Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing fluorescence detection and effective photothermal therapy of near‐infrared polymer nanoparticles using alginate tissue phantoms

Photothermal therapy (PTT) uses light absorbing materials to generate heat for treatment of diseases, like cancer. The advantages of using PTT components that absorb in the near‐infrared (NIR) include reduced tissue auto‐fluorescence and higher penetration depths. However, NIR laser light can still be scattered and absorbed by biological tissues, thus decreasing the amount of the energy reaching the PTT agents. We have developed two distinct formulations of NIR‐absorbing nanoparticles, one which can be utilized for PTT only, and another for both PTT and fluorescence imaging of colorectal cancer. In this work, the fluorescence detection limit and the PTT heating potential of the two nanoparticle types were determined using alginate tissue phantoms. The objective of this study was to determine the PTT efficiency and theranostic potential of the nanoparticles by irradiating 3D collagen tumor spheroids, containing nanoparticles and CT26 mouse colorectal cancer cells, through increasing tissue phantom thicknesses and then quantifying cell death.
High-Quality Genome Sequence of the Root-Knot Nematode Meloidogyne arenaria Genotype A2-O

Root-knot nematodes (Meloidogyne spp.) cause serious damage to many crops globally. We report the high-quality genome sequence of Meloidogyne arenaria genotype A2-O. ABSTRACT Root-knot nematodes (Meloidogyne spp.) cause serious damage to many crops globally. We report the high-quality genome sequence of Meloidogyne arenaria genotype A2-O. The genome assembly of M. arenaria A2-O is composed of 2,224 contigs with an N50 contig length of 204,551 bp and a total assembly length of 284.05 Mb.
Chrna5-Expressing Neurons in the Interpeduncular Nucleus Mediate Aversion Primed by Prior Stimulation or Nicotine Exposure

Genetic studies have shown an association between smoking and variation at the CHRNA5/A3/B4 gene locus encoding the α5, α3, and β4 nicotinic receptor subunits. The α5 receptor has been specifically implicated because smoking-associated haplotypes contain a coding variant in the CHRNA5 gene. The Chrna5/a3/b4 locus is conserved in rodents and the restricted expression of these subunits suggests neural pathways through which the reinforcing and aversive properties of nicotine may be mediated. Here, we show that, in the interpeduncular nucleus (IP), the site of the highest Chrna5 mRNA expression in rodents, electrophysiological responses to nicotinic acetylcholine receptor stimulation are markedly reduced in α5-null mice. IP neurons differ markedly from their upstream ventral medial habenula cholinergic partners, which appear unaltered by loss of α5. To probe the functional role of α5-containing IP neurons, we used BAC recombineering to generate transgenic mice expressing Cre-recombinase from the Chrna5 locus. Reporter expression driven by Chrna5Cre demonstrates that transcription of Chrna5 is regulated independently from the Chrna3/b4 genes transcribed on the opposite strand. Chrna5-expressing IP neurons are GABAergic and project to distant targets in the mesopontine raphe and tegmentum rather than forming local circuits. Optogenetic stimulation of Chrna5-expressing IP neurons failed to elicit physical manifestations of withdrawal. However, after recent prior stimulation or exposure to nicotine, IP stimulation becomes aversive. These results using mice of both sexes support the idea that the risk allele of CHRNA5 may increase the drive to smoke via loss of IP-mediated nicotine aversion. SIGNIFICANCE STATEMENT Understanding the receptors and neural pathways underlying the reinforcing and aversive effects of nicotine may suggest new treatments for tobacco addiction. Part of the individual variability in smoking is associated with specific forms of the α5 nicotinic receptor subunit gene. Here, we show that deletion of the α5 subunit in mice markedly reduces the cellular response to nicotine and acetylcholine in the interpeduncular nucleus (IP). Stimulation of α5-expressing IP neurons using optogenetics is aversive, but this effect requires priming by recent prior stimulation or exposure to nicotine. These results support the idea that the smoking-associated variant of the α5 gene may increase the drive to smoke via loss of IP-mediated nicotine aversion.
Cross-domain analysis of discourse markers in European Portuguese

