Miracle-dz/UniFAR-FTUs huggingface dataset:
Prospects of Perineural Administration of Autologous Mesenchymal Stem Cells of Adipose Tissue in Patients with Cerebral Infarction

Background: Frustrating statistics of therapy of patients with cerebral infarctions is well known. Longstanding deficiency of somatic and visceral functions is one of the negative consequences of existing therapy. Such state is developed due to persistent structural and functional lack of central control of various body activities by brain neural networks. Finally, most of the patients have severe invalidities even in distant period after performed treatment. Therefore, development and implementation of technologies that will allow activating reparative processes in brain after cerebral infarction is one of prospective tasks. Aims: development and implementation of new technique of cerebral infarctions treatment using autologous mesenchymal stem cells (MSC) of adipose tissue. Methods: Authors developed unique technique aimed at formation of conditions for natural migration of stem cells (SC) to the area of cerebral infarction after their endoscopic administration into nasal submucosa. Results: Autologous mesenchymal stem cells (MSC) of 25 patients naturally migrated to the area of cerebral infarction after their administration into nasal submucosa. The process of MSC differentiation into neuron-like elements after penetration to cranial cavity was previously shown in experiments. Patients with primary cerebral infarctions (n=12) and secondary ones (n=8) showed statistically significant improvement of physiological functions control in 6 months after course therapy with SC assessed by National Institutes of Health Stroke Scale (NIHSS) and Glasgow Coma Scale (GCS), respectively. Administration of allogeneic SC to patients with cerebral infarctions (n=5) was ineffective. Discussion and Conclusion: Combination of standard therapy of cerebral infarctions with endoscopic perineural implantation of autologous MSC of adipose tissue is accompanied with activation of reparative processes leading to recovery of neurologic functions.
A Modified Model for Electromagnetic Scattering of Sea Surface Covered with Crest Foam and Static Foam

With the increase of sea surface wind speed, whitecaps will appear on the sea surface. Generally, for Electromagnetic (EM) scattering of the foam-covered sea surface, medium-scale waves are used to replace the breaking waves of the real sea surface. Another treatment in computation is to adopt one of the whitecap coverages and fixed foam layer thickness. In fact, the evolution process of a breaking wave goes through two stages: stage A (crest foam) and stage B (static foam). In this paper, a geometric model of the sea surface covered with crest foam and static foam is established. The coverage ratio of stage A and stage B is proposed for the first time for a given sea state. In addition, different foam layer thickness distributions in each foam for various wind speeds are also considered. Based on the facet scattering theory of sea surface, this paper adopts the modified facet-based scattering model to deal with the scattering contribution of the sea surface and the effect of foam. Finally, in order to verify the accuracy of the geometric modeling and the scattering model of the sea surface, the EM backscattering of sea surface under different sea states are calculated. Simulation results show that the results of the proposed model are more consistent with the measured data than the results of the sea surface covered with individual crest foam or the sea surface covered with individual static foam.
3D Spatial Dependencies Study in the Hawk and Dove Model

The aim of the research was to check spatial dependencies in evolutionary games in 3D grids and compare them with simulation results (2D) and theoretical or analytical considerations obtained from the replicator dynamics equations. In order to compare the results, the classic Hawk and Dove model was used and a series of simulations for both v < c and v > c cases was performed using our own software. The results are almost the same as the theoretical analysis of this model, but some small differences were observed and discussed. It seems, however, that the 3D model better reflects the behaviour of the population than 2D simulations.
Estimating Ground-Level Particulate Matter in Five Regions of China Using Aerosol Optical Depth

