An Integrated Multi-Model Framework Utilizing Convolutional Neural Networks Coupled with Feature Extraction for Identification of 4mC Sites in DNA Sequences.

N4-methylcytosine (4mC) is a chemical modification that occurs on one of the four nucleotide bases in DNA and plays a vital role in DNA expression, repair, and replication. It also actively participates in the regulation of cell differentiation and gene expression. Consequently, it is important to comprehend the role of 4mC in the epigenetic regulation for revealing the complications of the gene expression and their associated governing cellular operations. However, the inherent resource requirements and time constraints of the experimental procedure, present challenges to the cellular culture process. While data-driven methodologies present promising solutions to mitigate the demand for extensive experimental efforts, their performance relies on the suitability and existence of high-quality data. This study presents a multi-model framework that integrates convolutional neural network (CNN) with the distributed k-mer and embedding feature extraction techniques to enhance the identification of 4mC sites in DNA sequences. The integration of k-mers ensures the effective representation of the local sequence patterns, while the utilization of embedding enables a more holistic encoding by considering the broader context and semantics of the sequence data. Following the initial step, the obtained distributed representation of the DNA sequence seamlessly enters the CNN, triggering a crucial convolution operation wherein a set of adaptable filters systematically convolves across the sequence to detect vital local patterns. The proposed integrated multi-model framework was applied to six publicly available datasets and evaluated against the cutting-edge 4mCPred, 4mCCNN, iDNA4mC, Meta-4mCpred, DeepTorrent, 4mCPred-SVM, and DMKL-HFIS methods. The evaluation was based on accuracy, specificity, sensitivity, and Matthews Correlation Coefficient. The results demonstrated that the proposed multi-model framework outperformed the state-of-the-art methods, as well as one-hot encoding and the hybrid of one-hot & TNC features, in accurately identifying 4mC sites.

On degree-based operators and topological descriptors of molecular graphs and their applications to QSPR analysis of carbon derivatives.

This work initiates a concept of reduced reverse degree based RR D M -Polynomial for a graph, and differential and integral operators by using this RR D M -Polynomial. In this study twelve reduced reverse degree-based topological descriptors are formulated using the RR D M -Polynomial. The topological descriptors, denoted as T D 's, are numerical invariants that offer significant insights into the molecular topology of a molecular graph. These descriptors are essential for conducting QSPR investigations and accurately estimating physicochemical attributes. The structural and algebraic characteristics of the graphene and graphdiyne are studied to apply this methodology. The study involves the analysis and estimation of Reduced reverse degree-based topological descriptors and physicochemical features of graphene derivatives using best-fit quadratic regression models. This work opens up new directions for scientists and researchers to pursue, taking them into new fields of study.

Degree-based topological indices and entropies of diamond crystals.

High hardness, low friction coefficient and chemical resistance are only a few of the exceptional mechanical qualities of diamond. Diamonds can be artificially created to have different levels of conductivity, or they can be single, micro or nanocrystalline and highly electrically insulating. It also has high biocompatibility and is famous for being mechanically robust. Due to its high hardness, lack of ductility and difficulty in welding, diamond is a challenging material to construct devices with. Diamonds have experienced a rise in attention as a biological material in recent decades due to new synthesis and fabrication techniques that have eliminated some of these disadvantages. In general, entropic measurements are used for investigating the chemical or biological properties of molecular structures. This study calculates several important K-Banhatti entropies, redefined Zagreb entropies and atom-bond sum connectivity entropy for diamond crystals. We also present a numeric and graphical explanations of obtain indices.

Computation of differential and integral operators using M -polynomials of gold crystal.

Gold is generally considered a noble metal since it is inherently inert in its bulk state. However, gold demonstrates reactivity when it is in its ionic state. The inherent inertness of bulk gold has resulted in its widespread recognition as a vital raw material in various biomedical processes. The applications of these technologies include drug delivery microchips, dental prostheses, reconstructive surgery, culinary additives, and cardiovascular stents. Gold can also exist in molecules or ions, particularly gold ions, which facilitates the production of gold nanomaterials. In this paper, we have computed differential and integral operators by using the M -Polynomial of gold crystals and by utilizing this polynomial, we have also computed eleven topological indices like 1 s t Zagreb, 2 n d Zagreb, Hyper, Sigma, Second Modified, General Randic, General Reciprocal Randic, 3 r d Redefined Zagreb, Symmetric Division Degree, Harmonic, Inverse Sum indices for the structure of Gold crystal.

