Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of indinavir on the pharmacodynamic activity of gliclazide in rats and rabbits

The objective of this study is to examine the effect of oral administration of indinavir on blood glucose and investigate the effect of indinavir on the activity of gliclazide in rats (normal and diabetic) and rabbits to evaluate the safety and effectiveness of the combination. Studies in normal and alloxan induced diabetic rats were conducted with oral doses of gliclazide, indinavir and their combination with adequate washout periods in between treatments. Concurrently, studies in normal rabbits were conducted with gliclazide, indinavir and their combination given orally. Blood samples were collected from rats and rabbits and were analysed for blood glucose by GOD/POD method. It was observed that Indinavir alone produced significant hyperglycemia at 1h in rats (normal and diabetic) and rabbits. Indinavir produced hyperglycemia was more in diabetic than normal rats. Gliclazide produced hypoglycaemic/antidiabetic activity in normal and diabetic rats with peak activity at 2 h and 8 h and hypoglycaemic activity in normal rabbits at 3h. In combination, indinavir significantly reduced the effect of gliclazide in rats and rabbits. Thus, it can be concluded that the combination may need dose adjustment and care should be taken when the combination is prescribed for their clinical benefit in diabetic patients.
A Refined Taxonomy Based on Protein Biomarkers for Early-Stage Breast Cancer Management

Breast cancer treatment decision-making has been radically changed by the discovery of biomarkers that enable the prescription of stratified therapies according to the cancer genomic subtype. First, estrogen (ER) and progesterone receptors played an important role in the selection of patients for anti-hormonal therapy. Then, human epidermal growth factor receptor 2 (HER2) was validated as predictor of breast cancer response to anti-HER2 therapies. Now, molecular profiling to classify ER-positive breast cancers in luminal A and B types is carried out routinely and triple negative breast cancers tend to be categorized according to their different outcome. Prognostic and predictive biomarkers of the response to hormone/chemotherapy are also needed to identify patients who will benefit from the currently available adjuvant therapies. Several genomic signatures have been described that should outperform these traditional markers, but few are available on the market and their cost-benefit has not been thoroughly assessed yet. New protein biomarkers that can be more easily tested in clinical laboratories are also available. Particularly, uPA and PAI-1 are the only biomarkers validated with the highest level of evidence and recommended by ASCO. Moreover, IHC4 could be used as a surrogate marker at the place of molecular classifiers for ER-positive HER2-negative breast cancers. This review focuses on new protein biomarkers used to refine early-stage breast cancer taxonomy in view of personalized medicine decision-making.
A Distributed Frequent Itemset Mining Algorithm for Uncertain Data

With the rapidly expansion of big data in all domains, it has become a major research topic to improve the performance of mining frequent patterns in massive uncertain datasets in recent years. Most conventional frequent pattern mining approaches take expect, probability, or weight as one single factor of item support, and algorithms that consider both probability and weight are unable to balance execution efficiency under the circumstances of big data. Therefore, we propose a distributed frequent itemset mining algorithm for uncertain data: Dfimud. Firstly, Dfimud calculates the maximum probability weight value of 1-items and prunes the items whose value is less than the given threshold. Secondly, to reduce the times of scanning the datasets, a distributed Dfimud-tree structure inspired by FP-Tree is designed to mine frequent patterns. Finally, experiments on publicly available UCI datasets demonstrate that Dfimud achieves more optimal results than other related approaches across various metrics. In addition, the empirical study also shows that Dfimud has good scalability.
Medical Therapy for Obstructive Sleep Apnoea

