Miracle-dz/UniFAR-FTUs huggingface dataset:
Stratus OCT Quality Control in Two Multi-Centre Multiple Sclerosis Clinical Trials

The University of California (UC) Davis Reading Center evaluated 19,961 scans from 981 subjects in two multiple sclerosis therapeutic trials with the aim of determining the influence of optical coherence tomography quality control procedures on error rates. There was no optical coherence tomography technician certification in Trial 1, and technicians had very limited monitoring and feedback during the trial in view of the fact that data were received retrospectively. However, technicians were certified in Trial 2 and submitted data in accordance with the protocol. Trial 2 scans had higher signal strengths, fewer errors, and more useable data than the scans in Trial 1. Thus, certified technicians and prompt transmission of data for ongoing quality control monitoring provided higher quality data in multiple sclerosis trials.
Thresholds and drivers of coral calcification responses to climate change

Increased temperature and CO2 levels are considered key drivers of coral reef degradation. However, individual assessments of ecological responses (calcification) to these stressors are often contradicting. To detect underlying drivers of heterogeneity in coral calcification responses, we developed a procedure for the inclusion of stress–effect relationships in ecological meta‐analyses. We applied this technique to a dataset of 294 empirical observations from 62 peer‐reviewed publications testing individual and combined effects of elevated temperature and pCO2 on coral calcification. Our results show an additive interaction between warming and acidification, which reduces coral calcification by 20% when pCO2 levels exceed 700 ppm and temperature increases by 3°C. However, stress levels varied among studies and significantly affected outcomes, with unaffected calcification rates under moderate stresses (pCO2 ≤ 700 ppm, ΔT < 3°C). Future coral reef carbon budgets will therefore depend on the magnitude of pCO2 and temperature elevations and, thus, anthropogenic CO2 emissions. Accounting for stress–effect relationships enabled us to identify additional drivers of heterogeneity including coral taxa, life stage, habitat, food availability, climate, and season. These differences can aid reef management identifying refuges and conservation priorities, but without a global effort to reduce CO2 emissions, coral capacity to build reefs will be at risk.
CAIRNS: an ambient tangible interface for shifting energy demand at work

In the past 10 years, interactive systems have been designed to encourage users in using energy when there is plenty of renewable energy. In this paper, we propose to shift the energy demand of laptops in the workplace. Using a tools we developed, we define instructions for helping employees in this task. In order to help employees applying these instructions, we propose to use a physical histogram for visualizing renewable energy forecasts. In result of several user studies, we converge to a circular horizontal physical histogram, interpretable by the user independently of his position around the physical histogram. Finally, we evoke the design and the study of CAIRNS, an ambient shape-changing interface using illuminated extensible disks, for representing this kind of physical histogram.
Resveratrol and ivacaftor are additive G551D CFTR‐channel potentiators: therapeutic implications for cystic fibrosis sinus disease

Mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene result in defective Cl− transport and cause chronic bacterial infections in the upper and lower airways of cystic fibrosis (CF) patients. Ivacaftor is a CFTR potentiator that improves Cl− transport in CF patients with at least 1 copy of the G551D mutation. Resveratrol is also a potent CFTR potentiator that increases determinants of mucociliary transport. The objective of this study is to determine whether resveratrol and ivacaftor improve Cl− secretion in G551D CFTR over either agent alone.
Quantification of Axial Abnormality Due to Cerebellar Ataxia with Inertial Measurements

