Large improvement in thermoelectric performance of pressure-tuned Mg3Sb2

Because of the modified electronic band structure, the thermoelectric properties of Mg3Sb2 can be improved by pressure tuning.

Acceleration in quantum mechanics and electric charge quantization

In this study, we have discussed the implications of acceleration in quantum mechanics by means of a generalized derivative operator (GDO). A new Schrödinger equation is obtained which depends on the reduced Compton wavelength of the particle. We have discussed its implications in quantum mechanics for different types of potentials mainly the infinite wall potential, the gravitational linear field potential, the Cornell potential and the Coulomb repulsive potential. The corresponding wave functions and discrete energies are modified and differ from the results obtained in the conventional formalism. The major results obtained concerned the large improvement of the ground energy of the electron subject to the gravitational acceleration in addition to Cornell potential and the emergence of quantized electric charge in the theory without including Dirac monopoles or using gauge theories.

Quality improvement in practice—part 1: creating learning systems

Applying quality improvement methods to solve complex quality issues involves people deeply. People discover solutions, test and adapt ideas, thereby giving them autonomy and control over the system in which they work. Focusing quality improvement on what matters most for staff and service users creates the opportunity to bring a deeper sense of meaning and connection to purpose, both of which are integral to joy in work. When applying quality improvement at scale within large organisations or systems, bringing teams together in learning systems can provide a critical structure to build skills, collaborate and learn from one another. This article describes the core elements of learning systems designed to support quality improvement and joy in work, illustrated through two examples. The framework can be applied at different levels of a system, including the individual, the team and the organisation, or even to a large improvement effort across organisational boundaries. The next article in this series will discuss the application of the joy in work framework to healthcare settings.

Efficient Nanocrystal Photovoltaics via Blade Coating Active Layer

CdTe semiconductor nanocrystal (NC) solar cells have attracted much attention in recent year due to their low-cost solution fabrication process. However, there are still few reports about the fabrication of large area NC solar cells under ambient conditions. Aiming to push CdTe NC solar cells one step forward to the industry, this study used a novel blade coating technique to fabricate CdTe NC solar cells with different areas (0.16, 0.3, 0.5 cm2) under ambient conditions. By optimizing the deposition parameters of the CdTe NC’s active layer, the power conversion efficiency (PCE) of NC solar cells showed a large improvement. Compared to the conventional spin-coated device, a lower post-treatment temperature is required by blade coated NC solar cells. Under the optimal deposition conditions, the NC solar cells with 0.16, 0.3, and 0.5 cm2 areas exhibited PCEs of 3.58, 2.82, and 1.93%, respectively. More importantly, the NC solar cells fabricated via the blading technique showed high stability where almost no efficiency degradation appeared after keeping the devices under ambient conditions for over 18 days. This is promising for low-cost, roll-by-roll, and large area industrial fabrication.

Disentangling a deep learned volume formula

We present a simple phenomenological formula which approximates the hyperbolic volume of a knot using only a single evaluation of its Jones polynomial at a root of unity. The average error is just 2.86% on the first 1.7 million knots, which represents a large improvement over previous formulas of this kind. To find the approximation formula, we use layer-wise relevance propagation to reverse engineer a black box neural network which achieves a similar average error for the same approximation task when trained on 10% of the total dataset. The particular roots of unity which appear in our analysis cannot be written as e2πi/(k+2) with integer k; therefore, the relevant Jones polynomial evaluations are not given by unknot-normalized expectation values of Wilson loop operators in conventional SU(2) Chern-Simons theory with level k. Instead, they correspond to an analytic continuation of such expectation values to fractional level. We briefly review the continuation procedure and comment on the presence of certain Lefschetz thimbles, to which our approximation formula is sensitive, in the analytically continued Chern-Simons integration cycle.

Impact of cardiovascular health status on the association between changes in physical activity and major cardiovascular events and mortality among 88,320 adults: outcomes of the Lifelines Cohort Study

