Miracle-dz/UniFAR-FTUs huggingface dataset:
IEEE Access Special Section Editorial: Artificial Intelligence Enabled Networking

With today’s computer networks becoming increasingly dynamic, heterogeneous, and complex, there is great interest in deploying artificial intelligence (AI) based techniques for optimization and management of computer networks. AI techniques—that subsume multidisciplinary techniques from machine learning, optimization theory, game theory, control theory, and meta-heuristics—have long been applied to optimize computer networks in many diverse settings. Such an approach is gaining increased traction with the emergence of novel networking paradigms that promise to simplify network management (e.g., cloud computing, network functions virtualization, and software-defined networking) and provide intelligent services (e.g., future 5G mobile networks). Looking ahead, greater integration of AI into networking architectures can help develop a future vision of cognitive networks that will show network-wide intelligent behavior to solve problems of network heterogeneity, performance, and quality of service (QoS).
Multichannel techniques in color image enhancement and modeling

We present novel multichannel methods in two target research areas. The first area is color image modeling. Multichannel AR models have been developed and applied to color texture segmentation and synthesis. The second area is color image equalization, which is performed on the three RGB channels simultaneously, using the joint PDF. Alternatively, equalization at the HSI domain is performed in order to avoid changes in digital image hue. A parallel algorithm is proposed for color image histogram calculation and equalization.
A Hybrid GA-BP Model for Bankruptcy Prediction

With the increase of economic globalization and evolution of information technology, accounting information are being generated and accumulated at an unprecedented pace. As a result, there has been a critical need for automated approaches to effective and efficient utilization of massive amount of accounting information to support companies' decision making. In this paper, we describe a hybrid GA-BP model in bankruptcy prediction. Optimization based on the genetic algorithm was executed on the neural networks thresholds and weights values. In addition, an example is given to validate the model; the results show our model has a high prediction accuracy in bankruptcy prediction
Psychiatric disorders in pregnant and postpartum women in the United States.

CONTEXT Psychiatric disorders and substance use during pregnancy are associated with adverse outcomes for mothers and their offspring. Information about the epidemiology of these conditions in this population is lacking. OBJECTIVE To examine sociodemographic correlates, rates of DSM-IV Axis I psychiatric disorders, substance use, and treatment seeking among past-year pregnant and postpartum women in the United States. DESIGN National survey. SETTING Face-to-face interviews conducted in the 2001-2002 National Epidemiologic Survey on Alcohol and Related Conditions. PARTICIPANTS A total of 43 093 respondents were interviewed, of whom 14 549 were women 18 to 50 years old with known past-year pregnancy status. MAIN OUTCOME MEASURES Prevalence of 12-month DSM-IV Axis I psychiatric disorders, substance use, and treatment seeking. RESULTS Past-year pregnant and postpartum women had significantly lower rates of alcohol use disorders and any substance use, except illicit drug use, than nonpregnant women. In addition, currently pregnant women had a lower risk of having any mood disorder than nonpregnant women. The only exception was the significantly higher prevalence of major depressive disorder in postpartum than in nonpregnant women. Age, marital status, health status, stressful life events, and history of traumatic experiences were all significantly associated with higher risk of psychiatric disorders in pregnant and postpartum women. Lifetime and past-year treatment-seeking rates for any psychiatric disorder were significantly lower among past-year pregnant than nonpregnant women with psychiatric disorders. Most women with a current psychiatric disorder did not receive any mental health care in the 12 months prior to the survey regardless of pregnancy status. CONCLUSIONS Pregnancy per se is not associated with increased risk of the most prevalent mental disorders, although the risk of major depressive disorder may be increased during the postpartum period. Groups of pregnant women with particularly high prevalence of psychiatric disorders were identified. Low rates of maternal mental health care underscore the need to improve recognition and delivery of treatment for mental disorders occurring during pregnancy and the postpartum period.
A mixture-of-experts approach for gene regulatory network inference

Gene regulatory network GRN inference is an important problem in bioinformatics. Many machine learning methods have been applied to increase the inference accuracy. Ensemble learning methods are shown in DREAM3 and DREAM5 challenges to yield a higher inference accuracy than individual algorithms. However, no ensemble method has been proposed to take advantage of the complementarity among existing algorithms from the perspective of network motifs. We propose an ensemble method based on the principle of Mixture-of-Experts ensemble learning. The method can quantitatively evaluate the accuracy of individual algorithms on predicting each type of the network motifs and assign weights to the algorithms accordingly. The individual predictions are then used to generate the ensemble prediction. By performing controlled experiments and statistical tests, the proposed ensemble method is shown to yield a significantly higher accuracy than the generic average ranking method used in the DREAM5 challenge. In addition, a new type of network motif is found in GRN, the inclusion of which can increase the accuracy of the proposed method significantly.
A novel mechanism of activity‐dependent NMDA receptor antagonism describes the effect of ifenprodil in rat cultured cortical neurones.

