A standing-wave thermoacoustic engine driven by liquid nitrogen

Thermoacoustic oscillation at cryogenic temperatures, such as Taconis oscillation, has been typically suppressed in the former studies, and few efforts have been made to enhance it. We proposed a standing-wave thermoacoustic engine (TE) driven by liquid cryogens instead of the conventional heat to enhance the thermoacoustic effects and utilize the cold energy. Experimental and theoretical work has been performed on a self-made standingwave TE to demonstrate the feasibility and the operating characteristics of the engine driven by the liquid nitrogen. Experiments show that with nitrogen at 0.5 MPa as a working gas, a pressure ratio of 1.21 is obtained on the TE driven by liquid nitrogen with a much lower temperature difference along the stack compared to that of the conventional TE. The onset temperature difference decreases by 28.9% with helium at 0.63 MPa as a working gas, compared to that of the conventional TE. This study verifies the feasibility of enhancing the thermoacoustic oscillation at cryogenic temperatures. The TEs driven by liquid cryogens such as liquid nitrogen and liquefied nature gas (LNG), may be an alternative for recovering the cold energy

The achievement of food and nutrition security in South Asia is deeply gendered

Women form an integral part of the agricultural sector, and in much of South Asia women make up a majority of the agricultural workforce, often compelled to work in order to meet their families’ basic needs. While their contributions are recognised as central to the food and nutrition security of households and communities, their work is not recognized or supported adequately by public policy and social institutions. Women continue to face inequality across key development indicators including health, education, and nutrition; discriminatory laws; and high levels of precarity in terms of income, employment conditions, safety and wellbeing. Social structures that promote gender inequality and inhibit the agency of women contribute to the South Asian enigma – the persistence of undernutrition despite economic growth – and must be addressed to achieve food and nutrition security

Remotely acting SMCHD1 gene regulatory elements: in silico prediction and identification of potential regulatory variants in patients with FSHD

Background: Facioscapulohumeral dystrophy (FSHD) is commonly associated with contraction of the D4Z4 macro-satellite repeat on chromosome 4q35 (FSHD1) or mutations in the SMCHD1 gene (FSHD2). Recent studies have shown that the clinical manifestation of FSHD1 can be modified by mutations in the SMCHD1 gene within a given family. The absence of either D4Z4 contraction or SMCHD1 mutations in a small cohort of patients suggests that the disease could also be due to disruption of gene regulation. In this study, we postulated that mutations responsible for exerting a modifier effect on FSHD might reside within remotely acting regulatory elements that have the potential to interact at a distance with their cognate gene promoter via chromatin looping. To explore this postulate, genome-wide Hi-C data were used to identify genomic fragments displaying the strongest interaction with the SMCHD1 gene. These fragments were then narrowed down to shorter regions using ENCODE and FANTOM data on transcription factor binding sites and epigenetic marks characteristic of promoters, enhancers and silencers

Regulation of the dystrophin-associated glycoprotein complex composition by the metabolic properties of muscle fibres

The dystrophin-glycoprotein complex (DGC) links the muscle cytoskeleton to the extracellular matrix and is responsible for force transduction and protects the muscle fibres from contraction induced damage. Mutations in components of the DGC are responsible for muscular dystrophies and congenital myopathies. Expression of DGC components have been shown to be altered in many myopathies. In contrast we have very little evidence of whether adaptive changes in muscle impact on DGC expression. In this study we investigated connection between muscle fibre phenotype and the DGC. Our study reveals that the levels of DGC proteins at the sarcolemma differ in highly glycolytic muscle compared to wild-type and that these changes can be normalised by the super-imposition of an oxidative metabolic programme. Importantly we show that the metabolic properties of the muscle do not impact on the total amount of DGC components at the protein level. Our work shows that the metabolic property of a muscle fibre is a key factor in regulating the expression of DGC proteins at the sarcolemma

Spatially Resolved Magnetic Field Structure in the Disk of a T Tauri Star

Magnetic fields in accretion disks play a dominant role during the star formation process but have hitherto been observationally poorly constrained. Field strengths have been inferred on T Tauri stars themselves and possibly in the innermost part of the accretion disk, but the strength and morphology of the field in the bulk of the disk have not been observed. Unresolved measurements of polarized emission (arising from elongated dust grains aligned perpendicular to the field) imply average fields aligned with the disks. Theoretically, the fields are expected to be largely toroidal, poloidal, or a mixture of the two, which imply different mechanisms for transporting angular momentum in the disks of actively accreting young stars such as HL Tau. Here we report resolved measurements of the polarized 1.25 mm continuum emission from HL Tau's disk. The magnetic field on a scale of 80 AU is coincident with the major axis (~210 AU diameter) of the disk. From this we conclude that the magnetic field inside the disk at this scale cannot be dominated by a vertical component, though a purely toroidal field does not fit the data well either. The unexpected morphology suggests that the magnetic field's role for the accretion of a T Tauri star is more complex than the current theoretical understanding.Comment: Accepted for publication in Natur

