Length Of Interval Research Articles

Involvement of MID-1 COMPLEMENTING ACTIVITY 1 encoding a mechanosensitive ion channel in prehaustorium development of the stem parasitic plant Cuscuta campestris.

Parasitic plants pose a substantial threat to agriculture as they attack economically important crops. The stem parasitic plant Cuscuta campestris invades the host's stem with a specialized organ referred to as the haustorium, which absorbs nutrients and water from the host. Initiation of the parasitic process in C. campestris requires mechanical stimuli to its stem. However, the mechanisms by which C. campestris perceives mechanical stimuli are largely unknown. Previous studies have shown that mechanosensitive ion channels (MSCs) are involved in the perception of mechanical stimuli. To examine if MSCs are involved in prehaustorium development upon tactile stimuli, we treated C. campestris plants with an MSC inhibitor, GsMTx-4, which resulted in a reduced density of prehaustoria. To identify the specific MSC gene involved in prehaustorium development, we analyzed the known functions and expression patterns of Arabidopsis MSC genes and selected MID-1 COMPLEMENTING ACTIVITY 1 (MCA1) as a primary candidate. The MSC activity of CcMCA1 was confirmed by its ability to complement the phenotype of a yeast mid1 mutant. To evaluate the effect of CcMCA1 silencing on prehaustorium development, we performed host-induced gene silencing using Nicotiana tabacum plants that express an artificial microRNA (amiRNA) targeting CcMCA1. In the CcMCA1-silenced C. campestris, the number of prehaustoria per centimeter of stem length decreased, and the interval length between prehaustoria increased. Additionally, the expression levels of known genes involved in prehaustorium development, such as CcLBD25, decreased significantly in the CcMCA1-silenced plants. The results suggest that CcMCA1 is involved in prehaustorium development in C. campestris.

Pharmacodynamics of NOSO-502 studied in vitro and in vivo: determination of the dominant pharmacodynamic index driver.

NOSO-5O2 is the first clinical candidate of a new antimicrobial class-the odilorhabdins. The pharmacodynamics of NOSO-502 were studied in vitro and in vivo to establish the pharmacodynamic index (PDI) driver. A dilutional pharmacokinetic system was used for in vitro experiments. In vivo experiments were conducted in the neutropenic murine thigh infection model. Three Escherichia coli and two Klebsiella pneumoniae strains were used. NOSO-502 pharmacokinetics (PK) in mice were described in a population PK model. Dose fractionation and escalation exposures were performed and bacteriostatic and 1 log10 and 2 log10 kill effects were determined using an Emax model. The coefficient of determination (R2) was used to estimate the variance that might be due to regression with various PDIs (fAUC/MIC, fCmax/MIC, %fT>MIC and fAUC/MIC per length of dosing interval fAUC/MIC·1/tau). Exposure fractionation in both in vitro and in vivo studies indicated that 6-hourly dosing resulted in a greater reduction in K. pneumoniae bacterial burden compared with either 12- or 24-hourly dosing. Similar observations were made with three E. coli strains in vitro and a single strain of E. coli in vivo. For K. pneumoniae, R2 for fAUC/MIC·1/tau was 0.8376 in vitro and 0.6001 in vivo, which was greater than R2 with other PDIs. Analysis of pooled in vitro data from three strains of E. coli produced an R2 of 0.7073 for fAUC/MIC and 0.6100 for fAUC/MIC·1/tau. NOSO-502 exhibits both dose- and time-dependent in vitro and in vivo activity against both E. coli and K. pneumoniae.

The Best ECG Lead for Predicting the Risk of Drug-Induced Torsade De Pointes Using Corrected QT Interval: A Comparative Prognostic Study.

