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Evaluation of the Digital Ventilated Cage® system for circadian phenotyping

Abstract The study of circadian rhythms has been critically dependent upon analysing mouse home cage activity, typically employing wheel running activity under different lighting conditions. Here we assess a novel method, the Digital Ventilated Cage (DVC®, Tecniplast SpA, Italy), for circadian phenotyping. Based upon capacitive sensors mounted under black individually ventilated cages with inbuilt LED lighting, each cage becomes an independent light-controlled chamber. Home cage activity in C57BL/6J mice was recorded under a range of lighting conditions, along with circadian clock-deficient cryptochrome-deficient mice (Cry1 −/− , Cry2 −/− double knockout). C57BL/6J mice exhibited a 24 h period under light/dark conditions, with a free-running period of 23.5 h under constant dark, and period lengthening under constant light. Animals displayed expected phase shifting responses to jet-lag and nocturnal light pulses. Sex differences in circadian parameters and phase shifting responses were also observed. Cryptochrome-deficient mice showed subtle changes in activity under light/dark conditions and were arrhythmic under constant dark, as expected. Our results show the suitability of the DVC system for circadian behavioural screens, accurately detecting circadian period, circadian disruption, phase shifts and mice with clock defects. We provide an evaluation of the strengths and limitations of this method, highlighting how the use of the DVC for studying circadian rhythms depends upon the research requirements of the end user.

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TERT-TP53 mutations: a novel biomarker pair for hepatocellular carcinoma recurrence and prognosis

Exploring imitation of within hand prehensile object manipulation using fMRI and graph theory analysis

Uncovering mercury accumulation and the potential for bacterial bioremediation in response to contamination in the Singalila National Park

Abstract Several recent investigations into montane regions have reported on excess mercury accumulation in high-altitude forest ecosystems. This study explored the Singalila National Park, located on the Singalila ridge of the Eastern Himalayas, revealing substantial mercury contamination. Particular focus was on Sandakphu (3636 m), the highest peak in West Bengal, India. It harboured 6.77 ± 0.01 mg/kg of total mercury in its topsoil. Further evidence was provided by accumulation in leaves (0.040 ± 0.01 mg/kg), and roots (0.150 ± 0.008 mg/kg) of local vegetation, litterfall (0.234 ± 0.019 mg/kg), mosses (0.367 ± 0.043 mg/kg), surface water from local lakes and waterbodies (0.010 ± 0.005 mg/l), fresh snow (0.014 ± 0.004 mg/l), and sleet (0.019 ± 0.009 mg/l). Samples from other points of varying elevation in the park also demonstrated contamination. The soil displayed a range of 0.068–5.28 mg/kg, while the mean concentration in leaves was 0.153 ± 0.105 mg/kg, roots was 0.106 ± 0.054 mg/kg, and leaf litter was 0.240 ± 0.112 mg/kg. Additionally, the microbial consortia isolated from the contaminated soil displayed a high tolerance to mercuric chloride, presumably gained through repeated and consistent exposure. Four high tolerance bacterial strains, MTS2C, MTS3A, MTS4B and MTS6A, were further characterized for potential use in bioremediation strategies. Their mercury removal capacities were determined to be 82.35%, 75.21%, 61.95%, and 37.47%, respectively. Overall, the findings presented provide evidence for a highly contaminated environment in the Singalila National Park, that poses significant ecological risk to the flora, fauna and local inhabitants of this biodiversity hotspot. This research also highlights the need for further exploration and monitoring of the Eastern Himalayas for its role as a sink for atmospheric mercury.

Identification of heat tolerant lentil genotypes through stress tolerance indices

Ion beam induced secondary electron tomography of acrylonitrile-styrene-acrylate/polycarbonate polymer blends for fused filament fabrication and injection moulding

Sequential testing with Xpert MTB/RIF assay for diagnosis of tuberculous meningitis in Maharaj Nakorn Chiang Mai University Hospital

Statistical-based detection of pilot contamination attack for NOMA in 5G networks

Abstract Fifth-generation (5G) communication technologies, such as millimeter wave communication, massive multiple-input-multiple-output and non-orthogonal-multiple-access (NOMA) are playing a pivotal role in promoting the modern applications of the Internet-of-Things. Using non-orthogonal resource allocation, NOMA can increase spectrum efficiency and achieve wide connectivity with low transmission delay and signaling cost. Despite the high potential of NOMA in 5G communications, NOMA is susceptible to a pilot contamination attack (PCA), in which an attacker resents the same pilot signals as authorized users. Currently, using the available detection methods in NOMA gives high false positive probability since the time-division-duplex or orthogonal resource block can be allocated by many authorized user. Since the pilot contamination attack changes the signal reception at the legitimate receiver, this work introduces a novel detection scheme for identifying Pilot Contamination attack (PCA) that statistically investigates the asymmetry in received signal power levels. The main idea of the proposed detection scheme is to use various statistical measurements for normal traffic attributes (CSI) as a reference profile. Then, compute the Mahalanobis distance between the reference profile and CSI for the incoming connection and use the probability of the uniform distribution to make the final detection decision. The performance of the proposed detection technique in terms of its detection rate and false positive probabilities has been evaluated through extensive simulation. The simulation results show that the proposed scheme succeeded in detecting the pilot contamination attack with a detection rate of up to 98% and a precision reached 97.88%.

