Miracle-dz/UniFAR-FTUs huggingface dataset:
Mobile Voice Over IP (MVOIP): An Application-level Protocol

Current Voice over Internet Protocol (VOIP) protocols require participating hosts to have fixed IP addresses for the duration of a VOIP call. When using a wireless-enabled host, such as a tablet computer on an 802.11 wireless network, it is possible for a participant in a VOIP call to roam around the network, moving from one subnet to another and needing to change IP addresses. This address change creates the need for mobility support in VOIP applications. We present the design of Mobile Voice over IP (MVOIP), an application-level protocol that enables such mobility in a VOIP application based on the ITU H.323 protocol stack. An MVOIP application uses hints from the surrounding network to determine that it has switched subnets. It then initiates a hand-off procedure that comprises pausing its current calls, obtaining a valid IP address for the current subnet, and reconnecting to the remote party with whom it was in a call. Testing the system shows that on a Windows 2000 platform there is a perceivable delay in the hand-off process, most of which is spent in the Windows API for obtaining DHCP addresses. Despite this bottleneck, MVOIP works well on a wireless network.
A semiparametric nonlinear mixed model approach to phase I profile monitoring

ABSTRACT When process data follow a particular curve in quality control, profile monitoring is suitable and appropriate for assessing process stability. Previous research in profile monitoring focusing on nonlinear parametric (P) modeling, involving both fixed and random-effects, was made under the assumption of an accurate nonlinear model specification. Lately, nonparametric (NP) methods have been used in the profile monitoring context in the absence of an obvious linear P model. This study introduces a novel technique in profile monitoring for any nonlinear and auto-correlated data. Referred to as the nonlinear mixed robust profile monitoring (NMRPM) method, it proposes a semiparametric (SP) approach that combines nonlinear P and NP profile fits for scenarios in which a nonlinear P model is adequate over part of the data but inadequate of the rest. These three methods (P, NP, and NMRPM) account for the auto-correlation within profiles and treats the collection of profiles as a random sample with a common population. During Phase I analysis, a version of Hotelling’s T2 statistic is proposed for each approach to identify abnormal profiles based on the estimated random effects and obtain the corresponding control limits. The performance of the NMRPM method is then evaluated using a real data set. Results reveal that the NMRPM method is robust to model misspecification and performs adequately against a correctly specified nonlinear P model. Control charts with the NMRPM method have excellent capability of detecting changes in Phase I data with control limits that are easily computable.
On the Predictive Power of the In-Training Examination for the Certification of Anesthesiologist Assistants

OBJECTIVES In-training examinations (ITEs) are a popular teaching tool for certification programs. This study examines the relationship between examinees’ performance on the National Commission for Certification of Anesthesiologist Assistants (NCCAA) ITE and the high-stakes NCCAA Certification Examination. METHODS We utilized a mixed-methods approach in our study. Before estimating the models for the predictive validity study, a series of interviews with program directors were conducted to discuss the role of the ITE in students’ education. Multiple linear regression analysis was then used to assess the strength of the relationship between the ITE and Certification Examination scores, while considering the percentage of program examinees completed in their anesthesiologist assistant program between their ITE and Certification Examination attempts. Logistic regression analysis was used to estimate the probability of passing the Certification Examination as a function of ITE score. RESULTS Interviews with program directors confirmed that the ITE provided a valuable testing experience for students and highlighted the areas where students need to focus. Moreover, both the ITE score and the percentage of the program between exams were deemed statistically significant predictors for Certification Examination scores. The logistic regression model indicated that higher scores on the ITE implied a higher probability of passing the Certification Examination. CONCLUSION This research demonstrated the high predictive validity of the ITE examination scores in predicting the Certification Examination outcomes. Together with the proportion of the program covered between exams, the variables explain a significant amount of variability in Certification Examination scores. The ITE feedback helped students assess their preparedness and better focus their studies for the high-stakes certification examination for the profession.
Fully Distributed Low-Complexity Control for Nonlinear Strict-Feedback Multiagent Systems With Unknown Dead-Zone Inputs

