Miracle-dz/UniFAR-FTUs huggingface dataset:
Complete plastome sequence of Atalantia kwangtungensis (Rutaceae): an endemic “near threatened” shrub in South China

Abstract Atalantia kwangtungensis (Rutaceae) is a small shrub (1–2 m tall) distributed in moist and shady places throughout evergreen broad-leaved forests having altitudes that range from 100 to 400 m in West Guangdong, Southeast Guangxi, Hainan province of China. It has been ranked as a near threatened (NT) species in China. Here, we report and characterize the complete plastid genome sequence of A. kwangtungensis in an effort to provide genomic resources useful for its conservation. The complete plastome is 160,248 bp in length and contains the typical structure and gene content of angiosperm plastomes, including two inverted repeat (IR) regions of 27,151 bp, a large single-copy (LSC) region of 87,483 bp and a small single-copy (SSC) region of 18,463 bp. The plastome contains 110 genes, consisting of 77 unique protein-coding genes (ycf1 is a pseudogene), 29 unique tRNA genes, and 4 unique rRNA genes. The overall A/T content in the plastome of A. kwangtungensis is 61.6%. Phylogenetic analyses were performed using the entire plastome, including spacers, introns, etc., and we determined that A. kwangtungensis and Merrillia caloxylon were closely related. The complete plastome sequence of A. kwangtungensis will provide a useful resource for the conservation genetics of this species as well as for the phylogenetic studies in Sapindales.
Path Planning of an UAV with the Help of Lidar for Slam Application

Today’s world there will be always a value for path planning. Be it the use of mobile robots such as UAVs, UGVs, USVs, etc., everything functions based on SLAM input. The input parameter can be extracted by any kind of sensors such as Kinect, LiDAR, etc. Here I am using RPLIDAR(LiDAR) as Sensor for UAV path generation. By getting a single Plan reading from lidar I will generate 3D mapping by a gazebo and visualize it by rviz in ROS. Here we use ROS as an interface of robot and sensor.
Sustainable City: Energy Usage Prediction Method for Electrified Refuse Collection Vehicles

With the initiative of sustainable smart city space, services and structures (3S), progress towards zero-emission municipal services has advanced the deployment of electric refuse collection vehicles (eRCVs). However, eRCVs are commonly equipped with oversized batteries which not only contribute to the majority of the weight of the vehicles but also remain a consistent weight, independent of the stage of charge (SoC), thus crucially jeopardising the significance of eRCVs in sustainability and economic strategies. Hence, customising the battery capacity in such a way that minimises its weight while storing ample energy for stalwart serviceability could significantly enhance its sustainability. In this study, taking only addresses as input, through an emergent two-stage data analysis, the energy required to collect refuse from a group of addresses was predicted. Therefore, predictions of the battery capacity requirement for the target location are possible. The theories and techniques presented in this paper were evaluated using real-life data from eRCV trials. For the same group of addresses, predicted results show an averaged error rate of 8.44%, which successfully demonstrates that using the proposed address-driven energy prediction approach, the energy required to collect refuse from a set of addresses can be predicted, which can provide a means to optimise the vehicle’s battery requirement.
A robust high dimensional estimation of a finite mixture of the generalized linear model

Abstract Robust high dimensional estimation is one of the most important problems in statistics. In a high dimensional structure with a small number of non-zero observations, the dimension of the parameters is larger than the sample size. For modeling the sparsity of outlier response vector, we randomly selected a small number of observations and corrupted them arbitrarily. There are two distinct ways to overcome sparsity in the generalized linear model (GLM): in the parameter space, or in the space output. According to several studies in corrupted observation modeling, there is a relationship between robustness and sparsity. In this paper for obtaining robust high dimensional estimation, we proposed a finite mixture of the generalized linear models (FMGLMs). By using simulation with the expectation-maximization (EM) algorithm, we show improved modeling performance.
Portfolio Efficiency with High-Dimensional Data As Conditioning Information

