Miracle-dz/UniFAR-FTUs huggingface dataset:
Pathogenesis of Rift Valley Fever Virus Aerosol Infection in STAT2 Knockout Hamsters

Rift Valley fever virus (RVFV) is an emerging pathogen capable of causing severe disease in livestock and humans and can be transmitted by multiple routes including aerosol exposure. Several animal models have been developed to gain insight into the pathogenesis associated with aerosolized RVFV infection, but work with these models is restricted to high containment biosafety level (BSL) laboratories limiting their use for antiviral and vaccine development studies. Here, we report on a new RVFV inhalation infection model in STAT2 KO hamsters exposed to aerosolized MP-12 vaccine virus by nose-only inhalation that enables a more accurate delivery and measurement of exposure dose. RVFV was detected in hepatic and other tissues 4–5 days after challenge, consistent with virus-induced lesions in the liver, spleen and lung. Furthermore, assessment of blood chemistry and hematological parameters revealed alterations in several liver disease markers and white blood cell parameters. Our results indicate that STAT2 KO hamsters develop a disease course that shares features of disease observed in human cases and in other animal models of RVFV aerosol exposure, supporting the use of this BSL-2 infection model for countermeasure development efforts.
Software Aging and Rejuvenation in the Cloud: A Literature Review

With cloud computing becoming pervasive in IT enterprises, the long-running performance of cloud-based applications and services and of the underlying virtualization systems is a paramount concern. Researchers have been studying the phenomenon of software aging, and the corresponding rejuvenation countermeasures, in cloud-related contexts since about 10 years. This paper reviews the effort conducted so far by the software aging and rejuvenation (SAR) community in the cloud domain. Figures about current research trends and future directions can be derived from the reported results.
A gel‐free SNP genotyping method: bioluminometric assay coupled with modified primer extension reactions (BAMPER) directly from double‐stranded PCR products

Inexpensive, high‐throughput genotyping methods are needed for analyzing human genetic variations. We have successfully applied the regular bioluminometric assay coupled with modified primer extension reactions (BAMPER) method to single‐nucleotide polymorphism (SNP) typing as well as the allele frequency determination for various SNPs. This method includes the production of single‐strand target DNA from a genome and a primer extension reaction coupled with inorganic pyrophosphate (PPi) detection by a bioluminometric assay. It is an efficient way to get accurate allele frequencies for various SNPs, while single‐strand DNA preparation is labor intensive. The procedure can be simplified in the typing of SNPs. We demonstrate that a modified BAMPER method in which we need not prepare a single‐strand DNA can be carried out in one tube. A PCR product is directly used as a template for SNP typing in the new BAMPER method. Generally, tremendous amounts of PPi are produced in a PCR process, as well as many residual dNTPs, and residual PCR primers remain in the PCR products, which cause a large background signal in a bioluminometric assay. Here, shrimp alkaline phosphatase (SAP) and E. coli exonuclease I were used to degrade these components prior to BAMPER detection. The specific primer extension reactions in BAMPER were carried out under thermocycle conditions. The primers were extended to produce large amounts of PPi only when their bases at 3′‐termini were complementary to the target. The extension products, PPis, were converted to ATP to be analyzed using the luciferin–luciferase detection system. We successfully demonstrated that PCR products can be directly genotyped by BAMPER in one tube for SNPs with various GC contents. As all reactions can be carried out in a single tube, the method will be useful for realizing a fully automated genotyping system. Hum Mutat 24:155–163, 2004. © 2004 Wiley‐Liss, Inc.
Geographical Prevalence of Polycystic Ovary Syndrome as Determined by Region and Race/Ethnicity

