast cancer and the effect of Claudin-20 overexpression in human breast cancer cells. Q-PCR data from breast cancer primary tumors (n = 114) and matched background tissue (n = 30) showed that high claudin-20 expression was correlated with poor survival of patients with breast cancer (p = 0.022). Following transformation of the breast cancer cell lines MDA-MB-231 and MCF7 with a Claudin-20 expression construct functional assays were performed to ascertain changes in cell behavior. Claudin-20 transformed cells showed significantly increased invasion (p < 0.005) and were significantly less adhesive than wild type cells (p < 0.05). There was no effect on growth (either in vitro or in vivo) for either cell line. Overexpression of Claudin-20 resulted in reduced transepithelial resistance (induced by the motogen HGF at 25 ng/ml, p = 0.0007). Interestingly, this was not mirrored by paracellular permeability, as overexpression of Claudin-20 caused a decrease in permeability. The introduction of Claudin-20 into human breast cancer cells resulted in breast cancer cells with an aggressive phenotype and reduced trans-epithelial resistance. There was no corresponding decrease in paracellular permeability, indicating that this Claudin has a differential function in epithelial TJ. This provides further insight into the importance of correctly functioning TJ in preventing the progression of human breast cancer.
A Sophisticated Framework for the Accurate Detection of Phishing Websites

Phishing is an increasingly sophisticated form of cyberattack that is inflicting huge financial damage to corporations throughout the globe while also jeopardizing individuals' privacy. Attackers are constantly devising new methods of launching such assaults and detecting them has become a daunting task. Many different techniques have been suggested, each with its own pros and cons. While machine learning-based techniques have been most successful in identifying such attacks, they continue to fall short in terms of performance and generalizability. This paper proposes a comprehensive methodology for detecting phishing websites. The goal is to design a system that is capable of accurately distinguishing phishing websites from legitimate ones and provides generalized performance over a broad variety of datasets. A combination of feature selection, greedy algorithm, cross-validation, and deep learning methods have been utilized to construct a sophisticated stacking ensemble classifier. Extensive experimentation on four different phishing datasets was conducted to evaluate the performance of the proposed technique. The proposed algorithm outperformed the other existing phishing detection models obtaining accuracy of 97.49%, 98.23%, 97.48%, and 98.20% on dataset-1 (UCI Phishing Websites Dataset), dataset-2 (Phishing Dataset for Machine Learning: Feature Evaluation), dataset-3 (Phishing Websites Dataset), and dataset-4 (Web page phishing detection), respectively. The high accuracy values obtained across all datasets imply the models' generalizability and effectiveness in the accurate identification of phishing websites.
Rethinking Low-quality Optical Flow in Unsupervised Surgical Instrument Segmentation

Video-based surgical instrument segmentation plays an important role in robot-assisted surgeries. Unlike supervised settings, unsupervised segmentation relies heavily on motion cues, which are challenging to discern due to the typically lower quality of optical flow in surgical footage compared to natural scenes. This presents a considerable burden for the advancement of unsupervised segmentation techniques. In our work, we address the challenge of enhancing model performance despite the inherent limitations of low-quality optical flow. Our methodology employs a three-pronged approach: extracting boundaries directly from the optical flow, selectively discarding frames with inferior flow quality, and employing a fine-tuning process with variable frame rates. We thoroughly evaluate our strategy on the EndoVis2017 VOS dataset and Endovis2017 Challenge dataset, where our model demonstrates promising results, achieving a mean Intersection-over-Union (mIoU) of 0.75 and 0.72, respectively. Our findings suggest that our approach can greatly decrease the need for manual annotations in clinical environments and may facilitate the annotation process for new datasets. The code is available at https://github.com/wpr1018001/Rethinking-Low-quality-Optical-Flow.git
Knowledge-based reward shaping with knowledge revision in reinforcement learning

