Identification Of Sites Research Articles

Polyanionic Receptors for Carboxylates in Water

AbstractReceptors for carboxylate anions have many possible biomedical applications, including mimicry of the vancomycin group of antibiotics. However, binding carboxylates in water, the biological solvent, is highly challenging due to the hydrophilicity of these polar anions. Here we report, for the first time, the recognition of simple carboxylates such as acetate and formate in water by synthetic receptors with charge‐neutral binding sites. The receptors are solubilised by polyanionic side‐chains which, remarkably, do not preclude anion binding. The tricyclic structures feature two identical binding sites linked by polyaromatic bridges, capable of folding into closed, twisted conformations. This folding is hypothesised to preorganise the structures for anion recognition, mimicking the process which generates many protein binding sites. The architecture is suitable for elaboration into enclosed structures with potential for selective recognition of biologically relevant carboxylates.

Deep learning in disease vector image identification.

Vector-borne diseases (VBDs) represent a critical global public health concern, with approximately 80% of the world's population at risk of one or more VBD. Manual disease vector identification is time-consuming and expert-dependent, hindering disease control efforts. Deep learning (DL), widely used in image, text, and audio tasks, offers automation potential for disease vector identification. This paper explores the substantial potential of combining DL with disease vector identification. Our aim is to comprehensively summarize the current status of DL in disease vector identification, covering data collection, data preprocessing, model construction, evaluation methods, and applications in identification spanning from species classification to object detection and breeding site identification. We also discuss the challenges and possible prospects for DL in disease vector identification for further research. © 2024 Society of Chemical Industry.

DPNN-ac4C: a dual-path neural network with self-attention mechanism for identification of N4-acetylcytidine (ac4C) in mRNA.

The modification of N4-acetylcytidine (ac4C) in RNA is a conserved epigenetic mark that plays a crucial role in post-transcriptional regulation, mRNA stability, and translation efficiency. Traditional methods for detecting ac4C modifications are laborious and costly, necessitating the development of efficient computational approaches for accurate identification of ac4C sites in mRNA. We present DPNN-ac4C, a dual-path neural network with a self-attention mechanism for the identification of ac4C sites in mRNA. Our model integrates embedding modules, bidirectional GRU networks, convolutional neural networks, and self-attention to capture both local and global features of RNA sequences. Extensive evaluations demonstrate that DPNN-ac4C outperforms existing models, achieving an AUROC of 91.03%, accuracy of 82.78%, MCC of 65.78%, and specificity of 84.78% on an independent test set. Moreover, DPNN-ac4C exhibits robustness under the Fast Gradient Method attack, maintaining a high level of accuracy in practical applications. The model code and dataset are publicly available on GitHub (https://github.com/shock1ng/DPNN-ac4C).

A simplified antibody-responsive fluorescence biosensor by proximity ligation-induced quasi-Y-DNA assembly to illuminate emissive DNA looped-Ag nanocluster

Exploring the proximity ligation-induced quasi-Y-DNA assembly (plqYDA) for creating label-free fluorescence biosensor might be intriguing based on DNA loop-hosted red Ag nanocluster (lrAgNC) as a signaling reporter. For proof-of-concept, a bivalent digoxigenin antibody (anti-Dig) with two identical binding sites was used as an analyte model, and it can specifically distinguish two Dig haptens that were separately modified in two modular split strands (SS1 and SS2) with complements each other and invading domains. The strong and robust anti-Dig-Dig binding guided the proximity ligation of SS1 and SS2 to construct geometric-structure plqYDA with a paired stem and two sticky linkers. After introducing a dsDNA duplex from a reporting strand (RS) with seven-polycytosine template (C7) of lrAgNC and a blocking strand (BS), entropy-driven hybridization and cooperative displacement occurred. As a result, RS was rationally immobilized in plqYDA to crimp single-coil C7 as a closed and extruded loop, which preferably favored the development of emissive lrAgNC when adding AgNO3 and NaBH4. By collecting the distinct fluorescence signal, the label- and enzyme-free assay of anti-Dig was readily achieved, exhibiting good specificity and high sensitivity. This would be the first example of our best understanding of integrating plqYDA and lrAgNC for applicable biosensing and bioanalysis.

