Identification Of Sites Research Articles

Effect of Substituents on Chitosan-Derived Sustainable Corrosion Inhibitors: Experimental and Computational Studies of Inhibition and Adsorption Performance.

This work involves the synthesis of two chitosan derivatives by reacting chitosan, extracted from shrimp shells in eastern Morocco, with 2-nitrobenzaldehyde via a Schiff base reaction. An amino derivative of chitosan was then produced by reducing the imine group created by sodium borohydride. We investigated the molecular weight (Mw), crystallinity index (CrI), and degree of deacetylation (DDA) of the isolated chitosan, among other characteristic features. Thermogravimetric analysis (TGA), X-ray diffraction (XRD), and attenuated total reflectance Fourier transform infrared spectroscopy (FTIR-ATR) were used to characterize the extracted chitosan (CS), 2-nitroben-chitosan Schiff derivative (CS-2NI), and chitosan amino derivative (CS-2NA). In a corrosive medium of 1 M HCl, the three ligands, CS, CS-2NI, and CS-2NA, were used as mild steel corrosion inhibitors. Electrochemical studies were used to analyze the surface shape and assess the inhibitory efficiency. They reveal a significant inhibitory efficiency of 92.31% for the CS-2NI derivative, highlighting the effectiveness of the imine group (═N-) and the nitro group (-NO2) compared to the two amino groups (-NH2 and -NH-) present in the CS-2NA derivative. To support experimental research, a computational study was carried out that combined the simulated annealing technique. The three inhibitors behave as mixed-type corrosion inhibitors. Thermodynamic analyses and adsorption studies revealed that the tested inhibitors create covalent bonds with the steel surface (chemisorption) as well as physical interactions (physisorption), in accordance with the Langmuir model. The identification of the ligands' adsorption sites on the steel surface was made more accessible by computational methods, demonstrating the relationship between the inhibitory properties and the chemical structure of these biodegradable and biocompatible biopolymers.

Unveiling coastal ecosystem dependencies: multifactorial analysis of climate change influencing mangrove system in the United Arab Emirates over the period 1990–2020

Mangroves are an integral part of the coastal ecosystems, demonstrating resiliency against adverse anthropogenic and environmental effects. They provide blue carbon stock and security against coastal erosion and act as a nutrient source for many aquatic inhabitants along with providing raw materials for human consumption. However, mangroves continue to perish due to several climate factors. Past research has indicated some environmental factors influencing the growth and sustenance of mangrove forests. This paper aims to study the impact of the climate changes on the UAE mangrove forests during the period 1990–2020. The climate change related factors studied include surface temperature, sea-level rise, salinity, and coastal inundation. Here, remote sensing techniques and supervised machine learning methods were utilized to derive the climatic factors. The correlations between mangrove and these factors were investigated as well. It was revealed that Land Surface Temperature is closely related to mangrove and that mangrove biomass was highest in the land surface temperature range 30–35 °C. Results have not shown a clear correlation between tidal inundation and mangroves. Furthermore, outputs also showed that mangrove biomass was negatively affected by sea-level rise. The analysis showed a general positive relationship between mangrove and salinity corresponding to an increase in mangrove biomass which was only found when conducting spatial analysis, however site-specific analysis (i.e., temporal) showed an increase in mangrove density with decrease in salinity. These findings could potentially aid in facilitating the identification of suitable new sites for plantation or existing sites for rehabilitation of mangroves in UAE in addition to formulating policies at the highest level for mangrove conservation.