Reinforcement learning has proven to be a successful artificial intelligence technique when an agent needs to act and improve in a given environment. The agent receives feedback about its behaviour in terms of rewards through constant interaction with the environment and in time manages to identify which actions are more beneficial for each situation. Typically reinforcement learning assumes the agent has no prior knowledge about the environment it is acting on. Nevertheless, in many cases (potentially abstract and heuristic) domain knowledge of the reinforcement learning tasks is available by domain experts, and can be used toimprove the learning performance. One way of imparting knowledge to an agent is through reward shaping which guides an agent by providing additional rewards. One common assumption when imparting knowledge to an agent, is that the domain knowledge is always correct. Given that the provided knowledge is of a heuristic nature, there are cases when this assumption is not met and it has been shown that in cases where the provided knowledge is wrong, the agent takes longer to learn the optimal policy. As reinforcement learning methods are shifting more towards informed agents, the assumption that expert domain knowledge is always correct needs to be relaxed in order to scale these methods to more complex, real-life scenarios. To accomplish that, the agents need to have a mechanism to deal with those cases where the provided expert knowledge is not perfect. This thesis investigates and documents the adverse effects erroneous knowledge can have to the learning process of an agent if care is not taken. Moreover, it provides a novel approach to deal with erroneous knowledge through the use of knowledge revision principles, in order to allow agents to use their experiences to revise knowledge and thus benefit from more accurate shaping. Empirical evaluation shows that agents that are able to revise erroneous parts of the provided knowledge, can reach better policies faster when compared to agents that do not have knowledge revision capabilities.
End-to-end Adaptive Dynamic Subsampling and Reconstruction for Cardiac MRI

Accelerating dynamic MRI is essential for enhancing clinical applications, such as adaptive radiotherapy, and improving patient comfort. Traditional deep learning (DL) approaches for accelerated dynamic MRI reconstruction typically rely on predefined or random subsampling patterns, applied uniformly across all temporal phases. This standard practice overlooks the potential benefits of leveraging temporal correlations and lacks the adaptability required for case-specific subsampling optimization, which holds the potential for maximizing reconstruction quality. Addressing this gap, we present a novel end-to-end framework for adaptive dynamic MRI subsampling and reconstruction. Our pipeline integrates a DL-based adaptive sampler, generating case-specific dynamic subsampling patterns, trained end-to-end with a state-of-the-art 2D dynamic reconstruction network, namely vSHARP, which effectively reconstructs the adaptive dynamic subsampled data into a moving image. Our method is assessed using dynamic cine cardiac MRI data, comparing its performance against vSHARP models that employ common subsampling trajectories, and pipelines trained to optimize dataset-specific sampling schemes alongside vSHARP reconstruction. Our results indicate superior reconstruction quality, particularly at high accelerations.
Understanding stress: A psychological perspective for health professionals.

Part 1 Stress and health: an introduction. Part 2 Understanding stress - historical approach: the origins of stress research person - environment fit stress - third wave plague or whipping boy? definitions of stress. Part 3 The nature of stress: factors intrinsic to the job role stress relationships at work career stress organizational structure and climate the home-work interface life cycles and life events. Part 4 Stress response moderators and individual differences: personality characteristics personal conditioning variables. Part 5 The consequences of stress: stress and health outcomes organizational costs of stress. Part 6 Stress and the health professions: dentists ambulance and paramedics nurses doctors conclusion. Part 7 Managing stress: coping with stress - individual approaches coping with stress - workplace approaches.
Precision Agriculture: Crop Mapping using Machine Learning and Sentinel-2 Satellite Imagery

Food security has grown in significance due to the changing climate and its warming effects. To support the rising demand for agricultural products and to minimize the negative impact of climate change and mass cultivation, precision agriculture has become increasingly important for crop cultivation. This study employs deep learning and pixel-based machine learning methods to accurately segment lavender fields for precision agriculture, utilizing various spectral band combinations extracted from Sentinel-2 satellite imagery. Our fine-tuned final model, a U-Net architecture, can achieve a Dice coefficient of 0.8324. Additionally, our investigation highlights the unexpected efficacy of the pixel-based method and the RGB spectral band combination in this task.
Analysis of 2004-2007 vessel-specific seabird bycatch data in Alaska demersal longline fisheries

This report does not constitute a publication and is for information only. All data herein are to be considered provisional. ABSTRACT The annual incidental bycatch of seabirds by demersal groundfish longline vessels compared to bycatch from the late 1990s. Despite the recent reductions resulting from mandatory mitigation requirements, seabirds continue to be caught at higher rates than would be expected given results of controlled studies that demonstrated bycatch reductions of nearly 100% with paired streamer lines. We characterize recent seabird bycatch data (2004-2007) from the Alaska demersal longline fisheries and analyze factors influencing seabird bycatch for two fisheries-Pacific cod (Gadus macrocephalus) and sablefish (Anoplopoma fimbria). Previous analyses of 1995-2000 bycatch data showed that individual vessel was the single most important source of variability in seabird bycatch rates in Alaska longline fisheries. Certain vessels consistently caught a higher proportion of birds across years and fisheries. Our results demonstrate that a few individual vessels continue to be responsible for the majority of seabird bycatch. Six vessels out of 39 contribute 38% of all birds caught in the cod demersal longline fishery when sampled rates are extrapolated to hooks deployed in observed sets. Based on this analysis, we recommend a variety of methods to further reduce seabird bycatch by longline vessels in Alaska.
Safe programmable speculative parallelism