Optimization of the intron sequences combined with the CMV promoter increases recombinant protein expression in CHO cells

Optimized sequential model for superior classification of plant disease

Design and manufacture of CubeSat-type nanosatellite thermal subsystem

East Asian expatriate football players and national team success: Chinese, Japanese, and South Korean players in Europe (2000–2024)

Construction of a rodent neural network-skeletal muscle assembloid that simulate the postnatal development of spinal cord motor neuronal network

Wanting and liking of Facebook functions and their correlation to problematic use

Re-locative guided search optimized self-sparse attention enabled deep learning decoder for quantum error correction

Abstract Heavy hexagonal coding is a type of quantum error-correcting coding in which the edges and vertices of a low-degree graph are assigned auxiliary and physical qubits. While many topological code decoders have been presented, it is still difficult to construct the optimal decoder due to leakage errors and qubit collision. Therefore, this research proposes a Re-locative Guided Search optimized self-sparse attention-enabled convolutional Neural Network with Long Short-Term Memory (RlGS2-DCNTM) for performing effective error correction in quantum codes. The integration of the self-sparse attention mechanism in the proposed model increases the feature learning ability of the model to selectively focus on informative regions of the input codes. In addition, the use of statistical features computes the statistical properties of the input, thus aiding the model to perform complex tasks effectively. For model tuning, this research utilizes the RIGS nature-inspired algorithm that mimics the re-locative, foraging, and hunting strategies, which avoids local optima problems and improves the convergence speed of the RlGS2-DCNTM for Quantum error correction. When compared with other methods, the proposed RlGS2-DCNTM algorithm offers superior efficacy with a Minimum Mean Squared Error (MSE) of 4.26, Root Mean Squared Error of 2.06, Mean Absolute Error of 1.14 and a maximum correlation and $$R^2$$ of 0.96 and 0.92 respectively, which shows that the proposed model is highly suitable for real-time error decoding tasks.

Comparison of mNGS with conventional methods for diagnosis of cryptococcal meningitis: a retrospective study

Considerations for establishment of a private virtual hospital identified using an implementation science approach

Abstract Virtual hospitals are rapidly being implemented internationally. Research has predominantly focused on clinical outcomes not implementation. We aimed to identify pre-implementation determinants to enable health services to tailor virtual hospital models, increasing likelihood of suitability, acceptability, uptake, clinical effectiveness, and sustainability. We aimed to inform the design and implementation of a private Australian virtual hospital by identifying contextual barriers, enablers, and considerations. We conducted a qualitative pre-implementation determinant study using snowball sampling and semi-structured interviews ( n  = 37) between February and July 2023 with consumers/carers/both ( n  = 11), clinicians (doctors/allied health/nursing/personal carers), hospital, health service and aged care leadership ( n  = 22), and public health stakeholders (n=4). Deductive framework analysis based on the PERCS implementation science framework was used. The following key determinants were identified: Enablers – strong executive leadership support; enthusiasm for expanding rural and remote services; need for a clear vision; strong tension for change; commitment to high-quality healthcare. Major barrier: restrictive funding models that stifle opportunities for innovation. Other barriers: technological limitations; communication challenges; workforce issues; clinicians’ opinions varied on safety and suitability of virtual healthcare. This implementation science approach enabled identification of a broad set of determinants not previously reported, relevant locally and for an international audience. Evaluation of implementation outcomes is necessary.

Effects of thermal aging on the performance of ordinary and novel superhydrophobic and oleophobic ultra-fine dry powder extinguishing agent

Leveraging survival analysis and machine learning for accurate prediction of breast cancer recurrence and metastasis

Abstract Breast cancer, with its high incidence and mortality globally, necessitates early prediction of local and distant recurrence to improve treatment outcomes. This study develops and validates predictive models for breast cancer recurrence and metastasis using Recurrence-Free Survival Analysis and machine learning techniques. We merged datasets from the Molecular Taxonomy of Breast Cancer International Consortium, Memorial Sloan Kettering Cancer Center, Duke University, and the SEER program, creating a comprehensive dataset of 272, 252 rows and 23 columns. Our methodology utilized three predictive strategies: assessing recurrence risk, differentiating local from distant recurrences, and identifying potential metastatic sites. Key prognostic factors were identified through survival analysis. LightGBM, XGBoost, and Random Forest models were employed and validated against data from the Baheya Foundation. The models demonstrated strong performance; the survival analysis achieved a C-index of 0.837. The LightGBM model reached an AUC of 92% in predicting recurrences, while XGBoost and Random Forest models distinguished recurrence types with up to 86% accuracy, and they effectively differentiated between bone metastasis and all other locations combined (brain, liver, and lungs). This study highlights the significant potential of machine learning in advancing breast cancer management and sets a new benchmark for predictive analytics. Future research will integrate genetic data to further enhance these models.