Transcriptome-wide RNA 5-methylcytosine profiles of human iPSCs and iPSC-derived cardiomyocytes

Cardiac regenerative therapy has recently progressed by reprogramming somatic cells into induced pluripotent stem cells (iPSCs) and advanced by large-scale differentiation-derived cardiomyocytes (hiPSC-CMs). However, repairing damaged cardiac tissues with hiPSC-CMs remains limited due to immune rejection, cardiac arrhythmias, and concerns over tumor formation after hiPSC-CM transplantation. Despite efforts in profiling epigenomic changes during cardiac differentiation, regulatory mechanisms underlying 5-methylcytosine (m5C) deposition in RNA m5C epitranscriptomic landscape during hiPSC-to-cardiomyocyte differentiation remain unclear. Herein, bisulfite RNA-sequencing analysis was conducted in human pluripotent stem cells (hPSCs) from three independent cellular origins, and their derived cardiomyocytes (hPSC-CM), metabolic-maturation of derived cardiomyocytes (hPSC-CM-lac) and biochemical-enhanced derived cardiomyocytes (hPSC-CM-TDI). Integrated analysis of differentially methylated RNA m5C profiles and transcriptome-wide expression facilitated the identification of m5C sites coupled to the cardiomyocyte differentiation and RNA-dependent regulatory mechanisms of stem cell pluripotency. The RNA m5C profiles in this dataset allow the evaluations of the m5C level and distribution of specific m5C loci and facilitate understanding of the m5C epitranscriptomic landscape in biological functions of hPSC-CM beyond in vivo transplantation barriers.

Potential Role of Bioactive Compounds of Purple Corn as Breast Cancer Drug Candidates Through Inhibition of Cyclin-Dependent Protein Kinase 6 (Cdk6): An In-Silico Approach

Cancer is characterized by disorderly cell proliferation. Upregulation and activation of the Cyclin-Dependent Protein Kinase 6 (CDK6) signalling pathway can induce uncontrolled proliferation of breast cancer cells. Therefore, inhibition of CDK6 persists to be developed as a target for the design and development of potential drugs to treat breast cancer. This study aims to predict the biofunction of anthocyanin compounds from purple corn extract as Cyclin- Dependent Protein Kinase 6 (CDK6) inhibitors in silico. The research methods used include data mining, ligand and receptor preparation, molecular docking, docking visualization, and data analysis. Our results show that six compounds of purple corn extract (cyanidin, cyanidin 3-glucoside, pelargonidin-3-glucoside, pelargonidin, peonidin, and peonidin-3-glucoside) can bind CDK6 at the C-terminal and N-terminal domains. The binding pattern indicated that cyanidin, cyanidin 3-glucoside, pelargonidin-3-glucoside, pelargonidin, peonidin, and peonidin-3-glucoside bind to the identical site CDK6 residues as Palbociclib (control). This finding indicated that compounds from purple corn are most likely competitive inhibitors of CDK6. Peonidin-3-glucoside showed the lowest binding energy of -282.5 kcal/mol, close to palbociclib (-329.4 kcal/mol).

Modeling Ligand Binding Site Water Networks with Site Identification by Ligand Competitive Saturation: Impact on Ligand Binding Orientations and Relative Binding Affinities.

Appropriate treatment of water contributions to protein-ligand interactions is a very challenging problem in the context of adequately determining the number of waters to investigate and undertaking conformational sampling of the ligands, the waters, and the surrounding protein. In the present study, an extension of the Site Identification by Ligand Competitive Saturation-Monte Carlo (SILCS-MC) docking approach is presented that enables the determination of the location of water molecules in the binding pocket and their impact on the predicted ligand binding orientation and affinities. The approach, termed SILCS-WATER, involves MC sampling of the ligand along with explicit water molecules in a binding site followed by selection of a subset of waters within specified energetic and distance cutoffs that contribute to ligand binding and orientation. To allow for convergence of both the water and ligand orientations, SILCS-WATER is based on just the overlap of the ligand and water with the SILCS FragMaps and the interaction energy between the waters and ligand. Results show that the SILCS-WATER methodology can capture important waters and improve ligand binding orientations. For 6 of 10 multiple ligand-protein systems, the method improved relative binding affinity prediction against experimental results, with substantial improvements in five systems, when compared to standard SILCS-MC. Improved reproduction of crystallographic ligand binding orientations is shown to be an indicator of when SILCS-WATER will yield improved binding affinity correlations. The method also identifies waters interacting with ligands that occupy unfavorable locations with respect to the protein whose displacement through the appropriate ligand modifications should improve ligand binding affinity. Results are consistent with the binding affinity being modeled as a ligand-water complex interacting with the protein. The presented approach offers new possibilities in revealing water networks and their contributions to the binding orientation and affinity of a ligand for a protein and is anticipated to be of utility for computer-aided drug design.