Cerebellar Ataxia (CA) leads to deficiencies in muscle movement and lack of coordination that is often manifested as gait and balance disabilities. Conventional CA clinical assessments are subjective, cumbersome and provide less insight into the functional capabilities of patients. This cross-sectional study investigates the use of wearable inertial sensors strategically positioned on the front-chest and upper-back locations during the Romberg and Trunk tests for objective assessment of human postural balance due to CA. The primary aim of this paper is to quantify the performance of postural stability of 34 patients diagnosed with CA and 22 healthy subjects as controls. Several forms of entropy descriptions were considered to uncover characteristics of movements intrinsic to CA. Indeed, correlation with clinical observation is vital in ascertaining the validity of the inertial measurements in addition to capturing unique features of movements not typically observed by the practicing clinician. Both of these aspects form an integral part of the underlying objective assessment scheme. Uncertainty in the velocity contained a significant level of information with respect to truncal instability and, based on an extensive clustering and discrimination analysis, fuzzy entropy was identified as an effective measure in characterising the underlying disability. Front-chest measurements demonstrated a strong correlation with clinical assessments while the upper-back measurements performed better in classifying the two cohorts, inferring that the standard clinical assessments are relatively influenced by the frontal observations. The Romberg test was confirmed to be an effective test of neurological diagnosis as well as a potential candidate for objective assessment resulting in a significant correlation with the clinical assessments. In contrast, the Trunk test is observed to be relatively less informative.
ParaNet - Using Dense Blocks for Early Inference

DenseNets have been shown to be a competitive model among recent convolutional network architectures. These networks utilize Dense Blocks, which are groups of densely connected layers where the output of a hidden layer is fed in as the input of every other layer following it. In this paper, we aim to improve certain aspects of DenseNet, especially when it comes to practicality. We introduce ParaNet, a new architecture that constructs three pipelines which allow for early inference. We additionally introduce a cascading mechanism such that different pipelines are able to share parameters, as well as logit matching between the outputs of the pipelines. We separately evaluate each of the newly introduced mechanisms of ParaNet, then evaluate our proposed architecture on CIFAR-100.
Arabidopsis RNA processing factor SERRATE regulates the transcription of intronless genes

Intron splicing increases proteome complexity, promotes RNA stability, and enhances transcription. However, introns and the concomitant need for splicing extend the time required for gene expression and can cause an undesirable delay in the activation of genes. Here, we show that the plant microRNA processing factor SERRATE (SE) plays an unexpected and pivotal role in the regulation of intronless genes. Arabidopsis SE associated with more than 1000, mainly intronless, genes in a transcription-dependent manner. Chromatin-bound SE liaised with paused and elongating polymerase II complexes and promoted their association with intronless target genes. Our results indicate that stress-responsive genes contain no or few introns, which negatively affects their expression strength, but that some genes circumvent this limitation via a novel SE-dependent transcriptional activation mechanism. Transcriptome analysis of a Drosophila mutant defective in ARS2, the metazoan homologue of SE, suggests that SE/ARS2 function in regulating intronless genes might be conserved across kingdoms.
Big Data Privacy Context: Literature Effects On Secure Informational Assets

This article's objective is the identification of research opportunities in the current big data privacy domain, evaluating literature effects on secure informational assets. Until now, no study has analyzed such relation. Its results can foster science, technologies and businesses. To achieve these objectives, a big data privacy Systematic Literature Review (SLR) is performed on the main scientific peer reviewed journals in Scopus database. Bibliometrics and text mining analysis complement the SLR. This study provides support to big data privacy researchers on: most and least researched themes, research novelty, most cited works and authors, themes evolution through time and many others. In addition, TOPSIS and VIKOR ranks were developed to evaluate literature effects versus informational assets indicators. Secure Internet Servers (SIS) was chosen as decision criteria. Results show that big data privacy literature is strongly focused on computational aspects. However, individuals, societies, organizations and governments face a technological change that has just started to be investigated, with growing concerns on law and regulation aspects. TOPSIS and VIKOR Ranks differed in several positions and the only consistent country between literature and SIS adoption is the United States. Countries in the lowest ranking positions represent future research opportunities.
High Electrochemical Performance of Nanotube Structured ZnS as Anode Material for Lithium–Ion Batteries

