Miracle-dz/UniFAR-FTUs huggingface dataset:
Copy Number Variations in Candidate Genes and Intergenic Regions Affect Body Mass Index and Abdominal Obesity in Mexican Children

Introduction. Increase in body weight is a gradual process that usually begins in childhood and in adolescence as a result of multiple interactions among environmental and genetic factors. This study aimed to analyze the relationship between copy number variants (CNVs) in five genes and four intergenic regions with obesity in Mexican children. Methods. We studied 1423 children aged 6–12 years. Anthropometric measurements and blood levels of biochemical parameters were obtained. Identification of CNVs was performed by real-time PCR. The effect of CNVs on obesity or body composition was assessed using regression models adjusted for age, gender, and family history of obesity. Results. Gains in copy numbers of LEPR and NEGR1 were associated with decreased body mass index (BMI), waist circumference (WC), and risk of abdominal obesity, whereas gain in ARHGEF4 and CPXCR1 and the intergenic regions 12q15c, 15q21.1a, and 22q11.21d and losses in INS were associated with increased BMI and WC. Conclusion. Our results indicate a possible contribution of CNVs in LEPR, NEGR1, ARHGEF4, and CPXCR1 and the intergenic regions 12q15c, 15q21.1a, and 22q11.21d to the development of obesity, particularly abdominal obesity in Mexican children.
Robust GANs against Dishonest Adversaries

Robustness of deep learning models is a property that has recently gained increasing attention. We formally define a notion of robustness for generative adversarial models, and show that, perhaps surprisingly, the GAN in its original form is not robust. Indeed, the discriminator in GANs may be viewed as merely offering "teaching feedback". Our notion of robustness relies on a dishonest discriminator, or noisy, adversarial interference with its feedback. We explore, theoretically and empirically, the effect of model and training properties on this robustness. In particular, we show theoretical conditions for robustness that are supported by empirical evidence. We also test the effect of regularization. Our results suggest variations of GANs that are indeed more robust to noisy attacks, and have overall more stable training behavior.
Hybrid networks based on epoxidized camelina oil

Abstract Lately, renewable resources received great attention in the macromolecular compounds area, regarding the design of the monomers and polymers with different applications. In this study the capacity of several modified vegetable oil-based monomers to build competitive hybrid networks was investigate, taking into account thermal and mechanical behavior of the designed materials. In order to synthesize such competitive nanocomposites, the selected renewable raw material, camelina oil, was employed due to the non-toxicity and biodegradability behavior. General properties of epoxidized camelina oil-based materials were improved by loading of different types of organic-inorganic hybrid compounds – polyhedral oligomeric silsesquioxane (POSS) bearing one (POSS1Ep) or eight (POSS8Ep) epoxy rings on the cages. In order to identify the chemical changes occurring after the thermal curing reactions, FT-IR spectrometry was employed. The new synthesized nanocomposites based on epoxidized camelina oil (ECO) were characterized by dynamic mechanical analyze and thermogravimetric analyze. The morphology of the ECO-based materials was investigate by scanning electron microscopy and supplementary information regarding the presence of the POSS compounds were establish by energy dispersive X-ray analysis and X-ray photoelectron spectroscopy. The smooth materials without any separation phase indicates a well dispersion of the Si–O–Si cages within the organic matrix and the incorporation of this hybrid compounds into the ECO network demonstrates to be a well strategy to improve the thermal and mechanical properties, simultaneously.
SiRCub - Brazilian Agricultural Crop Recognition System.

This paper presents a novel approach to classify agricultural crops using NDVI time series. The novelty lies in i) extracting a set of features from the each and every NDVI curve, and ii) using them to train a crop classification model using a Support Vector Machine (SVM). Specifically, we use the TIMESAT program package to: 1) smooth the time series, 2) decompose them into agricultural seasons? a season is the period between sowing and harvesting? , and 3) extract the features for each season.
Counting the cost of Afrophobia: Post-migration adaptation and mental health challenges of African refugees in South Africa

