Miracle-dz/UniFAR-FTUs huggingface dataset:
NLRR1 Is a Potential Therapeutic Target in Neuroblastoma and MYCN-Driven Malignant Cancers

Receptor tyrosine kinases (RTKs) receive different modulation before transmitting proliferative signals. We previously identified neuronal leucine-rich repeat 1 (NLRR1) as a positive regulator of EGF and IGF-1 signals in high-risk neuroblastoma cells. Here, we show that NLRR1 is up-regulated in various adult cancers and acts as a key regulator of tumor cell proliferation. In the extracellular domains of NLRR1, fibronectin type III (FNIII) domain is responsible for its function to promote cell proliferation. We generated monoclonal antibodies against the extracellular domains of NLRR1 (N1mAb) and screened the positive N1mAbs for growth inhibitory effect. The treatment of N1mAbs reduces tumor cell proliferation in vitro and in vivo, and sensitizes the cells to EGFR inhibitor, suggesting that NLRR1 is a novel regulatory molecule of RTK function. Importantly, epitope mapping analysis has revealed that N1mAbs with growth inhibitory effect recognize immunoglobulin-like and FNIII domains of NLRR1, which also indicates the importance of FNIII domain in the function of NLRR1. Thus, the present study provides a new insight into the development of a cancer therapy by targeting NLRR1 as a modulator of proliferative signals on cellular membrane of tumor cells.
Membrane-Suspended Nanopores in Microchip Arrays for Stochastic Transport Recording and Sensing

The transport of nutrients, xenobiotics, and signaling molecules across biological membranes is essential for life. As gatekeepers of cells, membrane proteins and nanopores are key targets in pharmaceutical research and industry. Multiple techniques help in elucidating, utilizing, or mimicking the function of biological membrane-embedded nanodevices. In particular, the use of DNA origami to construct simple nanopores based on the predictable folding of nucleotides provides a promising direction for innovative sensing and sequencing approaches. Knowledge of translocation characteristics is crucial to link structural design with function. Here, we summarize recent developments and compare features of membrane-embedded nanopores with solid-state analogues. We also describe how their translocation properties are characterized by microchip systems. The recently developed silicon chips, comprising solid-state nanopores of 80 nm connecting femtoliter cavities in combination with vesicle spreading and formation of nanopore-suspended membranes, will pave the way to characterize translocation properties of nanopores and membrane proteins in high-throughput and at single-transporter resolution.
Bacitracin Methylene Disalicylate Improves Intestinal Health by Modulating Its Development and Microbiota in Weaned Rabbits

Intestinal infections are a major cause of morbidity and mortality in humans and agricultural animals, especially newborns and weaned animals. Preventive treatments that help weaned animals maintain homeostasis and balance the hindgut microbial populations are desirable. The present study aimed to explore the impact of bacitracin methylene disalicylate (BMD) on the intestinal health by analyzing the intestinal environment, morphology, expression of peptidoglycan recognition proteins (PGRPs), and flora of weaned rabbits. A total of 300 New Zealand weaned rabbits were randomly divided into the following five treatment groups for a 35-day feed trial: control group (basal diet), bacitracin zinc (BZ) group (50 mg/kg BZ), BMDa group (100 mg/kg BMD), BMDb group (50 mg/kg BMD), and BMDc group (rabbits fed a basal diet supplemented with 25 mg/kg BMD). In each treatment group, 28 rabbits were slaughtered for experimental analysis. The results showed that the supplementation of BMD increased the environmental acidity of the cecum of the weaned rabbits and reduced the ammonia-nitrogen concentration, which was beneficial to the survival of useful bacteria in the intestine. The morphology analysis of the duodenum using hematoxylin and eosin staining revealed that the villus length, villus/crypt ratio, and intestinal wall thickness increased in the BMD group, thereby improving the structure of the duodenum and the absorption capacity of the small intestine. Moreover, real-time polymerase chain reaction test showed that PGRPs (especially PGLYRP-1 and PGLYRP-2) in the intestinal had an antagonistic effect with BMD in the process of inhibiting pathogenic bacteria, resulting in their decreased expression (P < 0.05). Furthermore, through 16S rRNA sequencing in the cecal content, the abundance of the predominant phyla in the BMDa and BZ groups was found to be the closest. The abundance of the genera Lachnospira, Erysipelotrichaceae (p-75-a5), Paraprevotellaceae (YRC22), Mogibacterium, Peptococcaceae (rc4-4), Anaerovibrio, Succinivibrio, and Sphaerochaeta increased in the BMDa and BZ groups (P < 0.05). The relative abundance of Alistipes, Sedimentibacter, and Dorea significantly increased only in the BMDa group (P < 0.05). Conclusively, BMD, as well as microbes, improved the intestinal environment and structure to maintain the intestinal health of weaned rabbits.
Optimized Synthesis of CdTe Nanoplatelets and Photoresponse of CdTe Nanoplatelets Films