In this paper, the distributed control problem of nonlinear strict-feedback multiagent systems is addressed under directed and time-invariant communication graphs. With the utilization of the prescribed performance control methodology, a control algorithm is proposed to ensure predefined bounds of overshoot, convergence rates, and steady-state values of the neighborhood synchronization errors in the presence of unknown dead-zone inputs. The algorithm is fully distributed in the sense that the control input for each agent is independent of any global information of the communication graph and is solely based on local relative output information from its neighborhood set. The approximating structures, e.g., neural networks or fuzzy systems, and the command filters that are typically incorporated to avoid the need for analytical derivatives in the backstepping design are not employed here, resulting in a low-complexity design. Simulation results are included to verify the algorithm.
Presence of the cloud cover and elevation angle of the sun affect measurements of eggshell coloration and patterning obtained from calibrated digital images

Abstract Calibrated digital photography is frequently used in studies focusing on avian eggshell appearance to measure colour and pattern features. Photographs are often taken in natural light conditions, yet little is known to what extent the normalisation process is able to control for varied light. Here, we photographed 36 blown eggs of the Japanese quail Coturnix japonica at five different elevation angles of the sun on both sunny and uniformly overcast days alongside grey standards. We normalised and processed the photographs in the MICA Toolbox software and checked how much noise was introduced by different natural light conditions to the colour and pattern measurements of the same set of eggs. Our results indicate that natural variation of light conditions affects eggshell colour and pattern measurements obtained by means of calibrated digital photography. Depending on a trait, the elevation angle of the sun had similar or even greater effect on the measurement than the presence of the cloud cover. Furthermore, measurements taken in cloudy conditions were more repeatable than those taken in sunny conditions. Based on the results, we propose practical guidelines regarding measuring colour and pattern of eggshells using calibrated digital photography in outdoor conditions.
In vitro Characterization of Polyethyleneimine–Oleic Acid Cationic Micelle as a Novel Protein Carrier

Background: Nanotechnology has introduced valuable carriers for vaccine delivery. The success of vaccination depends on many factors, such as the intact and safe presentation of vaccine candidates to immune cells. We have conjugated branched PEI-2k and oleic acid (OL) as the building block of the cationic micelle. We aimed to introduce a novel carrier for vaccine candidates. Materials and Methods: We conjugated polyethyleneimine and OL (POA) to synthesize the building blocks of cationic micelles. The critical micelle concentration (CMC), size and zeta potential of micelles, and their stability in 60 days were determined. Loading, encapsulation efficiency, and in vitro release study were assessed using bovine serum albumin (BSA) as a protein model. Furthermore, the cytotoxicity and hemocompatibility of developed nanosized micelles were evaluated to ascertain the biocompatibility of fabricated micelles. Cell uptake of cationic micelles in the macrophage cell line was also followed up. Results: The conjugation of two polymer parts was confirmed by Fourier transform infrared spectroscopy and 1H nuclear magnetic resonance techniques. The CMC of the developed micelles was around 5.62 × 10−8 mg/ ml, whereas the loading and encapsulation efficiencies were 16.5% and 70%, respectively. The size and zeta potential of the cationic micelles were 96.53 ± 18.53 nm and 68.3 mV, respectively. The release of BSA from POA micelles after 8 and 72 hours was 8.5% and 82%, respectively. Finally, fluorescence microscopy showed that the prepared micelles were successfully and effectively taken up by RAW264.7 cells. Conclusion: These results may provide a cutting-edge vaccine delivery solution and open up a new avenue for future vaccine research.
Preoperative Risk Factors in Patients With Pancreatic Cancer

