Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Endangered Species of Senna (Leguminosae) from the Atlantic Forest of Bahia, Brazil, Supported by X-ray Analysis of Leaflets

Abstract Senna (Leguminosae, Cassiinae) is a speciose genus widely distributed throughout tropical and subtropical regions of the world. During taxonomic studies of Senna sect. Chamaefistula ser. Bacillares, a new endemic and endangered species from the Atlantic Forest of Bahia, Brazil was discovered. The new species, Senna bahiensis, is compared with its morphologically similar species Senna gardneri, including x-ray analysis comparison of their leaflet venation patterns. The use of high resolution x-ray digital images provided the decisive information, being a promising technique to be used in systematic studies.
A Remarkable New Species of the Habenaria nuda Complex (Orchidaceae) from the Espinhaço Range of Eastern Brazil

Abstract Habenaria longissima, a new species from the H. nuda species complex, is described and illustrated. It is remarkable for the exceptionally long lateral segments of the petals and labellum, which are the longest among Neotropical Habenaria, both in absolute and in proportional terms. Despite the morphological similarity, Bayesian and parsimony phylogenetic analyses based on nuclear (ITS) and plastid DNA markers (matK), revealed that H. longissima is distantly related to other species of the complex, and constitutes an independent lineage. Its distribution is unusual in that it is the only species of Orchidaceae restricted to the Quadrilátero Ferrífero in the State of Minas Gerais and to Chapada Diamantina, in the central part of the Espinhaço range in the State of Bahia, with the populations 1000 km from each other. Habenaria longissima is a rare species, known only from three localities and four populations and informally proposed as Endangered due to the small area of occupancy and small number of known populations.
Checking vitals of CERES scanners: parameters of a slow mode filter

Clouds and the Earth’s Radiant Energy System (CERES) instruments are scanning radiometers on board the Terra, Aqua, NPP and NOAA20 satellites that use thermistor bolometers as detectors, with a sampling rate of 0.01s. During calibration testing a slow mode of detectors was found which had a magnitude of about three percent of the signal, and a characteristic time of 0.3 sec. To reduce the effect of this mode, a numerical filter was introduced. However, an analysis of the data from the CERES instruments aboard the Terra and Aqua satellites showed a difference of radiances in the forward scan compared to radiances from the backward scan. This difference was due to the fact that parameters of the digital filtered derived based on calibration data were not characterized correctly. A focus of this work is to show results of setting slow mode filter parameters based on in-flight data. A gradient-based minimization strategy is employed to effectively find the filter parameters for each instrument to meet the performance goal, namely the divergence of 0.15% for viewing zenith angles <65 degrees. The newly designed numerical filters are used in processing the newest editions of CERES Level-0 data products, and then in the Erbe-like and SSFs.
Gearbox fault diagnosis based on deep convolution generative adversarial network

The existing gearbox fault diagnosis models often need a lot of fault data and corresponding tags to complete the model training, but the actual fault data is often less and unevenly distributed. Aiming at the situation that the gearbox fault data is scarce and unevenly distributed, this paper proposes a fault diagnosis method based on the Deep Convolution Generative Adversarial Network (DCGAN). First, the original vibration signals collected in the early stage are processed through data processing to form a sample data set and input into the DCGAN model for confrontation training, and virtual samples with real sample characteristics are generated to expand the sample data set. Then, the convolution neural network model is constructed to complete the fault diagnosis and get the results. The results show that the model trained by this method is more accurate when generating a large amount of data, and the diagnostic effect is significantly better.
Investigation of vertical profiles of optical turbulence from mesoscale simulations runs and radiosonde data

We report the analysis of radio soundings launched at our permanent measurement site in North-western Germany. The data of potential temperature and wind speed are used to derive vertical profiles of the gradient Richardson number 𝑅𝑖𝑔 in the free troposphere, and subsequently, the strength of optical turbulence 𝐶𝑛 2. These values are compared to a numerical framework, which derives 𝐶𝑛 2 from mesoscale weather prediction data by two schemes based on the gradient Richardson number and Monin-Obukhov similarity theory, respectively.
Cloud liquid and ice water content estimation from satellite: a regression approach based on neural networks

