Abstract

Accurate molecular subtyping of cancer is crucial for advancing personalized medicine. Although multiomics data contain valuable predictive information, effectively integrating them is challenging domics sources, providing a robust framework for molecular characterizations-modal biological interactions. We propose HyperCLSA (Hypergraph Contrastive Learning with Self-Attention), a novel deep learning framework for efficient multi-omics integration in breast cancer subtyping. HyperCLSA combines hypergraph-based sample encoding, supervised contrastive learning for latent space alignment, and multi-head self-attention for adaptive fusion of omics modalities. Evaluated on The Cancer Genome Atlas Breast Cancer dataset (TCGA-BRCA) forPAM50 subtype classification, HyperCLSA achieves a state-of-the-art accuracy of 90.1%, significantly outperforming established baselines. Our results demonstrate HyperCLSA's effectiveness in extracting complementary information across heterogeneous omics sources, providing a robust framework for molecular characterization of breast cancer.

Abstract

Remote labs are a groundbreaking development in the education industry, providing students with access to laboratory education anytime, anywhere. However, most remote labs are costly and difficult to scale, especially in developing countries. With this as a motivation, this paper proposes a new remote labs (RLabs) solution that includes two use case experiments: Vanishing Rod and Focal Length. The hardware experiments are built at a low-cost by retrofitting Internet of Things (IoT) components. They are also made portable by designing miniaturised and modular setups. The software architecture designed as part of the solution seamlessly supports the scalability of the experiments, offering compatibility with a wide range of hardware devices and IoT platforms. Additionally, it can live-stream remote experiments without needing dedicated server space for the stream. The software architecture also includes an automation suite that periodically checks the status of the experiments using computer vision (CV). The software architecture is further assessed for its latency and performance. RLabs is qualitatively evaluated against seven non-functional attributes - affordability, portability, scalability, compatibility, maintainability, usability, and universality. Finally, user feedback was collected from a group of students, and the scores indicate a positive response to the students' learning and the platform's usability.

Abstract

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ACCEPTED MANUSCRIPT
A label-free sensing of creatinine using radio frequency-driven lab-on-chip (loc) system
Andleeb Zahra1, Swarnim Sinha2, Alimpan Modak3, Imran Siddiqui4, Azeemuddin Syed5, Prabhakar Bhimalapuram6, Tapan K. Sau7, Pawan Kumar8 and Zia Abbas9

Accepted Manuscript online 2 August 2024 • © 2024 IOP Publishing Ltd
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DOI 10.1088/2631-8695/ad6ad5
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Article and author information
Abstract
This paper presents a promising avenue of Radio Frequency (RF) biosensors for sensitive and real-time monitoring of creatinine detection. Knowing creatinine levels in the human body is related to its possible association with renal, muscular, and thyroid dysfunction. The detection was performed using an Inter-Digitated Capacitor (IDC) made of copper (Cu) metal over an FR4 substrate. To demonstrate our methodology, we have chosen Phosphate Buffer (PB) as our solvent for making the creatinine solutions of different concentrations. Moreover, Assayed Chemistry Control (ACC), a reference control consisting of human serum-based solutions has been mixed with the different concentrations of creatinine in a ratio of 1:9 to spike the creatinine value in the ACC solution. The sensor has been designed using a High-Frequency Structure Simulator (HFSS) tool with an operating frequency of 2.53 GHz. Then the design is fabricated over the FR4 printed circuit board (PCB) and tested using a Vector Network Analyzer (VNA). However, the sensitive area of the IDC is introduced to grade 4 Whatman filter paper for the Sample Under Test (SUT) handling unit. The main advantage of using Whatman filter paper is that the uniform spreading of liquid reduces experimental error, and less volume is required for testing the sample. The principal idea implemented in the biosensor design is to track the shift in the operating frequency in the presence of different concentrations of creatinine mix in ACC solution with Phosphate Buffer (PB) solution as a reference.

Abstract

Abstract--Remote laboratories have become increasingly pop-
ular in the field of education as they allow users to conduct real
scientific experiments from any location worldwide and at any
time. However, most of the work in remote labs focuses on physics
and engineering experiments. Remote labs on chemistry experi-
ments are uncommon. This paper proposes a remote IoT-based
setup for acid-base titration that makes use of an interactive web
platform to control and observe the experiment in real-time. The
setup requires no human intervention during experimentation as
each step of the experiment is performed remotely by retrofitting
the traditional setup. Data was gathered by having a selected
group of students carry out the experiment multiple times. The
paper performs an analysis of the accuracy of the experimental
setup and presents user feedback on the experimental setup, the
experience and its impact on their learning. The paper compares
an alternate remote titration experiment with the proposed setup
based on affordability using a comprehensive cost analysis.

