Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining Multiple Geospatial Data for Estimating Aboveground Biomass in North Carolina Forests

Mapping and quantifying forest inventories are critical for the management and development of forests for natural resource conservation and for the evaluation of the aboveground forest biomass (AGFB) technically available for bioenergy production. The AGFB estimation procedures that rely on traditional, spatially sparse field inventory samples constitute a problem for geographically diverse regions such as the state of North Carolina in the southeastern U.S. We propose an alternative AGFB estimation procedure that combines multiple geospatial data. The procedure uses land cover maps to allocate forested land areas to alternative forest types; uses the light detection and ranging (LiDAR) data to evaluate tree heights; calculates the area-total AGFB using region- and tree-type-specific functions that relate the tree heights to the AGFB. We demonstrate the procedure for a selected North Carolina region, a 2.3 km2 area randomly chosen in Duplin County. The tree diameter functions are statistically estimated based on the Forest Inventory Analysis (FIA) data, and two publicly available, open source land cover maps, Crop Data Layer (CDL) and National Land Cover Database (NLCD), are compared and contrasted as a source of information on the location and typology of forests in the study area. The assessment of the consistency of forestland mapping derived from the CDL and the NLCD data lets us estimate how the disagreement between the two alternative, widely used maps affects the AGFB estimation. The methodology and the results we present are expected to complement and inform large-scale assessments of woody biomass in the region.
Application of High-Dimensional Outlier Mining Based on the Maximum Frequent Pattern Factor in Intrusion Detection

As the Internet applications are growing rapidly, the intrusion detection system is widely used to detect network intrusion effectively. Aiming at the high-dimensional characteristics of data in the intrusion detection system, but the traditional frequentpattern-based outlier mining algorithm has the problems of difficulty in obtaining complete frequent patterns and high time complexity, the outlier set is further analysed to get the attack pattern of intrusion detection. /e NSL-KDD dataset and UNSWNB15 dataset are used for evaluating the proposed approach by conducting some experiments. /e experiment results show that the method has good performance in detection rate, false alarm rate, and recall rate and effectively reduces the time complexity.
RUNNING HEAD : CEREBELLUM , EMOTION , MORALITY 1 Cerebellum and Emotion in Morality

In the current chapter, I examined the relationship between the cerebellum, emotion, and morality with evidence from large-scale neuroimaging data analysis. Although the aforementioned relationship has not been well studied in neuroscience, recent studies have shown that the cerebellum is closely associated with emotional and social processes at the neural level. Also, debates in the field of moral philosophy, psychology, and neuroscience have supported the importance of emotion in moral functioning. Thus, I explored the potentially important but less-studies topic with NeuroSynth, a tool for large-scale brain image analysis, while addressing issues associated with reverse inference. The result from analysis demonstrated that brain regions in the cerebellum, the right Crus I and Crus II in particular, were specifically associated with morality in general. I discussed the potential implications of the finding based on clinical and functional neuroimaging studies of the cerebellum, emotional functioning, and neural networks for diverse psychological processes.
QTL mapping of lodging tolerance in soybean

Abstract. Lodging is an important agronomic trait that affects soybean seed yield. In this study, a recombinant inbred line (RIL) population derived from ‘Zhongdou 27’ × ‘Jiunong 20’ (including 112 lines) was used to identify quantitative trait loci (QTL) associated with lodging of soybean. A genetic map of 2050.27 cM was previously constructed using 4412 single nucleotide polymorphism (SNP) bins in this population. Three major QTL were identified in the single environment for 3 years, accounting for 12.38–16.5% of the phenotypic variation. Among these QTL, qldg-1 was stable for 3 years and qldg-2 was stable for 2 years. QTL by environment interactions (QEI) mapping was also used to detect QTL. A total of 14 QTL were detected, which could explain 2.62–11.28% of the phenotypic variation. The constructed residual heterozygous lines (RHL) were used for the verification of qldg-1 and qldg-2, and the results showed that these two QTL could significantly improve lodging resistance. In addition, genes in the confidence interval of qldg-1 and qldg-2 were designed to predict the candidates. The results of quantitative real-time PCR (qRT-PCR) verification of five genes revealed that two genes (Glyma.17G048100 and Glyma.09G239000) were expressed differentially during the dynamic stages between the parents, demonstrating that these two were the candidates associated with soybean lodging. The QTL and candidate genes related to soybean lodging identified in this study will be of great significance to the future soybean molecular-assisted breeding for lodging resistance.
Report on the 30th the web conference 2021 (TheWebConf2021)

