Miracle-dz/UniFAR-FTUs huggingface dataset:
Diabetes self care activities among adults 20 years and above residing in a resettlement colony in East Delhi

Background: Self-care activities are the cornerstone of diabetes care that ensures patients participation to achieve optimal glycemic control and to prevent complications. Objective: The aim of this study is to find the level of self-care activities among diabetics aged ≥20 years residing in a resettlement colony in East Delhi and its association with sociodemographic factors, disease, and treatment profile. Methods: Using cross-sectional survey, 168 known diabetic patients were selected from Nand Nagri, a resettlement colony in East Delhi. Data were collected using Hindi translation of revised version-Summary of Diabetic Self Care Activities along with a pretested semi-open-ended questionnaire. Self-care was assessed on six parameters as follows: (a) general diet, (b) specific diet, (c) exercise, (d) blood sugar testing, (e) foot-care, and (f) smoking. The study period was from November 2014 to April 2016. Results: Nearly 35.1% of respondents belonged to 60–69 years age group. About 52.4% of respondents were female. Fifty-two diabetics (31%) reported having practised diet control on all 7 days in the past 1 week. Nearly 39.3% of patients did not perform any physical activity. The blood test was not practised by 92.3% of respondents. Foot-care was practised by only 19% of patients. There was a significant association between general diet among diabetics with family support (P = 0.020), place of diagnosis (P = 0.033), and treatment funds (P = 0.017). The exercise score among diabetics who were below the poverty line was higher than those above poverty line (P = 0.029). Younger age (P = 0.005) and treatment with insulin (P = 0.008) were positively associated with blood glucose testing. The foot-care practice was better in patients aware of complications and foot-care practices (P < 0.001). Conclusion: Self-care activities among diabetic patients were very poor. Self-management educational programs at hospitals along with information, education, and communication activities at the community level and one-to-one counseling are recommended.
Improving Spelling Correction with Consumer Health Terminology

The demand of consumer health informatics (CHI) has grown rapidly due to the increase in Internet usage [1]. Consumers (patients, families, caregivers, and the general public) seek health information online every day. Automatic and semi-automatic consumer NLP systems are developed to reduce the cost for CHI. NLM launched the Consumer Health Information and Question Answering (CHIQA) project to help consumers find reliable health information. A consumer spelling tool (CSpell) was developed for spelling error correction in the NLP pipeline due to the high spelling error rate in consumer questions. The SPECIALIST Lexicon was used as the default dictionary in CSpell because all lexical entries are manually verified by linguists. A systematic approach was developed to retrieve consumer health terminology (CHT) from the UMLS Metathesaurus and MEDLINE. These terms were added to the dictionary in CSpell to improve system performance.
Time-Aware and Corpus-Specific Entity Relatedness

Entity relatedness has emerged as an important feature in a plethora of applications such as information retrieval, entity recommendation and entity linking. Given an entity, for instance a person or an organization, entity relatedness measures can be exploited for generating a list of highly-related entities. However, the relation of an entity to some other entity depends on several factors, with time and context being two of the most important ones (where, in our case, context is determined by a particular corpus). For example, the entities related to the International Monetary Fund are different now compared to some years ago, while these entities also may highly differ in the context of a USA news portal compared to a Greek news portal. In this paper, we propose a simple but flexible model for entity relatedness which considers time and entity aware word embeddings by exploiting the underlying corpus. The proposed model does not require external knowledge and is language independent, which makes it widely useful in a variety of applications.
Extensive nitrification and active ammonia oxidizers in two contrasting coastal systems of the Baltic Sea