1. Ifenprodil is a selective, atypical non‐competitive antagonist of NMDA receptors that contain the NR2B subunit with an undefined mechanism of action. Ifenprodil is neuroprotective in in vivo models of cerebral ischaemia but lacks many of the undesirable side‐effects associated with NMDA antagonist. 2. Using whole‐cell voltage‐clamp recordings, we have studied the mechanism of inhibition of NMDA‐evoked currents by ifenprodil in rat cultured cortical neurones in the presence of saturating concentrations of glycine. 3. Ifenprodil antagonized NMDA receptors in an activity‐dependent manner, whilst also increasing the receptor affinity for glutamate recognition‐site agonists. Ifenprodil inhibition curves against 10 and 100 microM NMDA‐evoked currents yielded IC50 values of 0.88 and 0.17 microM, respectively. Thus, the apparent affinity of ifenprodil for the NMDA receptor is increased in an NMDA concentration‐dependent manner. 4. Currents evoked by 0.3 and 1 microM NMDA were potentiated to approximately 200% of control levels in the presence of 3 microM ifenprodil. Thus, with increasing concentration of NMDA the effect of ifenprodil on NMDA‐evoked currents changed from one of potentiation to one of increasing inhibition. 5. These results are predicted by a reaction scheme in which ifenprodil exhibits a 39‐ and 50‐fold higher affinity for the agonist‐bound activated and desensitized states of the NMDA receptor, respectively, relative to the resting, agonist‐unbound state. Furthermore, ifenprodil binding to the NMDA receptor results in a 6‐fold higher affinity for glutamate site agonists. 6. This represents a novel mechanism of NMDA receptor antagonism that, together with the subunit selectivity, probably contributes to the attractive neuropharmacological profile of this and related compounds.
Gender differences in one‐year outcomes of first‐presentation psychosis patients in inner‐city UK Early Intervention Services

Men and women have historically been shown to differ in their presentation and outcome of psychotic disorders and thus are likely to have different treatment needs. It is unclear whether Early Intervention Services (EIS) are able to provide equitable care for both men and women presenting for the first time with psychosis. The main aim of this study was to explore gender differences for first‐presentation psychosis patients at the time of their referral to inner‐city EIS and their outcomes 1 year later.
Accounting for reconstruction kernel-induced variability in CT radiomic features using noise power spectra

Abstract. Large variability in computed tomography (CT) radiomics feature values due to CT imaging parameters can have subsequent implications on the prognostic or predictive significance of these features. Here, we investigated the impact of pitch, dose, and reconstruction kernel on CT radiomic features. Moreover, we introduced correction factors to reduce feature variability introduced by reconstruction kernels. The credence cartridge radiomics and American College of Radiology (ACR) phantoms were scanned on five different scanners. ACR phantom was used for 3-D noise power spectrum (NPS) measurements to quantify correlated noise. The coefficient of variation (COV) was used as the variability assessment metric. The variability in texture features due to different kernels was reduced by applying the NPS peak frequency and region of interest (ROI) maximum intensity as correction factors. Most texture features were dose independent but were strongly kernel dependent, which is demonstrated by a significant shift in NPS peak frequency among kernels. Percentage improvement in robustness was calculated for each feature from original and corrected %COV values. Percentage improvements in robustness of 19 features were in the range of 30% to 78% after corrections. We show that NPS peak frequency and ROI maximum intensity can be used as correction factors to reduce variability in CT texture feature values due to reconstruction kernels.
Introgression between invasive and native blue mussels (genus Mytilus) in the central California hybrid zone

The ecological and genetic factors determining the extent of introgression between species in secondary contact zones remain poorly understood. Here, we investigate the relative importance of isolating barriers and the demographic expansion of invasive Mytilus galloprovincialis on the magnitude and the direction of introgression with the native Mytilus trossulus in a hybrid zone in central California. We use double‐digest restriction‐site‐associated DNA sequencing (ddRADseq) to genotype 1337 randomly selected single nucleotide polymorphisms and accurately distinguish early and advanced generation hybrids for the first time in the central California Mytilus spp. hybrid zone. Weak levels of introgression were observed in both directions but were slightly more prevalent from the native M. trossulus into the invasive M. galloprovincialis. Few early and advanced backcrossed individuals were observed across the hybrid zone confirming the presence of strong barriers to interbreeding. Heterogeneous patterns of admixture across the zone of contact were consistent with the colonization history of M. galloprovincialis with more extensive introgression in northern localities furthest away from the putative site of introduction in southern California. These observations reinforce the importance of dynamic spatial and demographic expansions in determining patterns of introgression between close congeners, even in those with high dispersal potential and well‐developed reproductive barriers. Our results suggest that the threat posed by invasive M. galloprovincialis is more ecological than genetic as it has displaced, and continues to displace the native M. trossulus from much of central and southern California.
Efficiency, But At What Cost? Evidence from a DEA Analysis of WV School Districts

