Miracle-dz/UniFAR-FTUs huggingface dataset:
Biological soil crusts increase stability and invasion resistance of desert revegetation communities in northern China

Biological soil crusts (BSCs) in arid region provide critical ecosystem services. However, most previous studies on BSC – plant topic have had a narrow spatiotemporal and taxonomic focus, so context dependency concerning exotic plant responses to BSCs remains poorly understood. In this study, using a series of manipulative experiments under greenhouse conditions, we investigated the in ﬂ uence of BSCs in the Tengger Desert in northern China on seed germination and seedling growth of an exotic grass species, Stipa glareosa P. Smirn. , with or without two native plants species, Eragrostis poaeoides Beauv. and Artemisia capillaris Thunb. , both of whose ontogeny differs from that of the exotic species. BSC treatments included intact crusts, disturbed crusts, and bare soil. Compared to bare soil, the presence of BSCs reduced the germination of E. poaeoides and A. capillaris by 78% and 74%, respectively. For the exotic species, BSCs reduced the germination percentage by as much as 90%. With gradual increases in soil nutrients such as organic carbon, available phosphorus, and available nitrogen, the shoot biomass of the three plants increased along the sequence from bare soil to lichens and mosses. Shoot biomass of the two native plant species was reduced by the exotic species S. glareosa . The percent germination and shoot biomass of S. glareosa on the disturbed crusts were greater than those on the intact crusts and bare soils. Overall, the presence of BSCs on stabilized sand dunes can reduce the germination of these three plant species, but compared with native plant species, BSCs have a stronger inhibitory effect on the seed germination of the exotic plant species. However, the disturbance may weaken the inhibition of the seed germination of the exotic grass S. glareosa and may provide more nutrients for the exotic grass S. glareosa to establish a revegetation region. We con-cluded that compared with the absence of BSCs, BSCs in this desert ecosystem can provide more nutrients and a suitable environment for native vascular plants and exert an inhibitory effect on the exotic plants.
Intracranial Recordings and Computational Modeling of Music Reveal the Time Course of Prediction Error Signaling in Frontal and Temporal Cortices

Prediction is held to be a fundamental process underpinning perception, action, and cognition. To examine the time course of prediction error signaling, we recorded intracranial EEG activity from nine presurgical epileptic patients while they listened to melodies whose information theoretical predictability had been characterized using a computational model. We examined oscillatory activity in the superior temporal gyrus (STG), the middle temporal gyrus (MTG), and the pars orbitalis of the inferior frontal gyrus, lateral cortical areas previously implicated in auditory predictive processing. We also examined activity in anterior cingulate gyrus (ACG), insula, and amygdala to determine whether signatures of prediction error signaling may also be observable in these subcortical areas. Our results demonstrate that the information content (a measure of unexpectedness) of musical notes modulates the amplitude of low-frequency oscillatory activity (theta to beta power) in bilateral STG and right MTG from within 100 and 200 msec of note onset, respectively. Our results also show this cortical activity to be accompanied by low-frequency oscillatory modulation in ACG and insula—areas previously associated with mediating physiological arousal. Finally, we showed that modulation of low-frequency activity is followed by that of high-frequency (gamma) power from approximately 200 msec in the STG, between 300 and 400 msec in the left insula, and between 400 and 500 msec in the ACG. We discuss these results with respect to models of neural processing that emphasize gamma activity as an index of prediction error signaling and highlight the usefulness of musical stimuli in revealing the wide-reaching neural consequences of predictive processing.
Ensemble flood forecasting: Current status and future opportunities

Ensemble flood forecasting has gained significant momentum over the past decade due to the growth of ensemble numerical weather and climate prediction, expansion in high performance computing, growing interest in shifting from deterministic to risk‐based decision‐making that accounts for forecast uncertainty, and the efforts of communities such as the international Hydrologic Ensemble Prediction Experiment (HEPEX), which focuses on advancing relevant ensemble forecasting capabilities and fostering its adoption. With this shift, comes the need to understand the current state of ensemble flood forecasting, in order to provide insights into current capabilities and areas for improvement, thus identifying future research opportunities to allow for better allocation of research resources. In this article, we provide an overview of current research activities in ensemble flood forecasting and discuss knowledge gaps and future research opportunities, based on a review of 70 papers focusing on various aspects of ensemble flood forecasting around the globe. Future research directions include opportunities to improve technical aspects of ensemble flood forecasting, such as data assimilation techniques and methods to account for more sources of uncertainty, and developing ensemble forecasts for more variables, for example, flood inundation, by applying techniques such as machine learning. Further to this, we conclude that there is a need to not only improve technical aspects of flood forecasting, but also to bridge the gap between scientific research and hydrometeorological model development, and real‐world flood management using probabilistic ensemble forecasts, especially through effective communication.
Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.
The Axon Initial Segment is the Dominant Contributor to the Neuron's Extracellular Electrical Potential Landscape

