Miracle-dz/UniFAR-FTUs huggingface dataset:
Stem Cell Treatments for Parkinson’s Disease

ABSTRACT Parkinson’s disease (PD) manifests with a typical movement disorder, due to the loss of dopaminergic neurons of the substantia nigra. There are no disease-modifying treatments, and current management is centered on symptom control using predominantly dopaminergic drugs. While effective at improving the motor symptoms of PD, these treatments result in significant adverse effects, due to non-targeted and non-physiological delivery of dopamine to the brain. For many years, there has been interest in cell grafting as a potential means of restor­ing dopamine to the striatum in a physiological manner, which would theoreti­cally treat the symptoms of PD that are due to dopamine deficiency, without the motor and neuropsychiatric adverse effects that are seen with dopaminergic medications. A number of cell sources have been trialed in PD patients, but lack of efficacy, ethical and logistical barriers have meant that most of these do not offer useful treatment options. Stem cell-based treatments are emerging as the most promising approach for the development of a useful regenerative treatment that could be used in a large number of patients. Although progress in this field has been slow, a number of exciting clinical trials are now on the horizon, and there is genuine hope that stem cells will enter the clinic in the short- to medium-term future.
Rapid expansion and facilitating factors of the Ponto-Caspian invader Dikerogammarus villosus within the eastern Baltic Sea

Dikerogammarus villosus, an amphipod of Ponto-Caspian origin, has recently and rapidly spread along Baltic coastal lagoons and estuaries. By 2016 it had invaded Russian (Kaliningrad region), Lithuanian and Latvian waters, but was not recorded from Estonian waters. This species has a discontinuous distribution suggesting a “jump” was involved in its dispersal. A classification tree and GLM analyses confirm such an observed distribution pattern and suggest productivity of the environment, distance to the nearest lagoon/river mouth and distance to the nearest port/marina were the most influential explanatory variables of its distribution. Our data indicates this rapid east and northward expansion is very likely due to vessel transport, which would account for the “jump” dispersal. Other vectors facilitating further spread are almost certainly acting at a local scale such as overland transportation of vessels, movements of diving gear, drifting mats of algae, macrophytes and flotsam, as well as natural spread. We predict the “killer shrimp” will soon appear within the entire Gulf of Riga and the Gulf of Finland, and also expand up-rivers of the eastern Baltic Sea. Following the species expansion, alterations and changes to macroinvertebrate assemblages in invaded areas can be expected.
Role of Magnetic field on the Dynamical Analysis of Second Grade Fluid: An Optimal Solution subject to Non-integer Differentiable Operators

The dynamical analysis of MHD second grade fluid based on their physical properties has stronger resistance capabilities, low-frequency responses, lower energy consumption, and higher sensitivities; due to these facts externally applied magnetic field always takes the forms of diamagnetic, ferromagnetic and paramagnetic. The mathematical modeling based on the fractional treatment of governing equation subject to the temperature distribution, concentration, and velocity field is developed within a porous surfaced plate. Fractional differential operators with and without non-locality have been employed on the developed governing partial differential equations. The mathematical analysis of developed fractionalized governing partial differential equations has been established by means of systematic and powerful techniques of Laplace transform with its inversion. The fractionalized analytical solutions have been traced out separately through Atangana-Baleanu and Caputo-Fabrizio fractional differential operators. Our results suggest that the velocity profile decrease by increasing the value of the Prandtl number. The existence of a Prandtl number may reflect the control of the thickness of momentum and enlargement of thermal conductivity.
Photoreaction dynamics of functional photoreceptor proteins

