Running Time Research Articles

A Retrospective Analysis of Southeastern U.S. Police Recruit Health and Fitness From 4 Points in Time Within a 16-Year Period: Implications for Physical Fitness Training.

Gonzales, SM, Orr, RM, Coburn, JW, Hoffmann, MD, Kennedy, K, Dawes, JJ, and Lockie, RG. A retrospective analysis of southeastern U.S. police recruit health and fitness from 4 points in time within a 16-year period: Implications for physical fitness training. J Strength Cond Res 38(6): 1118-1126, 2024-Drawing from the general population for its recruiting needs, police departments often employ academy curricula to improve a recruit's fitness so they can perform occupational tasks. Recently, obesity and physical inactivity have increased in the general population, potentially influencing the health and fitness of incoming recruits. This study involved a retrospective, cross-sectional analysis of health and fitness data of police recruits. Data from 1 police department in southeastern United States were analyzed at 4 specific time points, splitting the recruits into natural immerging groups; 2003 (n = 93), 2006 (n = 137), 2009 (n = 74), and 2019 (n = 242). Health and fitness data for all recruits included age, height, body mass, and body mass index (BMI); systolic and diastolic blood pressure (BP); sit-and-reach; combined grip strength; push-ups and sit-ups completed in 60 seconds; physical ability test time; and 2.4-km run time. Several univariate analyses, with sex and age as covariates (analyses of covariance), and a Bonferroni's post hoc, determined whether there were significant between-group differences. Effect sizes (d) were also calculated. Key results demonstrated that 2019 recruits were heavier than the recruits in 2006 and 2009 (p ≤ 0.032; d = 0.14-0.38); had a greater BMI (p ≤ 0.028; d = 0.24-0.75) and systolic BP (p < 0.001; d = 0.47-0.65), and lesser sit-and-reach distance (p ≤ 0.020; d = 0.26-0.46), than all recruit groups; completed fewer sit-up repetitions than the recruits in 2006 and 2009 (p ≤ 0.025; d = 0.42-0.48); and were slower in the 2.4-km run than the recruits in 2006 (p = 0.009; d = 0.36). Police training staff may need to address lesser health (BMI, BP) and fitness (hamstring flexibility, abdominal endurance, aerobic fitness) in incoming recruits.

A new efficient decoder of linear block codes based on ensemble learning models: case of boosting

&lt;p&gt;Error-correcting codes are used to partially or completely correct errors as much as possible, while ensuring high transmission speeds. Several Machine Learning (ML) models such as Logistic Regression and Decision tree have been applied to correct transmission errors. Among the most powerful ML techniques are aggregation methods which have yielded to excellent results in many areas of research. It is this excellence that has prompted us to consider their application for the hard decoding problem. In this sense, we have successfully designed, tested and validated our proposed EL-BoostDec decoder (hard decision decoder based on Ensemble Learning - Boosting technique) which is based on computing of the syndrome of the received word and on using Ensemble Learning techniques to find the corresponding corrigible error. The obtained results with EL-BoostDec are very encouraging in terms of the binary error rate (BER) that it offers. Practically EL-BoostDec has succeed to correct 100% of errors that have weights less than or equal to the correction capacity of studied codes. The comparison of EL-BoostDec with many competitors proves its power. A study of parameters which impact on EL-BoostDec performances has been established to obtain a good BER with minimum run time complexity.&lt;/p&gt;

Face recognition with occluded face using improve intersection over union of region proposal network on Mask region convolutional neural network

Face recognition entails detecting and identifying facial attributes. Mask region convolutional neural network (R-CNN) method is a prominent approach, while prior research predominantly delved into refining loss functions and perfecting object and face detection, recognizing, and identifying faces using imperfect data remained relatively unexplored. This study focuses on an occluded dataset comprising Indonesian faces, wherein 'occluded' denotes facial data that lacks complete visibility-encompassing instances where objects obscure faces or are partially cropped. This investigation involves a deliberate experiment that tailors the intersection over union (IoU) of the region proposal network (RPN) to suit the nuances of occluded Indonesian faces, thereby augmenting accuracy in recognition and segmentation tasks. The innovation IoU in the strategic utilization of Anchors, which involves the exclusion of anchors falling beyond the image borders to optimize computational efficiency. The outcomes of this research are striking; it showcases a remarkable 14.75%, 10.9%, and 12.97% surge based on mean average precision (mAP), mean average recall (mAR), and F1-Scores compared to the conventional Mask R-CNN approach. Notably, our proposed model elevates the average accuracy by 10% to 15% and decreases running time by 21%, a noteworthy enhancement compared to the preceding model. This progress is substantiated by validation utilizing 300 instances dataset, reinforcing the robustness of our approach.