Extracellular voltage fields, produced by a neuron's action potentials, provide a widely used means for studying neuronal and neuronal‐network function. The neuron's soma and dendrites are thought to drive the extracellular action potential (EAP) landscape, while the axon's contribution is usually considered less important. However, by recording voltages of single neurons in dissociated rat cortical cultures and Purkinje cells in acute mouse cerebellar slices through hundreds of densely packed electrodes, it is found, instead, that the axon initial segment dominates the measured EAP landscape, and, surprisingly, the soma only contributes to a minor extent. As expected, the recorded dominant signal has negative polarity (charge entering the cell) and initiates at the distal end. Interestingly, signals with positive polarity (charge exiting the cell) occur near some but not all dendritic branches and occur after a delay. Such basic knowledge about which neuronal compartments contribute to the extracellular voltage landscape is important for interpreting results from all electrical readout schemes. Finally, initiation of the electrical activity at the distal end of the axon initial segment (AIS) and subsequent spreading into the axon proper and backward through the proximal AIS toward the soma are confirmed. The corresponding extracellular waveforms across different neuronal compartments could be tracked.
Adherence to the Traditional Mediterranean Diet and Human Milk Composition: Rationale, Design, and Subject Characteristics of the MEDIDIET Study

Introduction: Knowledge about how a lactating woman's diet influences the composition of her breast milk is still very limited. In particular, no study has evaluated the role of adherence to the Mediterranean diet on human milk characteristics. Aim: We carried out an observational study to investigate the influence of mother adherence to a Mediterranean diet on her breast milk composition. Methods: Between 2012 and 2014, 300 healthy mothers, who exclusively breastfed their babies, were enrolled from five centers across Italy. During a visit to the hospital center 6 weeks after childbirth these women were asked to provide a sample of their freshly expressed breast milk and to answer a series of questions on personal characteristics and lifestyle factors. The application of a validated food frequency questionnaire allowed the collection of detailed dietary habits. Milk was collected and then stored until chemical analyses were performed. The study has been registered (Trial Registration: Dutch Trial register NTR3468). Descriptive analyses on baseline characteristics of mothers and babies were carried out on the participants, overall and stratified by center. Results: The participants had a mean age of 33 years (SD = 4.06), and a pre-pregnancy BMI of 22.3 Kg/m2 (SD = 3.22). Forty-seven percent gave birth to their first child, 40% to the second 13% to the third or subsequent child. Babies had a mean birth weight of 3,324 g (DS = 389), and a mean length of 51 cm (SD = 1.94). Fifty-three percent were males. Conclusion: The present work provides the general description and the characteristics of mothers and babies included in the MediDiet study.
A 7000‐year history of changing plant trait composition in an Amazonian landscape; the role of humans and climate

Abstract Tropical forests are shifting in species and trait composition, but the main underlying causes remain unclear because of the short temporal scales of most studies. Here, we develop a novel approach by linking functional trait data with 7000 years of forest dynamics from a fossil pollen record of Lake Sauce in the Peruvian Amazon. We evaluate how climate and human disturbances affect community trait composition. We found weak relationships between environmental conditions and traits at the taxon level, but strong effects for community‐mean traits. Overall, community‐mean traits were more responsive to human disturbances than to climate change; human‐induced erosion increased the dominance of dense‐wooded, non‐zoochorous species with compound leaves, and human‐induced fire increased the dominance of tall, zoochorous taxa with large seeds and simple leaves. This information can help to enhance our understanding of forest responses to past environmental changes, and improve predictions of future changes in tropical forest composition.
Emotion Action Detection and Emotion Inference: the Task and Dataset

Many Natural Language Processing works on emotion analysis only focus on simple emotion classification without exploring the potentials of putting emotion into "event context", and ignore the analysis of emotion-related events. One main reason is the lack of this kind of corpus. Here we present Cause-Emotion-Action Corpus, which manually annotates not only emotion, but also cause events and action events. We propose two new tasks based on the data-set: emotion causality and emotion inference. The first task is to extract a triple (cause, emotion, action). The second task is to infer the probable emotion. We are currently releasing the data-set with 10,603 samples and 15,892 events, basic statistic analysis and baseline on both emotion causality and emotion inference tasks. Baseline performance demonstrates that there is much room for both tasks to be improved.
Somatic Embryogenesis in the Medicago truncatula Model: Cellular and Molecular Mechanisms