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): The Lifelines Biobank initiative received funding from the Dutch Ministry of Health, Welfare and Sport, the Dutch Ministry of Economic Affairs, the University Medical Center Groningen [UMCG], University Groningen and the Northern Provinces of the Netherlands. The work of T.M.H.E is supported by the Netherlands Heart Foundation [Senior E-Dekker grant #2017T051]. Background. Regular physical activity (PA) improves health. Many observational studies investigated the association between PA and health at a single time-point, but PA might change over time. Purpose. To examine the association between change in PA and major adverse cardiovascular events (MACE) and all-cause mortality, and to investigate the impact of cardiovascular health status at baseline on these outcomes. Methods. This study used data from the Lifelines Cohort Study (N = 88,320). Self-reported PA volumes were presented as Metabolic Equivalent of Task (MET) min/week. Change in PA was calculated by subtracting MET-min/week at the first assessment from the second assessment (median interval: 4 yrs), and 5 groups were created; large reduction (< -1500), moderate reduction (-1500 to -250), no change (-250 to 250), moderate improvement (-250 to 250) and large improvement (>1500). The outcome was a combination of MACE and all-cause mortality. Results. During a median follow-up of 7 years, 667 events occurred among healthy individuals (43 ± 12 yrs, 1% of 69,818) and 599 in individuals with CVRF (55 ± 11 yrs, 3% of 18,502). Adjusted for confounders and baseline PA, healthy individuals with a large reduction in PA had a greater risk of incident MACE and mortality (Table). In CVRF, moderate to large improvements in PA were associated with reductions in adverse outcomes. Risk estimates became stronger in individuals with lower baseline PA (<2000 MET-min/week), Table). Conclusions. Maintaining PA in healthy individuals and increasing PA in individuals with CVRF over time is important to prevent MACE and mortality. The impact of changes in PA was stronger for individuals with lower baseline PA. Table. Change of PA, MACE and mortality. Changes in PA Healthy CVRF Large reduction 1.40 [1.02;1.93] 1.27 [0.95;1.70] Moderate reduction 1.22 [0.89;1.68] 0.97 [0.72;1.30] No changes Ref Ref Moderate improvement 1.04 [0.74;1.44] 0.65 [0.47;0.91] Large improvement 0.96 [0.71;1.31] 0.69 [0.51; 0.94] Individuals with lower baseline PA Large reduction 2.24 [0.96;5.21] 2.85 [1.44;5.63] Moderate reduction 1.77 [1.10;2.84] 1.33 [0.89;1.98] No changes Ref Ref Moderate improvement 1.16 [0.73;1.83] 0.49 [0.31;0.76] Large improvement 0.77 [0.48;1.23] 0.58 [0.39;0.86]

Incorporating family history of disease improves polygenic risk scores in diverse populations

Polygenic risk scores derived from genotype data (PRS) and family history of disease (FH) both provide valuable information for predicting disease risk, enhancing prospects for clinical utility. PRS perform poorly when applied to diverse populations, but FH does not suffer this limitation. Here, we explore methods for combining both types of information (PRS-FH). We analyzed 10 complex diseases from the UK Biobank for which family history (parental and sibling history) was available for most target samples. PRS were trained using all British individuals (N=409K), and target samples consisted of unrelated non-British Europeans (N=42K), South Asians (N=7K), or Africans (N=7K). We evaluated PRS, FH, and PRS-FH using liability-scale R2, focusing on three well-powered diseases (type 2 diabetes, hypertension, depression) with R2 > 0.05 for PRS and/or FH in each target population. Averaging across these three diseases, PRS attained average prediction R2 of 5.8%, 4.0%, and 0.53% in non-British Europeans, South Asians, and Africans, confirming poor cross-population transferability. In contrast, PRS-FH attained average prediction R2 of 13%, 12%, and 10%, respectively, representing a large improvement in Europeans and an extremely large improvement in Africans; for each disease and each target population, the improvement was highly statistically significant. PRS-FH methods based on a logistic model and a liability threshold model performed similarly when covariates were not included in predictions (consistent with simulations), but the logistic model outperformed the liability threshold model when covariates were included. In conclusion, including family history greatly improves the accuracy of polygenic risk scores, particularly in diverse populations.

Efficient Agony Based Transfer Learning Algorithms for Survival Forecasting

Progression modeling is a mature subfield of cancer bioinformatics, but it has yet to make a proportional clinical impact. The majority of the research in this area has focused on the development of efficient algorithms for accurately reconstructing sequences of (epi)genomic events from noisy data. We see this as the first step in a broad pipeline that will translate progression modeling to clinical utility, with the subsequent steps involving inferring prognoses and optimal therapy programs for different cancers and using similarity in progression to enhance decision making. In this paper we take some initial steps in completing this pipeline. As a theoretical contribution, we introduce a polytime-computable pairwise distance between progression models based on the graph-theoretic notion of “agony”. Focusing on a particular progression model we can then use this agony distance to cluster (dis)similarities via multi-dimensional scaling. We recover known biological similarities and dissimilarities. Finally, we use the agony distance to automate transfer learning experiments and show a large improvement in the ability to forecast time to death.