An off-chip platform for on-demand, single-target encapsulation for ultrasensitive biomarker detection.

Closed-channel microfluidic systems offer versatile on-chip capabilities for bioanalysis but often face complex fabrication and operational challenges. In contrast, free-boundary off-chip microfluidic platforms are relatively simple to fabricate and operate but lack the ability to perform complex tasks such as on-demand single-target sorting and encapsulation. To address these challenges, we develop an off-chip platform powered by a fluorescent-activated mechanical droplet sorting and production (FAM-DSP) system.

Molecular precursors for the electrodeposition of 2D-layered metal chalcogenides.

Two-dimensional transition metal dichalcogenides (TMDCs) are highly anisotropic, layered semiconductors, with the general formula ME (M = metal, E = sulfur, selenium or tellurium). Much current research in this field focusses on TMDCs for catalysis and energy applications; they are also attracting great interest for next-generation transistor and optoelectronic devices. The latter high-tech applications place stringent requirements on the stoichiometry, crystallinity, morphology and electronic properties of monolayer and few-layer materials.

Cohort profile: creation of the SAIL MELD-B e-cohort (SMC) and SAIL MELD-B children and young adult e-cohort (SMYC) to investigate the lived experience of the 'burdensomeness' of multimorbidity.

Purpose: We have established the SAIL MELD-B electronic cohort (e-cohort SMC) and the SAIL MELD-B children and Young adults e-cohort (SMYC) as a part of the Multidisciplinary Ecosystem to study Lifecourse Determinants and Prevention of Early-onset Burdensome Multimorbidity (MELD-B) project. Each cohort has been created to investigate and develop a deeper understanding of the lived experience of the 'burdensomeness' of multimorbidity by identifying new clusters of burdensomeness concepts, exploring early life risk factors of multimorbidity and modelling hypothetical prevention scenarios.

Participants: The SMC and SMYC are longitudinal e-cohorts created from routinely collected individual-level population-scale anonymised data sources available within the Secure Anonymised Information Linkage (SAIL) Databank.

Continual deep reinforcement learning with task-agnostic policy distillation.

Central to the development of universal learning systems is the ability to solve multiple tasks without retraining from scratch when new data arrives. This is crucial because each task requires significant training time. Addressing the problem of continual learning necessitates various methods due to the complexity of the problem space.

Forward and Backward Private Searchable Encryption for Cloud-Assisted Industrial IoT.

In the cloud-assisted industrial Internet of Things (IIoT), since the cloud server is not always trusted, the leakage of data privacy becomes a critical problem. Dynamic symmetric searchable encryption (DSSE) allows for the secure retrieval of outsourced data stored on cloud servers while ensuring data privacy. Forward privacy and backward privacy are necessary security requirements for DSSE.

An Efficient Pairing-Free Ciphertext-Policy Attribute-Based Encryption Scheme for Internet of Things.

The Internet of Things (IoT) is a heterogeneous network composed of numerous dynamically connected devices. While it brings convenience, the IoT also faces serious challenges in data security. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising cryptography method that supports fine-grained access control, offering a solution to the IoT's security issues.

Few-shot learning for inference in medical imaging with subspace feature representations.

Unlike in the field of visual scene recognition, where tremendous advances have taken place due to the availability of very large datasets to train deep neural networks, inference from medical images is often hampered by the fact that only small amounts of data may be available. When working with very small dataset problems, of the order of a few hundred items of data, the power of deep learning may still be exploited by using a pre-trained model as a feature extractor and carrying out classic pattern recognition techniques in this feature space, the so-called few-shot learning problem. However, medical images are highly complex and variable, making it difficult for few-shot learning to fully capture and model these features.

Toward a Procedure for the Template Free Growth of Te Nanowires Across an Insulator by Electrodeposition.

In this work, we present a method for direct, site-selective growth of tellurium nanowires by electrochemical deposition. The Te nanowires were grown laterally between two specially designed nanoband electrodes across a gap, and over a dielectric material, forming a lateral device structure directly. The resulting wires are crystalline and phase pure, as evidenced by Raman spectroscopy, EDS (energy dispersive X-ray spectroscopy), and ADF-STEM (annular dark field scanning transmission electron microscopy).

Estimating cognitive workload using a commercial in-ear EEG headset.

This study investigated the potential of estimating various mental workload levels during two different tasks using a commercial in-ear electroencephalography (EEG) system, the IDUN 'Guardian'.Participants performed versions of two classical workload tasks: an n-back task and a mental arithmetic task. Both in-ear and conventional EEG data were simultaneously collected during these tasks.

A combined electro-optical deformability micro-cytometer.

We have developed a deformability cytometer that simultaneously measures the optical and electrical shape change of single cells in a viscoelastic shear flow. The optical deformability of single cells is measured using a low-cost CMOS camera illuminated with a high-power LED triggered from an electrical impedance signal created by a passing cell. Simultaneously the electrical deformability of the cell is determined using electrode arrays that measure shape changes along different axes.

