Miracle-dz/UniFAR-FTUs huggingface dataset:
Short‐term prediction of stream turbidity using surrogate data and a meta‐model approach: A case study

Many water‐quality monitoring programs aim to measure turbidity to help guide effective management of waterways and catchments, yet distributing turbidity sensors throughout networks is typically cost prohibitive. To this end, we built and compared the ability of dynamic regression (auto‐regressive integrated moving average [ARIMA]), long short‐term memory neural nets (LSTM), and generalized additive models (GAM) to forecast stream turbidity one step ahead, using surrogate data from relatively low‐cost in‐situ sensors and publicly available databases. We iteratively trialled combinations of four surrogate covariates (rainfall, water level, air temperature and total global solar exposure) selecting a final model for each type that minimized the corrected Akaike information criterion. Cross‐validation using a rolling time‐window indicated that ARIMA, which included the rainfall and water‐level covariates only, produced the most accurate predictions, followed closely by GAM, which included all four covariates. However, according to the no‐free‐lunch theorems in machine learning, no single model has an advantage over all other models for all instances. Therefore, we constructed a meta‐model, trained on time‐series features of turbidity, to take advantage of the strengths of each model over different time points and predict the best model (that with the lowest forecast error one‐step prior) for each time step. The meta‐model outperformed all other models, indicating that this methodology can yield high accuracy and may be a viable alternative to using measurements sourced directly from turbidity‐sensors where costs prohibit their deployment and maintenance, and when predicting turbidity across the short term. Our findings also indicated that temperature and light‐associated variables, for example underwater illuminance, may hold promise as cost‐effective, high‐frequency surrogates of turbidity, especially when combined with other covariates, like rainfall, that are typically measured at coarse levels of spatial resolution.
Transcriptome Analysis of Diffuse Large B-Cell Lymphoma Cells Inducibly Expressing MyD88 L265P Mutation Identifies Upregulated CD44, LGALS3, NFKBIZ, and BATF as Downstream Targets of Oncogenic NF-κB Signaling

During innate immune responses, myeloid differentiation primary response 88 (MyD88) functions as a critical signaling adaptor protein integrating stimuli from toll-like receptors (TLR) and the interleukin-1 receptor (IL-1R) family and translates them into specific cellular outcomes. In B cells, somatic mutations in MyD88 trigger oncogenic NF-κB signaling independent of receptor stimulation, which leads to the development of B-cell malignancies. However, the exact molecular mechanisms and downstream signaling targets remain unresolved. We established an inducible system to introduce MyD88 to lymphoma cell lines and performed transcriptomic analysis (RNA-seq) to identify genes differentially expressed by MyD88 bearing the L265P oncogenic mutation. We show that MyD88L265P activates NF-κB signaling and upregulates genes that might contribute to lymphomagenesis, including CD44, LGALS3 (coding Galectin-3), NFKBIZ (coding IkBƺ), and BATF. Moreover, we demonstrate that CD44 can serve as a marker of the activated B-cell (ABC) subtype of diffuse large B-cell lymphoma (DLBCL) and that CD44 expression is correlated with overall survival in DLBCL patients. Our results shed new light on the downstream outcomes of MyD88L265P oncogenic signaling that might be involved in cellular transformation and provide novel therapeutical targets.
Environmental enrichment promotes resilience to neuropathic pain-induced depression and correlates with decreased excitability of the anterior cingulate cortex

