Miracle-dz/UniFAR-FTUs huggingface dataset:
Expression profile analysis of MATE gene family in rice

Multidrug and toxic compound extrusion (MATE) proteins is a newly characterized transporter family in plants. However, knowledge of this family in systematic classification, molecular evolution, and expression patterns in plants is limited. In this study, MATE gene sequence, structure, and names as well as MATE protein size and subcellular localization in rice were analyzed using bioinformatics tools, chromosome localizations, and gene clusters. The function of MATE proteins was further elucidated on a basis of phylogenetic relationships. Using available transcriptomic data, the expression pattern and function of MATE were different in the selected organs and developments stages of rice. In addition, the relative abundance of OsMATE1 transcripts increased 3 h after copper treatment and so it was identified as a candidate gene for Cu tolerance in rice. This research provided basic data for further studies on MATE genes in rice and theoretical information about the biological function of MATE proteins.
Topology-Dependent Interference of Circuit Function by Growth Feedback

Growth-mediated feedback between synthetic gene circuits and host organisms leads to diverse emerged behaviors, including growth bistability and enhanced ultrasensitivity. However, the range of possible impacts of growth feedback on different gene circuits remains underexplored. Here, we mathematically and experimentally demonstrated that growth feedback affects the functions of memory gene circuits in a network topology-dependent way. Specifically, the memory of the self-activation circuit is quickly lost due to the fast growth-mediated dilution of the circuit products. Decoupling of growth feedback reveals its memory, manifested by its hysteresis property across a broad range of stimulus. On the contrary, the toggle switch is more refractory to the growth-mediated dilution and can retrieve its memory after the fast-growth phase. The underlying principle lies in the different dependence of active and repressive regulations in these circuits on the growth-mediated dilution. Our results unveil the topology-dependent mechanism on how growth-mediated feedback influences the behaviors of gene circuits.
Application of Azoxystrobin Fungicide Improves Drought Tolerance in Tomato, via Enhancing Physio-Biochemical and Anatomical Feature

To investigate whether the fungicide Azoxystrobin improves the potential to maintain physio-biochemical functions under drought, tomato plants were applied with Azoxystrobin under either well-watered and deficit irrigation conditions. Drought-stressed tomato plants showed significant reductions in most tested parameters of physiology [cell membrane stability (CMS), relative water content (RWC), relative water loss (RWL) and chlorophylls], growth attributes and leaflet and main stem anatomical features, while exhibited increases in contents of proline and total phenols, activities of catalase (CAT), peroxidase (POD) and polyphenol oxidase (PPO), fresh (FW) and dry (DW) weights of roots, and leaflet spongy tissue thickness compared to well-watered control plants. Under full irrigation, Azoxystrobin treatment significantly increased RWC and chlorophyll content, POD and PPO activities, root DW, number of fruits per plant and many features of leaflet and main stem, while significantly decreased CMS and RWL, root, shoot and plant lengths, shoot and plant FW, and stem xylem tissue thickness compared to the control plants sprayed with water. However, Azoxystrobin treatment ameliorated drought stress in tomato plants and significantly increased CMS and free proline content, activities of CAT, POD and PPO, and contents of free and total phenols, and root DW and number of fruits per plant, in addition to spongy tissue thickness of leaflet, but not affected chlorophylls and carotenoids contents, root FW, plant DW and most of anatomical features compared to the stressed plants without Azoxystrobin treatment. These results support that Azoxystrobin foliar application may have a positive effect on well-watered and drought-stressed tomato plants.
Factors Affecting Sugarcane Production by Small-Scale Growers in Ndwedwe Local Unicipality, South Africa

