Nanomechanical Crystalline AlN Resonators with High Quality Factors for Quantum Optoelectromechanics.

High-quality factor (Qm) mechanical resonators are crucial for applications where low noise and long coherence time are required, as mirror suspensions, quantum cavity optomechanical devices, or nanomechanical sensors. Tensile strain in the material enables the use of dissipation dilution and strain engineering techniques, which increase the mechanical quality factor. These techniques have been employed for high-Qm mechanical resonators made from amorphous materials and, recently, from crystalline materials such as InGaP, SiC, and Si. A strained crystalline film exhibiting substantial piezoelectricity expands the capability of high-Qm nanomechanical resonators to directly utilize electronic degrees of freedom. In this work, nanomechanical resonators with Qm up to 2.9 × 107 made from tensile-strained 290 nm-thick AlN are realized. AlN is an epitaxially-grown crystalline material offering strong piezoelectricity. Nanomechanical resonators that exploit dissipation dilution and strain engineering to reach a Qm × fm-product approaching 1013 Hz at room temperature are demonstrated. A novel resonator geometry is realized, triangline, whose shape follows the Al-N bonds and offers a central pad patterned with a photonic crystal. This allows to reach an optical reflectivity above 80% for efficient coupling to out-of-plane light. The presented results pave the way for quantum optoelectromechanical devices at room temperature based on tensile-strained AlN.

Causal inference on human behaviour.

Making causal inferences regarding human behaviour is difficult given the complex interplay between countless contributors to behaviour, including factors in the external world and our internal states. We provide a non-technical conceptual overview of challenges and opportunities for causal inference on human behaviour. The challenges include our ambiguous causal language and thinking, statistical under- or over-control, effect heterogeneity, interference, timescales of effects and complex treatments. We explain how methods optimized for addressing one of these challenges frequently exacerbate other problems. We thus argue that clearly specified research questions are key to improving causal inference from data. We suggest a triangulation approach that compares causal estimates from (quasi-)experimental research with causal estimates generated from observational data and theoretical assumptions. This approach allows a systematic investigation of theoretical and methodological factors that might lead estimates to converge or diverge across studies.

Pressurized intraperitoneal aerosolized chemotherapy (PIPAC) experience in patients with recurrent low grade serous ovarian carcinoma (LGSOC): sub-cohort report of phase 1 clinical trial.

Introduction: Low grade serous ovarian carcinoma (LGSOC) is a rare subtype of ovarian cancer (OC) that is challenging to treat due to its relative chemoresistance. Given that LGSOC patients often recur in the peritoneal cavity, novel intraperitoneal (IP) chemotherapy should be explored. Pressurized intraperitoneal aerosolized chemotherapy (PIPAC) is a method that has demonstrated peritoneal disease control in cancers with peritoneal metastases. Methods: NCT04329494 is a US multicenter phase 1 trial evaluating the safety of PIPAC in recurrent ovarian, uterine, and GI cancers with peritoneal metastases. This analysis describes the outcomes of a sub-cohort of four LGSOC patients treated with IP cisplatin 10.5 mg/m2, doxorubicin 2.1 mg/m2 PIPAC q4-6 weeks. Primary endpoints included dose-limiting toxicities (DLT) and incidence of adverse events (AE). Secondary endpoints were progression free survival (PFS) and treatment response based on radiographic, intraoperative, and pathological findings. Results: Four patients with LGSOC were enrolled of which three were heavily pretreated. Median prior lines of therapy was 5 (range 2-10). Three patients had extraperitoneal metastases, and two patients had baseline partial small bowel obstructive (SBO) symptoms. Median age of patients was 58 (38-68). PIPAC completion rate (≥2 PIPACs) was 75%. No DLTs or Clavien-Dindo surgical complications occurred. No G4/G5 AEs were observed, and one G3 abdominal pain was reported. One patient had a partial response after 3 cycles of PIPAC and completed an additional 3 cycles with compassionate use amendment. Two patients came off study after 2 cycles due to extraperitoneal progressive disease. One patient came off study after 1 cycle due to toxicity. Median decrease in peritoneal carcinomatosis index between cycles 1 and 2 was 5.0%. Ascites decreased in 2 out of 3 patients who had ≥2 PIPACs. Median PFS was 4.3 months (1.7-21.6), median overall survival was 11.6 months (5.4-30.1), and objective response rate was 25%. Conclusion: PIPAC with cisplatin/doxorubicin is well tolerated in LGSOC patients without baseline SBO symptoms. IP response was seen in 2 out of 3 patients that completed ≥2 PIPAC cycles. Further study of PIPAC for patients with recurrent disease limited to the IP cavity and with no partial SBO symptoms should be considered.