M5C‐TNKmer: Identification of 5‐Methylated Base Cytosine of Ribonucleic Acid Using Supervised Machine Learning Techniques

ABSTRACT5‐Methylcytosine (m5C) is a widely recognized epigenetic modification in ribonucleic acid (RNA), catalyzed by methyltransferases. This modification is crucial for various biological functions. While the role of m5C in deoxyribonucleic acid (DNA) has been extensively studied, its role in RNA is still in its early stages of exploration. Accurate and systematic detection and classification of m5C sites in RNA remain challenging tasks. Machine learning techniques offer an efficient alternative to traditional laboratory methods for identifying m5C sites in Homo sapiens. This study introduces a novel computational model m5C‐TNKmer, which utilizes k‐mer feature extraction to enhance the identification of m5C sites in RNA sequences. Four sub‐datasets derived from the primary dataset Di‐nucleotide (DNC), Tri‐nucleotide (TNC), Tetra‐nucleotide (Tetra‐NC), and Penta‐nucleotide (Penta‐NC) were used to train the model. The results demonstrated that m5C‐TNKmer achieved an impressive accuracy of 96.15%. This model provides a powerful tool for scientists to accurately identify RNA m5C sites, contributing to a deeper understanding of genetic functions and regulatory mechanisms.

Identification of viral protease-dependent cleavage sites within the human astrovirus polyprotein.

Infection by human astrovirus (HAstV), a small, positive-strand RNA virus, is a major cause of gastroenteritis and has been implicated in an increasing number of severe, sometimes fatal, neurological diseases since 2008. Currently, there are no vaccines or antiviral treatments available to treat HAstV infection. An attractive target for antiviral therapeutics is the viral protease due to its essential functions throughout infection. However, the molecular mechanisms of the HAstV protease, nonstructural protein 1a/3 (nsp1a/3), are poorly understood. In fact, the specific residues within the cleavage junctions that are targeted by nsp1a/3 during polyprotein processing have yet to be experimentally identified. To identify the junctions between viral proteins, we performed mass spectrometry and site-directed mutagenesis using epitope-tagged viral polyprotein expression plasmids. Using these strategies, we identified a consensus motif that is found throughout the polyprotein near previously proposed junctions. We found that cleavage occurs after a hydrophobic residue - X - Gln motif. Further mutagenesis of surrounding sequence identified the importance of basic residues following the motif for efficient processing. Cleavage at each junction was determined to be essential for the production of progeny virions. However, abolishing nsp1a/4-VPg cleavage allowed efficient replication, suggesting that VPg can function in an intermediate form. Overall, our results identify a conserved cleavage motif that is recognized by the nsp1a/3 protease within the viral polyprotein, and cleavage at this motif was found to be essential for the recovery of progeny virions. These findings will be instrumental in further understanding the basic functions of HAstV polyprotein processing during infection.IMPORTANCEHuman astroviruses (HAstVs) are a leading cause of non-bacterial gastroenteritis in children, elderly individuals, and immunocompromised patients. However, infection by divergent strains of HAstV is now recognized as a causative agent of severe neurological diseases, which can have fatal outcomes. Despite the global prevalence of HAstV, we currently have a limited understanding of the biology of these viruses. Translation of the viral genome leads to the production of polyproteins that are processed by viral and host proteases into functional proteins. In this study, we identified a conserved recognition sequence targeted by the viral protease for cleavage. Importantly, these findings elucidate the N- and C-termini of the nonstructural proteins within the HAstV polyprotein, offering valuable information for future studies on the function of individual viral proteins. Similar to other positive-sense RNA viruses, the necessity of proteolytic processing for the HAstV polyprotein highlights the viral protease as a promising target for antiviral development.