We study in detail the synthesis of CdTe nanoplatelets. Three populations of nanoplatelets with a thickness defined with atomic precision are obtained. We show that CdTe nanoplatelets can be extended laterally to obtain nanosheets with lateral dimensions in the micrometer range. We present the study of the reaction conditions for the formation of CdTe nanoplatelets and for their lateral extension. The reaction products are analyzed with optical spectroscopy, transmission electron microscopy, and small-angle X-ray scattering. We investigate the electro-optical properties of films formed with CdTe nanoplatelets, and we show that their current photoresponse is better than the one of comparable films formed with CdTe spherical nanocrystals.
Kinetics of carbothermic reduction of natural aluminosilicates

Thermogravimetric analysis was used to investigate the kinetics of the carbothermic reduction of aluminosilicates between 1450 and 1550°C. The majority of the experiments involved the reduction of meta-kaolin, mullite or silica with lamp black within an argon atmosphere This dissertation focused on the mechanism and the rate limiting step involved in the reduction process. Also investigated were various aluminosilicates, reductants and reactor atmosphere to identify less expensive sources of raw materials. The carbothermic reduction of aluminosilicates is believed to be simply the reduction of mullite and silica after their formation at the reaction temperatures. Thermodynamics show that only the silica phase will be reduced within the temperature range of this study. The reduction mechanism of aluminosilicates is therefore based on the reduction mechanism of silica. This mechanism can be described as follows: S i 0 2 + C = SiO ( g ) + CO Step (1) SiO (g) + 2C = SiC + CO Step (2) From examination of the mechanism, the two possibilities for the rate limiting step are the SiO formation step and the SiC formation step. Experiments involving temperature effect, particle size effect, Si and SiC seeding effect, reactor atmosphere effect (CO(g> and N2(g)) and various combinations of precursor powders were carried out. The collected weight loss data are presented as conversion, X, versus time, t, and conversion rate, , versus time, t. The activation energies for the different reaction mixtures dt have been determined and compared with values from literature. Product powders were
Evaluating total carrying capacity of tourism using impact indicators

ABS TRACT: The carrying capacity is well identified tool to manage problems due to uncontrolled tourism for any destination. This report highlights the carrying capacity estimation of Kerwa tourism area, Bhopal, India. The methodology used in this report is a new two-tier mechanism of impact analysis using index numbers derived from a survey of 123 stakeholders. From this the individual component impact analysis and the total carrying capacity of the area is computed in order to state the insight of the total carrying capacity left for the tourism activities in Kerwa tourism area. It is calculated from, the results so obtained, that the Kerwa catchment area falls in “very low impact category” and hence in a healthy state of the artwork in terms of total carrying capacity. The study conveys the current need in the destination management and tourism development as a road map for the destination managers for implementing sustainable tourism.
Order-disorder phase transition and multiferroic behaviour in a metal organic framework compound (CH3)2NH2Co(HCOO)3