Aerosol optical depth (AOD) has been widely used to estimate near-surface particulate matter (PM). In this study, ground-measured data from the Campaign on Atmospheric Aerosol Research network of China (CARE-China) and the Aerosol Robotic Network (AERONET) were used to evaluate the accuracy of Visible Infrared Imaging Radiometer Suite (VIIRS) AOD data for different aerosol types. These four aerosol types were from dust, smoke, urban, and uncertain and a fifth “type” was included for unclassified (i.e., total) aerosols. The correlation for dust aerosol was the worst (R2 = 0.15), whereas the correlations for smoke and urban types were better (R2 values of 0.69 and 0.55, respectively). The mixed-effects model was used to estimate the PM2.5 concentrations in Beijing–Tianjin–Hebei (BTH), Sichuan–Chongqing (SC), the Pearl River Delta (PRD), the Yangtze River Delta (YRD), and the Middle Yangtze River (MYR) using the classified aerosol type and unclassified aerosol type methods. The results suggest that the cross validation (CV) of different aerosol types has higher correlation coefficients than that of the unclassified aerosol type. For example, the R2 values for dust, smoke, urban, uncertain, and unclassified aerosol types BTH were 0.76, 0.85, 0.82, 0.82, and 0.78, respectively. Compared with the daily PM2.5 concentrations, the air quality levels estimated using the classified aerosol type method were consistent with ground-measured PM2.5, and the relative error was low (most RE was within ±20%). The classified aerosol type method improved the accuracy of the PM2.5 estimation compared to the unclassified method, although there was an overestimation or underestimation in some regions. The seasonal distribution of PM2.5 was analyzed and the PM2.5 concentrations were high during winter, low during summer, and moderate during spring and autumn. Spatially, the higher PM2.5 concentrations were predominantly distributed in areas of human activity and industrial areas.
Eye-pointer Coordination in a Decision-making Task Under Uncertainty

: Eye-tracking (ET) systems, which capture eye movements, are often used to measure human behavior while interacting with a user interface. Given the high costs and challenges of acquiring, installing and ensuring good calibration of ET systems, the use of pointer (or mouse) tracking is gaining interest as a viable alternative in research on human-computer interaction. In this study, we measured and evaluated temporal and spatial relationships between eye and pointer movements in a standardized task that allowed us to examine the relationship between eye and pointer movements while participants made decisions under conditions of high and low uncertainty. We collected data from N=81 participants and applied a range of metrics to a total of 5205 decision trials. The overall ﬁndings show that the convergence between eye and pointer movements is consistently high. Importantly, there are differences in levels of convergence depending on the temporal, spatial and combined temporo-spatial metrics used. There are also differences in eye-pointer convergence depending on the relative level of decision uncertainty in the task. In conclusion, the present ﬁndings favour the use of pointer tracking to analyse human-computer interaction in more complex tasks.
The Mother's Experience : A Phenomenological Study of Maternal Self-Efficacy Among Mothers Attending Outpatient Treatment for Substance Abuse

THE MOTHER’S EXPERIENCE: A PHENOMENOLOGICAL STUDY OF MATERNAL SELF-EFFICACY AMONG MOTHERS ATTENDING OUTPATIENT TREATMENT FOR SUBSTANCE ABUSE Lisa Marie Witkowski St. Mary's University, 2017 Dissertation Adviser: Jason Northrup, Ph.D. The purpose of this study is to better understand the perceptions of maternal selfefficacy among mothers in substance abuse recovery. A phenomenological research design using face-to-face, semi-structured interviews captured the lived experiences of mothers attending outpatient drug and alcohol treatment. For this study, the researcher distributed approved flyers to designated outpatient treatment centers to voluntarily recruit mothers for purposeful sampling. Eligibility requirements include women at least eighteen (18) years or older, at least one living child, attended prior rehabilitation services, and display good mental status after assessed by the researcher with the Mini Mental Status Exam. During the interview, the researcher collected demographical data and audio-taped the interview. Once interviews were completed, the researcher transcribed the audio-recorded interviews into a digital format and checked for accuracy. The researcher uploaded the digital transcriptions into a qualitative data analysis software Running head: THE MOTHER’S EXPERIENCE x program. Moustakas’ (1994) modified Stevick-Colaizzi-Keen method assisted in analyzing the transcriptions and identifying emerging themes. During data analysis, the “essence” evolved suggesting the lived experiences of mothers in outpatient substance abuse treatment: The integration process between good mothering beliefs and practices with sober living. The depicted essence formed from four identified categories: What It Means to Be a Good Mother, Consequences of Addiction on Motherhood, Being a Mother During Recovery, and Risk Factors. Within these categories, 11 themes emerged – Providing a Stable and Safe Environment, Nurturing the Relationship, Being a Good Role Model, Impact of Addiction on Me, Impact of Addiction on My Children, Hitting Rock Bottom, Treatment’s Role in Staying Sober, Fixing the Damage, Discovering My Purpose, Types of Trauma, and Life Stressors. Also, an additional 39 integral subthemes were identified. Member-checking with participants and peer debriefing was conducted to validate results. From the confirmed data, a depiction of the essence of the phenomenon is portrayed. This study concludes by discussing limitations, implications to clinical practice for mental health professionals, and recommendations for future research. Running head: THE MOTHER’S EXPERIENCE xi
Review and Evaluation of Deep Learning Architectures for Efficient Land Cover Mapping with UAS Hyper-Spatial Imagery: A Case Study Over a Wetland