Polycystic ovarian syndrome (PCOS) is thought to be the most common endocrine disorder found in women. Common symptoms include irregular menstrual cycle, polycystic ovaries, and hirsutism, as well as an increased risk for a multitude of conditions, including insulin resistance, dyslipidemia and infertility. The prevalence of polycystic ovarian syndrome is generally thought to be between 3% and 10% but it is widely unknown for specific subpopulations based on geographical location and race/ethnicity. Based on the high degree of variability and inconsistencies between the different diagnostic criteria, there is a unique challenge that exists when determining the prevalence of this syndrome. There are a large percentage of individuals that remain undiagnosed even after visiting multiple health care providers. Most studies conducted across the world are limited by small sample size, selection bias, and lack of comparability across studies. There have been very few studies that have examined the prevalence of polycystic ovary syndrome across the United States. Based on the National Institutes of Health (NIH)’s diagnostic criteria, there is a similar prevalence of PCOS documented across the United States, the United Kingdom, Spain, Greece, Australia, and Mexico. Other studies have shown some differences between geographical location and race. The existing data is not conclusive enough to determine whether or not there is any significant differences in the prevalence of PCOS across geographical location, racial or ethnic groups. This review will seek to determine the prevalence of polycystic ovarian syndrome based on geographical location and race/ethnicity.
MicroRNAs as Regulators of Insulin Signaling: Research Updates and Potential Therapeutic Perspectives in Type 2 Diabetes

The insulin signaling pathway is composed of a large number of molecules that positively or negatively modulate insulin specific signal transduction following its binding to the cognate receptor. Given the importance of the final effects of insulin signal transduction, it is conceivable that many regulators are needed in order to tightly control the metabolic or proliferative functional outputs. MicroRNAs (miRNAs) are small non-coding RNA molecules that negatively modulate gene expression through their specific binding within the 3′UTR sequence of messenger RNA (mRNA), thus causing mRNA decoy or translational inhibition. In the last decade, miRNAs have been addressed as pivotal cellular rheostats which control many fundamental signaling pathways, including insulin signal transduction. Several studies demonstrated that multiple alterations of miRNAs expression or function are relevant for the development of insulin resistance in type 2 diabetes (T2D); such alterations have been highlighted in multiple insulin target organs including liver, muscles, and adipose tissue. Indirectly, miRNAs have been identified as modulators of inflammation-derived insulin resistance, by controlling/tuning the activity of innate immune cells in insulin target tissues. Here, we review main findings on miRNA functions as modulators of insulin signaling in physiologic- or in T2D insulin resistance- status. Additionally, we report the latest hypotheses of prospective therapies involving miRNAs as potential targets for future drugs in T2D.
Fasting-Evoked En Route Hypoglycemia in Diabetes (FEEHD): An Overlooked Form of Hypoglycemia in Clinical Practice

Objective Many patients with diabetes opt to fast for lab tests, especially for lipid profiles, thus missing breakfast. In parallel, recent studies and international guidelines have indicated that routine fasting for lipid panels may not be necessary. Missing breakfast while fasting for lab tests may invoke hypoglycemia, if patients are not properly instructed about adjusting diabetes medications on the night before or on the day of the lab test. Our group described this form of hypoglycemia and introduced the term FEEHD to refer to it (fasting-evoked en route hypoglycemia in diabetes). In a recently published small study, we reported a rate of occurrence of FEEHD of 27.1%. The objective of this study was to evaluate the rate of occurrence of FEEHD in another clinic. Methods Patients with diabetes were asked to complete a simple, 2-page survey inquiring about hypoglycemic events while fasting for labs in the preceding 12 months. Results A total of 525 patients completed the surveys out of 572 patients invited (91.8% response rate). A total of 363 patients with complete data were analyzed, with a mean age of 60.6 (SD 12.5) years. A total of 62 (17.1%) patients reported having experienced one or more FEEHD events in the prior 12 months. Of the 269 patients who were at higher risk of FEEHD (on insulin secretagogues or on insulin), 59 (21.9%) reported having experienced FEEHD. Only 33 of FEEHD patients (53%) recalled having contacted their provider regarding the events and only 22 (35%) indicated having received some sort of FEEHD prevention instructions. Conclusion Our study shows a significant rate of occurrence of FEEHD in the real world (a clinical practice). FEEHD is especially dangerous, as patients often commute (drive) to and from the laboratory facility (potential risk of traffic accidents). Given study limitations, further studies are needed to assess prevalence of FEEHD in other settings and in the general populations.
Implementation of docker-based smart greenhouse data analysis platform

