Miracle-dz/UniFAR-FTUs huggingface dataset:
Monomers and Macromonomers from Renewable Resources

Renewable resources constitute an extremely rich and varied array of molecules and macromolecules incessantly produced by natural biological activities thanks to solar energy. Their exploitation by mankind has always been at the heart of its survival as sources of food, remedies, clothing, shelter, energy, etc., and of its leisure as sources of fl owers, dyes, fragrances, and other amenities. In the specifi c context of materials, good use has always been made of cotton, paper, starch products, wool, silk, gelatin, leather, natural rubber, vegetable oils and terpenes, among others, through progressively more sophisticated and scaled up technologies. The meteoric ascension of coal and petroleum chemistry throughout the twentieth century gave rise to the extraordinary surge of a wide variety of original macromolecules derived from the rich diversity of monomers available through these novel synthetic routes. This technical revolution is still very much alive today, but the dwindling of fossil resources and their unpredictable price oscillations, mostly on the increase, is generating a growing concern about fi nding alternative sources of chemicals, and hence of organic materials, in a similar vein as the pressing need for more ecological and perennial sources of energy. The new paradigm of the biorefi nery [1] represents the global strategic formulation of such an alternative in both the chemical and the energy fi elds, with progressive implementations, albeit with different approaches, throughout the planet. Within the specifi c context of this chapter, renewable resources represent the obvious answer to the quest for macromolecular materials capable of replacing their fossil based counterparts [2, 3] . This is not as original as it sounds, because, apart from the role of natural polymers throughout our history evoked above, the very fi rst synthetic polymer commodities, developed during the second half of the nineteenth century, namely cellulose esters, vulcanized natural rubber, rosin derivatives, terpene ‘ resins ’ , were all derived from renewable resources. What is new and particularly promising, has to do with the growing momentum that this 1
Gold Nanoparticles as Radiosensitizers in Cancer Radiotherapy

Abstract The rapid development of nanotechnology offers a variety of potential therapeutic strategies for cancer treatment. High atomic element nanomaterials are often utilized as radiosensitizers due to their unique photoelectric decay characteristics. Among them, gold nanoparticles (GNPs) are one of the most widely investigated and are considered to be an ideal radiosensitizers for radiotherapy due to their high X-ray absorption and unique physicochemical properties. Over the last few decades, multi-disciplinary studies have focused on the design and optimization of GNPs to achieve greater dosing capability and higher therapeutic effects and highlight potential mechanisms for radiosensitization of GNPs. Although the radiosensitizing potential of GNPs has been widely recognized, its clinical translation still faces many challenges. This review analyses the different roles of GNPs as radiosensitizers in cancer radiotherapy and summarizes recent advances. In addition, the underlying mechanisms of GNP radiosensitization, including physical, chemical and biological mechanisms are discussed, which may provide new directions for the optimization and clinical transformation of next-generation GNPs.
A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction

Unsupervised Bilingual Dictionary Induction methods based on the initialization and the self-learning have achieved great success in similar language pairs, e.g., English-Spanish. But they still fail and have an accuracy of 0% in many distant language pairs, e.g., English-Japanese. In this work, we show that this failure results from the gap between the actual initialization performance and the minimum initialization performance for the self-learning to succeed. We propose Iterative Dimension Reduction to bridge this gap. Our experiments show that this simple method does not hamper the performance of similar language pairs and achieves an accuracy of 13.64 55.53% between English and four distant languages, i.e., Chinese, Japanese, Vietnamese and Thai.
Unobservable causal loops explain both the quantum computational speedup and quantum nonlocality.

We consider the reversible processes between two one-to-one correlated measurement outcomes common to both the quantum computational speedup and quantum nonlocality. The two outcomes are the setting and the solution of the problem and respectively the outcomes of measuring in the same basis two maximally entangled observables in a state of spatial separation of the respective subsystems. Of course the ordinary quantum description of a reversible process leaves the direction of causality unspecified. We show that for this reason it is incomplete. It allows causal structures that ensure the correlation between the two measurement outcomes that violate the time symmetry required from the description of a reversible process. This also says that the process description can be completed by time-symmetrizing it. Doing this leaves the description unaltered, as it should be, but shows that it is the quantum superposition of unobservable time-symmetrization instances whose causal structure is completely specified. Both the speedup and nonlocality are explained by the fact that each of these instances hosts causal loops. The speedup is due to the fact that the final measurement of the solution retrocausally tells the problem solver half of the information about the solution she will produce in the future. She can use this knowledge to produce the solution with fewer computation steps (oracle queries). Nonlocality to the fact that the measurement on either subsystem retrocausally and locally changes the state of both subsystems when the two were not spatially separate.
Probabilistic Load Forecasting Based on Adaptive Online Learning

