Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Quality of Fresh Vegetables Through Salinity Eustress and Biofortification Applications Facilitated by Soilless Cultivation

Closed soilless cultivation systems (SCS) support high productivity and optimized year-round production of standardized quality. Efficiency and precision in modulating nutrient solution composition, in addition to controlling temperature, light, and atmospheric composition, renders protected SCS instrumental for augmenting organoleptic and bioactive components of quality. Effective application of eustress (positive stress), such as moderate salinity or nutritional stress, can elicit tailored plant responses involving the activation of physiological and molecular mechanisms and the strategic accumulation of bioactive compounds necessary for adaptation to suboptimal environments. For instance, it has been demonstrated that the application of salinity eustress increases non-structural carbohydrates and health-promoting phytochemicals such as lycopene, β-carotene, vitamin C, and the overall phenolic content of tomato fruits. Salinity eustress can also reduce the concentration of anti-nutrient compounds such as nitrate due to antagonism between nitrate and chloride for the same anion channel. Furthermore, SCS can be instrumental for the biofortification of vegetables with micronutrients essential or beneficial to human health, such as iodine, iron, selenium, silicon, and zinc. Accurate control of microelement concentrations and constant exposure of roots to the fortified nutrient solution without soil interaction can maximize their uptake, translocation, and accumulation in the edible plant parts; however, biofortification remains highly dependent on microelement forms and concentrations present in the nutrient solution, the time of application and the accumulation capacity of the selected species. The present article provides an updated overview and future perspective on scientific advances in SCS aimed at enhancing the sensory and bioactive value of vegetables.
The Implementation of a Data-Accessing Platform Built from Big Data Warehouse of Electric Loads

With the flourishing of Internet of Things (IoT) technology, ubiquitous power data can be linked to the Internet and be analyzed for real-time monitoring requirement. Numerous power data would be accumulated to even Terabyte level as the time goes. To approach a real-time power monitoring platform on them, an efficient and novel implementation techniques has been developed and formed to be the kernel material of this thesis. The generic power-data source is provided by the so-called smart meters equipped inside factories located in an enterprise practically. The data collection and storage are handled by the Hadoop subsystem and the data ingestion to Hive data warehouse is conducted by the Spark unit. On the aspect of system verification, under single record query, these software modules: Hive, Spark, and Impala had been tested in terms of query-response efficiency. And for the performance exploration on the statistical query function and data ETL processing. The kernel contributions of this research work can be highlighted by two parts: (1) Multi-layer software modules are adopted to design and implement the real-time power-monitoring platform embedded with some excellent characteristics of high efficiency, high feasibility and low cost. (2) The rudimental experiments are conducted to verify the query-response efficiency, and performance evaluations for the proposed real-time power monitoring platform, which reveals the high feasibility for the target research goals.
A software for complete calibration of multicamera systems

We present a software for complete metric calibration of synchronized multicamera setups. The minimum is three cameras, but there is no upper limit. The method is fully automatic and a freely moving bright spot is the only calibration object. No camera pre-calibration is required. The software computes complete set of intrinsic camera parameters including the parameters of non-linear distortion as well as external orientation of the cameras in one common coordinate system. The software is written in Matlab, runs in non-interactive mode and produces both textual and intuitive graphical output. It is very robust and may be run with the same set of parameters in different setups. The software is free.
Author name disambiguation for collaboration network analysis and visualization

In this paper we outline a heuristic algorithm for disambiguating author names of publications via deterministic clustering based on well-defined similarity measures between publications in which their names appear as authors. The algorithm is designed to be used in the construction of a collaboration network, i.e., a graph of author nodes and co-author links. In this context, the goal is to produce a co-authorship graph with network characteristics that are close to those of the “true” collaboration network, so that meaningful network metrics can be determined. The algorithm we present here is fairly easily comprehended as it does not depend on any sophisticated AI techniques. This is important in the context of policy studies, in which we successfully applied it, as it enables policy makers to judge the soundness of the methodology with considerable confidence. It is also quite fast, making it possible to run large-scale analyses (here, in the order of a hundred thousand publications and in the order of a million names to be disambiguated) on a moderately sized desktop computer within a few days. The algorithm is, finally, open to improvement via extensions that take into account additional kinds of fields in bibliographic records of publications to provide evidence that two occurrences of similar names belong to the same individual.
UPLC-MS/MS Based Identification of Dietary Steryl Glucosides by Investigation of Corresponding Free Sterols