By using ZnO nanorods as an ideal sacrificial template, one-dimensional (1-D) ZnS nanotubes with a mean diameter of 10 nm were successfully synthesized by hydrothermal method. The phase composition and microstructure of the ZnS nanotubes were characterized by using XRD (X-ray diffraction), SEM (scanning electron micrograph), and TEM (transmission electronic microscopy) analysis. X-ray photoelectron spectroscopy (XPS) and nitrogen sorption isotherms measurements were also used to study the information on the surface chemical compositions and specific surface area of the sample. The prepared ZnS nanotubes were used as anode materials in lithium-ion batteries. Results show that the ZnS nanotubes deliver an impressive prime discharge capacity as high as 950 mAh/g. The ZnS nanotubes also exhibit an enhanced cyclic performance. Even after 100 charge/discharge cycles, the discharge capacity could still remain at 450 mAh/g. Moreover, cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) measurements were also carried out to evaluate the ZnS electrodes.
A Review on Green Synthesis, Biomedical Applications, and Toxicity Studies of ZnO NPs

The advance of reliable and eco-friendly strategies for the development of nanoparticles is a fundamental key to the discipline of nanotechnology. Nanoparticles have been continuously evaluated and have been used in many industrial applications for a decade. In particular, the role of zinc oxide nanoparticles (ZnO NPs) has received a great interest because of various properties such as UV filter properties and photochemical, antifungal, high catalyst, and antimicrobial activities. Because of the high rate of poisonous chemicals and the extreme surroundings used within the chemical and physical methods, the green techniques have been adopted using plants, fungi, bacteria, and algae for the synthesis of nanoparticles. Therefore, this paper considers various green synthesis methods to provide the evidence of ZnO NP role to several applications, and in addition, biomedical applications and toxic effect were reviewed. Therefore, the paper used various secondary sources to collect the relevant review articles. From the findings, the green route of synthesis is rather safe and eco-friendly when compared to physical and chemical means of synthesis. On the other hand, its biomedical applications in this sector are increased day by day in various processes including bioimaging, drug delivery, biosensors, and gene delivery. With respect to its toxicity properties, ZnO NPs can act as smart weapons against multiple drug-resistant microorganisms and as a talented substitute for antibiotics.
Modeling Cell-to-Cell Spread of HIV-1 with Nonlocal Infections

This paper is devoted to develop a nonlocal and time-delayed reaction-diffusion model for HIV infection within host cell-to-cell viral transmissions. In a bounded spatial domain, we study threshold dynamics in terms of basic reproduction number R0 for the heterogeneous model. Our results show that if R0<1, the infection-free steady state is globally attractive, implying infection becomes extinct, while if R0>1, virus will persist in the host environment.
Quality of Secondary Schools Trained Geography Teachers in Universities and Diploma Colleges in Pedagogy

There has been a concern about the quality of secondary schools teachers being prepared and produced by University and Teachers Training Colleges especially in pedagogy and use of instructional resources which is naturally and technically a pedagogical issue. The study was guided by Shulman`s concept of pedagogical content knowledge. A total sample of the study was ten (10) secondary schools, ten (10) Heads of department, fifty (50) Geography teachers and five hundred (500) Geography students. Schools, Geography teachers and Heads/Chairs of departments were purposively selected while form three (3) and form four (4) classes were selected by simple random method. The researcher used a descriptive cross-sectional survey research design involving both qualitative and quantitative methodologies. Data were collected using questionnaires for Geography Teachers and interview schedules for Heads/Chairs of departments, observation schedules, and resources checklist. The major findings were most of University trained Geography teachers were not competent enough in pedagogy and faced difficulties in utilization of educational technology in teaching/ learning process which included: insufficient knowledge of meaningful instructional activities, fewer lessons in the timetable as recommended by the ministry, inadequacy of teaching/learning resources, examination oriented programme, cheating and inadequate teacher professional development in Geography. It was also noted most of TTCS trained teachers had been effectively oriented on the use of instructional resources but followup a mechanism like in-service training was inadequate. Based on research findings, the study recommended that Geography teachers should be in-service in the use of practical approaches and at least have six lessons per week to enable them to cover syllabus on time. It was also recommended that the ministry of education modernize all secondary schools in Kenya by technologizing them to make them tandem with the expectation of modern education. In addition, it was further recommended that most of the Geography departments should have Geography rooms which should be well equipped.
Chimeric Proteins Containing MAP-1 and Functional Domains of C4b-Binding Protein Reveal Strong Complement Inhibitory Capacities

