Regular Case Research Articles

Safety and Efficacy of Live Retrograde Intrarenal Surgery in Patients with Kindey Stone: Outcomes from a Boutique Course Series Which Complies with the Live Surgery Event Policies.

Live surgery events (LSEs) are frequently organized for sharing the surgical experiences with surgeons at the beginning of their learning curves. The aim of this study was to investigate whether the outcomes and complication rates of patients underwent retrograde intrarenal surgery (RIRS) at LSEs are comparable with regular cases. Ten courses were organized during 2017-2022. Data of 32 patients who operated in the LSEs were 1:3 matched (for stone burden and surgeon) with the data of patients who underwent regular RIRS within the course periods at the same centers (n = 96). All courses took place in concordance with the latest LSE policies. The primary outcomes were stone-free and complication rates. Fluoroscopy and operation times were the secondary outcomes. Stone-free rates of the groups were similar (84% in LSE and 79% in control group; p = 0.520). Similarly, there were no differences in complication rates (p = 0.428) and fluoroscopy time (p = 0.477). Duration of the LSE cases (82.24 ± 31.12 min) was slightly but insignificantly longer than regular cases (73.77 ± 20.89 min, p = 0.092). Moreover, guest surgeons tend to have longer operation time with statistically insignificant prolongation (74.92 ± 30.43 min for host, 89.52 ± 28.34 min for guest surgeons, p = 0.064). RIRS can be performed without jeopardizing operation efficiency and patient safety in LSEs. If surgeon is not familiar with operating room set-up or staff, live surgery must be performed by host surgeon to avoid extended operating time.

A Discussion of “A Note on Universal Inference” by Tse and Davison

We congratulate the authors for their insightful contribution to universal inference, as proposed in Wasserman et al. (2020). The split likelihood ratio test (SLRT) provided by the universal inference framework is valid in finite samples and requires essentially no regularity conditions. However, this amazing universality also comes at a price as the SLRT may be highly conservative. In their article, Tse and Davison (2022) explore how nuisance parameters impact the conservativeness of the SLRT. They study the loss of power in the basic normal location model, taking up the analysis in Dunn et al. (2022). Tse and Davison also propose a modification to reduce the conservatism of the SLRT. Universal inference is justified by an application of Markov's inequality, and the modification is based on the clever observation that, in the normal location model, the expectation to be considered may be more tightly controlled by exploiting the independence of two terms that emerge in the presence of nuisance parameters. The impact of using this trick for irregular testing problems is illustrated in an example based on a Gaussian mixture model. An independent recent work of our group studies universal inference from a related but also different angle (Strieder & Drton, 2022). We would like to take the opportunity of this discussion to point out similarities and also differences between this work and the paper of Tse and Davison. The main results in Strieder and Drton (2022) provide the large-sample theory for the SLRT under both the null hypothesis and local alternatives assuming the classical regularity conditions that lead to Wilks' theorem for the standard likelihood ratio test (see Theorem 3.1 and related Corollaries/Remarks in Strieder & Drton, 2022). The setup allows for general composite null hypotheses. The limiting distributions of the SLRT are then used to illustrate how (loss of) power of the test is impacted by dimensionality of the hypotheses. These limiting distributions coincide with the distributions that are considered in the paper of Tse and Davison (e.g. Equations 8, 13–14). The two articles differ in the way the (limiting) distribution of the SLRT is used in order to improve power. While Tse and Davison focus on using the independence of the two parts stemming from the parameter of interest and the nuisance parameter, Strieder and Drton concentrate on highlighting the impact of the data splitting. Tse and Davison utilize insights from the regular setting under the perspective of nuisance parameters to motivate the use of a smaller critical value. Such a smaller critical value obviously increases power and continues to control the size in the regular case. However, with the modified critical value, one no longer has a universal guarantee to control the size in general irregular problems. This raises the question: How far should we go in terms of lowering the critical value once we start setting it based on asymptotic distributions derived for regular testing problems? While this would be easy to do, we certainly do not want to go all the way to the relevant quantile of the regular limiting distribution. But then which principles should we invoke to justify a particular lowered critical value when applying it to irregular problems? As mentioned, the work of Strieder and Drton pursues a different goal and proposes a different modification. Instead of changing the critical value, this modification employs the regular limiting distribution merely to tune the data splitting ratio (at a target power level). Tuning the splitting ratio has a more limited impact on power, but importantly, an SLRT with tuned splitting remains universally valid in finite samples—no matter whether the problem is regular or not. Tse and Davison also investigate the power of the SLRT across various splitting ratios with the conclusion that a ratio of γ = 2 / 3 $$ \gamma =2/3 $$ performs best. We emphasize that this is a feature of the considered low-dimensional setting. Contrasting Figure 2 from the article of Tse and Davison, Figure 1 here displays the power and size of the SLRT when testing a 45-dimensional hypothesis in a 50-dimensional space and shows that in a higher-dimensional setting, a splitting ratio below 0.5 performs best. For a detailed analysis of the optimal choice of the splitting ratio based on the dimensionality of tested null and alternative hypotheses, we refer to Strieder and Drton (2022). Open Access funding enabled and organized by Projekt DEAL.