Sugarcane is an important crop worldwide due to its many nutritional and economic uses. Small-scale sugarcane growers (SSGs) are a significant sector of sugarcane production in South Africa. However, the number of SSGs is noted to have declined from as early as the 2000s to the present time. As a result of the declining sugarcane production, there are now generally fewer SSGs. However, it is not clear cut as to what caused the decrease in sugarcane production by SSGs. The primary objective of this paper is to determine the factors affecting the sugarcane production by SSGs in Mona and Sonkombo in Ndwedwe Local Municipality. Data collection was through a well-structured questionnaire administered to 100 SSGs (that is, 50 respondents each from the study sites, namely Mona and Sonkombo) that were randomly selected. The paper employs descriptive statistics to describe farm characteristics, and a production function (Cobb–Douglass production function (CDPF)) analysis using the ordinary least squares (OLS) criterion to estimate the parameters affecting sugarcane production. Results show that late harvesting (by up to three (3) weeks), late fertiliser application (by up to six (6) months, and chemicals (Gramoxone) application (by up to five (5) months) were primary challenges facing SSGs, likely to result in declining sugarcane yield. The CDPF regression analysis reveals that significant predictors of the production function are: labour and the amount of chemicals (Gramoxone) applied. Labour (man-days/ha), amount of chemicals (Gramoxone) applied are found to be statistically significant and positively correlated with sugarcane production. The government, through the relevant Department of Agriculture, including the private sector, should intensify out-grower technical services for SSGs to realise higher production per hectare. Such services would ensure optimal allocation and application of inputs, labour and chemicals (herbicides and pesticides), respectively, at the right time to ensure efficacy. There is also a need to introduce buying consortiums for SSGs to reduce the costs of inputs.
Three-fluorophore FRET-FLIM enables the study of trimeric protein interactions and complex formation with nanoscale resolution in living plant cells

Integration of signalling on the cellular level is essential for the survival of organisms. Protein-protein interaction studies provide valuable insights in these signalling events. One of the best understood signalling pathways in plants is the brassinosteroid (BR) hormone signalling pathway, which is mediated by the receptor BRASSINOSTEROID INSENSITIVE 1 (BRI1) with its co-receptor BRI1-ASSOCIATED KINASE (BAK1). Both BRI1 and BAK1 have been shown to interact with RECEPTOR LIKE PROTEIN 44 (RLP44), which was implicated in cell wall integrity sensing by modulation of BL signalling. Here we provide evidence by quantitative in vivo three-fluorophore FRET-FLIM measurements, that RLP44, BRI1 and BAK1 form a trimeric complex in the plasma membrane of N. benthamiana leaf cells, with an estimated distance between them below 15 nm. The immune receptor FLAGELLIN SENSING 2 (FLS2), which is also a receptor-like kinase like BRI1, is not integrated in a similar complex with RLP44 and BAK1. Our study supports that BRI1 and FLS2 are localized in distinct nanodomains in the PM. As the fluorescence lifetime of the donor is monitored, our method circumvents the extensive calculations necessitated by intensity-based FRET interaction assays and thus provides a feasible base for studying the sub-compartmentalization in the plasma membrane of living plant cells with a nanoscale resolution.
New 1,8-Naphthalimide Derivatives as Photoinitiators for Free-Radical Polymerization Upon Visible Light

Photopolymerization processes, and especially those carried out under visible light, are more and more widespread for their multiple advantages compared to thermal processes. In the present paper, new 1,8-naphthalimide derivatives are proposed as photoinitiators for free-radical polymerization upon visible light exposure using light-emitting diodes (LEDs) at 395, 405, and 470 nm. These photoinitiators are used in combination with both iodonium salts and phosphine. The synthesis of these compounds as well as their excellent polymerization initiation ability for methacrylate monomers are presented in this article. A full picture of the involved chemical mechanisms is also provided thanks to photolysis, radical characterization, and redox measurements.
Role of base strength, cluster structure and charge in sulfuric-acid-driven particle formation