Identification of suitable sites for different artificial groundwater recharge structures for sustainable water resources management in Ken River Basin

Identification of suitable sites for different artificial groundwater recharge structures for sustainable water resources management in Ken River Basin

PARP1 acetylation at K119 is essential in regulating the progression and proliferation of cervical cancer cells.

Cervical cancer, CC, is one of the malignant cancers in women worldwide. Many studies about the genesis and progression of CC have been done at genomic, transcriptional, translational, and epigenetic levels. However, much less is done at post-translational modification (PTM) level. We first used pan-PTM antibodies to compare the pan PTM levels between clinical normal cervical tissues and CC tissues; we then sent the selected samples for label-free identification of acetylation sites. Next, we employed WT or K119A mutant PARP1-EGFP-STREPII plasmid transfection in Hela cells and examined various indexes including colony formation, wound healing, ROS generation, early apoptosis, and immunofluorescence and quantification of proliferation markers (Ki67, PCNA, and p-P53). Last, we examined the levels of multiple important kinases regulating cervical cancer progression. We found that pan-acetylation was the most downregulated in clinical CC samples, whereas the acetylation of PARP1, Poly(ADP-ribose) polymerase-1, was upregulated at K119. Next, we showed that PARP1-WT overexpression significantly suppressed the proliferation and progression in CC cell line Hela, while K119A overexpression didn't show any impact. Finally, PARP1-WT overexpression significantly decreased p-ERK1/2 while didn't affect the phosphorylation levels of other important kinases such as AKT, MTOR, and RPS6. This study discovered a new type of PTM of PARP1 in CC, and showed that PARP1 acetylation at K119 is essential in regulating the proliferation and progression of CC through ERK1/2. Further studies are required to investigate how PARP1 acetylation impact its function.

Computational approach for decoding malaria drug targets from single-cell transcriptomics and finding potential drug molecule

Malaria is a deadly disease caused by Plasmodium parasites. While potent drugs are available in the market for malaria treatment, over the years, Plasmodium parasites have successfully developed resistance against many, if not all, front-line drugs. This poses a serious threat to global malaria eradication efforts, and the continued discovery of new drugs is necessary to tackle this debilitating disease. With recent unprecedented progress in machine learning techniques, single-cell transcriptomic in Plasmodium offers a powerful tool for identifying crucial proteins as a drug target and subsequent computational prediction of potential drugs. In this study, We have implemented a mutual-information-based feature reduction algorithm with a classification algorithm to select important proteins from transcriptomic datasets (sexual and asexual stages) for Plasmodium falciparum and then constructed the protein-protein interaction (PPI) networks of the proteins. The analysis of this PPI network revealed key proteins vital for the survival of Plasmodium falciparum. Based on the function and identification of a few strong binding sites on a couple of these key proteins, we computationally predicted a set of potential drug molecules using a deep learning-based technique. Lead drug molecules that satisfy ADMET and drug-likeliness properties are finally reported out of the generated drugs. The study offers a general computational pipeline to identify crucial proteins using scRNA-seq data sets and further development of potential new drugs.

How Does Visibility Count? An Open Data‐Driven Approach to Compare the Use of Ground Visibility in Archaeological Field‐Walking Surveys in the Mediterranean Region

ABSTRACTThere is a common agreement among archaeologists that assessing visibility in the field is essential to measure the accuracy of their observations. Archaeologists widely expect that low visibility negatively impacts the recovery rate of artefacts and sites during field‐walking surveys. However, they hold fundamentally divergent opinions on using recorded visibility values and on whether or how to weight the results. In this paper, I undertake a review and comparison of ground visibility assessments from three archaeological field‐walking surveys conducted in the eastern Mediterranean, all of which have published their data. Capitalizing on the availability of open data, I recode and analyse the algorithms employed in these surveys. The results highlight the impacts of weighting techniques, and I compare the maps produced with and without weighting. In all cases, the corrections do not substantially change the interpretations of the results at the scale of site identification. As such, this data‐driven experiment contributes to the ongoing debate on how to compare effectively and integrate data from various survey projects to study regional trends.