This paper presents an analysis of discourse markers in two spontaneous speech corpora for European Portuguese - university lectures and map-task dialogues - and also in a collection of tweets, aiming at contributing to their categorization, scarcely existent for European Portuguese. Our results show that the selection of discourse markers is domain and speaker dependent. We also found that the most frequent discourse markers are similar in all three corpora, despite tweets containing discourse markers not found in the other two corpora. In this multidisciplinary study, comprising both a linguistic perspective and a computational approach, discourse markers are also automatically discriminated from other structural metadata events, namely sentence-like units and disfluencies. Our results show that discourse markers and disfluencies tend to co-occur in the dialogue corpus, but have a complementary distribution in the university lectures. We used three acoustic-prosodic feature sets and machine learning to automatically distinguish between discourse markers, disfluencies and sentence-like units. Our in-domain experiments achieved an accuracy of about 87% in university lectures and 84% in dialogues, in line with our previous results. The eGeMAPS features, commonly used for other paralinguistic tasks, achieved a considerable performance on our data, especially considering the small size of the feature set. Our results suggest that turn-initial discourse markers are usually easier to classify than disfluencies, a result also previously reported in the literature. We conducted a cross-domain evaluation in order to evaluate the robustness of the models across domains. The results achieved are about 11%-12% lower, but we conclude that data from one domain can still be used to classify the same events in the other. Overall, despite the complexity of this task, these are very encouraging state-of-the-art results. Ultimately, using exclusively acoustic-prosodic cues, discourse markers can be fairly discriminated from disfluencies and SUs. In order to better understand the contribution of each feature, we have also reported the impact of the features in both the dialogues and the university lectures. Pitch features are the most relevant ones for the distinction between discourse markers and disfluencies, namely pitch slopes. These features are in line with the wide pitch range of discourse markers, in a continuum from a very compressed pitch range to a very wide one, expressed by total deaccented material or H+L* L* contours, with upstep H tones.
An Improved Scalable Synthesis of α- and β-Amyrin

The synthesis of α- and β-amyrin was accomplished starting from easily accessible starting materials, oleanolic, and ursolic acid. The procedures allow the preparation of β-amyrin in an exceptionally short scalable manner via selective iodation and reduction. For α-amyrin, a different synthetic approach had to be chosen providing access to α-amyrin in medium-to-large scale.
SLaTE: A System for Labeling Topics with Entities

In recent years, the Latent Dirichlet allocation (LDA) topic model (Blei, Ng, and Jordan, 2003) has become one of the most employed text mining techniques (Meeks and Weingart 2012) in the digital humanities (DH). Scholars have often noted its potential for text exploration and distant reading analyses, even when it is well known that its results are difficult to interpret (Chang et al, 2009) and to evaluate (Wallach et al, 2009). At last year’s edition of the Digital Humanities conference, we introduced a new corpus exploration method able to produce topics that are easier to interpret and evaluate than standard LDA topic models (Nanni and Ruiz, 2016). We did so by combining two existing techniques, namely Entity linking and Labeled LDA (L-LDA). At its heart, our method first identifies a collection of descriptive labels for the topics of arbitrary documents from a corpus, as provided from the vocabulary of entities found within wide-coverage knowledge resources (e.g., Wikipedia, DBpedia). Then it generates a specific topic for each label. Having a direct relation between topics and labels makes interpretation easier, and using a disambiguated knowledge resource as background knowledge limits label ambiguity. As our topics are described with a limited number of unambiguous labels, they promote interpretability, and this may sustain the use of the results as quantitative evidence in humanities research (Lauscher et al, 2016). The contributions of this poster cover the release of: a) a complete implementation of the processing pipeline for our entity-based LDA approach; b) a three-step evaluation platform that enables its extensive quantitative analysis.
GutenTag: A User-Friendly, Open-Access, Open-Source System for Reproducible Large-Scale Computational Literary Analysis