There are few studies on the role of migration within sub-Saharan Africa and its relation to the development of mental illness. We investigated post-resettlement adaptation and mental health challenges of African refugees/migrants in Durban, South Africa. We interviewed 335 African help-seeking refugees/migrants for anxiety, depression (25-item Hopkins Symptom Checklist) and post-traumatic stress symptoms (30-item Harvard Trauma Questionnaire). Socio-demographic and migration history, focusing on post-migration circumstances and experiences of discrimination in the host country, were obtained. Association between migration and post-settlement factors and mental health outcomes were assessed using adjusted logistic regression models. Prevalence of mental distress was high: 49.4% anxiety, 54.6% depression and 24.9% post-traumatic stress symptoms. After adjustment for family separation since migration, recent arrival in South Africa was associated with increased risk for depression (aOR = 4.0, 95% CI:1.3–11.8) and post-traumatic stress (aOR = 5.2, 95% CI:1.7–15.9), while in unadjusted models, older age on arrival was associated with anxiety (aOR = 5.3, 95% CI:1.4–19.8) and depression (aOR = 6.2, 95% CI:1.6–24.3). History of family separation since migration was independently associated with depression and post-traumatic stress in all models. Discriminatory experiences since migration was also an independent risk factor for all three mental health outcomes. Finally, being divorced/widowed was associated with an increased risk for post-traumatic stress, while higher income earners were protected against post-traumatic symptoms, even after adjustment. Refugees/migrants in South Africa show a significant burden of mental distress that is linked to challenges of adjustment in an often hostile context. Services addressing these and other health-related, social-economic needs should be developed as a priority.
Mapping of Rice Varieties and Sowing Date Using X-Band SAR Data

Rice is a major staple food for nearly half of the world’s population and has a considerable contribution to the global agricultural economy. While spaceborne Synthetic Aperture Radar (SAR) data have proved to have great potential to provide rice cultivation area, few studies have been performed to provide practical information that meets the user requirements. In rice growing regions where the inter-field crop calendar is not uniform such as in the Mekong Delta in Vietnam, knowledge of the start of season on a field basis, along with the planted rice varieties, is very important for correct field management (timing of irrigation, fertilization, chemical treatment, harvest), and for market assessment of the rice production. The objective of this study is to develop methods using SAR data to retrieve in addition to the rice grown area, the sowing date, and the distinction between long and short cycle varieties. This study makes use of X-band SAR data from COSMO-SkyMed acquired from 19 August to 23 November 2013 covering the Chau Thanh and Thoai Son districts in An Giang province, Viet Nam, characterized by a complex cropping pattern. The SAR data have been analyzed as a function of rice parameters, and the temporal and polarization behaviors of the radar backscatter of different rice varieties have been interpreted physically. New backscatter indicators for the detection of rice paddy area, the estimation of the sowing date, and the mapping of the short cycle and long cycle rice varieties have been developed and assessed. Good accuracy has been found with 92% in rice grown area, 96% on rice long or short cycle, and a root mean square error of 4.3 days in sowing date. The results have been discussed regarding the generality of the methods with respect to the rice cultural practices and the SAR data characteristics.
Predictive modeling of emergency cesarean delivery

Objective To increase discriminatory accuracy (DA) for emergency cesarean sections (ECSs). Study design We prospectively collected data on and studied all 6,157 births occurring in 2014 at four public hospitals located in three different autonomous communities of Spain. To identify risk factors (RFs) for ECS, we used likelihood ratios and logistic regression, fitted a classification tree (CTREE), and analyzed a random forest model (RFM). We used the areas under the receiver-operating-characteristic (ROC) curves (AUCs) to assess their DA. Results The magnitude of the LR+ for all putative individual RFs and ORs in the logistic regression models was low to moderate. Except for parity, all putative RFs were positively associated with ECS, including hospital fixed-effects and night-shift delivery. The DA of all logistic models ranged from 0.74 to 0.81. The most relevant RFs (pH, induction, and previous C-section) in the CTREEs showed the highest ORs in the logistic models. The DA of the RFM and its most relevant interaction terms was even higher (AUC = 0.94; 95% CI: 0.93–0.95). Conclusion Putative fetal, maternal, and contextual RFs alone fail to achieve reasonable DA for ECS. It is the combination of these RFs and the interactions between them at each hospital that make it possible to improve the DA for the type of delivery and tailor interventions through prediction to improve the appropriateness of ECS indications.
Emotional discussions reduce memory recall

ABSTRACT People often discuss events they have seen and these discussions can influence later recollections. We investigated the effects of factual, emotional, and free retelling discussion on memory recollections of individuals who have witnessed an event. Participants were shown a video, made an initial individual recall, participated in one of the three retelling conditions (emotional versus factual versus free) or a control condition, and then recalled the event individually again. Participants in the factual and free retelling conditions reported more items not previously recalled than participants in the control condition did, while the emotional condition did not show the same advantage. Participants in all three retelling conditions failed to report more previously recalled items as compared with the control condition. Finally, a memory conformity effect was observed for all three retelling conditions. These findings suggest that eyewitnesses’ discussions may influence the accuracy of subsequent memory reports, especially when these discussions are focused on emotional details and thoughts.
Difference approach to the analysis of resistivity recovery data for irradiated short-range ordered alloys