West Virginia schools are consistently below the national average on the NAEP. Using Data Envelopment Analysis, we estimate the technical efficiency of West Virginia school districts. We find less variation in technical efficiency in West Virginia than in similar studies conducted in other states. This appears to be because of state policy imposing homogeneity of input usage. Due to the limited variation in technical efficiency across districts, we cannot analyze how non-school inputs such as socioeconomic factors affect technical efficiency across districts. Summary statistics organized by county economic status, however, suggest that socioeconomic status plays a role. Our results highlight an important limitation of DEA analysis on schools.
Retail Mergers and Food Prices: Evidence from France

This paper analyzes the impact of a merger in the French supermarket industry on food prices. Using consumer panel data, we compare the changes in prices for merging and rival firms in affected and comparison markets. We use a novel definition of affected markets when some firms have a local pricing strategy and others a more centralized pricing strategy. We find that prices increase significantly following the merger, and that the merging firms lose market shares. For the rivals, the price increases are larger in local markets, in which concentration increased and differentiation changed after the merger.
Multilevel Governmental Efforts for Energy Efficiency: Policy Adoption, Implemenation, and Evaluation under the American Recovery and Reinvestment Act (ARRA)

This dissertation consists of three essays studying the impact of the American Recovery and Reinvestment Act in the energy policy field. The purpose of this research was to evaluate the effectiveness of ARRA funds, spent as temporary funding, on the change of energy efficiency policies, jobs, and technologies. The first essay examined variation in local level energy-efficiency grants and corresponding initiatives from American Reinvestment and Recovery Act (ARRA) in the United States. The analysis was based upon a hurdle model of counts of energyefficiency grants received by 348 local governments that received these grants from 2009 to 2013, as well as 348 matched local governments that did not receive such funds. Citylevel characteristics including amount of federal financial support, per capita income, signaling of preferences for sustainability policies, manufacturing and political influences were shown to be empirically important determinants of variation in local energyefficiency initiatives. The evidence suggested that all else held equal, the $21.8 billion in ARRA funds expended with the intent of promoting the diffusion of local energyefficiency programs and policies successfully led to this end.
A Proteomic View of Salmonella Typhimurium in Response to Phosphate Limitation

Salmonella enterica serovar Typhimurium (S. Typhimurium), an important foodborne pathogen, often encounters phosphate (Pi) shortage both in the environment and inside host cells. To gain a global view on its physiological responses to Pi starvation, we performed proteomic profiling of S. Typhimurium upon the shift from Pi-rich to Pi-low conditions. In addition to the Pho regulon, many metabolic processes were up-regulated, such as glycolysis, pentose phosphate pathway, pyrimidine degradation, glycogen, and trehalose metabolism, allowing us to chart an overview of S. Typhimurium carbon metabolism under Pi starvation. Furthermore, proteomic analysis of a mutant lacking phoB (that encodes a key regulator of Pi shortage response) suggested that only a small subset of the altered proteins upon Pi limitation was PhoB-dependent. Importantly, we present evidence that S. Typhimurium N-acetylglucosamine catabolism was induced under Pi-limiting conditions in a PhoB-dependent manner. Immunoblotting and β-galactosidase assays demonstrated that PhoB was required for the full activation of NagB, a key enzyme of this pathway, in response to low Pi. Thus, our study reveals that N-acetylglucosamine catabolism may represent an additional PhoB-regulated pathway to tackle bacterial Pi shortage.
Robust and computationally efficient approach for Heart Rate monitoring using photoplethysmographic signals during intensive physical excercise

Photoplethysmography (PPG) at the wrist can be used as a non-invasive technique for estimating the Heart Rate (HR). However, Motion Artifacts (MA) impede this estimation of the HR especially when the subject performs intensive physical exercise. This paper describes the implementation of a fast algorithm based on a statistical approach for extracting the HR from such MA corrupted PPG. Data-sets contain PPG signals with accelerometer data in 3-dimensions of subjects who were performing various forearm and upper arm activities. Simultaneously recorded Electrocardiogram (ECG) was used to verify the algorithm. The algorithm first de-noises the signal followed by a focus reduction in the spectrum and then peak tracking. The proposed algorithm resulted in an overall average-absolute-error of 1.5926 Beats Per Minute (BPM). The Bland-Altman was used to compare ECG derived HR and PPG derived HR where Limit of Agreement (LOA) was ±7 BPM. Also, this algorithm gives a great efficiency in time and robustness to track the HR on-the-go.
Expression of aquaporins 1 and 5 in a model of ventilator‐induced lung injury and its relation to tidal volume

