Miracle-dz/UniFAR-FTUs huggingface dataset:
A classification for extant ferns

We present a revised classification for extant ferns, with emphasis on ordinal and familial ranks, and a synopsis of included genera. Our classification reflects recently published phylogenetic hypotheses based on both morphological and molecular data. Within our new classification, we recognize four monophyletic classes, 11 monophyletic orders, and 37 families, 32 of which are strongly supported as monophyletic. One new family, Cibotiaceae Korall, is described. The phylogenetic affinities of a few genera in the order Polypodiales are unclear and their familial placements are therefore tentative. Alphabetical lists of accepted genera (including common synonyms), families, orders, and taxa of higher rank are provided.
Adaptive Data Transmission Method According to Wireless State in Long Range Wide Area Networks

: The Internet of Things (IoT) has enabled various intelligent services, and IoT service range has been steadily extended through long range wide area communication technologies, which enable very long distance wireless data transmission. End-nodes are connected to a gateway with a single hop. They consume very low-power, using very low data rate to deliver data. Since long transmission time is consequently needed for each data packet transmission in long range wide area networks, data transmission should be efficiently performed. Therefore, this paper proposes a multicast uplink data transmission mechanism particularly for bad network conditions. Transmission delay will be increased if only retransmissions are used under bad network conditions. However, employing multicast techniques in bad network conditions can significantly increase packet delivery rate. Thus, retransmission can be reduced and hence transmission efficiency increased. Therefore, the proposed method adopts multicast uplink after network condition prediction. To predict network conditions, the proposed method uses a deep neural network algorithm. The proposed method performance was verified by comparison with uplink unicast transmission only, confirming significantly improved performance.
Diversity and distribution of epiphytic macrolichens on tree trunks in two slopes of the montane rainforest of Gran Piedra, Santiago de Cuba

Abstract The macrolichens are a conspicuous component of the montane rainforest of Gran Piedra, Santiago de Cuba. This study aims at comparing the species composition, frequency and cover area of epiphytic macrolichens between the north and south slopes, as well as to determine the variation in species composition and frequency of epiphytic macrolichens in communities along and around the tree trunk. Six plots were sampled; three on the north-facing slope and three on the south slope. We found 21 species of macrolichens. Sticta beauvoisii, Hypotrachyna lineariloba and Pannaria tavaresii are new records for Cuba. Differences were observed in species composition between slopes, whereas neither the species composition nor the frequency changed significantly with cardinal orientation or vertical distribution on the trunk. Heterodermia leucomelaena and Leptogium austroamericanum were considered as indicators of the north slopes, and Cladonia ceratophylla of the south slope.
Neurons as Canonical Correlation Analyzers

Normative models of neural computation offer simplified yet lucid mathematical descriptions of murky biological phenomena. Previously, online Principal Component Analysis (PCA) was used to model a network of single-compartment neurons accounting for weighted summation of upstream neural activity in the soma and Hebbian/anti-Hebbian synaptic learning rules. However, synaptic plasticity in biological neurons often depends on the integration of synaptic currents over a dendritic compartment rather than total current in the soma. Motivated by this observation, we model a pyramidal neuronal network using online Canonical Correlation Analysis (CCA). Given two related datasets represented by distal and proximal dendritic inputs, CCA projects them onto the subspace which maximizes the correlation between their projections. First, adopting a normative approach and starting from a single-channel CCA objective function, we derive an online gradient-based optimization algorithm whose steps can be interpreted as the operation of a pyramidal neuron. To model networks of pyramidal neurons, we introduce a novel multi-channel CCA objective function, and derive from it an online gradient-based optimization algorithm whose steps can be interpreted as the operation of a pyramidal neuron network including its architecture, dynamics, and synaptic learning rules. Next, we model a neuron with more than two dendritic compartments by deriving its operation from a known objective function for multi-view CCA. Finally, we confirm the functionality of our networks via numerical simulations. Overall, our work presents a simplified but informative abstraction of learning in a pyramidal neuron network, and demonstrates how such networks can integrate multiple sources of inputs.
Floodplain Inundation and Salinization From a Recently Restored First‐Order Tidal Stream