Electrical resistivity recovery (RR) data for irradiated concentrated alloys typically consist of two inseparable parts, one resulting from defect annihilation and the other from short-range order (SRO) effects. These parts exhibit different behaviour and often follow opposite trends. Therefore, in this case, analysis of RR data within the conventional method is too complicated. A new approach to data analysis of such a two-component RR is proposed. The approach involves a new quantity, the difference RR (DRR), which is composed of RR dependences of two similar samples irradiated to different defect concentrations. It is shown that the SRO formation proper and the stages corresponding to the onset of long-range migration of Frenkel pair defects, formed in each part of RR, can be clearly related to certain features of the DRR plots. This interrelationship allows detecting and identifying these stages in each part of RR separately. The validity of the approach is illustrated by analysis of the available pairwise RR data for Fe–16Cr–20Ni and Fe–4Cr alloys. It makes it possible to detect the small contribution from the SRO formation to RR in Fe–4Cr, which we failed to observe previously. It is shown that stage III of Fe–4Cr, which has a negligible contribution to the part of RR induced by defect annihilation, is clearly observed in the part induced by SRO formation.
MR‐based measurements and simulations of the magnetic field created by a realistic transcranial magnetic stimulation (TMS) coil and stimulator

Transcranial magnetic stimulation (TMS) is an emerging technique that allows non‐invasive neurostimulation. However, the correct validation of electromagnetic models of typical TMS coils and the correct assessment of the incident TMS field (BTMS) produced by standard TMS stimulators are still lacking. Such a validation can be performed by mapping BTMS produced by a realistic TMS setup. In this study, we show that MRI can provide precise quantification of the magnetic field produced by a realistic TMS coil and a clinically used TMS stimulator in the region in which neurostimulation occurs. Measurements of the phase accumulation created by TMS pulses applied during a tailored MR sequence were performed in a phantom. Dedicated hardware was developed to synchronize a typical, clinically used, TMS setup with a 3‐T MR scanner. For comparison purposes, electromagnetic simulations of BTMS were performed. MR‐based measurements allow the mapping and quantification of BTMS starting 2.5 cm from the TMS coil. For closer regions, the intra‐voxel dephasing induced by BTMS prohibits TMS field measurements. For 1% TMS output, the maximum measured value was ~0.1 mT. Simulations reflect quantitatively the experimental data. These measurements can be used to validate electromagnetic models of TMS coils, to guide TMS coil positioning, and for dosimetry and quality assessment of concurrent TMS‐MRI studies without the need for crude methods, such as motor threshold, for stimulation dose determination.
Iwssip 2010 -17th International Conference on Systems, Signals and Image Processing Unsupervised Color Image Segmentation Based on Local Fractal Dimension

— This paper proposes an improved version for the JSEG color image segmentation algorithm, combining the classical JSEG algorithm with a local fractal operator that measures the fractal dimension of each pixel, thus improving the boundary detection. Furthermore, the sensitivity of color variation is enhanced when working with the original color value, instead of quantized color information. Experiments with natural color images of the " The Berkeley Segmentation Dataset and Benchmark " (BSDS) are presented, which show improved results, qualitatively and quantitatively, in comparison with the classical JSEG, the Fractal-only and the Fractal-JSEG methods.
Five Days Granulocyte Colony-Stimulating Factor Treatment Increases Bone Formation and Reduces Gap Size of a Rat Segmental Bone Defect: A Pilot Study

Bone is an organ with high natural regenerative capacity and most fractures heal spontaneously when appropriate fracture fixation is provided. However, additional treatment is required for patients with large segmental defects exceeding the endogenous healing potential and for patients suffering from fracture non-unions. These cases are often associated with insufficient vascularization. Transplantation of CD34+ endothelial progenitor cells (EPCs) has been successfully applied to promote neovascularization of bone defects, however including extensive ex vivo manipulation of cells. Here, we hypothesized, that treatment with granulocyte colony-stimulating factor (G-CSF) may improve bone healing by mobilization of CD34+ progenitor cells into the circulation, which in turn may facilitate vascularization at the defect site. In this pilot study, we aimed to characterize the different cell populations mobilized by G-CSF and investigate the influence of cell mobilization on the healing of a critical size femoral defect in rats. Cell mobilization was investigated by flow cytometry at different time points after five consecutive daily G-CSF injections. In a pilot study, bone healing of a 4.5-mm critical femoral defect in F344 rats was compared between a saline-treated control group and a G-CSF treatment group. In vivo microcomputed tomography and histology were applied to compare bone formation in both treatment groups. Our data revealed that leukocyte counts show a peak increase at the first day after the last G-CSF injection. In addition, we found that CD34+ progenitor cells, including EPCs, were significantly enriched at day 1, and further increased at day 5 and day 11. Upregulation of monocytes, granulocytes and macrophages peaked at day 1. G-CSF treatment significantly increased bone volume and bone density in the defect, which was confirmed by histology. Our data show that different cell populations are mobilized by G-CSF treatment in cell specific patterns. Although in this pilot study no bridging of the critical defect was observed, significantly improved bone formation by G-CSF treatment was clearly shown.
AMCP Partnership Forum: Improving Quality, Value, and Outcomes with Patient-Reported Outcomes

