Abstracts:This study aimed to develop doped carbon dots and coat them with carboxyl-polymer to explore their applications in imaging living tissue cells and achieving targeted drug release, particularly for tumor therapy. The synthesis of NP-CDs involved a one-pot hydrothermal reaction of seaweed powder, ethylene diamine, and phosphoric acid at atmospheric pressure. Subsequently, the NP-CDs were coated with carboxyl-mounted PEG to create PEG@NP-CDs, serving as a nano carrier for delivering the anti-cancer drug Doxorubicin (DOX). The drug delivery capabilities of PEG@NP-CDs were assessed, and their sensitivity to variations in pH value was studied. The hydrothermal reaction successfully yielded NP-CDs with distinctive fluorescence properties, exhibiting green fluorescence at 430 nm and varying emission peaks depending on the excitation wavelength used. The subsequent coating of NP-CDs with carboxyl-mounted PEG resulted in PEG@NP-CDs, which demonstrated biocompatibility and potential for drug delivery applications. The MTT assay confirmed the high biocompatibility of PEG@NP-CDs, rendering them suitable for biomedical applications. The study successfully developed a straightforward method to synthesize CDs doped with nitrogen and phosphorus, which exhibited green fluorescence and sensitivity to excitation wavelengths. These nanomaterials have potential for imaging living tissue cells and achieving slow drug release. Their drug delivery capabilities, especially pH sensitivity, make them promising for targeted therapy, particularly in tumors. The biocompatibility of PEG@NP-CDs further supports their safe biomedical use. Overall, PEG@NP-CDs offer a valuable tool for simultaneous imaging and drug delivery, with promising applications in tumor detection and therapy.

Abstracts:DNA data storage is a cutting-edge storage technique due to its high density, replicability, and long-term capability. It involves encoding, insertion, deletion, and substitution (IDS) channels for data synthesis and sequencing, and decoding processes. The IDS channels that feature multiple output sequences are prone to IDS errors, complicating the decoding process and degrading the performance of DNA data storage. To address this issue, we investigate effective IDS error correction algorithms considering two encoding schemes in DNA data storage. Specifically in the encoding process, we use marker codes (MC) and embedded marker codes (EMC) as inner codes, respectively, both connected to low-density parity-check (LDPC) codes as outer codes. First, we propose the segmented progressive matching (SPM) algorithm to infer the consensus sequence from multiple output sequences, thereby facilitating the decoding processes. Moreover, when using MC as the inner code, we propose a synchronous decoding algorithm based on the Hidden Markov Model (SDH) to infer the a posteriori probability (APP) of base symbols, which supports the external decoding algorithm. Furthermore, when the inner code is EMC, we propose the iterative external decoding (IED) algorithm. IED integrates synchronous decoding with embedded normalized min-sum decoding (ENMS) to achieve an enhanced APP for external decoding, enabling lower bit-error rate (BER) transmission. Meanwhile, we reduce the complexity of the external decoder by minimizing checksum node computations. Comparing the two schemes reveals that the SDH algorithm with MC as the inner code offers a lightweight solution for DNA data storage. In contrast, the IED with EMC demonstrates superior decoding performance with a linear complexity scale by the number of iterations. Compared with existing studies, simulation results show that our proposed decoding algorithm reduces the BER by ${21}.{72}\% \sim {99}.{75}\%$ .

Abstracts:DNA hybridization reaction is a significant technology in the field of semi-synthetic biology and holds great potential for use in biological computation. In this study, we propose a novel machine learning model based on a DNA hybridization reaction circuit. This circuit comprises a computation training component, a test component, and a learning algorithm. Compared to conventional machine learning models based on semiconductors, the proposed machine learning model harnesses the power of DNA hybridization reaction, with the learning algorithm implemented based on the unique properties of DNA computation, enabling parallel computation for the acquisition of learning results. In contrast to existing machine learning models based on DNA circuits, our proposed model constitutes a complete synthetic biology computation system, and utilizes the “dual-rail” mechanism to achieve the DNA compilation of the learning algorithm, which allows the weights to be updated to negative values. The proposed machine learning model based on DNA hybridization reaction demonstrates the ability to predict and fit linear functions. As such, this study is expected to make significant contributions to the development of machine learning through DNA hybridization reaction circuits.

