Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect Of Tele-Nursing ( Phone-Based Follow-Ups ) On Self-Efficacy , Healthy Lifestyle , And Glycemic Control In Diabetic Patients

Telenursing refers to the use of telecommunications and information technology to provide nursing care at a distance, which may help solve increasing shortages of nurses, reduce both travel distance and time, and keep patients out of the hospital. It is especially useful for diabetes patients who have problems of far distance to the health care clinics or need a long time waiting for visiting a doctor. Aim of the study: To determine the effect of tele-nursing (phone-based follow-ups) on self-efficacy, healthy lifestyle, and glycemic control in type 2 diabetic patients. Design: A quasi experimental design was adopted. Setting: The study was conducted at medical department and endocrinology outpatient clinic of Menoufia University Hospitals. Sample: One hundred patents with type 2 diabetes were enrolled in the study and randomly divided into two groups of experiment and control based on random numbers table. The experimental group received telephone counseling and follow-up intervention for 12 weeks by the researcher and the follow-ups included instructions on self-care and advices to follow their diets, exercise, and insulin titration. Tools: four tools were used for data collection: I) A structured interviewing questionnaire to assess demographic characteristics II) Self-efficacy scale III), Health promotion lifestyle profile scale, IV) physiological measurement of fasting blood sugar, Glycated Hemoglobin, and postprandial glucose. Results: There were statically significant differences between both groups regarding self-efficacy scores as well as the health promotion lifestyle profile after intervention. The mean level of FBS & PPG was significantly lower in the experimental group than the control after intervention (p < 0.05 & P < 0.001) respectively. Conclusion: According to our findings, we can conclude that telenursing was effective to improve selfefficacy, healthy lifestyle and blood glucose level in type 2 diabetic patients.
Real-Time Ionospheric Threat Adaptation Using a Space Weather Prediction for GNSS-Based Aircraft Landing Systems

The use of ground-based augmentation systems (GBASs) is increasing in the national airspace system and also in many nations to support aircraft precision approaches and landing. An anomalous ionospheric event if undetected can cause a potential threat to users of single-frequency-based global navigation satellite system augmentation systems. Current GBAS utilize the pre-defined “worst case” ionospheric threat model in their computation of user position errors to consider all possible ionospheric conditions. This could lead to an excessive availability penalty by adding conservatism on the resulting error bounds. This paper proposes a methodology of real-time ionospheric threat adaptation that adjusts the ionospheric threat model in real time instead of always using the same threat model. This is done by using predicted values of space weather indices for determining the corresponding threat model based on an established relationship between space weather indices and ionospheric threats. Since space weather prediction itself is not reliable due to prediction errors, an uncertainty model was derived from 17 years of historical data. When applied to Category I GBAS in the Conterminous United States, this method lowered the upper bound of the current threat model about 95% of the time during the 17 years (from 1995 to 2011) using the bounded prediction value of the disturbance-storm time index.
Knowledge Discovery in Healthcare databases: feature selection in diabetes classification

Artificial Intelligence may enhance and complement Medical Intelligence to deliver Healthcare of the 21 century. Medical databases accumulate vast amounts of data, holding potential remedies to many diseases. Data mining opens new dimensions and opportunities to the existing statistical approaches in medical domain. A better set of predictors is needed for diagnostics and classification of medical conditions and that is where feature selection become indispensable. The National Health and Nutrition Examination Surveys (NHANES) data was utilised in this research, with demographic data, details of laboratory tests and food components data accessed for knowledge discovery. Diabetes is one of the main causes of disabilities and deaths in the world and one of the disorders where causes are poorly understood. This was the main motivation for exploration of the data from a data scientist perspective. Glucose blood level (serum glucose) was selected as the target feature, as it is the main factor in identification of diabetes. Algorithms with feature selection may work with predictors that were never considered before but could help improve accuracy. "Gbm", "glmnet", "svmRadial" and "nnet" packages were applied for feature selection in this research within the R environment. Comparative analysis of features selected by different algorithms estimated with Receiver Operating Characteristic (ROC), sensitivity, specificity and kappa. Complex analysis revealed important features for predicting glucose level: blood osmolality, blood sodium and blood phosphorus level. Validation of the predictive accuracy using a ROC curve has been done on a test set with accuracy almost 87% with all modelling techniques. Pima Indian Diabetes data has been chosen as a reference against the proposed model, accuracy of 76% attained in comparison with the same methods.
Light and Electron Microscopic Localization of Presenilin-1 in Primate Brain

