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Abstract Superconducting microstrip single photon detectors (SMSPDs) are increasingly attracting the interest of the scientific community as a new platform for large area detectors with unprecedented advantaged in terms of fabrication. However, while their operativity at the telecommunication wavelength was achieved, working beyond 1.55 µm is challenging. Here, we experimentally demonstrate single-photon operation of NbRe microstrips at wavelengths of 1.55 and 2 µm. The devices are structured as pairs of parallel microstrips with widths ranging from 1.4 to 2.2 μm and lengths from 5 to 10 μm. This innovative design may assure large sensitive areas, without affecting the kinetic inductance, namely the time performance of the detectors. The results are discussed in the framework of the hot-spot two-temperature model.

Summary: Vesicle trafficking and the establishment of apicobasal polarity are essential processes in epithelial morphogenesis. UNC45A deficiency has been reported in a multi-organ syndrome presenting with severe diarrhea associated with enterocyte polarity defects. Myosin 1b, an actin motor able to bind membranes, regulates membrane shaping and vesicle trafficking. Here, we show that MYO1B is part of the UNC45A interactome. In the absence of UNC45A, myosin 1b is degraded and forms aggregates when proteasome activity is inhibited. In 3D Caco-2 cells, lumen formation is impaired in the absence of myosin 1b, associated with spindle orientation defects, Golgi apparatus fragmentation, and trafficking impairment. In zebrafish larvae, loss of myo1b results in intestinal bulb epithelium folding defects associated with terminal web disorganization and vesicle accumulation, reminiscent of villous atrophy. In conclusion, we show that myosin 1b plays an unexpected role in the development of the intestinal epithelium downstream of UNC45A, establishing its contribution in the gut defects reported in UNC45A patients.

Permafrost-agroecosystems include all cultivation and pastoral activities in areas underlain by permafrost. These systems support local livelihoods and food production and are rarely considered in global agricultural studies but may become more relevant as climate change is increasing opportunities for food production in high latitude and mountainous areas. The exact locations and amount of agricultural production in areas containing permafrost are currently unknown, therefore we provide an overview of countries where both permafrost and agricultural activities are present. We highlight the socioecological diversity and complexities of permafrost-agroecosystems through seven case studies: (1) crop cultivation in Alaska, USA; (2) Indigenous food systems and crop cultivation in the Northwest Territories, Canada; (3) horse and cattle husbandry and Indigenous hay production in the Sakha Republic, Russia; (4) mobile pastoralism and husbandry in Mongolia; (5) yak pastoralism in the Central Himalaya, Nepal; (6) berry picking and reindeer herding in northern Fennoscandia; and (7) reindeer herding in northwest Russia. We discuss regional knowledge gaps associated with permafrost and make recommendations to policy makers and land users for adapting to changing permafrost environments. A better understanding of permafrost-agroecosystems is needed to help sustainably manage and develop these systems considering rapidly changing climate, environments, economies, and industries.

IntroductionEwing Sarcoma (EWS) has been reported in seven children with Down syndrome (DS). To date, a detailed assessment of this solid tumour in DS patients is yet to be made.MethodsHere, we characterise a chemo-resistant mediastinal EWS in a 2-year-old DS child, the youngest ever reported case, by exploiting sequencing approaches.ResultsThe tumour showed a neuroectodermal development driven by the EWSR1-FLI1 fusion. The inherited myeloperoxidase deficiency of the patient caused failure of neutrophil-mediated cell death and promoted genomic instability.DiscussionIn this context, the tumour underwent genome-wide near haploidisation resulting in a massive overexpression of pro-inflammatory cytokines. Recruitment of defective neutrophils fostered rapid evolution of this EWS.

Abstract Magnetic Josephson junctions (MJJs) have emerged as a prominent playground to explore the interplay between superconductivity and ferromagnetism. A series of fascinating experiments have revealed striking phenomena at the superconductor/ferromagnet (S/F) interface, pointing to tunable phase transitions and to the generation of unconventional spin-triplet correlations. Here, we show that the Josephson effect, being sensitive to phase space variation on the nanoscale, allows a direct observation of the spin polarization of the S/F interface. By measuring the temperature dependence of the Josephson magnetic field patterns of tunnel MJJs with strong and thin F-layer, we demonstrate an induced nanoscale spin order in S along the superconducting coherence length at S/F interface, i.e., the inverse proximity effect, with the first evidence of full spin screening at very low temperatures, as expected by the theory. A comprehensive phase diagram for spin nanoscale ordering regimes at S/F interfaces in MJJs has been derived in terms of the magnetic moment induced in the S-layer. Our findings contribute to drive the design and the tailoring of S/F interfaces also in view of potential applications in quantum computing.

