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Abstract In this study, we introduce StructmRNA, a new BERT-based model that was designed for the detailed analysis of mRNA sequences and structures. The success of DNABERT in understanding the intricate language of non-coding DNA with bidirectional encoder representations is extended to mRNA with StructmRNA. This new model uses a special dual-level masking technique that covers both sequence and structure, along with conditional masking. This enables StructmRNA to adeptly generate meaningful embeddings for mRNA sequences, even in the absence of explicit structural data, by capitalizing on the intricate sequence-structure correlations learned during extensive pre-training on vast datasets. Compared to well-known models like those in the Stanford OpenVaccine project, StructmRNA performs better in important tasks such as predicting RNA degradation. Thus, StructmRNA can inform better RNA-based treatments by predicting the secondary structures and biological functions of unseen mRNA sequences. The proficiency of this model is further confirmed by rigorous evaluations, revealing its unprecedented ability to generalize across various organisms and conditions, thereby marking a significant advance in the predictive analysis of mRNA for therapeutic design. With this work, we aim to set a new standard for mRNA analysis, contributing to the broader field of genomics and therapeutic development.

Prime editors are CRISPR-based genome engineering tools with significant potential for rectifying patient mutations. However, their usage requires experimental optimization of the prime editing guide RNA (PegRNA) to achieve high editing efficiency. This paper introduces the deep transformer-based model for predicting prime editing efficiency (DTMP-Prime), a tool specifically designed to predict PegRNA activity and prime editing (PE) efficiency. DTMP-Prime facilitates the design of appropriate PegRNA and ngRNA. A transformer-based model was constructed to scrutinize a wide-ranging set of PE data, enabling the extraction of effective features of PegRNAs and target DNA sequences. The integration of these features with the proposed encoding strategy and DNABERT-based embedding has notably improved the predictive capabilities of DTMP-Prime for off-target sites. Moreover, DTMP-Prime is a promising tool for precisely predicting off-target sites in CRISPR experiments. The integration of a multi-head attention framework has additionally improved the precision and generalizability of DTMP-Prime across various PE models and cell lines. Evaluation results based on the Pearson and Spearman correlation coefficient demonstrate that DTMP-Prime outperforms other state-of-the-art models in predicting the efficiency and outcomes of PE experiments.

Data mining and techniques for analyzing big data play a crucial role in various practical fields, including financial markets. However, only a few quantitative studies have been focused on predicting daily stock market returns. The data mining methods used in previous studies are either incomplete or inefficient. This study used the FPC clustering algorithm and prominent clustering algorithms such as K-means, IPC, FDPC, and GOPC for clustering stock market data. The stock market data utilized in this study comprise data from cement companies listed on the Tehran Stock Exchange. These data concerning capital returns and price fluctuations will be examined and analyzed to guide investment decisions. The analysis process involves extracting the stock market data of these companies over the past two years. Subsequently, these companies are categorized based on two criteria: profitability percentage and short-term and long-term price fluctuations, using the FPC clustering algorithm and the classification above algorithms. Then, the results of these clustering analyses are compared against each other using standard and recognized evaluation criteria to assess the quality of the clustering analysis. The findings of this investigation indicate that the FPC algorithm provides more favorable results than other algorithms. Based on the results, companies demonstrating profitability, stability, and loss within short-term (weekly and monthly) and long-term (three-month, six-month, and one-year) time frames will be placed within their respective clusters and introduced accordingly.