What is the central question of this study? Although different studies have attempted to find factors that influence the expression of aquaporins (AQPs) in the lung in different situations, to date no research group has explored the expression of AQP1 and AQP5 jointly in rats mechanically ventilated with different tidal volumes in a model of ventilator‐induced lung injury. What is the main finding? Mechanical ventilation with a high tidal volume causes lung injury and oedema, increasing lung permeability. In rats ventilated with a high tidal volume, the pulmonary expression of AQP1 decreases.
Leading the Indian Managers to Satisfaction: The Mediating Role of Ethical Climate

The theme of this study revolves around the ethical approach of the top management and ethical climate of the organizations which directly or indirectly influence the job-related attitudinal behaviour of employees. In this article, an attempt is made to explore whether ethical leadership in Indian context impacts the ethical climate and perceived satisfaction of employees. The study also analyzes the meditating effect of ethical climate upon the relationship of ethical leadership and managerial satisfaction. This research study employed a quantitative method using a cross-sectional survey design to assess the effect of ethical leadership on the organizational ethical climate and managerial satisfaction. A non-probability, convenience sample was obtained from this population group and was based on accessibility. This study used internal consistency reliability, Pearson correlation and hierarchical regression as statistical techniques. The following research study examined the potential of ethical leadership to foster the levels of ethical climate and found that ethical leadership acts as an antecedent of ethical climate. Further, the employees led by highly ethical leaders reported highly satisfied, and moderating effect of ethical climate also found positive. Generalization of this study's results to circumstances in other countries may not be possible because the target population was employees in Indian corporations. The study is helpful in addressing the gap of empirical analysis in relation with ethical leadership, ethical climate and in managerial context in the Indian service sector corporations.
Iodine and iron status of pregnant women in Lubumbashi, Democratic Republic of Congo

Abstract Objective Adequate iodine and Fe intakes are imperative during pregnancy to prevent fetal defects, but such data are not available in the Democratic Republic of Congo. We aimed to assess iodine and Fe status in pregnant women from Lubumbashi. Design Cross-sectional study. We measured urinary iodine concentration (UIC) in random urine samples using a modified Sandell–Kolthoff digestion method; the WHO reference medians were used to classify iodine intake as deficient, adequate, more than adequate or excessive. Serum ferritin concentrations were measured by immunoenzymatic assay and considered insufficient when <12 ng/ml. Setting Maternity units from rural, semi-urban and urban areas of Lubumbashi, Democratic Republic of Congo. Subjects Two hundred and twenty-five randomly selected pregnant women attending prenatal consultation, seventy-five postpartum women and seventy-five non-pregnant women as controls. Results Overall median UIC in pregnant women was 138 (interquartile range: 105–172) μg/l, indicating iodine deficiency, whereas postpartum and non-pregnant women had adequate iodine intake: median UIC = 144 μg/l and 204 μg/l, respectively. Median UIC values were lower in late pregnancy than in early pregnancy: in the first, second and third trimester respectively 255 μg/l, 70 μg/l and 88 μg/l in the rural area; 306 μg/l, 166 μg/l and 68 μg/l in the semi-urban area; and 203 μg/l, 174 μg/l and 99 μg/l in the urban area. Fe was insufficient in 39 % of pregnant women compared with 21 % of non-pregnant and postpartum women. In the third trimester, deficiencies in both iodine and Fe were high: 40 %, 12 % and 18 % in the rural, semi-urban and urban areas, respectively. Conclusions Our data suggest that pregnant women are at risk of iodine and Fe deficiencies in Lubumbashi. Country policies fighting against iodine and Fe deficiencies during pregnancy should be reinforced.
Analysis of phonocardiogram signals through proactive denoising using novel self-discriminant learner

Phonocardiogram (PCG) records heart sound and murmurs, which contains significant information of cardiac health. Analysis of PCG signal has the potential to detect abnormal cardiac condition. However, the presence of noise and motion artifacts in PCG hinders the accuracy of clinical event detection. Thus, noise detection and elimination are crucial to ensure accurate clinical analysis. In this paper, we present a robust denoising technique, Proclean that precisely detects the noisy PCG signal through pattern recognition, and statistical learning. We propose a novel self-discriminant learner that ensures to obtain distinct feature set to distinguish clean and noisy PCG signals without human-in-loop. We demonstrate that our proposed denoising leads to higher accuracy in subsequent clinical analytics for medical investigation. Our extensive experimentations with publicly available MIT-Physionet datasets show that we achieve more than 85% accuracy for noisy PCG signal detection. Further, we establish that physiological abnormality detection improves by more than 20%, when our proposed denoising mechanism is applied.