Medicago truncatula is now widely regarded as a legume model where there is an increasing range of genomic resources. Highly regenerable lines have been developed from the wild-type Jemalong cultivar, most likely due to epigenetic changes. These lines with high rates of somatic embryogenesis (SE) can be compared with wild-type where SE is rare. Much of the research has been with the high SE genotype Jemalong 2HA (2HA). SE can be induced from leaf tissue explants or isolated mesophyll protoplasts. In 2HA, the exogenous phytohormones 1-naphthaleneacetic acid (NAA) and 6-benzylaminopurine (BAP) are central to SE. However, there are interactions with ethylene, abscisic acid (ABA), and gibberellic acid (GA) which produce maximum SE. In the main, somatic embryos are derived from dedifferentiated cells, undergo organellar changes, and produce stem-like cells. There is evidence that the SE is induced as a result of a stress and hormone interaction and this is discussed. In M. truncatula, there are connections between stress and specific up-regulated genes and specific hormones and up-regulated genes during the SE induction phase. Some of the transcription factors have been knocked down using RNAi to show they are critical for SE induction (MtWUSCHEL, MtSERF1). SE research in M. truncatula has utilized high throughput transcriptomic and proteomic studies and the more detailed investigation of some individual genes. In this review, these studies are integrated to suggest a framework and timeline for some of the key events of SE induction in M. truncatula.
High Spatial-Resolution MALDI-IT-TOF MS/MS Imaging for Depicting Longitudinal and Transverse Distribution of Drugs Incorporated into Hair.

This study aims to achieve high spatial-resolution tandem mass spectrometry (MS/MS) imaging for depicting longitudinal and transverse distribution of drugs in hair, which can provide indispensable information for the proper interpretation of hair test results, including the mechanism of drug incorporation into hair. Two types of hair samples were obtained and analyzed: User's Hair; sampled from a volunteer who took an over-the-counter medicine containing methoxyphenamine (MOP), a non-regulated analogue of methamphetamine; and Soaked Hair; prepared by soaking blank hair in MOP solution. Longitudinal and transverse-sectioning of single hair shafts was accomplished by freeze-sectioning using customized microtomes. Vapor deposition of α-cyano-4-hydroxycinnamic acid provided the finest matrix layer (resolution <1 μm, 0.7-μm thickness), although it provided less effective ionization of MOP compared to aerosol spraying or a combination of both. Matrix assisted laser desorption/ionization (MALDI)-ion trap (IT)-time of flight (TOF) MS/MS permitted the imaging of trace-level MOP in hair with a MS/MS window setting of ± 0.02 Da and a spatial resolution setting at 5 or 10 μm. For Soaked Hair, localization of MOP in the peripheral part was clearly depicted, but no such biased distribution was observed in the transverse sections of User's Hair. MOP-positive bands generated corresponding to the time periods of MOP intake could be observed on the longitudinal sections of User's Hair. This method can provide forensically crucial information regarding hair analysis for drugs: drug incorporation mechanism into hair, discrimination of undesired surface contamination from endogenous incorporation of ingested drugs, and precise elucidation of drug-use history.
pH-Responsive Dual Drug-Loaded Nanocarriers Based on Poly (2-Ethyl-2-Oxazoline) Modified Black Phosphorus Nanosheets for Cancer Chemo/Photothermal Therapy

Synergistic cancer therapy, such as those combining chemotherapeutic and photothermal methods, has stronger treatment effect than that of individual ones. However, it is challenging to efficiently deliver nanocarriers into tumor cells to elevate intracellular drug concentration. Herein, we developed an effective pH-responsive and dual drug co-delivery platform for combined chemo/photothermal therapy. An anticancer drug doxorubicin (DOX) was first loaded onto the surface of black phosphorus (BP). With poly(2-ethyl-2-oxazoline) (PEOz) ligand conjugated onto the polydopamine (PDA) coated BP nanosheets, targeted long circulation and cellular uptake in vivo was significantly improved. With another anticancer drug bortezomib (BTZ) loaded onto the surface of the nanocapsule, the platform can co-deliver two different drugs. The surface charge of the nanocapsule was reversed from negative to positive at the tumor extracellular pH (∼6.8), ionizing the tertiary amide groups along the PEOz chain, thus facilitating the cell internalization of the nanocarrier. The cytotoxicity therapeutic effect of this nanoplatform was further augmented under near-infrared laser irradiation. As such, our DOX-loaded BP@PDA-PEOz-BTZ platform is very promising to synergistic cancer therapy.
Training Over-parameterized Deep ResNet Is almost as Easy as Training a Two-layer Network