Low-Temperature NO2 Gas-Sensing System Based on Metal-Organic Framework-Derived In2O3 Structures and Advanced Machine Learning Techniques.

In the bustling metropolis of tomorrow, where pollution levels are a constant concern, a team of innovative researchers embarked on a quest to revolutionize air quality monitoring. In pursuit of this objective, this study embarked on the synthesis of indium oxide materials via a straightforward solvothermal method purposely for NO2 detection. Through meticulous analysis of their gas-sensing capabilities, a remarkable discovery came to light. Among the materials tested, In2O3 (IO-2) exhibited exceptional sensitivity toward 100 ppm of NO2 gas at an optimal working temperature of 150 °C and even at room temperature (RT). The response value reached an impressive 12.69, showcasing the material's outstanding capability to detect NO2 gas even at 100 ppb. Further investigation revealed a significant linear relationship (R2 = 0.89454) and commendable reproducibility, highlighting IO-2's potential as a reliable and stable sensing material. Moreover, machine learning techniques were utilized to predict the response characteristics of the sensing materials to various environmental conditions, concentrations of target gases, and operational parameters. This predictive capability can guide the design of more efficient and robust gas sensors, ultimately contributing to improved safety and environmental monitoring. As the demand for efficient, portable, and eco-friendly electronics continues to grow, these findings contribute to the development of sustainable and high-performance materials that can meet the needs of modern technology.

Correction: Multicriteria decision making attributes and estimation of physicochemical properties of kidney cancer drugs via topological descriptors.

[This corrects the article DOI: 10.1371/journal.pone.0302276.].

Greenhouse gas emissions and their driving factors among different flowering Chinese cabbage (Brassica campestris L.) varieties.

Crop cultivars have an influence on greenhouse gas (GHG) emissions, and there is variation between varieties. However, there are few reports available on the differences in GHG emissions and their driving factors among vegetable varieties. In this study, we conducted a field experiment to examine the variances in GHG emissions and their contributing factors among eight flowering Chinese cabbage varieties (considering growth period, leaf shape, and colour). The results showed significant differences in GHG emissions within varieties; early-maturing varieties exhibited GHG by 25.6% and 15.3%, respectively, when compared to mid- and late-maturing varieties. Among the different leaf types and color classifications, light-colored and sharp-leafed varieties had the lower global warming potential (GWP) overall. Cumulative CO2 emissions were influenced by leaf SPAD values and biomass, while cumulative N2O emissions were driven mainly by stem thickness, carbon accumulation, leaf SPAD values, and biomass. In summary, the selection of light-colored varieties with pointed leaves and shorter growth periods in actual production contributed positively to the reduction of carbon emissions from flowering Chinese cabbage production. Through efficient variety screening, this study provides a win-win strategy for achieving efficient vegetable production while also addressing the global climate challenge.

Multicriteria decision making attributes and estimation of physicochemical properties of kidney cancer drugs via topological descriptors.

Based on topological descriptors, QSPR analysis is an incredibly helpful statistical method for examining many physical and chemical properties of compounds without demanding costly and time-consuming laboratory tests. Firstly, we discuss and provide research on kidney cancer drugs using topological indices and done partition of the edges of kidney cancer drugs which are based on the degree. Secondly, we examine the attributes of nineteen drugs casodex, eligard, mitoxanrone, rubraca, and zoladex, etc and among others, using linear QSPR model. The study in the article not only demonstrates a good correlation between TIs and physical characteristics with the QSPR model being the most suitable for predicting complexity, enthalpy, molar refractivity, and other factors and a best-fit model is attained in this study. This theoretical approach might benefit chemists and professionals in the pharmaceutical industry to forecast the characteristics of kidney cancer therapies. This leads towards new opportunities to paved the way for drug discovery and the formation of efficient and suitable treatment options in therapeutic targeting. We also employed multicriteria decision making techniques like COPRAS and PROMETHEE-II for ranking of said disease treatment drugs and physicochemical characteristics.