Nitrification is important in nitrogen (N) cycling of aquatic environments, but knowledge about its regulation and importance is sparse. Here we examined nitrification and ammonia oxidizers in the Baltic Sea. We investigated two sites with different catchment characteristics (agricultural and forest), the Bay of Gdánsk (south) and the Öre Estuary (north), and measured pelagic nitrification rates and abundance, composition and expression of ammonia monooxygenase (amoA) genes. Highest nitrification rates were found in the nutrient rich Bay of Gdańsk. Interestingly, abundances of ammonia-oxidizing archaea (AOA) and bacteria (AOB) were orders of magnitude lower than reported from other sites. Although AOA were most abundant at both sites, the highest expression levels were from AOB. Interestingly, few AOA and AOB taxa dominated amoA gene expression, with a Nitrosomarinus related phylotype showing widespread expression. AOA and AOB communities differed between sites and depths, respectively, with the composition in rivers being distinct. A storm event, causing an even depth distribution of nitrification and particles in the Bay of Gdańsk, indicated that the presence of particles stimulate nitrification. The study highlights coastal regions as dynamic sites of extensive pelagic nitrification, which may affect local food web dynamics and loss of N mediated by denitrification.
Computed tomography super-resolution using deep convolutional neural network

The objective of this study is to develop a convolutional neural network (CNN) for computed tomography (CT) image super-resolution. The network learns an end-to-end mapping between low (thick-slice thickness) and high (thin-slice thickness) resolution images using the modified U-Net. To verify the proposed method, we train and test the CNN using axially averaged data of existing thin-slice CT images as input and their middle slice as the label. Fifty-two CT studies are used as the CNN training set, and 13 CT studies are used as the test set. We perform five-fold cross-validation to confirm the performance consistency. Because all input and output images are used in two-dimensional slice format, the total number of slices for training the CNN is 7670. We assess the performance of the proposed method with respect to the resolution and contrast, as well as the noise properties. The CNN generates output images that are virtually equivalent to the ground truth. The most remarkable image-recovery improvement by the CNN is deblurring of boundaries of bone structures and air cavities. The CNN output yields an approximately 10% higher peak signal-to-noise ratio and lower normalized root mean square error than the input (thicker slices). The CNN output noise level is lower than the ground truth and equivalent to the iterative image reconstruction result. The proposed deep learning method is useful for both super-resolution and de-noising.
News and consumer card payments

We exploit a unique daily data set on debit card expenditures to study the reaction of consumers to daily news relating to Economic Policy Uncertainty (EPU). Payments with debit cards are a proxy for consumption in the quarterly national accounts. Using big data techniques we construct daily EPU indexes, using either articles from Bloomberg news-wire or tweets from Twitter. Our empirical analysis at high frequency required estimates of daily seasonal components, finding strong patterns both within the week and within the month. Using local projections we find that daily shocks to EPU temporarily reduce debit card purchases, especially during the recent crisis; the main results are confirmed using monthly data and controlling for financial uncertainty and macroeconomic surprises. Furthermore, economic policy uncertainty affects the ratio between ATM withdrawals and debit card purchases, signaling an increase in households' preference for cash.
Development and validation of a new patient-reported outcome measure for peripheral nerve disorders of the hand, the I-HaND© Scale

Following guidelines from the Patient-Centred Outcomes Research Institute and using a mixed methods study, a new patient-reported outcome measure (PROM) for both nerve trauma and compression affecting the hand, the Impact of a Hand Nerve Disorders (I-HaND) Scale, was developed. Face-to-face interviews with 14 patients and subsequent pilot-testing with 61 patients resulted in the development of the 32-item PROM. A longitudinal validation study with 82 patients assessed the psychometric properties of the I-HaND. Content and construct validity was confirmed by cognitive interviews with patients and through principal component analysis. The I-HaND has high internal consistency (α = 0.98) and excellent test–retest reliability (intraclass correlation coefficient = 0.97). Responsiveness statistics showed that the I-HaND can detect change over 3 months and discriminate between improvers and non-improvers. We conclude that the I-HaND can be used as a PROM for people with a range of hand nerve disorders.
Optimization of gelatin–alginate composite bioink printability using rheological parameters: a systematic approach