GutenTag is a cutting-edge resource that allows literary researchers of all levels of technical expertise to perform large-scale computational literary analysis. It allows users to build large, clean, highly customized worksets and then either analyse them in-system or export them as plain text or richly-encoded TEI. It has been built from the ground up by literary scholars for literary scholars: rather than relying on off-the-shelf tools poorly suited to the domain of literature, we have developed many of the components ourselves based on the specific demands of literary research. GutenTag is fully open-source, its analyses are based on entirely open corpora, and researchers can save and distribute all the parameters of their analyses, allowing for unprecedented reproducibility of research in a field plagued by siloed corpora. GutenTag is easy to use, permitting casual non-programmers to perform complex computational literary analysis via an online interface, while offering additional offline customization options to more advanced users. Although GutenTag was initially designed to facilitate our own research in polyvocality and dialogism, we show here that it can be leveraged to intervene in pressing debates unrelated to our specific research, such as the discussion surrounding Matthew Jockers’s analysis of gender in Macroanalysis. Overview of GutenTag
Utilizing state‐of‐the‐art “omics” technology and bioinformatics to identify new biological mechanisms and biomarkers for coronary artery disease

Identification of the four standard modifiable cardiovascular risk factors (SMuRFs)—diabetes mellitus, hyperlipidaemia, hypertension, and cigarette smoking—has allowed the development of risk scores. These have been used in conjunction with primary and secondary prevention strategies targeting SMuRFs to reduce the burden of CAD. Recent studies show that up to 25% of ACS patients do not have any SMuRFs. Thus, SMuRFs do not explain the entire burden of CAD. There appears to be variation at the individual level rendering some individuals relatively susceptible or resilient to developing atherosclerosis. Important disease pathways remain to be discovered, and there is renewed enthusiasm to discover novel biomarkers, biological mechanisms, and therapeutic targets for atherosclerosis. Two broad approaches are being taken: traditional approaches investigating known candidate pathways and unbiased omics approaches. We review recent progress in the field and discuss opportunities made possible by technological and data science advances. Developments in network analytics and machine learning algorithms used in conjunction with large‐scale multi‐omic platforms have the potential to uncover biological networks that may not have been identifiable using traditional approaches. These approaches are useful for both biomedical research and precision medicine strategies.
Evaluation of Meropenem Extended Versus Intermittent Infusion Dosing Protocol in Critically Ill Patients

Extended infusion (EI) administration of β-lactams can improve target attainment in critically ill patients with altered pharmacokinetics/pharmacodynamics. To optimize meropenem dosing in patients with severe sepsis/septic shock, our Antimicrobial Stewardship Program implemented a EI meropenem (EIM) protocol in an 18-bed Medical Intensive Care Unit in March 2014. In this retrospective study, we compared intensive care unit (ICU) mortality and clinical response in patients who received meropenem for ≥72 hours administered per EIM protocol of 1 g over 3 hours every 8 hours versus intermittent infusion (IIM) protocol of 500 mg over 30 minutes every 6 hours. Age, weight, comorbidities, severity of illness, and vasopressor use were comparable between groups (EIM protocol n = 52, IIM protocol n = 96). The IIM protocol group had higher rates of renal dose adjustment at meropenem initiation. Among 56 identified gram-negative (GN) pathogens, 94% had meropenem minimal inhibitory concentration ≤0.25 mg/L. The ICU mortality was lower (19 vs 37%; P = .032) and clinical response was higher (83% vs 46%; P < .01) in the EIM protocol versus IIM protocol group. Total vasopressor days were shorter (2 vs 3 days; P = .038), and white blood cell normalization rate was higher (87% vs 51%; P < .01) in the EIM protocol versus IIM protocol group. There was no difference in days of mechanical ventilation, duration of therapy, and ICU stay. The IIM protocol was also identified as an independent risk factor associated with ICU mortality (hazard ratio: 3.653, 95% confidence interval: 1.689-7.981; P = .001) after adjusting for Sequential Organ Failure Assessment score. In this cohort of patients with severe sepsis/septic shock and highly susceptible GN pathogens, there was improved mortality and clinical response in the EIM protocol group.
Home range variation of two different-sized groups of golden snub-nosed monkeys (Rhinopithecus roxellana) in Shennongjia, China: implications for feeding competition

