Using reservoir computing to construct scarred wave functions

Scar theory is one of the fundamental pillars in the field of quantum chaos, and scarred functions are a superb tool to carry out studies in it. Several methods, usually semiclassical, have been described to cope with these two phenomena. In this paper, we present an alternative method, based on the novel machine learning algorithm known as reservoir computing, to calculate such scarred wave functions together with the associated eigenstates of the system. The resulting methodology achieves outstanding accuracy while reducing execution times by a factor of ten. As an illustration of the effectiveness of this method, we apply it to the widespread chaotic two-dimensional coupled quartic oscillator.

Binding affinity predictions with hybrid quantum-classical convolutional neural networks

Central in drug design is the identification of biomolecules that uniquely and robustly bind to a target protein, while minimizing their interactions with others. Accordingly, precise binding affinity prediction, enabling the accurate selection of suitable candidates from an extensive pool of potential compounds, can greatly reduce the expenses associated to practical experimental protocols. In this respect, recent advances revealed that deep learning methods show superior performance compared to other traditional computational methods, especially with the advent of large datasets. These methods, however, are complex and very time-intensive, thus representing an important clear bottleneck for their development and practical application. In this context, the emerging realm of quantum machine learning holds promise for enhancing numerous classical machine learning algorithms. In this work, we take one step forward and present a hybrid quantum-classical convolutional neural network, which is able to reduce by 20% the complexity of the classical counterpart while still maintaining optimal performance in the predictions. Additionally, this results in a significant cost and time savings of up to 40% in the training stage, which means a substantial speed-up of the drug design process.

Taking advantage of noise in quantum reservoir computing

The biggest challenge that quantum computing and quantum machine learning are currently facing is the presence of noise in quantum devices. As a result, big efforts have been put into correcting or mitigating the induced errors. But, can these two fields benefit from noise? Surprisingly, we demonstrate that under some circumstances, quantum noise can be used to improve the performance of quantum reservoir computing, a prominent and recent quantum machine learning algorithm. Our results show that the amplitude damping noise can be beneficial to machine learning, while the depolarizing and phase damping noises should be prioritized for correction. This critical result sheds new light into the physical mechanisms underlying quantum devices, providing solid practical prescriptions for a successful implementation of quantum information processing in nowadays hardware.

Drug-related problems in patients admitted for SARS-CoV-2 infection during the COVID-19 pandemic

Introduction: Drug-related problems (DRP) are events or circumstances in which drug therapy does or could interfere with desired health outcomes. In December 2019, a new coronavirus, SARS-CoV-2, appeared. Little knowledge about this type of infection resulted in the administration of various drugs with limited use in other pathologies. Evidence about DRP in patients with COVID-19 is lacking. Objective: The aim of the present study is to describe identified cases of DRP and those drugs involved in the first wave of patients with COVID-19, and evaluate associated risk factors. Material and methods: Observational, retrospective study performed in a tertiary university hospital between 14th March 2020 and 31 May 2020 (corresponding to the first COVID-19 wave). We recruited patients admitted during the study period. Exclusion criteria included age < 18 years; admission to critically ill units; and care received either in the emergency room, at-home hospitalization or a healthcare center. Results: A total of 817 patients were included. The mean age was 62.5 years (SD 16.4) (range 18-97), and 453 (55.4%) were male. A total of 516 DRP were detected. Among the patients, 271 (33.2%) presented at least one DRP. The mean DRP per patient with an identified case was 1.9. The prevailing DRPs among those observed were: incorrect dosage (over or underdosage) in 145 patients (28.2%); wrong drug combination in 131 (25.5%); prescriptions not in adherence to the then COVID-19 treatment protocol in 73 (14.1%); prescription errors due to the wrong use of the computerized physician order entry in 47 (9.2%); and incorrect dosage due to renal function in 36 (7%). The logistic regression analysis showed that patients who received only prescriptions of antibacterials for systemic use (J01 ATC group) faced a higher likelihood of experiencing a DRP (OR 2.408 (1.071-5.411), p = 0.033). Conclusion: We identified several factors associated with an increased risk of DRPs, similar to those reported in other pre-pandemic studies, including a prolonged length of stay, higher number of prescribed drugs and antimicrobial administration. The relevance of pharmacists and tools like pharmacy warning systems can help prevent, identify and resolve DRP efficiently.