The systematic response of coastal ecosystems to inundation and salinity exposure is fundamental to their ecology and biogeochemical function. Here we observe and model freshwater‐seawater interactions in a first‐order stream—floodplain system where tidal access was recently restored. Subsurface flow and transport modeling were used to quantify and better understand the interplay of processes, properties, and conditions that control water level and salinity in the floodplain to the tidal stream. Water levels in the stream were highly correlated with tidal forcing, which resulted in episodic inundation of the floodplain at quasi‐monthly frequency. The tidal stream is the only source of salinity to the floodplain, yet shallow groundwater salinity was considerably higher than average stream salinity. The low‐permeability clay floodplain soils limit lateral groundwater flow and transport, resulting in floodplain groundwater and salinity dynamics driven almost exclusively by infiltration during inundation events. As inundation occurs during high tide, estuarine waters reach the floodplain with minor attenuation in salinity from the stream's freshwater discharge. Infiltration and salinity exposure are topography controlled and regulated by ponding depth and duration, seasonal ground saturation, and depth to water table. The model suggests that floodplain salinity is currently in an early stage of transition from pre‐restoration freshwater conditions and will not reach equilibrium for ~20 years. These findings have broad relevance for understanding how and over what time scales coastal ecosystems will respond to increasing seawater exposure from sea level rise, ocean‐originating storms, and changes in natural and man‐made barriers.
Xanthomonas oryzae pv. oryzae infection triggers accumulation of phenolics, defense-related enzymes and thaumatin-like proteins in rice leaves

Abstract The effect of Xanthomonas oryzae pv. oryzae infection on induction of phenylalanine ammonia-lyase (PAL), peroxidase (PO), phenolics and thaumatin-like proteins (TLPs) in rice was studied. PAL activity increased significantly one day after inoculation with X. o. pv. oryzae and the maximum enzyme activity was observed two days after inoculation. The phenolic content in rice leaves increased significantly one day after inoculation and the maximum accumulation of phenols was observed two days after inoculation. Significant increase in peroxidase activity was observed in rice leaves one day after inoculation with X. o. pv. oryzae. Isozyme analysis indicated that three peroxidase isozymes (PO-1, PO-2 and PO-3) were induced after inoculation with X. o. pv. oryzae. Immunoblot analysis of protein extracts from control and pathogen inoculated rice plants revealed the induced accumulation of 16 and 24 kDa TLPs in rice leaves in response to X. o. pv. oryzae infection. TLP mRNA accumulation was induced strongly in rice leaves in response to infection by X. o. pv. oryzae.
Compressive sensing‐based two‐dimensional scattering‐center extraction for incomplete RCS data

We propose a two‐dimensional (2D) scattering‐center‐extraction (SCE) method using sparse recovery based on the compressive‐sensing theory, even with data missing from the received radar cross‐section (RCS) dataset. First, using the proposed method, we generate a 2D grid via adaptive discretization that has a considerably smaller size than a fully sampled fine grid. Subsequently, the coarse estimation of 2D scattering centers is performed using both the method of iteratively reweighted least square and a general peak‐finding algorithm. Finally, the fine estimation of 2D scattering centers is performed using the orthogonal matching pursuit (OMP) procedure from an adaptively sampled Fourier dictionary. The measured RCS data, as well as simulation data using the point‐scatterer model, are used to evaluate the 2D SCE accuracy of the proposed method. The results indicate that the proposed method can achieve higher SCE accuracy for an incomplete RCS dataset with missing data than that achieved by the conventional OMP, basis pursuit, smoothed L0, and existing discrete spectral estimation techniques.
High‐throughput screening in natural product drug discovery in Australia utilising Australia's biodiversity