Deep learning-based spike sorting: a survey.

Deep learning is increasingly permeating neuroscience, leading to a rise in signal-processing applications for extracellular recordings. These signals capture the activity of small neuronal populations, necessitating 'spike sorting' to assign action potentials (spikes) to their underlying neurons. With the rise in publications delving into new methodologies and techniques for deep learning-based spike sorting, it is crucial to synthesise these findings critically.

Observation and enhancement through alkali metal doping of p-type conductivity in the layered oxyselenides SrZnOCuSe and BaZn O CuSe.

The optoelectronic properties of two layered copper oxyselenide compounds, with nominal composition SrZnOCuSe and BaZnOCuSe, have been investigated to determine their suitability as p-type conductors. The structure, band gaps and electrical conductivity of pristine and alkali-metal-doped samples have been determined. We find that the strontium-containing compound, SrZnOCuSe, adopts the expected tetragonal structure with 4/ symmetry, and has a band gap of 2.

Looping Flexible Fluoropolymer Microcapillary Film Extends Analysis Times for Vertical Microfluidic Blood Testing.

The microfluidic measurement of capillary flow can be used to evaluate the response of biological samples to stimulation, where distance and velocity are altered. Melt-extruded multi-bored microfluidic capillaries allow for high-throughput testing with low device cost, but simple devices may limit control over sample flow when compared to the more complex "lab-on-a-chip" devices produced using advanced microfluidic fabrication methods. Previously, we measured the dynamics of global haemostasis stimulated by thrombin by dipping straight vertical microcapillaries into blood, but only the most rapid response could be monitored, as flow slowed significantly within 30 s.

Advancements in synthetic CT generation from MRI: A review of techniques, and trends in radiation therapy planning.

Background: Magnetic resonance imaging (MRI) and Computed tomography (CT) are crucial imaging techniques in both diagnostic imaging and radiation therapy. MRI provides excellent soft tissue contrast but lacks the direct electron density data needed to calculate dosage. CT, on the other hand, remains the gold standard due to its accurate electron density information in radiation therapy planning (RTP) but it exposes patients to ionizing radiation.

Nonclassical Spin-Multiplexing Metasurfaces Enabled Multifunctional Meta-Scope.

Dielectric metasurfaces have emerged as attractive devices for advanced imaging systems because of their high efficiency, ability of wavefront manipulation, and lightweight. The classical spin-multiplexing metasurfaces can only provide two orthogonal circular polarization channels and require high phase contrast which limits their applications. Here, metasurfaces with arbitrary three independent channels are demonstrated by proposing a nonclassical spin-multiplexing approach exploring the low refractive index meta-atoms.

Hybrid mHealth care: Patient perspectives of blended treatments for psychosis. A systematic review.

Background: mHealth interventions use mobile and wireless technologies to deliver aspects of healthcare, and have been extensively employed in mental health research, showcasing their potential to address the significant treatment gap. While numerous studies underscore the advantages and functionalities of mHealth, challenges persist regarding patient uptake and sustained engagement among individuals with psychosis spectrum disorder. This review aims to explore individual-level barriers and facilitators to engagement with hybrid digital systems, which involves the integration of digital tools alongside in-person care.

Remote Assessment of Eczema Severity via AI-powered Skin Image Analytics: A Systematic Review.

Various studies have been published on the remote assessment of eczema severity from digital camera images. Successful deployment of an accurate and robust AI-powered tool for such purposes can aid the formulation of eczema treatment plans and assist in patient monitoring. This review aims to provide an overview of the quality of published studies on this topic and to identify challenges and suggestions to improve the robustness and reliability of existing tools.

Discrimination of Microplastics and Phytoplankton Using Impedance Cytometry.

Both microplastics and phytoplankton are found together in the ocean as suspended microparticles. There is a need for deployable technologies that can identify, size, and count these particles at high throughput to monitor plankton community structure and microplastic pollution levels. In situ analysis is particularly desirable as it avoids the problems associated with sample storage, processing, and degradation.

Neurovascular coupling methods in healthy individuals using transcranial doppler ultrasonography: A systematic review and consensus agreement.

Neurovascular coupling (NVC) is the perturbation of cerebral blood flow (CBF) to meet varying metabolic demands induced by various levels of neural activity. NVC may be assessed by Transcranial Doppler ultrasonography (TCD), using task activation protocols, but with significant methodological heterogeneity between studies, hindering cross-study comparisons. Therefore, this review aimed to summarise and compare available methods for TCD-based healthy NVC assessments.

Insights from explainable AI in oesophageal cancer team decisions.

Background: Clinician-led quality control into oncological decision-making is crucial for optimising patient care. Explainable artificial intelligence (XAI) techniques provide data-driven approaches to unravel how clinical variables influence this decision-making. We applied global XAI techniques to examine the impact of key clinical decision-drivers when mapped by a machine learning (ML) model, on the likelihood of receiving different oesophageal cancer (OC) treatment modalities by the multidisciplinary team (MDT).