Several genes have been implicated in the pathogenesis of early-onset familial Alzheimer’s disease. A majority of the autosomal dominant cases are linked to recently identified mutations in the presenilin-1 gene on chromosome 14. The native presenilin-1 protein in primates has not been well characterized, and its precise localization is unknown. We have studied the native presenilin-1 protein in monkey brain and peripheral tissues by using a monoclonal antibody specific for the N-terminal domain of human presenilin-1. Western blots detect polypeptide species of ∼49 and ∼32 kDa from COS-7 and PC12 cells transfected with full-length human presenilin-1 cDNA and from in vitro translations of the normal human presenilin-1 mRNA. A 32 kDa polypeptide is detected in monkey peripheral tissues, with the highest expression in testis and lung. In all brain regions the 32 kDa band is the predominant form of presenilin-1, and it is found in particulate subfractions. Light microscopic immunocytochemistry reveals presenilin-1 staining in all brain regions, with the strongest labeling in neurons and neuropil. In addition, weaker immunoreactivity is also present in glia and blood vessels. Neuronal staining shows significant variability, with particularly intense labeling of certain cell types, including large neocortical and hippocampal pyramidal neurons, magnocellular basal forebrain neurons, brainstem motoneurons, and some populations of interneurons. By electron microscopic immunocytochemistry, highly selective presenilin-1 staining is seen on the cytoplasmic surfaces of membranous organelles, which suggest localization to the endoplasmic reticulum–Golgi intermediate compartment, a subdomain of the endoplasmic reticulum, and some coated transport vesicles.
A novel process variation tolerant wide fan-in dynamic OR gate with reduced contention

Register file structures in modern microprocessors usually employ wide fan-in dynamic CMOS OR gates. Weak keepers have been traditionally used to resolve the low noise margin problem of dynamic CMOS design. Scaling trends and process variation issues in CMOS design have reduced the effectiveness of this weak PMOS keeper. On the other hand large sized PMOS keeper used in wide fan-in dynamic OR gate results in contention between the pull down network (PDN) and the keeper. As a consequence of contention there is an unnecessary increase in power dissipation and loss in performance. In this paper a process variation tolerant wide fan-in dynamic OR gate with a new keeper design is proposed which is capable of reducing the contention between the keeper and PDN and hence capable of reducing the power dissipation and delay. Simulation results at 50 nm shows that the power dissipation and delay have been reduced by 40% and 35% respectively as compared to the wide fan-in dynamic OR gate with conventional keeper under different levels of process variation.
E-Customer Satisfaction in the E-Tailing Industry: An Empirical Survey for Turkish E-Customers

The main objective of this study is to provide theoretical and empirical frameworks for determining drivers of e-customer satisfaction from e-tailers and highlight what is needed to increase e-customer satisfaction level in Turkey. First, literature was reviewed about customer satisfaction to verify the drivers of e-customer satisfaction. Second, an empirical study was conducted to determine the drivers of e-customer satisfaction and measure e-customer satisfaction level from e-stores. In conclusion, the empirical survey’s results of 552 respondents show that e-customer satisfaction is positively influenced from e-shopping cost, e-store design quality, e-store service quality, e-store information quality and cargo carriers’ service quality. E-customers are also strongly satisfied with e-shopping cost, e-store design quality, e-store service quality and cargo carriers’ service quality. On the other hand, apart from choosing cargo carriers, e-customer satisfaction is negatively influenced from e-store shipping policy, and e-customers are strongly dissatisfied from e-stores’ information quality and shipping policies.
Development of a Framework for the Assessment of the Role and Impact of Technology on the Public Procurement Process: an Irish Health Sector Study

This paper is focused on the public sector supply strategy in the Republic of Ireland. There has been considerable change in practices and processes over the past 10 years with the modernisation of the public sector. Programs such as the Strategic Management Initiative, Management Information Framework and more recently the Benchmarking review have brought the processes of the sector into the public forum. Harland’s (2000) conceptual model for public sector supply, offers a framework for analysis at the top level of public procurement policy. It offers an opportunity to assess the drivers and factors that have influenced change in the public sector procurement arena. It will allow for the isolation of the technological factors. For the purpose of this research, the isolation of technological factors is critical in establishing a link between the technology and the actions taken. Harland’s model was constructed for the UK public sector, and in particular, the health sector. It is worth noting at this stage that the model itself has not been tested in the Irish context. This paper will present the initial results of a case study currently underway with a Regional Materials Management Group (RMMG). The Irish health service is in a period of transition. Recent reports on organization and financial management have demanded an even greater focus on value for money and “lean” thinking across the service. The Minister for Health and Children set up the Health Board Executive (HeBE). Its board comprises the chief executives of the seven health boards in Ireland, the Eastern Regional Health Authority and its 3 area health boards. It was established to enable joint working between Health Agencies. The HeBE defines the role of the RMMG clearly. The Regional Materials Management Groups and in particular the Regional Materials Manager is the principal initiator / interpreter of purchasing policy and procedures in the Health Agencies in Ireland. They have responsibility for the central procurement of all goods and services for the regional authority, the development of policy and procedures, as well as Inventory Management and Systems Development. The RMMG plays a significant role in the Procurement Process for the Health Sector. A longitudinal case study is underway with the RMMG. This paper will present initial findings from the case study using Harland’s conceptual model for public sector supply.
Packer identification based on metadata signature