Novel Australian biodiversity is being examined as a potential source of new therapeutic agents. Drug discovery begins with attempts to find a molecule that causes a specific biological response, i.e., a “hit. ” The trend in drug discovery is clearly towards rapid, high‐throughput screening (HTS) of large libraries of compounds. A particularly attractive option for HTS targets that has become available with the ability to clone human genes is to utilise human targets in the screening process. There is increased probability that the compounds discovered would be more effective in their eventual human target than may be the case for compounds discovered using animal models or, indeed, animal gene products. HTS strategies involve screening of compound libraries, combinatorial libraries, and natural product extracts. The limiting factor in HTS is the ability to access large numbers of chemically diverse substances. Natural products are the greatest source of structural diversity. HTS of the unparalleled diversity in natural product extracts thereby offers one of the best chances for discovery of novel lead compounds and is complementary to the newly emerging source of compounds provided by combinatorial chemistry. However, the complexity of natural product extracts brings with it the added effort required to obtain a single pure compound. Drug Dev. Res. 46:250–254, 1999. © 1999 Wiley‐Liss, Inc.
Evaluation of Experimental Multi-Particulate Polymer-Coated Drug Delivery Systems with Meloxicam

The objectives of this study are the development and evaluation of modified release multi-particulate drug delivery systems containing a BCS class II drug (meloxicam), formulated as polymer-coated pellets. Inert seeds containing microcrystalline cellulose, lactose monohydrate, and polyvinylpyrrolidone were prepared by extrusion-spheronization. The obtained cores were loaded with meloxicam using the drug layering technique, by spray coating in a fluidized bed with a liquid dispersion of the drug. The resulting drug pellets were film-coated with various polymers (Acryl-EZE® 93O, Eudragit® RS 30-D as well as experimental composite obtained by adding Methocel™ E5 Premium LV as pore forming agent to the extended release polymer Eudragit® RS 30-D). All experimental systems were evaluated by scanning electron microscopy and in vitro release testing, in an attempt to investigate the characteristics of the film coatings and their influence on drug release from the multi-particulate systems. The in vitro release study was performed in two stages, using two media with pH values corresponding to the gastric and intestinal environment (HCl 0.1N, pH = 1.2 for the first two hours of the test and phosphate buffer 50 mM, pH 6.8 for the next 4 h). The in vitro release data have highlighted the impact of the formulation factors on the drug release.
Color vision, accommodation and visual acuity in the largemouth bass

: The largemouth bass is a sight feeder and one of the most popular freshwater fish for lure fishing. While lures a have variety of colors and forms, there is no information on the color vision and form perception in the largemouth bass. We recorded S-potentials from the isolate retinae of the largemouth bass to examine color vision electrophysiologically. The occurrence of the C-response provided direct evidence for the possession of color vision. L-responses recorded indicated a high spectral sensitivity to red. The direction and distance of lens movement of a specimen of 43 cm in fork length indicated a horizontal visual axis and the near point at 13.5 cm, respectively. The visual acuity calculated from the cone density and the focal distance of the lens was 0.10. Histological characteristics of the retina indicated that the eyes are well adapted to both form and movement perceptions.
Determining Growth Sensitivity of Star Math With a Latent Growth Curve Model

Repeated measurements of student ability (i.e., progressing monitoring) is an essential element of informed decision-making when adjusting instruction. An important characteristic of progress monitoring measures is frequent administration to identify areas of concern and to evaluate academic growth. The purpose of this study was to determine if STAR Math is sensitive to small incremental growth across a semester. Within two southern school districts, 114 fifth grade students’ progress monitoring data were collected weekly, and a latent growth curve was used to estimate students’ change in math ability. Results indicated STAR Math is sensitive to small incremental growth, with a statistically significant and positive slope, suggesting students using STAR Math showed improvement in ability over the semester.
Genetic divergence of black gram genotypes (Vigna mungo (L.) Hepper).

