Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of NO-Induced Nitrosative Status in Human Placenta from Pregnant Women with Gestational Diabetes Mellitus

Dysregulation of NO production is implicated in pregnancy-related diseases, including gestational diabetes mellitus (GDM). The role of NO and its placental targets in GDM pregnancies has yet to be determined. S-Nitrosylation is the NO-derived posttranslational protein modification that can modulate biological functions by forming NO-derived complexes with longer half-life, termed S-nitrosothiol (SNO). Our aim was to examine the presence of endogenous S-nitrosylated proteins in cysteine residues in relation to antioxidant defense, apoptosis, and cellular signal transduction in placental tissue from control (n = 8) and GDM (n = 8) pregnancies. S-Nitrosylation was measured using the biotin-switch assay, while the expression and protein activity were assessed by immunoblotting and colorimetric methods, respectively. Results indicated that catalase and peroxiredoxin nitrosylation levels were greater in GDM placentas, and that was accompanied by reduced catalase activity. S-Nitrosylation of ERK1/2 and AKT was increased in GDM placentas, and their activities were inhibited. Activities of caspase-3 and caspase-9 were increased, with the latter also showing diminished nitrosylation levels. These findings suggest that S-nitrosylation is a little-known, but critical, mechanism by which NO directly modulates key placental proteins in women with GDM and, as a consequence, maternal and fetal anomalies during pregnancy can occur.
A Class-Based Approach to Lexical Discovery

In this paper I propose a generalization of lexical association techniques that is intended to facilitate statistical discovery of facts involving word classes rather than individual words. Although defining association measures over classes (as sets of words) is straightforward in theory, making direct use of such a definition is impractical because there are simply too many classes to consider. Rather than considering all possible classes, I propose constraining the set of possible word classes by using a broad-coverage lexical/conceptual hierarchy [Miller, 1990].
Distributed Scheduling Framework - A Load Distribution Facility on Mach

With a network of workstations, load distribution strategies can be employed to better utilize resources. In practice, there is no single load distribution algorithm that outperforms all others in all cases. Therefore , it is desirable that diierent systems can use diierent load distribution algorithms. Even in the same system, the necessity of changing the load distribution algorithm may arise when the system connguration or other parameters, such as task types, are changed. Generally speaking, changing of load distribution algorithm is not easy since the scheduler running the algorithm is an operating system protected object. This paper presents the Distributed Scheduling Framework (DSF) | a load distribution facility on Mach, which allows various kinds of load distribution algorithms to be installed at user-level, and minimizes the eeorts needed with their implementation. The DSF thus provides a basis for experimentation with new load distribution algorithms, and allows such algorithms to be used to improve system performance as well.
An Examination of Non-linear Relationships between Intellectual Property Rights Protection and Growth

This paper examines the possibility of a non-linear relationship existing between intellectual property rights protection (IPR) and gross domestic product (GDP) growth rates. A theoretical justification is developed for the potential existence of a non-linear relationship in terms of a quadratic relationship. This is then examined using panel data from 191 countries and taken in 5 year intervals, although the data had many missing observations. Results indicate there is statistically significant evidence that a quadratic relationship exists between IPR and GDP growth, however there are reservations about this evidence due to a dearth of observations in countries with very weak intellectual property rights protections.
Graphomotoric coding in recognizing Chinese Characters

Graphomotoric Coding (GMC) involves encoding of proprioceptive feedback during writing, analysis of the temporal and spatial sequences of strokes and construction of the configurational relations among component parts of the written characters. The present study seeks to (i) determine the ability of normal right-handed writers to recognize Chinese characters using GMC exclusively; (ii) reveal whether they show a right-hand preference in GMC; and (iii) elucidate the influence of the proposed mirror-reversal hypotheses on left hand coding. The results reveal that sole kinaesthesia allowed one to recognize an average of ten out of twenty Chinese characters taught between four and six. No hand preference in coding was found and three plausible causes are discussed. Mirror-reversed characters were recognized more poorly than normal ones and none of the proposed mirror-reversal hypotheses significantly influence the character recognition ability in normal right-handed writers. The application of GMC to langauge rehabilitation for alexic and aphasic patients is suggested and discussed.
A node management scheme for R2V connections in RSU-supported Vehicular Adhoc Networks