Cloud microphysics in terms of their liquid/ice water content and particle size are the principal factors addressed to study and understand the behavior behind the climate change phenomenon. Based on remotely sensed measurements, in the last decades, some evidence exists that an increase in temperature leads to an increase in cloud liquid water content (CLWC). The temperature dependence of ice water content (CIWC) is also evident from measurements of midlatitude cirrus clouds. Hence, innovative methods, such as those based on the use of Artificial Intelligence (AI) allowing a more relevant investigation of how clouds influence the hydrological cycle and radiative components of the Earth's climate system, are required. This work investigates the capability of a statistical regression scheme of CLWC and CIWC, implemented through the use of a multilayer feed-forward neural network (NN). The whole methodology is applied to a set of simulated IASI-NG L1C and MWS acquisitions, covering the global scale. The NN regression analysis shows good agreement with the test data. The retrieved cloud liquid water and ice profiles have an accuracy of 20 to 60% depending on the given layer. Finally, the layer with the maximum concentration is accurately identified.
Integrated robotic measurement and machining technology for large castings

In the past, for large castings, manual or large special equipment are need to complete the measurement and machining. With the advantages of high flexibility and large workspace, industrial robots provide a promising alternative. In this work, the rear axle housing machining is taken as an example. Firstly, the blank model is obtained by multi-angle point cloud scanning and fusion based on FPFH method. Then, after aligning the coordinate system, the height of the residual material is calculated. To ensure that the robot can process a complete workpiece with high stiffness, machining region division is carried out, and the robot posture and workpiece’s orientation are optimized. Finally, the process of integrated robotic measurement and machining is verified by simulation and experiment, and the machining results are analyzed.
A Visual Classification Method for Milling Surface Roughness Based on Convolutional Neural Network

∗ At present, most milling surface roughness detection is still completed by the traditional contact roughness measuring instrument. Aiming at the problem that the traditional contact roughness measuring instrument has a strong dependence and the measurement speed is slow, this paper will design a visual detection method that is consistent with the working environment, which belongs to the non-contact measurement. This method, firstly design special light source, illumination mode, complete the fill light to highlight image roughness related characteristics, and then the key hardware such as CCD camera is used to collect image data sets, finally based on convolutional neural network detection technology, namely through the end-to-end image analysis, then using convolution operation and comprehensive processing roughness classification model. Finally, the surface roughness can be classified accurately and rapidly predicted. The results show that the accuracy of surface roughness classification is 97%, and the time is much higher than that of the traditional contact roughness measuring instrument.
Latest Trend of Milk Derived Exosomes: Cargos, Functions, and Applications

Exosomes are nanosized phospholipid bilayer vesicles released to the extracellular environment. Exosomes from various tissues or cells are being studied and there has been a growing interest in milk exosomes research due to their emerging role as messengers between cells and the fact that it can be produced in large quantities with rich source of milk. Milk derived exosomes (MDEs) contain lipids, microRNAs, proteins, mRNAs as well as DNA. Studies of exosome cargo have been conducted widely in many research areas, especially exosomal miRNAs. In this paper, we reviewed the current knowledge in isolation and identification, cargos, functions mainly in intestinal tract and immunity system of MDEs. Its application as drug carriers and diseases biomarker are also discussed. Furthermore, we also consider critical challenges of MDEs application and provide possible directions for future research.
Performances of Hybridized-, Embedded-, and Weighted-Interior Penalty Discontinuous Galerkin Methods for Heterogeneous and Anisotropic Diffusion Problems

The present paper discusses families of Interior Penalty Discontinuous Galerkin (IP) methods for solving heterogeneous and anisotropic diffusion problems. Specifically, we focus on distinctive schemes, namely the Hybridized-, Embedded-, and Weighted-IP schemes, leading to final matrixes of different sizes and sparsities. Both the Hybridized- and Embedded-IP schemes are eligible for static condensation, and their degrees of freedom are distributed on the mesh skeleton. In contrast, the unknowns are located inside the mesh elements for the Weighted-IP variant. For a given mesh, it is well-known that the number of degrees of freedom related to the standard Discontinuous Galerkin methods increases more rapidly than those of the skeletal approaches (Hybridized- and Embedded-IP). We then quantify the impact of the static condensation procedure on the computational performances of the different IP classes in terms of robustness, accuracy, and CPU time. To this aim, numerical experiments are investigated by considering strong heterogeneities and anisotropies. We analyze the fixed error tolerance versus the run time and mesh size to guide our performance criterion. We also outlined some relationships between these Interior Penalty schemes. Eventually, we confirm the superiority of the Hybridized- and Embedded-IP schemes, regardless of the mesh, the polynomial degree, and the physical properties (homogeneous, heterogeneous, and/or anisotropic).
Egg and Dietary Cholesterol Consumption and Mortality Among Hypertensive Patients: Results From a Population-Based Nationwide Study