Deep learning has already been proved as a powerful state-of-the-art technique for many image understanding tasks in computer vision and other applications including remote sensing (RS) image analysis. Unmanned aircraft systems (UASs) offer a viable and economical alternative to a conventional sensor and platform for acquiring high spatial and high temporal resolution data with high operational flexibility. Coastal wetlands are among some of the most challenging and complex ecosystems for land cover prediction and mapping tasks because land cover targets often show high intra-class and low inter-class variances. In recent years, several deep convolutional neural network (CNN) architectures have been proposed for pixel-wise image labeling, commonly called semantic image segmentation. In this paper, some of the more recent deep CNN architectures proposed for semantic image segmentation are reviewed, and each model’s training efficiency and classification performance are evaluated by training it on a limited labeled image set. Training samples are provided using the hyper-spatial resolution UAS imagery over a wetland area and the required ground truth images are prepared by manual image labeling. Experimental results demonstrate that deep CNNs have a great potential for accurate land cover prediction task using UAS hyper-spatial resolution images. Some simple deep learning architectures perform comparable or even better than complex and very deep architectures with remarkably fewer training epochs. This performance is especially valuable when limited training samples are available, which is a common case in most RS applications.
An Autoregressive Multiple Model Probabilistic Framework for the Detection of SSVEPs in Brain-Computer Interfaces

: This work investigates a novel autoregressive multiple model (AR-MM) probabilistic framework for the detection of steady-state visual evoked potentials (SSVEPs) in brain-computer interfaces (BCIs). The proposed method is compared to standard SSVEP detection techniques using a 12-class SSVEP dataset recorded from 10 subjects. The results, obtained from a single-channel analysis, reveal that the AR-MM probabilistic framework signiﬁcantly improves the SSVEP detection performance compared to the standard single-channel power spectral density analysis (PSDA) method. Speciﬁcally, an average classiﬁcation accuracy of 82.02 ± 16.21 % and an information transfer rate (ITR) of 48.22 ± 17.25 bpm are obtained with a 2 s period for SSVEP detection with the AR-MM probabilistic framework. These results are found to be on average only 2.29 % and 3.73 % lower in classiﬁcation accuracy compared to the state-of-the-art multichannel SSVEP detection methods, speciﬁcally the canonical correlation analysis (CCA) and the ﬁlter bank canonical correlation analysis (FBCCA) methods, respectively. In terms of training, it is shown that the proposed approach requires only a few seconds of data to train each model. This study revealed the potential of using the AR-MM probabilistic approach to distinguish between different classes using single-channel SSVEP data. The proposed method is particularly appealing for practical use in real-world BCI applications where a minimal amount of channels and training data are desirable.
A Synergetic Approach to Burned Area Mapping Using Maximum Entropy Modeling Trained with Hyperspectral Data and VIIRS Hotspots