Patient-reported outcomes (PROs), which provide a direct measure of a patient’s health status or treatment preferences, represent a key component of the shift toward patient-centered health care. PROs can measure the state of a patient’s disease-specific and overall health throughout the care continuum, enabling them to have a variety of uses for key health care stakeholders. Currently, PROs are used in drug development, aligning patient and clinician goals in care, quality-of-care measures, and coverage and reimbursement decisions. While there have been significant strides by key health care stakeholders to further the development and use of PROs, there are a number of challenges limiting more widespread use. In light of these current challenges and the potential for PROs to improve health care quality and value, on October 19, 2017, the Academy of Managed Care Pharmacy convened a forum of key stakeholders representing patients, payers, providers, government, and pharmaceutical companies to discuss and identify solutions to the current challenges and barriers to further use of PROs. These discussions informed the development of participants’ ideal future state in which PROs maximize the goals of all health care stakeholders and the actionable steps required to make the future state a reality. While stakeholders shared unique perspectives throughout the forum, they had consensus on 2 overarching issues: the importance of PROs in defining value, improving patient care, and implementing value-based payment models and the need for strong organizational and operational systems to achieve optimal adoption and use. Participants identified several key challenges in PRO use and adoption: achieving a representative patient population, inclusion of PRO data in medication labels, the necessity for both standardized and customizable PROs, and operational and organizational barriers to collecting and analyzing PROs. To overcome these challenges, participants recommended that manufacturers should engage key stakeholders early and throughout the drug development process to ensure the most valid and representative PROs and patient populations will be included. To streamline the PRO collection process, participants suggested engaging pharmacists and other providers who may have more frequent interaction with patients. Participants also recommended that PRO collection and analysis should use common technology platforms, streamline components of clinician care to reduce workflow, and be integrated with claims data to provider payers a better understanding of patient health in real time. Finally, additional work should be done to develop patient-reported outcome measures that contain relevant measures for all healthcare stakeholders. While significant challenges remain in PRO development and adoption, participants agreed that greater use can only be achieved through collaboration and patient-centered care.
The natural compound sulforaphene, as a novel anticancer reagent, targeting PI3K-AKT signaling pathway in lung cancer

Lung cancer is one of the leading causes of cancer death worldwide. Isothiocyanates from cruciferous vegetables been shown to possess anticarcinogenic activities in lung malignances. We previously found sulforaphene (4-methylsufinyl-3-butenyl isothiocyanate, SFE), one new kind of isothiocyanates, existing in a relative high abundance in radish seeds. An efficient methodology based on macroporous resin and preparative high-performance liquid chromatography was developed to isolate SFE in reasonably large quantities, high purity and low cost. However, it is still largely unclear whether SFE could function as an antineoplastic compound, especially in lung cancer. In this study, we systematically investigated the anti-cancer effects of SFE in vitro as well as its possible underling molecular mechanisms in lung cancer. The acute toxicity tests and pharmacokinetics tests for SFE were performed to evaluate its drugability in mice. Also, we evaluated the in vivo anti-cancer effects of SFE using nude Balb/C mice with lung cancer xenograft. SFE can induce apoptosis of multiple lung cancer celllines and, thus, inhibited cancer cell proliferation. Lung cancer cells treated with SFE exhibit significant inhibition of the PI3K-AKT signaling pathway, including depressed PTEN expression and inhibition of AKT phosphoralation. At well-tolerated doses, administration of SFE to mice bearing lung cancer xenografts leads to significant inhibitions of tumor growth. In summary, our work identifies SFE as a novel natural broad-spectrum small molecule inhibitor for lung cancer.
The Multiproduct Newsvendor Problem with Customer Choice

We address the multiproduct newsvendor problem under a general specification of customer choice behavior. We develop a methodology that yields upper bounds on the optimal value as well as feasible inventory solutions. The methodology is based on an approximate Jordan decomposition of the state transition matrix. Two specializations of the methodology are presented: one leads to a decomposition by customer into a sequence of assortment optimization problems and the second leads to a decomposition by product into a collection of independent newsvendor problems. We conduct computational experiments and find that the proposed methodology outperforms existing bounds. The electronic companion is available at https://doi.org/10.1287/opre.2017. 1654.