Load forecasting is crucial for multiple energy management tasks such as scheduling generation capacity, planning supply and demand, and minimizing energy trade costs. Such relevance has increased even more in recent years due to the integration of renewable energies, electric cars, and microgrids. Conventional load forecasting techniques obtain single-value load forecasts by exploiting consumption patterns of past load demand. However, such techniques cannot assess intrinsic uncertainties in load demand, and cannot capture dynamic changes in consumption patterns. To address these problems, this paper presents a method for probabilistic load forecasting based on the adaptive online learning of hidden Markov models. We propose learning and forecasting techniques with theoretical guarantees, and experimentally assess their performance in multiple scenarios. In particular, we develop adaptive online learning techniques that update model parameters recursively, and sequential prediction techniques that obtain probabilistic forecasts using the most recent parameters. The performance of the method is evaluated using multiple datasets corresponding with regions that have different sizes and display assorted time-varying consumption patterns. The results show that the proposed method can significantly improve the performance of existing techniques for a wide range of scenarios.
Single Image Super-resolution with a Switch Guided Hybrid Network for Satellite Images

The major drawbacks with Satellite Images are low resolution, Low resolution makes it difficult to identify the objects present in Satellite images. We have experimented with several deep models available for Single Image Superresolution on the SpaceNet dataset and have evaluated the performance of each of them on the satellite image data. We will dive into the recent evolution of the deep models in the context of SISR over the past few years and will present a comparative study between these models. The entire Satellite image of an area is divided into equal-sized patches. Each patch will be used independently for training. These patches will differ in nature. Say, for example, the patches over urban areas have non-homogeneous backgrounds because of different types of objects like vehicles, buildings, roads, etc. On the other hand, patches over jungles will be more homogeneous in nature. Hence, different deep models will fit on different kinds of patches. In this study, we will try to explore this further with the help of a Switching Convolution Network. The idea is to train a switch classifier that will automatically classify a patch into one category of models best suited for it.
Cationic Solid Lipid Nanoparticles of Resveratrol for Hepatocellular Carcinoma Treatment: Systematic Optimization, in vitro Characterization and Preclinical Investigation

Aim The present study focuses on the development and evaluation of the resveratrol (RV)-loaded cationic solid lipid nanoparticles (RV-c-SLNs) for the management of hepatocellular carcinoma (HCC). Materials and Methods Optimization of formulation was performed using factorial design, and further in vitro drug release, cytotoxicity, biodistribution, in vivo preclinical, and biochemical evaluation were carried out. Results The optimized formulation exhibited uniform size, homogeneous disparity, positive zeta potential, and stability over 12-week storage at 25°C/60% RH. The in vitro drug release and cytotoxicity study showed 60% drug release within the first 6 hours and comparatively higher cytotoxicity on HepG2 cell line by resveratrol-solid lipid nanoparticle (RV-SLN) as compared to the RV solution. In addition, an anticancer action and biodistribution study on a rat model of HCC showed significant reduction of tumor volume and higher accumulation in the tumor tissue from RV-c-SLN (P<0.01) over RV solution and RV-SLN. Furthermore, RV-c-SLN showed significant down-regulation in the levels of pro-inflammatory cytokines and balancing of antioxidant enzymes. Histopathological investigation showed reduced occurrence of hepatic nodules, necrosis formation, infiltration of inflammatory cells, blood vessels inflammation, and cell swelling. Conclusion Overall, the obtained results construed that RV-c-SLN with improved antitumor activity as clearly evident from in vitro, in vivo, and biochemical investigations.
A Deductive Verification Framework For Higher Order Programs

In this report, we present the preliminary work developed for our research project for the APDC (\'Area Pr\'atica de Desenvolvimento Curricular) course. The main goal of this project is to develop a framework, on top of the Why3 tool, for the verification of effectful higher-order programs. We use defunctionalization as an intermediate transformation from higher-order OCaml implementations into first order ones. The target for our translation is WhyML, the Why3's programming language. We believe defunctionalization can be an interesting route for the automated verification of higher-order programs, since one can employ off-the-shelf automated program verifiers to prove the correctness of the generated first-order program. This report also serves to introduce the reader to the subject of deductive program verification and some of the tools and concepts used to prove higher order effectful programs.
Overexpression of monocarboxylate transporter 4 promotes the migration and invasion of non-carcinogenic L929 fibroblast cells