Cardioneuroablation (CNA) is a novel interventional procedure for the treatment of recurrent vasovagal syncope (VVS) and advanced atrioventricular block secondary to hyperactivation of vagal tone in young patients. By damaging the cardiac parasympathetic ganglia, CNA seems to be able to mitigate and/or abolish the excessive vagal activity and improve patients’ outcome. This review is intended to give a detailed and comprehensive overview of the current evidences regarding (1) the clinical applications of CNA (2) the identification of ablation targets and procedural endpoints (3) the medium-long term effect of the procedure and its future perspectives. However, clinical data are still limited, and expert consensus or recommendations in the guidelines regarding this technique are still lacking.

Interest in tracking and monitoring animals in livestock farming using wearable sensors has been steadily increasing. The use of these devices is particularly crucial in extensive livestock systems where direct interaction between animals and farmers is infrequent, necessitating strenuous efforts in long-distance herd monitoring. Internet of Things (IoT) technologies offer a promising solution to address the challenges posed by vast distances, enabling real-time and remote animal monitoring. In this study, an experimental trial was conducted using a custom-designed device, located in a Polyvinyl Chloride (PVC) case, specifically tailored to fit onto a collar. This case incorporates an integrated SigFox communication system, i.e., a Low Power Global Positioning System (LP-GPS) omnidirectional system, and a power supply. The trial took place in two grazing areas located in different territorial zones, designated as Case Study I and II. A LP-GPS collar was provided for each selected animal, and the data were recorded at 20-min intervals for Case Study I and 10-min intervals for Case Study II. The acquired data were then imported and analysed using Geographical Information Systems (GIS) software. Information was collected through a purpose-built web application (AppWeb). The objective was to analyze those territorial areas mostly occupied by animals within the two considered grazing areas by developing a GIS-based methodology. Specifically, customized algorithms such as Heatmap and Kernel Density Estimation (KDE) plugins were employed to conduct spatial analyses. The maps obtained through Heatmap plugin, showed the temporal-spatial distribution of animals within their grazing areas. Additionally, the KDE tool was used to classify preferred territorial areas, generating tailored charts for each animal in the sample. The individual Core Areas, determined through KDE evaluation for each animal, were overlaid to provide a comprehensive analysis of the monitored animals.The results achieved applying the GIS-based methodology facilitated the identification of animal positions and could be adopted to provide insights into feeding behavior and soil erosion, thereby aiding in the prevention of environmental issues.

Detectors capable of resolving the number of photons are essential in many applications, ranging from classic photonics to quantum optics and quantum communication. In particular, photon-number-resolving detectors based on arrays of superconducting nanostrips can offer a high detection efficiency, a low dark count rate, and a recovery time of a few nanoseconds. In this work, we use a detector of this kind for the unbiased generation of random numbers by following two different methods based on the detection of photons. In the former, we exploit the property that the light is equally distributed on each strip of the entire detector, whereas in the latter, we exploit the fact that, for a high average number of photons, the parity of the Poisson distribution of the number of photons emitted by the laser tends to be zero. In addition, since these two methods are independent, it is possible to use them at the same time.

BackgroundEarly-onset colorectal cancer (CRC), defined as diagnosis before age 50, has increased in recent decades. Although more often diagnosed at advanced stage, associations with other histological and molecular markers that impact prognosis and treatment remain to be clarified. We conducted a systematic review and meta-analysis concerning the prevalence of prognostic and predictive tumor markers for early- vs. late-onset CRC, including oncogene mutations, microsatellite instability (MSI), and emerging markers including immune cells and the consensus molecular subtypes.MethodsWe systematically searched PubMed for original research articles published between April 2013–January 2024. Included studies compared the prevalence of tumor markers in early- vs. late-onset CRC. A meta-analysis was completed and summary odds ratios (ORs) with 95% confidence intervals (CIs) were obtained from a random effects model via inverse variance weighting. A sensitivity analysis was completed to restrict the meta-analysis to studies that excluded individuals with Lynch syndrome, a hereditary condition that influences the distribution of tumor markers for early-onset CRC.ResultsIn total, 149 articles were identified. Tumors from early-onset CRC are less likely to include mutations in KRAS (OR, 95% CI: 0.91, 0.85-0.98), BRAF (0.63, 0.51-0.78), APC (0.70, 0.58-0.84), and NRAS (0.88, 0.78-1.00) but more likely to include mutations in PTEN (1.68, 1.04-2.73) and TP53 (1.34, 1.24-1.45). After limiting to studies that excluded Lynch syndrome, the associations between early-onset CRC and BRAF (0.77, 0.64-0.92) and APC mutation (0.81, 0.67-0.97) were attenuated, while an inverse association with PIK3CA mutation was also observed (0.88, 0.78-0.99). Early-onset tumors are less likely to develop along the CpG Island Methylator Phenotype pathway (0.24, 0.10-0.57), but more likely to possess adverse histological features including high tumor grade (1.20, 1.15-1.25), and mucinous (1.22, 1.16-1.27) or signet ring histology (2.32, 2.08-2.57). A positive association with MSI status (1.31, 1.11-1.56) was also identified. Associations with immune markers and the consensus molecular subtypes are inconsistent.DiscussionA lower prevalence of mutations in KRAS and BRAF is consistent with extended survival and superior response to targeted therapies for metastatic disease. Conversely, early-onset CRC is associated with aggressive histological subtypes and TP53 and PTEN mutations, which may serve as therapeutic targets.