We have investigated the multiferroic and glassy behaviour of metal-organic framework (MOF) material (CH3)2NH2Co(CHOO)3. The compound has perovskite–like architecture in which the metal–formate forms a framework. The organic cation (CH3)2NH2+ occupies the cavities in the formate framework in the framework via N–H···O hydrogen bonds. At room temperature, the organic cation is disordered and occupies three crystallographically equivalent positions. Upon cooling, the organic cation is ordered which leads to a structural phase transition at 155 K. The structural phase transition is associated with a para-ferroelectric phase transition and is revealed by dielectric and pyroelectric measurements. Further, a PE hysteresis loop below 155 K confirms the ferroelectric behaviour of the material. Analysis of dielectric data reveal large frequency dispersion in the values of dielectric constant and tanδ which signifies the presence of glassy dielectric behaviour. The material displays a antiferromagnetic ordering belo.
Blood RNA sequencing shows overlapping gene expression across COPD phenotype domains

Rationale COPD can be assessed using multidimensional grading systems with components from three domains: pulmonary function tests, symptoms and systemic features. Clinically, measures may be used interchangeably, though it is not known if they share similar pathobiology. Objective To use RNA sequencing (RNA-seq) to determine if there is an overlap in the underlying biological mechanisms and consequences driving different components of the multidimensional grading systems. Methods Whole blood was collected for RNA-seq from current and former smokers in the Genetic Epidemiology of COPD study. We tested the overlap in gene expression and biological pathways associated with case–control status and quantitative COPD phenotypes within and between the three domains. Results In 2647 subjects, there were 3030 genes differentially expressed in any of the three domains or case–control status. There were five genes that overlapped between the three domains and case–control status, including G protein-coupled receptor 15(GPR15), sestrin 1 (SESN1) and interferon-induced guanylate-binding protein 1 (GBP1), which were associated with longitudinal decline in FEV1. The overlap between the three domains was enriched for pathways related to cellular components. Conclusions We identified gene sets and pathways that overlap between 12 COPD-related phenotypes and case–control status. There were no pathways represented in the overlap between the three domains and case–control status, but we identified multiple genes that demonstrated a consistent pattern of expression across several of the phenotypes. Patterns of gene expression correlation were generally similar to the correlation of clinical phenotypes in the PFT and symptom domains but not the systemic features.
Ammonia-Oxidizing Bacteria in Biofilters Removing Trihalomethanes Are Related to Nitrosomonas oligotropha

ABSTRACT Ammonia-oxidizing bacteria (AOB) in nitrifying biofilters degrading four regulated trihalomethanes—trichloromethane, bromodichloromethane, dibromochloromethane, and tribromomethane—were related to Nitrosomonas oligotropha. N. oligotropha is associated with chloraminated drinking water systems, and its presence in the biofilters might indicate that trihalomethane tolerance is another reason that this bacterium is dominant in chloraminated systems.
Direct Low-Temperature Smelting of Spent Lead Acid Battery

Direct low-temperature smelt process of battery paste and battery plates was studied and a new technology for treating spent lead battery was developed.The effects of dosage of sodium hydroxide,reductant and sodium carbonate on the smelt process were investigated.Good results of laboratary and pilot experiment were achieved.The results of the pilot experiment indicated that direct recovery of lead is up to 91.4%,and rate of slag and rate of dust are 17.5% and 4.18% respectively under the conditions of temperature 920 ℃,dosage of sodium hydroxide theoretical amount and dosage of reductant 6%.
LightGuide

Precise and reliable directional feedback is crucial for electronic traveling aids that guide visually impaired people along safe paths. A large proportion of visually impaired people can determine light position using their light perception. This work presents LightGuide, a directional feedback solution that indicates a safe direction of travel via the position of a light within the user's visual field. We prototyped LightGuide using an LED strip attached to the brim of a cap, and conducted three user studies to explore the effectiveness of LightGuide compared to HapticBag, a state-of-the-art baseline solution that indicates directions through on-shoulder vibrations. Results showed that, with LightGuide, participants turned to target directions in place more quickly and smoothly, and navigated along basic and complex paths more efficiently, smoothly, and accurately than HapticBag. Users' subjective feedback implied that LightGuide was easy to learn and intuitive to use. The potential limitations of using LightGuide in real environments are subsequently discussed.
Handling Acoustic Variation in Dysarthric Speech Recognition Systems Through Model Combination