It has been proved that gradient descent converges linearly to the global minima for training deep neural network in the over-parameterized regime. However, according to \citet{allen2018convergence}, the width of each layer should grow at least with the polynomial of the depth (the number of layers) for residual network (ResNet) in order to guarantee the linear convergence of gradient descent, which shows no obvious advantage over feedforward network. In this paper, we successfully remove the dependence of the width on the depth of the network for ResNet and reach a conclusion that training deep residual network can be as easy as training a two-layer network. This theoretically justifies the benefit of skip connection in terms of facilitating the convergence of gradient descent. Our experiments also justify that the width of ResNet to guarantee successful training is much smaller than that of deep feedforward neural network.
An Effective Label Noise Model for DNN Text Classification

Because large, human-annotated datasets suffer from labeling errors, it is crucial to be able to train deep neural networks in the presence of label noise. While training image classification models with label noise have received much attention, training text classification models have not. In this paper, we propose an approach to training deep networks that is robust to label noise. This approach introduces a non-linear processing layer (noise model) that models the statistics of the label noise into a convolutional neural network (CNN) architecture. The noise model and the CNN weights are learned jointly from noisy training data, which prevents the model from overfitting to erroneous labels. Through extensive experiments on several text classification datasets, we show that this approach enables the CNN to learn better sentence representations and is robust even to extreme label noise. We find that proper initialization and regularization of this noise model is critical. Further, by contrast to results focusing on large batch sizes for mitigating label noise for image classification, we find that altering the batch size does not have much effect on classification performance.
Gestión del conocimiento: aplicaciones y prácticas organizacionales

En la actualidad, las organizaciones requieren de procesos de renovación constantes y continuos dados los cambios impuestos por la modernidad y la globalización, la construcción de este nuevo entorno ha motivado cambios tanto en líderes como en los miembros de las organizaciones bajo un nuevo esquema de toma de decisiones, que ha generado una nueva percepción de manera consciente dentro del espacio organizacional, que permite generar relaciones en sus ERPs que promueven la creatividad y la innovación continua, pero sobre todo la promoción de la implementación de proyectos de administración del conocimiento, como procesos estructurados de análisis y uso de información para la generación de ventaja competitiva apoyados en el uso de la tecnología de manera integral.
Aktivitas Antiangiogenesis Ekstrak Daun Dewandaru (Eugenia uniflora L.) pada Retina Tikus Wistar yang Diinduksi Streptozotocin

Retinopati diabetik adalah komplikasi vaskular yang paling spesifik dari kedua tipe diabetes. Penyebab retinopati diabetik adalah adanya angiogenesis. Angiogenesis diinduksi oleh aktivasi Vascular Endothelial Growth Factor (VEGF). Kandungan Quercetin , salah satu flavonoid yang ada dalam daun Dewandaru ( Eugenia uniflora L. ) dapat menghambat VEGF. Penelitian ini bertujuan untuk mengetahui akativitas angiogenesis ekstrak Eugenia uniflora L. pada retina tikus yang diinduksi Streptozotocin (STZ). Penelitian ini menggunakan True Experimental Laboratory melalui metode Randomized Posttest Only Controlled Group Design dengan tikus wistar jantan yang dibagi menjadi 5 kelompok, yaitu kelompok kontrol negatif (K-) yang hanya diberikan diet standar tikus, pemberian STZ yaitu dengan dosis 40 mg/kgBB secara injeksi di intraperitoneal diberikan pada kelompok kontrol positif (K+), dan kelompok perlakuan (P1, P2, P3) dan dosis pemberian ekstrak Eugenia uniflora L. yaitu P1=50mg/kgBB, P2=100mg/kgBB, dan P3=200mg/kgBB secara peroral sehari sekali. Selama 1 bulan perlakuan, hasil glukosa darah 1 dan 2, kelompok K (-), K (+), dan P3 menunjukkan peningkatan kadar glukosa, sedangkan P1 dan P2 menunjukkan penurunan kadar glukosa. Selama penelitian K (-), K (+), P1, P2, P3 menunjukkan peningkatan berat badan. Hasil pewarnaan HE didapatkan penurunan jumlah pembuluh darah pada kelompok perlakuan dengan perbedaan signifikan antara kelompok penelitian (p<0.05). Dapat disimpulkan bahwa pemberian ekstrak Eugenia uniflora L. pada retina tikus Wistar yang diinduksi STZ berpotensi menurunkan respon angiogenesis pada retina tikus yaitu dengan menurunkan jumlah pembuluh darah retina tikus.