The complement system is a tightly regulated network of proteins involved in defense against pathogens, inflammatory processes, and coordination of the innate and adaptive immune responses. Dysregulation of the complement cascade is associated with many inflammatory disorders. Thus, inhibition of the complement system has emerged as an option for treatment of a range of different inflammatory diseases. MAP-1 is a pattern recognition molecule (PRM)-associated inhibitor of the lectin pathway of the complement system, whereas C4b-binding protein (C4BP) regulates both the classical and lectin pathways. In this study we generated chimeric proteins consisting of MAP-1 and the first five domains of human C4BP (C4BP1−5) in order to develop a targeted inhibitor acting at different levels of the complement cascade. Two different constructs were designed and expressed in CHO cells where MAP-1 was fused with C4BP1−5 in either the C- or N-terminus. The functionality of the chimeric proteins was assessed using different in vitro complement activation assays. Both chimeric proteins displayed the characteristic Ca2+-dependent dimerization and binding to PRMs of native MAP-1, as well as the co-factor activity of native C4BP. In ELISA-based complement activation assays they could effectively inhibit the lectin and classical pathways. Notably, MAP-1:C4BP1−5 was five times more effective than rMAP-1 and rC4BP1−5 applied at the same time, emphasizing the advantage of a single inhibitor containing both functional domains. The MAP-1/C4BP chimeras exert unique complement inhibitory properties and represent a novel therapeutic approach targeting both upstream and central complement activation.
Breast Cancer Detection Using Infrared Thermal Imaging and a Deep Learning Model

Women’s breasts are susceptible to developing cancer; this is supported by a recent study from 2016 showing that 2.8 million women worldwide had already been diagnosed with breast cancer that year. The medical care of a patient with breast cancer is costly and, given the cost and value of the preservation of the health of the citizen, the prevention of breast cancer has become a priority in public health. Over the past 20 years several techniques have been proposed for this purpose, such as mammography, which is frequently used for breast cancer diagnosis. However, false positives of mammography can occur in which the patient is diagnosed positive by another technique. Additionally, the potential side effects of using mammography may encourage patients and physicians to look for other diagnostic techniques. Our review of the literature first explored infrared digital imaging, which assumes that a basic thermal comparison between a healthy breast and a breast with cancer always shows an increase in thermal activity in the precancerous tissues and the areas surrounding developing breast cancer. Furthermore, through our research, we realized that a Computer-Aided Diagnostic (CAD) undertaken through infrared image processing could not be achieved without a model such as the well-known hemispheric model. The novel contribution of this paper is the production of a comparative study of several breast cancer detection techniques using powerful computer vision techniques and deep learning models.
Detecting spamming reviews using long short-term memory recurrent neural network framework

Some unethical companies may hire workers (fake review spammers) to write reviews to influence consumers' purchasing decisions. However, it is not easy for consumers to distinguish real reviews posted by ordinary users or fake reviews post by fake review spammers. In this current study, we attempt to use Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) framework to detect spammers. In the current, we used a real case of fake review in Taiwan, and compared the analytical results of the current study with results of previous literature. We found that the LSTM method was more effective than Support Vector Machine (SVM) for detecting fake reviews. We concluded that deep learning could be use to detect fake reviews.
Conformational Design and Characterisation of a Truncated Diamine Oxidase from Arthrobacter globiformis