The Web Conference (TheWebConf2021) is the premier conputer science conference on the state of the Web and its related areas. It covers the Web from a wide spectrum of aspects, including technical areas of artificial intelligence, security, privacy, knowledge representation, mobile computing as well as social sciences, economics, policy, accessibility and others. The year 2021 marked the 30th edition that was organised in Ljubljana, Slovenia, but took place fully online due to the COVID-19 pandemic.
Fibrous Scaffolds From Elastin-Based Materials

Current cutting-edge strategies in biomaterials science are focused on mimicking the design of natural systems which, over millions of years, have evolved to exhibit extraordinary properties. Based on this premise, one of the most challenging tasks is to imitate the natural extracellular matrix (ECM), due to its ubiquitous character and its crucial role in tissue integrity. The anisotropic fibrillar architecture of the ECM has been reported to have a significant influence on cell behaviour and function. A new paradigm that pivots around the idea of incorporating biomechanical and biomolecular cues into the design of biomaterials and systems for biomedical applications has emerged in recent years. Indeed, current trends in materials science address the development of innovative biomaterials that include the dynamics, biochemistry and structural features of the native ECM. In this context, one of the most actively studied biomaterials for tissue engineering and regenerative medicine applications are nanofiber-based scaffolds. Herein we provide a broad overview of the current status, challenges, manufacturing methods and applications of nanofibers based on elastin-based materials. Starting from an introduction to elastin as an inspiring fibrous protein, as well as to the natural and synthetic elastin-based biomaterials employed to meet the challenge of developing ECM-mimicking nanofibrous-based scaffolds, this review will follow with a description of the leading strategies currently employed in nanofibrous systems production, which in the case of elastin-based materials are mainly focused on supramolecular self-assembly mechanisms and the use of advanced manufacturing technologies. Thus, we will explore the tendency of elastin-based materials to form intrinsic fibers, and the self-assembly mechanisms involved. We will describe the function and self-assembly mechanisms of silk-like motifs, antimicrobial peptides and leucine zippers when incorporated into the backbone of the elastin-based biomaterial. Advanced polymer-processing technologies, such as electrospinning and additive manufacturing, as well as their specific features, will be presented and reviewed for the specific case of elastin-based nanofiber manufacture. Finally, we will present our perspectives and outlook on the current challenges facing the development of nanofibrous ECM-mimicking scaffolds based on elastin and elastin-like biomaterials, as well as future trends in nanofabrication and applications.
A Personalized Therapeutics Approach Using an In Silico Drosophila Patient Model Reveals Optimal Chemo- and Targeted Therapy Combinations for Colorectal Cancer