Obstructive Sleep Apnoea (OSA) is increasingly recognised as a condition that not only causes excessive daytime sleepiness, but is also an important cardiovascular risk factor. Treatment of OSA should include conservative measures such as weight loss and positional therapy, the nuances of which are discussed in this article. However this in itself is not sufficient treatment for most patients. The first line of treatment for OSA that is recommended across the entire spectrum of disease is continuous positive airway pressure (CPAP) therapy. Indications for initiating CPAP are discussed, as well as possible alternatives to CPAP such as surgery or dental devices. In initiating CPAP treatment, machine-patient interface needs to be carefully considered, as there is a wide range of masks available. Factors to be considered in the choice of CPAP machine and mode are discussed. Finally, patient-specific factors such as patient acceptance of treatment, common problems encountered in CPAP follow-up and the importance of patient education are addressed.
Comparison of ability requirements for UAS operators

Since the introduction of UAS in the German Bundeswehr, their deployment has steadily grown and therefore the demand for operating personnel. Due to the lack of knowledge about specific demands created by operating UAS, the DLR German Aerospace Center, Department of Aviation and Space Psychology in cooperation with the German Air Force, Center of Aerospace Medicine conducts a study to empirically analyze ability requirements of UAS operators. Aim of the study is the development of specific requirement profiles for UAS operating personnel as well as the identification of possible differences in requirements between unmanned and manned military aviation. Requirement profiles will be differentiated for different types of UAS and different operating positions (pilot vs. sensor operator). Experienced UAS operators as well as pilots of manned aircraft answered a German version of the Fleishman Job Analysis Survey (FJAS). The FJAS consists of 73 scales for assessment of required abilities and skills from cognitive, psychomotor and sensory domains as well as interactive and social domains and was extended for two additional scales developed at DLR. First results suggest that general requirements irrespective of operating position can be deduced such as high mental persistence, high reliability and high selective attention. Also, position specific requirements were identified: For UAS pilots, operational monitoring and problem recognition are central aspects. For sensor operators, demands are higher in abilities like visualization and perceptual speed. Differences between systems will be discussed. The results can contribute valuable information about human factors relevant for selection, training, and stress management of future UAS operators.
Inverse synthetic aperture radar imaging using complex‐value deep neural network

: As compared with traditional ISAR imaging methods, the compressive sensing (CS)-based imaging methods can obtain high-quality images using much less under-sampled data. However, the availability or appropriateness of the sparse representation of the target scene and the relatively low computational efficiency of image reconstruction algorithms limit the performance and application of the CS-based ISAR imaging methods. In recent years, the deep learning technology has been applied in many fields and achieved outstanding performance in image classification, image reconstruction etc. DL implements the tasks using the deep neural network (DNN), which composes multiple hidden layers and non-linear activation layer. In this study, a novel ISAR imaging method that uses a complex-value deep neural network (CV-DNN) to perform the image formation using under-sampled data is proposed. The CV-DNN architecture can extract and exploit the sparse feature of the target image extremely well by multilayer non-linear processing. The experimental results show that the proposed CV-DNN-based ISAR imaging method can provide better shape reconstruction of target with less data than state-of-the-art CS reconstruction algorithms and improve the imaging efficiency obviously.
Power Quality Performance Evaluation of Multilevel Inverter With Reduced Switching Devices and Minimum Standing Voltage

Multilevel inverters (MLIs) have been extensively employed to improve the power quality of the photovoltaic (PV) systems. However, the need for large number of components, higher standing voltage, and high harmonic content in the output of a conventional MLI greatly affects the system efficiency. Asymmetrical MLIs have been therefore developed as a suitable alternative to address these issues. This article aims at developing such a hybrid asymmetrical structure suitable for PV application that has a high level per component ratio and minimum standing voltage. The proposed MLI is assembled using a reduced switch H-bridge-based (RSHB) MLI structure with n asymmetrical repeating units and different level doubling circuit (LDC) combinations. The two dc sources used in the repeating units are in the ratio of 1:n voltage ratio, and using n such units, the proposed MLI structures, i.e., PS1 and PS2 can synthesize 4n + 5 and 4n + 7 levels, respectively, at the output instead of 2n + 3 levels with only RSHB MLI. Comparative analysis reveals that both PS1 and PS2 have fewer switches, low standing voltage, less power loss, and lower cost. A 3.9-kW standalone solar PV system is considered for performance evaluation of the PS1 structure applying both the selective harmonic elimination and carrier-based pulsewidth modulation control schemes. In light of this, dc-link voltage balancing and self-voltage balancing mechanism of the LDC are warranted. Extensive simulation of the proposed MLI is performed in MATLAB/Simulink platform under a change in modulation index, sudden load change, frequency change, and step change in solar insolation. Furthermore, theoretical and simulation findings are validated experimentally by performing similar tests on a prototype of the proposed 17-level MLI.
Factors affecting usability of 3D model learning in a virtual reality environment

