Miracle-dz/UniFAR-FTUs huggingface dataset:
Emergence of Brain Rhythms: Model Interpretation of EEG Data

Electroencephalography (EEG) monitors ---by either intrusive or noninvasive electrodes--- time and frequency variations and spectral content of voltage fluctuations or waves, known as brain rhythms, which in some way uncover activity during both rest periods and specific events in which the subject is under stimulus. This is a useful tool to explore brain behavior, as it complements imaging techniques that have a poorer temporal resolution. We here approach the understanding of EEG data from first principles by studying a networked model of excitatory and inhibitory neurons which generates a variety of comparable waves. In fact, we thus reproduce $\alpha$, $\beta,$ $\gamma$ and other rhythms as observed by EEG, and identify the details of the respectively involved complex phenomena, including a precise relationship between an input and the collective response to it. It ensues the potentiality of our model to better understand actual mind mechanisms and its possible disorders, and we also describe kind of stochastic resonance phenomena which locate main qualitative changes of mental behavior in (e.g.) humans. We also discuss the plausible use of these findings to design deep learning algorithms to detect the occurence of phase transitions in the brain and to analyse its consequences.
A note on Pottia intermedia (Turner) Fürnr. (Pottiaceae, Bryopsida) with special reference to its phylogeny and new localities in SW Japan

Eight new localities of Pottia intermedia (Turner) Fürnr. are recorded from Honshu and Shikoku (Hiroshima, Kagawa and Ehime Prefs.), SW Japan. Although it is widely distributed in the world, this species is a rare moss in Japan and is previously known from Honshu, Shikoku and Kyushu for Japan. Japanese populations of P. intermedia grow on soil in open sites, especially in citrus orchards. Analytical illustrations with SEM images of peristome, spores and leaf papillae and a discussion on the phylogenetic position of this species are provided based on the materials collected.
Finding Insights through Interactive Visualization

The ability to analyze and turn the data into useful information is becoming increasingly important to businesses as they need to quickly react to changing demands and needs. Data visualization has become progressively important to enable data to be visually explored and analyzed. A known challenge lies in how to move beyond exploration and focus attention in helping decision makers to capture further insights and reason about what they are seeing. In this paper, we show how data visualization can play a critical role not only for patterns visualization but also towards developing new insights through exploratory data analysis. To highlight the effectiveness of finding insights, we use a motivating example drawing from an airline industry for predicting flight delays.
New Dependencies of Hierarchies in Polynomial Optimization

We compare four key hierarchies for solving Constrained Polynomial Optimization Problems (CPOP) arising from semialgebraic proof systems: Sum of Squares (SOS), Sum of Diagonally Dominant Polynomials (SDSOS), Sum of Nonnegative Circuits (SONC), and the Sherali Adams (SA) hierarchies. We prove a collection of dependencies among these hierarchies both for general CPOPs and for optimization problems on the Boolean hypercube. Key results include for the general case that the SONC and SOS hierarchy are polynomially incomparable, while SDSOS is contained in SONC. On the Boolean hypercube, we show as a main result that Schmudgen-like versions of the hierarchies SDSOS*, SONC*, and SA* are polynomially equivalent. Moreover, we show that SA* is contained in any Schmudgen-like hierarchy that provides a O(n) degree bound.
Soil Microbiota of Dystric Cambisol in the High Tatra Mountains (Slovakia) after Windthrow