Uncovering the heritable components of multimorbidities and disease trajectories using a nationwide cohort.

Quantifying the contribution of genetics and environmental effects on disease initiation and progression, as well as the shared genetics of different diseases, is vital for the understanding of the disease etiology of multimorbidities. In this study, we leverage nationwide Danish registries to provide a granular atlas of the genetic origin of disease phenotypes for a cohort of all Danes 1978-2018 with partially known pedigree (n = 6.3 million). We estimate the heritability and genetic correlation between thousands of disease phenotypes using a novel approach that can be scaled to nationwide data. Our findings confirm the importance of genetics for a number of known associations and increase the resolution of heritability by adding numerous associations, some of which point to shared biologically origin of different phenotypes. We also establish the heritability of disease trajectories and the importance of sex-specific genetic contributions. Results can be accessed at https://h2.cpr.ku.dk/ .

Multi-cancer risk stratification based on national health data: a retrospective modelling and validation study.

BACKGROUND: Health care is experiencing a drive towards digitisation, and many countries are implementing national health data resources. Although a range of cancer risk models exists, the utility on a population level for risk stratification across cancer types has not been fully explored. We aimed to close this gap by evaluating pan-cancer risk models built on electronic health records across the Danish population with validation in the UK Biobank. METHODS: In this retrospective modelling and validation study, data for model development and internal validation were derived from the following Danish health registries: the Central Person Registry, the Danish National Patient Registry, the death registry, the cancer registry, and full-text medical records from secondary care records in the capital region. The development data included adults aged 16-86 years without previous malignant cancers in the time period from Jan 1, 1995, to Dec 31, 2014. The internal validation period was from Jan 1, 2015, to April 10, 2018, and the data included all adults without a previous indication of cancer aged 16-75 years on Dec 31, 2014. The external validation cohort from the UK Biobank included all adults without a previous indication of cancer aged 50-75 years. We used time-dependent Bayesian Cox hazard models built on the combined medical history of Danish individuals. A set of 1392 covariates from available clinical disease trajectories, text-mined basic health factors, and family histories were used to train predictive models of 20 major cancer types. The models were validated on cancer incidence between 2015 and 2018 across Denmark and on individuals in the UK Biobank. The primary outcomes were discrimination and calibration performance. FINDINGS: From the Danish registries, we included 6 732 553 individuals covering 60 million hospital visits, 90 million diagnoses, and a total of 193 million life-years between Jan 1, 1978, and April 10, 2018. Danish registry data covering the period from Jan 1, 2015, to April 10, 2018, were used to internally validate risk models, containing a total of 4 248 491 individuals who remained at risk of a primary malignant cancer diagnosis and 67 401 cancer cases recorded. For the external validation, we evaluated the same time period in the UK Biobank covering 377 004 individuals with 11 486 cancer cases. The predictive performance of the models on Danish data showed good discrimination (concordance index 0·81 [SD 0·08], ranging from 0·66 [95% CI 0·65-0·67] for cervix uteri cancer to 0·91 [0·90-0·92] for liver cancer). Performance was similar on the UK Biobank in a direct transfer when controlling for shifts in the age distribution (concordance index 0·66 [SD 0·08], ranging from 0·55 [95% CI 0·44-0·66] for cervix uteri cancer to 0·78 [0·77-0·79] for lung cancer). Cancer risks were associated, in addition to heritable components, with a broad range of preceding diagnoses and health factors. The best overall performance was seen for cancers of the digestive system (oesophageal, stomach, colorectal, liver, and pancreatic) but also thyroid, kidney, and uterine cancers. INTERPRETATION: Data available in national electronic health databases can be used to approximate cancer risk factors and enable risk predictions in most cancer types. Model predictions generalise between the Danish and UK health-care systems. With the emergence of multi-cancer early detection tests, electronic health record-based risk models could supplement screening efforts. FUNDING: Novo Nordisk Foundation and the Danish Innovation Foundation.