Background Pancreatic cancer is gastrointestinal cancer with a poor prognosis. Although surgical techniques and chemotherapy have improved treatment outcomes, the 5-year survival rate for pancreatic cancer is less than 10%. In addition, resection of pancreatic cancer is highly invasive and is associated with high rates of postoperative complications and hospital mortality. The Japanese Pancreatic Association states that preoperative body composition assessment may predict postoperative complications. However, although impaired physical function is also a risk factor, few studies have examined it in combination with body composition. We examined preoperative nutritional status and physical function as risk factors for postoperative complications in pancreatic cancer patients. Methods Fifty-nine patients with pancreatic cancer who underwent surgical treatment and were discharged alive from January 1, 2018, to March 31, 2021, at the Japanese Red Cross Medical Center. This retrospective study was conducted using electronic medical records and a database of departments. Body composition and physical function were evaluated before and after surgery, and the risk factors between patients with and without complications were compared. Results Fifty-nine patients were analyzed: 14 and 45 patients in the uncomplicated and complicated groups, respectively. The major complications were pancreatic fistulas (33%) and infections (22%). There were significant differences in: age, 74.0 (44 - 88) (P = 0.02); walking speed, 0.93 m/s (0.3 - 2.2) (P = 0.01); and fat mass, 16.50 kg (4.7 - 46.2) (P = 0.02), in the patients with complications. On Multivariable logistic regression analysis, age (odds ratio: 2.28; confidence interval (CI): 1.3400 - 569.00; P = 0.03), preoperative fat mass (odds ratio: 2.28; CI: 1.4900 - 168.00; P = 0.02), and walking speed (odds ratio: 0.119; CI: 0.0134 - 1.07; P = 0.05) were identified as risk factors. Walking speed (odds ratio: 0.119; CI: 0.0134 - 1.07; P = 0.05) was the risk factor that was extracted. Conclusions Older age, more preoperative fat mass, and decreased walking speed were possible risk factors for postoperative complications.
Blended learning during and beyond the COVID-19 pandemic: Attitudes of nurse educators in Gauteng

Background The use of blended learning (BL) pedagogy has become inevitable due to contemporary technological innovations in the nursing education sector. As of late, the need to use BL pedagogy has resulted by the sudden occurrence of the COVID-19 pandemic. However, several nurse educators still experience uncertainties in using BL due to technological, psychological, infrastructure and equipment readiness barriers. Aim To report the attitudes of nurse educators towards the use of BL pedagogy as a new norm of teaching and learning in public nursing education institutions (NEIs) in the Gauteng Province (GP), South Africa, during and beyond the COVID-19 pandemic period. Setting The study was conducted in five Gauteng public NEIs. Methods A descriptive non-experimental quantitative design was conducted with 144 nurse educators. Data was collected through a questionnaire. Statistical Analysis Software (SAS) was used to analyse data with the help of a biostatistician. Results Technologically, only 50% (N = 72) found BL easy to use while 48% (n = 69) were ready and willing to use the BL Psychologically, more than half, that is, 65% (n = 94) lacked the confidence to use BL pedagogy. About 55% (n = 79) reported having inadequate BL infrastructure, while 32% (n = 46) seemed to be satisfied with the availability of effective equipment to support BL pedagogy. Conclusion Based on the results, it is apparent that nurse educators in Gauteng are not technologically and psychologically ready, since the infrastructure and equipment to support the BL are not adequately provided. Contribution The study emphasised the purpose of performing regular assessments to establish the overall readiness of nurse educators to successfully implement the BL pedagogy.
Effectiveness of Comprehensive Physical Activity Health Promotion Program on the Essential Physical Functions of Older Patients With Multiple Diseases and Dementia in Rural Area

Against the backdrop of aging populations worldwide, physical activity programs aimed at promoting the health of older adults have begun to flourish. However, few studies have focused on elderly residents in rural areas who may have multiple comorbidities. Therefore, the current study aimed to investigate the effects of a 12-week physical activity program on health promotion among rural elderly with multiple diseases. The study included 18 elderly participants, with a mean age of 82.39 years, who had dementia and one or more additional diseases. Among the participants, 89% were female. The results showed that the 12-week physical activity program intervention significantly improved participants’ walking speed and range of motion of the arm joints. Based on these findings, it is hoped that this study will provide a reference for future researchers and practitioners targeting rural or elderly populations who may have multiple diseases to develop more comprehensive physical activity programs.
The Impact of Sensor Response and Airspeed on the Representation of the Convective Boundary Layer and Airmass Boundaries by Small Unmanned Aircraft Systems