In this paper, we build efficient portfolios using different frameworks proposed in the literature with several datasets containing an increasing number of predictors as conditioning information. We carry an extensive empirical study to investigate several approaches to impose sparsity and dimensionality reduction, as well as possible latent factors driving the returns of the risky assets. In contrast to previous studies that made use of naive OLS and low-dimension information sets, we find that (i) accounting for large conditioning information sets, and (ii) the use of variable selection, shrinkage methods and factors models, such as the principal component regression and the partial least squares provide better out-of-sample results as measured by Sharpe ratios.
Impact of alfalfa and NPK fertilization in agricultural reclamation on the transformation of Technosols in an area following lignite mining

Open‐cast mining of lignite leads to considerable degradation of the natural environment, which then must be reclaimed. The aim of this study was to assess the transformation of Technosols in an area after lignite mining which had been under 40‐year cultivation of alfalfa with grass at various NPK fertilization rates. We hypothesized that 40‐year cultivation of plants caused morphological transformation of the parent material and resulted in differentiation of physical and chemical properties in the profile of the Technosols; fertilization had an added advantageous effect on their properties. The surface layer was transformed into an Ap‐horizon. Under alfalfa, with grass and NPK fertilization, Ap‐horizons demonstrated lower bulk density (BD) (7–11%) and particle density (PD) (1–2%), and higher total porosity (TP) (18–35%), drainable porosity (DP) (2–94%), field water capacity (FC) (11–21%), unavailable water (UW) (4–17%), soil organic carbon (SOC) (80–142%), total nitrogen (TN) (261–330%), available P (39–435%), available K (32–89%), cation exchange capacity (CEC) (25–43%), and soil acidity (Hext) (42–81%) compared to Cd‐horizons. Fertilized Technosols (1NPK, 2NPK), compared with control (0NPK), showed in the Ap‐horizon an increase in SOC (18–35%), TP (8–15%), DP (81–90%), FC (9–10%), P (110–285%), K (14–71%), and TN (0–18%), and a decrease in BD (4–7%). In contrast, fertilization had no effect on the UW, PD, soil inorganic carbon (SIC), CaCO3, available Mg, and pHKCl. The alfalfa and orchard grass played a key role in the reclamation and soil‐forming processes, while fertilization played a supporting role.
The social evolution of siderophore production in Pseudomonas aeruginosa is environmentally determined

Bacteria secrete various exoproducts whose benefits can be shared by all cells in the vicinity. The potential importance of these “public goods” in bacterial evolutionary ecology has been extensively studied. Cheating by siderophore-null mutants of the opportunistic pathogen Pseudomonas aeruginosa has received particular attention. The potential of siderophore mutants to attenuate virulence, and the possibility of exploiting this for clinical ends, have generated a wealth of publications. However, the possibility that genotype · environment interactions govern the evolutionary consequences of siderophore loss has been almost entirely ignored. A review of the available literature revealed (i) widespread use of an undefined mutant as a siderophore cheat; and (ii) a reliance on experiments conducted in iron-limited minimal medium. Whole genome sequencing of the undefined mutant revealed a range of mutations affecting phenotypes other than siderophore production. We then conducted cheating assays using defined deletion mutants, grown in conditions designed to model infected fluids and tissue in CF lung infection and non-healing wounds. Depending on the environment, we found that siderophore loss could lead to cheating, simple fitness defects, or no fitness effect at all. It is therefore crucial to develop appropriate in vitro growth conditions in order to better predict the social evolution of traits in vivo.
Studies on identification and in vivo function of novel drug target enzymes of Leishmania donovani using biomolecular approaches

SHORT ABSTRACT Leishmaniasis is a neglected parasitic disease caused by several species of Leishmania parasite. The current drug scenario against this disease is not very good, due to poor efficacy, high cost and major side effects. Moreover, there are several reports about resistance against available drugs. There is an eminent requirement for drugs with higher efficacy and lower side effects. Discovery and validation of novel drug targets are prerequisite for any drug discovery effort. In our study, we have chosen two proteins of the pathogen Leishmania, for evaluation as potential drug targets, viz. a conserved hypothetical protein and a functionally annotated protein, CAAX prenyl protease II. The conserved hypothetical protein, LdBPK_070020, was conserved throughout the other species of Leishmania hinting towards important role of the protein. The other possible target, functionally annotated protein CAAX prenyl protease II, has key role in Ras proteins maturation and localization. The sequence similarity of both the proteins with human genome was significantly low.
Growth of Jatropha curcas plants submitted to water deficit and increasing nitrogen doses