Dietary plant foods are characterized by a vast molecular diversity of glycosylated sterols (SG) that differ in the structure of the steryl backbone. The identification of these polar steryl conjugates represents a major challenge as they are structurally highly similar, and commercial standards are limited to a few naturally abundant species. Spectral databases do not yet contain MS/MS spectra of these sterol conjugates obtained by electrospray ionization (ESI), which would facilitate their reliable identification. Thus, this study aimed at providing novel information on ESI-MS/MS spectra of both abundant and minor SG found in foods. As a first step, however, free sterols (FS) were investigated for their fragmentation behavior as they share the same intermediate ion as SG. Pure SG were obtained from commercially available standard mixtures and minor SG were extracted from different food sources (oat bran, wheat bran, pumpkin seeds, melon, rapeseeds, and potato peel). ESI-MS/MS spectra of 15 FS were assessed and fragment ions reflective of structural features were identified and rationalized. Subsequently, 14 SG were identified at four different levels, while relative retention times from chromatographic separation and spectral features of FS served to identify five SG. Spectral data from FS were directly transferable to SG when analyzed as aglycone ions as shown by similarity scores while SG were characterized by shorter retention times in reverse phase chromatography and the additional analysis as sodiated adduct confirmed their glycosidic nature. Moreover, we report for the first time the occurrence of 24-methylenecholesterol and a 4-monomethyl sterol as glycosidic conjugates in higher plants. The presented data will serve as a valuable tool for SG profiling of foods by facilitating their identification.
Electrocardiographic Imaging Using a Spatio-Temporal Basis of Body Surface Potentials—Application to Atrial Ectopic Activity

Electrocardiographic imaging (ECGI) strongly relies on a priori assumptions and additional information to overcome ill-posedness. The major challenge of obtaining good reconstructions consists in finding ways to add information that effectively restricts the solution space without violating properties of the sought solution. In this work, we attempt to address this problem by constructing a spatio-temporal basis of body surface potentials (BSP) from simulations of many focal excitations. Measured BSPs are projected onto this basis and reconstructions are expressed as linear combinations of corresponding transmembrane voltage (TMV) basis vectors. The novel method was applied to simulations of 100 atrial ectopic foci with three different conduction velocities. Three signal-to-noise ratios (SNR) and bases of six different temporal lengths were considered. Reconstruction quality was evaluated using the spatial correlation coefficient of TMVs as well as estimated local activation times (LAT). The focus localization error was assessed by computing the geodesic distance between true and reconstructed foci. Compared with an optimally parameterized Tikhonov-Greensite method, the BSP basis reconstruction increased the mean TMV correlation by up to 22, 24, and 32% for an SNR of 40, 20, and 0 dB, respectively. Mean LAT correlation could be improved by up to 5, 7, and 19% for the three SNRs. For 0 dB, the average localization error could be halved from 15.8 to 7.9 mm. For the largest basis length, the localization error was always below 34 mm. In conclusion, the new method improved reconstructions of atrial ectopic activity especially for low SNRs. Localization of ectopic foci turned out to be more robust and more accurate. Preliminary experiments indicate that the basis generalizes to some extent from the training data and may even be applied for reconstruction of non-ectopic activity.
Prevalence and Risk Factors for Dental Caries among Preschool Children: A Cross-sectional Study in Eastern India