An efficient exact algorithm for planted motif search on large DNA sequence datasets.

DNA motif is the pattern shared by similar fragments in DNA sequences, which plays a key role in regulating gene expression, and DNA motif discovery has become a key research topic. Exact planted (l,d)-motif search (PMS) is one of the motif discovery approaches, which aims to find from t sequences all the (l,d)-motifs that are motifs of l length appearing in at least qt sequences with at most d mismatches. The existing exact PMS algorithms are only suitable for small datasets of DNA sequences. The development of high-throughput sequencing technology generates vast amount of DNA sequence data, which brings challenges to solving exact PMS problems efficiently. Therefore, we propose an efficient exact PMS algorithm called PMmotif for large datasetsof DNA sequences, after analyzing the time complexity of the existing exact PMS algorithms. PMmotif finds (l,d) -motifs with strategy by searching the branches on the pattern tree that may contain (l,d) -motifs. It is verified by experiments that the running time ratio of the existing excellentPMS algorithmstoPMmotif isbetween14.83and 58.94. In addition, for the first time, PMmotif can solve the (15,5) and(17,6) challenge problem instances on large DNA sequence datasets within 24 hours.

SuiETAS: Developing and Testing ETAS-Based Earthquake Forecasting Models for Switzerland

ABSTRACT We present the development and testing of multiple epidemic-type aftershock sequence (ETAS)-based earthquake forecasting models for Switzerland, aiming to identify suitable candidate models for operational earthquake forecasting (OEF) at the Swiss Seismological Service. We consider seven model variants: four variants use parameters obtained through fitting the ETAS model to the Swiss earthquake catalog, and three use generic parameters that were fit to Californian seismicity or global seismicity from regions tectonically similar to Switzerland. The model variants use different pieces of information from the current state-of-the-art time-independent earthquake rate forecast underlying the Swiss seismic hazard model (SUIhaz2015), and one is calibrated on a larger local data set that includes smaller earthquakes by allowing a time-dependent estimate of the completeness magnitude. We test all variants using pseudoprospective short-term (7-day) forecasting experiments and retrospective long-term (30-year) consistency tests. Our results suggest that all ETAS-based models outperform the time-independent SUIhaz2015 forecast in the short term, but two of the model variants overestimate event numbers in the long term. ETAS parameters are found not to be universally transferrable across tectonic regimes, and region-specific calibration is found to add value over generic parameters. Finally, we rank all model variants based on six criteria, including the results of the pseudoprospective and retrospective tests, as well as other criteria such as model run time or consistency with the existing long-term model, using a multicriteria decision analysis approach. Based on this ranking, we propose the ETAS model calibrated on the Swiss catalog, and with the spatial background seismicity distribution of SUIhaz2015 as the ideal candidate for the first Swiss OEF system. All procedures and choices involved in the development and testing of the Swiss ETAS model follow recently established expert recommendations and can act as a reference in the establishment of time-variant earthquake forecasting models for other regions.

3D Refraction Travel Time Accuracy Study in High Contrasted Media

Among all the existing methods to solve the eikonal equation, three methods are chosen to verify accuracy, symmetry, reciprocity and error propagation along large offsets of refracted waves in seismic near surface exploration context. Performance is extremely highlighted nowadays and accuracy is being neglected, then an eikonal solver poorly explored in geoscience is used. A classical solver, the Fast Iterative and the modified Fast Sweeping Method are applied in three modeling schemes: a simple two layers model, a large four layers and a complex benchmark model. The three methods compute the first arrival of refracted waves in high contrast media and the results are compared to the analytical solution. A circular geometry is considered in all experiments to explore the method applicability using full azimuth angles. On the first scheme, the errors in travel time are computed among the three methods using different model sample spacing and we discuss accuracy, symmetry and reciprocity of first arrivals. On the second scheme, three circular receivers are placed in different offsets to check errors along refracted wave propagation. Finally, the third scheme, four shots are strategically positioned over the SEG/EAGE Overthrust model in order to compare the full acoustic wavefield with the eikonal solvers and then check the similarities. Although the focus is on methods accuracy, the algorithm run time is also considered and the comparison shows that the modified Fast Sweeping Method is the most accurate. The most computational efficient eikonal solver is the Fast Iterative Method, but its geoscience applicability needs to be cautious, because of its inaccurate results.