The objective of the present study was to assess the severity of water deficit in Jatropha curcas plants under different nitrogen doses. The experiment was set up in a randomized complete block design with a 5 x 2 factorial [five doses of urea with 45% nitrogen (0; 300; 600; 900; 1200 mg per pot)] and two water supply levels, irrigated and water deficit, and four replications. The Jatropha curcas seeds were sown in 5 L pots with substrate consisting of oxisol, sand and manure at the ratio 3: 1: 0.5, respectively. The nitrogen was added as organic matter in the soil may have contributed to absence of interaction between the factors water deficit and nitrogen dose because it already supplied the minimum nitrogen for the plant metabolism. The nitrogen doses studied did not interfere in the water deficit severity in Jatropha curcas plants, but the plants presented tolerance to water deficit and used delayed dehydration as tolerance strategy. Jatropha curcas plant production in substrate containing red-yellow latossol, sand and manure at the ratio 3:1:0.5, respectively, and organic matter content similar or equal to 1.9% did not require nitrogen fertilization.
Adaptive Machine Learning-Based Temperature Prediction Scheme for Thermal-Aware NoC System

Because of the high-complex interconnection in the contemporary manycore system, the Network-on-Chip (NoC) technology is proven as an efficient way to solve the communication problem in multicore systems. However, the thermal problem becomes the main design challenge in the current NoC systems due to the high-diverse workload distribution and large power density. Therefore, the Proactive Dynamic Thermal Management (PDTM) is employed as an efficient way to control the system temperature in modern multicore systems. Based on the predicted temperature information, the PDTM can control the system temperature in advance, which helps to reduce the performance impact during the temperature control period. However, the conventional temperature prediction model is usually built based on several specific physical parameters, which are usually temperature-sensitive as well. As a result, the current temperature prediction models still suffer from large prediction errors, which reduces the benefit of the PDTM. To solve this problem, we combine the artificial neural network and LMS adaptive filter theory to propose an adaptive machine learning-based temperature prediction model. Because the proposed model can adapt to the hyperplane of the temperature behavior of NoC system during the runtime, the proposed approach can reduce average error by 37.2% to 62.3%, which helps to improve the system performance by 9.16% to 38.37% and can bring smaller area overhead than the related works by 18.59% to 22.11%.
Quantifying trophic interactions and niche sizes of juvenile fishes in an invaded riverine cyprinid fish community

© 2018 John Wiley & Sons A/S. Quantifying feeding interactions between nonindigenous and indigenous fishes in invaded fish communities is important for determining how introduced species integrate into native food webs. Here, the trophic interactions of invasive 0+ European barbel Barbus barbus (L.) and the three other principal 0+ fishes in the community, Squalius cephalus (L.), Leuciscus leuciscus (L.) and Phoxinus phoxinus (L.), were investigated in the River Teme, a River Severn tributary in Western England. B. barbus has been present in the River Teme for approximately 40 years. Analyses of stomach contents from samples collected from three sites between June and September 2015 revealed that, overall, fishes displayed a generalist feeding strategy, with most prey having low frequency of selection. Relationships of diet composition versus body length and gape height were species-specific, with increasing dietary specialisms apparent as the 0+ fishes increased in length and gape height. The trophic niche size of invasive B. barbus was always significantly smaller than S. cephalus and L. leuciscus and was significantly smaller than P. phoxinus at two sites. This was primarily due to differences in the functional morphology of the fishes; 0+ B. barbus were generally restricted to foraging on the benthos, whereas the other fishes were able to forage on prey present throughout the water column. Nevertheless, the invasive B. barbus were exploiting very similar prey items to populations in their native range, suggesting these invaders were strongly pre-adapted to the River Teme and this arguably facilitated their establishment and invasion.
Use of Genetic Microsatellite Markers to Identify Factors Affecting Stocking Success in Striped Bass