Combining crystallographic and binding affinity data towards a novel dataset of small molecule overlays

Although small molecule superposition is a standard technique in drug discovery, a rigorous performance assessment of the corresponding methods is currently challenging. Datasets in this field are sparse, small, tailored to specific applications, unavailable, or outdated. The newly developed LOBSTER set described herein offers a publicly available and method-independent dataset for benchmarking and method optimization. LOBSTER stands for “Ligand Overlays from Binding SiTe Ensemble Representatives”. All ligands were derived from the PDB in a fully automated workflow, including a ligand efficiency filter. So-called ligand ensembles were assembled by aligning identical binding sites. Thus, the ligands within the ensembles are superimposed according to their experimentally determined binding orientation and conformation. Overall, 671 representative ligand ensembles comprise 3583 ligands from 3521 proteins. Altogether, 72,734 ligand pairs based on the ensembles were grouped into ten distinct subsets based on their volume overlap, for the benefit of introducing different degrees of difficulty for evaluating superposition methods. Statistics on the physicochemical properties of the compounds indicate that the dataset represents drug-like compounds. Consensus Diversity Plots show predominantly high Bemis–Murcko scaffold diversity and low median MACCS fingerprint similarity for each ensemble. An analysis of the underlying protein classes further demonstrates the heterogeneity within our dataset. The LOBSTER set offers a variety of applications like benchmarking multiple as well as pairwise alignments, generating training and test sets, for example based on time splits, or empirical software performance evaluation studies. The LOBSTER set is publicly available at https://doi.org/10.5281/zenodo.12658320, representing a stable and versioned data resource. The Python scripts are available at https://github.com/rareylab/LOBSTER, open-source, and allow for updating or recreating superposition sets with different data sources. Graphical abstractSimplified illustration of the LOBSTER dataset generation.

Regarding the history of the inclusion of European beech forests and ancient forests in the list of UNESCO World Heritage sites and the problems of sustainable development in Ukraine in the region of their location

The current issues on the history of establishment and management of the transnational serial UNESCO World Heritage Site "Ancient and primeval beech forests of the Carpathians and other regions of Europe" are considered. The site was inscribed to the Heritage list in 2007 as "Primeval beech forests of the Carpathians". It was extended and reorganized in 2011, 2017 and 2021 into "Ancient and primeval beech forests of the Carpathians and other regions of Europe". It is located on the territories of 18 European countries (Albania, Austria, Belgium, Bulgaria, Bosnia and Herzegovina, Italy, Spain, Germany, North Macedonia, Poland, Romania, Slovakia, Slovenia, Ukraine, France, Croatia, Czech Republic and Switzerland). It comprises 94 components with an area of 98,125.15 hectares. The buffer zone of the site constitutes 294,720, 87 hectares. An important role for its establishment and extension had an active environmental position and fundamental research of many Ukrainian and foreign scientists, representatives of different times, first of all professors Alois Zlatnik (Czech Republic), Štefan Korpel, Ivan Voloschuk and William Pichler (Slovakia), Vasyl Komendar, Stepan Stoiko and Vasyl Parpan (Ukraine), Mario Brodgi and Brigitta Kommarmot (Switzerland) and others. The support of the expert of International Union for Nature Conservation David Michalik (USA) was extremely important in this case. A significant role in this difficult process was played also by the following international scientific conferences and seminars organized by the Carpathian Biosphere Reserve: "Natural forests of the temperate zone of Europe - values and utilization" and "Identification of potential World Natural Heritage sites"; a monograph published in Switzerland in Ukrainian and German "Virgin forests of the Carpathians. Guidebook to the forests of the Carpathian Biosphere Reserve", a Ukrainian-Dutch project on the inventory of Transcarpathian primeval forests, support by German researchers Peter Schmidt and Harald Plachter. Here are analyzed also the lost opportunities caused by the unsatisfactory implementation of three Decrees and Orders of the President and the Government of Ukraine (2009-2023) on the issues on conservation of primeval beech forests as a World Heritage site and the sustainable development of mountain settlements adjacent to them.