There has been much more damage to forests in the Slovak Republic in the second half of the 20th century than to other European countries. Forested mountain massifs have become a filter of industrial and transportation emissions from abroad, as well as from domestic origins. There are not only acidic deposits of sulphur and heavy metals present in forest soils, but other additional environmental problems, such as climate change, storms, fires, floods, droughts, are worsening the situation. Therefore, forest terrestrial ecosystems are becoming more vulnerable due to changes in natural and environmental conditions. In the High Tatra Mountains in Slovakia, which are protected as a national park, four internationally monitored localities were established after the windthrow disaster in 2004 and fire in 2005: REF, with intact forest; EXT, with extracted wood mass; NEX, with non-extracted wood mass; and FIR, the burnt locality. Soils from these localities were microbiologically analysed with special attention to fungi. Bacterial microbiota detected by high-throughput sequencing showed the prevalence of the genera Acidothermus, Mycobacterium, and Nocardia, and a very low presence of the genera Acidibacter, Burkholderia-Paraburkholderia, Optitus and the uncultured genus Desulfurellaceae H16 in the soil sample from the burnt locality when compared with the unburned sites. Additionally, soil mycocoenoses showed a low similarity between the locality with an intact forest ecosystem and the localities with extracted (REF–EXT) and non-extracted (REF–NEX) wood mass. There was no similarity with the burnt locality (FIR), where heat-resistant fungi dominated. It was shown that the windthrow disaster and subsequent extraction or non-extraction of wood mass did not affect the soil microbial communities or their development. On the other hand, the influence of fire was significant.
The Effect of Transport Time Interval on Neurological Recovery after Out-of-Hospital Cardiac Arrest in Patients without a Prehospital Return of Spontaneous Circulation

Background Longer transport adversely affects outcomes in out-of-hospital cardiac arrest (OHCA) patients who do not return to spontaneous circulation (ROSC). The aim of this study was to determine the association between the transport time interval (TTI) and neurological outcomes in OHCA patients without ROSC. Methods We analyzed adult OHCA patients with presumed cardiac etiology and without prehospital ROSC from 2012 to 2015. The study population was divided into 2 groups according to STI (short STI [1–5 minutes] and long STI [≥ 6 minutes]). The primary exposure was TTI, which was categorized as short (1–5 minutes), intermediate (6–10 minutes), or long (≥ 11 minutes). The primary outcome was a good neurological recovery at discharge. Multiple logistic regression analysis was used in each STI group. Results Among 57,822 patients, 23,043 (40%), 20,985 (36%), and 13,794 (24%) were classified as short, intermediate, and long TTI group. A good neurological recovery occurred in 1.0%, 0.6%, and 0.3% of the patients in the short, intermediate and long TTI group, respectively. Among 12,652 patients with short STI, a good neurological recovery occurred in 2.2%, 1.0%, and 0.4% of the patients in the short, intermediate and long TTI group, respectively. Among 45,570 patients with long STI, a good neurological recovery occurred in 0.7%, 0.5%, and 0.3% of the patients in the short, intermediate and long TTI group, respectively. When short TTI was used as a reference, the adjusted odds ratios (AOR) of TTI for good neurological recovery was different between short STI group and long STI group (AOR [95% confidence interval, 0.46 [0.32–0.67] vs. 0.72 [0.59–0.89], respectively, for intermediate TTI and 0.31 [0.17–0.55] vs. 0.49 [0.37–0.65], respectively, for long TTI). Conclusion A longer TTI adversely affected the likelihood of a good neurological recovery in OHCA patients without prehospital ROSC. This negative effect was more prominent in short STI group.
Software-Defined Networking for Unmanned Aerial Vehicular Networking and Security: A Survey

Despite the immense benefits offered by the utilization of unmanned aerial vehicles (UAVs) in civilian and military applications, significant work needs to be done to ensure that these systems are able to securely communicate and resiliently operate to accomplish the mission. As the UAVs grow with their popularity and usability for different applications, there is a dire need to ensure that UAVs and their networks are capable of mitigating cyber-attacks on the fly. One approach that has gained considerable popularity is Software-Defined Networking (SDN) based solutions. SDN is a networking paradigm that has gained attention due to its dynamic flexibility to program networks and increase network visibility, and its potential to assist in the mitigating security vulnerabilities in the network including the network of UAVs. This article provides an overview of recent advances, and current state of art related to security vulnerabilities and SDN enabled countermeasures. This paper also presents a comparison of different approaches in a tabular form and a discussion of challenges and future research directions with respect to UAV security.
Advancing Marine Biogeochemical and Ecosystem Reanalyses and Forecasts as Tools for Monitoring and Managing Ecosystem Health