Torsade de pointes (TdP) is a deadly complication from drug-induced QT prolongation. Each of the 12 lead of an electrocardiogram (ECG) has a different length of QT interval, and thus might have a different performance in TdP prediction. This study aimed to determine the best ECG lead or set of leads in this regard. This is a comparative prognostic accuracy study using a two-gate data gathering design. The population in this study was from two sources, a case group (Patients who had drug-induced TdP, which were identified through a systematic Medline search) and a control group (those who overdosed on QT-prolonging drugs, which included patients who were under the consultation of Medical Toxicology Services). The areas under the receiver operating characteristic curve (AUROC) of heart rate-corrected QT (QTc) in each single ECG lead and of a mean/median QTc from a set of ECG leads (17 index test) in predicting the risk of TdP were calculated and compared with each other, trying to find the best lead for this propose. QTc Interval measurements were done by four investigators (Interrater reliabilities 0.95). Finally, we included 136 and 148 ECGs from TdP cases and controls, respectively. V3 lead had the highest frequency of longest QTc interval, among the leads. The lead having the longest QTc yielded the greatest AUROC in predicting TdP regardless of QT correction formulas (QTcFRA=0.9915, QTcRTH=0.9893, QTcBZT=0.9904). The mean QTc of 3 leads (lead II, plus any two of leads V2-V4), and a median QTc of 6 leads (I, II, aVF, V2, V4, V6) provided similar overall performance for TdP prediction (regardless of the type of QTc formula). The longest QTc provided the greatest AUROC in predicting drug-induced TdP, however, the longest QTc is not located in a fixed individual lead in any patient. A less time-consuming method with comparable performance to that of the longest QTc was to use a mean QTc from 3 leads (lead II, plus any two of leads V2-V4). The potential clinical impact of this finding needs to be verified in a prospective cohort study.

Estimating percentiles of time-to-failure distribution obtained from a Weibull accelerated degradation model

We propose a nonparametric kernel estimation method to estimate the percentiles of the time-to-failure distribution under the usual use condition obtained from a Weibull accelerated degradation model. We discuss some of the well-known parametric method that used to estimate the time-to-failure distribution and its percentile under the usual use condition including ordinary least square method and maximum likelihood method. The different exiting methods were compared with the kernel method through simulation by using the mean square error and the bootstrap confidence interval length. In general, when the distributional assumption is available, the maximum likelihood estimator performs better than the other two estimators, while the kernel estimator performs better than the other two estimators when the distributional assumption is not available. Application to real data set was disscuced.

Switching from natalizumab to antiCD20 monoclonal antibodies: Short transition interval is associated with improved outcome.

To investigate the impact of transition interval length when switching from natalizumab (NTZ) to anti-CD20 monoclonal antibodies (antiCD20) on recurrent disease activity and safety in relapsing multiple sclerosis (RMS). Aggregating data from 8 MS centres in Austria, Switzerland, and Germany, we included RMS patients who (i) continuously received NTZ for ≥3 months, (ii) were switched to antiCD20, and (iii) had ≥12 months follow-up after switch. The primary endpoint was occurrence of relapse after switch, secondary endpoints included severe infections (CTCAE grade ≥3). Overall, 139 RMS patients were included (70.5% females, mean age at switch 38.8 years [SD 9.7], mean disease duration at switch 11.3 years [SD 6.2], median duration on NTZ 4.4 years [range: 0.3-16.4], median transition interval 58 days [0-180]). Relapse occurred in 18 patients (12.9%) after NTZ discontinuation. Of those, 11 (61.1%) patients relapsed during the transition interval. No patient with a transition interval below 30 days experienced a relapse, compared to 11.1% and 16.1% with transition intervals of 30-44 days and ≥ 45 days, respectively. In multivariable Cox regression, a transition interval ≥ 45 days predicted a 4.73-fold increased risk of relapse. Over approximately 4 years of follow-up, six severe infections were reported without any noticeable effect of transition interval length. No PML occurred. Switching from NTZ to antiCD20 is generally both effective and safe. Keeping the transition interval below 30 days provides the optimal balance between preventing recurrent disease activity and ensuring safety.