Introduction Dental caries is one of the major widespread health issues that continue to negatively affect the oral health of children globally. Aim To estimate the prevalence of dental caries and its risk factors among preschool children of Bhubaneswar, Odisha, India. Materials and methods The study was a community-based cross-sectional one among preschool children with (N = 425) participants recruited from the Anganwadi centers (AWCs) of Bhubaneswar, Odisha, India. By using a cross-sectional study design, dental caries was assessed using the World Health Organization (WHO) guidelines, and other socioeconomic and risk factors data were collected through parental interview using questionnaires. Caries was identified at both tooth and surface levels through visual dental examinations by trained and calibrated dentists. Logistic regression analyses were used to identify associations among variables and caries. Results The proportion of preschool children suffering from dental caries was found to be 47.29%. The multivariable-adjusted model depicted that longer duration of breastfeeding was a significant predictor as follows: Those who had breastfed for more than 36 months [adjusted odds ratio (AOR): 5.41; 95% confidence interval (CI): 2.97-9.85; p = 0.001], 12 to 24 months (AOR: 2.1; 95% CI: 1.04-4.36; p = 0.037), followed by increase in age 61 to 72 months (AOR: 5.39; 95% CI: 2.72-10.67; p = 0.001), 49 to 60 months (AOR: 2.53; 95% CI: 1.41-4.52; p = 0.002), more than two children in the family (AOR: 2.70; 95% CI: 1.55-4.69; p = 0.001), and children who did not brush the teeth under the parent’s supervision (AOR: 2.70; 95% CI: 1.55-4.69; p = 0.001). Conclusion The study highlights the need to increase awareness about the oral health and hygiene among parents of preschool children in India. How to cite this article: Chugh VK, Sahu KK, Chugh A. Prevalence and Risk Factors for Dental Caries among Preschool Children: A Cross-sectional Study in Eastern India. Int J Clin Pediatr Dent 2018;11(3):238-243.
Increased HSF1 expression predicts shorter disease-specific survival of prostate cancer patients following radical prostatectomy

Prostate cancer is a highly heterogeneous disease and the clinical outcome is varying. While current prognostic tools are regarded insufficient, there is a critical need for markers that would aid prognostication and patient risk-stratification. Heat shock transcription factor 1 (HSF1) is crucial for cellular homeostasis, but also a driver of oncogenesis. The clinical relevance of HSF1 in prostate cancer is, however, unknown. Here, we identified HSF1 as a potential biomarker in mRNA expression datasets on prostate cancer. Clinical validation was performed on tissue microarrays from independent cohorts: one constructed from radical prostatectomies from 478 patients with long term follow-up, and another comprising of regionally advanced to distant metastatic samples. Associations with clinical variables and disease outcomes were investigated. Increased nuclear HSF1 expression correlated with disease advancement and aggressiveness and was, independently from established clinicopathological variables, predictive of both early initiation of secondary therapy and poor disease-specific survival. In a joint model with the clinical Cancer of the Prostate Risk Assessment post-Surgical (CAPRA-S) score, nuclear HSF1 remained a predictive factor of shortened disease-specific survival. The results suggest that nuclear HSF1 expression could serve as a novel prognostic marker for patient risk-stratification on disease progression and survival after radical prostatectomy.
Nanoparticle Conjugation of Human Papillomavirus 16 E7-long Peptides Enhances Therapeutic Vaccine Efficacy against Solid Tumors in Mice