Some New Time and Cost Efficient Quadrature Formulas to Compute Integrals Using Derivatives with Error Analysis

In this research, some new and efficient quadrature rules are proposed involving the combination of function and its first derivative evaluations at equally spaced data points with the main focus on their computational efficiency in terms of cost and time usage. The methods are theoretically derived, and theorems on the order of accuracy, degree of precision and error terms are proved. The proposed methods are semi-open-type rules with derivatives. The order of accuracy and degree of precision of the proposed methods are higher than the classical rules for which a systematic and symmetrical ascendancy has been proved. Various numerical tests are performed to compare the performance of the proposed methods with the existing methods in terms of accuracy, precision, leading local and global truncation errors, numerical convergence rates and computational cost with average CPU usage. In addition to the classical semi-open rules, the proposed methods have also been compared with some Gauss–Legendre methods for performance evaluation on various integrals involving some oscillatory, periodic and integrals with derivative singularities. The analysis of the results proves that the devised techniques are more efficient than the classical semi-open Newton–Cotes rules from theoretical and numerical perspectives because of promisingly reduced functional cost and lesser execution times. The proposed methods compete well with the spectral Gauss–Legendre rules, and in some cases outperform. Symmetric error distributions have been observed in regular cases of integrands, whereas asymmetrical behavior is evidenced in oscillatory and highly nonlinear cases.

Evaluating an implementation model of evidence-based therapy for eating disorders in non-specialist regional mental health settings

BackgroundMany people with eating disorders (EDs) either do not access treatment, access it well after symptoms first start, or drop out of treatment. This study evaluated ways to improve early access to evidence-based interventions for those with EDs in a non-specialist community setting.MethodsIn an Australian regional community, links were formed between general medical practitioners and treatment providers (psychologists, mental health social workers and dietitians), who received ongoing training, feedback and support. Service users had access to 20–40 subsidised treatment sessions. Data were collected from 143 patients over 18 months. Our outcomes are reported according to the RE-AIM implementation framework: Reach (we measured uptake and treatment completion); Effectiveness (impact on disordered eating cognitions, body mass index, remission, and moderators of effectiveness including illness duration, previous treatment, presence of comorbidities, presence of a normative level of disordered eating, presence of any ED behaviours, weighing in treatment, multidisciplinary case conferencing, number of dietetic sessions); Adoption (drop-out and predictors); Implementation (barriers encountered); Maintenance (subsequent activity designed to embed new practices).ResultsTreatment was completed by 71%; significant large decreases in eating disorder cognitions were achieved; remission was obtained by 37% (intent-to-treat). Treatment completion was predicted by lower baseline levels of disordered eating, uptake of ≥ 3 dietetic sessions, and ≥ 2 team case conferences. Greater improvement over time was predicted by regular case conferencing and in-session weighing.ConclusionsImplementation of this model in a regional community setting produced completion rates and outcomes comparable to those found in specialist clinical trials of ED treatments. Service providers identified care coordination as the most important factor to connect users to services and help navigate barriers to ongoing treatment.Trial Registration: This research was an invited evaluation of a project implemented by the Australian Department of Health. The project did not introduce any new clinical practice but sought to improve access to evidence-based multidisciplinary treatment for people with EDs by removing four known systemic barriers: securing an accurate diagnosis, availability of multidisciplinary treatment, cost of treatment, and intensity of treatment. As such, the project did not require trial registration. Notwithstanding, this evaluation obtained ethics approval (Bellberry Human Research Ethics Committee, Application No: 2018-09-728-FR-1).