Through the convergence of ICT-technology with traditional greenhouse, the smart greenhouse has been able to reduce costs and increase production by using the large amount of data for making the optimal growth environment. Korean agricultural agency EPIS provides the raw data that is gathered from the greenhouses to public users using open API. In order to get an optimal growth algorithm, it needs to build up a platform for data analysis with various combination of factors easily. This paper describes the experience of implementing a platform that can easily analyze farm environment information. This platform consists of data gathering engine, data visualizer, database, and traffic router. We have implemented data gathering engine with Python to fetch the data using SOAP-based Open API, and uses NoSQL time-series database for storing them, open sources server applications for visualization and traffic routing. Furthermore, this application servers are all running as a container. The use of Docker makes it much easier to deploy this platform services regardless of underlying operating systems. In this paper, we describes the implementation of data gathering engine and how we composed the micro services to build up agricultural data analysis platform.
ARStudio: AR Broadcasting System based on Multiple RGB-D Cameras

Many television shows have been produced that synthesize virtual objects or make use of interactions between cast members and virtual objects. Recently, many attempts have been made to apply augmented reality (AR) technology to the production of interactive broadcast content using virtual objects and special effects, even during a live broadcast. In this study, we present ARStudio, an AR-based broadcasting system that can transmit virtual objects by synthesizing them in real time to the 3D structure of the broadcast set space recognized by multiple cameras. Also, we introduce a new calibration method that can easily and quickly merge multiple broadcast cameras and RGB-D cameras into one coordinate system. Additionally, we built a spatial editing and control tool for traditional broadcast producers to help them easily implement AR broadcasting. We applied the proposed system to a real broadcasting production environment by working with a Korean national broadcasting company, EBS, and demonstrated its functionality through a public TV network.
WLAN throughput improvement using dynamic sensitivity control technique

This paper represents a dynamic sensitivity control technique to improve spectral efficiency in wireless local area network. The proposed technique can be used when traffic is highly imbalanced, in other words, downlink or uplink traffic is dominant. We performed a simulation to evaluate the proposed technique and the simulation results shows 20.6% loss at a single worst case but it shows 98.6% gain at optimal case. Overall gain is 31.3% and it shows the usefulness of the traffic imbalance information in the dynamic sensitivity control in WLAN.
Robust cross-domain disfluency detection with pattern match networks

In this paper we introduce a novel pattern match neural network architecture that uses neighbor similarity scores as features, eliminating the need for feature engineering in a disfluency detection task. We evaluate the approach in disfluency detection for four different speech genres, showing that the approach is as effective as hand-engineered pattern match features when used on in-domain data and achieves superior performance in cross-domain scenarios.
Intake of an Obesogenic Cafeteria Diet Affects Body Weight, Feeding Behavior, and Glucose and Lipid Metabolism in a Photoperiod-Dependent Manner in F344 Rats