ABSTRACT The aim of the present study was to explore the learning usability factors of 3D modeling in virtual reality environment (VRE). Users’ 3D modeling usability factors in VR were investigated through principal component analysis (PCA). Fifty industrial design students participated in the 3D modeling learning in VR experience experiment, and their average age was 23.9 years (SD = 1.6). System Usability Scale (SUS) scores and user experience of the 3D modeling interface of the participants in the VR environment were also assessed. The results show that there are three major usability factors in 3D modeling learning in VR work: interactive quality, dynamic compatibility, and flow effects. In addition, the users’ learning experiences of the 3D modeling in VR are described. The results can be provided to 3D modeling software developers as a key reference in VR learning interface design.
Job Gendering: Occupational Choice and the Marriage Market (PRESENTATIONS OF PAPER)

Existing theories of occupational segregation appeal to family-related responsibilities, employer discrimination, and socialization to explain why women and men occupy such different jobs. We suggest that occupational segregation is perpetuated because women (and men) may be penalized in the marriage market for making nontraditional occupational choices. As an empirical test, we asked students to rate personal ad-like vignettes. Holding all else equal, having a gender-nonconforming occupation reduced the attractiveness of both men and women.
<inline-formula><tex-math notation="LaTeX">$M^3$</tex-math><alternatives><mml:math><mml:msup><mml:mi>M</mml:mi><mml:mn>3</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="xu-ieq1-2950315.gif"/></alternatives></inline-formula>: Multipath Assisted Wi-Fi Localization with a Single Access Point

Owing to the ubiquitous penetration of Wi-Fi in our daily lives, Wi-Fi indoor localization has attracted intensive attentions in the last decade or so. Despite some significant progresses, the high accuracy of existing systems is still achieved at the cost of dense access point (AP) deployment. The more practical single AP localization is largely left as an open problem because the hardware-induced time delay “contaminates” the measurement of signal propagation time in the air. In this article, we design and implement <inline-formula><tex-math notation="LaTeX">$M^3$</tex-math><alternatives><mml:math><mml:msup><mml:mi>M</mml:mi><mml:mn>3</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="xu-ieq2-2950315.gif"/></alternatives></inline-formula> to tackle this challenge with commodity Wi-Fi cards. <inline-formula><tex-math notation="LaTeX">$M^3$</tex-math><alternatives><mml:math><mml:msup><mml:mi>M</mml:mi><mml:mn>3</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="xu-ieq3-2950315.gif"/></alternatives></inline-formula> exploits a <italic>multipath-assisted</italic> approach that turns the harmful multipath from foe to friend to enable single AP localization: a device can be pinpointed through the combination of azimuths and the <italic>relative</italic> time of flight (ToF) of Line-of-Sight (LoS) signal and reflection signals, eliminating the need for multiple APs along with their absolute ToF measurements. <inline-formula><tex-math notation="LaTeX">$M^3$</tex-math><alternatives><mml:math><mml:msup><mml:mi>M</mml:mi><mml:mn>3</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="xu-ieq4-2950315.gif"/></alternatives></inline-formula> further utilizes frequency hopping to combine <italic>multiple channels</italic> to form a virtually wider-spectrum channel for higher ToF resolution. As a prominent feature of <inline-formula><tex-math notation="LaTeX">$M^3$</tex-math><alternatives><mml:math><mml:msup><mml:mi>M</mml:mi><mml:mn>3</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="xu-ieq5-2950315.gif"/></alternatives></inline-formula>, the channels do not need to be adjacent. Comprehensive experiments demonstrate that <inline-formula><tex-math notation="LaTeX">$M^3$</tex-math><alternatives><mml:math><mml:msup><mml:mi>M</mml:mi><mml:mn>3</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="xu-ieq6-2950315.gif"/></alternatives></inline-formula> outperforms the state-of-the-art systems and achieves a median localization accuracy of 71 cm in three environments with a single AP.
Grid tied PV System using modular multilevel inverter