Neural network emulation of the human ventricular cardiomyocyte action potential for more efficient computations in pharmacological studies.

Computer models of the human ventricular cardiomyocyte action potential (AP) have reached a level of detail and maturity that has led to an increasing number of applications in the pharmaceutical sector. However, interfacing the models with experimental data can become a significant computational burden. To mitigate the computational burden, the present study introduces a neural network (NN) that emulates the AP for given maximum conductances of selected ion channels, pumps, and exchangers. Its applicability in pharmacological studies was tested on synthetic and experimental data. The NN emulator potentially enables massive speed-ups compared to regular simulations and the forward problem (find drugged AP for pharmacological parameters defined as scaling factors of control maximum conductances) on synthetic data could be solved with average root-mean-square errors (RMSE) of 0.47 mV in normal APs and of 14.5 mV in abnormal APs exhibiting early afterdepolarizations (72.5% of the emulated APs were alining with the abnormality, and the substantial majority of the remaining APs demonstrated pronounced proximity). This demonstrates not only very fast and mostly very accurate AP emulations but also the capability of accounting for discontinuities, a major advantage over existing emulation strategies. Furthermore, the inverse problem (find pharmacological parameters for control and drugged APs through optimization) on synthetic data could be solved with high accuracy shown by a maximum RMSE of 0.22 in the estimated pharmacological parameters. However, notable mismatches were observed between pharmacological parameters estimated from experimental data and distributions obtained from the Comprehensive in vitro Proarrhythmia Assay initiative. This reveals larger inaccuracies which can be attributed particularly to the fact that small tissue preparations were studied while the emulator was trained on single cardiomyocyte data. Overall, our study highlights the potential of NN emulators as powerful tool for an increased efficiency in future quantitative systems pharmacology studies.

A phase I study of Mirvetuximab Soravtansine and gemcitabine in patients with FRα-positive recurrent ovarian, primary peritoneal, fallopian tube, or endometrial cancer, or triple negative breast cancer.

OBJECTIVE: Platinum-resistant epithelial ovarian cancer (EOC), recurrent endometrial cancer (EC), and triple negative breast cancer (TNBC) are difficult to treat after failing standard therapies. This phase I study evaluated mirvetuximab soravtansine (MIRV) and gemcitabine in patients with recurrent FRα-positive EOC, EC, or TNBC to determine the maximum tolerated dose (MTD)/recommended phase 2 dose (RP2D) (primary endpoint). METHODS: FRα-positive patients with platinum-resistant EOC, EC, or TNBC with ≤4 prior chemotherapy regimens (2 for EC) were enrolled. FRα expression requirement varied among eligible tumors and changed during the study. RESULTS: Twenty patients were enrolled; 17 were evaluable for DLT. Half the patients received ≥3 prior chemotherapy lines. Most EOC and EC patients (78%) were medium (50-74%) or high(75-100%) FRα expressors. TNBC patients were low (25-49%) FRα expressors. The MTD/RP2D was MIRV 6 mg/kg AIBW D1 and gemcitabine 800 mg/m2 IV, D1 and D8, every 21 days (Dose Level [DL] 3), where 5/7 patients demonstrated a partial response (PR) as their best response, including 2 confirmed ovarian responses whose time-to-progression and duration of response were 7.9/5.4 and 8.0/5.7 months respectively. Most common treatment-related adverse events at MTD were anemia and neutropenia (3/7 each, 43%), diarrhea, hypophosphatemia, thrombocytopenia, and leukopenia (2/7 each, 29%). DLTs were thrombocytopenia (DL1), oral mucositis (DL4) and diarrhea (DL4). Nine of 20 patients (45%; 95% CI: 21.1-68.9%) achieved PR as their best response, with 3/20 patients or 15% (95%CI, 0-32.1%) confirmed PR. CONCLUSION: MIRV and gemcitabine demonstrate promising activity in platinum resistant EOC at RP2D, but frequent hematologic toxicities.

Mechanoelectric effects in healthy cardiac function and under Left Bundle Branch Block pathology.