Effect of copper and nickel exposure on ribosomal DNA variation in Daphnia pulex mutation accumulation lines.

The release of heavy metals from industrial, agricultural, and mining activities poses significant risks to aquatic ecosystems by degrading water quality and generating reactive oxygen species (ROS) that can damage DNA in aquatic organisms. Daphnia is a widespread keystone species in freshwater ecosystems that is routinely exposed to a range of anthropogenic and natural stressors. With a fully sequenced genome, a well-understood life history and ecology, and an extensive library of responses to toxicity, Daphnia serves as an ideal model organism for studying the impact of environmental stressors on genomic stability. Ribosomal DNA (rDNA) encodes ribosomal RNA, which is essential for protein synthesis, and the spacers that separate the rRNA genes contain regulatory elements. However, the effects of heavy metals on this region of the genome are not well documented. We used short-read whole-genome sequences to analyze copy number and sequence variation in Daphnia pulex mutation accumulation lines exposed to nickel and copper, both individually and in combination, at concentrations that mimic levels often found at contaminated sites. We found no significant direct effect of chronic exposure to either metal on rDNA copy number or sequence variation. However, the results suggest that nickel and copper exposure may indirectly influence rDNA by altering recombination rates. We also emphasize the importance of interval length between generational samples for accurately assessing the frequency and magnitude of rDNA copy number changes. In addition, we observed differential expansion of rDNA haplotypes, suggesting that they may be clustered within the rDNA array.

Factors associated with time to first birth interval among ever married Bangladeshi women: A comparative analysis on Cox-PH model and parametric models.

The fertility rate of a married woman can be measured by the length of the first birth interval (FBI). This length is influenced by some significant factors. Better knowledge about the factors affecting the birth interval can help in controlling population growth and fertility progress. The main focus of this study was to compare the performance of Cox-Proportional Hazard (Cox-PH) and the parametric Accelerated Failure Time (AFT) model in assessing the impact of significant factors affecting the time to FBI of ever-married Bangladeshi women. Information of 14941 women having at least one birth was included in this study from the most recent nationally representative data 2017-18 Bangladesh Demographic and Health Survey (BDHS). We used the Cox-PH model and AFT model under various parametric forms of survival time distributions (Weibull, Exponential, and Log-normal distribution) to measure the effect of factors influencing FBI. And then, a respective Akaike information criterion (AIC) was calculated for selecting the best-fitted model. According to the AIC and BIC values, the log-normal model fitted better than other AFT models. Based on the log-normal model, women's age and age at first marriage, maternal and paternal education, contraceptive use status, used anything to avoid pregnancy, sex of household head, and spousal age difference had a significant association with FBI of ever married Bangladeshi women. The parametric AFT model (log-normal distribution) was a better fitted model in evaluating the covariates associated with FBI of ever-married Bangladeshi Women. Higher education, the right age at marriage, and proper knowledge about family planning (i.e., contraception use) should be ensured for every married person to control the gap of the first birth.

Comparison of long- and short-rest periods during high-intensity interval exercise on transcriptomic responses in equine skeletal muscle.