Southern European countries, particularly Spain, are greatly affected by forest fires each year. Quantification of burned area is essential to assess wildfire consequences (both ecological and socioeconomic) and to support decision making in land management. Our study proposed a new synergetic approach based on hotspots and reflectance data to map burned areas from remote sensing data in Mediterranean countries. It was based on a widely used species distribution modeling algorithm, in particular the Maximum Entropy (MaxEnt) one-class classifier. Additionally, MaxEnt identifies variables with the highest contribution to the final model. MaxEnt was trained with hyperspectral indexes (from Earth-Observing One (EO-1) Hyperion data) and hotspot information (from Visible Infrared Imaging Radiometer Suite Near Real-Time 375 m active fire product). Official fire perimeter measurements by Global Positioning System acted as a ground reference. A highly accurate burned area estimation (overall accuracy = 0.99%) was obtained, and the indexes which most contributed to identifying burned areas included Simple Ratio (SR), Red Edge Normalized Difference Vegetation Index (NDVI750), Normalized Difference Water Index (NDWI), Plant Senescence Reflectance Index (PSRI), and Normalized Burn Ratio (NBR). We concluded that the presented methodology enables accurate burned area mapping in Mediterranean ecosystems and may easily be automated and generalized to other ecosystems and satellite sensors.
Evaluating Pretrained Transformer Models for Citation Recommendation

Citation recommendation systems for the scientiﬁc literature, to help authors ﬁnd papers that should be cited, have the potential to speed up discoveries and uncover new routes for scientiﬁc exploration. We treat this task as a ranking problem, which we tackle with a two-stage approach: candidate generation followed by re-ranking. Within this framework, we adapt to the scientiﬁc domain a proven combination based on “bag of words” retrieval followed by re-scoring with a BERT model. We experimentally show the eﬀects of domain adaptation, both in terms of pretraining on in-domain data and exploiting in-domain vocabulary. In addition, we evaluate eleven pretrained transformer models and analyze some unexpected failure cases. On three diﬀerent collections from diﬀerent scientiﬁc disciplines, our models perform close to or at the state of the art in the citation recommendation task.
3D Displacement Field of Wenchuan Earthquake Based on Iterative Least Squares for Virtual Observation and GPS/InSAR Observations

The acquisition of a 3D displacement field can help to understand the crustal deformation pattern of seismogenic faults and deepen the understanding of the earthquake nucleation. The data for 3D displacement field extraction are usually from GPS/interferometric synthetic aperture radar (InSAR) observations, and the direct solution method is usually adopted. We proposed an iterative least squares for virtual observation (VOILS) based on the maximum a posteriori estimation criterion of Bayesian theorem to correct the errors caused by the GPS displacement interpolation process. Firstly, in the simulation examples, both uniform and non-uniform sampling schemes for GPS observation were used to extract 3D displacement. On the basis of the experimental results of the reverse fault, the normal fault with a strike-slip component, and the strike-slip fault with a reverse component, we found that the VOILS method is better than the direct solution method in both horizontal and vertical directions. When a uniform sampling scheme was adopted, the percentages of improvement for the reverse fault ranged from 3% to 9% and up to 70%, for the normal fault with a strike-slip component ranging from 4% to 8% and up to 68%, and for the strike-slip fault with a reverse component ranging from 1% to 8% and up to 22%. After this, the VOILS method was applied to extract the 3D displacement field of the 2008 Mw 7.9 Wenchuan earthquake. In the East–West (E) direction, the maximum displacement of the hanging wall was 1.69 m and 2.15 m in the footwall. As for the North–South (N) direction, the maximum displacement of the hanging wall was 0.82 m for the southwestern, 0.95 m for the northeastern, while that of the footwall was 0.77 m. In the vertical (U) direction, the maximum uplift was 1.19 m and 0.95 m for the subsidence, which was significantly different from the direct solution method. Finally, the derived vertical displacements were also compared with the ruptures from field investigations, indicating that the VOILS method can reduce the impact of the interpolated errors on parameter estimations to some extent. The simulation experiments and the case study of the 3D displacement field for the 2008 Wenchuan earthquake suggest that the VOILS method proposed in this study is feasible and effective, and the degree of improvement in the vertical direction is particularly significant.
Using ASAR for Analysis of Electrogenic and Human Gut Microbial Communities

