An adaptive trimming approach to Bayesian additive regression trees

A machine learning technique merging Bayesian method called Bayesian Additive Regression Trees (BART) provides a nonparametric Bayesian approach that further needs improved forecasting accuracy in the presence of outliers, especially when dealing with potential nonlinear relationships and complex interactions among the response and explanatory variables, which poses a major challenge in forecasting. This study proposes an adaptive trimmed regression method using BART, dubbed BART(Atr) to improve forecasting accuracy by identifying suspected outliers effectively and removing these outliers in the analysis. Through extensive simulations across various scenarios, the effectiveness of BART(Atr) is evaluated against three alternative methods: default BART, robust linear modeling with Huber’s loss function, and data-driven robust regression with Huber’s loss function. The simulation results consistently show BART(Atr) outperforming the other three methods. To demonstrate its practical application, BART(Atr) is applied to the well-known Boston Housing Price dataset, a standard regression analysis example. Furthermore, random attack templates are introduced on the dataset to assess BART(Atr)’s performance under such conditions

Lesional Intractable Epileptic Spasms in Children: Electroclinical Localization and Postoperative Outcomes

To analyze the influence of seizure semiology, electroencephalography (EEG) features and magnetic resonance imaging (MRI) change on epileptogenic zone localization and surgical prognosis in children with epileptic spasm (ES) were assessed. Data from 127 patients with medically intractable epilepsy with ES who underwent surgical treatment were retrospectively analyzed. ES semiology was classified as non-lateralized, bilateral asymmetric, and focal. Interictal epileptiform discharges were divided into diffusive or multifocal, unilateral, and focal. MRI results showed visible local lesions for all patients, while the anatomo-electrical-clinical value of localization of the epileptogenic zone was dependent on the surgical outcome. During preoperative video EEG monitoring, among all 127 cases, 53 cases (41.7%) had ES only, 46 (36.2%) had ES and focal seizures, 17 (13.4%) had ES and generalized seizures, and 11 (8.7%) had ES with focal and generalized seizures. Notably, 35 (27.6%) and 92 cases (72.4%) showed simple and complex ES, respectively. Interictal EEG showed that 22 cases (17.3%) had bilateral multifocal discharges or hypsarrhythmia, 25 (19.7%) had unilateral dominant discharges, and 80 (63.0%) had definite focal or regional discharges. Ictal discharges were generalized/bilateral in 71 cases (55.9%) and definite/lateralized in 56 cases (44.1%). Surgically resected lesions were in the hemisphere (28.3%), frontal lobe (24.4%), temporal lobe (16.5%), temporo-parieto-occipital region (14.2%), and posterior cortex region (8.7%). Seizure-free rates at 1 and 4 years postoperatively were 81.8 and 72.7%, respectively. There was no significant difference between electroclinical characteristics of ES and seizure-free rate. Surgical treatment showed good outcomes in most patients in this cohort. Semiology and ictal EEG change of ES had no effect on localization, while focal or lateralized epileptiform discharges of interictal EEG may affect lateralization and localization. Complete resection of epileptogenic lesions identified via MRI was the only factor associated with a positive surgical outcome

Efficient Table-Based Masking with Pre-processing

Masking is one of the most investigated countermeasures against sidechannel attacks. In a nutshell, it randomly encodes each sensitive variable into a number of shares, and compiles the cryptographic implementation into a masked one that operates over the shares instead of the original sensitive variables. Despite its provable security benefits, masking inevitably introduces additional overhead. Particularly, the software implementation of masking largely slows down the cryptographic implementations and requires a large number of random bits that need to be produced by a true random number generator. In this respect, reducing the< overhead of masking is still an essential and challenging task. Among various known schemes, Table-Based Masking (TBM) stands out as a promising line of work enjoying the advantages of generality to any lookup tables. It also allows the pre-processing paradigm, wherein a pre-processing phase is executed independently of the inputs, and a much more efficient online (using the precomputed tables) phase takes place to calculate the result. Obviously, practicality of pre-processing paradigm relies heavily on the efficiency of online phase and the size of precomputed tables. In this paper, we investigate the TBM scheme that offers a combination of linear complexity (in terms of the security order, denoted as d) during the online phase and small precomputed tables. We then apply our new scheme to the AES-128, and provide an implementation on the ARM Cortex architecture. Particularly, for a security order d = 8, the online phase outperforms the current state-of-the-art AES implementations on embedded processors that are vulnerable to the side-channel attacks. The security order of our scheme is proven in theory and verified by the T-test in practice. Moreover, we investigate the speed overhead associated with the random bit generation in our masking technique. Our findings indicate that the speed overhead can be effectively balanced. This is mainly because that the true random number generator operates in parallel with the processor’s execution, ensuring a constant supply of fresh random bits for the masked computation at regular intervals