Evaluation of proppant injection based on a data-driven approach integrating numerical and ensemble learning models

Injecting proppant to prop open fluid-driven fractures in subsurface reservoirs is one of the key missions of hydraulic fracturing. However, quantitative evaluation of the distribution of successfully propped fractures is limited due to the infeasibility of direct measurement. This work defines an indexing parameter for field practice to estimate the proportion of proppant-filled fractures in the reservoir – the proppant filling index (PFI). A new data-driven workflow, combining numerical models and an ensemble learning algorithm, is proposed and trained on field records of both screen-out and near screen-out cases and is then applied to predict PFIs for regular cases. The algorithm performance is promoted via variable importance measure (VIM) analyses and a backward elimination strategy. Four screen-out and twelve regular cases are presented to demonstrate the predicted PFI and highlight its potential utilizations. The new PFI and workflow evaluate the proppant injection quantitatively and reveal any mismatch between proppant injection and underground fractures, which may be essential for post-fracturing analyses and reservoir characterization to improve both oil & gas recovery, the sequestration of CO2, storage then recovery of H2 and the recovery of deep geothermal fluids as important components in enabling the energy transition.

Tests for isotropy in spatial point patterns – A comparison of statistical indices

Isotropy of a point process, defined as invariance of the distribution under rotation, is often assumed in spatial statistics. Formal tests for the hypothesis of isotropy can be created by comparing directional summary statistics in different directions. In this paper, the statistical powers of tests based on a variety of summary statistics and several choices of deviance measures are compared in a simulation study. Four models for anisotropic point processes are considered covering both regular and clustered cases. We discuss the robustness of the results to changes of the tuning parameters, and highlight the strengths and limitations of the methods.

The Singularity Theorems of General Relativity and Their Low Regularity Extensions

On the occasion of Sir Roger Penrose’s 2020 Nobel Prize in Physics, we review the singularity theorems of General Relativity, as well as their recent extension to Lorentzian metrics of low regularity. The latter is motivated by the quest to explore the nature of the singularities predicted by the classical theorems. Aiming at the more mathematically minded reader, we give a pedagogical introduction to the classical theorems with an emphasis on the analytical side of the arguments. We especially concentrate on focusing results for causal geodesics under appropriate geometric and initial conditions, in the smooth and in the low regularity case. The latter comprise the main technical advance that leads to the proofs of C^{1}-singularity theorems via a regularisation approach that allows to deal with the distributional curvature. We close with an overview on related lines of research and a future outlook.

Numerical Simulation and Design of COVID-19 Disease Detection System Based on Improved Computing Techniques

The high demand for testing the sickness has led to a lack of resources at emergency clinics as the coronavirus epidemic continues. PC vision-based frameworks can be used to increase the productivity of Coronavirus localization. However, a significant amount of information preparation is needed to create an accurate and reliable model, which is currently impractical given the peculiar nature of the illness. One such model is for differentiating pneumonia cases by using radiographs, and it has achieved sufficiently high exactness to be used on patients. Various models are currently being used inside the medical services sector to order different illnesses. This proposal evaluates the benefit of using motion learning to broaden the presentation of the Coronavirus location model, starting from the premise that there is limited information available for Coronavirus ID. Infections that affect the human lungs include viral pneumonia caused by the coronavirus and other viruses. The World Health Organization (W.H.O.) proclaimed Covid a pandemic in 2020; the sickness originated in China and quickly spread to other countries. Early diagnosis of infected patients aids in saving the patient's life and prevents the infection's further spread. As one of the quickest and least expensive methods for diagnosing the condition, the convolutional neural organization (CNN) model is suggested in this research study to assist in the early detection of the infection using chest X-Beam images. Two convolutional brain organizations (CNN) models were created using two different datasets. The primary model was created for double characterization using one of the datasets that only included pneumonia cases and common chest X-Beam images. The second model made use of the information advanced by the primary model using move learning and was created for three class divisions on chest X-Beam images of cases with the coronavirus, pneumonia, and regular cases.