In silico models of biomolecular regulation in cancer, annotated with patient-specific gene expression data, can aid in the development of novel personalized cancer therapeutic strategies. Drosophila melanogaster is a well-established animal model that is increasingly being employed to evaluate such preclinical personalized cancer therapies. Here, we report five Boolean network models of biomolecular regulation in cells lining the Drosophila midgut epithelium and annotate them with colorectal cancer patient-specific mutation data to develop an in silico Drosophila Patient Model (DPM). We employed cell-type-specific RNA-seq gene expression data from the FlyGut-seq database to annotate and then validate these networks. Next, we developed three literature-based colorectal cancer case studies to evaluate cell fate outcomes from the model. Results obtained from analyses of the proposed DPM help: (i) elucidate cell fate evolution in colorectal tumorigenesis, (ii) validate cytotoxicity of nine FDA-approved CRC drugs, and (iii) devise optimal personalized treatment combinations. The personalized network models helped identify synergistic combinations of paclitaxel-regorafenib, paclitaxel-bortezomib, docetaxel-bortezomib, and paclitaxel-imatinib for treating different colorectal cancer patients. Follow-on therapeutic screening of six colorectal cancer patients from cBioPortal using this drug combination demonstrated a 100% increase in apoptosis and a 100% decrease in proliferation. In conclusion, this work outlines a novel roadmap for decoding colorectal tumorigenesis along with the development of personalized combinatorial therapeutics for preclinical translational studies.
The Role of Epigenetic Changes in the Progression of Alcoholic Steatohepatitis

Alcoholic steatohepatitis (ASH) is a progression hepatitis with severe fatty liver and its mortality rate for 30-days in patients are over 30%. Additionally, ASH is well known for one-fifth all alcoholic related liver diseases in the world. Excessive chronic alcohol consumption is one of the most common causes of the progression of ASH and is associated with poor prognosis and liver failure. Alcohol abuse dysregulates the lipid homeostasis and causes oxidative stress and inflammation in the liver. Consequently, metabolic pathways stimulating hepatic accumulation of excessive lipid droplets are induced. Recently, many studies have indicated a link between ASH and epigenetic changes, showing differential expression of alcohol-induced epigenetic genes in the liver. However, the specific mechanisms underlying the pathogenesis of ASH remain elusive. Thus, we here summarize the current knowledge about the roles of epigenetics in lipogenesis, inflammation, and apoptosis in the context of ASH pathophysiology. Especially, we highlight the latest findings on the roles of Sirtuins, a conserved family of class-III histone deacetylases, in ASH. Additionally, we discuss the involvement of DNA methylation, histone modifications, and miRNAs in ASH as well as the ongoing efforts for the clinical translation of the findings in ASH-related epigenetic changes.
Chromosome-Scale Genome for a Red-Fruited, Perpetual Flowering and Runnerless Woodland Strawberry (Fragaria vesca)

Citation: Alger EI, Platts AE, Deb SK, Luo X, Ou S, Cao Y, Hummer KE, Xiong Z, Knapp SJ, Liu Z, McKain MR and Edger PP (2021) Chromosome-Scale Genome for a Red-Fruited, Perpetual Flowering and Runnerless Woodland Strawberry (Fragaria vesca). Front. Genet. 12:671371. doi: 10.3389/fgene.2021. 671371 Chromosome-Scale Genome for a Red-Fruited, Perpetual Flowering and Runnerless Woodland Strawberry (Fragaria vesca)
Cell assembly formation and structure in a piriform cortex model

Abstract The piriform cortex is rich in recurrent excitatory synaptic connections between pyramidal neurons. We asked how such connections could shape cortical responses to olfactory lateral olfactory tract (LOT) inputs. For this, we constructed a computational network model of anterior piriform cortex with 2000 multicompartment, multiconductance neurons (500 semilunar, 1000 layer 2 and 500 layer 3 pyramids; 200 superficial interneurons of two types; 500 deep interneurons of three types; 500 LOT afferents), incorporating published and unpublished data. With a given distribution of LOT firing patterns, and increasing the strength of recurrent excitation, a small number of firing patterns were observed in pyramidal cell networks: first, sparse firings; then temporally and spatially concentrated epochs of action potentials, wherein each neuron fires one or two spikes; then more synchronized events, associated with bursts of action potentials in some pyramidal neurons. We suggest that one function of anterior piriform cortex is to transform ongoing streams of input spikes into temporally focused spike patterns, called here “cell assemblies”, that are salient for downstream projection areas.
Influence of zinc substitution on structural, elastic, magnetic and optical properties of cobalt chromium ferrites