Divergence in MiRNA targeting of AchAco and its role in citrate accumulation in kiwifruit

BackgroundMicroRNA (miRNA) is a crucial post-transcriptional regulatory factor in plant growth and development. Duplicated genes often exhibit functional divergence due to competition for the identical miRNA binding sites. Kiwifruit (Actinidia spp.) is an economically significant horticultural crop renowned for its distinctive flavor, which is largely attributable to elevated citrate levels during fruit development. However, the mechanisms through which miRNA-targeted modules evolve following duplication events and regulate citrate biosynthesis, thereby influencing the unique flavor profile of kiwifruits, remain poorly understood.ResultsIn this study, we examined the expression patterns of miRNAs and interactions with their targets in kiwifruit fruit samples from various pulp tissues and developmental stages. Our analysis identified 46 miRNAs, comprising 44 known miRNAs and two novel/kiwifruit-specific miRNAs, which targeted a total of 1,474 genes. Correlation analysis revealed weak relationships between the expression levels of miRNAs and their target genes. Among these targets, 27 tandemly duplicated genes, and 782 whole genome duplication (WGD) genes exhibited a loss of miRNA binding sites in one of their duplicated copies. Furthermore, weighted gene co-expression network analysis demonstrated that most duplicated genes clustered into distinct gene modules. These findings suggest that the loss of miRNA targets following duplications contributes to expression divergence among gene duplicates, thereby facilitating stable gene expression within the miRNA-targeted network. For instance, the aconitate hydratase genes AchAco4 and AchAco6 were each targeted by different miRNAs, ach-miR-3774 and ach-miR-10371, respectively. Notably, these genes exhibited distinct expression patterns compared to their duplicated counterparts.ConclusionsThis study enhances our understanding of how the miRNA-AchAco module regulates citrate content and provides insights into the molecular network that influences the flavor profile of kiwifruit.Clinical trial numberNot applicable.

Stacking based ensemble learning framework for identification of nitrotyrosine sites

Protein nitrotyrosine is an essential post-translational modification that results from the nitration of tyrosine amino acid residues. This modification is known to be associated with the regulation and characterization of several biological functions and diseases. Therefore, accurate identification of nitrotyrosine sites plays a significant role in the elucidating progress of associated biological signs. In this regard, we reported an accurate computational tool known as iNTyro-Stack for the identification of protein nitrotyrosine sites. iNTyro-Stack is a machine-learning model based on a stacking algorithm. The base classifiers in stacking are selected based on the highest performance. The feature map employed is a linear combination of the amino composition encoding schemes, including the composition of k-spaced amino acid pairs and tri-peptide composition. The recursive feature elimination technique is used for significant feature selection. The performance of the proposed method is evaluated using k-fold cross-validation and independent testing approaches. iNTyro-Stack achieved an accuracy of 86.3% and a Matthews correlation coefficient (MCC) of 72.6% in cross-validation. Its generalization capability was further validated on an imbalanced independent test set, where it attained an accuracy of 69.32%. iNTyro-Stack outperforms existing state-of-the-art methods across both evaluation techniques. The github repository is create to reproduce the method and results of iNTyro-Stack, accessible on: https://github.com/waleed551/iNTyro-Stack/

IN SILICO BASED RE-ENGINEERING OF A COMPUTATIONALLY DESIGNED BIOSENSOR WITH ALTERED SIGNALLING MODE AND IMPROVED DYNAMIC RANGE.