Mechanoelectric feedback (MEF) in the heart operates through several mechanisms which serve to regulate cardiac function. Stretch activated channels (SACs) in the myocyte membrane open in response to cell lengthening, while tension generation depends on stretch, shortening velocity, and calcium concentration. How all of these mechanisms interact and their effect on cardiac output is still not fully understood. We sought to gauge the acute importance of the different MEF mechanisms on heart function. An electromechanical computer model of a dog heart was constructed, using a biventricular geometry of 500K tetrahedral elements. To describe cellular behavior, we used a detailed ionic model to which a SAC model and an active tension model, dependent on stretch and shortening velocity and with calcium sensitivity, were added. Ventricular inflow and outflow were connected to the CircAdapt model of cardiovascular circulation. Pressure-volume loops and activation times were used for model validation. Simulations showed that SACs did not affect acute mechanical response, although if their trigger level was decreased sufficiently, they could cause premature excitations. The stretch dependence of tension had a modest effect in reducing the maximum stretch, and stroke volume, while shortening velocity had a much bigger effect on both. MEF served to reduce the heterogeneity in stretch while increasing tension heterogeneity. In the context of left bundle branch block, a decreased SAC trigger level could restore cardiac output by reducing the maximal stretch when compared to cardiac resynchronization therapy. MEF is an important aspect of cardiac function and could potentially mitigate activation problems.

Neural network emulation of the human ventricular cardiomyocyte action potential: a tool for more efficient computation in pharmacological studies.

Computer models of the human ventricular cardiomyocyte action potential (AP) have reached a level of detail and maturity that has led to an increasing number of applications in the pharmaceutical sector. However, interfacing the models with experimental data can become a significant computational burden. To mitigate the computational burden, the present study introduces a neural network (NN) that emulates the AP for given maximum conductances of selected ion channels, pumps, and exchangers. Its applicability in pharmacological studies was tested on synthetic and experimental data. The NN emulator potentially enables massive speed-ups compared to regular simulations and the forward problem (find drugged AP for pharmacological parameters defined as scaling factors of control maximum conductances) on synthetic data could be solved with average root-mean-square errors (RMSE) of 0.47mV in normal APs and of 14.5mV in abnormal APs exhibiting early afterdepolarizations (72.5% of the emulated APs were alining with the abnormality, and the substantial majority of the remaining APs demonstrated pronounced proximity). This demonstrates not only very fast and mostly very accurate AP emulations but also the capability of accounting for discontinuities, a major advantage over existing emulation strategies. Furthermore, the inverse problem (find pharmacological parameters for control and drugged APs through optimization) on synthetic data could be solved with high accuracy shown by a maximum RMSE of 0.21 in the estimated pharmacological parameters. However, notable mismatches were observed between pharmacological parameters estimated from experimental data and distributions obtained from the Comprehensive in vitro Proarrhythmia Assay initiative. This reveals larger inaccuracies which can be attributed particularly to the fact that small tissue preparations were studied while the emulator was trained on single cardiomyocyte data. Overall, our study highlights the potential of NN emulators as powerful tool for an increased efficiency in future quantitative systems pharmacology studies.

A lower leg surrogate study to investigate the effect of quadriceps-hamstrings activation ratio on ACL tensile force.

OBJECTIVES: Many studies have investigated the relationship between muscle activation and tensile force of the anterior cruciate ligament. These studies lacked a holistic representation of the muscle status. For instance, they were limited with respect to the peak muscle forces, number of muscles, and possible muscle activation patterns. DESIGN: This study used a knee surrogate including ten muscles with motor-controlled muscle force activation crossing the knee joint, thus providing a fully muscle-supported knee joint. METHODS: Anterior cruciate ligament tensile force is measured in different knee flexion and extension movements to evaluate ratios of quadriceps/hamstring muscle activations in low hip angle setups. RESULTS: Increasing the extension of the leg increased anterior cruciate ligament tension forces. Different quadriceps/hamstring ratios had different effects on anterior cruciate ligament tension forces during unrestricted flexion and extension movements. This was dependent on the direction of movement. Sole hamstring activation increased the anterior cruciate ligament tensile forces in extension movements compared with flexion movements. Sole quadriceps activation provoked greater anterior cruciate ligament tensile forces in flexion than in extension. This was not prominent in the test in which the other muscle groups counteracted the dominant muscle group. CONCLUSIONS: The findings from the present study demonstrate that active hamstring activation can reduce the load on the anterior cruciate ligament, and the dominant quadriceps increase anterior cruciate ligament loads for knee flexions of less than 40°. Moreover, the anterior cruciate ligament is loaded differently in flexion or extension movements with flexion movements, resulting in higher anterior cruciate ligament loads.