Efficient Selective Carbon Dioxide Separation via Task-Specific Ionic Liquids Incorporated in ZIF-8.

Owing to the rapid increase in anthropogenic emission of carbon dioxide (CO2) in the atmosphere, which has resulted in a number of global climate challenges, a decrease in CO2 emissions is urgently needed in the current scenario. This study focuses on the development and characterization of composites for carbon dioxide (CO2) separation. The composites consist of two task-specific ionic liquids (TSILs), namely, tetramethylgunidinium imidazole [TMGHIM] and tetramethylgunidinium phenol [TMGHPhO], impregnated in ZIF-8. The performance of CO2 separation, including sorption capacity and selectivity, was evaluated for pristine ZIF-8 and composites of TMGHIM@ZIF-8 and TMGHPhO@ZIF-8. To demonstrate the thermal stability of the material, thermogravimetric analysis (TGA) was performed. Additionally, powder X-ray diffraction (XRD) and scanning electron microscopy (SEM) were utilized to showcase the crystal structures and morphology. Fourier transform infrared spectroscopy (FTIR) and BET were also utilized to confirm the successful incorporation of TSILs into ZIF-8. The composite synthesized with TMGHIM@ZIF-8 demonstrated superior CO2 sorption performance as compared with TMGHPhO@ZIF-8. This is attributed to its strong attraction toward CO2, resulting in a higher CO2/CH4 selectivity of 110 while pristine MOFs showed 12 that is 9 times higher than that of the pristine ZIF-8. These TSILs@ZIF-8 composites have significant potential in designing sorbent materials for efficient acid gas separation applications.

TimeTector: A Twin-Branch Approach for Unsupervised Anomaly Detection in Livestock Sensor Noisy Data (TT-TBAD).

Unsupervised anomaly detection in multivariate time series sensor data is a complex task with diverse applications in different domains such as livestock farming and agriculture (LF&A), the Internet of Things (IoT), and human activity recognition (HAR). Advanced machine learning techniques are necessary to detect multi-sensor time series data anomalies. The primary focus of this research is to develop state-of-the-art machine learning methods for detecting anomalies in multi-sensor data. Time series sensors frequently produce multi-sensor data with anomalies, which makes it difficult to establish standard patterns that can capture spatial and temporal correlations. Our innovative approach enables the accurate identification of normal, abnormal, and noisy patterns, thus minimizing the risk of misinterpreting models when dealing with mixed noisy data during training. This can potentially result in the model deriving incorrect conclusions. To address these challenges, we propose a novel approach called "TimeTector-Twin-Branch Shared LSTM Autoencoder" which incorporates several Multi-Head Attention mechanisms. Additionally, our system now incorporates the Twin-Branch method which facilitates the simultaneous execution of multiple tasks, such as data reconstruction and prediction error, allowing for efficient multi-task learning. We also compare our proposed model to several benchmark anomaly detection models using our dataset, and the results show less error (MSE, MAE, and RMSE) in reconstruction and higher accuracy scores (precision, recall, and F1) against the baseline models, demonstrating that our approach outperforms these existing models.

Plant extract preparation and green synthesis of silver nanoparticles using Swertia chirata: Characterization and antimicrobial activity against selected human pathogens.