Fabrication of boron modified bi-functional air diffusion electrode for ciprofloxacin degradation: In-situ H2O2 generation and self-activation

In order to improve H2O2 in-situ generation in electrochemical system, a boron (B) modified air diffusion electrode (B@ADE) was fabricated in the present study. Carbon black (CB) doped by B was deposited on ADE surface as catalytic layer. Performance of B@ADE with different B/C mass ratios has been investigated and 0.3 was identified as the optimal one·H2O2 yield can be achieved 204.85 mg L-1 within 45 min at 0.3-B@ADE, which was 1.51 times for that of ADE. Meanwhile, H2O2 self-activation on B@ADE surface was observed and effects of different operating parameters on •OH formation have been analyzed. XPS analyzation demonstrated that BC2O, BC2O and C=O/O-C=O were related to H2O2 generation. DFT calculation revealed that BCO2 was benefit for O2 adsorption, while BC2O promoted *OOH desorption with lower free energy barriers for H2O2 and •OH generation. Quenching experiments for ciprofloxacin (CIP) demonstrated that •OH, O2•- and 1O2 were co-existed in the system with their contribution quantified. Four possible degradation pathways were proposed through active sites identification by DFT calculation and intermediates detection by HPLC-MS/MS. Bio-toxicity analyzation results showed that the toxicity of most intermediates was lowered than CIP. These results proved that electrochemical oxidation system with B@ADE enhanced in-situ H2O2 generation and self-activation, with great potential for practical application.

Expanding Short-Term Pedestrian Volume Counts to Average Annual Daily Counts at Intersections: Temporal Pattern Indices and a Comparison of Statistical Land Use and Simple Threshold Methods

Quantifying pedestrian exposure for intersections is important for developing pedestrian-centric strategies. Exposure is typically expressed as the annual-average daily pedestrian traffic (AADPT). AADPT can be directly calculated when continuous counts (CCs) are available. However, CCs are rarely available for all intersections, requiring estimation methods. This work explores the steps involved in expanding pedestrian short-term counts (STCs) to AADPT using factor groups. Day-of-week-of-month expansion factors were used to expand 8-h STCs. The first step consists of grouping sites with similar pedestrian activity patterns (factor groups). Five factor groups were identified, and three pedestrian temporal pattern indicators were proposed to characterize them. The thresholds of these indicators for grouping sites with CCs are available in this work. The second step involves the challenge of associating sites where only STCs are available (unknown traffic patterns) with a particular factor group. Multinomial logistic regression (MLR) was developed to link factor groups to land use, socioeconomic, and operational attributes. Explanatory variables that represent commercial and industrial areas and the number of schools and transit stops were found to be significant. Simpler methods were also introduced to identify factor groups strongly affected by schools. The last step assesses the expansion of STCs to AADPT using the proposed approaches. The results showed improved performance when compared with averaging expansion factors across all sites. It was also demonstrated that, in certain cases, simple approaches that rely exclusively on the identification of sites that are highly affected by schools present similar performance to more complex approaches, such as MLR.

Phytoconstituent and In silico Screening of Active Compounds from Red Ginger (Zingiber officinale var. rubrum Theilade) Rhizome and Avocado (Persea americana Mill.) Leaves Extracts as Novel Inhibitors of MRSA