A current challenge in the rational design of biomolecular sensors is the ability to custom design binding affinities and detection mode in silico. To this end, we re-engineered a previously reported computationally-designed fluorescent maltooligosaccharide (MOS)-detecting biosensor to both alter its ligand-binding affinity and to analyse the underlying sensing mechanism. The dynamic range of the biosensor was expanded through the computer aided introduction of a series of amino acid substitutions in the starting protein scaffold (MalX from Streptococcus pneumoniae), which generated a biosensor set with binding affinities spanning over five orders of magnitude. The impact of the introduced substitutions on the underlying mode of signal generation was assessed in silico using our previously reported Computational Identification of Non-disruptive Conjugation sites (CINC) pipeline. CINC utilizes molecular dynamics simulations and an in-house developed algorithm to examine and exploit the structural dynamics of a protein at amino acid-level resolution. Using CINC, we demonstrate that re-engineering of the MOS-detecting biosensor set resulted in sensors with two distinct output modes which differed based on local conformational changes at the fluorescently modified reporter position. These output modes were classified as "ligand-sensing"-type biosensors (readout based on the tool sensing a unique conformation in the ligand-bound state), and "apo-sensing"-type biosensors (readout based on the tool sensing a unique conformation in the apo state). Together, these results demonstrate that structural dynamics at the individual amino acid residue level can be used as an engineer-able feature to rationally alter the fluorescence reporting properties of a biosensing device. Moving forward, the CINC workflow can also be adapted for the rational design of protein dynamic properties maximizing its utility as an in silico design platform for custom biomolecular tools.

Identification of key antigenic sites in hemagglutinin of H10N3 avian influenza virus

The H10 avian influenza viruses (AIV) have been detected in both birds and mammals. Recently, the cases of human infection with H10N8 and H10N3 in China pose high risk to public health. However, the antigenic sites in hemagglutinin (HA) of H10 are poorly understood. In this study, 3 monoclonal antibodies (MAb), designated as 1F4, 6B3 and 6G12, against the HA protein of the H10N3 strain A/chicken/Taizhou/498/2021(H10N3) (TZ498), were first generated. All of these MAb could effectively inhibit TZ498 in haemagglutination inhibition assay and microneutralization assay. Four novel antigenic sites at positions 135, 208, 227, and 266 (H10 numbering) were identified in the HA of TZ498 through escape mutants selected by these 3 MAb. Moreover, natural mutations at positions 135 and 227 were found in the H10 field strains. All these not only provide novel insights into the molecular markers for monitoring the antigenic variation of H10 but also be helpful for developing efficient control strategies against H10.

Assessing Large Language Models for Oncology Data Inference From Radiology Reports.

We examined the effectiveness of proprietary and open large language models (LLMs) in detecting disease presence, location, and treatment response in pancreatic cancer from radiology reports. We analyzed 203 deidentified radiology reports, manually annotated for disease status, location, and indeterminate nodules needing follow-up. Using generative pre-trained transformer (GPT)-4, GPT-3.5-turbo, and open models such as Gemma-7B and Llama3-8B, we employed strategies such as ablation and prompt engineering to boost accuracy. Discrepancies between human and model interpretations were reviewed by a secondary oncologist. Among 164 patients with pancreatic tumor, GPT-4 showed the highest accuracy in inferring disease status, achieving a 75.5% correctness (F1-micro). Open models Mistral-7B and Llama3-8B performed comparably, with accuracies of 68.6% and 61.4%, respectively. Mistral-7B excelled in deriving correct inferences from objective findings directly. Most tested models demonstrated proficiency in identifying disease containing anatomic locations from a list of choices, with GPT-4 and Llama3-8B showing near-parity in precision and recall for disease site identification. However, open models struggled with differentiating benign from malignant postsurgical changes, affecting their precision in identifying findings indeterminate for cancer. A secondary review occasionally favored GPT-3.5's interpretations, indicating the variability in human judgment. LLMs, especially GPT-4, are proficient in deriving oncologic insights from radiology reports. Their performance is enhanced by effective summarization strategies, demonstrating their potential in clinical support and health care analytics. This study also underscores the possibility of zero-shot open model utility in environments where proprietary models are restricted. Finally, by providing a set of annotated radiology reports, this paper presents a valuable data set for further LLM research in oncology.