Three-dimensional bioprinting has emerged as a promising technique in tissue engineering applications through the precise deposition of cells and biomaterials in a layer-by-layer fashion. However, the limited availability of hydrogel bioinks is frequently cited as a major issue for the advancement of cell-based extrusion bioprinting technologies. It is well known that highly viscous materials maintain their structure better, but also have decreased cell viability due to the higher forces which are required for extrusion. However, little is known about the effect of the two distinct components of dynamic modulus of viscoelastic materials, storage modulus (G′) and loss modulus (G″), on the printability of hydrogel-based bioinks. Additionally, ‘printability’ has been poorly defined in the literature, mostly consisting of gross qualitative measures which do not allow for direct comparison of bioinks. This study developed a framework for evaluating printability and investigated the effect of dynamic modulus, including storage modulus (G′), loss modulus (G″), and loss tangent (G″/G′) on the printing outcome. Gelatin and alginate as model hydrogels were mixed at various concentrations to obtain hydrogel formulations with a wide range of storage and loss moduli. These formulations were then evaluated for the quantitatively defined values of extrudability, extrusion uniformity, and structural integrity. For extrudability, increasing either the loss or storage modulus increased the pressure required to extrude the bioink. A mathematical model relating the G′ and G″ to the required extrusion pressure was derived based on the data. A lower loss tangent was correlated with increased structural integrity while a higher loss tangent correlated with increased extrusion uniformity. Gelatin–alginate composite hydrogels with a loss tangent in the range of 0.25–0.45 exhibited an excellent compromise between structural integrity and extrusion uniformity. In addition to the characterization of a common bioink, the methodology introduced in this paper could also be used to evaluate the printability of other bioinks in the future.
Quasi-Monte Carlo Variational Inference

Many machine learning problems involve Monte Carlo gradient estimators. As a prominent example, we focus on Monte Carlo variational inference (MCVI) in this paper. The performance of MCVI crucially depends on the variance of its stochastic gradients. We propose variance reduction by means of Quasi-Monte Carlo (QMC) sampling. QMC replaces N i.i. d. samples from a uniform probability distribution by a deterministic sequence of samples of length N. This sequence covers the underlying random variable space more evenly than i.i. d. draws, reducing the variance of the gradient estimator. With our novel approach, both the score function and the reparameterization gradient estimators lead to much faster convergence. We also propose a new algorithm for Monte Carlo objectives, where we operate with a constant learning rate and increase the number of QMC samples per iteration. We prove that this way, our algorithm can converge asymptotically at a faster rate than SGD. We furthermore provide theoretical guarantees on QMC for Monte Carlo objectives that go beyond MCVI, and support our findings by several experiments on large-scale data sets from various domains.
Xylem anatomical adjustments prioritize hydraulic efficiency over safety as Norway spruce trees grow taller

As a tree grows taller, the increase in gravitational pressure and path length resistance results in lower water potentials at a given flow rate and higher carbon construction costs to transport a given amount of water to the leaves. We investigated how hydraulic safety and efficiency are coordinated under the constraints of higher cavitation risks and higher carbon construction costs with increasing tree height. We combined measurements of xylem tracheid anatomical traits with the vulnerability to drought-induced embolism and hydraulic conductivity of the apical shoots of 2- to 37-m tall Picea abies trees growing at two sites in the Dolomites (Italian Eastern Alps). We found that the theoretical hydraulic conductivity of the apical shoots increased with tree height at both sites (P < 0.001) as a result of an increase in either total tracheid number or mean hydraulic diameter. The xylem water potential inducing 50% loss of apical conductance significantly increased from small (-4.45 ± 0.20 MPa) to tall trees (-3.65 ± 0.03 MPa) (P = 0.007). The more conductive xylem at the treetop of taller trees allows the full compensation for the height-related hydraulic constraints and minimizes the additional carbon costs of transporting water over a longer path length. The corresponding increase in vulnerability to cavitation shows that hydraulic efficiency is prioritized over safety during height growth.
Effective-medium inversion of induced-polarization data for mineral exploration and mineral discrimination: Case study for the copper deposit in Mongolia