Red ginger rhizome (Zingiber officinale var. Rubrum) and avocado leaves (Persea americana Mill.) are empirically known as one of the medicinal plants used in Taro Village, Gianyar Regency, Bali which have great potential in treating infectious diseases caused by antibiotic resistance, such as MRSA. This study aims to analyze the phytoconstituents and anti-MRSA potential contained in red ginger rhizome and avocado leaves extracts by assessing their inhibitory effects on three proteins related to MRSA resistance and virulence (PBAP2a, transglycosylase, and glycosyltransferase). Phytoconstituents of avocado leaf and red ginger extracts were analyzed using GC-MS. Molecular docking was performed in silico to determine the similarity properties of predicted drugs, bioactivity, toxicity, identification of active sites and validation of protein structures, and docking simulations were performed between compounds found in the extract and their target proteins. Phytoconstituent analysis revealed that avocado leaves and red ginger extracts as a whole have 43 types of compounds and 10 bioactive compounds each with beneficial drug-like properties. The compound 6,11-hexadecadien-1-ol from avocado leaves extracts was predicted to have hepatotoxic properties. There were at least 3 compounds, namely beta-bisabolene from avocado leaves extract, zingiberenol and gamma-curcumene from red ginger rhizome extract, showing the lowest binding affinity for the target protein. Red ginger rhizome and avocado leaves extracts showed valuable potential as anti-MRSA agents through the mechanism of inhibition of three resistance-related proteins, as predicted by in silico analysis.

Pair-EGRET: Enhancing the prediction of protein-protein interaction sites through graph attention networks and protein language models.

Proteins are responsible for most biological functions, many of which require the interaction of more than one protein molecule. However, accurately predicting protein-protein interaction (PPI) sites (the interfacial residues of a protein that interact with other protein molecules) remains a challenge. The growing demand and cost associated with the reliable identification of PPI sites using conventional experimental methods call for computational tools for automated prediction and understanding of PPIs. We present Pair-EGRET, an edge-aggregated graph attention network that leverages the features extracted from pre-trained transformer-like models to accurately predict PPI sites. Pair-EGRET works on a k-nearest neighbor graph, representing the three-dimensional structure of a protein, and utilizes the cross-attention mechanism for accurate identification of interfacial residues of a pair of proteins. Through an extensive evaluation study using a diverse array of experimental data, evaluation metrics, and case studies on representative protein sequences, we demonstrate that Pair-EGRET can achieve remarkable performance in predicting PPI sites. Moreover, Pair-EGRET can provide interpretable insights from the learned cross-attention matrix. Pair-EGRET is freely available in open source form at the GitHub Repository https://github.com/1705004/Pair-EGRET. Supplementary data are available at Bioinformatics online.

Application of thin slice technology of loose cuttings sample in early stage of experimental geology

Abstract Thin section of rock is a highly technical work in the laboratory, and its application in logging site and rock identification is helpful to solve geological problems quickly in the process of exploration drilling. The cuttings samples cover a variety of lithologies, including sandstone and volcanic rocks with significant diagenetic differences. Due to the special properties of the cuttings, such as loose, fear of water, moisture absorption, brittleness, large hardness differences, inconsistent particle size, and not easy to penetrate glue, the production process has been challenged. In the temperature and humid environment, the cuttings are very susceptible to delixing, especially in the cuttings with cleavage development, more cracks and poor diagenesis. After the sticking sheet is affected by vibration during rough grinding, it is easy to produce warping edge and warping Angle, which leads to the destruction of the original structure of the mineral. The previous production process cycle is long, the operation is relatively not convenient, especially in the grinding into the international standard 0.03mm thick pieces there are certain difficulties. Therefore, it is very important to improve and optimize the rock wafer technology. This will help improve the efficiency of the production of cuttings and reduce the likelihood of damage to mineral structures, which in turn can be used more effectively in exploration drilling sites and rock identification, providing faster and more accurate support for solving geological problems. The results of this study have important significance for improving the efficiency and convenience of the production process and improving the application prospect of the rock sheet technology.

Validating claims-based algorithms for a systemic lupus erythematosus diagnosis in Medicare data for informed use of the Lupus Index: a tool for geospatial research

ObjectiveThis study aimed to validate claims-based algorithms for identifying SLE and lupus nephritis (LN) in Medicare data, enhancing the use of the Lupus Index for geospatial research on SLE prevalence...

A top-down spatial scenario approach for identifying the locations of rainwater harvesting sites in an urban region.