Efficient singlet exciton fission in pentacene prepared from a soluble precursor

Carrier multiplication using singlet exciton fission (SF) to generate a pair of spin-triplet excitons from a single optical excitation has been highlighted as a promising approach to boost the photocurrent in photovoltaics (PVs) thereby allowing PV operation beyond the Shockley-Queisser limit. The applicability of many efficient fission materials, however, is limited due to their poor solubility. For instance, while acene-based organics such as pentacene (Pc) show high SF yields (up to 200%), the plain acene backbone renders the organic molecule insoluble in common organic solvents. Previous approaches adding solubilizing side groups such as bis(tri-$\textit{iso}$-propylsilylethynyl) to the Pc core resulted in low vertical carrier mobilities due to reduction of the transfer integrals via steric hindrance, which prevented high efficiencies in PVs. Here we show how to achieve good solubility while retaining the advantages of molecular Pc by using a soluble precursor route. The precursor fully converts into molecular Pc through thermal removal of the solubilizing side groups upon annealing above 150 °C in the solid state. The annealed precursor shows small differences in the crystallinity compared to evaporated thin films of Pc, indicating that the Pc adopts the bulk rather than surface polytype. Furthermore, we identify identical SF properties such as sub-100 fs fission time and equally long triplet lifetimes in both samples.M.T. thanks the Gates Cambridge Trust and the Winton Programme for the Physics of Sustainability for funding. A.H.K. acknowledges the Cambridge Nehru Bursary, the Cambridge Bombay Society, a Trinity-Henry Barlow- and Haidar Scholarship as well as Rana Denim Pvt. Ltd. for financial support. K.B. and J.N. would like to thank Dr. Tom Arnold and Jakub Rozboril for assistance during the beam time at Diamond Light Source. Financial support for K.B. from Diamond Light Source, Swiss Light Source, and the German Research Foundation (Grant No. BR 4869/1-1) is gratefully acknowledged. M.L.B. is a research fellow of Christ’s College, Cambridge. This work was supported by the Engineering and Physical Sciences Research Council (Grant Nos. EP/M005143/1, EP/G060738/1 and Cambridge NanoDTC EP/G037221/1, EP/L015978/1)

Pre-transplant CDKN2A expression in kidney biopsies predicts renal function and is a future component of donor scoring criteria

CDKN2A is a proven and validated biomarker of ageing which acts as an off switch for cell proliferation. We have demonstrated previously that CDKN2A is the most robust and the strongest pre-transplant predictor of post- transplant serum creatinine when compared to “Gold Standard” clinical factors, such as cold ischaemic time and donor chronological age. This report shows that CDKN2A is better than telomere length, the most celebrated biomarker of ageing, as a predictor of post-transplant renal function. It also shows that CDKN2A is as strong a determinant of post-transplant organ function when compared to extended criteria (ECD) kidneys. A multivariate analysis model was able to predict up to 27.1% of eGFR at one year post-transplant (p = 0.008). Significantly, CDKN2A was also able to strongly predict delayed graft function. A pre-transplant donor risk classification system based on CDKN2A and ECD criteria is shown to be feasible and commendable for implementation in the near future

A computational framework to emulate the human perspective in flow cytometric data analysis

Background: In recent years, intense research efforts have focused on developing methods for automated flow cytometric data analysis. However, while designing such applications, little or no attention has been paid to the human perspective that is absolutely central to the manual gating process of identifying and characterizing cell populations. In particular, the assumption of many common techniques that cell populations could be modeled reliably with pre-specified distributions may not hold true in real-life samples, which can have populations of arbitrary shapes and considerable inter-sample variation. <p/>Results: To address this, we developed a new framework flowScape for emulating certain key aspects of the human perspective in analyzing flow data, which we implemented in multiple steps. First, flowScape begins with creating a mathematically rigorous map of the high-dimensional flow data landscape based on dense and sparse regions defined by relative concentrations of events around modes. In the second step, these modal clusters are connected with a global hierarchical structure. This representation allows flowScape to perform ridgeline analysis for both traversing the landscape and isolating cell populations at different levels of resolution. Finally, we extended manual gating with a new capacity for constructing templates that can identify target populations in terms of their relative parameters, as opposed to the more commonly used absolute or physical parameters. This allows flowScape to apply such templates in batch mode for detecting the corresponding populations in a flexible, sample-specific manner. We also demonstrated different applications of our framework to flow data analysis and show its superiority over other analytical methods. <p/>Conclusions: The human perspective, built on top of intuition and experience, is a very important component of flow cytometric data analysis. By emulating some of its approaches and extending these with automation and rigor, flowScape provides a flexible and robust framework for computational cytomics

On the Schoenberg Transformations in Data Analysis: Theory and Illustrations

The class of Schoenberg transformations, embedding Euclidean distances into higher dimensional Euclidean spaces, is presented, and derived from theorems on positive definite and conditionally negative definite matrices. Original results on the arc lengths, angles and curvature of the transformations are proposed, and visualized on artificial data sets by classical multidimensional scaling. A simple distance-based discriminant algorithm illustrates the theory, intimately connected to the Gaussian kernels of Machine Learning