Optimal quantum reservoir computing for the noisy intermediate-scale quantum era

Universal fault-tolerant quantum computers require millions of qubits with low error rates. Since this technology is years ahead, noisy intermediate-scale quantum (NISQ) computation is receiving tremendous interest. In this setup, quantum reservoir computing is a relevant machine learning algorithm. Its simplicity of training and implementation allows to perform challenging computations on today's available machines. In this Letter, we provide a criterion to select optimal quantum reservoirs, requiring few and simple gates. Our findings demonstrate that they render better results than other commonly used models with significantly less gates and also provide insight on the theoretical gap between quantum reservoir computing and the theory of quantum states' complexity.

Adapting reservoir computing to solve the Schrödinger equation

Reservoir computing is a machine learning algorithm that excels at predicting the evolution of time series, in particular, dynamical systems. Moreover, it has also shown superb performance at solving partial differential equations. In this work, we adapt this methodology to integrate the time-dependent Schrödinger equation, propagating an initial wavefunction in time. Since such wavefunctions are complex-valued high-dimensional arrays, the reservoir computing formalism needs to be extended to cope with complex-valued data. Furthermore, we propose a multi-step learning strategy that avoids overfitting the training data. We illustrate the performance of our adapted reservoir computing method by application to four standard problems in molecular vibrational dynamics.

Perceptions from nurses, surgeons, and anesthetists about the use and benefits of the surgical checklist in a teaching hospital

OBJECTIVE

To assess attitudes and perceptions from nursing staff, surgeons and anesthetists about compliance, utility, and impact on patient's safety of the surgical checklist in a teaching hospital. We also aimed to identify improvement opportunities for strengthening the usefulness of the checklist in the operating theater.

METHODS

We carried out a questionnaire-based an observational cross-sectional study. A questionnaire was distributed to operating room staff, including nursing staff, surgeons, and anesthetists. In addition to the information about surgical checklist, We also collected information regarding years of experience in the operating theater. Fisher's exact was used to compare proportions in each statement. Group discussion meetings with key professionals were held to jointly assess the results, propose improvement actions, and evaluate their feasibility.

RESULTS

The overall response rate was 36.2% (131/362). Nursing staff was perceived as the most supportive group in the use of surgical checklist. A 64.3% of surgeons considered that using the checklist prevented adverse events vs 84.2% and 85.7% among anesthetists and nurses, respectively; p=0.028. Junior staff showed a supportive attitude toward the use of surgical checklist, considering it as a tool that gives them confidence. We ended up with a list of improvement actions aiming at strengthening the surgical checklist reliability and compliance.

CONCLUSIONS

The perception of the surgical checklist usefulness as a tool to prevent adverse events was moderate among surgeons, but well appreciated by junior staff. Nursing staff were especially critical regarding compliance and support by other professionals. To reinforce the usefulness perception of the surgical checklist it is needed to increase the involvement of all professionals, especially senior staff and surgical leaders.

A decade of the Catalonian Arthroplasty Register (RACat): Variability, exhaustivity, and survival of prostheses between 2005 and 2014

BACKGROUND AND AIM

The Catalonian Arthroplasty Register (RACat) is a public health-based population register used to analyse and evaluate hip and knee replacements in Catalonia. The aim of this study is to present the outcomes after 10 years in operation (January 2005-December 2014).

METHODOLOGY

Using the information from the RACat and the minimum basic data set at hospital discharge, an analysis was made of the quality and exhaustivity of the data, as well as a descriptive analysis of the patients, prostheses, and care process. Survival was also analysed by calculating the accumulated incidence of revisions (according to the cause of intervention in hip replacements and conservation or sacrifice of the posterior cruciate ligament in knee replacement). The relationship between revision risk and the fixation technique of the prosthesis is also analysed, using competitive risk models adjusted for gender, age, and comorbidities.