Depression is a common comorbidity of chronic pain with many patients being affected. However, efficient pharmacological treatment strategies are still lacking. Therefore, it is desirable to find additional alternative approaches. Environmental enrichment has been suggested as a method to alleviate pain-induced depression. However, the neuronal mechanisms of its beneficial effects are still elusive. The anterior cingulate cortex (ACC) plays a central role in processing pain-related negative affect and chronic pain-induced plasticity in this region correlates with depressive symptoms. We studied the consequences of different durations of environmental enrichment on pain sensitivity and chronic pain-induced depression-like behaviors in a mouse model of neuropathic pain. Furthermore, we correlated the behavioral outcomes to the activity levels of pyramidal neurons in the ACC by analyzing their electrophysiological properties ex vivo. We found that early exposure to an enriched environment alone was not sufficient to cause resilience against pain-induced depression-like symptoms. However, extending the enrichment after the injury prevented the development of depression and reduced mechanical hypersensitivity. On the cellular level, increased neuronal excitability was associated with the depressive phenotype that was reversed by the enrichment. Therefore, neuronal excitability in the ACC was inversely correlated to the extended enrichment-induced resilience to depression. These results suggest that the improvement of environmental factors enhanced the resilience to developing chronic pain-related depression. Additionally, we confirmed the association between increased neuronal excitability in the ACC and depression-like states. Therefore, this non-pharmacological intervention could serve as a potential treatment strategy for comorbid symptoms of chronic pain.
The effect of fractional vegetation cover on the relationship between EVI and soil moisture in non-forest regions

Surface soil moisture (SM) is critical for terrestrial ecological and hydrological processes, particularly in non-forested arid and semi-arid regions. On the large scale, the relationship of vegetation greenness to SM is often considered to be a reflection of spatiotemporal changes in SM. In this study, we investigated the empirical relationship between the remotely sensed enhanced vegetation index (EVI) and in situ SM, using observations from 16 Ameriflux sites in non-forest regions. The linear relationship between SM and EVI could be classified by the fractional vegetation cover (FVC), and the ratio of EVI to SM became higher as the FVC increased. In addition, multiple regressions for SM using both EVI and FVC showed a higher correlation than that of the single regressions using EVI or FVC separately. We found that EVI and FVC were critical parameters in representing the SM variations related with the surface vegetation changes, but further studies are necessary before the relationship can be applied.
The effect of plant spacing and cutting interval on growth of Moringa oleifera.

Two provenances of Moringa oleifera grown in Zimbabwe, namely Malawi and Mutoko were evaluated to determine the effect of plant spacing and cutting interval on plant growth. A split-split plot experimental design was set up with provenance as main plot factor, plant spacing as sub-plot factor and cutting interval sub-sub plot factor at Bindura University of Science Education's research unit. The provenances were tested at two plant spacings of 15 cm x 15 cm and 20 cm x 20 cm, and two cutting intervals of 60 days and 75 days. The results showed that at first establishment, 90 days after planting, Mutoko provenance had higher (p<0.05) mean of plant height and root collar diameter. The 60 days cutting interval had the highest (p<0.05) plant growth performance for Mutoko provenance at each subsequent cutting to most of the growth parameters. At 75 days cutting interval Mutoko provenance demonstrated a higher (p<0.05) mean growth of sprout height.
Graphene nanoribbon antenna modeling based on MoM-GEC method for electromagnetic nanocommunications in the terahertz range

Abstract In this paper, we present a graphene nanoribbon (GNR)-based terahertz antenna array modeling using the MoM-GEC method (the moments method combined with the generalized equivalent circuit). The quantum mechanical conductivity of graphene is used to introduce the graphene’s effect in the mathematical formulation. The GNR is excited by a gap source with unit voltage localized in its center. The antenna structure is shielded in a rectangular waveguide with electric boundary walls. The proposed formulation is firstly validated and it yields efficient and accurate solutions. The obtained input impedance, current density distribution, and radiated electric field are presented to discuss the antenna performance in cases of single and antennas array. It is demonstrated that all results verify the boundary conditions and behave correctly corresponding to the physical laws. Furthermore, we showed that the antenna array leads to enhance the far-field communications for lower distances compared to conventional antenna.
Classification of cassava leaf diseases using deep Gaussian transfer learning model