Abstract. In atmospheric sulfuric-acid-driven particle formation, bases are able to stabilize the initial molecular clusters and thus enhance particle formation. The enhancing potential of a stabilizing base is affected by different factors, such as the basicity and abundance. Here we use weak (ammonia), medium strong (dimethylamine) and very strong (guanidine) bases as representative atmospheric base compounds, and we systematically investigate their ability to stabilize sulfuric acid clusters. Using quantum chemistry, we study proton transfer as well as intermolecular interactions and symmetry in clusters, of which the former is directly related to the base strength and the latter to the structural effects. Based on the theoretical cluster stabilities and cluster population kinetics modeling, we provide molecular-level mechanisms of cluster growth and show that in electrically neutral particle formation, guanidine can dominate formation events even at relatively low concentrations. However, when ions are involved, charge effects can also stabilize small clusters for weaker bases. In this case the atmospheric abundance of the bases becomes more important, and thus ammonia is likely to play a key role. The theoretical findings are validated by cluster distribution experiments, as well as comparisons to previously reported particle formation rates, showing a good agreement.
Interoperability of multiscale visual representations for satellite image big data

In this paper, we propose an interoperable solution for dealing with hierarchical representations of satellite images. Computationally intensive construction of the tree representation is performed on the server side, while the need for computational resources on the client side are greatly reduced (tree postprocessing or visualization). The proposed scheme is interoperable in the sense that it does not impose any constraints on the client environment, and API for C++, Java and Python languages are currently available. The communication is performed nodewise in a binary format using array structure for limiting the memory footprint.
Evaluation of proximate content and vitamin profile of Moringa oleifera seed hull

Moringa plant is a plant with increase awareness of its health and nutritional benefits in Nigeria. This study was conducted to examine the nutrient composition of underutilized hull of Moringa oleifera seed. Proximate composition, antinutrients, minerals and vitamins profile of the hull were determined using standard analytical methods. The hull contained crude protein 5.25±0.011%, crude fibre 19.5±0.038%, ash 5.0±0.026%, crude lipid 4.25±0.011%, carbohydrate 55.2±0.062 and moisture content of 10.8±0.033%. Sodium (45.49 mg/100 g) and potassium (24.52 mg/100 g) were the most abundant minerals with appreciable amount of vitamin E and beta-carotene. Alkaloid (0.25%), saponin (10%), oxalate (0.015 mg/100 g) and phytate (0.487 mg/100 g) were found in the hull. It was therefore concluded that the findings of M. oleifera seed hull contain nutritive values that may necessitate its usage in alternative feed formulation instead of just remaining an agro waste. Key words: Antinutrients, hull, minerals, Moringa oleifera, vitamins.
Aluminum Matrix Composites Reinforced with Graphene: A Review on Production, Microstructure, and Properties

Abstract Graphene, a single-atom-thick sheet of sp2 hybridized carbon atoms densely packed within a hexagonal crystal lattice, owes a significant portion of its rapidly expanding usage in various fields of industry and science to its large surface area, lightweight, unique electronic and thermal properties, and extraordinary mechanical properties that it possesses. Using lightweight and high-strength materials leads to a substantial decrease in fuel consumption as well as an increase in payload. Among the numerous candidates to fulfill the aforementioned requirements, Al alloys and specifically, Al matrix composites (AMCs) reinforced with various graphene particles (nano-sheets, nano-platelets, etc.) stand out owing to their inherent lightweight, high specific strength and modulus, superior ductility and excellent thermal conductivity. In this paper, it has been endeavored to provide a comprehensive overview of the various methods of the synthesis and fabrication of graphene-reinforced AMCs with an overall intention of achieving a homogeneous distribution of graphene within the Al matrix. The emphasis of this review has been largely placed upon the detailed examination of the mechanical properties of these composites described in the recent literature published in this field. The strengthening mechanisms of Al/graphene composites, as well as the parameters affecting the strength and ductility achieved by graphene, such as the agglomeration of graphene within the Al matrix and Al/graphene interfacial reactions, have been elucidated. The role of graphene on the electrical and thermal properties of graphene-reinforced AMCs and the directions for future research are addressed. It should be noted, however, that while a diligent analysis of various works published in the field of Al/graphene composites has been carried out in this review, an exact comparison between the varying stages of each work, or their final properties is impractical, mainly due to the differences in the used initial substances, processing, and analyses.
Algorithm Level Timing Speculation for Convolutional Neural Network Accelerators

