Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiscale Object-Based Classification and Feature Extraction along Arctic Coasts

Permafrost coasts are experiencing accelerated erosion in response to above average warming in the Arctic resulting in local, regional, and global consequences. However, Arctic coasts are expansive in scale, constituting 30–34% of Earth’s coastline, and represent a particular challenge for wide-scale, high temporal measurement and monitoring. This study addresses the potential strengths and limitations of an object-based approach to integrate with an automated workflow by assessing the accuracy of coastal classifications and subsequent feature extraction of coastal indicator features. We tested three object-based classifications; thresholding, supervised, and a deep learning model using convolutional neural networks, focusing on a Pleaides satellite scene in the Western Canadian Arctic. Multiple spatial resolutions (0.6, 1, 2.5, 5, 10, and 30 m/pixel) and segmentation scales (100, 200, 300, 400, 500, 600, 700, and 800) were tested to understand the wider applicability across imaging platforms. We achieved classification accuracies greater than 85% for the higher image resolution scenarios using all classification methods. Coastal features, waterline and tundra, or vegetation, line, generated from image classifications were found to be within the image uncertainty 60% of the time when compared to reference features. Further, for very high resolution scenarios, segmentation scale did not affect classification accuracy; however, a smaller segmentation scale (i.e., smaller image objects) led to improved feature extraction. Similar results were generated across classification approaches with a slight improvement observed when using deep learning CNN, which we also suggest has wider applicability. Overall, our study provides a promising contribution towards broad scale monitoring of Arctic coastal erosion.
Instrumented Office Chair With Low-Cost Plastic Optical Fiber Sensors for Posture Control and Work Conditions Optimization

People spend more and more time sitting and this habit has been shown to cause spine pathologies. Thus, the scientific community and the industry have become interested in instrumented chairs development to detect incorrect user postures. In this work we present the development and implementation of invisibles and non-intrusive plastic optical fiber sensor cells to monitor the posture and evaluate the ergonomic behavior of a seated person. The low-cost plastic optical fiber (POF) based sensing devices developed in this work, were implemented in an office chair, to evaluate the workers posture throughout their work day. In addition to the sensors, Android and PC software applications were developed, to provide real time feedback and alerts to the user whenever an inadequate posture is detected, or the seating position is the same for a long time. The proposed approach was evaluated in a study involving six users, and results show that it can detect the user’s position with 96.6% accuracy.
Differential Response to Cytotoxic Drugs Explains the Dynamics of Leukemic Cell Death: Insights from Experiments and Mathematical Modeling

This study presents a framework whereby cancer chemotherapy could be improved through collaboration between mathematicians and experimentalists. Following on from our recently published model, we use A20 murine leukemic cells transfected with monomeric red fluorescent proteins cells (mCherry) to compare the simulated and experimental cytotoxicity of two Federal Drug Administration (FDA)-approved anticancer drugs, Cytarabine (Cyt) and Ibrutinib (Ibr) in an in vitro model system of Chronic Lymphocytic Leukemia (CLL). Maximum growth inhibition with Cyt (95%) was reached at an 8-fold lower drug concentration (6.25 μM) than for Ibr (97%, 50 μM). For the proposed ordinary differential equations (ODE) model, a multistep strategy was used to estimate the parameters relevant to the analysis of in vitro experiments testing the effects of different drug concentrations. The simulation results demonstrate that our model correctly predicts the effects of drugs on leukemic cells. To assess the closeness of the fit between the simulations and experimental data, RMSEs for both drugs were calculated (both RMSEs < 0.1). The numerical solutions of the model show a symmetrical dynamical evolution for two drugs with different modes of action. Simulations of the combinatorial effect of Cyt and Ibr showed that their synergism enhanced the cytotoxic effect by 40%. We suggest that this model could predict a more personalized drug dose based on the growth rate of an individual’s cancer cells.
Impact of Seasonal and Organ-Related Fluctuations on the Anthelmintic Properties and Chemical Profile of Cladium mariscus (L.) Pohl Extracts