Abstract Here we present a deep learning-based image analysis platform (DLAP), tailored to autonomously quantify cell numbers, and fluorescence signals within cellular compartments, derived from RNAscope or immunohistochemistry. We utilised DLAP to analyse subtypes of tyrosine hydroxylase (TH)-positive dopaminergic midbrain neurons in mouse and human brain-sections. These neurons modulate complex behaviour, and are differentially affected in Parkinson’s and other diseases. DLAP allows the analysis of large cell numbers, and facilitates the identification of small cellular subpopulations. Using DLAP, we identified a small subpopulation of TH-positive neurons (~5%), mainly located in the very lateral Substantia nigra (SN), that was immunofluorescence-negative for the plasmalemmal dopamine transporter (DAT), with ~40% smaller cell bodies. These neurons were negative for aldehyde dehydrogenase 1A1, with a lower co-expression rate for dopamine-D2-autoreceptors, but a ~7-fold higher likelihood of calbindin-d28k co-expression (~70%). These results have important implications, as DAT is crucial for dopamine signalling, and is commonly used as a marker for dopaminergic SN neurons.

The transition from a linear to circular economy is driving a growing emphasis on utilizing bio-based materials for bioenergy and construction purposes. This literature review seeks to offer a thorough bibliometric and critical analysis of bio-based building materials, particularly those that incorporate agricultural residues. A selection of pertinent articles was analyzed using text-mining techniques, revealing a substantial increase in research output on this topic, from 74 publications in 2000 to 1238 in 2023. Key areas such as sustainability, sources of bio-based materials, building applications, design and analysis, material properties, and processes have been extensively examined. The cluster “Sustainability” was the most frequently discussed topic, comprising 28.85% of the content, closely followed by “Building Materials and Techniques” at 28.07%. Given the critical role of life cycle assessment (LCA) in sustainability, an additional analysis was conducted focusing on existing research addressing this subject. The findings of this study are aimed at advancing the incorporation of waste-derived bio-based materials into a circular economy framework, thereby supporting the broader objectives of sustainability and resource efficiency.

Craniosynostoses (CRS) are caused by the premature fusion of one or more cranial sutures, with isolated nonsyndromic CRS accounting for most of the clinical manifestations. Such premature suture fusion impacts both skull and brain morphology and involves regions far beyond the immediate area of fusion. The combined use of different neuroimaging tools allows for an accurate depiction of the most prominent clinical–radiological features in nonsyndromic CRS but can also contribute to a deeper investigation of more subtle alterations in the underlying nervous tissue organization that may impact normal brain development. This review paper aims to provide a comprehensive framework for a better understanding of the present and future potential applications of neuroimaging techniques for evaluating nonsyndromic CRS, highlighting strategies for optimizing their use in clinical practice and offering an overview of the most relevant technological advancements in terms of diagnostic performance, radiation exposure, and cost-effectiveness.

Long-term patient monitoring is required for detection of episodes of atrial fibrillation, one of the most widespread cardiac pathologies. Today, the most used non-invasive technique is Holter electrocardiographic (ECG) monitoring, which can often prove ineffective because of the short duration of recordings (e.g., one day). Other techniques such as photo-plethysmography are adopted by smartwatches for much longer duration monitoring, but this has the disadvantage of offering only intermittent measurements. This study proposes an Internet of Things (IoT) sensor that can provide a very long period of continuous monitoring. The sensor consists of an ECG-integrated Analog Front End (MAX30003), a microcontroller (STM32F401RE), and an IoT narrowband module (STEVAL-STMODLTE). The instantaneous heart rate is extracted from the ECG recording in real time. At intervals of two minutes, the sequence of inter-beat intervals is transmitted to an IoT cloud platform (ThingSpeak). Settled atrial fibrillation event recognition software runs on the cloud and generates alerts when it recognizes such arrhythmia. Performances of the proposed sensor were evaluated by generating analog ECG signals from a public dataset of ECG signals with atrial fibrillation episodes, the MIT-BIH Atrial Fibrillation Database, each recording lasting approximately 10 h. Software implementing the Lorentz algorithm, one of the best detectors of atrial fibrillation, was implemented on the cloud platform. The accuracy, sensitivity, and specificity in recognizing atrial fibrillation episodes of the proposed system was calculated by comparison with a cardiologist’s reference data. Across all patients, the proposed method achieved an accuracy of 0.88, a sensitivity 0.71, and a specificity 0.99. The results obtained suggest that the developed system can continuously record and transmit heart rhythms effectively and efficiently and, in addition, offers considerable performance in recognizing atrial fibrillation episodes in real time.