A functional mini protein can be developed by miniaturising its size. The minimisation technique provides an excellent model system for studying native enzymes, especially in creating an alternative novel biocatalyst. Miniaturised proteins may have enhanced stability, a crucial characteristic for large-scale production and industrial applications. In this study, a huge enzyme molecule, known as diamine oxidase (DAO, comprising 700 amino acids), was selected to undergo the process. By retaining the arrangement of the original functional sites of DAO in the fourth domain, a mini DAO can be designed via homology modelling. After several downsizing processes, a final configuration of 220 amino acids displayed high binding affinity towards histamine, a short-chain substrate that was catalysed by the parental DAO. The configuration also showed enhanced affinity towards a long-chain substrate known as spermidine. The gene for the designed protein was cloned and expressed in pET102/TOPO vector and overexpressed in E. coli BL21 (DE3). The new mini DAO had similar temperature tolerance and versatile substrates specificity characteristics as its parental protein. An active mini-protein with these characteristics is potentially useful for several applications such as detecting biogenic amines in the biological fluids and the environment that may give rise to health issues.
Advanced 3D Photogrammetric Surface Reconstruction of Extensive Objects by UAV Camera Image Acquisition

This paper proposes a replicable methodology to enhance the accuracy of the photogrammetric reconstruction of large-scale objects based on the optimization of the procedures for Unmanned Aerial Vehicle (UAV) camera image acquisition. The relationships between the acquisition grid shapes, the acquisition grid geometric parameters (pitches, image rates, camera framing, flight heights), and the 3D photogrammetric surface reconstruction accuracy were studied. Ground Sampling Distance (GSD), the necessary number of photos to assure the desired overlapping, and the surface reconstruction accuracy were related to grid shapes, image rate, and camera framing at different flight heights. The established relationships allow to choose the best combination of grid shapes and acquisition grid geometric parameters to obtain the desired accuracy for the required GSD. This outcome was assessed by means of a case study related to the ancient arched brick Bridge of the Saracens in Adrano (Sicily, Italy). The reconstruction of the three-dimensional surfaces of this structure, obtained by the efficient Structure-From-Motion (SfM) algorithms of the commercial software Pix4Mapper, supported the study by validating it with experimental data. A comparison between the surface reconstruction with different acquisition grids at different flight heights and the measurements obtained with a 3D terrestrial laser and total station-theodolites allowed to evaluate the accuracy in terms of Euclidean distances.
Efficacy and Safety of Immunosuppressive Therapy for PBC–AIH Overlap Syndrome Accompanied by Decompensated Cirrhosis: A Real-World Study

Aim To explore the efficacy and safety of immunosuppressive therapy for the treatment of primary biliary cirrhosis-autoimmune hepatitis (PBC-AIH) overlap syndrome accompanied by decompensated cirrhosis. Methods A cohort study was performed to evaluate the usefulness of immunosuppressive therapy in this unique group. This cohort study was performed between October 2013 and June 2017 and included 28 biopsy-proven patients diagnosed according to the Paris criteria. The therapies included ursodeoxycholic acid (UDCA) alone (N=14) or in combination with immunosuppression (IS) therapy (N=14). The primary endpoints were biochemical remission, liver-related adverse events, transplant-free survival, and drug side-effects. Results The frequency of biochemical remission for the AIH features was significantly higher in the UDCA+IS group than in the UDCA-only group (60.0 versus 9.1%, P=0.024) after 12 months of therapy but not after 3 and 6 months (28.6 versus 0%, P=0.165; 35.7 versus 7.1%, P=0.098). The rates of liver-related adverse events were lower in the combined group (2/14 versus 9/14, P=0.018). The Kaplan-Meier estimate showed that the transplant-free survival was distinct between the two groups (P=0.019). In the UDCA+IS group, mild and transient leukopenia occurred in two patients receiving azathioprine (AZA), and an infection was observed in one patient receiving mycophenolate mofetil (MMF). Conclusions PBC-AIH patients with decompensated cirrhosis receiving a combination of UDCA and immunosuppressors presented with higher biochemical remission rates and experienced fewer liver-related adverse events, implying that the combined treatment might be a better therapeutic option for strictly defined decompensated PBC-AIH overlap syndrome.