Ocean ecosystems are subject to a multitude of stressors, including changes in ocean physics and biogeochemistry, and direct anthropogenic influences. Implementation of protective and adaptive measures for ocean ecosystems requires a combination of ocean observations with analysis and prediction tools. These can guide assessments of the current state of ocean ecosystems, elucidate ongoing trends and shifts, and anticipate impacts of climate change and management policies. Analysis and prediction tools are defined here as ocean circulation models that are coupled to biogeochemical or ecological models. The range of potential applications for these systems is broad, ranging from reanalyses for the assessment of past and current states, and short-term and seasonal forecasts, to scenario simulations including climate change projections. The objectives of this article are to illustrate current capabilities with regard to the three types of applications, and to discuss the challenges and opportunities. Representative examples of global and regional systems are described with particular emphasis on those in operational or pre-operational use. With regard to the benefits and challenges, similar considerations apply to biogeochemical and ecological prediction systems as do to physical systems. However, at present there are at least two major differences: (1) biogeochemical observation streams are much sparser than physical streams presenting a significant hinderance, and (2) biogeochemical and ecological models are largely unconstrained because of insufficient observations. Expansion of biogeochemical and ecological observation systems will allow for significant advances in the development and application of analysis and prediction tools for ocean biogeochemistry and ecosystems, with multiple societal benefits.
Relationship Between the Reciprocal Change in Inflammation-Related Biomarkers (Fibrinogen-to-Albumin and hsCRP-to-Albumin Ratios) and the Presence and Severity of Coronary Slow Flow

Inflammation has been implicated in the pathogenesis of endothelial dysfunction, atherosclerosis, and microvascular coronary dysfunction. In this context, it is thought that fibrinogen, high-sensitive C-reactive protein (hsCRP), and albumin may be associated with the pathogenesis of coronary slow flow (CSF). We aimed to evaluate the ratios of fibrinogen-to-albumin and hsCRP-to-albumin in patients with CSF compared to patients with angiographically normal coronary arteries and stable coronary artery disease (CAD). In all, 65 patients with CSF, 65 patients with newly diagnosed stable CAD, and 65 control participants with angiographically normal coronary arteries were included. The coronary flow rates of all patients were determined by the Thrombolysis in Myocardial Infarction frame count method. Fibrinogen, hsCRP, and albumin levels were analyzed in all patients, and the fibrinogen-to-albumin and hsCRP-to-albumin ratios were calculated. The baseline characteristics of the 3 groups were similar. The plasma albumin level was significantly lower, whereas the fibrinogen and the hsCRP levels were significantly higher, in the CSF and CAD groups compared to the controls. The fibrinogen-to-albumin and hsCRP-to-albumin ratios were significantly higher in both the CSF and the CAD groups compared to the control group. The hsCRP-to-albumin ratio was positively correlated with the mean Thrombolysis in Myocardial Infarction frame count in the whole study population. According to the receiver–operating characteristic analysis, the efficacies of the fibrinogen-to-albumin and hsCRP-to-albumin ratios in predicting CSF were significant. The fibrinogen-to-albumin and hsCRP-to-albumin ratios, which were increased by a reciprocal change, suggest that inflammation may play a role in the pathogenesis of CSF.
Deep Learning for Automated Medical Image Analysis