Abstracts:This paper introduces an improved redundant residue number system (RRNS) encoding method to enhance the reliability of information transmission in diffusive molecular communication (DMC). In addressing the 2-1 mapping issue in RRNS encoding, we propose a simplified low-mapping solution that effectively avoids the 2-1 mapping problem, thereby simplifying the decoding process. Leveraging the superior performance of the low-mapping algorithm, we further developed a direct decision algorithm that further simplifies the decoding algorithm by omitting the traditional minimum distance decision-making steps. Furthermore, this study delves into the impact of modulus selection on RRNS decoding performance and provides guidelines for optimizing code construction. Through simulation experiments on DMC channels, we have validated the effectiveness of the proposed RRNS encoding method, especially when employing binary concentration shift keying (BCSK) modulation and considering intersymbol interference (ISI). The simulation results show that the proposed encoding method not only significantly reduces the bit error rate (BER) but also fully meets the requirements of DMC systems, offering a promising new direction for the development of molecular communication technology. With these improvements, our method not only enhances the reliability of information transmission in DMC systems but also lays a solid foundation for future research and applications in molecular communication technology.

Abstracts:The advent of evanescent field based fiber optic biosensor and advancements in nanotechnology has create an excellent opportunity in label-free detection of biomarkers which plays vital role in the early, rapid and accurate diagnosis of acute diseases. In this work, we demonstrate a high sensitive Molybdenum Tungsten Disulfide (MoWS2) coated side polished fiber (SPF) biosensor for accurate and early diagnosis of cardio vascular disease (CVD). The Cardiac Troponins I (cTnI) is identified as a biomarker of interest for early and rapid diagnosis of CVD. The proposed SPF biosensor exhibits surface plasmonic resonance (SPR) detection due to the evanescent field interaction between MoWS2 nano coated side polished region and anti-CTnI. The proposed SPF biosensor possess the high sensitivity of 82% to detect the cTnI antibody with a limit of detection (LOD) about 17.5 pg/mL. The peak SPR shift have been calculated as 61 nm for analyte concentrations of 500 pg/mL Moreover, the proposed SPF biosensor possess the high degree of selectivity and environmental stability to CTnI among three analytes such as CTnI, Estrogen and Glucose. The hydrophobic interactions of MoWS2 and cTnI antibody leads to chemical free biofunctionalization of antibody in the sensing region. Hence, the simulation results shows the surface interaction strength calculated as 1.29 KJ mol−1/nm2 in order to evaluate the hydrophobic interactions. Thus, the proposed optical biosensor is a promising candidate for “point-of-care” testing of CVD disorders and preclinical assessments.

Abstracts:The new generation of glucose biosensors has attracted significant research interest due to its fast response, high stability, reproducibility, portability and low detection limit. In this work, various types of high-performance non-enzymatic glucose sensors are proposed, based on carbon nitride supported copper oxide nanoparticles (CNCO). The hybrid system was synthesized using a modified deposition-precipitation route where the copper oxide nanoparticles were dispersed on the carbon nitride matrix. The X-ray diffraction pattern revealed that the copper oxide nanoparticles exhibit a high degree of crystallinity with a monoclinic structure. The synthesized hybrid material was used as a catalyst for the electrochemical detection of glucose in the range of 0 to 15.6 mM, demonstrating a detection limit of 0.59 mM and a sensitivity of 0.53 mA.mM ${}^{-{1}}$ .cm ${}^{-{2}}$ . The CNCO based extended gate field effect transistor, at different glucose concentrations (1-9 mM), showed limit of detection and sensitivity values of 0.59 mM and 0.065 mA.mM ${}^{-{1}}$ .cm ${}^{-{2}}$ , respectively. A microcontroller-based glucose sensor was also implemented in this study that exhibited the sensitivity value of 1.46 mV/mM within the concentration range of 2-8 mM. The carbon nitride-supported copper oxide-based glucose sensors exhibit excellent reproducibility, sufficient stability and high selectivity, making them a promising candidate for real-life sensing applications.