Channelrhodopsin (ChR) is a key protein of the optogenetic toolkit. C1C2, a functional chimeric protein of Chlamydomonas reinhardtii ChR1 and ChR2, is the only ChR whose crystal structure has been solved, and thus uniquely suitable for structure-based analysis. We report C1C2 photoreaction dynamics with ultrafast transient absorption and multi-pulse spectroscopy combined with target analysis and structure-based hybrid quantum mechanics/molecular mechanics calculations. Two relaxation pathways exist on the excited (S1) state through two conical intersections CI1 and CI2, that are reached via clockwise and counter-clockwise rotations: (i) the C13=C14 isomerization path with 450 fs via CI1 and (ii) a relaxation path to the initial ground state with 2.0 ps and 11 ps via CI2, depending on the hydrogen-bonding network, hence indicating active-site structural heterogeneity. The presence of the additional conical intersection CI2 rationalizes the relatively low quantum yield of photoisomerization (30±3%), reported here. Furthermore, we show the photoreaction dynamics from picoseconds to seconds, characterizing the complete photocycle of C1C2. This chapter is based on the following publication: Scientific Reports 7, 7217 (2017), collaborated with K. Stehfest and P. Hegemann for protein purification, M. Marazzi and M. Elstner for QM/MM calculation, and I. H. M. van Stokkum for target analysis.
Does Tourism Boost Economic Growth: Evidence from Italy

Purpose: Tourism plays a crucial role in enhancing the nation's welfare. Therefore, this study's core idea is to investigate tourist arrival, tourism growth, tourism expenditure, and its associated economic headway impact. Approach/Methodology/Design: Using an annual time series data from 1995-2018, capturing the effect of tourism growth, tourism expenditure on economic growth, and finally revealing the association amongst these endogenous and exogenous parameters. Findings: The unit root test analysis outcomes illustrate that all parameters become stationary when a first-order difference is considered as having a lag value of one. ADRL Bound test reveals the presence of short-run association among parameters. The outcomes of t-statistics and the Wald F-test reveal bidirectional and unidirectional causation among the parameters. Practical Implications: The study will contribute positively to the understanding of tourism and its associated effect to boost the economy. Originality/Value: The study believes to contribute positively to the understanding of the tourism sector.
Extreme genetic signatures of local adaptation during plant colonization

Colonization of new habitats is expected to require genetic adaptations to overcome environmental challenges. Here we use full genome re-sequencing and extensive common garden experiments to investigate demographic and selective processes associated with the recent colonization of Japan by Lotus japonicus. We carefully track the colonization process where L. japonicus gradually spread from subtropical conditions to much colder climates in northern Japan. We characterize the loss of diversity during this process and identify genomic regions with extreme genetic differentiation. Next, we perform population structure-corrected association mapping of phenotypic traits measured in a common garden and discover a number of genome-wide significant associations. Contrasting these analyses, we find that there is a strong concordance between phenotypic variation and extreme differentiation for overwintering and flowering time traits. Our results provide evidence that these traits were direct targets of selection by local adaptation during the colonization process and point to associated candidate genes.
Implementation of the Perfect Plasticity Approximation with biogeochemical compartments in R

Modeling forest ecosystems is a landmark challenge in science, due to the complexity of the processes involved and their importance in predicting future planetary conditions. While there are a number of open-source forest biogeochemistry models, few papers exist detailing the software development approach used to develop these models. This has left many forest biogeochemistry models large, opaque, and/or difficult to use, typically implemented in compiled languages for speed. Here, we present a forest biogeochemistry model from the SORTIE-PPA class of models, PPA-SiBGC. Our model is based on the Perfect Plasticity Approximation with simple biogeochemistry compartments and uses empirical vegetation dynamics rather than detailed prognostic processes to drive the estimation of carbon and nitrogen fluxes. This allows our model to be used with traditional forest inventory data, making it widely applicable and simple to parameterize. We detail the conceptual design of the model as well as the software implementation in the R language for statistical computing. Our aim is to provide a useful tool for the biogeochemistry modeling community that demonstrates the importance of vegetation dynamics in biogeochemical models.
Genetic Relationship among Paphiopedilum Subgenus Brachypetalum Section Brachypetalum Using HAT-RAPD Markers