Knowledge on the home range size of a species or population is important for understanding its behavioral and social ecology and improving the effectiveness of conservation strategies. We studied the home range size of two different-sized groups of golden snub-nosed monkeys (Rhinopithecus roxellana) in Shennongjia, China. The larger group (236 individuals) had a home range of 22.5 km2 from September 2007 to July 2008, whereas the smaller group (62 individuals) occupied a home range of 12.4 km2 from November 2008 to July 2009. Both groups exhibited considerable seasonal variation in their home range size, which was likely due to seasonal changes in food availability and distribution. The home range in any given season (winter, spring, summer, or winter+spring+summer) of the larger group was larger than that of the smaller group. As the two groups were studied in the same area, with the confounding effects of food availability thus minimized, the positive relationship between home range size and group size suggested that scramble feeding competition increased within the larger group.
Deletion of Abca 7 increases cerebral amyloid-β accumulation in the J 20 mouse model of Alzheimer ' s disease

ATP-binding cassette transporter A7 (ABCA7) is expressed in the brain and has been detected in macrophages, microglia, and neurons. ABCA7 promotes efflux of lipids from cells to apolipoproteins and can also regulate phagocytosis and modulate processing of amyloid precursor protein (APP) to generate the Alzheimer's disease (AD) amyloid-β (Aβ) peptide. Genome-wide association studies have indicated that ABCA7 single nucleotide polymorphisms confer increased risk for late-onset AD; however, the role that ABCA7 plays in the brain in the AD context is unknown. In the present study, we crossed ABCA7-deficient (A7-/-) mice with J20 amyloidogenic mice to address this issue. We show that ABCA7 loss doubled insoluble Aβ levels and thioflavine-S-positive plaques in the brain. This was not related to changes in APP processing (assessed by analysis of full-length APP and the APP β C-terminal fragment). Apolipoprotein E regulates cerebral Aβ homeostasis and plaque load; however, the apolipoprotein E concentration was not altered by ABCA7 loss. Spatial reference memory was significantly impaired in both J20 and J20/A7-/mice compared with wild-type mice; however, there were no cognitive differences between J20 and J20/A7-/mice. There were also no major differences detected in hippocampal or plaqueassociated microglial/macrophage markers between J20 and J20/A7-/mice, whereas the capacity for bone marrow-derived macrophages derived from A7-/mice to take up oligomeric Aβ was reduced by 51% compared with wild-type mice. Our results suggest that ABCA7 plays a role in the regulation of Aβ homeostasis in the brain and that this may be related to altered phagocyte function.
Spatial-temporal Variation based Innovation History Visualization: A Case Study of the Liquid Crystal Institute at Kent State University

Innovation began taking root as a term associated with science and industry in the nineteenth century, matching the forward march of the Industrial Revolution, although the language of that period focused more strongly on the invention, particularly technical invention (Green, 2013). Nowadays, innovation is defined simply as a “new idea, device, or method” (Wikipedia). In the Big Data era, innovation history research not only shows the raw data but also demonstrates and reveals the deep relationships of data and how the innovation was generated. This poster mainly describes an innovation history analysis system, integrating and visualizing multisource and isomerism data in a static and dynamic representation manner, considering spatial-temporal factors. A case study of the Liquid Crystal Institute (LCI) at Kent State University is presented. Liquid Crystal Institute (LCI) was founded in 1965 by Glenn H. Brown, a chemistry professor at Kent State University. The birthplace of liquid crystal displays (LCD), the LCI is the world’s first research center focused on the basic and applied science of liquid crystals. The dramatic rise of the liquid crystal display (LCD) industry through the subsequent 40 years has fundamentally changed our modern life. As shown in Figure 1, to research the innovation history of LCI, there are varieties of data sources in the forms of audio, video, digitalized images, text from the website, annual reports from 1965-2013, interviews with key researchers in LCI, booklets from LCI’s 50 year anniversary that covers significant scientists and important events, as well as biennial International Liquid Crystal Conference (ILCC) materials about the largest academic meeting in the field of liquid crystals that was started by the LCI founding director Glenn H. Brown in 1965. At the data processing level, data searching rules related to the publications, grants, and patents are appointed. For text, text mining tools, such as Open CALAIS and Cogito Intelligence API, are used to extract people, locations, and event information from annual reports, booklets etc. After the initial processing of raw data, data are then imported into our system, including publications records, grants, patents, inventions, researchers (such as research staff, post-doctoral fellows, visiting scholars, etc.), special events, spin-offs, and conferences.
STORM without enzymatic oxygen scavenging for correlative atomic force and fluorescence superresolution microscopy