Author(s): Zhu, Wentao | Advisor(s): Xie, Xiaohui | Abstract: Medical imaging is an essential tool in many areas of medical applications, used for both diagnosis and treatment. However, reading medical images and making diagnosis or treatment recommendations require specially trained medical specialists. The current practice of reading medical images is labor-intensive, time-consuming, costly, and error-prone. It would be more desirable to have a computer-aided system that can automatically make diagnosis and treatment recommendations. Recent advances in deep learning enable us to rethink the ways of clinician diagnosis based on medical images. Early detection has proven to be critical to give patients the best chance of recovery and survival. Advanced computer-aided diagnosis systems are expected to have high sensitivities and small low positive rates. How to provide accurate diagnosis results and explore different types of clinical data is an important topic in the current computer-aided diagnosis research. In this thesis, we will introduce 1) mammograms for detecting breast cancers, the most frequently diagnosed solid cancer for U.S. women, 2) lung Computed Tomography (CT) images for detecting lung cancers, the most frequently diagnosed malignant cancer, and 3) head and neck CT images for automated delineation of organs at risk in radiotherapy. First, we will show how to employ the adversarial concept to generate the hard examples improving mammogram mass segmentation. Second, we will demonstrate how to use the weakly labelled data for the mammogram breast cancer diagnosis by efficiently design deep learning for multi-instance learning. Third, the thesis will walk through DeepLung system which combines deep 3D ConvNets and Gradient Boosting Machine (GBM) for automated lung nodule detection and classification. Fourth, we will show how to use weakly labelled data to improve existing lung nodule detection system by integrating deep learning with probabilistic graphic model. Lastly, we will demonstrate the AnatomyNet which is thousands’ faster and more accurate than previous methods on automated anatomy segmentation.
A mixed analysis approach to elucidate the multiple chronic condition experience of English- and Spanish-speaking older adults

Background Having multiple chronic conditions (MCCs) is the most common health condition in older adults. The management of each condition presents many challenges. A key factor in patients’ ability to manage their own health care is their level of health literacy. Purpose The purpose of this mixed analysis study was to elucidate the experience of older adults living with MCCs and identify associations among the experiences to determine targets for future mobile health (mHealth) interventions focused on MCCs and health literacy. Participants and methods Using the Abilities, Skills, and Knowledge Model as a framework, semi-structured interviews were completed with 25 older adults in English and Spanish. Interviews were analyzed using inductive thematic analysis and hierarchical cluster analysis. Results The main themes from these results included 1) Reflections of chronic disease; 2) Emotional aspects of chronic disease; 3) Physical barriers to well-being; 4) Quality-of-care factors; and 5) Cognitive strategies for self-management. Qualitative results highlighted the importance of a multi-targeted approach to chronic disease self-management (CDSM). Cluster analysis identified associations within the qualitative data, revealing the importance of the subthemes related to coping with pain and the impact of the patient–provider relationship on treatment adherence. Conclusion Results support the utility of mHealth interventions to improve health literacy and promote CDSM.
Advertising and Content Differentiation: Evidence from YouTube

This paper studies the effect of advertising on content differentiation on YouTube, the secondmost visited website in the world. I demonstrate that an exogenous increase in the feasible advertising quantity leads to a considerable decrease in the YouTubers’ probability to duplicate mainstream content, i.e., the type of content that attracts the largest number of views. The result is driven by an intuitive mechanism: Mainstream content is provided by many competing YouTubers; thus, viewers who perceive advertising as a nuisance – and therefore as an implicit price they have to pay – could easily switch to a competitor if a YouTuber increased her advertising quantity. Switching is less likely, however, if the YouTuber differentiates her content from the mainstream, gains market power in a niche, and thereby softens competition in the ad “price. ”
A Deep Learning Based Resource Allocation Scheme in Vehicular Communication Systems

In vehicular communications, intracell interference and the stringent latency requirement are challenging issues. In this paper, a joint spectrum reuse and power allocation problem is formulated for hybrid vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Recognizing the high capacity and low-latency requirements for V2I and V2V links, respectively, we aim to maximize the weighted sum of the capacities and latency requirement. By decomposing the original problem into a classification subproblem and a regression subproblem, a convolutional neural network (CNN) based approach is developed to obtain real-time decisions on spectrum reuse and power allocation. Numerical results further demonstrate that the proposed CNN can achieve similar performance as the Exhaustive method, while needs only 3.62% of its CPU runtime.
Design of Ultra Wide Octave Electrically Tunable Bandpass Filter for Wireless Communication