Incorporation of spatial- and connectivity-based cortical brain region information in regularized regression: Application to Human Connectome Project data.

Studying the association of the brain's structure and function with neurocognitive outcomes requires a comprehensive analysis that combines different sources of information from a number of brain-imaging modalities. Recently developed regularization methods provide a novel approach using information about brain structure to improve the estimation of coefficients in the linear regression models. Our proposed method, which is a special case of the Tikhonov regularization, incorporates structural connectivity derived with Diffusion Weighted Imaging and cortical distance information in the penalty term. Corresponding to previously developed methods that inform the estimation of the regression coefficients, we incorporate additional information via a Laplacian matrix based on the proximity measure on the cortical surface. Our contribution consists of constructing a principled formulation of the penalty term and testing the performance of the proposed approach via extensive simulation studies and a brain-imaging application. The penalty term is constructed as a weighted combination of structural connectivity and proximity between cortical areas. Simulation studies mimic the real brain-imaging settings. We apply our approach to the study of data collected in the Human Connectome Project, where the cortical properties of the left hemisphere are found to be associated with vocabulary comprehension.

Data science and IoT based mobile monitoring framework for hyper-local PM2.5 assessment in urban setting

Ambient air quality shows a high degree of spatiotemporal variation. It is known to impact human life and health. Obtaining meaningful estimates of human exposure to air pollutants is key to mitigating its ill effects. In order to ascertain individual exposure, pollutant information is required at high resolution. Current air quality monitoring approaches utilize static monitoring stations that are sparsely distributed across a city which in turn results in poor spatial resolution. Such stations allow only for gross assessment of air quality across a city. This work proposes a comprehensive framework for hyperlocal assessment of ambient air quality through a mobile monitoring approach. PM2.5 concentration is considered in this work as a specific air pollutant to demonstrate the framework. In order to test the efficacy of the approach, two case studies were undertaken in the city of Chennai, India as a part of this work. The first study aims at locating diurnal, spatio-temporal PM2.5 hotspots in an urban environment. These hotspots are then associated with daily anthropogenic activity. The second study enables an event centric understanding of spatio-temporal short-lived extremities in PM2.5 concentration. The results are then retrospectively associated with land use profile and anthropological activities. The mobile sensing paradigm provides a unique perspective in the assessment of such localized events. This micro-level assessment offers significant insights on the spatio-temporal variations within a city in a regular case and an extreme, event centric case. Real world applications of hyperlocal air quality assessment, such as least exposure route estimation, are also explored at the end of this work.

Time-dependent contact mechanics

Contact geometry allows us to describe some thermodynamic and dissipative systems. In this paper we introduce a new geometric structure in order to describe time-dependent contact systems: cocontact manifolds. Within this setting we develop the Hamiltonian and Lagrangian formalisms, both in the regular and singular cases. In the singular case, we present a constraint algorithm aiming to find a submanifold where solutions exist. As a particular case we study contact systems with holonomic time-dependent constraints. Some regular and singular examples are analyzed, along with numerical simulations.

A novel approach to treat polycystic ovarian syndrome (PCOS) patients

The precise underlying pathophysiology for the progression of Polycystic Ovary Syndrome (PCOS) remains unclear. Eminent scientists and clinicians normally target the given clinical symptoms to curtail the syndrome in a given period. For years, multiple varied techniques have been developed and followed to treat this disorder. A novel non-invasive, pain-free treatment ‘Neurotherapy’ has been highlighted in this article that can be considered as a boon in the field of treatment and healing. This study aimed to evaluate the effectiveness of Neurotherapy in a patient with PCOS. A regular case of PCOS with ultrasonography denoting bilateral ovarian cysts is hereby discussed. Post the therapy, ultrasound reports have shown no trace of cysts in both the ovaries thereby concluding that this novel therapy can be very fruitful in treating one of the most common gynaecology related female problems, without any side effects or after-effects.