Isolation of phytoconstituents from an extract of Murraya paniculata with cytotoxicity and antioxidant activities and in silico evaluation of their potential to bind to aldose reductase (AKR1B1)

The study on Murraya paniculata (Orange Jasmine) stem bark extract found it to have antioxidant and cytotoxic proper-ties. The structures of the isolated phytoconstituents were determined using NMR spectroscopy. Compounds were evaluated for their potential to be aldose reductase inhibitors using molecular docking and dynamics (MD) simulations. Phytochemical screening of methanolic crude extract was performed from which different fractions of the extract were screened for antioxidant activity using the DPPH radical scavenging method and cytotoxicity using the brine shrimp lethality bioassay. The aqueous fraction showed strong antioxidant activity as compared to the standard butylated hy-droxytoluene, whereas pet ether, dichloromethane, chloroform and methanolic extract exhibited moderate antioxidant activity. Activities in the DPPH assay ranged from 17 to 63 µg/ml and all fractions showed cytotoxic activity. Five identified phytochemical compounds (1-5) include ergosterol endoperoxide (1), the coumarin derivatives 7-methoxy-8-(3-methylbut-2-enyl)-1-benzopyran-2-one (2) and 5,7-dimethoxy-8-(3-methylbut-2-enyl)-1-benzopyran-2-one (3) and a mixture of β-sitosterol (4), and stigmasterol (5). Among them ergosterol endoperoxide has been isolated from the stem bark of the M. paniculata for the first time. MD simulations of the identified compounds indicated their potential to bind to the aldose reductase (AKR1B1) protein. Predicted binding affinities of the compounds based on the site identification the ligand competitive saturation (SILCS) technology was −15.04, −8.85, −9.83, −11.95, and −11.75 kcal/mol for 1 through 5, respectively. The present results are anticipated to lead to further study of the activities of the five compounds including experimental evaluation of their inter-actions with AKR1B1.

GIS-MCDM integrated approach for suitable landfill site selection: a case study of the southwestern part of Bejaia Province, Algeria.

Selecting optimal landfill sites is a critical challenge in household waste management. In response to the environmental issues posed by open dumps in southwestern Bejaia province, this study adopts an integrated GIS-MCDM approach to identify suitable locations for new landfill sites. Initially, key environmental and socio-economic criteria were determined through a review of national regulations, expert opinions, and relevant literature. Fourteen factors, categorized into physiological and geological, hydrological and climatic, land use, and accessibility criteria, were then evaluated using freely available data processed within a GIS framework. Moreover, the Analytic Hierarchy Process (AHP) method was employed to assign weights to each factor based on its significance and compliance with regulatory and environmental standards. Finally, the identification of optimal landfill sites was determined by calculating the landfill site suitability index (LSSI), revealing that 0.48% of the area is highly suitable for landfilling, 66.79% is moderately suitable, 28.25% has low suitability, and 4.48% is unsuitable for landfill implementation. Furthermore, in the highly suitable category, 13 candidate sites were initially identified as suitable for landfill installation; further prioritization was conducted based on waste quantity per commune, population density, site area, distance from waste production centers, and accessibility. Following a detailed evaluation, four final sites were deemed optimal for landfill construction in the study area. These findings provide valuable insights for authorities and stakeholders in managing household waste disposal in Bejaia province. Future assessments should include additional criteria, evaluate long-term environmental impacts, and consider public acceptance to ensure sustainable waste management in the region.

Development of cell-free platforms for discovering, characterizing, and engineering post-translational modifications.

Post-translational modifications (PTMs) are important for the stability and function of many therapeutic proteins and peptides. Current methods for studying and engineering PTM installing proteins often suffer from low-throughput experimental techniques. Here we describe a generalizable, in vitro workflow coupling cell-free protein synthesis (CFPS) with AlphaLISA for the rapid expression and testing of PTM installing proteins. We apply our workflow to two representative classes of peptide and protein therapeutics: ribosomally synthesized and post-translationally modified peptides (RiPPs) and conjugate vaccines. First, we demonstrate how our workflow can be used to characterize the binding activity of RiPP recognition elements, an important first step in RiPP biosynthesis, and be integrated into a biodiscovery pipeline for computationally predicted RiPP products. Then, we adapt our workflow to study and engineer oligosaccharyltransferases (OSTs) involved in conjugate vaccine production, enabling the identification of mutant OSTs and sites within a carrier protein that enable high efficiency production of conjugate vaccines. In total, we expect that our workflow will accelerate design-build-test cycles for engineering PTMs.