Genetic relationship of lady’s slipper orchids, Paphiopedilum subgenus Brachypetalum section Brachypetalum, 14 varieties found in Thailand, was demonstrated by HAT-RAPD markers. The total of 72 random primers were screened and 29 primers could be used for DNA amplification. Twenty-two primers were selected and used to analyze each DNA species of all samples. A dendrogram was constructed, based on polymorphic bands, and showed genetic similarities among 4 clusters with the similarity coefficients ranging from 0.32 to 0.81 as well as the analysis of genetic diversity.
A machine learning approach for the spatiotemporal forecasting of ecological phenomena using dates of species occurrence records

Spatiotemporal forecasts of ecological phenomena are highly useful and significant in scientific and socio-economic applications. Nevertheless, developing the correlative models to make these forecasts is often stalled by the inadequate availability of the ecological time-series data. On the contrary, considerable amounts of temporally discrete biological records are being stored in public databases, and often include the sites and dates of the observation. While these data are reasonably suitable for the development of spatiotemporal forecast models, this possibility remains mostly untested. In this paper, we test an approach to develop spatiotemporal forecasts based on the dates and locations found in species occurrence records. This approach is based on ‘time-series classification’, a field of machine learning, and involves the application of a machine-learning algorithm to classify between time-series representing the environmental conditions that precede the occurrence records and time-series representing other environmental conditions, such as those that generally occur in the sites of the records. We employed this framework to predict the timing of emergence of fruiting bodies of two mushroom species (Boletus edulis and Macrolepiota procera) in countries of Europe, from 2009 to 2015. We compared the predictions from this approach with those from a ‘null’ model, based on the calendar dates of the records. Forecasts made from the environmental-based approach were consistently superior to those drawn from the date-based approach, averaging an area under the receiver operating characteristic curve (AUC) of 0.9 for B. edulis and 0.88 for M. procera, compared to an average AUC of 0.83 achieved by the null models for both species. Prediction errors were distributed across the study area and along the years, lending support to the spatiotemporal representativeness of the values of accuracy measured. Our approach, based on species occurrence records, was able to provide useful forecasts of the timing of emergence of two mushroom species across Europe. Given the increased availability and information contained in this type of records, particularly those supplemented with photographs, the range of events that could be possible to forecast is vast.
A configural model of expert judgement as a preliminary epidemiological study of injury problems: An application to drowning

Robust epidemiological studies identifying determinants of negative health outcomes require significant research effort. Expert judgement is proposed as an efficient alternative or preliminary research design for risk factor identification associated with unintentional injury. This proposition was tested in a multi-factorial balanced experimental design using specialist judges (N=18), lifeguards and surfers, to assess the risk contribution to drowning for swimming ability, surf bathing experience, and wave height. All factors provided unique contributions to drowning risk (p<.001). An interaction (p=.02) indicated that occasional surf bathers face a proportionally increased risk of drowning at increased wave heights relative to experienced surf bathers. Although findings were limited by strict criteria, and no gold standard comparison data were available, the study provides new evidence on causal risk factors for a drowning scenario. Countermeasures based on these factors are proposed. Further application of the method may assist in developing new interventions to reduce unintentional injury.
Determinants of Electronic Word-of-Mouth among Line Users in Thailand