General variable neighborhood search for the parallel machine scheduling problem with two common servers

We address in this paper the parallel machine scheduling problem with a shared loading server and a shared unloading server. Each job has to be loaded by the loading server before being processed on one of the available machines and unloaded immediately by the unloading server after its processing. The objective function involves the minimization of the overall completion time, known as the makespan. This important problem raises in flexible manufacturing systems, automated material handling, healthcare, and many other industrial fields, and has been little studied up to now. To date, research on it has focused on the case of two machines. The regular case of this problem is considered. A mixed integer programming formulation based on completion time variables is suggested to solve small-sized instances of the problem. Due to its mathcal{NP}mathcal{}-hardness, we propose two greedy heuristics based on the minimization of the loading, respectively unloading, server waiting time, and an efficient General Variable Neighborhood Search (GVNS) algorithm. In the computational experiments, the proposed methods are compared using 120 new and publicly available instances. It turns out that, the proposed GVNS with an initial solution-finding mechanism based on the unloading server waiting time minimization significantly outperforms the other approaches.

Total graph of a signed graph

The total graph is built by joining the graph to its line graph by means of the incidences. We introduce a similar construction for signed graphs. Under two similar defnitions of the line signed graph, we defne the corresponding total signed graph and we show that it is stable under switching. We consider balance, the frustration index and frustration number, and the largesteigenvalue. In the regular case we compute the spectrum of the adjacency matrix of the total graph and the spectra of certain compositions, and we determine some with exactly two main eigenvalues.

The Sum and the Product of Two Quadratic Matrices: Regular Cases

The Sum and the Product of Two Quadratic Matrices: Regular Cases

Epidemiology-constrained Seating Plan Problem

Abstract The emergence of an infectious disease pandemic may result in the introduction of restrictions in the distance and number of employees, as was the case of COVID-19 in 2020/2021. In the face of fluctuating restrictions, the process of determining seating plans in office space requires repetitive execution of seat assignments, and manual planning becomes a time-consuming and error-prone task. In this paper, we introduce the Epidemiology-constrained Seating Plan problem (ESP), and we show that it, in general, belongs to the NP-complete class. However, due to some regularities in input data that could a affect computational complexity for practical cases, we conduct experiments for generated test cases. For that reason, we developed a computational environment, including the test case generator, and we published generated benchmarking test cases. Our results show that the problem can be solved to optimality by CPLEX solver only for specific settings, even in regular cases. Therefore, there is a need for new algorithms that could optimize seating plans in more general cases.

On Stein’s lemma in hypotheses testing in general non-asymptotic case

The problem of testing two simple hypotheses in a general probability space is considered. For a fixed type-I error probability, the best exponential decay rate of the type-II error probability is investigated. In regular asymptotic cases (i.e., when the length of the observation interval grows without limit) the best decay rate is given by Stein’s exponent. In the paper, for a general probability space, some non-asymptotic lower and upper bounds for the best rate are derived. These bounds represent pure analytic relations without any limiting operations. In some natural cases, these bounds also give the convergence rate for Stein’s exponent. Some illustrating examples are also provided.

Does Multisystem Inflammatory Syndrome Only Mimic Acute Appendicitis in Children or Can It Coexist: When Should We Suspect MIS-C?