On Computing the \(k\) -Shortcut Fréchet Distance

The Fréchet distance is a popular measure of dissimilarity for polygonal curves. It is defined as a min-max formulation that considers all orientation-preserving bijective mappings between the two curves. Because of its susceptibility to noise, Driemel and Har-Peled introduced the shortcut Fréchet distance in 2012, where one is allowed to take shortcuts along one of the curves, similar to the edit distance for sequences. We analyse the parameterized version of this problem, where the number of shortcuts is bounded by a parameter \(k\) . The corresponding decision problem can be stated as follows: Given two polygonal curves \(T\) and \(B\) of at most \(n\) vertices, a parameter \(k\) and a distance threshold \(\delta\) , is it possible to introduce \(k\) shortcuts along \(B\) such that the Fréchet distance of the resulting curve and the curve \(T\) is at most \(\delta\) ? We study this problem for polygonal curves in the plane. We provide a complexity analysis for this problem with the following results: (i) there exists a decision algorithm with running time in \(\mathcal{O}(kn^{2k+2}\log n)\) ; (ii) assuming the exponential-time-hypothesis (ETH), there exists no algorithm with running time bounded by \(n^{o(k)}\) . In contrast, we also show that efficient approximate decider algorithms are possible, even when \(k\) is large. We present a \((3+\varepsilon)\) -approximate decider algorithm with running time in \(\mathcal{O}(kn^{2}\log^{2}n)\) for fixed \(\varepsilon\) . In addition, we can show that, if \(k\) is a constant and the two curves are \(c\) -packed for some constant \(c\) , then the approximate decider algorithm runs in near-linear time.

Development of HPLC method for isomeric impurities of key starting material of novel oral anticoagulant drug; Edoxaban Tosylate Monohydrate

Reverse-phase high-performance liquid chromatography method has been developed for the determination of EDO-S1 stereoisomeric impurities such as isomer 1, isomer 2, isomer 3, isomer 4, isomer 5, isomer 6 and isomer 7 with good resolution using the column, Bakerbond C18 (150 × 4.6 mm; 3 μm). The separation was achieved with mobile phase-A (10 mM dipotassium hydrogen phosphate pH-7.0 with 10% orthophosphoric acid solution in Milli-Q water) and mobile phase-B (n-Propanol: Acetonitrile ratio of 20:30 % V/V), which consisted of mobile phase mixture in the combination of moilephase-A: mobile phase-B (85:15). The total run time was 30 min at 0.8 mL/min flow rate, 20 µL injection volume and 30 ℃ column oven temperature. The column eluate was monitored at 210 nm to quantify the impurities The method showed adequate specificity, sensitivity, linearity, accuracy, precision, and robustness inline to ICH tripartite guidelines. The limit of detection and quantification limits were 0.1 and 0.3 μg mL−1, respectively, for all isomeric impurities and EDO-S1. The developed method was found to be linear over the concentration range of LOQ to 150% of specification range for isomeric impurities with a correlation coefficient &gt;0.999. The method was precise (%RSD &lt; 5.0), robust, and accurate (with 85%–115% recovery).

Quantitation of tazemetostat in human plasma using liquid chromatography-tandem mass spectrometry.