Abstract

Biomolecules play indispensable roles in all life processes, including disease development, so their accurate detection is critical to cell investigation, medical diagnosis, and treatment. Radio Frequency (RF) sensing has become a popular testing technique compared to traditional methods for biomolecule analysis, providing results in less time using less volume of samples. It allows rapid, sensitive, real-time measurements and label-free techniques. However, for taking the signals from biomolecules over the RF-based sensors we need to connect them with the Vector Network Analyzer (VNA) which is a bulky and costly device. To develop an RF sensing based a true lab-on-chip (LoC) device, the paper presents a fully integrated low-power less-area on-chip single port CMOS VNA designed in 65nm CMOS technology to detect the bio-molecule The proposed design works in a tunable frequency range of 0.5 GHz to 2.5 GHz, ensuring much higher precision. Using an RLC equivalent of an Interdigitated capacitor (IDC) sensor, a fully integrated architecture for detecting $S_{11}$ of the biomolecules has been introduced. A resistive bridge coupler is used for wideband operation with a directivity of 14.89dB up to 2.5 GHz. Also, a high-linearity LNA (low noise amplifier) is designed with a linearity of -13dB and a gain of 14dB. The LNA has a low NF of 3.21dB. The design occupies an active area of 0.01767mm^2 and consumes power of 41mW from a 1 V supply.

Abstract

Discovering new reaction pathways lies at the heart of drug discovery and chemical experimentation. A huge amount of drug reaction data lies in unannotated patent texts which are not machine readable. Reaction roles play an important role in analysing chemical pathways, and tracing chemicals through them, and while there is a vast body of chemical data available, the unavailability of reaction role annotated data is a blocker to effectively deploy deep learning methods for reaction discovery. This paper introduces a new dataset, WEAVE 2.0, obtained from chemical patents, along with full, manual, annotations of novel chemical reactions with reaction role information. We also provide baseline and state of the art models for chemical entity recognition from our raw dataset. Our dataset and associated models form the foundation of neural understanding of chemical reaction pathways via reaction roles.

Abstract

In this work, the role of gold nanoparticles in enhancing the sensitivity of Radio Frequency based biomolecule detection is assessed. A facile, efficient and rapid microstrip patch antenna based sensor, resonating at 61.747 GHz is utilized for the analysis. Gold nanoparticles are placed in the vicinity of biomolecules, and the extent of improvement is studied under various conditions. In particular, different morphological variations and arrangements of gold particles are investigated. The results indicate that, in general, the presence of gold nanoparticles contributes towards sensitivity improvement. However, the amount of enhancement strongly depends upon the morphological features and arrangements of gold particles and varies within 5-65% for the scenarios studied in this case.

Abstract

In this work, we explore the enhancement of sensitivity of sensing of biomolecules using Radio Frequency (RF) based sensing, by utilizing gold particles. In particular, we investigate the impact of various parameters of gold particles such as shape, size and arrangements on enhancing sensitivity. The work uses a Microstrip Patch Antenna designed to resonate at 96.84 GHz. The sensor is facile, rapid and efficient, and uses sample volume of 17.5 nL. The results indicate that the presence of gold particles helps in improving the performance of RF sensors in detecting biomolecules. However, the extent of improvement depends on the properties of gold particles and is in the range of 30-80% for the different cases studied in this work.

Abstract

We present three related schemes to generate novel molecules based on seed molecule; all the methods use as input the voxelized representation of the seed molecule to generate the SMILES representation of the novel molecule. At heart of these methods are two networks (a) a variational autoencoder that uses a Riemannian metric to encode the latent space of (hence named RHVAE) and can use pharmacophoric requirements as additional input for decoder and (b) attentive captioning network (a type of recurring neural network) that can efficiently focus, capture and use the 'content' of input to generate the SMILES output of novel molecules. We analyze the performance of the three proposed methods. We demonstrate the generation of meaningful new molecules, by generating shapes through an autoencoder network which can then be passed to our attentive captioning network, while requiring smaller data sets for training and retaining similar performance to existing state-of-art methods. Keywords. De Novo Drug Design; Chemical Modeling; Variational autoencoders, Attentive RNNs, Remannian

Abstract

The thermodynamics of the hydrophobic effect, as measured primarily through the temperature dependence of solubility, is reviewed, and then a class of models that incorporate the basic mechanism of hydrophobicity is described. These models predict a quantitative relation between the free energy of hydrophobic hydration and the strength of the solvent-mediated attraction between pairs of solute molecules. It is remarked that the free energy of attraction being just of the order of the thermal energy kT may be important for the effective operation of the hydrophobic effect in proteins. Deviations from pairwise additivity of hydrophobic forces are also briefly discussed.