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Neutrinos caught on camera: Testing the first prototype of a new elementary particle detector

Some innovations in physics come from entirely new technologies, others from fresh theoretical insights. Others still take shape by bringing together existing tools in new ways, working out how to combine them to outperform other solutions. The branch of particle physics that studies weakly interacting particles—such as neutrinos and some types of dark-matter candidates—could use innovative detection approaches: technological challenges in this research area quickly become practical as well as economic, as increases in detector volume and spatial resolution improve the sensitivity to the processes producing the particles of interest. Similarly, demanding targets on instrument capability apply to the calorimeters used in collider experiments.

Three-dimensional (3D) tracking of elementary particles in large-volume, dense materials is required in most particle physics experiments. In a scintillator, this is commonly achieved through fine segmentation of the material into many smaller active units, with each unit emitting light in the visible frequency range when a charged particle passes through it. Typically, the photons produced in every active unit are collected by optical fibers and carried outside of the scintillator to the photomultiplier tubes or silicon photomultipliers used for photon counting.

In the T2K neutrino-oscillation experiment in Japan, for example, one detector boasts about two tons of sensitive volume assembled from approximately two million cubes and 60,000 fibers. Over at CERN and the Paul Scherrer Institute, the LHCb and Mu3e experiments achieve sub-millimeter spatial resolution thanks to millions of thin scintillating optical fibers. With these figures, it’s clear that the scalability of this kind of scintillator material segmentation may turn into a bottleneck when larger volumes become necessary.

Phage therapy case reveals hidden antibodies can block treatment of drug-resistant infections

A new treatment for patients with life-threatening infectious diseases is being pioneered in Melbourne by researchers at The Alfred and Monash University. VICPhage, a clinical partnership between The Alfred and Monash, is one of the first in Australia to offer end-to-end capacity in phage therapy to treat some of the most challenging infections.

It involves injecting a patient with viruses called bacteriophages, or phages for short, to kill bacterial infections that have not responded to other treatments.

Professor Anton Peleg, Director of the Department of Infectious Diseases at The Alfred and Monash University and the Center to Impact AMR at Monash University, is co-lead of VICPhage and senior author of a new paper published in Nature Medicine.

How parasites exit host cells

After infecting host cells and reproducing, the parasite life cycle requires them to egress so that they can move to the next host. Past studies on the genes required for this process have been conducted but show conflicting results.

The methodology of past studies often involved opening the host cells during the screening process. Consequently, researchers were unable to reliably identify when mutations prevent parasites from egressing.

To avoid the same limitations, the team used an in vivo approach to screen for essential genes instead.

“Our in vivo screen, based on CRISPR, identified for the first time that the MIC11 gene is essential for host cell membrane permeabilization and parasite egress.” Explains the lead author.

Further tests demonstrated that deleting the MIC11 gene led the parasites to be unable to rupture the host cell membrane. By incapacitating parasites in this way, they could no longer exit the host cells, majorly disrupting the parasite life cycle.

“We also found evidence that MIC11 interacts with PLP1, providing further evidence of MIC11’s crucial role,” explains the senior author. “PLP1 is another parasite protein that was already known to be essential for egress.” ScienceMission sciencenewshighlights.


Bridging structure and function: artificial intelligence-based modelling of kidney proteins

Advances in artificial intelligence-driven algorithms and experimental technologies have revolutionized the field of protein modelling. This Review describes how these developments have provided unprecedented insights into the structure of key proteins within the kidney, improved understanding of the relationships between protein structure and stability, and enabled mechanistic interpretation of variants that underlie a variety of kidney pathologies.

Quantum Simulations Now Model Energy Loss With Greater Accuracy

A new computational technique accurately models decoherence’s impact on light–matter interactions within waveguide quantum electrodynamics. Matias Bundgaard-Nielsen and colleagues at the Technical University of Denmark present a matrix product state (MPS) method capable of modelling decoherence processes via density matrices, representing a key advancement over previous approaches. The method utilises collision quantum optics and efficiently incorporates various loss mechanisms, including emitter pure dephasing and off-chip radiative decay, to simulate complex waveguide QED systems such as two-level systems and multi-emitter setups. By modelling these realistic dissipation dynamics, the research offers vital insights into the behaviour of quantum systems and enables improved designs for quantum technologies.