RESULTS

The main reason for the primary hip and knee replacement surgery was arthrosis. The accumulated incidence of revisions at 10 years was 3.9% in hip replacements caused by arthrosis, and 2.3% in those caused by fracture. Conservation of the posterior cruciate ligament was achieved in 4.4% of knee replacements, with sacrifice in 5.1%.

DISCUSSION

The RACat is consolidated as a tool for the evaluation of joint replacements, with great potential in the analysis of medium and long-term efficacy, the study of the variability in clinical practice, and post-marketing surveillance.

Primary total hip arthroplasty in Catalonia: What is the clinical evidence that supports our prosthesis?

INTRODUCTION

The implementation of National Prostheses Registries allows us to obtain a large amount of data and make conclusions in order to improve the use of them. Sweden was the first country to implement a National Prostheses Registry in 1979. Catalonia has been doing this since 2005. The aim of our study is to analyse the evidence that supports primary total hip replacement in Catalonia in the last 9 years, based on the Arthroplasty Registry of Catalonia (RACat).

MATERIAL AND METHODS

A review of the literature was carried out of the prosthesis (acetabular cups/stems) reported in the RACat between the period 2005 to 2013 in the following databases: ODEP (Orthopaedic Data Evaluation Panel), TRIP database, PubMed, and Google Scholar. Those prostheses implanted in less than 10 units (182 acetabular components corresponding to 49 models/228 stems corresponding to 63 models) were excluded.

RESULTS

A total of 18,634 (99%) implanted acetabular cups were analysed out of a total number of 18,816, corresponding to 74 different models. In 18 models (2527 acetabular cups) no clinical evidence to support its use was found. An analysis was performed on 19,367 (98.84%) out of a total number of 19,595 implanted stems, corresponding to 75 different models. In 16 models (1845 stems) no clinical evidence was found to support their use. Variable evidence was found in the 56 models of acetabular cups (16,107) and 59 models of stems (17,522), most of it corresponding to level iv clinical evidence.

CONCLUSIONS

There was a significant number implanted prostheses evaluated (13.56% acetabular cups/9.5% stems) for which no clinical evidence was found. The elevated number of models is highlighted (49 types for acetabular cups/63 types for stems) with less than 10 units implanted, which corresponds to only 1% of the total implants. The use of arthroplasty registers is shown to be an extremely helpful tool that allows analyses and conclusions to be made for the follow-up and post-marketing surveillance period.

Living arrangements in four Asian countries: a comparative perspective

The findings of the 4 preceding country studies are examined here from a comparative perspective identifying some of the similarities and differences that underlie living arrangements of the elderly. More specifically, we compare the normative basis underlying living arrangements, mechanisms that help perpetuate co-residence, strains inherent to co-residence, preferences for co-residents, alternative forms of living arrangements, and views of social changes in relation to living arrangements. Overall, the focus group data on which the studies are based highlight the importance of culture in influencing the living arrangements of elderly people in Asia. The results suggest that at least for the next generation, co-residential living by elderly with children will continue to be a viable option, although the extent to which it persists is likely to vary among the 4 countries studied.

Variability analysis of the respiratory volume based on non-linear prediction methods

This work proposed and studied a method of automatically classifying respiratory volume signals as high or low variability by means of non-linear analysis of the respiratory volume. The analysis used volume signals generated by the respiratory system to construct a model of its dynamics and to estimate the quality of the predictions made with the model. Different methods of prediction evaluation, prediction horizons and embedding dimensions were also analysed. Assessment of the method was made using a database that contained 40 respiratory volume signals classified using clinical criteria into two classes: low or high variability. The results obtained using the method of surrogate data provided evidence of non-linear determinism in the respiratory volume signals. A discriminant analysis carried out using non-linear prediction variables classified the respiratory volume signals with an accuracy of 95%.