We previously demonstrated that chronic exposure to different photoperiods induced marked variations in several glucose and lipid metabolism-related parameters in normoweight Fischer 344 (F344) rats. Here, we examined the effects of the combination of an obesogenic cafeteria diet (CAF) and the chronic exposure to three different day lengths (L12, 12 h light/day; L18, 18 h light/day; and L6, 6 h light/day) in this rat strain. Although no changes were observed during the first 4 weeks of adaptation to the different photoperiods in which animals were fed a standard diet, the addition of the CAF for the subsequent 7 weeks triggered profound physiologic and metabolic alterations in a photoperiod-dependent manner. Compared with L12 rats, both L6 and L18 animals displayed lower body weight gain and cumulative food intake in addition to decreased energy expenditure and locomotor activity. These changes were accompanied by differences in food preferences and by a sharp upregulation of the orexigenic genes Npy and Ghsr in the hypothalamus, which could be understood as a homeostatic mechanism for increasing food consumption to restore body weight control. L18 rats also exhibited higher glycemia than the L6 group, which could be partly attributed to the decreased pAkt2 levels in the soleus muscle and the downregulation of Irs1 mRNA levels in the gastrocnemius muscle. Furthermore, L6 animals displayed lower whole-body lipid utilization than the L18 group, which could be related to the lower lipid intake and to the decreased mRNA levels of the fatty acid transporter gene Fatp1 observed in the soleus muscle. The profound differences observed between L6 and L18 rats could be related with hepatic and muscular changes in the expression of circadian rhythm-related genes Cry1, Bmal1, Per2, and Nr1d1. Although further research is needed to elucidate the pathophysiologic relevance of these findings, our study could contribute to emphasize the impact of the consumption of highly palatable and energy dense foods regularly consumed by humans on the physiological and metabolic adaptations that occur in response to seasonal variations of day length, especially in diseases associated with changes in food intake and preference such as obesity and seasonal affective disorder.
Model Checking the Uncertainties in Software Systems Introduced by Intelligent Components

An intelligent software system consists of intelligent components, fundamentally modeled by neural networks, and traditional software components, in which the intelligent components are responsible to make decisions. Thus, intelligent systems can adapt to environment changes in the running time. However, due to the fact that a neural network highly relies on the training data, its structure and quality are both uncertain. These uncertainties will be passed to the rest of the software system by the intelligent components. This paper presents a framework to model checking such kind of intelligent system software.
Randomised cross-over study of automated oxygen control for preterm infants receiving nasal high flow

Objective To evaluate a prototype automated controller (IntellO2) of the inspired fraction of oxygen (FiO2) in maintaining a target range of oxygen saturation (SpO2) in preterm babies receiving nasal high flow (HF) via the Vapotherm Precision Flow. Design Prospective two-centre order-randomised cross-over study. Setting Neonatal intensive care units. Patients Preterm infants receiving HF with FiO2 ≥25%. Intervention Automated versus manual control of FiO2 to maintain a target SpO2 range of 90%–95% (or 90%–100% if FiO2=21%). Main outcome measures The primary outcome measure was per cent of time spent within target SpO2 range. Secondary outcomes included the overall proportion and durations of SpO2 within specified hyperoxic and hypoxic ranges and the number of in-range episodes per hour. Results Data were analysed from 30 preterm infants with median (IQR) gestation at birth of 26 (24–27) weeks, study age of 29 (18–53) days and study weight 1080 (959–1443) g. The target SpO2 range was achieved 80% of the time on automated (IntellO2) control (IQR 70%–87%) compared with 49% under manual control (IQR 40%–57%; p<0.0001). There were fewer episodes of SpO2 below 80% lasting at least 60 s under automated control (0 (IQR 0–1.25)) compared with manual control (5 (IQR 2.75–14)). There were no differences in the number of episodes per hour of SpO2 above 98% (4.5 (IQR 1.8–8.5) vs 5.5 (IQR 1.9–14); p=0.572) between the study arms. Conclusions The IntellO2 automated oxygen controller maintained patients in the target SpO2 range significantly better than manual adjustments in preterm babies receiving HF. Trial registration number NCT02074774.
ETArch-SG - A SDN-based Architecture to Support the Communication Requirements of Future Smart Grids