The study of Iranian architectural spaces as well as historical houses demonstrates the combination and connection among the three open, closed, and semi-open spaces. The connection among these three spaces and generally between inside and outside is a principle of Iranian architecture. The porch, as a semi-open space, has various functional, climatic, cultural, and physical functions, besides the common connection between open and closed spaces. Moreover, it is often considered a criterion for identification of the architectural and social value and identity of owners of historical houses. In this study, the descriptive-analytical method and comparative study are employed through library sources, and the typology and concept of the porch are addressed in order to realize the study’s first goal, i.e. identifying and typology of porches in the Qajar and first Pahlavi eras. Subsequently, in accordance with field observations and perceptions of the houses’ porch, it is tried to analyze the physical features of the porch in Qajar and first Pahlavi eras based on the two “physical and spatial” and “performance and functional” precise systems. The findings below are obtained after presenting the models and comparing the physical systems in four eras (early, middle, and late Qajar, and the first Pahlavi). Through investigating the porches in terms of model, they were studied and classified based on the houses’ systematic physical features in three types of “porch in general”, “porch in the middle”, and “protruding porch”. Concerning the key objective of the study, i.e. investigating the way and reason for changes in porches, it is worth noting that like other components and elements of historic houses, the porch is flexible and the changes in society and architecture were followed by the changes in configuration, space organization, and particularly the porches’ body. After the developments occurred in Iran as a result of the Iran-Russia war, a chain of events was created, reaching its peak in the first Pahlavi era. These events included a series of political, economic, social, cultural, and urban factors affecting the architectural model of houses and porches. The relationship with Western countries, the weakening of beliefs and values, the increased population, the financial ability of the owners of the houses, as well as the renovation and urban developments can be referred to as the key factors with a great effect on changes in the porches.

Clustered regularly interspaced short palindromic repeats (CRISPR)-based gene editing has been widely used in various cell types and organisms. To make genome editing with Clustered regularly interspaced short palindromic repeats far more precise and practical, we must concentrate on the design of optimal gRNA and the selection of appropriate Cas enzymes. Numerous computational tools have been created in recent years to help researchers design the best gRNA for Clustered regularly interspaced short palindromic repeats researches. There are two approaches for designing an appropriate gRNA sequence (which targets our desired sites with high precision): experimental and predicting-based approaches. It is essential to reduce off-target sites when designing an optimal gRNA. Here we review both traditional and machine learning-based approaches for designing an appropriate gRNA sequence and predicting off-target sites. In this review, we summarize the key characteristics of all available tools (as far as possible) and compare them together. Machine learning-based tools and web servers are believed to become the most effective and reliable methods for predicting on-target and off-target activities of Clustered regularly interspaced short palindromic repeats in the future. However, these predictions are not so precise now and the performance of these algorithms -especially deep learning one’s-depends on the amount of data used during training phase. So, as more features are discovered and incorporated into these models, predictions become more in line with experimental observations. We must concentrate on the creation of ideal gRNA and the choice of suitable Cas enzymes in order to make genome editing with Clustered regularly interspaced short palindromic repeats far more accurate and feasible.

Objective(s): Pseudomonas aeruginosa is the bacterium that causes of pulmonary infection among chronically hospitalized patients. Alginate is a common surface antigen of P. aeruginosa with a constant structure that which makes it an appropriate target for vaccines. In this study, P. aeruginosa alginate was conjugated with to PLGA nanoparticles, and its immunogenicity was characterized as a vaccine.Materials and Methods: Alginate was isolated from a mucoid strain of P. aeruginosa and conjugated with to PLGA with˝ N-(3-Dimethylaminopropyl)-N′-ethylcarbodiimide hydrochloride ˝= ˝EDAC˝ and N-Hydroxysuccinimide (NHS). Chemical characterization of prepared nano-vaccine was performed using FTIR Spectroscopy, Zetasizer, and Atomic Force Microscopy (AFM). The immunogenicity of this nano-vaccine was evaluated through intramuscular injection into BALB/c mice. Four groups of mice were subjected to the injection of alginate–PLGA, and two weeks after the last administration step, opsonophagocytosis assay, IgG detection, challenge, and cytokine determination via ELISA were carried out.Results: Alginate-PLGA conjugation was corroborated by FTIR, Zetasizer, and AFM. The ELISA consequence showed that alginate was prospering in the instigation of the humoral immunity.The immunogenicity enhanced against the alginate-PLGA. Remarkably diminished bacterial titer in the spleen of the immunized mice posterior to challenge with PAO1 strain in comparison with the alginate alone and control groups.Conclusion: The bacterial burden in the spleen significantly decreased after the challenge (p