Superresolution microscopy based on localisation is usually performed in a buffer containing enzymatic oxygen scavenger, which facilitates reversible photoswitching of the dye molecules. This makes correlative fluorescence localisation and atomic force microscopy (AFM) challenging, because enzymatic oxygen scavenging interferes with the AFM cantilevers. Here we report on the blinking kinetics of a new red cyanine dye, iFluor-647, which is similar to the Alexa-647 dye commonly used for superresolution microscopy, but with brightness and blinking properties which are superior to Alexa-647 in a buffer without enzymatic oxygen scavenger. We measure the blinking behaviour of iFluor-647 in buffers with and without enzymatic oxygen scavenger with different thiol concentrations. We then apply this dye for correlative localisation and atomic force microscopy in a buffer without enzymatic oxygen scavenger, which allows acquisition of AFM and superresolution images without buffer change.
PLC-based temperature controller applied to a distiller of essential oils by water-steam drag

The size of the world market for essential oils has been constantly growing in the last decade, with an increase of between 10% and 15% of production per year. Among the most used techniques to extract essential oils is distillation by water-steam drag, however to guarantee safety in terms of work accidents, and, in order to homogenize the quality of the extracted oil, there is evidence of the need for these processes to have an automatic temperature controller, which governs the process. Based on the previous problem, the objective of the present project is to develop an architecture for automatic regulation of temperature in processes of extraction of essential oils. As a result of the present project, a prototype temperature controller was implemented and its behavior was evaluated in a laboratory distiller. In the evaluation of the behavior of the temperature controller, a rise time (Tr) of approximately 15 min., settling time (Ts) greater than 40 min., and steady state error (Ess) less than 2 °C were obtained.
Prioritising references for systematic reviews with RobotAnalyst: A user study

Screening references is a time‐consuming step necessary for systematic reviews and guideline development. Previous studies have shown that human effort can be reduced by using machine learning software to prioritise large reference collections such that most of the relevant references are identified before screening is completed. We describe and evaluate RobotAnalyst, a Web‐based software system that combines text‐mining and machine learning algorithms for organising references by their content and actively prioritising them based on a relevancy classification model trained and updated throughout the process. We report an evaluation over 22 reference collections (most are related to public health topics) screened using RobotAnalyst with a total of 43 610 abstract‐level decisions. The number of references that needed to be screened to identify 95% of the abstract‐level inclusions for the evidence review was reduced on 19 of the 22 collections. Significant gains over random sampling were achieved for all reviews conducted with active prioritisation, as compared with only two of five when prioritisation was not used. RobotAnalyst's descriptive clustering and topic modelling functionalities were also evaluated by public health analysts. Descriptive clustering provided more coherent organisation than topic modelling, and the content of the clusters was apparent to the users across a varying number of clusters. This is the first large‐scale study using technology‐assisted screening to perform new reviews, and the positive results provide empirical evidence that RobotAnalyst can accelerate the identification of relevant studies. The results also highlight the issue of user complacency and the need for a stopping criterion to realise the work savings.