The use of plants and their metabolites stands as a promising option to tackle parasitic infections by gastrointestinal nematodes (GIN) in integrated control strategies. Still, the influence of environmental and phenological factors, and their interactions, in the wild on the metabolomics and biological properties of target plant species, is often disregarded. In this work, we hypothesized that variations in the anthelmintic (AH) properties and chemical composition of extracts from the salt tolerant species Cladium mariscus L. Pohl (sawgrass) may be influenced by seasonal factors and organ-parts. To test this hypothesis, acetone/water extracts were prepared from dried biomass obtained from aerial organs collected from sawgrass in consecutive seasons and tested against Haemonchus contortus and Trichostrongylus colubriformis by the larval exsheathment inhibition assay (LEIA) and egg hatching inhibition assay (EHIA). To ascertain the role of plant organ, the activity of leaves and inflorescences extracts from summer samples was compared. The role of polyphenols in the anthelmintic activity depending on GINs and fluctuations across seasons and plant organs was assessed using polyvinylpolypyrrolidone (PVPP), coupled with an in-depth chemical profiling analysis using high-performance liquid chromatography completed with electrospray ionization mass spectrometric detection (HPLC-ESI-MSn). Main differences in anthelmintic activities were observed for summer and autumn samples, for both assays. Moreover, inflorescences’ extracts were significantly more active than those from leaves against both parasite species on EHIA and against H. contortus on LEIA. Application of PVPP totally inhibit the AH effects based on EHIA and only partly for LEIA. Non-treated PVPP extracts were predominantly composed of flavan-3-ols, proanthocyanidins, luteolin and glycosylated flavonoids, while two flavonoid glycosides were quantified in all PVPP-treated samples. Thus, the activity of such compounds should be further explored, although some unknown metabolites remain to be identified. This study reinforces the hypothesis of the AH potential of sawgrass and of its polyphenolic metabolites uses as nutraceutical and/or phytotherapeutic drugs.
Conducting an online synchronous interview: Implications for online assessment

This paper highlights key points and practicalities regarding using online interviews using a recently completed undergraduate thesis as a case study. This case entitles “The consumers’ perceptions towards green products in food and beverages sector in Ho Chi Minh City. ” Online interviewing using Google Meet has become one of the most common data collection methods in Vietnam as it is reasonably easy to use, provides almost all the required functionalities for online research, and is free of charge. Inspired by the experiences and practices from the case study, this paper is intended for use in online research methods and provides implications for application in online assessment.
Atividades realizadas utilizando tecnologias wearables durante a avaliação de indivíduos com a doença de Parkinson: Uma revisão sistemática

— A variabilidade da doença de Parkinson torna a avaliação da doença um desafio. A doença possui além dos sintomas motores as flutuações que progridem com diferentes velocidades, dificultando as decisões terapêuticas. Além disso, atualmente o diagnóstico e avaliação são realizados por meio de escalas subjetivas que estão sujeitas a percepção de cada avaliador. Diante disso, tecnologias vêm sendo utilizadas para auxiliar os clínicos e promover maior qualidade de vida entre os acometidos. Dentre essas tecnologias, as vestíveis ou wearables são amplamente difundidas em serviços de saúde, especialmente na doença de Parkinson. Embora sejam bastante difundidas, ainda não estão consolidadas em ambientes clínicos. Para que haja adesão clínica dessas tecnologias, diversas etapas devem ser cumpridas que vão desde o desenvolvimento até a validação. Dentre essas etapas, destaca-se a escolha da atividade realizada para validação da tecnologia. Atualmente, muitos trabalhos utilizam as atividades propostas pela MDS-UPDRS (Movement Disorders Society - Unified Parkinson’s Disease Rating Scale), sendo que outros utilizam atividades funcionais. Porém, em muitos casos a escolha da atividade não é simples, exigindo um grande esforço dos pesquisadores. Diante do exposto esse trabalho propôs uma revisão das atividades mais recorrentes nos últimos cinco anos que utilizam tecnologias vestíveis na avaliação dos sintomas motores da doença de Parkinson. Essa revisão poderá auxiliar protocolos experimentais de trabalhos futuros .
Epigenetics in Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurodegenerative disease with unknown pathogenesis and complex pathological manifestations. At present, a large number of studies on targeted drugs for the typical pathological phenomenon of AD (Aβ) have ended in failure. Although there are some drugs on the market that indirectly act on AD, their efficacy is very low and the side effects are substantial, so there is an urgent need to develop a new strategy for the treatment of AD. An increasing number of studies have confirmed epigenetic changes in AD. Although it is not clear whether these epigenetic changes are the cause or result of AD, they provide a new avenue of treatment for medical researchers worldwide. This article summarizes various epigenetic changes in AD, including DNA methylation, histone modification and miRNA, and concludes that epigenetics has great potential as a new target for the treatment of AD.
Pathogenesis of Primary Aldosteronism: Impact on Clinical Outcome