Electrically tunable bandpass filters are crucial components in wireless communication system. In this paper, a combline filter structure is used to design an electrically tunable filter, whose working frequency range is 200MHz ~ 600MHz. In the entire tuning range, 1dB relative bandwidth is about 12%, the insertion loss is less than 3dB, the input 2rd-order intercept point(IIP2) is more than 60dBm, the input 3th-order intercept point(IIP3) is more than 20dBm, and the input and output voltage standing wave ratio(VSWR) is less than 1.8. The test and simulation results are in good agreement. Finally, we apply this tunable filter to a 20MHz~1350MHz tuned filter bank and achieve good results.
POLYMORPHISMS OF CYP1B1 AND COMT GENES AND THE RISK OF ENDOMETRIAL CANCER IN SOUTH IRAQI WOMEN

This study was designed to study the polymorphisms of CYP1B1 and COMT genes and the risk of endometrial cancer in south Iraqi women. A total of fifty patient with endometrial cancer, aged between 15-72 year were contacted after surgery in cancer centre in Basrah, and fifty healthy women with same age and state were served as control. The blood samples were collected in sterilized tube with EDTA for DNA extraction and PCR experiments. The results showed that the frequency of CYP1B1 homozygous wild type Leu /Leu was 14% and 68% in patients and control respectively, wile the homozygous mutant Val /Val was 32% and 28%, respectively. The heterozygous mutant allele Leu /Val was 54% in endometrial cancer cases compared to 4% in control. The statistical analysis showed increased risk of endometrial cancer with homozygous mutant Val/Val genotype with an OR of 5.55 (95% CI 2.57811.951 ) and with heterozygous Leu/Val with an OR of 65.5 (59% CI = 20.407-210.687 ). When the patients stratified according to different stages , we found no association between the endometrial caner and stage IC, the risk increased to two fold with stage IIB , while in stage IIIA and IVB the risk was highly increased to more than six fold with an OR of 6.138 and 6.277, respectively . The frequency of COMT homozygous wild type Met/Met genotype was 2% in patients and 70% in controls, while the homozygous mutant Val/Val genotype was 40% and 14% in cases and controls respectively. The heterozygous allele Val/Met increased to 58% in Cassese compared to 16% in controls. When stratified the patients according to different stages, the risk of cancer increased to 2 folds with stage IC with an OR of 2.66 ( 95% CI = 0.686-10.356 )and to 4 folds with stage IIB (OR of 4.0, 95% CI = 0.84918.836), while the risk was less than 2 with stage IIIA and IVB with an OR of 1.777 and 1.333 respectively. According to the grade of the cancer, the OR was 1.714 (95% CI=0.468-6.499) with grade II. The risk highly increased with grade III with an OR of 6.584 (CI=6.210-16.25). The risk of endometrial cancer increased to 2.5 fold in patient how had CYP1B1 mutant allele Leu/Val and COMT mutant allele.
Leisure‐Time Sedentary Behavior and Obesity Among 116,762 Adolescents Aged 12‐15 Years from 41 Low‐ and Middle‐Income Countries

Rates of adolescent obesity are increasing worldwide, with steeper increases being observed in low‐ and middle‐income countries (LMICs). Sedentary behavior (SB) has been associated with various physical and mental health conditions. Little is known about the association between SB and obesity among adolescents in LMICs. This cross‐sectional study explored the associations between SB and obesity in adolescents from 41 LMICs.
Syllable-based Neural Named Entity Recognition for Myanmar Language

Named Entity Recognition (NER) for Myanmar Language is essential to Myanmar natural language processing research work. In this work, NER for Myanmar language is treated as a sequence tagging problem and the effectiveness of deep neural networks on NER for Myanmar language has been investigated. Experiments are performed by applying deep neural network architectures on syllable level Myanmar contexts. Very first manually annotated NER corpus for Myanmar language is also constructed and proposed. In developing our in-house NER corpus, sentences from online news website and also sentences supported from ALT-Parallel-Corpus are also used. This ALT corpus is one part of the Asian Language Treebank (ALT) project under ASEAN IVO. This paper contributes the first evaluation of neural network models on NER task for Myanmar language. The experimental results show that those neural sequence models can produce promising results compared to the baseline CRF model. Among those neural architectures, bidirectional LSTM network added CRF layer above gives the highest F-score value. This work also aims to discover the effectiveness of neural network approaches to Myanmar textual processing as well as to promote further researches on this understudied language.