Execution order constraints imposed by dependences can serialize computation, preventing parallelization of code and algorithms. Speculating on the value(s) carried by dependences is one way to break such critical dependences. Value speculation has been used effectively at a low level, by compilers and hardware. In this paper, we focus on the use of speculation by programmers as an algorithmic paradigm to parallelize seemingly sequential code. We propose two new language constructs, speculative composition and speculative iteration. These constructs enable programmers to declaratively express speculative parallelism in programs: to indicate when and how to speculate, increasing the parallelism in the program, without concerning themselves with mundane implementation details. We present a core language with speculation constructs and mutable state and present a formal operational semantics for the language. We use the semantics to define the notion of a correct speculative execution as one that is equivalent to a non-speculative execution. In general, speculation requires a runtime mechanism to undo the effects of speculative computation in the case of mis predictions. We describe a set of conditions under which such rollback can be avoided. We present a static analysis that checks if a given program satisfies these conditions. This allows us to implement speculation efficiently, without the overhead required for rollbacks. We have implemented the speculation constructs as a C# library, along with the static checker for safety. We present an empirical evaluation of the efficacy of this approach to parallelization.
Comparative Framework Linking Some Capabilities of Small Array Dispositions of Generic Points Absorbers

This thesis completion works it out to deepen into diverse modeling techniques for Point Absorbers. A combined frequency-time domain model is conceived, designed and developed in Matlab with Fortran as a base, leading to obtain physical variables of primary importance, namely position, velocity and power to energy net balance relationships of absorption. Integration of different degrees of freedom with heave as main executable leads in turn to a single buoy motion focus. Acquisition of the needed hydrodynamic coefficients is provided through application of NEMOH & BEMIO solvers due to the Boundary Element Methodology. Initially, this Wave-to-motion model is validated by comparison with previous experimental results for a floating cone cylinder shape (Buldra-FO3). A single, generic, vertical floating cylinder is contemplated then, that responds to the action of the passing regular waves excitation. Later, two equally sized vertical floating cylinders aligned with the incident wave direction are modeled for a variable distance between the bodies. For both unidirectional regular and irregular waves as an input in deep water, we approximate the convolutive radiation force function term through the Prony method. By changing the spatial disposition of the axisymmetric buoys, using for instance triangular or rectangular shaped arrays of three and four bodies respectively, the study delves into motion characteristics for regular waves. The results highlight efficient layouts for maximizing the energy production whilst providing important insights into their performance, revealing displacement amplificationand capture width-ratios, while deriving in possible interpretations of scenarios related to the the known park effect. These terms are encompassed by the novelty of a new conceptual Post-Processing methodology in the field, which leads to obtain an optimal distance for the separated bodies with effective energy absorption in a regular wave regime. In conclusion, this computational excursion envisions and proposes potential fields of study, which will surely enhance new connections and link this renewable energy form.
2(5H)-Furanone: A Prospective strategy for biofouling-control in membrane biofilm bacteria by quorum sensing inhibition

Biofouling of membranes demands costly periodic cleaning and membrane replacement. A sustainable and environmentally friendly solution for maintenance is not available and would be of great interest for many purposes including economical. As complex biofilm formation by environmental strains is the major cause of biofouling and biofilm formation in most cases are controlled by N-Acylhomoserine lactone (AHL)-mediated Quorum Sensing (QS). An effort was made to understand the appropriateness of 2(5H)-furanone, to use against biofouling of membranes. QS inhibition activity by 2(5H)-furanone was studied using bioindicator strains and known AHL of different acyl chain lengths. The biofilm inhibition was studied by growth analysis on polystyrene plate of Aeromonas hyrdrophila, an environmental biofilm strain isolated from a bio-fouled reverse osmosis (RO) membrane. Results showed a QS inhibition activity against a wide range of AHLs and also biofilm formation by 2(5H)-furanone, which is believed to act as a potential quorum inhibition agent in a bacterial biofilm community.
The condition‐dependence of male genital size and shape

Abstract The male genitals of internal fertilisers evolve rapidly and divergently, and sexual selection is generally responsible for this. Many sexually selected traits are condition‐dependent—with their expression dependent upon the resources available to be allocated to them—as revealed by genetic or environmental manipulations of condition. However, it is not clear whether male genitals are also condition‐dependent. Here we manipulate condition in two ways (via inbreeding and diet) to test the condition‐dependence of the genital arch of Drosophila simulans. We found that genital size but not genital shape suffered from inbreeding depression, whereas genital size and shape were affected by dietary manipulation of condition. The differential effects of these treatments likely reflect underlying genetic architecture that has been shaped by past selection: inbreeding depression is only expected when traits have a history of directional selection, while diet impacts traits regardless of historical selection. Nonetheless, our results suggest genitals can be condition‐dependent like other sexually selected traits.
Construction of Music Composition System with Interactive Genetic Algorithm