Alternative water sources are necessary in developing nations because surface water is not always accessible, and groundwater is depleted. In such situations, rainwater harvesting is considered a promising sustainable water resource management solution. Numerous studies have been conducted to determine suitable locations for rainwater harvesting (RWH) using bottom-up approaches applied to large watersheds. The bottom-up methods begin with various geographic criteria and end with regions suitable for RWH intervention, even considering the distance from settlements to be one of the criteria, excluding urban areas from RWH site identification. This study developed a top-down methodology that began with the distributed pinpoint locations of potential RWH sites, as determined by distributed flow accumulation values produced from a digital elevation model (DEM), and then filtered out the sites based on various criteria in the context of urban areas. The flow accumulation values were apportioned according to the flow-contributing area of each RWH site. Five flow-contributing areal scenarios corresponding to 1 km2, 2.5 km2, 5 km2, 7.5 km2, and 10 km2 were considered in this study, as it is challenging to choose a suitable location for RWH sites in urban zones for efficient water storage owing to a variety of land uses. Based on this technique, a case study was conducted in Jaipur, Rajasthan, India, where it was found that the volumetric potential of rainwater storage is maximum (403,679,424.9 cu. m) for 1 km2 and minimum (169,951,322 cu. m) for 10 km2 flow contributing areal distribution per RWH site.

Recovery of vanadium element from wastewater of petroleum refineries using effective adsorbent: Mathematical approach via isothermal, kinetics and thermodynamic simulation

Adsorption behavior can be determined using different essential studies such as adsorption isotherm, kinetics, and thermodynamics. In this study, adsorption isotherm models, and kinetic, and thermodynamics studies are used to describe the use of alumina as an effective adsorbent and a remover of vanadium (V+5) ions from aqueous solutions contaminated by this metal. The aqueous solutions used simulate the wastewater of most traditional oil refineries. Efficiency can be determined by comparing the correlation coefficients of the linear relationships used with each model. Using alumina, the perfect removal of vanadium ions was achieved. Vanadium removal increases with an increase in the operating conditions, which are time temperature, agitation speed, pH, and adsorbent’s media dose. However, it also increases with the elimination of the initial concentration. This study shows that the vanadium’s adsorption based on the Langmuir isotherm model gives a correlation coefficient of 0.9999, while when it is based on the Temkin and Freundlich isotherms the correlation coefficient is less. Hence, adsorption on the surface of alumina takes place in monolayer surfaces with a regular distribution of particle’s binding energy and a narrow quantity of identical sites on the surface of alumina. Subsequently, the kinetic study shows that the adsorption behavior matched the pseudo-second-order kinetic model with R2 equaling 0.9999. Also, thermodynamics studies approves that the adsorption is a spontaneous endothermic process of enthalpy change.

Comprehensive Review and Assessment of Computational Methods for Prediction of N6-Methyladenosine Sites.

N6-methyladenosine (m6A) plays a crucial regulatory role in the control of cellular functions and gene expression. Recent advances in sequencing techniques for transcriptome-wide m6A mapping have accelerated the accumulation of m6A site information at a single-nucleotide level, providing more high-confidence training data to develop computational approaches for m6A site prediction. However, it is still a major challenge to precisely predict m6A sites using in silico approaches. To advance the computational support for m6A site identification, here, we curated 13 up-to-date benchmark datasets from nine different species (i.e., H. sapiens, M. musculus, Rat, S. cerevisiae, Zebrafish, A. thaliana, Pig, Rhesus, and Chimpanzee). This will assist the research community in conducting an unbiased evaluation of alternative approaches and support future research on m6A modification. We revisited 52 computational approaches published since 2015 for m6A site identification, including 30 traditional machine learning-based, 14 deep learning-based, and 8 ensemble learning-based methods. We comprehensively reviewed these computational approaches in terms of their training datasets, calculated features, computational methodologies, performance evaluation strategy, and webserver/software usability. Using these benchmark datasets, we benchmarked nine predictors with available online websites or stand-alone software and assessed their prediction performance. We found that deep learning and traditional machine learning approaches generally outperformed scoring function-based approaches. In summary, the curated benchmark dataset repository and the systematic assessment in this study serve to inform the design and implementation of state-of-the-art computational approaches for m6A identification and facilitate more rigorous comparisons of new methods in the future.