A nanoparticle-based long peptide vaccine activates CD8+ T cells and improves therapeutic efficacy in mouse models of HPV-driven cancer. This strategy may be effective in enhancing responses in other solid tumors, suggesting its potential for clinical development. Treatment of patients bearing human papillomavirus (HPV)-related cancers with synthetic long-peptide (SLP) therapeutic vaccines has shown promising results in clinical trials against premalignant lesions, whereas responses against later stage carcinomas have remained elusive. We show that conjugation of a well-documented HPV-E7 SLP to ultra-small polymeric nanoparticles (NP) enhances the antitumor efficacy of therapeutic vaccination in different mouse models of HPV+ cancers. Immunization of TC-1 tumor-bearing mice with a single dose of NP-conjugated E7LP (NP-E7LP) generated a larger pool of E7-specific CD8+ T cells with increased effector functions than unconjugated free E7LP. At the tumor site, NP-E7LP prompted a robust infiltration of CD8+ T cells that was not accompanied by concomitant accumulation of regulatory T cells (Tregs), resulting in a higher CD8+ T-cell to Treg ratio. Consequently, the amplified immune response elicited by the NP-E7LP formulation led to increased regression of large, well-established tumors, resulting in a significant percentage of complete responses that were not achievable by immunizing with the non-NP–conjugated long-peptide. The partial responses were characterized by distinct phases of regression, stable disease, and relapse to progressive growth, establishing a platform to investigate adaptive resistance mechanisms. The efficacy of NP-E7LP could be further improved by therapeutic activation of the costimulatory receptor 4-1BB. This NP-E7LP formulation illustrates a “solid-phase” antigen delivery strategy that is more effective than a conventional free-peptide (“liquid”) vaccine, further highlighting the potential of using such formulations for therapeutic vaccination against solid tumors. Cancer Immunol Res; 6(11); 1301–13. ©2018 AACR.
Markov Boundary-Based Outlier Mining

It is a grand challenge to identify the outliers existing in subspaces from a high-dimensional data set. A brute-force method is computationally prohibitive since it requires examining an exponential number of subspaces. Current state-of-the-art methods explore various heuristics to significantly prune subspaces, facing the tradeoff between the subspace completeness and search efficiency. In this brief, we discuss a principal type of subspace outliers whose behaviors are different from the others on individual attributes. We formulate such outliers by a novel notion of the Markov boundary-based (MBB) outliers. The central idea is that for each attribute <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> in a data set, we consider only the subspace representing the knowledge needed to predict the behavior on <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula>, which is captured by the MB of <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula>. Then, the outliers whose behavior is different from others on <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> can be detected in the subspace of the MB, and thus, our approach reduces the number of possible subspaces from exponential to linear with respect to dimensionality. Using both synthetic and real data sets, we validate the effectiveness and efficiency of our method.
Mining Rules from Real-Valued Time Series: A Relative Information-Gain-Based Approach

Time series data is collected in almost every industrial field; mining knowledge from it has been attracting extensive attention in the data mining community. In this paper, we focus on temporal association rule mining from real-valued time series. Early work employs symbolization-based methods, but the symbolized representation misses out similarity of the original series, resulting in mining invalid rules. Although state-of-the-art work directly manipulates the original series, it may still find false rules due to the lack of correlation analysis. In our work, we present a hybrid approach combining the idea of direct manipulation and symbolization, which not only preserves the information about the raw data but also realizes the correlation analysis. Specifically, we leverage the similarity-preserving property of motifs, i.e. frequent occurring subsequences in time series, to partially symbolize the raw data. Then, for each rule candidate as a pair of motifs, we propose a rule searching framework to investigate the underlying relationships between them. To evaluate rule candidates, we accommodate the shape similarity by utilizing the relative information gain based on Minimum Description Length principle, and further develop a novel rule interestingness measure R_cos, which generalizes the classical measure cosine for association rules. We perform comprehensive experiments on both artificial and real world datasets, and the results show that the proposed rule searching framework and the rule interestingness measure are effective for mining valid temporal association rules from real-valued time series.
Addressing externalized behavioral concerns in primary care: Listening to the voices of parents

ABSTRACT This article presents the perceptions parents have of the causative and curative factors for their child’s externalized behaviors and what treatment they prefer to receive from their primary care integrated behavioral health team. This is a qualitative study, using interpretative phenomenological analysis. Semi-structured interviews were conducted with a purposive sample of 12 parents representing 14 patients with a disruptive behavior disorder (DBD) who sought care from their primary care physician for treatment of the DBD. Participants spoke of uncertainty of the cause of the DBD and the desire to find parenting approaches that augment the effectiveness of pharmacological intervention provided by the primary care team. Parents’ responses suggest that they are eager for more education about their child’s DBD and how to engage at-home management of the symptoms. Discussion focused on the import of considering the voices of these parents when implementing brief parent management training programs in integrated behavioral health primary care programs.
Stepwise phosphorylation of leukotriene B4 receptor 1 defines cellular responses to leukotriene B4