This paper aims at the construction of the music composition system that has two important points. The one is that the system helps musically unskilled people to compose their own music. The other is that the system composes musical works reflecting users subjective evaluation of music. This paper uses the technique of Interactive Genetic Algorithm, since the interaction between users and the system is necessary to compose 16-bars music reflecting users feeling. The Interactive GA is a kind of an Interactive Evolutionary Computation as one of soft computing techniques. In the interactive GA, human evaluation is used instead of fitness functions. The subjects experiments are carried out in order to confirm the validity of the present system. From the results of the experiment, it is found that the subjects evaluation values become high with the progress of generations. That is, the system evolves and composes musical works reflecting user's feeling.
An in-the-wild study of learning mid-air gestures to browse hierarchical information at a large interactive public display

This paper describes the design and evaluation of our Media Ribbon, a large public interactive display for browsing hierarchical information, with mid-air gestures. Browsing a hierarchical information space is a fundamental form of interaction. Designing learnable mid-air gestures is a current challenge for large display interaction. Our in-the-wild evaluation draws on 41 days of quantitative log data, with 4484 gestures detected, and qualitative data from 15 interviews, and associated video. We explored: whether our design enabled people to learn the gestures; how our tutorial and feedback mechanisms supported learning; and the effectiveness of support for browsing hierarchical information. Our contributions are: (1) design of large public display for browsing of hierarchical information; (2) with its gesture set; (3) insights into the ways people learn and use this interface in our context; and (4) guidelines for designing learnable mid-air gestures.
R2: An Application-Level Kernel for Record and Replay

Library-based record and replay tools aim to reproduce an application's execution by recording the results of selected functions in a log and during replay returning the results from the log rather than executing the functions. These tools must ensure that a replay run is identical to the record run. The challenge in doing so is that only invocations of a function by the application should be recorded, recording the side effects of a function call can be difficult, and not executing function calls during replay, multithreading, and the presence of the tool may change the application's behavior from recording to replay. These problems have limited the use of such tools. R2 allows developers to choose functions that can be recorded and replayed correctly. Developers annotate the chosen functions with simple keywords so that R2 can handle calls with side effects andmultithreading. R2 generates code for record and replay from templates, allowing developers to avoid implementing stubs for hundreds of functions manually. To track whether an invocation is on behalf of the application or the implementation of a selected function, R2 maintains a mode bit, which stubs save and restore. We have implemented R2 on Windows and annotated large parts (1,300 functions) of the Win32 API, and two higher-level interfaces (MPI and SQLite). R2 can replay multithreaded web and database servers that previous library-based tools cannot replay. By allowing developers to choose high-level interfaces, R2 can also keep recording overhead small; experiments show that its recording overhead for Apache is approximately 10%, that recording and replaying at the SQLite interface can reduce the log size up to 99% (compared to doing so at the Win32 API), and that using optimization annotations for BitTorrent and MPI applications achieves log size reduction ranging from 13.7% to 99.4%.
Land use and soil legacy in the Lower Coastal Plain: A case study of Wormsloe State Historic Site, Georgia

Agriculture alters forest soil physical and chemical properties, often for decades to centuries, following agricultural cessation. Wormsloe State Historic Site, near Savannah, Georgia, has nearly a 300-year documented history of land use. To investigate agricultural soil legacies at Wormsloe, 120 random sample points were established across two land use history areas: Agriculture (some agricultural activity between 1736 and the present) and Reference (areas never farmed and under some forest cover from 1736 to present) within two mapped soil series (Chipley and Olustee). Landscape variables; profile characteristics; soil pH, carbon (C) and nitrogen (N) concentrations; extractable phosphorus (P), calcium (Ca), and magnesium (Mg) concentrations; and dominant vegetation were evaluated. An A horizon was always described in never farmed reference areas, and an Ap horizon was often, but not always, described in reforested agricultural areas. Soil P concentrations (Mehlich-1) were elevated (p = 0.05) in former agricultural areas. Soil pH (p = 0.04) differed across soil series but not land use. Discriminant analysis of sample plots reclassified by agricultural abandonment period (Reference/No Agricultural History, Civil War, Depression, and Modern) indicated that the Reference/No Agricultural History and Civil War period plots were similar and distinct from plots in areas abandoned during the Depression and Modern periods. Our ability to discriminate among dominant overstory and understory vegetation types using soil and site variables was relatively low.