Synchrotron-Based Characterizations of Electrocatalytic Metal-Organic Frameworks and Covalent Organic Framework-Based Solid Electrolyte Interphase for Stabilizing Anode of Rechargeable Batteries

Considering energy-related technologies including rechargeable batteries and water electrolysis, effective catalytic materials play a crucial role in the rise and unceasing development. Metal-organic frameworks (MOFs) have drawn attention due to their abundant and identical catalytic sites along the periodic network with tunable catalytic functionality and stability. Herein, structural information, catalytic sites, and their corresponding mechanistic pathways have been investigated by employing synchrotron-based X-ray diffraction (XRD) and X-ray absorption spectroscopy (XAS). The framework stability, the accessible active sites related to pre-designed metal clusters, the alteration of oxidation state of, and the coordination geometry around the active metal centers evolving along the electrochemical oxygen evolution reaction (OER) are thoroughly revealed. In addition, aqueous zinc-ion batteries (AZIBs) are promising energy storage systems due to their cost-effectiveness and eco-friendliness. However, there are some limitations restricting their practical utilizations such as the high activity of water leading to Zn corrosion and hydrogen evolution, along with the formation of dendrites on the Zn surface during repeated charge–discharge cycles. To limit parasitic side reactions, an artificial solid electrolyte interphase (ASEI) protective layer made of a covalent organic framework (COFs) is an effective strategy. Herein, grazing-incidence XAS was employed to specifically characterize the Zn speciation on the electrode surface. According to XAS results, the protective COF-ASEI layer can retard the coordination of water at the electrolyte–Zn interface, improve Zn plating/stripping kinetics, and increase the stability of the Zn anode. This presentation highlights the significance of synchrotron characterizations in energy-related technologies.

In situ reconstruction of bimetallic heterojunctions encapsulated in N/P co-doped carbon nanotubes for long-life rechargeable zinc-air batteries

Efficient and stable bifunctional electrocatalysts are crucial for the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) in high-performance rechargeable zinc-air batteries (ZABs). Despite significant advancements in non-noble metal-based catalysts over the past decades, the rational tuning of various active materials for efficient ORR and OER, along with the identification of genuine active sites, remains a formidable challenge. Encouraged by the development of in-situ characterization technologies, research on bifunctional catalysts for ZABs has gradually shifted to exploring the true active sites and investigating catalyst evolution and degradation, which is vital for further breakthroughs in bifunctional catalysts. In this study, we developed a facile method to encapsulate bimetallic heterojunction CoFe/CoFeP in N/P co-doped carbon nanotubes (CNTs) as a bifunctional catalyst for ZABs. This unique heterojunction structure prevents the dissolution and erosion of transition metals, enhances continuous electron transfer and mass transport, and effectively boosts the catalyst's bifunctional activity and stability. Importantly, ex-situ and in-situ techniques were employed to track the dynamic ORR and OER catalytic processes, capture the reconstructions of bifunctional catalysts, and reveal the real active sites. The CoFe/CoFeP@NPC catalyst exhibits superior bifunctional catalytic activity and stability, especially with a half-wave potential of 0.887 V for ORR and 1.55 V at 10 mA cm−2 for OER. Impressively, the assembled rechargeable ZABs demonstrate a high-power density (550 mW cm−2), a low charge-discharge voltage difference (0.7 V), and ultralong cycling for over 1600 h.

Identification of Suitable Sites for Farm Ponds in Drought-Prone Areas for Sustainable Water Management: A Case Study on Lower Bindusara Watershed, Beed District, Maharashtra

Identification of Suitable Sites for Farm Ponds in Drought-Prone Areas for Sustainable Water Management: A Case Study on Lower Bindusara Watershed, Beed District, Maharashtra