Vehicular Adhoc Networks (VANETs) have become a popular research topic in recent years. In VANETs, RSU-to-Vehicle (R2V) unicast has a great deal of potential because it enables a Road-Side Unit (RSU) to contact a specific car and provide customized pushing services. However, to date, R2V has only been considered by a few works as it is difficult for the RSU to gather information about cars that are several hops way. To overcome this difficulty, we propose a scheme called RSU-Based Node Tracking (RBNT). Given on a predetermined road framework, RBNT chooses vehicles as Relay Nodes that connect with each other distributedly and form a hierarchical structure. Then, the RSU can connect to cars in its management area through the Relay Nodes. The results of simulations conducted to evaluate RBNT's performance demonstrate that the scheme's connection delay is significantly lower than that of existing approaches, and its coverage range is stable under different traffic densities. To the best of our knowledge, this is the first work that focuses on the R2V unicast issue and provides all the above advantages.
Non-intrusive head movement analysis of videotaped seizures of epileptic origin

In this work we propose a non-intrusive video analytic system for patient's body parts movement analysis in Epilepsy Monitoring Unit. The system utilizes skin color modeling, head/face pose template matching and face detection to analyze and quantify the head movements. Epileptic patients' heads are analyzed holistically to infer seizure and normal random movements. The patient does not require to wear any special clothing, markers or sensors, hence it is totally non-intrusive. The user initializes the person-specific skin color and selects few face/head poses in the initial few frames. The system then tracks the head/face and extracts spatio-temporal features. Support vector machines are then used on these features to classify seizure-like movements from normal random movements. Experiments are performed on numerous long hour video sequences captured in an Epilepsy Monitoring Unit at a local hospital. The results demonstrate the feasibility of the proposed system in pediatric epilepsy monitoring and seizure detection.
Benchmarking Concurrent Priority Queues: Performance of k-LSM and Related Data Structures

A number of concurrent, relaxed priority queues have recently been proposed and implemented. Results are commonly reported for a throughput benchmark that uses a uniform distribution of keys drawn from a large integer range, and mostly for single systems. We have conducted more extensive benchmarking of three recent, relaxed priority queues on four different types of systems with different key ranges and distributions. While we can show superior throughput and scalability for our own k-LSM priority queue for the uniform key distribution, the picture changes drastically for other distributions, both with respect to achieved throughput and relative merit of the priority queues. The throughput benchmark alone is thus not sufficient to characterize the performance of concurrent priority queues. Our benchmark code and k-LSM priority queue are publicly available to foster future comparison.
Exploring Single-Mother College Students’ Perceptions of Their College-Related Experiences and of Campus Services

Abstract The purpose of this qualitative study was to explore single-mother college students’ perceptions of their college-related experiences and to learn about campus services related to these experiences that could help them during their college journey. Ten single mothers were interviewed using a semi-structured interview guide. Five themes emerged from the data: mothers are motivated by their children, mothers are managers, mothers are time pressured, mothers want the university to consider them as individuals, and mothers want the university to consider their children. Study limitations, future research, and recommendations for policy and practice were presented.
Effects of Indicators of Trustworthiness on Overall Brand Trust in Customer Relationship Management

The paper prompts further investigation of the relationship between brand trust and three trustworthiness indicators (i.e., perceived benevolence, perceived competence and perceived integrity). Moreover, it finds that customer relationship orientation has a moderating effect on the association between brand trust and some indicators (i.e., perceived benevolence and perceived integrity).
Identification and Characterization of the Lamprey High-Mobility Group Box 1 Gene