Malware applies lots of obfuscation techniques, which are often automatically generated by the use of packers. This paper presents a packer identification of packed code based on metadata signature, which is a frequency vector of occurrences of classified obfuscation techniques. First, BE-PUM (Binary Emulator for PUshdown Model generation) disassembles and generates the control flow graph of malware in an on-the-fly manner, using concolic testing. Second, obfuscation techniques in the generated control flow graph are detected based on the formal criteria of each obfuscation technique. Last, the used packer is identified with the chisquare test on the metadata signature of a packed code. The precision is evaluated with experiments on 12814 malware from VX heaven and Virusshare, in which 608 examples are detected inconsistent with commercial packer identification at PEiD, CFF Explore, and VirusTotal. We manually confirm that, except for 1 example, BE-PUM is correct. The only case that BE-PUM misunderstands is between MEW and FSG, which are quite similar packers and current BE-PUM extension does not support MEW.
Characteristics of Human Endometrium-Derived Mesenchymal Stem Cells and Their Tropism to Endometriosis

Human endometrial tissue has become an attractive source of mesenchymal stem cells (MSCs) for cell-based therapies because these MSCs can be easily harvested and have tumour tropism as well as reduced immunogenic and inflammatory properties. Our study aimed to obtain and characterise human endometrial mesenchymal stem cells (EMSCs) and assess their endometriosis tropism. EMSCs were successfully isolated from the endometrium of women undergoing laparoscopy for idiopathic infertility. The EMSCs presented a fibroblast-like morphology during culture. Flow cytometry analyses showed that the cells were positive for the specific stem cell markers CD73, CD90, CD105, CD166, and HLA-ABC (major histocompatibility complex class I (MHC I)) but negative for CD14, CD34, CD45, and HLA-DR (MHC II). Reverse transcription polymerase chain reaction results showed that the EMSCs expressed the stem cell marker OCT4. The EMSCs could differentiate into osteocytes, adipocytes, and chondrocytes under certain conditions. The EMSCs had a high tropism to endometriosis without tumourigenicity. This study enhances the possibility of using EMSCs as drug carriers in human cell-based therapies. Meanwhile, future research could also focus on developing targeted therapies for endometriosis.
Evaluation of Volumetric Changes of Augmented Maxillary Sinus With Different Bone Grafting Biomaterials

AbstractExtensive alveolar bone resorption because of pneumatized maxillary sinus is a common problem that limits dental implant placement. Maxillary sinus floor augmentation (MSFA) is an accepted treatment protocol that provides sufficient bone volume. The aim of this study was to evaluate the percentage of graft volume reduction following MSFA using cone beam computed tomography. In this retrospective study, cone beam computed tomography scans of MSFA were measured to evaluate the volume of the grafted sinus with deproteinized bovine bone (DBB), mineralized allograft (MA), or a mixture of MA and demineralized allograft as a composite. The volumetric changes in sinus augmentation between 2 weeks (T-I) and 6 months (T-II) after operation were analyzed. Thirty-nine patients were included in this study. The average percent volume reduction was 8.14 ± 3.76%, 19.38 ± 9.22%, and 24.66 ± 4.68% for DBB, MA, and composite graft, respectively. A significant graft volume reduction was found between T-I and T-II for all groups (P < 0.01). The DBB group showed the least volume reduction (P < 0.01). Biomaterials can influence the bone graft volume change before implant placement. Deproteinized bovine bone may offer greater volume stability during healing than mineralized and composite allografts.
Interaction of multiple factors with a GC‐rich rlement within the mitogen responsive region of the human transferrin receptor gene

Nuclear factors from HeLa cells were isolated by elution of DNA‐cellulose bound proteins with a double stranded synthetic oligonucleotide corresponding to the region from −34 to −79 of the human transferrin receptor (TR) gene promoter. The eluted proteins were further purified and separated from the oligonucleotide by ion exchange chromatography. Proteins within the resulting fraction bound with specificity to the TR promoter. Retardation gel analysis and competition with specific double‐stranded oligonucleotides show that multiple factors present in this fraction compete for binding within the same region of the TR promoter. Footprinting experiments demonstrate that these factors contact a GC‐rich element that is within the region that is required for enhanced expression of the gene in proliferating cells. One of the factors protects an extended DNA sequence but, still contacts the GC‐rich element.
Genome Sequence of a Novel Nicotine-Degrading Strain, Pseudomonas geniculata N1