Background: Hypertensive patients are sensitive to the amount of dietary cholesterol intake, especially cholesterol from the whole eggs. Whether whole egg and dietary cholesterol consumption are suitable for hypertensive patients is still controversial. Aim: The objective of the study was to examine the associations of intake of eggs as well as the dietary cholesterol with total mortality in a Chinese nationwide cohort. Methods: We utilized data from the China Health and Nutrition Survey (CHNS) from the year of 1991 to 2015. Cumulative averages of egg and cholesterol intake were calculated to represent the consumption of the long-term diet of the participants in each available round of the survey. Cox regression models were employed to estimate the effects of eggs and dietary cholesterol from the different sources on mortality among hypertensive patients. Results: A total of 8,095 participants were included in the final analysis and followed up for a mean of 11.4 years. Finally, 927 cases of death were detected. After adjustment for the multivariate factors, consuming more than seven eggs per week was related to 29% lower mortality among the hypertensive patients compared with the consumers with not more than two eggs per week [hazard ratio (HR): 0.71; 95% CI: 0.59–0.85; P < 0.001]. Similarly, the egg-sourced cholesterol intake was inversely associated with mortality (P = 0.002) whereas intake of the dietary cholesterol from the non-egg sources was significantly related to the higher mortality (P < 0.001). However, total cholesterol intake was not related to mortality among hypertensive patients. Substituting eggs for an equivalent amount of non-egg-sourced protein-abundant foods was also associated with lower mortality. Conclusion: Higher consumption of eggs and egg-sourced dietary cholesterol was associated with lower mortality among the enrolled Chinese hypertensive patients but non-egg-sourced cholesterol intake was related to higher mortality. Therefore, our findings do not support the view that hypertensive patients should avoid whole egg consumption for the purpose of restricting dietary cholesterol intake.
Educational Strategies for Secondary Stroke Prevention: An Integrative Literature Review

ABSTRACT Background Stroke has the highest global burden of any neurological disease. Many studies investigate secondary stroke prevention strategies, but the outcomes remain poor, and the rates of recurrent stroke stay high. Purpose This review synthesizes the existing studies that focus on educational strategies for stroke survivors to reduce their risk of further stroke. Methods A systematic search of the literature was undertaken across PubMed, CINAHL Plus, PsycINFO and ERIC, for articles published from 2010 to 2020. The selected articles were critically appraised using tools from the Joanna Briggs Institute (JBI), before being thematically analyzed. Results A total of 20 studies were identified. Three main themes and sub-themes: 1) Focus (healthy behaviors, physical and psychological aspects, health literacy); 2) Types of intervention (program characteristics, structure and approach); 3) Future implications (positive values and challenges). Discussions Current programs are feasible and can have significant benefits on physical, psychological, and health literacy but are inadequate for improving smoking cessation, alcohol consumption and weight control behaviors. Translation to Health Education Practice How to reinforce healthy behaviors remains an issues to resolve. Understanding the stroke survivor’s health literacy, the utilization of technology may offer the benefits of making education easily tailored to the individual’s needs.
Social experience dependent plasticity of mouse song selectivity without that of song components

Syllable sequences in male mouse ultrasonic-vocalizations (USVs), “songs”, contain structure -quantified through predictability, like birdsong and aspects of speech. Apparent USV innateness and lack of learnability, discount mouse USVs for modelling speech-like social communication and its deficits. Informative contextual natural sequences (SN) were theoretically extracted and they were preferred by female mice. Primary auditory cortex (A1) supragranular neurons show differential selectivity to the same syllables in SN and random sequences (SR). Excitatory neurons (EXNs) in females showed increases in selectivity to whole SNs over SRs based on extent of social exposure with male, but syllable selectivity remained unchanged. Thus mouse A1 single neurons adaptively represent entire order of acoustic units without altering selectivity of individual units, fundamental to speech perception. Additionally, observed plasticity was replicated with silencing of somatostatin positive neurons, which had plastic effects opposite to EXNs, thus pointing out possible pathways involved in perception of sound sequences.
Predictive coding across the left fronto-temporal hierarchy during language comprehension.

We used magnetoencephalography (MEG) and event-related potentials (ERPs) to track the time-course and localization of evoked activity produced by expected, unexpected plausible, and implausible words during incremental language comprehension. We suggest that the full pattern of results can be explained within a hierarchical predictive coding framework in which increased evoked activity reflects the activation of residual information that was not already represented at a given level of the fronto-temporal hierarchy ("error" activity). Between 300 and 500 ms, the three conditions produced progressively larger responses within left temporal cortex (lexico-semantic prediction error), whereas implausible inputs produced a selectively enhanced response within inferior frontal cortex (prediction error at the level of the event model). Between 600 and 1,000 ms, unexpected plausible words activated left inferior frontal and middle temporal cortices (feedback activity that produced top-down error), whereas highly implausible inputs activated left inferior frontal cortex, posterior fusiform (unsuppressed orthographic prediction error/reprocessing), and medial temporal cortex (possibly supporting new learning). Therefore, predictive coding may provide a unifying theory that links language comprehension to other domains of cognition.
Comprehensive evaluation of deconvolution methods for human brain gene expression