The purpose of this study was to elucidate the skeletal muscle transcriptomic response unique to rest duration during high-intensity interval exercise. Thoroughbred horses performed three 1-min bouts of exercise at their maximal oxygen uptake (10.7-12.5 m/s), separated by 15 min (long) or 2 min (short) walking at 1.7 m/s. Gluteus medius muscle was collected before and at 4 h after the exercise and used for RNA sequencing. We identified 1,756 and 1,421 differentially expressed genes in response to the long and short protocols, respectively, using DEseq2 analysis [false discovery rate (FDR) cutoff = 0.05, minimal fold change = 1.5]. The overall transcriptional response was partially aligned, with 43% (n = 949) of genes altered in both protocols, whereas no discordant directional changes were observed. K-means clustering and gene set enrichment analyses based on Gene Ontology biological process terms showed that genes associated with muscle adaptation and development were upregulated regardless of exercise conditions; genes related to immune and cytokine responses were more upregulated following the long protocol, and protein folding and temperature response were highly expressed after the short protocol. We found that 11 genes were upregulated to a greater extent by the short protocol and one was by the long protocol, with GNA13, SPART, PHAF1, and PTX3 identified as potential candidates for skeletal muscle remodeling. Our results suggest that altered metabolic fluctuations dependent on the intermittent pattern of interval exercise modulate skeletal muscle gene expression, and therefore, rest interval length could be an important consideration in optimizing skeletal muscle adaptation.NEW & NOTEWORTHY This is the first study to address the comparison of transcriptional responses to high-intensity interval exercise with two different rest periods in skeletal muscle. The expression of genes related to metabolic adaptations altered in both conditions, while genes associated with immune and cytokine responses and protein folding and temperature response were varied with the length of the rest period. These results provide evidence for rest duration-specific transcriptional response to high-intensity interval training.

IMPACT OF BOX-COX TRANSFORMATION TECHNIQUE ON THE BAYESIAN MAXIMUM ENTROPY (BME) PREDICTION ACCURACY

This study investigated whether increasing the normality of an attribute using Box-Cox transformation improves Bayesian Maximum Entropy (BME) prediction accuracy. Furthermore, we examined if BME accuracy is affected by sample size or spatial dependence. For hard data, the unconditional sequential approach was used to simulate symmetric data (skewness = 0) and data positively skewed (skewness: 1, 3, 6, and 9) with sample size ranging from 100 to 500 at the interval length of 50. Soft data was randomly distributed throughout a square of unit size and a width of 1.5. Data was then transformed using Box-Cox transformation. The prediction accuracy was assessed using the Mean Square Error (MSE) and bias, and transformation methods were compared using the Multivariate Analysis of Variance (MANOVA). The results showed BME accuracy is affected by transformation methods but not the sample size and the spatial dependency. However, in comparing the transformed data with the untransformed data, the MSE and bias of the untransformed data (lambda = 1) were closer to zero than the transformed data lambda 1. As a result, we concluded that BME is robust to skewness, sample size, and spatial dependency.

Chemotherapy-Surgery Interval Effects on Tumor Necrosis and Outcome in Children and Young Adults With Osteosarcoma.

Osteosarcoma treatment incorporates chemotherapy and surgery. Resection of the primary tumor usually occurs after induction chemotherapy. Occasionally, scheduling challenges and medical complications result in delay. The goal of this study is to determine if an increased interval between completion of neoadjuvant therapy and surgical resection correlates with decreased tumor necrosis and inferior outcomes in children and young adults with osteosarcoma. We conducted a retrospective chart review of 121 patients age less than 40years diagnosed with osteosarcoma treated at a single tertiary medical center between 2000 and 2022. Inclusion criteria included receipt of two cycles of neoadjuvant methotrexate, cisplatin, and doxorubicin. Association of the interval from completion of induction chemotherapy to resection with tumor necrosis (Spearman's correlation) and outcomes (multivariable Cox hazard regression) were analyzed. There was no significant correlation between interval length and tumor necrosis. However, patients with an interval greater than 16days had lower 5-year event-free survival (p = 0.019). Multivariable adjusted analysis of patients with initially localized disease revealed that each day increase in interval length corresponds with a 1.1 times greater hazard of having an event (95% CI: 1.0-1.2; p = 0.016). Delays in local control were not associated with tumor necrosis. This is consistent with the hypothesis that tumor necrosis is a biologic marker of a tumor's sensitivity to chemotherapy and may not be affected by minor regimen aberrations. However, surgical delay from completion of induction chemotherapy may confer worse outcomes. Longer intervals generally confer worse outcomes in patients with initially localized disease.