A six-fold increase in simulated timescales for waveguide quantum electrodynamics has been achieved, surpassing limitations that previously restricted simulations to Markovian dynamics. This advancement results from employing a density matrix-based matrix product state (MPS) method, enabling accurate modelling of non-Markovian effects arising from time delays and memory effects within the system.

Traditionally, waveguide QED simulations have relied on the Markov approximation, which assumes that the system’s memory of past events is negligible. However, in many realistic scenarios—particularly those involving long propagation delays within the waveguide or slow emitter dynamics—this approximation breaks down. The method explicitly accounts for the system’s history, allowing the simulation of phenomena that depend on non-Markovian effects. In particular, it incorporates realistic decoherence mechanisms such as pure dephasing, which perturbs the phase coherence of quantum states, and off-chip radiative decay, where excitation energy is lost to the environment outside the waveguide.

Explainable Deep Reinforcement Learning for Anomaly Detection in IoT-Enabled Metaverse Healthcare: Toward Trustworthy Cyber Threat Intelligence

JUST PUBLISHED:Click here to read the latest free, Open Access article from Research.


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Nvidia becomes first company to cross $5 trillion in market value

Nvidia has achieved a historic milestone. The chipmaker is now the world’s most valuable listed company. Its market capitalization has surpassed five trillion dollars. This surge places Nvidia ahead of tech giants like Alphabet and Apple. The company’s success is driven by its crucial role in supplying GPUs for artificial intelligence models. Nvidia’s stock performance reflects its strong market position.

Frontiers: Year 2020 this gene therapy in mice shows promise for als gene therapy in humans

Gene therapy is an emerging and powerful therapeutic tool to deliver functional genetic material to cells in order to correct a defective gene. During the past decades, several studies have demonstrated the potential of AAV-based gene therapies for the treatment of neurodegenerative diseases. While some clinical studies have failed to demonstrate therapeutic efficacy, the use of AAV as a delivery tool has demonstrated to be safe. Here, we discuss the past, current and future perspectives of gene therapies for neurodegenerative diseases. We also discuss the current advances on the newly emerging RNAi-based gene therapies which has been widely studied in preclinical model and recently also made it to the clinic.

Gene therapy is an emerging therapeutic tool used to deliver functional genetic material to cells in order to correct a defective gene. By delivering a copy of a therapeutic gene to affected cells, the product encoded by that gene [i.e., its messenger RNA (mRNA) and/or proteins] will be continuously synthesized within the cell, utilizing the cell’s own transcriptional and translational machinery (Porada et al., 2013). The main advantage of this technology is that it offers a potentially life-long therapeutic effect without the need for repeated administration. Gene therapy can be used to correct defective genes by introducing a functional copy of the gene, by silencing a mutant allele using RNA interference (RNAi), by introducing a disease-modifying gene, or by using gene-editing technology (Grimm and Kay, 2007; Dow et al., 2015; Saraiva et al., 2016).

Gene therapy vectors can be either viral or non-viral. Different physical and chemical systems can be applied to deliver therapeutic genes to cells without the need of a viral vector. Non-viral vectors have no size limitation for the therapeutic gene, generally have a low immunogenicity risk, and can be produced at relatively low costs (Nayerossadat et al., 2012). However, due to the fact that high therapeutic doses are required when using non-viral technologies, and the resulting gene expression is generally transient, most gene therapies now rely on viral vectors. Numerous viral vector types have been tested in clinic, including vaccinia, measles, vesicular stomatitis virus (VSV), polio, reovirus, adenovirus, lentivirus, γ-retrovirus, herpes simplex virus (HSV) and adeno-associated virus (AAV) (Lundstrom, 2018).

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