Metastasis is a primary contributor to the low survival rates of patients with cancer. Enhanced migration and invasion are two key features of the metastatic transformation of cancer cells. Furthermore, despite the fact that overexpression of the monocarboxylate transporter (MCT)1 and 4 proteins has been found to promote the migration or invasion of cancer cells, previous findings have not been conclusive and have even been contradictory. The majority of these previous studies have relied on the silencing or inhibition of MCT1/4 expression or function in highly metastatic cell lines. Silencing can be transient or incomplete, and inhibition can result in off-target effects. Employing a different approach, the present study stably transfected human MCT1 and MCT4 into the non-carcinogenic murine NCTC clone 929 (L929) cell line, which had undetectable endogenous MCT1 and MCT4 expression. It was observed that overexpression of MCT4, and not MCT1, promoted the migration and invasion of L929 cells. It was also found that overexpression of an inactive form of the MCT4 transporter with a single amino acid mutation failed to promote either migration or invasion, which suggested that MCT4 activity is required. Since an epidermal growth factor receptor (EGFR) inhibitor could reverse the effect of MCT4-overexpression, it was concluded that MCT4-overexpression exert its functions through modulating the EGF/EGFR pathway.
Functional Attributes and Anticancer Potentialities of Chico (Pachycereus Weberi) and Jiotilla (Escontria Chiotilla) Fruits Extract

Mexico has a great diversity of cacti, however, many of their fruits have not been studied in greater depth. Several bioactive compounds available in cacti juices extract have demonstrated nutraceutical properties. Two cactus species are interesting for their biologically active pigments, which are chico (Pachycereus weberi (J. M. Coult.) Backeb)) and jiotilla (Escontria chiotilla (Weber) Rose)). Hence, the goal of this work was to evaluate the bioactive compounds, i.e., betalains, total phenolic, vitamin C, antioxidant, and mineral content in the extract of the above-mentioned P. weberi and E. chiotilla. Then, clarified extracts were evaluated for their antioxidant activity and cytotoxicity (cancer cell lines) potentialities. Based on the obtained results, Chico fruit extract was found to be a good source of vitamin C (27.19 ± 1.95 mg L-Ascorbic acid/100 g fresh sample). Moreover, chico extract resulted in a high concentration of micronutrients, i.e., potassium (517.75 ± 16.78 mg/100 g) and zinc (2.46 ± 0.65 mg/100 g). On the other hand, Jiotilla has a high content of biologically active pigment, i.e., betaxanthins (4.17 ± 0.35 mg/g dry sample). The antioxidant activities of clarified extracts of chico and jiotilla were 80.01 ± 5.10 and 280.88 ± 7.62 mg/100 g fresh sample (DPPH method), respectively. From the cytotoxicity perspective against cancer cell lines, i.e., CaCo-2, MCF-7, HepG2, and PC-3, the clarified extracts of chico showed cytotoxicity (%cell viability) in CaCo-2 (49.7 ± 0.01%) and MCF-7 (45.56 ± 0.05%). A normal fibroblast cell line (NIH/3T3) was used, as a control, for comparison purposes. While jiotilla extract had cytotoxicity against HepG2 (47.31 ± 0.03%) and PC-3 (53.65 ± 0.04%). These results demonstrated that Chico and jiotilla are excellent resources of biologically active constituents with nutraceuticals potentialities.
Predicting the 2¹/₂-year outcome of dysthymic disorder: The roles of childhood adversity and family history of psychopathology.

Follow-up studies of dysthymic disorder (DD) indicate that demographic and clinical variables are not strong predictors of its outcome. The present study extended this literature by examining the relationship between the early home environment and family history of psychopathology and outcome in DD. Eighty-six outpatients with DD were followed up over a 30-month period using structured clinical interviews. A number of measures of childhood adversity and familial psychopathology assessed at baseline predicted outcome, even after controlling for baseline severity and clinical variables. The best predictors included a history of sexual abuse, quality of the patient's relationship with both parents, and higher familial loadings for drug abuse and Cluster A personality disorders. These findings indicated that childhood adversity and familial psychopathology have greater predictive utility for DD than for demographic and clinical variables.
Modeling the Responses of TSM Resonators under Various Loading Conditions

We develop a general model that describes the electrical responses of thickness shear mode resonators subject to a variety of surface loadkgs. The model incorporates a physically diverse set of single component loadings, including rigid solids, viscoelastic media and fluids (Newtonian or Maxwellian). The model allows any number of these components to be combined in any configuration. Such multiple loadings are representative of a variety of physical situations encountered in electrochemical and other liquid phase applications, as well as gas phase applications. In the general case, the response of the composite is not a linear combination of the individual component responses. We discuss application of the model in a qualitative diagnostic fashion, to gain insight into the nature of the interracial structure, and in a quantitative fashion, to extract appropriate physical parameters, such as liquid viscosity and density and polymer shear moduli.
Fast algorithm for blind optimisation of optical systems; statistics and methodology