High-mobility group box 1 (HMGB1), a highly conserved DNA-binding protein, plays an important role in maintaining nucleosome structures, transcription, and inflammation. We identified a homolog of HMGB1 in the Japanese lamprey (Lampetra japonica). The Lampetra japonica HMGB1 gene (Lj-HMGB1) has over 70% sequence identity with its homologs in jawed vertebrates. Despite the reasonably high sequence identity with other HMGB1 proteins, Lj-HMGB1 did not group together with these proteins in a phylogenetic analysis. We examined Lj-HMGB1 expression in lymphocyte-like cells, and the kidneys, heart, gills, and intestines of lampreys before and after the animals were challenged with lipopolysaccharide (LPS) and concanavalin A (ConA). Lj-HMGB1 was initially expressed at a higher level in the heart, but after treatment with LPS and ConA only the gills demonstrated a significant up-regulation of expression. The recombinant Lj-HMGB1 (rLj-HMGB1) protein bound double-stranded DNA and induced the proliferation of human adenocarcinoma cells to a similar extent as human HMGB1. We further revealed that Lj-HMGB1 was able to induce the production of tumor necrosis factor-α (TNF-α), a pro-inflammatory mediator, in activated human acute monocytic leukemia cells. These results suggest that lampreys use HMGB1 to activate their innate immunity for the purpose of pathogen defense.
On optimal detection for matrix multiplicative data hiding

This paper analyzes a multiplicative data hiding scheme, where the watermark bits are embedded within frames of a Gaussian host signal by two different, but arbitrary, embedding matrices. A closed form expression for the bit error rate (BER) of the optimal detector is derived when the frame sizes tend to infinity. Furthermore, a structure is proposed for the optimal detector which divides the detection process into two main blocks: host signal estimation and decision making. The proposed structure preserves optimality, and allows for a great deal of flexibility: The estimator can be selected according to the a priori knowledge about host signal. For example, if the host signal is an Auto-Regressive (AR) process, we argue that a Kalman filter may serve as the estimator. Compared to a direct implementation of the Neyman-Pearson detector, this approach results in significantly reduced complexity while keeping optimal performance.
Oracle performance estimation of Bernoulli-distributed sparse vectors

Compressed Sensing (CS) is now a well-established research area and a plethora of applications has emerged in the last decade. In this context, assuming N available noisy measurements, lower bounds on the Bayesian Mean Square Error (BMSE) for the estimated entries of a sparse amplitude vector are derived in the proposed work for (i) a Gaussian overcomplete measurement matrix and (ii) for a random support, assuming that each entry is modeled as the product of a continuous random variable and a Bernoulli random variable indicating that the current entry is non-zero with probability P. A closed-form expression of the Expected CRB (ECRB) is proposed. In the second part, the BMSE of the Linear Minimum MSE (LMMSE) estimator is derived and it is proved that the LMMSE estimator tends to be statistically efficient in asymptotic conditions, i.e., if product (1 - P)2 SNR is maximized. This means that in the context of the Gaussian CS problem, the LMMSE estimator gathers together optimality in the low noise variance regime and a simple derivation (as opposed to the derivation of the MMSE estimator). This result is original because the LMMSE estimator is generally sub-optimal for CS when the measurement matrix is a single realization of a given random process.
Association between Sickle Cell Disease and Dental Caries among Sudanese Children