The future use of smart grid scenarios with massive amounts of data poses stringent requirements to the communication protocols. IEC 61850, the standard regarding communication within power systems, defines the minimum requirements by the technology supporting smart grids. Considering the new scenarios and their requirements, support to multicast communication can fulfill the time constraints. Current networking technologies based on the TCP/IP architecture have limitations to support multicast inside the network. Based on the Entity Title Architecture (ETArch), an SDN based network architecture, capable of satisfying requirements such as multicast, this work proposes ETArch-SG to support future smart grid communication requirements. By using an experimental approach, ETArch-SG was compared to the current TCP/IP architecture. Our results demonstrate that ETArch-SG can configure IED with lower latencies and communicate PMUs with better performance when compared to IP Multicast, reaching the IEC 61850 time constraints.
Fast Failure Recovery for In-Band Controlled Multi-Controller OpenFlow Networks

Benefit from central control paradigm, SDN owns flexible and agile controllability to the networks. However, the central controller becomes the most vulnerable device. The failure of the controller results in the malfunction of the whole network. OpenFlow specification allows a network to equip multiple controllers to enhance the reliability on the control plane. How to perform fast failure recovery that takes advantage of a network with multiple controllers especially for networks operating with in-band control has not been well investigated. In this paper, we design a fast failure recovery scheme that takes the multiple controller architecture into consideration. In our system, there is a main controller that is responsible for controlling the network in the normal state. The other controllers are standby controllers used to take over the network control in failure state. The failure recovery in the proposed scheme includes three phases- fast failure detection, fast failure location identification, and traffic reroute. The fast failure detection uses monitoring cycles to examine the status of the network. As a failure is detected, the main controller and the standby controllers work together to perform failure location identification to pin-point the locations of the failure devices. In the traffic reroute phase, the main controller and the standby controllers collaborate to complete the fast failover. A control path provisioning algorithm is proposed to resolve the disjoint path planning problem for the routing of the in-band control channels from the controllers to the switches. As a result, a failure can be fast recovered even if it damages both working control paths and data paths. We have implemented our design in an experimental network. The experimental results show that the proposed scheme can protect any single link or single node failure within 50 msec.
Towards a Hybrid Storage Architecture for IoT

Internet of Things (IoT) is becoming part of our daily life. Indeed, studies predict a sharp market growth by 2020. One of the challenges of this fast-growing market is how to store and manage the amount of non-structured data generated by IoT devices. In this paper, we propose a hybrid storage architecture for IoT for addressing scalability, performance, and heterogeneity issues. We selected three of the most commonly used NoSQL databases (Redis, MongoDB, and Cassandra) to perform the first evaluation of our architecture. Our results suggest that the hybrid storage architecture is a feasible and promising approach to address some of the issues related to the ever-growing amount of data generated by IoT devices. Additionally, our findings also show that Redis achieves a better overall performance for the two chosen types of data, namely scalar and positional.
The effectiveness of a couple-based intervention for people living with mild Alzheimer’s disease and their spousal caregivers in Korea

Background and objectives We evaluated the effectiveness of a Korean version of the Couples Life Story Approach (CLSA-K), a structured life-review program originally developed in the U.S. to help older adults with dementia and their spousal caregivers engage with each other and improve their quality of life. Research design and methods: Fifty mild people living with mild Alzheimer’s disease and their spouses were recruited and 37 couples completed the five-week CLSA-K program. Data on psychosocial outcomes—i.e., depressive symptoms, talkativeness, mutuality (for both caregivers and care recipients) and burden (for caregivers)—were collected one week prior to (Time 1) and one week after the intervention (Time 2). Using repeated measures generalized linear models, we examined the differences in the amounts and patterns of the changes in outcomes according to age, gender, and the care-recipients’ level of cognitive impairment. Results Some participants benefited from the CLSA-K program, while others did not. For caregivers, mental health, and marital quality remained stable, while caregiver burden increased among those with spouses who had moderate levels of cognitive impairment. For care-recipients, younger and male participants showed increased talkativeness. Discussion and implications CLSA-K appears to be promising for specific subgroups of participants in Korea. Multi-component or other approaches may be more beneficial for other couples. Cultural differences as well as social positions may play a role in the acceptability and efficacy of the couple-based intervention.