Adaptive Optics (AO) improves the efficiency of the optical devices in confocal imaging systems by reducing wavefront aberrations. Aberration is caused by imperfections within the system and reduces the optical signal to noise ratio of the resultant images besides deteriorating the quality of the images. An AO system, comprises a wavefront sensor and a deformable mirror (DM), is a straightforward solution to compensate for this distortion. In confocal microscopy, the wavefront sensor can be supplanted with an optimisation algorithm. We have previously implemented the general simulated annealing (SA) algorithm for optimising confocal microscopes. In this paper the modified version of the SA algorithm, fast simulated annealing (FSA) is investigated which takes less than one hundredth of the optimisation time required by the general version.
NMDA Receptor Alterations After Mild Traumatic Brain Injury Induce Deficits in Memory Acquisition and Recall

Mild traumatic brain injury (mTBI) presents a significant health concern with potential persisting deficits that can last decades. Although a growing body of literature improves our understanding of the brain network response and corresponding underlying cellular alterations after injury, the effects of cellular disruptions on local circuitry after mTBI are poorly understood. Our group recently reported how mTBI in neuronal networks affects the functional wiring of neural circuits and how neuronal inactivation influences the synchrony of coupled microcircuits. Here, we utilized a computational neural network model to investigate the circuit-level effects of N-methyl D-aspartate receptor dysfunction. The initial increase in activity in injured neurons spreads to downstream neurons, but this increase was partially reduced by restructuring the network with spike-timing-dependent plasticity. As a model of network-based learning, we also investigated how injury alters pattern acquisition, recall, and maintenance of a conditioned response to stimulus. Although pattern acquisition and maintenance were impaired in injured networks, the greatest deficits arose in recall of previously trained patterns. These results demonstrate how one specific mechanism of cellular-level damage in mTBI affects the overall function of a neural network and point to the importance of reversing cellular-level changes to recover important properties of learning and memory in a microcircuit.
A Novel Neural Model With Lateral Interaction for Learning Tasks

We propose a novel neural model with lateral interaction for learning tasks. The model consists of two functional fields: an elementary field to extract features and a high-level field to store and recognize patterns. Each field is composed of some neurons with lateral interaction, and the neurons in different fields are connected by the rules of synaptic plasticity. The model is established on the current research of cognition and neuroscience, making it more transparent and biologically explainable. Our proposed model is applied to data classification and clustering. The corresponding algorithms share similar processes without requiring any parameter tuning and optimization processes. Numerical experiments validate that the proposed model is feasible in different learning tasks and superior to some state-of-the-art methods, especially in small sample learning, one-shot learning, and clustering.
Ecological Risk of Water Resource Use to the Wellbeing of Macroinvertebrate Communities in the Rivers of KwaZulu-Natal, South Africa

The rivers of KwaZulu-Natal, South Africa, are being impacted by various anthropogenic activities that threaten their sustainability. Our study demonstrated how Bayesian networks could be used to conduct an environmental risk assessment of macroinvertebrate biodiversity and their associated ecosystem to assess the overall effects of these anthropogenic stressors in the rivers. We examined the exposure pathways through various habitats in the study area using a conceptual model that linked the sources of stressors through cause-effect pathways. A Bayesian network was constructed to represent the observed complex interactions and overall risk from water quality, flow and habitat stressors. The model outputs and sensitivity analysis showed ecosystem threat and river health (represented by macroinvertebrate assessment index – MIRAI) could have high ecological risks on macroinvertebrate biodiversity and the ecosystem, respectively. The results of our study demonstrated that Bayesian networks can be used to calculate risk for multiple stressors and that they are a powerful tool for informing future strategies for achieving best management practices and policymaking. Apart from the current scenario, which was developed from field data, we also simulated three other scenarios to predict potential risks to our selected endpoints. We further simulated the low and high risks to the endpoints to demonstrate that the Bayesian network can be an effective adaptive management tool for decision making.
Novel Pre- and Postoperative Care Using Telemedicine

The use of telemedicine and telehealth services has grown exponentially over the past decade and has become increasingly relevant and necessary during the coronavirus 2019 (COVID-19) pandemic. There remains ample opportunity to electronically connect cardiothoracic surgeons with their patients during both preoperative and postoperative visits. In this review, we examine the various implementations of telemedicine within thoracic surgery and explore future applications in this quickly developing field.