In Sub‐Saharan Africa, experts visually examine the plants and look for disease symptoms on the leaves to diagnose cassava diseases, a subjective method. Machine learning algorithms have been employed to quickly identify and classify crop diseases. In this study, we propose a model that integrates a transfer learning approach with a deep Gaussian convolutional neural network model. In this study, two pre‐trained transfer learning models were used, that is, MobileNet V2 and VGG16, together with three different kernels: a hybrid kernel (a product of a squared exponential kernel and a rational quadratic kernel), a squared exponential kernel, and a rational quadratic kernel. In experiments using MobileNet V2 and the three kernels, the hybrid kernel performed better, with an accuracy of 90.11%, compared to 86.03% and 85.14% for the squared exponential kernel and a rational quadratic kernel, respectively. Additionally, experiments using VGG16 and the three kernels showed that the hybrid kernel performed better, with an accuracy of 88.63%, compared to the squared exponential kernel's accuracy of 84.62% and the rational quadratic kernel's accuracy of 83.95%, respectively. All the experiments were done using a traditional computer with no access to GPU and this was the major limitation of the study.
Reliable Detection Method of Variable Series Arc Fault in Building Integrated Photovoltaic Systems Based on Nonstationary Time Series Analysis

Arc fault characteristics would be influenced by diverse system elements and operation levels in building integrated photovoltaic (BIPV) systems, which would challenge the arc fault detection. This article aims at improving the detection reliability and speed by applying the time series analysis at feature and classification layers to reflect the random evolution process of arc fault current. According to the arc ignition method in UL1699B, arc fault signals are first sampled with a residential storage element and ten dc load elements at three voltage levels in BIPV systems. After evaluating time–frequency information with established indexes, variable and early arc fault are discovered to have no obvious current and harmonic indications due to strong system element noises, sensor precision, and low arc energy. Then, the sample entropy (SE) feature and gated recurrent unit (GRU) classifier are proposed to improve the detection performance through involving the nonstationary time series correlation of the arc fault current. An arc fault simulation platform is built to generate arc current with low signal-to-noise ratio (SNR), which proves the detection algorithm integrates multisource information to grasp the appearance of new signal pattern. Next, constrained optimization problems are designed for both feature and classifier layers to achieve the most distinguishable arc fault and fault-like performance. With matched optimization methods, the modified SE and GRU method could improve the detection accuracy by an average of 21.08% after comparing with existing features and classifiers. Finally, hardware implementation results indicate a higher detection accuracy and less runtime of the proposed detection method.
Local rules for approximating strong dislocation interactions in discrete dislocation dynamics

We consider the problem of approximating the strong dislocation interactions which occasionally take place during the numerical simulation of dense dislocation systems. Local rules are proposed to govern the behaviour of those tracking points that find themselves in an under-resolved environment. It is shown that these rules, utilizing only nearest-neighbour information, satisfactorily approximate more fully resolved calculations of localized dislocation–dislocation interactions, of extended structures such as junctions, and of higher order processes such as dislocation–junction and junction–junction interactions. Use of these rules should make it possible to do efficient large-scale numerical dislocation simulations, while still retaining the important qualitative effects arising from strong local interactions.
Generalization of the perturbation theory by using a repeating equation: A case that reveals the plasma chaotic state

Abstract Among the theoretical and experimental situations of interest in plasma physics are the waves that rise into the plasma and are strongly affected from the plasma parameters. Therefore, the development of strong relations between unperturbed and perturbed quantities is proved to exist both experimentally and theoretically. In the present paper, the mathematical solution ended in a relation between drift and perturbed velocities, which presupposes the perturbed velocity value to be very small in comparison with the drift velocity. However, the experiment has shown that the presupposition of the small value of the perturbations is not satisfied in many laboratory instances; as a result the use of the perturbation theory is limited. To surpass this difficulty, the big perturbed quantity was divided into small and equal parts, and by using the equation between unperturbed and perturbed velocities as a repeating relation, the perturbed theory may be extended and generalized beyond the small disturbances. In addition, calculations with a little change in the initial conditions were carried out, to determine when the plasma obtains chaotic behavior.
Identification of circular RNAs expression pattern in caprine fetal fibroblast cells exposed to a chronic non-cytotoxic dose of graphene oxide-silver nanoparticle nanocomposites