Abstracts:DNA-based storage has emerged as a promising storage paradigm due to its immense storage potential. However, the error-prone nature of DNA sequencing and synthesis processes limits this potential. Image data is typically compressed before storage, and even a single mismatch can lead to catastrophic error propagation during decompression, rendering the image unrecoverable. To reduce the error rate of DNA storage-based image compression, we have designed a high fault-tolerant DNA image storage system and applied it to image compression for DNA storage. This system achieves significant improvements in both image data compression ratio and resilience through three key innovations: 1) Using a Variational Autoencoder (VAE) to compress the image into uniformly sized latent variable blocks, followed by further compression via Singular Value Decomposition (SVD); 2) Quantizing the floating-point numbers in the latent variable blocks and applying rotational coding to the resulting ternary sequences, effectively ensuring positive constraints on homopolymer run lengths and GC content; 3) Optimizing the error-correction scheme to best recover each type of error by quantizing it back to its original value. Through image scaling, we adjust the compression ratio, and the comparative results of image compression simulations demonstrate the performance of the proposed model, highlighting its superiority in fault tolerance and storage density.

Abstracts:This study proposed a miniaturized multi-sensor device prepared using a flexible printed circuit board (FPCB) and applied to detect glucose (Glu) and pH value, where both the readout circuit board and the sensors possess flexible characteristics. Additionally, this work implemented the potentiometric readout circuit. It integrated the die onto the readout circuit board using wire bonding techniques, while the area of the readout circuit board is 5.5 cm $\times 4.0$ cm. The readout circuit board is equipped with a power supply, a readout circuit chip, and a multi-sensor. It is worth mentioning that this study designs the multi-sensor in a double-sided manner. The advantage of this design lies in the fact that both sides of the sensor can be utilized as a working electrode or reference electrode, providing convenience to users during measurement analysis. In addition, the magnesium oxide (MgO) multi-sensor is interconnected with the readout circuit board using slot type. This means the MgO multi-sensor can also be used as a disposable sensor. In this study, the multi-sensor system can measure hydrogen ions and Glu at the same time. The sensitivity of the two is 25.27 mV/pH and 16.78 mV/mM, respectively, and the linearity can reach 99.9 %.

Abstracts:DNA is emerging as a promising medium for storing huge volumes of data in a confined space that remains intact for thousands of years. Although this technique is very efficient, especially for multimedia data like images, there is a lack of efficient searching and retrieval technique. This paper addresses this issue and proposes a novel Content Based Image Retrieval (CBIR) technique to retrieve similar images from the generated DNA-based image feature vectors. The features are obtained by a novel encoding scheme that uses the three Most-Significant Bits from the images and converts them into a string of nucleotides that follow run length and GC constraints to form DNA planes stored in a DNA medium. The nucleotides in these planes are interpreted through three consecutive sequences forming codons. The codon-based features are then utilized to perform instance-based image retrieval. The DNA planes are further adapted and implemented on diverse deep learning architectures, including ResNet-50, VGG-16, VGG-19, and Inception V3, to facilitate classification-based image retrieval tasks. The system’s performance has been assessed using a range of datasets, encompassing coral, medical, and multi-label images. Experimental results demonstrate that the proposed approach achieves notable improvements when compared to existing state-of-the-art methods.

Abstracts:Recent experimental studies have shed light on the intriguing possibility that ion channels exhibit cooperative behaviour. However, a comprehensive understanding of such cooperativity remains elusive, primarily due to limitations in measuring separately the response of each channel. Rather, only the superimposed channel response can be observed, challenging existing data analysis methods. To address this gap, we propose IDC (Idealisation, Discretisation, and Cooperativity inference), a robust statistical data analysis methodology that requires only voltage-clamp current recordings of an ensemble of ion channels. The framework of IDC enables us to integrate recent advancements in idealisation techniques and coupled Markov models. Further, in the cooperativity inference phase of IDC, we introduce a minimum distance estimator and establish its statistical guarantee in the form of asymptotic consistency. We demonstrate the effectiveness and robustness of IDC through extensive simulation studies. As an application, we investigate gramicidin D channels. Our findings reveal that these channels act independently, even at varying applied voltages during voltage-clamp experiments. An implementation of IDC is available from GitLab.