Primary aldosteronism (PA) is the most common form of secondary arterial hypertension, with a prevalence of approximately 20% in patients with resistant hypertension. In the last decade, somatic pathogenic variants in KCNJ5, CACNA1D, ATP1A1 and ATP2B3 genes, which are involved in maintaining intracellular ionic homeostasis and cell membrane potential, were described in aldosterone-producing adenomas (aldosteronomas). All variants in these genes lead to the activation of calcium signaling, the major trigger for aldosterone production. Genetic causes of familial hyperaldosteronism have been expanded through the report of germline pathogenic variants in KCNJ5, CACNA1H and CLCN2 genes. Moreover, PDE2A and PDE3B variants were associated with bilateral PA and increased the spectrum of genetic etiologies of PA. Of great importance, the genetic investigation of adrenal lesions guided by the CYP11B2 staining strongly changed the landscape of somatic genetic findings of PA. Furthermore, CYP11B2 staining allowed the better characterization of the aldosterone-producing adrenal lesions in unilateral PA. Aldosterone production may occur from multiple sources, such as solitary aldosteronoma or aldosterone-producing nodule (classical histopathology) or clusters of autonomous aldosterone-producing cells without apparent neoplasia denominated aldosterone-producing micronodules (non-classical histopathology). Interestingly, KCNJ5 mutational status and classical histopathology of unilateral PA (aldosteronoma) have emerged as relevant predictors of clinical and biochemical outcome, respectively. In this review, we summarize the most recent advances in the pathogenesis of PA and discuss their impact on clinical outcome.
Fine-Grained Benchmarking and Targeted Optimization: Enabling Green IoT-Oriented Blockchain in the 6G Era

For 6G-empowered IoT, blockchain is widely regarded as a fundamental technique for addressing the security and privacy issues. Nonetheless, the current blockchains suffer from several flaws, especially the low performance and scalability. They seriously hinder the blockchain deployments in 6G-empowered IoT with high concurrency and complexity. To address this problem, we need to better understand blockchains and try to optimize them. However, we still lack a standard tool for comprehensively benchmarking the IoT-oriented blockchains in 6G era. As to blockchain optimization, the current general-purpose strategies can hardly hold efficiency all the time due to the heterogeneity of 6G-empowered IoT. In this paper, we present a novel pipeline for facilitating the green IoT-oriented blockchains in 6G era, called “fine-grained benchmarking + targeted optimization. ” Specifically, we first design a benchmarking framework which realizes the whole workflow of data acquisition, log parsing, and metric visualization, offering quantitative blockchain performance reports. Moreover, our framework provides ten metrics divided into four dimensions, leading blockchain benchmarking to the fine-grained level. Based on the acquired insights, we further present the concept of targeted optimization, i.e., directly designing optimization strategies based on the metrics that perform poorly in benchmarking. To better illustrate this pipeline, we take IOTA, the most potential IoT-oriented blockchain in 6G era, as a case study. We observe two defects of IOTA, i.e., low power efficiency and slow ledger synchronization. Following our pipeline, we design two targeted optimization mechanisms, namely Proof-of-Resource Efficiency and Dynamic Local Snapshot Protocol. Extensive experiments demonstrate that our pipeline can maximize the optimization efficiency, successfully addressing the issues hindering IOTA. Last but not least, we emphasize that the proposed pipeline can also be applied by other IoT-oriented blockchains in the 6G era.
Utility of CK Metrics in Predicting Size of Board-Based Software Games