Herbal medicinal plants have been used for centuries in traditional medicine, and it is interesting to see how modern research has identified the active compounds responsible for their therapeutic effects. The green synthesis of silver nanoparticles using herbal medicinal plants, such as Swertia chirata, is particularly noteworthy due to its antimicrobial properties. In the current study, the Swertia chirata plant was collected for the first time from the region of Murree, Punjab, Pakistan. After collection, extracts were prepared in different solvents (ethanol, methanol, chloroform, and distilled water), and silver nanoparticles were synthesized by reducing silver nitrate (AgNO3). The UV-visible spectrophotometer, SEM, and EDX were used to characterize the synthesized nanoparticles in terms of their size and shape. The phytochemical analysis of crude extract was performed to determine the presence of different kinds of phytochemicals. The antibacterial activity of plant extracts and the silver nanoparticles were then assessed using the agar well diffusion method against various pathogenic bacteria. The results showed that the plant contains several phytochemicals with remarkable antioxidant potential. The antibacterial analysis revealed that silver nanoparticles and the plant extracts exhibited a significant zone of inhibition against human pathogenic bacteria (Escherichia coli, S. capitis, B. subtilis, and Pseudomonas aeruginosa) as compared to the cefixime and norfloxacin. This implies that the nanoparticles have the potential to be used in nano-medicine applications, such as drug delivery systems, as well as for their antibacterial, antifungal, and antiviral activities. Additionally, the development and application of materials and technologies at the nanometer scale opens possibilities for the creation of novel drugs and therapies. Overall, the study highlights the promising potential of herbal medicinal plants found in Murree, Punjab, Pakistan, and green-synthesized silver nanoparticles in various fields of medicine and nanotechnology.

Epigenetic disruption of the RARγ complex impairs its function to bookmark AR enhancer interactions required for enzalutamide sensitivity in prostate cancer.

The current study in prostate cancer (PCa) focused on the genomic mechanisms at the cross-roads of pro-differentiation signals and the emergence of lineage plasticity. We explored an understudied cistromic mechanism involving RARγ's ability to govern AR cistrome-transcriptome relationships, including those associated with more aggressive PCa features. The RARγ complex in PCa cell models was enriched for canonical cofactors, as well as proteins involved in RNA processing and bookmarking. Identifying the repertoire of miR-96 bound and regulated gene targets, including those recognition elements marked by m6A, revealed their significant enrichment in the RARγ complex. RARγ significantly enhanced the AR cistrome, particularly in active enhancers and super-enhancers, and overlapped with the binding of bookmarking factors. Furthermore, RARγ expression led to nucleosome-free chromatin enriched with H3K27ac, and significantly enhanced the AR cistrome in G2/M cells. RARγ functions also antagonized the transcriptional actions of the lineage master regulator ONECUT2. Similarly, gene programs regulated by either miR-96 or antagonized by RARγ were enriched in alternative lineages and more aggressive PCa phenotypes. Together these findings reveal an under-investigated role for RARγ, modulated by miR-96, to bookmark enhancer sites during mitosis. These sites are required by the AR to promote transcriptional competence, and emphasize luminal differentiation, while antagonizing ONECUT2.

Taxonomic study and diversity of Postia s.lat. in Swat, Pakistan: addition of five brown rot Polypores to the country.

Brown rot Polypores are ecologically significant as they play a crucial role in maintaining the carbon cycle and contribute to humus formation in forest ecosystems through their lignocellulose degradation ability. It is important to note that some species can significantly impact timber, potentially causing decay in economically valuable wood. Many Asian countries including Pakistan are still under the exploratory phase and have undocumented species diversity in Polypore fungi. In the current study, collections representing five different species belonging to two families, Postiaceae and Adustoporiaceae, were subjected to detailed morphoanatomical and molecular analyses. A combined matrix of two gene datasets (ITS and nrLSU) was analyzed using three different phylogenetic methods viz. Maximum Parsimony (MP), Maximum Likelihood (ML), and Bayesian inference (BI). Our study presents descriptions of five previously undocumented brown rot Polypore species from the country including Fuscopostia fragilis (Fr.) B.K. Cui, L.L. Shen & Y.C. Dai, Amaropostia stiptica (Pers.) B.K. Cui, L.L. Shen & Y.C. Dai, Cyanosporus piceicola B.K. Cui, L.L. Shen & Y.C. Dai, Spongiporus balsameus (Peck) A. David, Rhodonia placenta (Fr.) Niemelä, K.H. Larss. & Schigel. Regarding the molecular data, nodes of our subject sequences were substantially supported and fell under their respective species clades with high ML bootstrap values (≥ 95), MP bootstrap ≥ 74 and BI probabilities ≥ 0.98. Findings of the study will not only contribute to our understanding of local Polypores species diversity but also enhance knowledge of geographical distribution in global context.