The emerging use of genetic markers for stock identification is advantageous as this technique utilizes an inherent permanent mark, it elimi- nates stress associated with conventional tagging methods, and mark recovery is nonlethal. In controlled experiments, increased accuracy and reliability have been demonstrated with genetic tags, as compared to other commonly used markers. While a variety of genetic markers exist for fish identification, microsatellites are preferred because of their polymorphic nature and versatile use, including identification of population structure, measures of genetic diversity, and parent- age analysis. Microsatellites provide a cost-effective tool for a variety of applica- tions, including their use in complex experimental designs, as well as responsible genetic population management. We provide examples of the use of microsatel- lites as genetic tags in South Carolina striped bass Morone saxatilis, describing an experimental restoration effort in the Ashley River and a large-scale stock enhancement effort for the Santee-Cooper reservoirs.
Distribution and community structure of Ostracoda (Crustacea) in shallow waterbodies of southern Kenya

The current study presents the ostracod communities recovered from 26 shallow waterbodies in southern Kenya, combined with an ecological assessment of habitat characteristics. A total of 37 waterbodies were sampled in 2001 and 2003, ranging from small ephemeral pools to large permanent lakes along broad gradients in altitude (700–2 800 m) and salinity (37–67 200 µS cm−1). Between 0 and 12 species were recorded per site. Lack of ostracods was associated with either hypersaline waters, or the presence of fish in fresh waters. Three of the 32 recovered ostracod taxa, Physocypria sp. , Sarscypridopsis cf. elizabethae and Oncocypris mulleri, combined a wide distribution with frequent local dominance. Canonical correspondence analysis on species–environment relationships indicated that littoral vegetation, altitude, surface water temperature and pH best explain the variation in ostracod communities. Presence of fish and water depth also influence species occurrence, with the larger species being more common in shallow waterbodies lacking fish. Based on Chao’s estimator of total regional species richness, this survey recovered about two-thirds (60–68%) of the regional ostracod species pool. Scanning electron micrographs (SEM) of the valve morphology of 14 ostracod taxa are provided, in order to facilitate their application in biodiversity and water-quality assessments and in palaeoenvironmental reconstruction.
A Deep Learning Approach to Detect COVID-19 Patients from Chest X-ray Images

Deep Learning has improved multi-fold in recent years and it has been playing a great role in image classification which also includes medical imaging. Convolutional Neural Networks (CNNs) have been performing well in detecting many diseases including coronary artery disease, malaria, Alzheimer’s disease, different dental diseases, and Parkinson’s disease. Like other cases, CNN has a substantial prospect in detecting COVID-19 patients with medical images like chest X-rays and CTs. Coronavirus or COVID-19 has been declared a global pandemic by the World Health Organization (WHO). As of 8 August 2020, the total COVID-19 confirmed cases are 19.18 M and deaths are 0.716 M worldwide. Detecting Coronavirus positive patients is very important in preventing the spread of this virus. On this conquest, a CNN model is proposed to detect COVID-19 patients from chest X-ray images. Two more CNN models with different number of convolution layers and three other models based on pretrained ResNet50, VGG-16 and VGG-19 are evaluated with comparative analytical analysis. All six models are trained and validated with Dataset 1 and Dataset 2. Dataset 1 has 201 normal and 201 COVID-19 chest X-rays whereas Dataset 2 is comparatively larger with 659 normal and 295 COVID-19 chest X-ray images. The proposed model performs with an accuracy of 98.3% and a precision of 96.72% with Dataset 2. This model gives the Receiver Operating Characteristic (ROC) curve area of 0.983 and F1-score of 98.3 with Dataset 2. Moreover, this work shows a comparative analysis of how change in convolutional layers and increase in dataset affect classifying performances.
Avian cultural services peak in tropical wet forests