Background: Pseudomonas aeruginosa has an important role in nosocomial infections. Objective: To evaluate biological activity of the detoxified LPS (D-LPS) entrapped into Poly lactic-co-glycolic acid (PLGA) nanoparticles. Materials: LPS was extracted and detoxified from the P. aeruginosa strain PAO1. The D-LPS, conjugated to the PLGA nanoparticles with 1-ethyl-3-dimethyl aminopropyl carbodiimide (EDAC) and N-hydroxy-succinimide (NHS). The connection was evaluated by FTIR (Fourier transform infrared), Zetasizer, and Atomic Force Microscope (AFM). The BALB/c mice injected intramuscularly with the D-LPS-PLGA with two-week intervals and then challenged two weeks after the last immunization. The bioactivity of the induced specific antisera and cytokines responses against D-LPS-PLGA antigen was assessed by ELISA. Results: D-LPS-PLGA conjugation was confirmed by FTIR, Zetasizer, and AFM. The ELISA results showed that D-LPS was successful in the stimulation of the humoral immune response. The immune responses raised against the D-LPS-PLGA, significantly decreased bacterial titer in the spleen of the immunized mice after challenge with PAO1 strain in comparison with the control groups. Conclusion: The conjugation of the bacterial LPS to the PLGA nanoparticle increased their functional activity by decrease in bacterial dissemination and increase the killing of opsonized bacteria.

In this study, a review of second-generation voltage conveyor (VCII) and current conveyor (CCII) circuits for the conditioning of bio signals and sensors is presented. The CCII is the most known current-mode active block, able to overcome some of the limitations of the classical operational amplifier, which provides an output current instead of a voltage. The VCII is nothing more than the dual of the CCII, and for this reason it enjoys almost all the properties of the CCII but also provides an easy-to-read voltage as an output signal. A broad set of solutions for relevant sensors and biosensors employed in biomedical applications is considered. This ranges from the widespread resistive and capacitive electrochemical biosensors now used in glucose and cholesterol meters and in oximetry to more specific sensors such as ISFETs, SiPMs, and ultrasonic sensors, which are finding increasing applications. This paper also discusses the main benefits of this current-mode approach over the classical voltage-mode approach in the realization of readout circuits that can be used as electronic interfaces for different types of biosensors, including higher circuit simplicity, better low-noise and/or high-speed performance, and lower signal distortion and power consumption.

This study reviews second-generation voltage conveyor (VCII)-based read-out circuits for sensors and bioelectrical signal conditioning from existing literature. VCII is the dual circuit of a second-generation current conveyor (CCII), which provides the possibility of processing signals in the current domain while providing output signals in the voltage form. The scope of this paper is to discuss the benefits and opportunities of new VCII-based read-out circuits over traditional ones and bioelectrical signals. The achieved main benefits compared to conventional circuits are the simpler read-out circuits, producing an output signal in a voltage form that can be directly used, improved accuracy, possibility of gain adjustment using a single grounded resistor, and the possibility of connecting several SiPM sensors to the readout circuit. The circuits studied in this paper include VCII- based read-out circuits suitable for all types of sensors configured in the current-mode Wheatstone bridge (CMWB) topology, the VCII-based read-out circuits solutions reported for silicon photomultiplier, spiral-shaped ultrasonic PVDF and differential capacitive sensors, and, finally, a simple readout circuitry for sensing bioelectrical signals. There are still not many VCII-based readout circuits, and we hope that the outcome of this study will enhance this area of research and inspire new ideas.

Introduction: Pseudomonas aeruginosa is a particular importance due to the numerous factors of pathogenicity and the prevalence of multi-resistance strains throughout the world. Therefore, the need to prevent and produce an effective vaccine seems necessary. The aim of this study was to use PLGA nanoparticles in the design of vaccine with alginate antigens, lipopolysaccharide, and exotoxin, A Pseudomonas aeruginosa. In this study, strain PAO1 of Pseudomonas aeruginosa is used. Then, antigens including, alginate, lipopolysaccharide, and exotoxin A were extracted. Then, lipopolysaccharide was detoxified by hot phenol method. Exotoxin A was purified by chromatography and detoxified with formalin. Then, antigens separately conjugated to PLGA. FT-IR and AFM methods were usedto confirm conjugation with nanoparticles. A rabbit model was used to study the pyrogenic effects of conjugates. The mortality effect of conjugates on mouse model was tested. Materials and methods: The success of conjugation based on the size and charge of the nanoparticle was confirmed. The presence of antigenic functional groups in the structure of the nanoparticle and the formation of a steric bond confirmed with FT-IR results and the corresponding courier form. Results: The 3D conjugate images of nanoparticles before and after conjugation showed an increase in the height of nanoparticle binding sites. The change from initial sharpness to puff after the conjugation confirmed the success of conjugation. Failure to observe the pyrogenic effects in the rabbit and no mortality observed in mice was proved. Discussion and conclusion: All of the results showed that conjugates were effective in immunization. Therefore, it can be a candidate vaccine with a great potential against Pseudomonas causing diseases.