Software size is one of the most important inputs of many software cost and effort estimation models. Early estimation of software plays an important role at the time of project inception. An accurate estimate of software size is, therefore, crucial for planning, managing, and controlling software development projects dealing with the development of software games. However, software size is unavailable during early phase of software development. This research determines the utility of CK (Chidamber and Kemerer) metrics, a well-known suite of object-oriented metrics, in estimating the size of software applications using the information from its UML (Unified Modeling Language) class diagram. This work focuses on a small subset dealing with board-based software games. Almost sixty games written using an object-oriented programming language are downloaded from open source repositories, analyzed and used to calibrate a regression-based size estimation model. Forward stepwise MLR (Multiple Linear Regression) is used for model fitting. The model thus obtained is assessed using a variety of accuracy measures such as MMRE (Mean Magnitude of Relative Error), Prediction of x(PRED(x)), MdMRE (Median of Relative Error) and validated using K-fold cross validation. The accuracy of this model is also compared with an existing model tailored for size estimation of board games. Based on a small subset of desktop games developed in various object-oriented languages, we obtained a model using CK metrics and forward stepwise multiple linear regression with reasonable estimation accuracy as indicated by the value of the coefficient of determination (R 2 = 0.756). Comparison results indicate that the existing size estimation model outperforms the model derived using CK metrics in terms of accuracy of prediction.
Choosing Sides: Impact of Prismatic Adaptation on the Lateralization of the Attentional System

Seminal studies revealed differences between the effect of adaptation to left- vs. right-deviating prisms (L-PA, R-PA) in normal subjects. Whereas L-PA leads to neglect-like shift in attention, demonstrated in numerous visuo-spatial and cognitive tasks, R-PA has only minor effects in specific aspects of a few tasks. The paucity of R-PA effects in normal subjects contrasts with the striking alleviation of neglect symptoms in patients with right hemispheric lesions. Current evidence from activation studies in normal subjects highlights the contribution of regions involved in visuo-motor control during prism exposure and a reorganization of spatial representations within the ventral attentional network (VAN) after the adaptation. The latter depends on the orientation of prisms used. R-PA leads to enhancement of the ipsilateral visual and auditory space within the left inferior parietal lobule (IPL), switching thus the dominance of VAN from the right to the left hemisphere. L-PA leads to enhancement of the ipsilateral space in right IPL, emphasizing thus the right hemispheric dominance of VAN. Similar reshaping has been demonstrated in patients. We propose here a model, which offers a parsimonious explanation of the effect of L-PA and R-PA both in normal subjects and in patients with hemispheric lesions. The model posits that prismatic adaptation induces instability in the synaptic organization of the visuo-motor system, which spreads to the VAN. The effect is lateralized, depending on the side of prism deviation. Successful pointing with prisms implies reaching into the space contralateral, and not ipsilateral, to the direction of prism deviation. Thus, in the hemisphere contralateral to prism deviation, reach-related neural activity decreases, leading to instability of the synaptic organization, which induces a reshuffling of spatial representations in IPL. Although reshuffled spatial representations in IPL may be functionally relevant, they are most likely less efficient than regular representations and may thus cause partial dysfunction. The former explains, e.g., the alleviation of neglect symptoms after R-PA in patients with right hemispheric lesions, the latter the occurrence of neglect-like symptoms in normal subjects after L-PA. Thus, opting for R- vs. L-PA means choosing the side of major IPL reshuffling, which leads to its partial dysfunction in normal subjects and to recruitment of alternative or enhanced spatial representations in patients with hemispheric lesions.
What You Always Wanted to Know about the Deterministic Part of IPC 2014 (But Were too Afraid to Ask)