Identification of gene mutations in six Chinese patients with maple syrup urine disease

Background: Maple syrup urine disease (MSUD) is a rare autosomal recessive amino acid metabolic disease. This study is to identify the pathogenic genetic factors of six cases of MUSD and evaluates the application value of high-throughput sequencing technology in the early diagnosis of MUSD.Methods: Clinical examination was carried out for patients and used blood tandem mass spectrometry (MS/MS), urine gas chromatography-mass spectrometry (GC/MS), and the application of high-throughput sequencing technology for detection. Validate candidate mutations by polymerase chain reaction (PCR)—Sanger sequencing technology. Bioinformatics software analyzed the variants’ pathogenicity. Using Swiss PDB Viewer software to predict the effect of mutation on the structure of BCKDHA and BCKDHB proteins.Result: A total of six MSUD patients were diagnosed, including four males and two females. Nine variants were found in three genes of six MSUD families by high-throughput sequencing, including four missense mutations: c.659C&gt;T(p.A220V), c.818C&gt;T(p.T273I), c.1134C&gt;G(p.D378E), and c.1006G&gt;A(p.G336S); two non-sense mutations: c.1291C&gt;T(p.R431*) and c.331C&gt;T(p.R111*); three deletion mutations: c.550delT (p.S184Pfs*46), c.718delC (p.P240Lfs*14), and c.795delG (p.N266Tfs*64). Sanger sequencing’s results were consistent with the high-throughput sequencing. The bioinformatics software revealed that the mutations were harmful, and the prediction results of Swiss PDB Viewer suggest that variation affects protein conformation.Conclusion: This study identified nine pathogenic variants in the BCKDHA, BCKDHB, and DBT genes in six MSUD families, including two novel pathogenic variants in the BCKDHB gene, which enriched the genetic mutational spectrum of the disease. High-throughput sequencing is essential for the MSUD’s differential diagnosis, early treatment, and prenatal diagnosis

Line Loss Calculation and Optimization in Low Voltage Lines with Photovoltaic Systems Using an Analytical Model and Quantum Genetic Algorithm

With the increasing integration of distributed photovoltaic (PV) generation into distribution networks, challenges such as power reverse flow and high line losses have emerged, leading to greater uncertainty in power systems. To address these issues, this paper presents an analytical model for calculating line losses in low-voltage distribution networks with PV generation, utilizing power flow calculations. A simulation model of a 15 node low-voltage network is developed using SIMULINK to validate the accuracy of the analytical model under the scenario of uniform load distribution (ULD). Additionally, a line loss optimization algorithm based on quantum genetic algorithms (QGA) is proposed for low-voltage distribution networks with distributed PV generation, along with an optimization model. The objective function of the optimization model is based on the reduction in line losses resulting from the integration of the PV system. The example results demonstrate the consistency between the line loss optimization using QGA and the analytical results, highlighting the significant advantages of QGA in terms of speed and accuracy. This research provides valuable insights for line loss optimization in low-voltage distribution networks with distributed PV generation and serves as a theoretical reference for future studies in this field

Novel STAMBP mutations in a Chinese girl with rare symptoms of microcephaly-capillary malformation syndrome and Mowat-Wilson syndrome

Microcephaly-capillary malformation syndrome (MIC-CAP) and Mowat-Wilson syndrome (MWS) are both rare hereditary diseases with several overlapping symptoms. We here report a Chinese patient simultaneously affected by MIC-CAP and MWS, presenting with moderate anaemia because of repeated, unilateral refractory epistaxis. The girl was initially diagnosed with MWS after discovery of a pathogenic nonsense mutation in ZEB2. Starting from the age of 3 years old, the child experienced repeated epistaxis on the right side without obvious incentive or trauma. The bleeding was quite difficult to stop and her hemoglobin dropped from 124 g/L to 64 g/L in three months. Both coagulation disorders and allergic rhinitis were excluded by extensive workup and experimental therapeutics. Retrospective genetic analysis revealed that she carried two novel compound heterozygous mutations in STAMBP (c.610T > C: p.Ser204Pro and c.945C > G: p.Asn315Lys). This case report demonstrates a rare presentation of MIC-CAP in the pediatric population and enriches the variant spectrum of STAMBP

Predictions of machine learning with mixed-effects in analyzing longitudinal data under model misspecification