Dependence of metrics and noise intensity on escape dynamics: mean first exit time and escape probability

Abstract This paper focuses on analyzing two deterministic quantities of the dynamics of stochastic systems, mean first exit time and escape probability. Through a detailed exploration of the escape phenomenon, we have successfully fitted the complex dynamics of the mean first exit time as a function of interval length (area size) and noise intensity, respectively. We have found that the mean first exit time tends to increase monotonically with the interval length (area size). Meanwhile, we also accurately captured the dynamical behavior of the minimum escape probability under various parameter cases. The relationship between them displays a range of patterns when we observed different intervals. Our research highlights the importance of noise effects when considering escape processes, especially in the context of interaction of interval length (area size) and noise intensity. A concrete error analysis is conducted to ensure the accuracy and generalization ability of the model’s predictions.

Calculating the Optimal Point Cloud Density for Airborne LiDAR Landslide Investigation: An Adaptive Approach

Ensuring that ground point density after raw point cloud processing meets the accuracy requirements for subsequent DEM construction represents a challenge for field operators during airborne LiDAR data acquisition. In this study, we propose a method to quantify DEM quality by combining the RMSE of elevation and terrain complexity, analyzing the DEM quality error curves constructed with different point cloud densities by a discrete difference peak-seeking method, to determine the optimal ground point density, and then constructing an ICP-NN algorithm for predicting the collected point cloud density. After analysis of DEM quality at eight point cloud dilution levels, the optimal ground point cloud densities were determined to be 2.43 pts/m2 (0.2 m resolution), 2.08 pts/m2 (1 m and 0.5 m resolution), and 1.84 pts/m2 (2 m resolution). Using the obtained optimal ground point densities, survey area slopes, canopy density, and elevation differences as eigenvalues, the ICP-NN model can be used to directly predict the collected point cloud density intervals in other regions, and the model has interval lengths ranging from 36 to 70.33 pts/m2 at 5 CLs. This method solves the problem of determining point cloud density in landslide surveys using airborne LiDAR and provides direct guidance for practical applications.

A Note on Confidence Intervals for a Binomial p: Andersson–Nerman vs. Wilson

ABSTRACTNumerous approximate confidence intervals in closed form have been suggested over the years for the parameter in the binomial distribution. One of the oldest and still most advocated is the Wilson (score) interval, which in this article will be compared with the less well‐known Andersson–Nerman (henceforth AN) interval. These intervals are quite closely related, but it will be shown analytically and illustrated by examples that the coverage probability of the AN interval always equals or exceeds that of the Wilson interval, while also having uniformly larger expected length. Asymptotic expressions for the coverage probability and expected length of the AN interval are provided, using Edgeworth and Taylor expansions, respectively. The well‐behaved Wilson and AN pivots are furthermore contrasted with the problematic Wald pivot. The latter gives rise to the Wald interval, which is probably one of the best known and most used procedures altogether in the history of statistical inference. Unfortunately, this interval performs poorly, but even to this day it is to be found in many current textbooks. Therefore, it is still of relevance to search for attractive alternatives based on sound statistical principles.

An optimal exact confidence interval for the difference of two independent binomial proportions.

The difference between two proportions is the most important parameter in comparing two treatments based on independent two binomials and has garnered widespread application across various fields, particularly in clinical trials. There exists significant interest in devising optimal confidence intervals for the difference. Approximate intervals relying on asymptotic normality may lack reliability, thus calling for enhancements in exact confidence interval construction to bolster reliability and precision. In this paper, we present a novel approach that leverages the most probable test statistic and employs the -function method to construct an optimal exact interval for the difference. We juxtapose the proposed interval against other exact intervals established through methodologies such as the Agresti-Min exact unconditional method, the Wang method, the fiducial method, and the hybrid score method. Our comparative analysis, employing the infimum coverage probability and total interval length as evaluation metrics, underscores the uniformly superior performance of the proposed interval. Additionally, we elucidate the application of these exact intervals using two real datasets.

The benefits of social insurance system prediction using a hybrid fuzzy time series method.