Gene expression measurements, similar to DNA methylation and proteomic measurements, are influenced by the cellular composition of the sample analysed. Deconvolution of bulk transcriptome data aims to estimate the cellular composition of a sample from its gene expression data, which in turn can be used to correct for composition differences across samples. Although a multitude of deconvolution methods have been developed, it is unclear whether their performance is consistent across tissues with different complexities of cellular composition. The human brain is unique in its transcriptomic diversity, expressing the highest diversity of alternative splicing isoforms and non-coding RNAs. It comprises a complex mixture of cell-types including transcriptionally similar sub-types of neurons, which undergo gene expression changes in response to neuronal activity. However, a comprehensive assessment of the accuracy of transcriptome deconvolution methods on human brain data is currently lacking. Here we carry out the first comprehensive comparative evaluation of the accuracy of deconvolution methods for human brain transcriptome data, and assess the tissue-specificity of our key observations by comparison with transcriptome data from human pancreas and heart. We evaluate 8 transcriptome deconvolution approaches, covering all main classes: 4 partial deconvolution methods, each applied with 9 different cell-type signatures, 2 enrichment methods, and 2 complete deconvolution methods. We test the accuracy of cell-type estimates using in silico mixtures of single-cell RNA-seq data, mixtures of neuronal and glial RNA, as well as nearly 2,000 human brain samples. Our results bring several important insights into the performance of transcriptome deconvolution: (a) We find that cell-type signature data has a stronger impact on brain deconvolution accuracy than the choice of method. (b) We demonstrate that biological factors influencing brain cell-type signature data (e.g. brain region, in vitro cell culturing), have stronger effects on the deconvolution outcome than technical factors (e.g. RNA sequencing platform). (c) We find that partial deconvolution methods outperform complete deconvolution methods on human brain data. To facilitate wider implementation of correction for cellular composition, we develop a webtool that implements the best performing methods, and is available at https://voineagulab.shinyapps. io/BrainDeconvShiny/ .
NHR-23 activity is necessary for C. elegans developmental progression and apical extracellular matrix structure and function

This work shows how a C. elegans transcription factor controls remodeling of the apical extracellular matrix during development and in which tissues it acts. ABSTRACT Nematode molting is a remarkable process where animals must repeatedly build a new apical extracellular matrix (aECM) beneath a previously built aECM that is subsequently shed. The nuclear hormone receptor NHR-23/NR1F1 is an important regulator of C. elegans molting. NHR-23 expression oscillates in the epidermal epithelium, and soma-specific NHR-23 depletion causes severe developmental delay and death. Tissue-specific RNAi suggests that nhr-23 acts primarily in seam and hypodermal cells. NHR-23 coordinates the expression of factors involved in molting, lipid transport/metabolism, and remodeling of the aECM. NHR-23 depletion causes dampened expression of a nas-37 promoter reporter and a loss of reporter oscillation. The cuticle collagen ROL-6 and zona pellucida protein NOAH-1 display aberrant annular localization and severe disorganization over the seam cells following NHR-23 depletion, while the expression of the adult-specific cuticle collagen BLI-1 is diminished and frequently found in patches. Consistent with these localization defects, the cuticle barrier is severely compromised when NHR-23 is depleted. Together, this work provides insight into how NHR-23 acts in the seam and hypodermal cells to coordinate aECM regeneration during development.
Artificial intelligence reveals nuclear pore complexity

Nuclear pore complexes (NPCs) mediate nucleocytoplasmic transport. Their intricate 120 MDa architecture remains incompletely understood. Here, we report a near-complete structural model of the human NPC scaffold with explicit membrane and in multiple conformational states. We combined AI-based structure prediction with in situ and in cellulo cryo-electron tomography and integrative modeling. We show that linker Nups spatially organize the scaffold within and across subcomplexes to establish the higher-order structure. Microsecond-long molecular dynamics simulations suggest that the scaffold is not required to stabilize the inner and outer nuclear membrane fusion, but rather widens the central pore. Our work exemplifies how AI-based modeling can be integrated with in situ structural biology to understand subcellular architecture across spatial organization levels. One sentence summary An AI-based, dynamic model of the human nuclear pore complex reveals how the protein scaffold and the nuclear envelope are coupled inside cells.