The objective of the research presented is to assess the impact of sensor response and aircraft airspeed on the accuracy of in situ observations collected by small unmanned aircraft systems profiling the convective boundary layer or transecting airmass boundaries. Estimates are made using simulated aircraft flown within large-eddy simulations. Both instantaneous errors (differences between observed temperature, which include the effects of sensor response and airspeed, and actual temperature) and errors in representation (differences between serial observations and representative snapshots of the atmospheric state) are considered. Synthetic data are retrieved assuming a well-aspirated first-order sensor mounted on rotary-wing aircraft operated as profilers in a simulated CBL and fixed-wing aircraft operated through transects across a simulated airmass boundary. Instantaneous errors are found to scale directly with sensor response time and airspeed for both CBL and airmass boundary experiments. Maximum errors tend to be larger for airmass boundary transects compared to the CBL profiles. Instantaneous errors for rotary-wing aircraft profiles in the CBL simulated for this work are attributable to the background lapse rate and not to turbulent temperature perturbations. For airmass boundary flights, representation accuracy is found to degrade with decreasing airspeed. This signal is most pronounced for flights that encounter the density current wake. When representation errors also include instantaneous errors resulting from sensor response, instantaneous errors are found to be dominant for flights that remain below the turbulent wake. However, for flights that encounter the wake, sensor response times generally need to exceed ~5 s before instantaneous errors become larger than errors in representation.
Occurrence and preventability of adverse events in hospitals: a retrospective study

ABSTRACT Objectives: to analyze the incidence of preventable adverse events related to health care in adult patients admitted to public hospitals in Brazil. Methods: observational, analytical, retrospective study based on medical records review. Results: medical records from 370 patients were evaluated, 58 of whom had at least one adverse event. The incidence of adverse events corresponded to 15.7%. Adverse events were predominantly related to healthcare-related infection (47.1%) and procedures (24.5%). Regarding the adverse event severity, 13.7% were considered mild, 51.0% moderate, and 35.3% severe. 99% of adverse events were classified as preventable. Patients admitted to the emergency room had a 3.73 times higher risk for adverse events. Conclusions: this study’s results indicate a high incidence of avoidable adverse events and highlight the need for interventions in care practice.
The importance of graph databases and graph learning for clinical applications

Abstract The increasing amount and complexity of clinical data require an appropriate way of storing and analyzing those data. Traditional approaches use a tabular structure (relational databases) for storing data and thereby complicate storing and retrieving interlinked data from the clinical domain. Graph databases provide a great solution for this by storing data in a graph as nodes (vertices) that are connected by edges (links). The underlying graph structure can be used for the subsequent data analysis (graph learning). Graph learning consists of two parts: graph representation learning and graph analytics. Graph representation learning aims to reduce high-dimensional input graphs to low-dimensional representations. Then, graph analytics uses the obtained representations for analytical tasks like visualization, classification, link prediction and clustering which can be used to solve domain-specific problems. In this survey, we review current state-of-the-art graph database management systems, graph learning algorithms and a variety of graph applications in the clinical domain. Furthermore, we provide a comprehensive use case for a clearer understanding of complex graph learning algorithms. Graphical abstract
Research on a real-time dynamic monitoring method for silent aspiration after stroke based on semisupervised deep learning: A protocol study