Decision-making in many industries relies heavily on accurate forecasts, including the insurance sector. The Social Insurance System (SIS) in Egypt, operating under a fully funded paradigm, depends on reliable predictions to ensure effective financial planning. This research introduces a hybrid predictive model that combines fuzzy time series (FTS) Markov chains with the tree partition method (TPM) and difference transformation to forecast total pension benefits within Egypt's SIS. A key feature of the proposed model is its ability to optimize the partitioning process, resulting in the creation of nine intervals that reduce computational complexity while maintaining forecasting accuracy. These intervals were consistently applied across all fuzzy time series models for comparison. The model's performance is evaluated using established metrics such as MAPE, Thiels' U statistic, and RMSE. Additionally, prediction interval coverage probability (PICP) and mean prediction interval length (MPIL) are used to assess the quality of prediction intervals, with a 95% prediction interval serving as the baseline. The proposed model achieved a PICP of approximately 95%, indicating well-calibrated prediction intervals, although the MPIL of 424.5 reflects a wider uncertainty range. Despite this, the model balances coverage accuracy and interval precision effectively. The results demonstrate that the proposed model significantly outperforms traditional models like linear regression, ARIMA, and exponential smoothing and conventional FTS models like Song, Chen, Yu, and Cheng by achieving the lowest MAPE with the value of 11.8% for training and 10.65% for testing. This superior performance highlights the model's reliability and potential applicability to further forecasting tasks in the field of insurance and beyond.

Short-Term Predictions of the Trajectory of Mpox in East Asian Countries, 2022–2023: A Comparative Study of Forecasting Approaches

The 2022–2023 mpox outbreak exhibited an uneven global distribution. While countries such as the UK, Brazil, and the USA were most heavily affected in 2022, many Asian countries, specifically China, Japan, South Korea, and Thailand, experienced the outbreak later, in 2023, with significantly fewer reported cases relative to their populations. This variation in timing and scale distinguishes the outbreaks in these Asian countries from those in the first wave. This study evaluates the predictability of mpox outbreaks with smaller case counts in Asian countries using popular epidemic forecasting methods, including the ARIMA, Prophet, GLM, GAM, n-Sub-epidemic, and Sub-epidemic Wave frameworks. Despite the fact that the ARIMA and GAM models performed well for certain countries and prediction windows, their results were generally inconsistent and highly dependent on the country, i.e., the dataset, as well as the prediction interval length. In contrast, n-Sub-epidemic Ensembles demonstrated more reliable and robust performance across different datasets and predictions, indicating the effectiveness of this model on small datasets and its utility in the early stages of future pandemics.

AI-Driven Prediction of Symptom Trajectories in Cancer Care: A Deep Learning Approach for Chemotherapy Management.

This study presents an advanced method for predicting symptom escalation in chemotherapy patients using Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs). The accurate prediction of symptom escalation is critical in cancer care to enable timely interventions and improve symptom management to enhance patients' quality of life during treatment. The analytical dataset consists of daily self-reported symptom logs from chemotherapy patients, including a wide range of symptoms, such as nausea, fatigue, and pain. The original dataset was highly imbalanced, with approximately 84% of the data containing no symptom escalation. The data were resampled into varying interval lengths to address this imbalance and improve the model's ability to detect symptom escalation (n = 3 to n = 7 days). This allowed the model to predict significant changes in symptom severity across these intervals. The results indicate that shorter intervals (n = 3 days) yielded the highest overall performance, with the CNN model achieving an accuracy of 81%, precision of 87%, recall of 80%, and an F1 score of 83%. This was an improvement over the LSTM model, which had an accuracy of 79%, precision of 85%, recall of 79%, and an F1 score of 82%. The model's accuracy and recall declined as the interval length increased, though precision remained relatively stable. The findings demonstrate that both CNN's temporospatial feature extraction and LSTM's temporal modeling effectively capture escalation patterns in symptom progression. By integrating these predictive models into digital health systems, healthcare providers can offer more personalized and proactive care, enabling earlier interventions that may reduce symptom burden and improve treatment adherence. Ultimately, this approach has the potential to significantly enhance the overall quality of life for chemotherapy patients by providing real-time insights into symptom trajectories and guiding clinical decision making.