An Integrated Workflow for Building Digital Twins of Cardiac Electromechanics-A Multi-Fidelity Approach for Personalising Active Mechanics.

Personalised computer models of cardiac function, referred to as cardiac digital twins, are envisioned to play an important role in clinical precision therapies of cardiovascular diseases. A major obstacle hampering clinical translation involves the significant computational costs involved in the personalisation of biophysically detailed mechanistic models that require the identification of high-dimensional parameter vectors. An important aspect to identify in electromechanics (EM) models are active mechanics parameters that govern cardiac contraction and relaxation. In this study, we present a novel, fully automated, and efficient approach for personalising biophysically detailed active mechanics models using a two-step multi-fidelity solution. In the first step, active mechanical behaviour in a given 3D EM model is represented by a purely phenomenological, low-fidelity model, which is personalised at the organ scale by calibration to clinical cavity pressure data. Then, in the second step, median traces of nodal cellular active stress, intracellular calcium concentration, and fibre stretch are generated and utilised to personalise the desired high-fidelity model at the cellular scale using a 0D model of cardiac EM. Our novel approach was tested on a cohort of seven human left ventricular (LV) EM models, created from patients treated for aortic coarctation (CoA). Goodness of fit, computational cost, and robustness of the algorithm against uncertainty in the clinical data and variations of initial guesses were evaluated. We demonstrate that our multi-fidelity approach facilitates the personalisation of a biophysically detailed active stress model within only a few (2 to 4) expensive 3D organ-scale simulations-a computational effort compatible with clinical model applications.

Dynamic Sparse Subspace Clustering for Evolving High-Dimensional Data Streams.

In an era of ubiquitous large-scale evolving data streams, data stream clustering (DSC) has received lots of attention because the scale of the data streams far exceeds the ability of expert human analysts. It has been observed that high-dimensional data are usually distributed in a union of low-dimensional subspaces. In this article, we propose a novel sparse representation-based DSC algorithm, called evolutionary dynamic sparse subspace clustering (EDSSC). It can cope with the time-varying nature of subspaces underlying the evolving data streams, such as subspace emergence, disappearance, and recurrence. The proposed EDSSC consists of two phases: 1) static learning and 2) online clustering. During the first phase, a data structure for storing the statistic summary of data streams, called EDSSC summary, is proposed which can better address the dilemma between the two conflicting goals: 1) saving more points for accuracy of subspace clustering (SC) and 2) discarding more points for the efficiency of DSC. By further proposing an algorithm to estimate the subspace number, the proposed EDSSC does not need to know the number of subspaces. In the second phase, a more suitable index, called the average sparsity concentration index (ASCI), is proposed, which dramatically promotes the clustering accuracy compared to the conventionally utilized SCI index. In addition, the subspace evolution detection model based on the Page-Hinkley test is proposed where the appearing, disappearing, and recurring subspaces can be detected and adapted. Extinct experiments on real-world data streams show that the EDSSC outperforms the state-of-the-art online SC approaches.

Genomic reconstruction of the SARS-CoV-2 epidemic in England.

The evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus leads to new variants that warrant timely epidemiological characterization. Here we use the dense genomic surveillance data generated by the COVID-19 Genomics UK Consortium to reconstruct the dynamics of 71 different lineages in each of 315 English local authorities between September 2020 and June 2021. This analysis reveals a series of subepidemics that peaked in early autumn 2020, followed by a jump in transmissibility of the B.1.1.7/Alpha lineage. The Alpha variant grew when other lineages declined during the second national lockdown and regionally tiered restrictions between November and December 2020. A third more stringent national lockdown suppressed the Alpha variant and eliminated nearly all other lineages in early 2021. Yet a series of variants (most of which contained the spike E484K mutation) defied these trends and persisted at moderately increasing proportions. However, by accounting for sustained introductions, we found that the transmissibility of these variants is unlikely to have exceeded the transmissibility of the Alpha variant. Finally, B.1.617.2/Delta was repeatedly introduced in England and grew rapidly in early summer 2021, constituting approximately 98% of sampled SARS-CoV-2 genomes on 26 June 2021.