Developing automatic speech recognition (ASR) systems that recognise dysarthric speech as well as control speech from unimpaired speakers remains challenging. Including more highly variable dysarthric speech during training can also negatively af-fect the performance on control speakers, which is not desirable when developing speech recognisers for a wider audience. In this work, we analyse how the acoustic variability of dysarthric speech affects ASR systems and propose the combination of multiple acoustic models trained on different subsets of speakers to mitigate this effect. This approach shows improvements for both dysarthric and control speakers on the Torgo and UA-Speech corpora.
Statistical modelling and climate variability of compound surge and precipitation events in a managed water system: a case study in the Netherlands

The co-occurrence of (not necessarily extreme) precipitation and surge can lead to extreme inland water levels in coastal areas. In a previous work the positive dependence between the two meteorological drivers was demonstrated in a managed water system in the Netherlands by empirically investigating an 800-year time series of water levels, which were simulated via a physical-based hydrological model driven by a regional climate model large ensemble. In this study, we present an impact-focused multivariate statistical framework to model the dependence between these flooding drivers and the resulting return periods of inland water levels. This framework is applied to the same managed water system using the aforementioned large ensemble. Composite analysis is used to guide the selection of suitable predictors and to obtain an impact function that optimally describes the relationship between high inland water levels (the impact) and the explanatory predictors. This is complex due to the high degree of human management affecting the dynamics of the water level. Training the impact function with subsets of data uniformly distributed along the range of water levels plays a major role in obtaining an unbiased performance. The dependence structure between the defined predictors is modelled using twoand three-dimensional copulas. These are used to generate paired synthetic precipitation and surge events, transformed into inland water levels via the impact function. The compounding effects of surge and precipitation and the return water level estimates fairly well reproduce the earlier results from the empirical analysis of the same regional climate model ensemble. Regarding the return levels, Published by Copernicus Publications on behalf of the European Geosciences Union. 3596 V. M. Santos et al.: Multivariate statistical modelling of water levels this is quantified by a root-mean-square deviation of 0.02 m. The proposed framework is able to produce robust estimates of compound extreme water levels for a highly managed hydrological system. Even though the framework has only been applied and validated in one study area, it shows great potential to be transferred to other areas. In addition, we present a unique assessment of the uncertainty when using only 50 years of data (what is typically available from observations). Training the impact function with short records leads to a general underestimation of the return levels as water level extremes are not well sampled. Also, the marginal distributions of the 50-year time series of the surge show high variability. Moreover, compounding effects tend to be underestimated when using 50-year slices to estimate the dependence pattern between predictors. Overall, the internal variability of the climate system is identified as a major source of uncertainty in the multivariate statistical model.
Faster lifting for two-variable logic using cell graphs

We consider the weighted ﬁrst-order model counting (WFOMC) task, a problem with important applications to inference and learning in structured graphical models. Bringing together earlier work [Van den Broeck et al., 2011, 2014], a formal proof was given by Beame et al. [2015] showing that the two-variable fragment of ﬁrst-order logic, FO 2 , is domain-liftable , meaning it admits an algorithm for WFOMC whose runtime is polynomial in the given domain size. However, applying this theoretical upper bound is often impractical for real-world problem instances. We show how to adapt their proof into a fast algorithm for lifted inference in FO 2 , using only off-the-shelf tools for knowledge compilation, and several careful optimizations in-volving the cell graph of the input sentence, a novel construct we deﬁne that encodes the interactions between the cells of the sentence. Experimental results show that, despite our approach being largely orthogonal to that of F ORCLIFT [Van den Broeck et al., 2011], our algorithm often outperforms it, scaling to larger domain sizes on more complex input sentences. , ,
Rain Attenuation in 5G Wireless Broadband Backhaul Link and Develop (IoT) Rainfall Monitoring System