A grid tied photovoltaic system using modular multilevel inverter topology is proposed in this paper. Basic unit structure of modular multilevel inverter used in this system is capable of converting DC power from PV array to AC power for feeding power to the household loads or utility grid. The proposed modular multilevel inverter structure has lesser power electronic devices compared to the existing multilevel inverter topologies. The proposed system generates a nearly sinusoidal signal and achieves better output profile with low total harmonic distortion. Simulation of the proposed system is carried out in MATLAB/Simulink software and the results are presented.
English to Luganda SMT: Ganda Noun Class Prefix Segmentation for Enriched Machine Translation

Luganda or Ganda is a morphologically rich and low-resource language from Uganda. The morphological richness of Luganda sentences has an impact on the quality of translation and this work looks at improving machine translation (MT) for English to Luganda. Luganda sentence formation bases on 10 noun classes with a prefix for singular and plural. In various aspects, the interaction of these class prefixes in sentences usually enforces different words such as nouns, verbs, adverbs and adjectives to be in agreement with the subject in a given sentence. Such scenarios have resulted in various Luganda word inflectional and derivational tendencies because each noun, verb, adverb or adjective finds itself having to combine with a prefix from a class it belongs and thus creating new word forms. Using 6 statistical machine translation (SMT) models divided equally into base and morphology models, we propose a procedure to segment Luganda sentences from our English-Luganda parallel Bible corpus. Our morphological segmentation approach bases on Ganda Noun Class (GNC) prefixes and we design a tool we call GandaKIT to segment Luganda sentences at pre-processing stage and desegement them after translation at post processing stage. In experiments, we compare translation performance of SMT base models against systems trained with morphological segmentation at pre-processing stage. Our results show an improvement in MT performance over base models ranging by a difference of 1.58 BLEU points and 0.2257 NIST score for our best system.
Expanding Receptive Field YOLO for Small Object Detection

State-of-art object detection networks like YOLO, SSD and Faster R-CNN all have achieved great success in object detection. However, these algorithms have a low performance in small object detection. So, we produce the Expanding receptive field YOLO (ERF-YOLO) to deal with this problem. At first, we propose an efficient block which is called expanding receptive field block (ERF-block) to capture more information in larger areas. Base on YOLOv2, we down-sample the low-level location information by ERF-block, and up-sample feature information by deconvolution. Then we further assemble these two parts together to make the prediction. After training the network on VOC dataset, we have a good result with 82.6% mAP (mean Average Precision) which is 4.0% higher than the original YOLOv2 network. Thanks to the efficient block, it takes 62fps to detect one image when the input size is 416×416, which could keep a real-time speed. In addition, we also evaluate the model on a remote sensing dataset which contains many small targets, and it also shows that ours model has a better performance.
Is Immigration Responsible for the Growth in the Number of Uninsured

This paper examines the contribution of immigration to the increase in the number of uninsured Americans between 1994 and 1998. The study found that non-citizens comprised 20 percent of the total uninsured in the U.S. Recent immigrants and their children who lacked health coverage (2.3 million) constitute only 5 percent of the 44 million uninsured. The number of new immigrants entering the U.S. between 1994 and 1998 actually fell, resulting in a decrease of 100,000 in the number of recent immigrants who are uninsured. Thus while recent immigrants have high rates of being uninsured (46 percent), they are not significant reason for the growth in the number of uninsured. Native citizens contributed most of the growth in the number of uninsured, 2.7 million of the 4.2 million increase (or 64 percent) in the number of uninsured between 1994 and 1998. (Published by the Kaiser Commission on Medicaid and the Uninsured; 2001 February.)
State of the art in tilt-quadrotors, modelling, control and fault recovery