A heuristic model-based approach for compensating wind effects in ski jumping.

Wind influences the jump length in ski jumping, which raises questions about the fairness. To counteract the wind problem, the International Ski Federation has introduced a wind compensation system in 2009: time-averaged wind velocity components tangential to the landing slope are obtained from several sites along the landing slope, and these data are used in a linear statistical model for estimating the jump length effect of wind. This is considered in the total score of the ski jump. However, it has been shown that the jump length effect estimates can be inaccurate and misleading. The present article introduces an alternative mathematical wind compensation approach that is based on an accurate mechanistic model of the flight phase. This estimates the jump length effect as difference between the jump length of the real ski jump at the given wind condition and the computed jump length of the simulated ski jump at calm wind. Inputs for the computer simulation are the initial flight velocity and aerodynamic coefficients of the real ski jump that can be obtained from kinematic and wind velocity data collected during the flight. The initial flight velocity is readily available from the kinematic data and inverse dynamics can be used to compute the aerodynamic coefficients. The accuracy of the estimated jump length effect of the mechanistic model-based approach depends only on the measurement errors in the kinematic and wind velocity data, but not on inaccuracies of an approach that is based on a linear statistical model.

Generic and specific recurrent neural network models: Applications for large and small scale biopharmaceutical upstream processes.

The calculation of temporally varying upstream process outcomes is a challenging task. Over the last years, several parametric, semi-parametric as well as non-parametric approaches were developed to provide reliable estimates for key process parameters. We present generic and product-specific recurrent neural network (RNN) models for the computation and study of growth and metabolite-related upstream process parameters as well as their temporal evolution. Our approach can be used for the control and study of single product-specific large-scale manufacturing runs as well as generic small-scale evaluations for combined processes and products at development stage. The computational results for the product titer as well as various major upstream outcomes in addition to relevant process parameters show a high degree of accuracy when compared to experimental data and, accordingly, a reasonable predictive capability of the RNN models. The calculated values for the root-mean squared errors of prediction are significantly smaller than the experimental standard deviation for the considered process run ensembles, which highlights the broad applicability of our approach. As a specific benefit for platform processes, the generic RNN model is also used to simulate process outcomes for different temperatures in good agreement with experimental results. The high level of accuracy and the straightforward usage of the approach without sophisticated parameterization and recalibration procedures highlight the benefits of the RNN models, which can be regarded as promising alternatives to existing parametric and semi-parametric methods.

Validation Is Not Verification: Precise Terminology and Scientific Methods in Bioprocess Modeling.

Advanced statistical approaches and new modeling procedures for biopharmaceutical development and manufacturing have received increasing attention. With this forum article, we would like to highlight the need for a consistent terminology and the necessity for appropriate model validation strategies.

Artificial neural networks for the prediction of solvation energies based on experimental and computational data.

The knowledge of thermodynamic properties for novel electrolyte formulations is of fundamental interest for industrial applications as well as academic research. Herewith, we present an artificial neural networks (ANN) approach for the prediction of solvation energies and entropies for distinct ion pairs in various protic and aprotic solvents. The considered feed-forward ANN is trained either by experimental data or computational results from conceptual density functional theory calculations. The proposed concept of mapping computed values to experimental data lowers the amount of time-consuming and costly experiments and helps to overcome certain limitations. Our findings reveal high correlation coefficients between predicted and experimental values which demonstrate the validity of our approach.

Towards a Digital Bioprocess Replica: Computational Approaches in Biopharmaceutical Development and Manufacturing.

Quantitative unit operation models for the optimization and refinement of modern late-stage biopharmaceutical drug manufacturing processes have recently attracted increasing attention. The supplementary benefits of these models include increased process robustness and control in combination with a more stringent design of the bioprocess due to a reduced number of exploratory experiments. In addition to unit operations, further efforts also focus on digital bioprocess replicas, which are straightforward combinations of unit operation and process models from inoculum to the fill and finish phase. In this review, we shed more light on digital bioprocess replicas in addition to standard unit operation models and discuss their strengths and weaknesses. We comment on the current usage of these approaches for late stage processes and outline the associated benefits, challenges and limitations.