Background: Sickle cell disease is an autosomal recessive hemoglobinopathy predominant among Afro-descendants, and has been categorized as a public health issue as it affects a significant percentage of the world’s population. The aim of this study is to determine the association of dental caries among Sudanese sickle cell disease children in relation to healthy controls children. Methods: dmft and DMFT were recorded for 212 sickle cell disease children aged 3-15 years old, matched with 212 control healthy children in Khartoum, Sudan. Statistical analysis was done using statistical software SPSS 17.5 version. Chi square test was used for the comparison of study and control groups, the level of significance was set at p Results: dmft was significant among age group 4-6 and 7-11 years old while DMFT was only significant among group 12-14 years old sickle cell disease. There is highly significant association exists in dmft and DMF. Sickle cell disease group is 1.115 likely to have present component in dmft when compared to control group and 1.82 likely to have present component of DMFT when compared to control group. Conclusion: Sudanese sickle cell disease children had high prevalence of dental caries than the healthy children. Establishment of frequent dental examination schedule for sickle cell disease children, including preventive dental care and promoting oral hygiene practices with toothbrushes, toothpaste, and mouthwash are recommended.
Ensuring tight computational security against higher-order DPA attacks

While DES has been proven to be breakable within a day given sufficient computational power, AES is still in use because it is extremely resistant to cryptanalytic attacks. Power Analytic Attacks use power consumption traces of the hardware or software implementation of these algorithms to reduce search space exponentially in the size of the key, thereby making computational complexity several orders of magnitude lower. This paper analyzes the increase in the computational advantage of an adversary who uses DPA and higher order power analysis attacks as opposed to algorithmic cryptanalysis. We highlight why there can be no perfect masking against DPA, and then define a standard for the security of masking countermeasures to such attacks. The main contribution is a security metric for systems and a cut-off for the number of encryptions allowable for a given order of masking to make the system immune to higher order DPA attacks.
Aviation in the Ville: Promoting Science and Civic Engagement through Aviation

Aviation in the Ville is an ongoing collaborative series of activities designed to promote diversity in aviation by introducing aviation topics to the youth of an underrepresented neighborhood and proving discipline-specific service learning opportunities to aviation undergraduate students. Situated in three interrelated settings: in-school, after school, and over the summer, this paper focuses on the initial analysis of data collected during the 2018 summer camp. Utilizing a qualitative, participatory action research model, and drawing on the Harvard Family Research Project out-of-school performance measures, a number of themes emerged which seem to support the effort. Keywords : aviation outreach, diversity in aviation, minority in aviation, participatory action, qualitative research
Collaborative Filtering in the News Domain with Explicit and Implicit Feedback

In online recommender systems, we use computerized algorithms to present articles targeted at the preferences of each individual user. One such technique, called collaborative filtering, works by selecting articles that is preferred by users with preferences similar to those of the target user. There are different ways to determine a users preference. A common practice is to ask for it, for instance by offering the user to provide a rating from one to five. Users are, however, often reluctant to provide such information. From a usability perspective, it is better to look at their behaviour, or implicit feedback, and use that to infer their preferences. The main goal of this thesis is to determine whether we can use such implicit feedback in order to improve the accuracy a collaborative filtering method. We have implemented and tested a number of standard machine learning algorithms for mapping implicit feedback to explicit ratings. These algorithms are applied to a news data set consisting of user interactions collected while reading and rating news articles. After the mapping techniques have inferred possible new ratings, the next step is to generate recommendations, which is done with a matrix factorization algorithm. To measure the effect of adding implicit feedback to the recommendations we calculate the root mean square error (RMSE). We have developed a fast and scalable collaborative filtering component and integrated it with a news aggregation service called SmartMedia. The component uses a combination of nearest neighbor search on articles with more than 30% correlation and a time threshold of 20 seconds to infer ratings based on user interactions collected. We show that these methods improve the RMSE by 5.14% compared to using only ratings users have given. Our work shows that utilizing implicit feedback can improve recommendation accuracy in the news domain. The techniques we have developed require less explicit rating to be given from users in order to create good recommendations. Thus we believe that the next generation of recommender systems will be less dependent on explicit rating while still being able to provide high quality recommendations.
The Effective Use of Crowdsourcing in E-Governance