Sequential phosphorylation of the leukotriene B4 receptor determines specific cellular responses to low and high concentrations of LTB4. Phosphorylation order matters The inflammatory lipid LTB4 stimulates immune cells to migrate toward sites of inflammation and then degranulate. Like other G protein–coupled receptors, the LTB4 receptor BLT1 becomes phosphorylated after ligand-induced activation. Nakanishi et al. identified five residues in the cytoplasmic C-terminal domain of BLT1 that were variably phosphorylated in the absence of LTB4 and two that were phosphorylated upon stimulation with LTB4. Phosphorylation at the LTB4-dependent sites stimulated additional phosphorylation events at the basal phosphorylation sites. The sequence of these phosphorylation events depended on the concentration of LTB4 and was required for chemotaxis and degranulation in response to high concentrations of LTB4. These findings suggest a mechanism whereby increasing concentrations of LTB4 induce distinct responses as cells migrate up an LTB4 gradient. Leukotriene B4 (LTB4) receptor type 1 (BLT1) is abundant in phagocytic and immune cells and plays crucial roles in various inflammatory diseases. BLT1 is phosphorylated at several serine and threonine residues upon stimulation with the inflammatory lipid LTB4. Using Phos-tag gel electrophoresis to separate differentially phosphorylated forms of BLT1, we identified two distinct types of phosphorylation, basal and ligand-induced, in the carboxyl terminus of human BLT1. In the absence of LTB4, the basal phosphorylation sites were modified to various degrees, giving rise to many different phosphorylated forms of BLT1. Different concentrations of LTB4 induced distinct phosphorylation events, and these ligand-induced modifications facilitated additional phosphorylation events at the basal phosphorylation sites. Because neutrophils migrate toward inflammatory sites along a gradient of LTB4, the degree of BLT1 phosphorylation likely increases in parallel with the increase in LTB4 concentration as the cells migrate. At high concentrations of LTB4, deficiencies in these two types of phosphorylation events impaired chemotaxis and β-hexosaminidase release, a proxy for degranulation, in Chinese hamster ovary (CHO-K1) and rat basophilic leukemia (RBL-2H3) cells, respectively. These results suggest that an LTB4 gradient around inflammatory sites enhances BLT1 phosphorylation in a stepwise manner to facilitate the precise migration of phagocytic and immune cells and the initiation of local responses, including degranulation.
Real time evaluation of tissue optical properties during thermal ablation of ex vivo liver tissues

Abstract Complete ablation of liver tumors is vital for minimizing the risk of local tumor recurrence. Accurately identifying the hallmarks of tissue necrosis during thermal ablative therapies may significantly increase the efficacy of ablation, while minimizing unnecessary damage to the surrounding normal tissues or critical structures. Light propagation in biological tissues is sensitive to the tissue microstructure and chromophore concentrations. In our previous studies, we found that the wavelength (λ) averaged liver tissue absorption coefficient (µa) and reduced scattering coefficient (µs′) change significantly upon heating which may be used for assessment of tissue damage during thermal ablation of solid tumors. Here, we seek to demonstrate the use of an integrated fiber-optic probe for continuous monitoring of the local tissue temperature (T), µa(λ) and µs′(λ) during thermal ablation of ex vivo porcine livers. The wavelength-averaged (435–630 nm) tissue absorption and scattering (µa and µs′ ) increased rapidly at 45 °C and plateaued at 67 °C. The mean µa and µs′ for liver tissue at 37 °C (n = 10) were 8.5 ± 3.7 and 2.8 ± 1.1 cm−1, respectively. The relative changes in µa and µs′ at 37, 55, and 65 °C were significantly different (p < .02) from each other. A relationship between the relative changes in µa and µs′ and the degree of tissue damage estimated using the temperature-based Arrhenius model for porcine liver tissues was established and studied.