In this paper, we propose a technique for improving the efficiency of hardware accelerators based on timing speculation (overclocking) and fault tolerance. We augment the accelerator with a lightweight error detection mechanism to protect against timing errors, enabling aggressive timing speculation. We demonstrate the validity of our approach for the convolution layers in Convolutional Neural Networks (CNN). We present an implementation of a fault-tolerant CNN accelerator combined with the lightweight error detection for convolution layers. The error detection mechanism we have developed works at the algorithm level, based on algebraic properties of the computation, allowing the full implementation to be realized using High-Level Synthesis tools. We use a set of Zybo boards to experimentally demonstrate that overclocking boosts the frequency by 17-36% with low chances of error, and that the infrequent errors can be detected with a negligible overhead (only 1000 LUTs).
Role of acidic additives in noncaloric sweeteners in causation of dental erosion

Dental erosion is a nondestructive carious process, slowly dissolving tooth structure because of extrinsic, intrinsic, and idiopathic causes, resulting in painless loss of tooth structure without the involvement of microorganisms. Polyols/noncaloric sweeteners are promoted extensively owing to cariostatic action and low-glycemic response, but pose a risk of dental erosion because of acidic additives incorporated into sugar-free products which cause demineralization of enamel. Erythritol, sorbitol, mannitol, and xylitol are some of the polyols publicized in maintaining good oral health by the American Dental Association. A review of the existing literature was done by searching through databases such as PubMed, EBSCO, Hinari, and Sage on noncaloric sweeteners from February to end of May 2016 using keywords noncaloric sweetener, polyols, dental erosion, casein phosphopeptide (CPP) and amorphous calcium phosphate (ACP), nanohydroxyapatite, and prevention of erosion. Novel preventive strategies by infusing CPP ACP, milk protein casein, and fluoride into sugar-free formulations may resolve the cause of dental erosion.
Using Deep Learning to Estimate Systolic and Diastolic volumes from MRI-images

Deep learning with convolutional neural networks has become a widely used tool for computer vision tasks. In this paper, we focus on the problem of automatically annotating the volumes of the left ventricle in the heart, based on DICOM MRI-images. We discuss the solution, which was the second best in an international data mining competition. We show that it is possible to achieve near-human performance using a deep learning approach when using task-specific model architectures.
Risk Assessment Approach for Prioritizing Danube
Basin-Specific Pollutants: a Case Study
in the Novi Sad Region

The aim of this work was to determine the most relevant pollutants in Danube surface water and wastewater in the city of Novi Sad, and to conduct a risk assessment approach on substances for the optimization of future monitoring programmes. According to the requirements of Serbian law, obligatory and operating monitoring was focused on physico-chemical and biological parameters while the expanded monitoring programmes have not been applied – often due to practical circumstances. Novi Sad, with a population of more than 300.000 inhabitants, does not have a wastewater treatment plant, and about 2 m3 of wastewater is discharged directly to the Danube river every day. Screening analyses of the water within the Danube basin around city of Novi Sad included more than 300 different organic substances, while target analysis was conducted for all WFD pollutants. The methodology for generating the list of priority substances was applied for the first time in the Danubian region around the city of Novi Sad. By applying the prioritization procedure, 86 organic substances were determined in screening analyses and 27 substances obtained within target analyses were identified as the most relevant. In addition, seasonal variation analysis was conducted to determine the occurrence trends of specific compounds in different seasons.
The Role the N-terminal Domain Plays in Spidroin Assembly