To support a phase 1 trial in patients with lymphomas, we developed a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for tazemetostat quantitation in 20 μL of human plasma. After protein precipitation, chromatographic separation employed a Kinetex C18 column and a gradient of 0.1% formic acid in both water and acetonitrile, during a 3-min run time. Detection was achieved using a SCIEX 6500+ tandem mass spectrometer with electrospray positive-mode ionization. Validation was based on the latest Food and Drug Administration guidance. With a stable isotopic internal standard, the assay was linear within the range of 10-5000 ng/mL and proved to be accurate (91.9%-103.7%) and precise (<4.4% imprecision). Recovery varied between 93.3% and 121.1%, and matrix effect ranged from -25.5% to -4.9%. Hemolysis, lipemia, and dilution did not impact quantitation. Plasma stability was confirmed after three freeze-thaw cycles, 24 h at room temperature, and 4months at -80°C. Incurred sample reanalysis yielded 94.4% samples within 20% difference (n = 36). External validation showed a mean bias of -11.1%. Pharmacokinetic (PK) data obtained from three patients suggested variable concentration time profiles, warranting collection of further data. The assay proved to be suitable for tazemetostat quantitation in human plasma and will support clinical studies by defining tazemetostat PKs.

A Sampling-Based Approach to Solve Difficult Path Planning Queries Efficiently in Narrow Environments for Autonomous Ground Vehicles

Path planning is an essential subproblem of autonomous robots’ navigation. Reaching a given goal pose or covering the available space are typical navigation missions, that require different planning approaches. We focus on such problems in this paper, where a goal pose must be reached by a wheeled autonomous ground vehicle in challenging situations, i.e. in complex environments with limited free space. Many path-planning methods are available, from which the sampling-based approaches gained the highest interest due to their computational efficiency. However, the performance of such methods degrades if the free space is limited and narrow passages have to be crossed on the way to the goal. Finding real-time planning methods to deliver high-quality paths in such situations is challenging. This paper aims to take steps toward solving this problem. On the one hand, an approach is presented to characterize free space narrowness and the difficulty of planning tasks. This can be used as a tool to compare planning queries and evaluate the performance of planning methods from the perspective of their sensitivity to environmental narrowness. On the other hand, an improved variant of our previously proposed RTR planner, an incremental sampling-based path-planning method, is introduced that exhibits good performance even in narrow and difficult planning situations. It is shown by simulations that it outperforms the popular RRT and RRT* planners in terms of running time and path quality, and that it is less sensitive to the narrowness of the environment where the planning task has to be solved.

Rapid measurement method for key dimensions of train wheelset based on improved image processing algorithm

The key dimensions of train wheelsets change with the increase of running time. To ensure the safe operation of the train, the key dimensions should meet the relevant technical requirements. Maintenance of wheelsets and measurement of key dimensions are important part of the daily maintenance work. A rapid measurement method based on image processing algorithms for key dimensions of hand-held wheelsets integrating multiple parameter measurements is studied. The general framework of the system is examined and the roles of the modules are described. The optical center for the initial position of the continuous line spot is searched. The continuous line spot center extraction algorithm during tread measurement is optimized. A three-dimensional model is reconstructed using point cloud data, then key dimensions are measured and calculated from the model. In order to verify the feasibility of the method, traditional measurement methods are used for comparison tests. The results show that the method is effective in the daily maintenance of trains.

Lentinula edodes substrate formulation using multilayer perceptron-genetic algorithm: a critical production checkpoint

Shiitake (Lentinula edodes) is one of the most widely grown and consumed mushroom species worldwide. They are a potential source of food and medicine because they are rich in nutrients and contain various minerals, vitamins, essential macro- and micronutrients, and bioactive compounds. The reuse of agricultural and industrial residues is crucial from an ecological and economic perspective. In this study, the running length (RL) of L. edodes cultured on 64 substrate compositions obtained from different ratios of bagasse (B), wheat bran (WB), and beech sawdust (BS) was recorded at intervals of 5 days after cultivation until the 40th day. Multilayer perceptron-genetic algorithm (MLP-GA), multiple linear regression, stepwise regression, principal component regression, ordinary least squares regression, and partial least squares regression were used to predict and optimize the RL and running rate (RR) of L. edodes. The statistical values showed higher prediction accuracies of the MLP-GA models (92% and 97%, respectively) compared with those of the regression models (52% and 71%, respectively) for RL and RR. The high degree of fit between the forecasted and actual values of the RL and RR of L. edodes confirmed the superior performance of the developed MLP-GA models. An optimization analysis on the established MLP-GA models showed that a substrate containing 15.1% B, 45.1% WB, and 10.16% BS and a running time of 28 days and 10 h could result in the maximum L. edodes RL (10.69 cm). Moreover, the highest RR of L. edodes (0.44 cm d−1) could be obtained by a substrate containing 30.7% B, 90.4% WB, and 0.0% BS. MLP-GA was observed to be an effective method for predicting and consequently selecting the best substrate composition for the maximal RL and RR of L. edodes.