Background and Objectives: Acute abdominal pain in children has been noticed to be a primary reason to seek medical attention in multisystem inflammatory disorder (MIS-C), which can prevail separately or together with acute appendicitis. Our aim was to distinguish regular appendicitis cases from MIS-C and to suggest the best clinical and laboratory criteria for it. Materials and methods: Cases of patients, admitted to the Pediatric Surgery Department over a six-month period in 2021, were retrospectively analyzed. Confirmed MIS-C or acute appendicitis cases were selected. MIS-C cases were either separate/with no found inflammation in the appendix or together with acute appendicitis. Acute appendicitis cases were either regular cases or with a positive COVID-19 test. Four groups were formed and compared: A-acute appendicitis, B-MIS-C with acute appendicitis, C-MIS-C only and D-acute appendicitis with COVID-19. Results: A total of 76 cases were overall analyzed: A-36, B-6, C-29 and D-5. The most significant differences were found in duration of disease A—1.4 days, B—4.5 days, C—4 days, D—4 days (p < 0.0001), C reactive protein (CRP) values A-19.3 mg/L B-112.5 m/L, C-143.8 mg/L and D-141 mg/L (p < 0.0001), presence of febrile fever A-13.9%, B-66.7%, C-96.6% and D-40% (p < 0.0001) and other system involvement: A 0%, B 100%, C 100% and D 20%. A combination of these factors was entered into a ROC curve and was found to have a possibility to predict MIS-C in our analyzed cases (with or without acute appendicitis) with an AUC = 0.983, p < 0.0001, sensitivity of 94.3% and specificity of 92.7% when at least three criteria were met. Conclusions: MIS-C could be suspected even when clinical data and performed tests suggest acute appendicitis especially when at least three out of four signs are present: CRP > 55.8 mg, symptoms last 3 days or longer, febrile fever is present, and any kind of other system involvement is noticed, especially with a known prior recent COVID-19 contact, infection or a positive COVID-19 antibody IgG test.

Care practices for patients requiring mechanical ventilation more than seven days in Swedish intensive care units: A national survey

ObjectiveTo identify care practices in Swedish intensive care units specific to patients requiring mechanical ventilation for >7 days. Research MethodologyWe conducted a national cross-sectional survey inviting all adult Swedish ICUs (n = 79). Nurse managers were invited by email to complete a questionnaire by telephone. The questionnaire included seven domains: ventilator weaning, mobilisation, communication, nutrition, symptom assessment, psychosocial support and organisational characteristics. ResultsWe received responses from 77 units (response rate, 97%). Weaning protocols were available in 42 (55%) units, 52 (68%) used individualised weaning strategies and 50 (65%) involved physicians and nurses in collaborative decision making. In 48 units (62%), early mobilisation was prioritised using bed cycling but only 26 (34%) units had mobilisation protocols. Most of the intensive care units (74, 96%) had nutrition protocols but only 2 (3%) had dedicated dieticians. Delirium screening tools were available in 49 (64%) ICUs, 3 (4%) assessed anxiety and none assessed dyspnoea. Nineteen (25%) units employed a primary nursing model and 11 (14%) indicated person-centred care policies. Regular case conferences, including family participation, were held by 39 (51%) units. ConclusionWe found that an individualised approach to ventilator weaning, decided by physicians and nurses in collaboration, was the predominant approach, although weaning protocols were available in some intensive care units. Most units prioritised early mobilisation, though few used protocols. Nutritional protocols were widely adopted, as few units had a dedicated dietician.

Symptom provocation for treatment of obsessive-compulsive disorder using transcranial magnetic stimulation: A step-by-step guide for professional training.

Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation technique that was cleared by the Food and Drug Administration (FDA) for the treatment of Obsessive-Compulsive Disorder (OCD) in 2018. The approved protocol includes individualized symptom provocation before each stimulation session, to elicit a moderate level of obsessional distress. Although symptom provocation can be a delicate, demanding, and uncomfortable procedure, structured training methods for those who are going to apply it are not available. Here, we describe a model for training in symptom provocation for TMS technicians, developed at the Champalimaud Clinical Centre in Lisbon, Portugal. Our programme includes two-sessions dedicated to clinical communication and symptom provocation techniques from a theoretical and practical perspective. Additionally, supervision meetings are conducted during treatment of patients, allowing regular case discussion and redefinition of symptom provocation hierarchy, as needed. In addition to having a strong practical component, our training program is short and pragmatic, allowing for easy implementation and fluid transition to clinical practice. By sharing our experience, we hope to contribute to systematize training procedures required for symptom provocation in the context of TMS, and to qualitatively describe a methodology that can be used for implementation of TMS programmes for the treatment of OCD.