75 Introduction . 76 Materials and Methods . 79 Results . 86 Discussion . 95 References . 99 IV. DIMERIZATION OF Nephila clavipes MAJOR AMPULLATE SPIDROIN 1 N-TERMINAL DOMAIN . 104 Abstract . 104 Introduction . 104 Materials and Methods . 108 Results . 109 Discussion . 114 References . 121. 104 Introduction . 104 Materials and Methods . 108 Results . 109 Discussion . 114 References . 121 V. PRODUCTION OF “MINI-SPIDROINS” IN Saccharomyces cerevisiae . 125 Abstract . 125 Introduction . 125 Materials and Methods . 129 Results . 132 Discussion . 135 References . 137 VI. CONCLUTIONS AND FUTURE DIRECTIONS . 142. 125 Introduction . 125 Materials and Methods . 129 Results . 132 Discussion . 135 References . 137 VI. CONCLUTIONS AND FUTURE DIRECTIONS . 142 References . 148
Medical Image Segmentation Algorithm Based on Feedback Mechanism CNN

With the development of computer vision and image segmentation technology, medical image segmentation and recognition technology has become an important part of computer-aided diagnosis. The traditional image segmentation method relies on artificial means to extract and select information such as edges, colors, and textures in the image. It not only consumes considerable energy resources and people's time but also requires certain expertise to obtain useful feature information, which no longer meets the practical application requirements of medical image segmentation and recognition. As an efficient image segmentation method, convolutional neural networks (CNNs) have been widely promoted and applied in the field of medical image segmentation. However, CNNs that rely on simple feedforward methods have not met the actual needs of the rapid development of the medical field. Thus, this paper is inspired by the feedback mechanism of the human visual cortex, and an effective feedback mechanism calculation model and operation framework is proposed, and the feedback optimization problem is presented. A new feedback convolutional neural network algorithm based on neuron screening and neuron visual information recovery is constructed. So, a medical image segmentation algorithm based on a feedback mechanism convolutional neural network is proposed. The basic idea is as follows: The model for obtaining an initial region with the segmented medical image classifies the pixel block samples in the segmented image. Then, the initial results are optimized by threshold segmentation and morphological methods to obtain accurate medical image segmentation results. Experiments show that the proposed segmentation method has not only high segmentation accuracy but also extremely high adaptive segmentation ability for various medical images. The research in this paper provides a new perspective for medical image segmentation research. It is a new attempt to explore more advanced intelligent medical image segmentation methods. It also provides technical approaches and methods for further development and improvement of adaptive medical image segmentation technology.
A room temperature operated ammonia gas sensor based on Ag-decorated TiO2 quantum dot clusters

In this research, nanometer size aggregates (clusters) of titanium dioxide (TiO2) quantum dot clusters (QDs) have been successfully prepared via a convenient hydrolysis method at a low temperature (80 °C). Then different amounts (0–5%) of Ag were further decorated on the TiO2 QDs via dipping and annealing under a nitrogen atmosphere. After Ag decoration, the TiO2 QD sensing materials were synthesized, and characterization and NH3 gas sensing performance studies were carried out. Analysis via XRD and EDS was conducted, and the results showed that Ag+ ions were successfully reduced to Ag and decorated on the surface of the anatase TiO2 QDs. Noble metal Ag acted as the sites for adsorbates, catalysts, or promoters during the surface reactions, and as the element improving the thermal stability of the nanostructure. Therefore, the Ag-decorated gas sensor possessed better gas sensing performance than an undecorated gas sensor, and 3% Ag dopant proved to be the optimal amount of addition. The fabricated 3% Ag-decorated TiO2 QDs gas sensor, compared with the undecorated TiO2 QDs sensor, displayed a 6-times-higher sensing response at room temperature and demonstrated excellent gas sensing properties toward 10–100 ppm NH3 gas, good selectivity, gas sensitivity and stability, rapid response/recovery time, and a linear relationship between the response and the target gas concentration. In particular, the excellent performance of the Ag decorated-TiO2 QDs gas sensor was achieved at room temperature, which suggests the great possibility of a prompt gas sensing response, with the use of paper as a substrate, that requires a low operation temperature.