Climate change is the cause of more frequent and intense rainfall where they affect wireless communications because they cause severe weakening of the power of the emitted signal. These losses reduce network coverage and, therefore, system availability. The proposed solution is to integrate an Internet of Things (IoT) rainfall monitoring system where it will be able to collect real-time data on the height of rain that erupts in a particular place. This data will help areas where base stations install and the distance of the link that may need to be changed to reduce rainfall's harmful effects. So, the prediction of attenuation due to rain is an essential parameter in both terrestrial and satellite connections. The present study uses the ITU-R P 838 and ITU-R P 530 models to theoretically calculate losses in a 5G wireless broadband link with 99.9% link availability. This study conducts three frequency bands, 24 GHz, 28 GHz, and 38GHz, in Palo Alto, California. The travel distance is 5km, while the rainfall rate for the analyzed area is in zone D. The results show that the attenuations are proportional to the frequency, polarization, and rainfall rate. Keywords—Rain attenuation; Internet of Things; wireless broadband
Data-Fusion for Epidemiological Analysis of Covid-19 Variants in UAE

Since December 2019, a new pandemic has appeared causing a considerable negative global impact. The SARS-CoV-2 first emerged from China and transformed to a global pandemic within a short time. The virus was further observed to be spreading rapidly and mutating at a fast pace, with over 5,775 distinct variations of the virus observed globally (at the time of submitting this paper). Extensive research has been ongoing worldwide in order to get a better understanding of its behaviour, influence and more importantly, ways for reducing its impact. Data analytics has been playing a pivotal role in this research to obtain valuable insights into understanding and fighting against the spread of infection. However, this is time and resource intensive, making it difficult to observe and quickly identify the impact of mutations. Factors such as the spread or virulence could explain the three months delay in revealing the new virus variant in the UK. This paper presents an extensive correlation analysis of the effect caused by the different SARS-CoV-2 strains, and their influence on the population across diverse factors, such as propagation and fatality rates, during the peak of the pandemic, with a focus on two major countries in theMiddle East, the United Arab Emirate (UAE) and the Kingdom of Saudi Arabia (KSA). This research aims to investigate the epidemiological behaviour of the Coronavirus' genomic variants over time in the UAE, compared with the KSA, where correlation analysis is carried out for a number of cases, deaths and their statistical deviations. The results of the analysis highlight very interesting insights into the epidemiological impact of the Covid-19 genomic behaviour in both countries, which could lead to important actions taken to minimize the impact on wider public health, possibly saving lives, and the economy. For instance, our method identifies a potential correlation between a spike in the number of deaths per case of 5.5 observed in the UAE by March 24th, with the emergence of new genomic variants of the Coronavirus (G0_c, G0_e1 and G0_e2). Our proposed methodology can be instrumental in identifying and classifying new variations of the virus earlier, and possibly predicting foreseeable mutations through pattern analysis, hence creating proactivemeasures to control its spread, such as the recent case of the new virus variant, recently discovered in the UK. © 2021 Tech Science Press. All rights reserved.
Image-based Onion Disease (Purple Blotch) Detection using Deep Convolutional Neural Network

Agriculture on earth is the biggest need for human sustenance. Over years, many farming methods and components have become computerized to guarantee quicker production with higher quality. Because of the enlarged demand in the farming industry, agricultural produce must be cultivated using an efficient process. Onion (Allium cepa L.) is an economically valuable crop and is the second-largest vegetable crop in the world. The spread of various diseases highly affected the production of the onion crop. One of the serious and most common diseases of onion worldwide is purple blotch. To compensate for a limited amount of training dataset of healthy and infected onion crops, the proposed method employs a pretrained enhanced InceptionV3 model. The proposed model detects onion disease (purple blotch) from images by recognizing the abnormalities caused by the disease. The suggested approach achieves a classification accuracy of 85.47% in recognizing the disease. This research investigates a novel approach for the rapid and accurate diagnosis of plant/crop diseases, laying the theoretical foundation for the use of deep learning in agricultural information. Keywords—Disease detection; disease classification; artificial intelligence; inceptionv3; deep convolutional neural network