In this paper a high-performance low-voltage low-power CMOS second-generation current conveyor (CCII) is presented. The proposed CCII is based on super transistor (ST) and enables low input impedance at X terminal and high output impedance at Z terminal. It also utilizes a novel power saving strategy in which a single NMOS cascode current mirror conveys X terminal current to Z terminal, provides high impedance at Z terminal and reduces the X terminal impedance all together resulting in a low-power and compact structure. As another advantage, only NMOS transistors are used in processing voltage and current signals granting the proposed CCII high frequency operation. PMOS transistors are used only for biasing. However, the proposed CCII cannot provide infinite impedance at Y terminal. HSPICE simulations using 0.18 µm parameters and supply voltage of ±0.9 V confirms that the proposed CCII exhibits impedances of 0.155 Ω, 1.6 MΩ and 47 kΩ at X, Z and Y terminals, respectively. Voltage and current bandwidths are also 377 MHz and 159 MHz, respectively. Some of the applications of the proposed CCII are given. DOI: http://dx.doi.org/10.5755/j01.eie.24.2.17948

In this paper, a novel approach to implement a stray insensitive CMOS interface for differential capacitive sensors is presented. The proposed circuit employs, for the first time, second-generation voltage conveyors (VCIIs) and produces an output voltage proportional to differential capacitor changes. Using VCIIs as active devices inherently allows the circuit to process the signal in the current domain, and hence, to benefit from its intrinsic advantages, such as high speed and simple implementation, while still being able to natively interface with voltage mode signal processing stages at necessity. The insensitiveness to the effects of parasitic capacitances is achieved through a simple feedback loop. In addition, the proposed circuit shows a very simple and switch-free structure (which can be used for both linear and hyperbolic sensors), improving its accuracy. The readout circuit was designed in a standard 0.35 μm CMOS technology under a supply voltage of ±1.65 V. Before the integrated circuit fabrication, to produce tangible proof of the effectiveness of the proposed architecture, a discrete version of the circuit was also prototyped using AD844 and LF411 to implement a discrete VCII. The achieved measurement results are in good agreement with theory and simulations, showing a constant sensitivity up to 412 mV/pF, a maximum linearity error of 1.9%FS, and acknowledging a good behavior with low baseline capacitive sensors (10 pF in the proposed measurements). A final table is also given to summarize the key specs of the proposed work comparing them to the available literature.

In this paper a novel high performance all current-mode fully-differential (FD) Current mode Operational Amplifier (COA) in BIPOLAR technology is presented. The unique true current mode simple structure grants the proposed COA the largest yet reported unity gain frequency while providing low voltage low power operation. Benefiting from some novel ideas, it also exhibits high gain, high common mode rejection ratio (CMRR), high power supply rejection ratio (PSRR), high output impedance, low input impedance and most importantly high current drive capability. Its most important parameters are derived and its performance is proved by PSPICE simulations using 0.8 μm BICMOS process parameters at supply voltage of ±1.2V indicating the values of 82.4 dB,52.3º, 31.5 Ω, 31.78 MΩ, 179.2 dB, 2 mW and 698 MHz for gain, phase margin, input impedance, output impedance, CMRR, power and unity gain frequency respectively. Its CMRR also shows very high frequency of 2.64 GHz at zero dB. Its very high PSRR+/PSRR- of 182 dB/196 dB makes the proposed COA a highly suitable block in Mixed-Mode (SOC) chips. Most favourably it can deliver up to ±1.5 mA yielding a high current drive capability exceeding 25. To demonstrate the performance of the proposed COA, it is used to realize a constant bandwidth voltage amplifier and a high performance Rm amplifier.