Electronic Word-of-Mouth (eWOM) plays an important role as a new marketing tool in many countries nowadays. eWOM has been used through social media such as Facebook, Instagram, etc. eWOM in social media allows a continuous connection to a broad audience. Line application is the latest social media introduced in 2011. This study thus explored factors impacting on the eWOM intention via Line application which is a famous social media used, especially in Thailand. The four factors (user preference, user similarity, user interaction and user concern for others) drawn from Consumer Behaviors theory and two factors (ease of use, and usefulness) drawn from Technology Acceptance Model (TAM) theory were used in this study. The samples of 196 respondents were collected by using convenience sampling method via Google form survey. Multiple regression with stepwise method was used to analyze the data. The results showed that user preference, Line ease of use and Line usefulness have positive relationship with eWOM, while user similarity, user interaction and user concern have no relationship with eWOM. For theoretical contribution, the study expanded the application of TAM to explore the changing consumer behaviors and eWOM. For managerial implication, this study could help marketer develop marketing strategies that allow businesses to build on communication platform. Keywords: Electronic Word-of-Mouth, Consumer Behaviors Theory, Technology Acceptance Theory, Line Application
Capital Structure of Property Sector in Malaysia

Article history: Received 25 June 2020 Accepted 18 August 2020 Published 30 September 2020 The inconclusiveness of findings on the capital structure has been the main debatable topic and repeatedly discussed to date. This paper is providing new empirical evidence on the relationship between capital structure and firm efficiency by investigating the sample of Syariahcompliant property firms in Malaysia from 2002 to 2011. By employing panel data, this study is examining the factors influencing the firm leverage and analysing the dynamic aspect of the capital structure of the listed Syariah-compliant property firms in Malaysia. The study employs a parametric Stochastic Frontier Analysis (SFA) method as well, to empirically construct the property industry's 'best practice' frontier and measure the firm's efficiency as the distance from that frontier. Using these performance measures, the researchers examine how efficient firms decide debt level in their capital structure. In summary, it is found that the effects of efficiency on capital structure are as stated in the efficiency-risk hypothesis. Based on the Generalised Method of Moment (Differenced GMM), the results indicate that seven determinants are found significantly influencing the capital structure decision of the firms, particularly in the property sector in Malaysia. Those determinants are tangibility, profitability, interest coverage ratio, quick ratio, Z-Score, market-to-book ratio and efficiency. Generally, the findings support both static trade-off theory and pecking order theory for Malaysian Shariah-compliant property firms.
Genetic Variability and Yielding Ability of Selected Bread Wheat (Triticum aestivum L.) Genotypes under Winter Conditions, Gezira State, Sudan

Wheat (Triticum aestivum L.) is the most widely grown cereal in temperate and some tropical environments but is faced with heat stress problems in the tropics. The aim of this study was to estimate the genetic variability and yielding ability of selected bread wheat genotypes under heat stress conditions, Gezira State, Sudan. The experiment was conducted by using 14 selected genotypes selected from the bread wheat cultivar 'Norin 61. ' The genotypes were grown under the natural heat stress conditions, Gezira Research Station Farm, Agricultural Research Corporation , Wad Medani, Sudan, for one season (2016/17). The experiment was laid out in an alpha lattice design with two replications and six characters (number of days to heading, number of days to maturity, canopy temperature at heading , chlorophyll content, biomass and grain yield) were measured. Results revealed that the genotypes differed significantly in all characters studied. Genotypic coefficient of variation (GCV) ranged from 1.97% (canopy temperature at heading ) to 18.94% (grain yield per ha), while phenotypic coefficient of variation (PCV) ranged between 4.34% (canopy temperature at heading ) to 29.27% (grain yield). High heritability and genetic advance were recorded for days to maturity (98.88% and 23.82% , respectively), whereas the moderate heritability and high genetic advance was observed for grain yield (41.87% and 25.25%, respectively). Genotypes MSD212 and MSD185 were early heading and maturing genotypes, while MSD63 and MSD220 were late genotypes for both characters. These genotypes can be recommended for solving the problems of early and late maturing in breeding programs. Genotype MSD156 gave the highest yield (3937kg/ha) which can be grown to increase production in the Gezira area and to be subjected for further testing in different locations and seasons to test its stability.
New hand-cart for Old-Delhi whole sale market designed and engineered using generic product development process