The current biodiversity crisis involves major shifts in biological communities at local and regional scales. The consequences for Earth's life‐support systems are increasingly well‐studied, but knowledge of how community shifts affect cultural services associated with wildlife lags behind. We integrated bird census data (3 years across 150 point‐count locations) with questionnaire surveys (>400 people) to evaluate changes in culturally important species across climate and land‐use gradients in Costa Rica. For farmers, urbanites, and birdwatchers alike, species valued for identity, bequest, birdwatching, acoustic aesthetics, and education were more likely to occupy wetter regions and forested sites, whereas disliked species tended to occupy drier and deforested sites. These results suggest that regional climate drying and habitat conversion in the Neotropics are likely to threaten the most culturally important bird species. This study provides a novel and generalizable pathway for assessing the effects of environmental changes on cultural services and integrating the sociocultural and ecological dimensions of biodiversity.
Managing Water Levels in Rice Paddies to Conserve the Itasenpara Host Mussel Unio douglasiae nipponensis

ABSTRACT Floodplains, considered hotspots of biodiversity, are used by a wide range of aquatic and terrestrial species. Over recent decades, floodplains have been modified for cultivation. This contributes to a loss of biodiversity, which has become a key issue in biological conservation in recent years. Paddy agriculture impacts substantially on the biodiversity of floodplains, including freshwater mussels and bitterlings. To model the habitat suitability for Unio douglasiae nipponensis, the itasenpara bitterling host mussel, we used geographical information system tools and field survey methods along with a generalized linear model to examine the environmental conditions of rivers surrounding paddy fields that were subject to water management practices. Water level fluctuations on the floodplains and artificial management of the water level in rivers around the paddy fields both influenced the spatial distribution of U. d. nipponensis, which suggests that traditional rice paddy management practices provide better support for floodplain species than artificially managed water levels.
Subject Review: Text Clustering Algorithms

Clustering algorithms are taking attention in recent times, according to a huge amount of datasets and the growth of parallelized computing architectures. The goal of clustering algorithms is to divided the dataset into clusters, such that objects within the same cluster are similar to each other and differ from objects of other clusters. Clustering algorithms play an important role in information retrieval, indexing and text summarization. In this paper a brief overview of several clustering algorithms is discussed
The ethical conflict of truth, hope, and the experience of suffering: A discussion of non-disclosure of terminal illness and clinical placebos

In medical practice, physicians are often faced with tough ethical and moral dilemmas, one such example is the reoccurring conflict between a patient’s hope and the truth. This paper explores two ethical dilemmas centered on compassion and the reduction of suffering: truth-telling with terminal patients and the clinical use of placebos. In each case the disclosure of truthful information could interfere with hope and suffering relief.
Visible-Light Initiated Free Radical Polymerization by Homomolecular Triplet-Triplet Annihilation

Summary Polymerization reactions initiated by ultraviolet light are ubiquitous in scores of industrial applications but would markedly benefit from visible-light activation to overcome stability, energy consumption, light penetration, and sample geometry limitations. The current work leverages visible-light-driven homomolecular triplet-triplet annihilation (TTA) in zinc(II) meso-tetraphenylporphyrin (ZnTPP) to initiate facile free-radical polymerization in trimethylolpropane triacrylate (TMPTA) and methyl acrylate (MA) monomers through ultrafast electron transfer quenching. Selective Q-band (S1) excitation of ZnTPP in the green or yellow sensitizes TTA occurring between two 3ZnTPP∗ energized chromophores, ultimately generating the highly reducing S2 excited state on one ZnTPP molecule (Ered = −2.13 V versus saturated calomel electrode, SCE). Subsequently, this S2 state engages in electron transfer with TMPTA or MA, thereby initiating the radical polymerization process. Bimolecular electron transfer was confirmed through optically gated fluorescence upconversion. FT-IR and EPR spin-trapping experiments verified visible-light-initiated polymerization leading to the formation of well-defined macro- and microscopic objects.