We consider predictions in longitudinal studies, and investigate the well known statistical mixed-effects model, piecewise linear mixed-effects model and six different popular machine learning approaches: decision trees, bagging, random forest, boosting, support-vector machine and neural network. In order to consider the correlated data in machine learning, the random effects is combined into the traditional tree methods and random forest. Our focus is the performance of statistical modelling and machine learning especially in the cases of the misspecification of the fixed effects and the random effects. Extensive simulation studies have been carried out to evaluate the performance using a number of criteria. Two real datasets from longitudinal studies are analysed to demonstrate our findings. The R code and dataset are freely available at https://github.com/shuwen92/MEML.</p

Bioactivity and health effects of garlic essential oil: A review

Abstract Garlic (Allium sativum L.), the underground bulb of the Allium plant in the family Liliaceae, is a common and popular spice that has historically been used to prevent and treat many different diseases such as pain, deafness, diarrhea, tumors, and other healthy problems. Garlic essential oil contains a variety of organosulfur compounds, such as the most representative diallyl disulfides (DADS) and diallyl trisulfides (DATS), which have attracted great interest in medicine, food, and agriculture because of their rich biological activities. This paper reviews the research progress on the composition and bioactivities of garlic essential oil mixtures and the bioactivity of some typical monomeric sulfides in garlic essential oil. The active mechanisms of representative sulfides in garlic essential oil were analyzed, and the applications of garlic essential oil in functional food, food additives, and clinical treatment were discussed. Combined with the current research status, the limitations and development direction of garlic essential oil in the study of molecular mechanism were discussed, which is of great significance to the development of garlic essential oil as a natural and safe alternative medicine for treatment

Construction and validation of a risk prediction model for perianal infection in patients with haematological malignancies during chemotherapy: a prospective study in a tertiary hospital in China

Objectives Perianal infection is a serious complication in patients undergoing chemotherapy for haematological malignancies. Therefore, we aimed to develop a predictive model to help medical staff promptly screen patients at a high risk of perianal infection during chemotherapy.Design This was a single-centre prospective observational study.Setting This study was conducted in a tertiary teaching hospital in Chengdu, China.Participants The study sample comprised 850 patients with haematological malignancies who underwent chemotherapy at the department of haematology or our hospital between January 2021 and June 2022.Interventions The included patients were randomly divided into training and validation groups in a 7:3 ratio. Based on the discharge diagnosis, patients with perianal infection were selected as the case group and the other patients were selected as the control group.Outcome measure The main outcome measure was the occurrence of perianal infections.Results A predictive model for perianal infections was established. A history of perianal infection, haemorrhoids, constipation and duration of diarrhoea were independent risk factors. The area under the curve of the The area under the receiver operating characteristic (ROC) curve for the training and validation groups were 0.784 (95% CI 0.727 to 0.841) and 0.789 (95% CI 0.818 to 0.885), respectively. Additionally, the model had good calibration in both the training and validation groups with a non-significant Hosmer-Lemeshow test (p=0.999 and 0.482, respectively).Conclusions The risk prediction model, including a history of perianal infection, history of haemorrhoids, constipation and duration of diarrhoea ≥3 days of perianal infection in patients with haematological malignancies during chemotherapy, has good prediction reliability and can be helpful in guiding clinical medical staff in screening and early intervention of high-risk groups

The Performance of Electronic Current Transformer Fault Diagnosis Model: Using an Improved Whale Optimization Algorithm and RBF Neural Network

With the widely application of electronic transformers in smart grids, transformer faults have become a pressing problem. However, reliable fault diagnosis of electronic current transformers (ECT) is still an open problem due to the complexity and diversity of fault types. In order to solve this problem, this paper proposes an ECT fault diagnosis model based on radial basis function neural network (RBFNN) and optimizes the model parameters and the network size of RBFNN simultaneously via an improved whale optimization algorithm (WOA) to improve the classification accuracy and robustness of RBFNN. Since the classical WOA is easy to fall into a locally optimal performance, a hybrid multi-strategies WOA algorithm (CASAWOA) is proposed for further improvement in optimization performance. Firstly, we introduced the tent chaotic map strategy to improve the population diversity of WOA. Secondly, we introduced nonlinear convergence factor and adaptive inertia weight to enhance the exploitation ability of the WOA. Finally, on the premise of ensuring the convergence speed of the algorithm, we modified the simulated annealing mechanism in order to prevent premature convergence. The benchmark function tests show that the CASAWOA outperforms other state-of-the-art WOA algorithms in terms of convergence speed and exploration ability. Furthermore, to validate the performance of ECT fault diagnosis model based on CASAWOA-RBFNN, a comprehensive analysis of eight fault diagnosis methods is conducted based on the ECT fault samples collected from the detection circuit. The experimental results show that the CASAWOA-RBFNN achieves an accuracy of 97.77% in ECT fault diagnosis, which is 9.8% better than WOA-RBF and which shows promising engineering practicality