Research studies on quadrotors have recently drawn significant interest from academia and industry. Faults and failures handling are the major weaknesses of conventional quadrotor platforms; therefore, an innovative actuation mechanism was introduced to allow tilting the rotors. Tilting rotors of multirotor platforms provide high dexterity for flying between adjacent obstacles and assist the platforms in dealing with various failure scenarios. This paper reviews the state of the research on tilt-quadrotor platforms. Several platforms, software and hardware architectures, were discussed in the literature. Most of the latest developments were focused on conventional quadrotor modelling, combined with rotor tilting dynamics. On the other hand, controlling such platform was mainly studied using two types of controllers: Feedback Linearisation technique and Control Allocator. Recovery strategy in case of fault or failure has been covered extensively for conventional quadrotors, but very limited known work for tilt-quadrotor. This review concludes that the system dynamic modelling is relatively well covered compared to exploring new control techniques for more stringent requirements. However, recovery strategies as the main advantage of tilt-quadrotor platforms are not explored extensively and require more research attention.
Extrapancreatic actions of incretin-based therapies on bone in diabetes mellitus

Diabetes mellitus is correlated with modifications in bone microarchitectural and mechanical strength, leading to increased bone fragility. The incretin hormones, with a classical effect to increase insulin secretion following food ingestion, are now postulated to have important direct effects on bone. As such, glucose-dependent insulinotropic polypeptide (GIP) has dual actions on bone cells; enhancing bone-forming activity of osteoblasts and suppressing bone resorption by osteoclasts. The sister incretin of GIP, glucagon-like peptide-1 (GLP-1), is also suspected to directly influence bone health in a beneficial manner, although mechanism are less clear at present. The physiological actions of incretins are attenuated by dipeptidyl peptidase (DPP-4) activity and it is speculated that introduction of DPP-4 inhibitor may also positively affect quality of the skeleton. As such, this thesis evaluates the potential beneficial effects of a DPP-4 resistant GIP analogue, namely [D-Ala2]GIP, on osteoblastic-derived, SaOS-2 cells, and also preliminary in vivo studies on the impact of genetic deficiencies of GIPRs and GLP-1Rs on bone mineral density and content. Further studies characterised the beneficial effects of incretin-based therapies on metabolic control, bone microstructure and bone mechanical integrity in animal models of pharmacologically-, genetically- and environmentally-induced diabetes. GIP and related stable analogue increased bone-forming biomarkers in SaOS-2 cells and importantly, [D-Ala2]GIP was shown to be more potent than native GIP. Knockout mouse studies revealed that both GIPR and GLP-1R signaling are important for optimum bone mass. All diabetic mouse models displayed reduced bone mass, altered bone micromorphology and impairment of bone mechanical strength, similar to the human situation, confirming their appropriateness. The incretin-based therapeutics, [D-Ala2]GIP and Liraglutide, in streptozotocin-diabetic significantly increased bone matrix properties, indicating recovery of bone strength at the tissue level. The beneficial effects of administration of [D-Ala2]GIP-oxyntomodulin on bone health in db/db mice were more prominent as the Oxm analogue did not only improve bone strength at tissue level, but also at whole-bone level. These modifications were independent of metabolic status. Twice-daily Exendin-4 therapy improved glycaemic control and increased work required to resist bone fracture in high-fat fed mice. It was also established that Sitagliptin had neutral effects on bone microstructure and mechanical strength in high-fat mice. In xv summary, these data demonstrate the negative impact of diabetes mellitus on normal skeleton development and bone quality. Moreover, this thesis highlights the growing potential of incretin-based therapies for ameliorating bone defects and improving the increased fragility fracture risk associated with diabetes.