The widespread use of graphene oxide-silver nanoparticle nanocomposites (GO-AgNPs) in biomedical sciences is increasing the chances of human and animal exposure to its chronic non-toxic doses. Exposure to AgNPs-related nanomaterials may result in the negative effect on the dam, fetus and offspring. However, there are only little available information for profound understanding of the epigenetic alteration in the cells and animals caused by low-dose chronic exposure of GO-AgNPs. The present study investigated the effect of 0.5 μg/mL GO-AgNPs for 10 weeks on the differential expression of circular RNAs (circRNAs) in caprine fetal fibroblast cells (CFFCs), and this dose of GO-AgNPs did not affect cell viability and ROS level. We predicted the functions of those differentially expressed (DE) circRNAs in CFFCs by bioinformatics analysis. Furthermore, we validated the expression of ten DE circRNAs using quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR) to ensure the reliability of the sequencing data. Our results showed that the DE circRNAs may potentially regulate the GO-AgNPs-inducing epigenetic toxicity through a regulatory network consisted of circRNAs, miRNAs and messenger RNAs (mRNAs). Therefore, the epigenetics toxicity is essential to assess the biosafety level of GO-AgNPs.
Eicosanoids in human heart failure: pilot study of plasma epoxyeicosatrienoic and dihydroxyeicosatrienoic acid levels

Introduction The role of eicosanoids, metabolites of arachidonic acid with cardio-renal activity, remains unclear in human heart failure (HF). Methods We enrolled 50 patients with HF to measure plasma 14,15-EET and 14,15-DHET levels using commercial ELISA kits and compared them with 25 age- and sex-matched controls. Results Both of the measured eicosanoids were significantly higher in the HF group: 14,15-EET (91.3 ±25.7 ng/ml vs. 64.95 ±35.4 ng/ml) and 14,15-DHET (10.58 ±2.06 ng/ml vs. 9.07 ±1.60 ng/ml), p for both < 0.001. Conclusions We found that peripheral plasma eicosanoid (14,15-EET, 14,15-DHET) levels are raised in patients with HF compared to age- and sex-matched controls.
Big data help to define climate change challenges for the typical Mediterranean species Cistus ladanifer L.

Climate change’s huge impact on Mediterranean species’ habitat suitability and spatial and temporal distribution in the coming decades is expected. The present work aimed to reconstruct rockrose (Cistus ladanifer L.) historical and future spatial distribution, a typically Mediterranean species with abundant occurrence in North Africa, Iberian Peninsula, and Southern France. The R ensemble modeling approach was made using the biomod2 package to assess changes in the spatial distribution of the species in the Last Interglacial (LIG), the Last Glacial Maximum (LGM), and the Middle Holocene (MH), in the present, and in the future (for the years 2050 and 2070), considering two Representative Concentration Pathways (RCP 4.5 and RCP 8.5). The current species potential distribution was modeled using 2,833 occurrences, six bioclimatic variables, and four algorithms, Generalized Linear Model (GLM), MaxEnt, Multivariate Adaptive Regression Splines (MARS), and Artificial Neural Networks (ANN). Two global climate models (GCMs), CCSM4 and MRI-CGCM3, were used to forecast past and future suitability. The potential area of occurrence of the species is equal to 15.8 and 14.1% of the study area for current and LIG conditions, while it decreased to 3.8% in the LGM. The species’ presence diaminished more than half in the RCP 4.5 (to 6.8% in 2050 and 7% in 2070), and a too low figure (2.2%) in the worst-case scenario (RCP 8.5) for 2070. The results suggested that the current climatic conditions are the most suitable for the species’ occurrence and that future changes in environmental conditions may lead to the loss of suitable habitats, especially in the worst-case scenario. The information unfolded by this study will help to understand future predictable desertification in the Mediterranean region and to help policymakers to implement possible measures for biodiversity maintenance and desertification avoidance.
Acute Colonic Pseudo-Obstruction in a Patient With COVID-19 Pneumonia: A Case Report