Objective This study aims to establish a real-time dynamic monitoring system for silent aspiration (SA) to provide evidence for the early diagnosis of and precise intervention for SA after stroke. Methods Multisource signals, including sound, nasal airflow, electromyographic, pressure and acceleration signals, will be obtained by multisource sensors during swallowing events. The extracted signals will be labeled according to videofluoroscopic swallowing studies (VFSSs) and input into a special dataset. Then, a real-time dynamic monitoring model for SA will be built and trained based on semisupervised deep learning. Model optimization will be performed based on the mapping relationship between multisource signals and insula-centered cerebral cortex–brainstem functional connectivity through resting-state functional magnetic resonance imaging. Finally, a real-time dynamic monitoring system for SA will be established, of which the sensitivity and specificity will be improved by clinical application. Results Multisource signals will be stably extracted by multisource sensors. Data from a total of 3200 swallows will be obtained from patients with SA, including 1200 labeled swallows from the nonaspiration category from VFSSs and 2000 unlabeled swallows. A significant difference in the multisource signals is expected to be found between the SA and nonaspiration groups. The features of labeled and pseudolabeled multisource signals will be extracted through semisupervised deep learning to establish a dynamic monitoring model for SA. Moreover, strong correlations are expected to be found between the Granger causality analysis (GCA) value (from the left middle frontal gyrus to the right anterior insula) and the laryngeal rise time (LRT). Finally, a dynamic monitoring system will be established based on the former model, by which SA can be identified precisely. Conclusion The study will establish a real-time dynamic monitoring system for SA with high sensitivity, specificity, accuracy and F1 score.
Type-I interferons promote innate immune tolerance in macrophages exposed to Mycobacterium ulcerans vesicles

Buruli ulcer is a chronic infectious disease caused by Mycobacterium ulcerans. The pathogen persistence in host skin is associated with the development of ulcerative and necrotic lesions leading to permanent disabilities in most patients. However, few of diagnosed cases are thought to resolve through an unknown self-healing process. Using in vitro and in vivo mouse models and M. ulcerans purified vesicles and mycolactone, we showed that the development of an innate immune tolerance was only specific to macrophages from mice able to heal spontaneously. This tolerance mechanism depends on a type I interferon response and can be induced by interferon beta. A type I interferon signature was further detected during in vivo infection in mice as well as in skin samples from patients under antibiotics regiment. Our results indicate that type I interferon-related genes expressed in macrophages may promote tolerance and healing during infection with skin damaging pathogen.
Counting pseudoalignments to novel splicing events

Abstract Motivation Alternative splicing (AS) of introns from pre-mRNA produces diverse sets of transcripts across cell types and tissues, but is also dysregulated in many diseases. Alignment-free computational methods have greatly accelerated the quantification of mRNA transcripts from short RNA-seq reads, but they inherently rely on a catalog of known transcripts and might miss novel, disease-specific splicing events. By contrast, alignment of reads to the genome can effectively identify novel exonic segments and introns. Event-based methods then count how many reads align to predefined features. However, an alignment is more expensive to compute and constitutes a bottleneck in many AS analysis methods. Results Here, we propose fortuna, a method that guesses novel combinations of annotated splice sites to create transcript fragments. It then pseudoaligns reads to fragments using kallisto and efficiently derives counts of the most elementary splicing units from kallisto’s equivalence classes. These counts can be directly used for AS analysis or summarized to larger units as used by other widely applied methods. In experiments on synthetic and real data, fortuna was around 7× faster than traditional align and count approaches, and was able to analyze almost 300 million reads in just 15 min when using four threads. It mapped reads containing mismatches more accurately across novel junctions and found more reads supporting aberrant splicing events in patients with autism spectrum disorder than existing methods. We further used fortuna to identify novel, tissue-specific splicing events in Drosophila. Availability and implementation fortuna source code is available at https://github.com/canzarlab/fortuna.
Evaluation of the changes in electrocardiographic ventricular repolarization parameters after COVID‐19 recovery in hemodialysis patients

Coronavirus disease‐2019 (COVID‐19) has cardiac manifestations. Data about electrocardiogram (ECG) changes after COVID‐19 recovery is limited in hemodialysis patients. We aimed to investigate the changes in ventricular repolarization parameters after COVID‐19 recovery in hemodialysis patients.