Identification of novel allosteric sites of SARS-CoV-2 papain-like protease (PLpro) for the development of COVID-19 antivirals

Coronaviruses such as SARS-CoV-2 encode a conserved papain-like protease (PLpro) that is crucial for viral replication and immune evasion, making it a prime target for antiviral drug development. In this study, three surface pockets on SARS-CoV-2 PLpro that may function as sites for allosteric inhibition were computationally identified. To evaluate the effects of these pockets on proteolytic activity, 52 residues were separately mutated to alanine. In Pocket 1, located between the Ubl and thumb domains, the introduction of alanine at T10, D12, T54, Y72, or Y83 reduced PLpro activity to <12% of that of WT. In Pocket 2, situated at the interface of the thumb, fingers, and palm domains, Q237A, S239A, H275A, and S278A inactivated PLpro. Finally, introducing alanine at five residues in Pocket 3, between the fingers and palm domains, inactivated PLpro: S212, Y213, Y251, K254, and Y305. Pocket 1 has a higher druggability score than Pockets 2 and 3. MD simulations showed that interactions within and between domains play critical roles in PLpro activity and thermal stability. The essential residues in Pockets 1 and 2 participate in a combination of intra- and inter-domain interactions. By contrast, the essential residues in Pocket 3 predominantly participate in inter-domain interactions. The most promising targets for therapeutic development are Pockets 1 and 3, which have the highest druggability score and the largest number of essential residues, respectively. Non-competitive inhibitors targeting these pockets may be antiviral agents against COVID-19 and related coronaviruses.

Remote sensing identification and hazard assessment methods for spoil sites

Spoil sites, as loose accumulations, are prone to instability after disturbances and are a serious threat to the safety of downstream communities. This research aims to rapidly and accurately assess the potential hazards of the numerous spoil sides. An integrated approach that combines spatial, aerial, and ground techniques was developed to rapidly identify and interpret the key factors influencing the stability of spoil sites. Subsequently, based on the results of numerical simulation of multiple combinations of unfavorable working conditions by the material point method, a hazard assessment methodology for spoil sites is proposed, which combines the limit equilibrium method with the improved hierarchical analysis. The proposed method is applied and validated on spoil sites along an expressway. The achieved results reveal that remote sensing combined with unmanned aerial vehicle (UAV) verification technology is capable of quickly and accurately identifying the critical elements of spoil sites. The hazard assessment of the spoil site is represented by the hazard coefficient (P), where 0 < P < 1 indicates safety, 1 ≤ P < 5 signifies instability, and P ≥ 5 represents extreme instability. The effectiveness of the proposed assessment approach is then verified through the on-site validation, which provides a vital reference for rapid identification and batch assessment of spoil sites.

Spatially resolved profiling of protein conformation and interactions by biocompatible chemical cross-linking in living cells

Unlocking the intricacies of protein structures and interactions within the dynamic landscape of subcellular organelles presents a significant challenge. To address this, we introduce SPACX, a method for spatially resolved protein complex profiling via biocompatible chemical cross(x)-linking with subcellular isolation, designed to monitor protein conformation, interactions, and translocation in living cells. By rapidly capturing protein complexes in their native physiological state and efficiently enriching cross-linked peptides, SPACX allows comprehensive analysis of the protein interactome within living cells. Leveraging structure refinement with cross-linking restraints, we identify subcellular-specific conformation heterogeneity of PTEN, revealing dynamic differences in its dual specificity domains between the nucleus and cytoplasm. Furthermore, by discerning conformational disparities, we identify 83 cytoplasm-exclusive and 109 nucleus-exclusive PTEN-interacting proteins, each associated with distinct biological functions. Upon induction of ubiquitin-proteasome system stress, we observe dynamic alterations in PTEN assembly and its interacting partners during translocation. These changes, including the identification of components and interaction sites, are characterized using the SPACX approach. Notably, SPACX enables identification of unique interacting proteins specific to PTEN isoforms, including PTEN and PTEN-Long, through the determination of sequence-specific cross-linking interfaces. These findings underscore the potential of SPACX to elucidate the functional diversity of proteins within distinct subcellular sociology.