This paper develops a novel method of 3D inversion of induced polarization (IP) survey data, based on a generalized effective-medium model of the IP effect (GEMTIP). The electrical parameters of the effective-conductivity model are determined by the intrinsic petrophysical and geometrical characteristics of composite media, such as the mineralization and/or fluid content of rocks and the matrix composition, porosity, anisotropy, and polarizability of formations. The GEMTIP model of multiphase conductive media provides a quantitative tool for evaluation of the type of mineralization, and the volume content of different minerals using electromagnetic (EM) data. The developed method takes into account the nonlinear nature of both electromagnetic induction and IP phenomena and inverts the EM data in the parameters of the GEMTIP model. The goal of the inversion is to determine the electrical conductivity and the intrinsic chargeability distributions, as well as the other parameters of the relaxation model simultaneously. The recovered parameters of the relaxation model can be used for the discrimination of different rocks, and in this way may provide an ability to distinguish between uneconomic mineral deposits and zones of economic mineralization using geophysical remote sensing technology.
Identification of Bronchoalveolar Lavage Components Applying Confocal Laser Endomicroscopy

Background In many studies, confocal laser endomicroscopy (CLE) has proven to be a useful tool in pulmonology; nevertheless, the application in this field is still experimental. By contrast, CLE is almost a standard technique in gastroenterology. The aim of the present study was to demonstrate the identification of bronchoalveolar lavage (BAL) components applying CLE, using a dye. Material/Methods In 21 patients with various underlying diseases a bronchoscopy with BAL was performed. As in routine clinical practice common, BAL fluid (BALF) was analyzed in terms of cytologic, virologic, and microbiologic aspects. To one fraction of BALF, we added acriflavine. After centrifugation CLE was applied and the video sequences were analyzed by an experienced investigator. Results Using CLE, BALF components (such as alveolar macrophages or leucocytes) could be easily identified. A further subdivision of leucocytes (neutrophilic, eosinophilic granulocytes, and lymphocytes) was not possible. Analogous to conventional cytology, a precise distinction of lymphocyte subpopulation (cd 4/cd 8 ratio) was not feasible. In terms of quantification, this is still the application field of flow cytometry and immunohistochemistry. Conclusions Using CLE, alveolar macrophages and leucocytes in stained BALF can be differentiated independent of smoking status. Further studies should be initiated in order to subclassify leucocytes in eosinophilic, neutrophilic granulocytes, and lymphocytes, which is important for routine clinical practice.
Combining ozone with UV and H2O2 for the degradation of micropollutants from different origins: lab-scale analysis and optimization

ABSTRACT The degradation of micropollutants (MPs), including pesticides, herbicides, pharmaceuticals and endocrine disrupting compounds, by ozone-based advanced oxidation techniques (AOP) was investigated in this study. The effect of different factors, such as ozone concentration, hydrogen peroxide concentration and initial pH, on the removal rate was studied in detail. The combination of UV with ozone/ H2O2 increased the MPs degradation. For example, atrazine removal increased from 12.6% to 66.9%. Increasing the concentration of ozone and H2O2 can enhance the degradation efficiency of MPs, while excess H2O2 plays a role as a scavenger for •OH. In addition, the optimizing conditions of degradation of MPs by an ozone-based AOP were investigated in this study. The optimal dosages of ozone for atrazine (ATZ), alachlor (ALA), carbamazepine (CBZ), 17-α-ethinylestradiol (EE2) and pentachlorophenol (PCP), were in the range of 0.6–0.75, while for ATZ a much higher dosage (5.4 mg/l) is needed. The optimal dosages of H2O2 concentration were at 0.75, 0.2, 0.47, 0.75 and 0.63 mM, and pH were at 10, 10, 7, 10 and 10, and reaction time at 38.5, 33.5 43, 6 and 6 min, respectively. Ozone-based AOP and in particular combination of UV with ozone and H2O2 is efficient to degrade atrazine, alachlor, carbamazepine, 17-α-ethinylestradiol and pentachlorophenol, and is attractive for future application of real wastewater treatment. GRAPHICAL ABSTRACT
Chemotherapy Sensitizes Therapy-Resistant Cells to Mild Hyperthermia by Suppressing Heat Shock Protein 27 Expression in Triple-Negative Breast Cancer