Divergence analysis was carried out among hundred black gram genotypes using Mahalanobis D2 analysis. Among ten characters studied, seed yield per plant (g) contributes maximum to the genetic diversity (33.31%). Black gram genotypes were grouped into 14 clusters. Among this, cluster I had maximum number of genotypes. Maximum intra-cluster distance was found in cluster II and the maximum inter-cluster distance was observed between cluster IV and XI. Cluster XI (VBG 19-002) showed the highest cluster mean for seed yield per plant (g), the number of branches per plant and the total number of pods. Cluster IV (VBG 18-076) had superior mean for days to 50% flowering, plant height (cm) and pod length (cm). Hence, hybridization of genotypes VBG 18-076 (Cluster IV) and Mash 114 (Cluster XII) with VBG 19-002 (Cluster XI) could give wide range of variability in segregating population.
Molecular Phylogeny Implicates New World Opossums (Didelphidae) as the Definitive Hosts of Sarcocystis ramphastosi, a Parasite of the Keel-billed Toucan (Ramphastos sulfuratus)

Macroscopic sarcocysts of Sarcocystis ramphastosi Dubey, Lane, and van Wilpe, 2004 were found in a toucan (Ramphastos sulfuratus) from Costa Rica. The DNA extracted from 11 individual sarcocysts was subjected to PCR amplification assays targeting 1208 bp of 18S ribosomal DNA; data revealed a close phylogenetic relationship to S. falcatula and S. neurona, transmitted by didelphid marsupials, indigenous to Americas. These results suggest opossums as a definitive hosts for Sarcocystis ramphastosi.
Effect of different oil cake sources on growth, nutrient retention and digestibility, muscle nucleic acid content, gut enzyme activities and whole‐body composition in silver barb, Puntius gonionotus fingerlings

Six iso-nitrogenous (30% crude protein) and iso-energetic (15 kJ g−1) diets were prepared using different oil cake sources, viz. groundnut, soybean, sunflower, sesame, mustard and mixed oil cakes as major ingredients, and protein sources along with a minimum of 5% fish meal in each diet and were fed to silver barb Puntius gonionotus fingerlings (16.20±0.11 g) ad libitum four times a day close to an apparent satiation level for a period of 60 days to determine the effect of diets on growth, nutrient utilization, apparent digestibility coefficient (ADC) of the nutrients in the diets, gut enzyme activity, muscle nucleic acid content and whole-body chemical composition of fish. Significantly higher (P<0.05) weight gain, specific growth rate, protein efficiency ratio, nutrient retention, ADC of nutrients in the diets, DNA:RNA ratio, protease and amylase activity with lower (P<0.05) feed:gain values were recorded in fish-fed groundnut and soybean oil cake-based diets than other diets tested. Among the dietary treatment groups, significantly higher (P<0.05) whole-body protein, lipid and energy were also found in groundnut oil cake- and soybean oil cake-based diets. The study suggests that the groundnut and soybean oil cake-based diets, which led to significantly higher (P<0.05) growth and nutrient utilization than the other oil cake-based diets in P. gonionotus fingerlings, may be used for pond culture of this species.
A Parallel Military-Dog-Based Algorithm for Clustering Big Data in Cognitive Industrial Internet of Things