ABSTRACT A newly isolated bacterium, Pseudomonas geniculata N1, can efficiently degrade nicotine. Here we present a 4.51-Mb assembly of its genome, which is the first sequence of the P. geniculata group. The sequence contains the genes related to nicotine catabolism and may provide insights into its molecular mechanism for N-heterocyclic degradation.
Automatic Extraction of Opt-Out Choices from Privacy Policies

Online “notice and choice” is an essential concept in the US FTC’s Fair Information Practice Principles. Privacy laws based on these principles include requirements for providing notice about data practices and allowing individuals to exercise control over those practices. Internet users need control over privacy, but their options are hidden in long privacy policies which are cumbersome to read and understand. In this paper, we describe several approaches to automatically extract choice instances from privacy policy documents using natu-ral language processing and machine learning techniques. We deﬁne a choice instance as a statement in a privacy policy that indicates the user has discretion over the collection, use, sharing, or retention of their data. We describe machine learning approaches for extracting instances containing opt-out hyper-links and evaluate the proposed methods using the OPP-115 Corpus, a dataset of annotated privacy policies. Extracting information about privacy choices and controls enables the development of concise and usable interfaces to help Internet users better understand the choices offered by online services.
The Impact of Relational Supplier Exchange on Financial Performance: A Study of the Hospital Sector

In the hospital context, when responsiveness is high, relational exchange with suppliers associates with better financial performance. But when a hospital’s responsiveness is low, relational exchange has no effect on performance—the hospital’s inability to adopt “things that are new” and extirpate “things that are old” eclipses the potential financial benefits of long-term, relational exchange with suppliers. Furthermore, the quality orientation of the hospital and supplier uncertainty associate with relational supplier exchange—these effects are not moderated by hospital responsiveness. The net effect is that a quality orientation in a hospital impacts financial performance only when hospital responsiveness is high.
Input action classification in a 3D gesture interface for mobile devices

In this research we propose a new motion classification method to improve operability of a 3D gesture interface that assists text input on mobile devices. A certain range of time-series finger scale data is cropped and is classified using linear discriminant analysis. To confirm possibility of linear separation, data were visualized using principle component analysis. Experimental result with changing cropping ranges and sampling rates showed that the recognition rate improved when the cropped time is longer, and more than 97.9% recognition rates were achieved with cropping time of 0.77s/0.38s from both/one sides of the peak.
Aqueous humor suppression of dendritic cell function helps maintain immune regulation in the eye during human uveitis.

PURPOSE Noninfectious uveitis is characterized by a dysregulated inflammatory or immune response in the eye. It is unclear whether this represents a failure of immune privilege or an overwhelming inflammatory drive that has exceeded the capacity of regulatory mechanisms that are still functioning. The authors investigated immune regulation in the human eye during intraocular inflammation (uveitis) and its impact on dendritic cell (DC) function and subsequent T-cell responses. METHODS Myeloid DCs were isolated from the aqueous humor (AqH) and peripheral blood of patients with active uveitis and characterized by flow cytometry. The effect of uveitis AqH was interrogated in an in vitro model of peripheral blood monocyte-derived DCs from healthy controls. RESULTS Myeloid DCs isolated from uveitic AqH were characterized by elevated major histocompatibility complex classes I and II (MHC I/II), but reduced CD86 compared with matched peripheral blood DCs. Exposure of peripheral blood monocyte-derived DCs from healthy controls to the inflammatory AqH supernatant recapitulated this phenotype. Despite interferon gamma (IFNγ)-dependent upregulation of MHC I, inflammatory AqH was overall suppressive to DC function, with reduced CD86 expression and diminished T-cell responses. This suppressive effect was equal to or greater than that induced by noninflammatory AqH, but was glucocorticoid independent (in contrast to noninflammatory AqH). CONCLUSIONS These data indicate that the ocular microenvironment continues to regulate DC function during uveitis, despite IFNγ-driven upregulation of MHC expression, supporting the hypothesis that immune regulation within the eye is maintained during inflammation.
Fast pipelined pseudo-random number generator in programmable SoC device

In this paper we propose a novel architecture of chaotic pseudo-random number generator (PRNG) based on the pipelined processing and frequency dependent negative resistances (FDNR). The design of PRNG has been optimized to achieve maximum output rate of pseudo-random sequences. The PRNG has been tested for 16-, 32-, 48-, and 64-bit precision of arithmetic by NIST 800-22 tests performed for each individual bit position. Then, the selected bit positions have been composed into the final output stream and verified by NIST test again. The PRNG has been implemented in programmable SoC device from Xilinx. Using the Zynq-7000 chip with 28-nm programmable logic and dual core ARM Cortex-A9 we get the maximum generation rate equal to 11.48 Gbps. An efficiency of the proposed approach in terms of maximum throughput and required logic resources has been compared with other implementations of chaotic PRNGs in programmable devices.