The rise of Web 2.0 paradigm has empowered the Internet users to share information and generate content on social networking and media sharing platforms such as wikis and blogs. The trend of harnessing the wisdom of public using Web 2.0 distributed networks through open calls is termed as ‘Crowdsourcing’. In addition to businesses, this powerful idea of using collective intelligence or the ‘wisdom of crowd’ applies to different situations, such as in governments and non-profit organizations which have started utilizing crowdsourcing as an essential problem-solving tool. In addition, the widespread and easy access to technologies such as the Internet, mobile phones and other communication devices has resulted in an exponential growth in the use of crowdsourcing for government policy advocacy, e-democracy and e-governance during the past decade. However, utilizing collective intelligence and efforts of public to find solutions to real life problems using web 2.0 tools does come with its share of associated challenges and limitations. This paper aims at identifying and examining the value-adding strategies which contribute to the success of crowdsourcing in e-governance. The qualitative case study analysis and emphatic design methodology are employed to evaluate the effectiveness of the identified strategic and functional components, by analyzing the characteristics of some of the notable cases of crowdsourcing in e-governance and the findings are tabulated and discussed. The paper concludes with the limitations and the implications for future research.
The Submergence Tolerance Regulator Sub1A Mediates Stress-Responsive Expression of AP2/ERF Transcription Factors1[C][W][OA]

We previously characterized the rice (Oryza sativa) Submergence1 (Sub1) locus encoding three ethylene-responsive factor (ERF) transcriptional regulators. Genotypes carrying the Sub1A-1 allele are tolerant of prolonged submergence. To elucidate the mechanism of Sub1A-1-mediated tolerance, we performed transcriptome analyses comparing the temporal submergence response of Sub1A-1-containing tolerant M202(Sub1) with the intolerant isoline M202 lacking this gene. We identified 898 genes displaying Sub1A-1-dependent regulation. Integration of the expression data with publicly available metabolic pathway data identified submergence tolerance-associated pathways governing anaerobic respiration, hormone responses, and antioxidant systems. Of particular interest were a set of APETALA2 (AP2)/ERF family transcriptional regulators that are associated with the Sub1A-1-mediated response upon submergence. Visualization of expression patterns of the AP2/ERF superfamily members in a phylogenetic context resolved 12 submergence-regulated AP2/ERFs into three putative functional groups: (1) anaerobic respiration and cytokinin-mediated delay in senescence via ethylene accumulation during submergence (three ERFs); (2) negative regulation of ethylene-dependent gene expression (five ERFs); and (3) negative regulation of gibberellin-mediated shoot elongation (four ERFs). These results confirm that the presence of Sub1A-1 impacts multiple pathways of response to submergence.
Assessing future nitrogen deposition and carbon cycle feedback using a multimodel approach: Analysis of nitrogen deposition

n this study, we present the results of nitrogen deposition on land from a set of 29 simulations from six different tropospheric chemistry models pertaining to present-day and 2100 conditions. Nitrogen deposition refers here to the deposition (wet and dry) of all nitrogen-containing gas phase chemical species resulting from NOx (NO + NO2) emissions. We show that under the assumed IPCC SRES A2 scenario the global annual average nitrogen deposition over land is expected to increase by a factor of ∼2.5, mostly because of the increase in nitrogen emissions. This will significantly expand the areas with annual average deposition exceeding 1 gN/m2/year. Using the results from all models, we have documented the strong linear relationship between models on the fraction of the nitrogen emissions that is deposited, regardless of the emissions (present day or 2100). On average, approximately 70% of the emitted nitrogen is deposited over the landmasses. For present-day conditions the results from this study suggest that the deposition over land ranges between 25 and 40 Tg(N)/year. By 2100, under the A2 scenario, the deposition over the continents is expected to range between 60 and 100 Tg(N)/year. Over forests the deposition is expected to increase from 10 Tg(N)/year to 20 Tg(N)/year. In 2100 the nitrogen deposition changes from changes in the climate account for much less than the changes from increased nitrogen emissions.