With the advancement of wireless communication, Internet of Things (IoT), and big data, high performance data analytic tools and algorithms are required. Data clustering, a promising analytic technique is widely used to solve the IoT and big-data-based problems, since it does not require labeled datasets. Recently, metaheuristic algorithms have been efficiently used to solve various clustering problems. However, to handle big datasets produced from IoT devices, these algorithm fail to respond within the desired time due to high computation cost. This article presents a new metaheuristic-based clustering method to solve the big data problems by leveraging the strength of MapReduce. The proposed methods leverages the searching potential of military dog squad to find the optimal centroids and MapReduce architecture to handle the big datasets. The optimization efficacy the proposed method is validated against 17 benchmark functions, and the results are compared with five other recent algorithms, namely, bat, particle swarm optimization, artificial bee colony, multiverse optimization, and whale optimization algorithm. Furthermore, a parallel version of the proposed method is introduced using MapReduce [MapReduce-based MDBO (MR-MDBO)] for clustering the big datasets produced from industrial IoT. Moreover, the performance of MR-MDBO is studied on two benchmark UCI datasets and three real IoT-based datasets produced from industry. The F-measure and computation time of the MR-MDBO is compared with the six other state-of-the-art methods. The experimental results witness that the proposed MR-MDBO-based clustering outperforms the other considered algorithms in terms of clustering accuracy and computation times.
High-Resolution Squinted SAR Imaging Algorithm Applied in the Near-Field Environment

In the squinted synthetic aperture radar (SAR) imaging of the near-field environment, range-dependent characteristic of squint angle cannot be ignored, which causes azimuth-dependent range cell migration (RCM) after linear range walk correction (LRWC). In this study, an efficient SAR imaging algorithm applied in the near-field environment is proposed. In the processing of the range focusing, LRWC is firstly used to remove the linear RCM. Then, the residual LRCM is expanded into azimuth-invariant and azimuth-variant terms in consideration of the residual LRCM of azimuth-dependent. Range cell migration azimuth scaling (RCMAS) is designed to remove the azimuth-variant term before secondary range compression (SRC) and range compression (RC). In the azimuth focusing, azimuth distortion compensation (ADC) is performed to compensate the azimuth distortion, following which azimuth nonlinear chirp scaling (ANCS) is applied to equalize the frequency modulation (FM) rate for azimuth compression (AC). The simulated results show that more accurate and improved imaging result can be obtained with the proposed algorithm.
Left ventricle segmentation using a Bayesian approach with distance dependent shape priors

We propose a method for segmentation of the left ventricle in magnetic resonance cardiac images. The framework consists of an initial Bayesian segmentation of the central slice of the volume. This segmentation is used to locate a shape prior for the LV myocardial tissue. This shape prior is determined using the fact that the myocardium is approximately annular as seen in the short-axis. Then a second Bayesian segmentation is performed to obtain the final result. This procedure is repeated for the rest of the slices. An extrapolation of the area of the LV is used to determine a stopping criterion. The method was evaluated on the databases of the Cardiac Atlas project. Our results demonstrate a suitable accuracy for myocardial segmentation (≈0.8 Dice’s coefficient). For the endocardium and the epicardium the Dice’s coefficients are 0.94 and 0.9 respectively. The accuracy was also evaluated in terms of the Hausdorff distance and the average distance. For the myocardium we obtain 8 mm and 2 mm respectively. Our results demonstrate the capability and merits of the proposed method to estimate the structure of the LV. The method requires minimal user input and generates results with quality comparable to more complex approaches. This paper suggests a new efficient approach for automatic LV quantification based on a Bayesian technique with shape priors with errors comparable to state-of-the-art techniques.
Digital Twin for Variation Management: A General Framework and Identification of Industrial Challenges Related to the Implementation

Digital twins have gained a lot of interest in recent years. This paper presents a survey among researchers and engineers with expertise in variation management confirming the interest of digital twins in this area. The survey shows, however, a gap between future research interest in academia and industry, identifying a larger need in industry. This indicates that there are some barriers in the industry to overcome before the benefits of a digital twin for variation management and geometry assurance can be fully capitalized on in an industrial context. To identify those barriers and challenges, an extensive interview study with engineers from eight different companies in the manufacturing sectors was accomplished. The analysis identifies industrial challenges in the areas of system-level, simulation working process, management issues, and education. One of the main challenges is to keep the 3D models fully updated, including keeping track of changes during the product development process and also feedback changes during full production to the development engineers. This is a part of what is called the digital thread, which is also addressed in this paper.