Ce-doped ZnO nanostructures: A promising platform for NO2 gas sensing.

In this comprehensive study, Ce-doped ZnO nanostructures were hydrothermally synthesized with varying Ce concentrations (0.5%, 1.0%, 1.5%, and 2.0%) to explore their gas-sensing capabilities, particularly towards NO2. Structural characterization revealed that as Ce doping increased, crystal size exhibited a slight increment while band gap energies decreased. Notably, the 0.5% Ce-doped ZnO nanostructure demonstrated the highest NO2 gas response of 8.6, underscoring the significance of a delicate balance between crystal size and band gap energy for optimal sensing performance. The selectivity of the 0.5% Ce-doped ZnO nanostructures to NO2 over other gases like H2, acetone, NH3, and CO at a concentration of 100 ppm and an optimized temperature of 250 °C was exceptional, highlighting its discriminatory prowess even in the presence of potential interfering gases. Furthermore, the sensor displayed reliability and reversibility during five consecutive tests, showcasing consistent performance. Long-term stability testing over 30 days revealed that the gas response remained almost constant, indicating the sensor's remarkable durability. In addition to its robustness against humidity variations, maintaining effectiveness even at 41% humidity, the sensor exhibited impressive response and recovery times. While the response time was swift at 11.8 s, the recovery time was slightly prolonged at 56.3 s due to the strong adsorption of NO2 molecules onto the sensing material hindering the desorption process. The study revealed the intricate connection between Ce-doping levels, structure, and gas-sensing. It highlighted the 0.5% Ce-doped ZnO nanostructure as a highly selective, reliable, and durable NO2 gas sensor, with implications for future environmental monitoring and safety.

Selective efficient photocatalytic degradation of antibiotics and direct Z-type migration pathway for hierarchical core-shell TiO2/g-C3N4 composites.

Constructing superior Z-type photocatalytic heterojunction is beneficial to effectively enlarge interface contact, improve the photo-generated carrier separation rate, and retain the high redox ability. In this work, we designed a hierarchical core-shell g-C3N4/TiO2 structure to build Z-type heterojunction via combining simple template method and pyrolysis process. A close-knit Z-type heterojunction was constructed using TiO2 as a thick core and g-C3N4 as an ultra-thin shell. The effects of lamp source, wavelength, tetracycline (TC) concentration, and photocatalyst dose on the degradation performance on TC of g-C3N4/TiO2 were inspected. 0.1TiO2/g-C3N4 photocatalyst had the best degradation rate and highest removal rate within 30 min, and its degradation rate was about 49, 23, and 5 times than pure g-C3N4, TiO2, and commercial TiO2/g-C3N4 in respect. Moreover, compared with degradation ability under Xenon lamp, LED irradiation for g-C3N4/TiO2 composites showed a remarkable selective degradation. The fast and efficient Z-type transfer pathway of 0.1 g-C3N4/TiO2 was realized by forming an optimized interface and abundant surface active sites ascribed to the combined action of thick TiO2 core and ultra-thin g-C3N4 shell. In addition, the degradation intermediates were analyzed by LC-MS and suggested pathways of degradation. The work could provide novel design concept to obtain reliable Z-type photocatalysts with hierarchical core-shell structure applied in degradation of antibiotic wastewater.

An estimation of physiochemical properties of bladder cancer drugs via degree-based chemical bonding topological descriptors.

The use of topological descriptors remains a significant approach due to numerous advances in the field of drug design. Descriptors provide numerical representations of a molecule's chemical characteristics when used with QSPR models. The QSPR analysis for bladder medications is the main focus of this study. Linear regression model is developed for the computed indices values, the physicochemical properties of the bladder medications are examined.Communicated by Ramaswamy H. Sarma.

The MYC axis in advanced prostate cancer is impacted through concurrent targeting of ERß and AR using a novel ERß-selective ligand alongside Enzalutamide.