Coronavirus disease (COVID-19) is primarily a respiratory illness caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. However, the disease is also known to cause a range of extrapulmonary manifestations, including gastrointestinal (GI) symptoms such as nausea, vomiting, and diarrhea. The exact mechanisms by which the virus causes extrapulmonary manifestations are not fully understood, but it is theorized that the virus can enter cells in other organs including the GI tract, through the angiotensin-converting enzyme 2 (ACE2) receptor. This can result in inflammation and damage to the affected organs. In rare cases, COVID-19 can also cause acute colonic pseudo-obstruction (ACPO), a condition characterized by symptoms of bowel obstruction but without a physical obstruction present. Acute colonic pseudo-obstruction is a serious and potentially life-threatening complication of COVID-19 that requires prompt recognition and treatment to prevent further complications such as bowel ischemia and perforation. We hereby present a case report of a patient with COVID-19 pneumonia developing ACPO and discuss the suggested pathophysiology, diagnostic approach, and treatment options.
Static structure and wavefront error analysis of the primary mirror of an experimental telescope

This paper presents the analysis of static structure and wavefront error (WFE) of a telescope experimental model’s primary mirror. The experimental telescope with 280mm diameter primary mirror had been assembled and aligned in 2011. The WFE result was not perfect. In order to find out the root cause of the WFE, the static structure analysis had been applied to the structure model of the telescope assembly. Some assumed effects which may cause deformation of the primary mirror had been proposed, such as gravity effect, iso-static mount (ISM) bonding effect, thermal expansion effect, etc. According to each assumed effect, we established a corresponding model and boundary setup. The numerical model was also analyzed by static structure analysis software and opto-mechanical analysis software to obtain numerical WFE and Zernike polynomials. The numerically synthetic optical system WFE was compared with measured system WFE of the telescope. According to the results of these deformation effects of the primary mirror, we could get the root cause of the system WFE. In the next generation of telescope, the opto-mechanical design and alignment process would be refined, and applied to the new optical system.
Removable Pressure-Sensitive Adhesives Based on Acrylic Telomer Syrups

Removable pressure-sensitive adhesives (PSAs) are used in the production of self-adhesive materials such as protective films, masking tapes or biomedical electrodes. This work presents a new and environmentally friendly method of obtaining this type of adhesive materials, i.e., photochemically induced free radical telomerization. Adhesive binders to removable PSAs, i.e., the photoreactive acrylic telomer syrups (ATS) were prepared from n-butyl acrylate, acrylic acid, and 4-acrylooxybenzophenone. Tetrabromomethane (CBr4) or bromotrichloromethane (CBrCl3) were used as the telogens. ATS was modified with unsaturated polybutadiene resin and a radical photoinitiator. Adhesive compositions were coated onto a carrier and UV cross-linked. The effects of the chemical nature of telomers (i.e., terminal Br or Cl atoms) and their molecular weight (K-value), as well as the cross-linking degree on adhesive properties of PSAs, were studied. It was found that with the increase in telogen content in the system, the dynamic viscosity of ATS and K-value of acrylic telomers decrease, and the conversion of monomers increases. CBr4 turned out to be a more effective chain transfer agent than CBrCl3. Moreover, telomers with terminal Br-atoms (7.5 mmol of CBr4), due to slightly lower molecular weights and viscosity, showed a higher photocrosslinking ability (which was confirmed by high cohesion results at 20 and 70 °C, i.e., >72 h). Generally, higher values of the temperature at which adhesive failure occurred were noted for PSAs based on ATS with lower telogen content (7.5 mmol), both CBr4 and CBrCl3. The excellent result for removable PSA was obtained in the case of telomer syrup Br-7.5 crosslinked with a 5 J/cm2 dose of UV-radiation (adhesion ca.1.3 N/25 mm, and cohesion > 72 h).