The International Planning Competition (IPC) is a prominent event of the AI planning community that has been organised since 1998; it aims at fostering the development and comparison of planning approaches, assessing the state-of-the-art in planning, and identifying new challenging benchmarks. IPC has a strong impact also outside the planning community, by providing a large number of ready-to-use planning engines and testing pioneering applications of planning techniques. This paper focuses on the deterministic part of IPC 2014, and describes format, participants, benchmarks as well as a thorough analysis of the results. Generally, results of the competition indicates some significant progress, but they also highlight issues and challenges that the planning community will have to face in the future.
Investing with Style in Liquid Private Debt

Abstract This paper extends the analysis of systematic investment approaches to broadly syndicated leveraged loans. We find that exposures linked to (short-term) momentum and valuation styles (and a combination thereof) are well-compensated: monthly rebalanced long-only portfolios of high value and momentum loans generate Sharpe and information ratios well above one and economically and statistically significant alphas. Factor portfolio performance deteriorates but remains significant over longer investment horizons. An important implication of our research is that active credit managers employing loan trading strategies that are momentum- and value-neutral do not make use of a viable source of additional return.
Molecular Mechanisms of Intercellular Rhizobial Infection: Novel Findings of an Ancient Process

Establishment of the root-nodule symbiosis in legumes involves rhizobial infection of nodule primordia in the root cortex that is dependent on rhizobia crossing the root epidermal barrier. Two mechanisms have been described: either through root hair infection threads or through the intercellular passage of bacteria. Among the legume genera investigated, around 75% use root hair entry and around 25% the intercellular entry mode. Root-hair infection thread-mediated infection has been extensively studied in the model legumes Medicago truncatula and Lotus japonicus. In contrast, the molecular circuit recruited during intercellular infection, which is presumably an ancient and simpler pathway, remains poorly known. In recent years, important discoveries have been made to better understand the transcriptome response and the genetic components involved in legumes with obligate (Aeschynomene and Arachis spp.) and conditional (Lotus and Sesbania spp.) intercellular rhizobial infections. This review addresses these novel findings and briefly considers possible future research to shed light on the molecular players that orchestrate intercellular infection in legumes.
Modeling the Effect of Team Collaboration on the Creation of New Knowledge

To survive global competition in a knowledge economy, respective fields of the enterprise knowledge must be upgraded constantly. There are two means to achieve this goal: inducing new knowledge from outside of the enterprise, or by creating new knowledge from within. The advantages of creating new knowledge from within are to create the core knowledge of the enterprise directly, easily protect commercial confidentialities, especially when the knowledge cannot be acquired from the outside, and since the knowledge is tailor made within enterprise, advantages such as higher applicability and higher compatibility can be achieved. When an enterprise has decided to create new knowledge from within, the following attributes must be contemplated: selecting suitable members, determining the kind of knowledge needed, knowledge complexity, knowledge level, the pressure of time, and the like. This study develops a mathematical model, which utilizes team collaboration in the creation of new knowledge, and helps select a suitable team under limited resources, while achieving the best benefit for knowledge creation. In light of the target knowledge, three knowledge dimensions must be considered concurrently, namely, knowledge complexity, the knowledge level of each employee, and the knowledge correlation between existing knowledge and target knowledge. Furthermore, the model can quantify the contribution of team collaboration on the creation of new knowledge, growth of the new knowledge of members, the time required to increase knowledge, and the total time required to create the target knowledge.