We have dissected the role of Estrogen receptor beta (ERß) in prostate cancer (PCa) with a novel ERß ligand, OSU-ERb-12. Drug screens revealed additive interactions between OSU-ERB-12 and either epigenetic inhibitors or the androgen receptor antagonist, Enzalutamide (Enza). Clonogenic and cell biolody studies supported the potent additive effects of OSU-ERB-12 (100nM) and Enza (1µM). The cooperative behavior was in PCa cell lines treated with either OSU-ERB-12 plus Enza or combinations involving 17ß-estradiol (E2). OSU-ERb-12 plus Enza uniquely impacted the transcriptiome, accessible chromatin, and the AR, MYC and H3K27ac cistromes. This included skewed transcriptional responses including suppression of the androgen and MYC transcriptomes, and repressed MYC protein. OSU-ERb-12 plus Enza uniquely impacted chromatin accessibility at approximately 3000 nucleosome-free sites, enriched at enhancers, enriched for basic Helix-Loop-Helix motifs. CUT&RUN experiments revealed combination treatment targeting of MYC, AR, and H3K27ac again shaping enhancer accessibility. Specifically, it repressed MYC interactions at enhancer regions enriched for bHLH motifs, and overlapped with publicly-available bHLH cistromes. Finally, cistrome-transcriptome analyses identified ~200 genes that distinguished advanced PCa tumors in the SU2C cohort with high androgen and low neuroendocrine scores.

Towards enhancing security of IoT-Enabled healthcare system.

The Internet-of-Things (IoT)-based healthcare systems are comprised of a large number of networked medical devices, wearables, and sensors that collect and transmit data to improve patient care. However, the enormous number of networked devices renders these systems vulnerable to assaults. To address these challenges, researchers advocated reducing execution time, leveraging cryptographic protocols to improve security and avoid assaults, and utilizing energy-efficient algorithms to minimize energy consumption during computation. Nonetheless, these systems still struggle with long execution times, assaults, excessive energy usage, and inadequate security. We present a novel whale-based attribute encryption scheme (WbAES) that empowers the transmitter and receiver to encrypt and decrypt data using asymmetric master key encryption. The proposed WbAES employs attribute-based encryption (ABE) using whale optimization algorithm behaviour, which transforms plain data to ciphertexts and adjusts the whale fitness to generate a suitable master public and secret key, ensuring security against unauthorized access and manipulation. The proposed WbAES is evaluated using patient health record (PHR) datasets collected by IoT-based sensors, and various attack scenarios are established using Python libraries to validate the suggested framework. The simulation outcomes of the proposed system are compared to cutting-edge security algorithms and achieved finest performance in terms of reduced 11 s of execution time for 20 sensors, 0.121 mJ of energy consumption, 850 Kbps of throughput, 99.85 % of accuracy, and 0.19 ms of computational cost.

Layered Architectural Fabrication of a Novel Sulfite Nanobiosensor by Encapsulation of Sulfite Oxidase on a Polypyrrole-Multiwalled Carbon Nanotubes Composite Decorated with Platinum Nanoparticles.

The fabrication of a highly selective and ultrasensitive sulfite nanobiosensor based on a layered architectural fabrication aided by the encapsulation of sulfite oxidase (SOx) in Nafion (Naf) matrix on a multiwalled carbon nanotubes-polypyrrole (MWCNTs-PPy) composite decorated with platinum nanoparticles (PtNPs) is described. The MWCNTs are deposited in the inner layer on a Pt electrode during electropolymerization of pyrrole (Py), followed by decoration with a PtNPs layer and subsequent encapsulation of SOx with Naf in the third layer capped with a fourth thin PtNPs layer. Images obtained by field emission scanning electron microscopy (FESEM) reveal that high-density PtNPs are deposited onto the 3D nanostructured inner MWCNTs-PPy layer and the electrochemical behavior is investigated. A large surface area provided by the incorporation of MWCNTs in the composite and decoration with PtNPs enables increased SOx loading, SOx retention, and substantial improvement in sensing performance. The resulting layered PtNPs/SOx-Naf/PtNPs/MWCNTs-PPy nanobiosensor exhibits a fast response time (within 3 s), a linear calibration range of 20 nmm - 6 m with an excellent sensitivity of 71 µA mm-1  cm-2 and a detection limit of 5.4 nm. The nanobiosensor  was effective in discriminating against common interferants and  was successfully applied to sulfite determination in real samples.