In this paper we describe applications of our ASAR package to functional, taxonomic and pathways analysis of metagenomes and propose future plans and perspectives. To illustrate an analytical potential of ASAR, we discuss outcomes of several projects. The main focus is made on metabolic plasticity of electrochemically active microbial communities and a potential role of integrated symbiotic bacterial interactions; antipathogenic properties of BES, manifested in its capacity to remove some pathogens from waste streams; and medical applications of this technology. We present ASAR-based metagenome analysis of evolving bacterial community from distillery waste over period of 36 months in BES environment as an example. Application of ASAR to personalised analyses of gut microbiome (GM) and the data interpretation based on publically available association studies are also discussed in this publication.
Development of HIV-1 Coreceptor Tropism Classifiers: An Approach to Improve X4 and R5X4 Viruses Prediction

: The pathway of human immunodeficiency virus (HIV) infection depends on the composition of a 35-amino acid variable region in its envelope, known as the V3 loop. Since this discovery, many tools have been developed to diagnose and predict viral tropism, from biochemical tests to various computational algorithms. To date, the biggest developmental difficulty is the correct prediction of X4 or R5X4-tropism virions. In this study, we evaluated some of these recommended criteria and proposed a random forest-based approach for better prediction of X4-capable (i.e., either X4-only, or R5X4-dual/mixed capability). All methods achieved a specificity higher than 87%, with geno2pheno 2.5% showing the best performance (98.2%). Nevertheless, the sensitivity (73.3%) was lower compared to the other approaches. The highest sensitivity was attained by our Complete Model with an undersampling strategy (90.1%). The accuracy of all approaches ranged from 87.4% to 93.0%. Complete Model with oversampling and Reduced Model with no balancing showed the highest MCC value (both with 0.796 score). Considering error rates and the number of explanatory variables, our main objective of increasing the ability to predict viral specimens with X4-tropism was achieved.
Wind-Temperature Regime and Wind Turbulence in a Stable Boundary Layer of the Atmosphere: Case Study

The paper presents the results of probing the stable atmospheric boundary layer in the coastal zone of Lake Baikal with a coherent Doppler wind lidar and a microwave temperature profiler. Two-dimensional height–temporal distributions of the wind velocity vector components, temperature, and parameters characterizing atmospheric stability and wind turbulence were obtained. The parameters of the low-level jets and the atmospheric waves arising in the stable boundary layer were determined. It was shown that the stable atmospheric boundary layer has an inhomogeneous fine scale layered structure characterized by strong variations of the Richardson number Ri. Layers with large Richardson numbers alternate with layers where Ri is less than the critical value of the Richardson number Ricr = 0.25. The channels of decreased stability, where the conditions are close to neutral stratification 0 < Ri < 0.25, arise in the zone of the low-level jets. The wind turbulence in the central part of the observed jets, where Ri > Ricr, is weak, increases considerably to the periphery of jets, at heights where Ri < Ricr. The turbulence may intensify at the appearance of internal atmospheric waves.
Investigation the Effect of Financial Ratios, Operating Cash Flows and Firm Size on Earnings Per Share: Evidence from the Tehran Stock Exchange

Financial ratios not only are used to understand the current and past performance and future prediction of company, but also they are used as a tool for planning and controlling the activities of the company. Therefore, the main objective of this study is to investigate the effect of financial ratios, operating cash flows and firm size on earnings per share of listed companies in Tehran Stock Exchange. To test the research hypotheses, the financial data of 140 companies listed in Tehran stock exchange during the time span 2006-2010 has been used. In the present study, the ratios of profitability, liquidity, leverage, activity and market value to book value and also operating cash flows and firm size are used as independent variables and the earnings per share is used as the dependent variable. The results indicate that there is a positive and significant relationship between financial ratios and firm size with earnings per share. But a significant relationship between the ratio of operating cash flows and earnings per share has not been observed.