Abstract The polycrystalline Co1−xZnxCr0.5Fe1. 5O4 series with (x = 0, 0.2, 0.4, 0.6, 0.8, 1.0) has been synthesized by conventional ceramic rout method. The structural and elastic properties have been investigated by X-ray diffractometer and Fourier transform spectroscopy. Both XRD and FTIR confirm the formation of single phase cubic spinel ferrites. The cationic distribution for all samples has been proposed. The lattice parameter, X-ray density, hoping length, bond length, and packing factors–in accompaniment with variations in the zinc concentration–have been studied. The IR band position has been explained by the cations involved in the structure. The elastic moduli such as Young's modulus, bulk modulus, rigidity modulus and Poison's ratio have bee calculated using force constants. Scanning electron microscope (SEM) observation conveys information about the agglomeration of particles. The hysteresis curve obtained from vibrating sample magneto meter (VSM) conveys information about the soft nature of prepared compositions. The saturation magnetization decreases with addition of zinc ions and coercivity is almost zero. An increase in band gap energy has been observed with addition of zinc by Ultraviolet Visible Spectroscopy (UV-VIS), which is due to small crystallite size.
Spike-based symbolic computations on bit strings and numbers

The brain uses recurrent spiking neural networks for higher cognitive functions such as symbolic computations, in particular, mathematical computations. We review the current state of research on spike-based symbolic computations of this type. In addition, we present new results which show that surprisingly small spiking neural networks can perform symbolic computations on bit sequences and numbers and even learn such computations using a biologically plausible learning rule. The resulting networks operate in a rather low firing rate regime, where they could not simply emulate artificial neural networks by encoding continuous values through firing rates. Thus, we propose here a new paradigm for symbolic computation in neural networks that provides concrete hypotheses about the organization of symbolic computations in the brain. The employed spike-based network models are the basis for drastically more energy-efficient computer hardware – neuromorphic hardware. Hence, our results can be seen as creating a bridge from symbolic artificial intelligence to energy-efficient implementation in spike-based neuromorphic hardware.
When the Whole Is Less Than the Sum of Its Parts: Maximum Object Category Information and Behavioral Prediction in Multiscale Activation Patterns

Neural codes are reflected in complex, temporally and spatially specific patterns of activation. One popular approach to decode neural codes in electroencephalography (EEG) is multivariate decoding. This approach examines the discriminability of activity patterns across experimental conditions to test if EEG contains information about those conditions. However, conventional decoding analyses ignore aspects of neural activity which are informative. Specifically, EEG data can be decomposed into a large number of mathematically distinct features (e.g., entropy, Fourier and Wavelet coefficients) which can reflect different aspects of neural activity. We previously compared 30 such features of EEG data, and found that visual category, and participant behavior, can be more accurately predicted using multiscale spatiotemporally sensitive Wavelet coefficients than mean amplitude (Karimi-Rouzbahani et al., 2021b). Here, we considered that even this larger set of features may only partially capture the underlying neural code, because the brain could use a combination of encoding protocols within a single trial which is not reflected in any one mathematical feature alone. To check, we combined those mathematical features using state-of-the-art supervised and unsupervised feature selection procedures (n = 17). Across 3 datasets, we compared decoding of visual object category between these 17 sets of combined features, and between combined and individual features. Object category could be robustly decoded using the combined features from all of the 17 algorithms. However, the combination of features, which were equalized in dimension to the individual features, were outperformed in most of the time points by the most informative individual feature (Wavelet coefficients). Moreover, the Wavelet coefficients also explained the behavioral performance more accurately than the combined features. These results suggest that a single but multiscale encoding protocol may capture the neural code better than any combination of features. Our findings put new constraints on the models of neural information encoding in EEG.
Inferring viral occurrence patterns through a synthetic data simulation