Assessing the impact of hard data patterns on Bayesian Maximum Entropy: a simulation study

This study empirically tested the robustness of Bayesian Maximum Entropy (BME) in predicting spatiotemporal data, with an emphasis on skewness, sample size, and spatial dependency level. Simulated data, both Gaussian and non-Gaussian, were generated using the unconditional sequential simulation method, with sample sizes ranging from 100 to 500 at the interval length of 50 and varying skewness (0, 1, 3, 6 and 9) and spatial dependency levels (weak, moderate, and strong). Findings revealed sample size variations and spatial dependence levels did not significantly influence BME prediction’s Mean Square Error (MSE) and bias. While skewness significantly impacted MSE (p-value < 0.001), bias remained unaffected. Moreover, skewness and spatial dependence interactions affected both MSE and bias. Despite this, BME proved robust to sample size and skewness, demonstrating a negligible MSE on the graphical plot (heatmap).

Evaluation of BLE Star Network for Wireless Wearable Prosthesis/Orthosis Controller

Concomitant improvements in wireless communication and sensor technologies have increased capabilities of wearable biosensors. These improvements have not transferred to wireless prosthesis/orthosis controllers, in part due to strict latency and power consumption requirements. We used a Bluetooth Low Energy 5.3 (BLE) network to study the influence of the connection interval (10–100 ms) and event length (2500–7500 μs), ranges appropriate for real-time myoelectric prosthesis/orthosis control on the maximum network size, power consumption, and latency. The number of connections increased from 4 to 12 as the connection interval increased from 10 to 50 ms (event length of 2500 μs). For connection intervals ≤50 ms, the number of connections reduced by ≥50% with the increasing event length. At a connection interval of 100 ms, little change was observed in the number of connections vs. event length. Across event lengths, increasing the connection interval from 10 to 100 ms decreased the average power consumed by approximately 16%. Latency measurements showed that an average of one connection interval (maximum of just over two) elapses between the application of the signal at the peripheral node ADC input and its detection on the central node. Overall, reducing the latency using shorter connection intervals reduces the maximum number of connections and increases power consumption.

The Statistics of Parametrized Syncmers in a Simple Mutation Process Without Spurious Matches.

Introduction: Often, bioinformatics uses summary sketches to analyze next-generation sequencing data, but most sketches are not well understood statistically. Under a simple mutation model, Blanca et al. analyzed complete sketches, that is, the complete set of unassembled k-mers, from two closely related sequences. The analysis extracted a point mutation parameter θ quantifying the evolutionary distance between the two sequences. Methods: We extend the results of Blanca et al. for complete sketches to parametrized syncmer sketches with downsampling. A syncmer sketch can sample k-mers much more sparsely than a complete sketch. Consider the following simple mutation model disallowing insertions or deletions. Consider a reference sequence A (e.g., a subsequence from a reference genome), and mutate each nucleotide in it independently with probability θ to produce a mutated sequence B (corresponding to, e.g., a set of reads or draft assembly of a related genome). Then, syncmer counts alone yield an approximate Gaussian distribution for estimating θ. The assumption disallowing insertions and deletions motivates a check on the lengths of A and B. The syncmer count from B yields an approximate Gaussian distribution for its length, and a p-value can test the length of B against the length of A using syncmer counts alone. Results: The Gaussian distributions permit syncmer counts alone to estimate θ and mutated sequence length with a known sampling error. Under some circumstances, the results provide the sampling error for the Mash containment index when applied to syncmer counts. Conclusions: The approximate Gaussian distributions provide hypothesis tests and confidence intervals for phylogenetic distance and sequence length. Our methods are likely to generalize to sketches other than syncmers and may be useful in assembling reads and related applications.