Purpose: Triple-negative breast cancer (TNBC) is a clinically aggressive disease with poor prognosis. Conventional chemotherapeutics are generally able to shrink the tumor mass, but often fail to completely eradicate cancer stem–like cells (CSCs) that are responsible for high risk of relapse and frequent metastases. In this study, we examined thermal sensibility of CSCs, developed an approach that enabled concurrent elimination of both the bulk of cancer cells and CSCs, and investigated the underlying mechanism. Experimental Design: We designed a platform consisting of gold nanoparticle-coated porous silicon microparticle (AuPSM) that was also loaded with docetaxel micelles (mDTXs) to enable concurrent killing of the bulk of cancer cells by released mDTX and CSCs by mild hyperthermia upon stimulation of AuPSM with near infrared. In addition, we examined the role of heat shock proteins in sensitizing CSC killing. Finally, we applied mDTX-loaded AuPSM to treat mice with SUM159 and 4T1 orthotopic tumors and evaluated tumor growth and tumor metastasis. Results: MDA-MB-231 and SUM159 TNBC cells treated with mDTX-loaded AuPSM and mild hyperthermia displayed significantly reduced efficiencies in mammosphere formation than those treated with mDTX alone or mild hyperthermia alone. Combination treatment also completely inhibited SUM159 orthotopic tumor growth and 4T1 tumor metastasis. Mechanistically, DTX treatment suppressed expression of heat shock protein 27 in cancer cells including the CSCs, rendering cells sensitive to mild hyperthermia. Conclusions: Our results indicate that chemotherapy sensitizes CSC to mild hyperthermia. We have developed an effective therapeutic approach to eliminate therapy-resistant cells in TNBC. Clin Cancer Res; 24(19); 4900–12. ©2018 AACR.
A Multi-tiered Network with Aerial and Ground Coverage

This paper examines a special type of aerial network with multiple unmanned aerial vehicles (UAVs). It focuses on multiple-hop communication and self-organization among UAVs. Each airborne UAV can communicate directly with other UAVs in the air or with ground stations. We provide a vision for a multi-tiered network consisting of airborne UAVs and traditional networks on the ground. We study how communication and self-organization evolve when network disconnections or outages occur in dynamic or hostile environments. Such network disconnections can occur in the air due to the fast movement of UAVs and/or in the ground due to changes in terrain, disasters, or military missions. We discuss five key challenges in the design: scalability and reliability, network formation, network connectivity, information delivery, and energy management. These challenges include a subset of key issues for this futuristic network structure.
Understanding the Reproducibility of Crowd-reported Security Vulnerabilities

Today’s software systems are increasingly relying on the “power of the crowd” to identify new security vulnerabilities. And yet, it is not well understood how reproducible the crowd-reported vulnerabilities are. In this paper, we perform the ﬁrst empirical analysis on a wide range of real-world security vulnerabilities (368 in total) with the goal of quantifying their reproducibility. Following a carefully controlled workﬂow, we organize a focused group of security analysts to carry out reproduction experiments. With 3600 man-hours spent, we obtain quantitative evidence on the prevalence of missing information in vulnerability reports and the low reproducibility of the vulnerabilities. We ﬁnd that relying on a single vulnerability report from a popular security forum is generally difﬁcult to succeed due to the incomplete information. By widely crowdsourcing the information gathering, security analysts could increase the reproduction success rate, but still face key challenges to trou-bleshoot the non-reproducible cases. To further explore solutions, we surveyed hackers, researchers, and engineers who have extensive domain expertise in software security (N=43). Going beyond Internet-scale crowd-sourcing, we ﬁnd that, security professionals heavily rely on manual debugging and speculative guessing to infer the missed information. Our result suggests that there is not only a necessity to overhaul the way a security forum collects vulnerability reports, but also a need for automated mechanisms to collect information commonly missing in a report.