Viruses infecting humans are manifold and several of them provoke significant morbidity and mortality. Simulations creating large synthetic datasets from observed multiple viral strain infections in a limited population sample can be a powerful tool to infer significant pathogen occurrence and interaction patterns, particularly if limited number of observed data units is available. Here, to demonstrate diverse human papillomavirus (HPV) strain occurrence patterns, we used log-linear models combined with Bayesian framework for graphical independence network (GIN) analysis. That is, to simulate datasets based on modeling the probabilistic associations between observed viral data points, i.e different viral strain infections in a set of population samples. Our GIN analysis outperformed in precision all oversampling methods tested for simulating large synthetic viral strain-level prevalence dataset from observed set of HPVs data. Altogether, we demonstrate that network modeling is a potent tool for creating synthetic viral datasets for comprehensive pathogen occurrence and interaction pattern estimations.
Binding free energy decomposition and multiple unbinding paths of buried ligands in a PreQ1 riboswitch

Riboswitches are naturally occurring RNA elements that control bacterial gene expression by binding to specific small molecules. They serve as important models for RNA-small molecule recognition and have also become a novel class of targets for developing antibiotics. Here, we carried out conventional and enhanced-sampling molecular dynamics (MD) simulations, totaling 141.5 μs, to characterize the determinants of binding free energies and unbinding paths for the cognate and synthetic ligands of a PreQ1 riboswitch. Binding free energy analysis showed that two triplets of nucleotides U6-C15-A29 and G5-G11-C16, contribute the most to the binding of the cognate ligands, by hydrogen bonding and by base stacking, respectively. Mg2+ ions are essential in stabilizing the binding pocket. For the synthetic ligands, the hydrogen-bonding contributions of the U6-C15-A29 triplet are significantly compromised, and the bound state resembles the apo state in several respects, including the disengagement of the C15-A14-A13 and A32-G33 base stacks. The bulkier synthetic ligands lead to significantly loosening of the binding pocket, including extrusion of the C15 nucleobase and a widening of the C15-C30 groove. Enhanced-sampling simulations further revealed that the cognate and synthetic ligands unbind in almost opposite directions. Our work offers new insight for designing riboswitch ligands.
Correlated transcriptional responses provide insights into the synergy mechanisms of the furazolidone, vancomycin and sodium deoxycholate triple combination in Escherichia coli

Effective therapeutic options are urgently needed to tackle antibiotic resistance. Furazolidone (FZ), vancomycin (VAN), and sodium deoxycholate (DOC) show promise as their combination can synergistically inhibit the growth of, and kill, multidrug-resistant Gram-negative bacteria that are classified as critical priority by the World Health Organization. Here, we investigated the mechanisms of action and synergy of this drug combination using a transcriptomics approach in the model bacterium Escherichia coli. We show that FZ and DOC elicit highly similar gene perturbations indicative of iron starvation, decreased respiration and metabolism, and translational stress. In contrast, VAN induced envelope stress responses, in agreement with its known role in peptidoglycan synthesis inhibition. FZ induced the SOS response consistent with its DNA damaging effects, but we demonstrate that using FZ in combination with the other two compounds enables use of lower dosages and largely mitigates its mutagenic effects. Based on the gene expression changes identified, we propose a synergy mechanism where the combined effects of FZ, VAN, and DOC amplify damage to Gram-negative bacteria while simultaneously suppressing antibiotic resistance mechanisms. Importance Synergistic combinations of existing antibacterials against multidrug-resistant “superbugs” are an alternative strategy to costly and arduous development of novel antibacterial molecules. The synergistic combination of nitrofurans, vancomycin and sodium deoxycholate shows promise in inhibiting and killing multidrug-resistant Gram-negative bacteria. We examined the mechanism of action and synergy of these three antibacterials, and proposed a mechanistic basis for their synergy. Our results highlight much needed mechanistic information necessary to advance this combination as a potential therapy.