The study aimed to design a hand cart for the cart puller in the old Delhi area. We used a Generic Product Development Process as a base to design and test our new cart design for the cart pullers. The process had been used in a sequential procedure to get the best design solution. It also saves time and money which would be wasted in the reiteration of the cart design. It starts with User Research where we find out the basic user needs, which can be conducted via interview. Then to convert the user needs into applicable design specifications, we used a Quality Function Deployment (QFD). The next stage was to design the concepts, to do so we used the Morphological chart which helped us to generate concepts logically. After the generation of concepts, we had to select one concept which is the most promising one, to do so we used Pugh’s Concept Selection method and we landed on one concept. To make the detailed design of the cart we used dimensions from the existing cart and used Indian anthropometric data to add ergonomics into the cart design. A 3D CAD Design was made using detailed dimensions. Finally, based on CAD design a low fidelity prototype was made, and then it was translated into a 1:1 scale prototype. The last stage of testing was done and results were compared with the existing cart design in terms of Rapid Entire Body Assessment (REBA), Hierarchical Task Analysis (HTA) and Verbal interview.
PRECISION OF ANTHROPOMETRIC EQUATIONS TO ESTIMATE TOTAL ENERGY EXPENDITURE OF OLDER ADULTS WITH SARCOPENIA: A CROSS SECTIONAL STUDY

Purpose: The accuracy and errors of prediction of equations that estimate energy expenditure are unknown to seniors with sarcopenia. This study assessed the precision of predictive equations to estimate energy needs of sarcopenic seniors. Methods: Ninety-four community-dwelling older adults (female: 66; mean age: 75.9[5.7]) were tested for body dimensions derived from DXA. Performance tests and diagnosis of sarcopenia were performed. The total energy expenditure (TEE) of sarcopenic seniors (n=10) was measured by accelerometry and compared with three anthropometric equations: DRIs, Escott-Stump with upper (Escott-Stumpupp) and low energy margin (Escott-Stumplow). Results: Except for height, all other variables of body dimensions of sarcopenic seniors were smaller than non-sarcopenic ones (p<0.05). The slightly lower TEE values in the Escott-Stumplow equation were not different from accelerometry (-53kcal; t=0.606; p=0.560) but were overestimated by the DRIs (+358kcal; t=3.163; p=0.011) and Escott-Stumpupp (+240kcal; t=-5.817; p<0.001), confirmed due to lack of agreement (Bland-Altman) with measured TEE. Conclusion: Smaller body dimensions of sarcopenic seniors suggest that their energy needs should be estimated from specific resources. The TEE assessed by the Escott-Stumplow equation was similar to that measured by accelerometry and therefore may be a good alternative for sarcopenic seniors where direct measurement of TEE is not possible.
Verification of a classification prediction method for the development of musical expression in early childhood using a machine learning method based on 3D motion capture data

It is widely acknowledged that children take developmental steps in performing musical expression through body movement dynamics. The author presents an approach to verify the method of classification prediction by machine learning using multiple classifiers to evaluate and classify the developmental degree of musical expressions in early childhood. The author addresses this potential solution by showing statistical analysis of full-body 3D motion captured data using such statistical analysis, and applies machine learning measures to predict developmental degrees of musical expression. In 2016, the author extracted the feature quantities of movement based on the results of the movement analysis of the musical expressions in the MEB program regarding 3-year-old, 4-year-old, and 5-year-old children (n=76). The developmental degree of musical expression was classified into three stages based on video analysis of the musical expression of each child. The 2016 training data as the feature quantity of movement (factors) and the three-stage evaluation (categorical dependent variables) were used as the model training for machine learning. Based on the results in 2017 with 2018, the classification model training results were applied to the acquired data (n=87) in 2019. The result of sensitivity analysis showed that the moving average acceleration of pelvis, the moving distance of right foot and the moving distance of right hand had a strong influence on the development of musical expression in early childhood. From those analysis results, the appropriateness of the machine learning method using decision trees for classifying the developmental degree of musical expression in early childhood was verified.