Models for Test Cost Minimization in Database Migration

Database migration is a ubiquitous need faced by enterprises that generate and use vast amounts of data. This is because of database software updates, or it is from changes to hardware, project standards, and other business factors. Migrating a large collection of databases is a way more challenging task than migrating a single database because of the presence of additional constraints. These constraints include capacities of shifts and sizes of databases. In this paper, we present a comprehensive framework that can be used to model database migration problems of different enterprises with customized constraints by appropriately instantiating the parameters of the framework. These parameters are the size of each database, the size of each shift, and the cost of testing each application. Each of these parameters can be either constant or arbitrary. Additionally, the cost of testing an application can be proportional to the number of databases that the application uses. We establish the computational complexities of a number of instantiations of this framework. We present fixed-parameter intractability results for various relevant parameters of the database migration problem. We also provide approximability and inapproximability results as well as lower bounds for the running time of any exact algorithm for the database migration problem. We show that the database migration problem is equivalent to a variation of the classical hypergraph partitioning problem. Our theoretical results also imply new theoretical results for the hypergraph partitioning problem that are interesting in their own right. Finally, we adapt heuristic algorithms devised for the hypergraph partitioning problem to the database migration problem, and we also give experimental results for the adapted heuristics. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods &amp; Analysis. Funding: B. Caskurlu and U. U. Acikalin are supported by The Scientific and Technological Research Council of Türkiye [Grant 122E599]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0021 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0021 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

OptiComm-GPT: a GPT-based versatile research assistant for optical fiber communication systems

With the increasing capacity and complexity of optical fiber communication systems, both academic and industrial requirements for the essential tasks of transmission systems simulation, digital signal processing (DSP) algorithms verification, system performance evaluation, and quality of transmission (QoT) optimization are becoming significantly important. However, due to the intricate and nonlinear nature of optical fiber communication systems, these tasks are generally implemented in a divide-and-conquer manner, which necessitates a profound level of expertise and proficiency in software programming from researchers or engineers. To lower this threshold and facilitate professional research easy-to-start, a GPT-based versatile research assistant named OptiComm-GPT is proposed for optical fiber communication systems, which flexibly and automatically performs system simulation, DSP algorithms verification, performance evaluation, and QoT optimization with only natural language. To enhance OptiComm-GPT’s abilities for complex tasks in optical fiber communications and improve the accuracy of generated results, a domain information base containing rich domain knowledge, tools, and data as well as the comprehensive prompt engineering with well-crafted prompt elements, techniques, and examples is established and performs under a LangChain-based framework. The performance of OptiComm-GPT is evaluated in multiple simulation, verification, evaluation, and optimization tasks, and the generated results show that OptiComm-GPT can effectively comprehend the user’s intent, accurately extract system parameters from the user’s request, and intelligently invoke domain resources to solve these complex tasks simultaneously. Moreover, the statistical results, typical errors, and running time of OptiComm-GPT are also investigated to illustrate its practical reliability, potential limitations, and further improvements.

Effect of 6-Week Sprint Training on Long-Distance Running Performance in Highly Trained Runners.

Long-distance running performance has been reported to be associated with sprint performance in highly trained distance runners. Therefore, we hypothesized that sprint training could enhance distance running and sprint performance in long-distance runners. This study examined the effect of 6-week sprint training on long-distance running and sprint performance in highly trained distance runners. Nineteen college runners were divided into control (n = 8) and training (n = 11) groups. Participants in the training group performed 12 sprint training sessions in 6weeks, while those in the control group performed 12 distance training sessions. Before and after the interventions, maximal oxygen uptake (V˙O2max), O2 cost during submaximal running (290m·min-1 and 310m·min-1 of running velocity), and time to exhaustion (starting at 290m·min-1 and increased 10m·min-1 every minute) were assessed on a treadmill. Additionally, the 100-m and 400-m sprinting times and 3000-m running time were determined on an all-weather track. In the control group, no measurements significantly changed after the intervention. In the training group, the time to exhaustion, 100-m and 400-m sprinting times, and 3000-m running time improved significantly, while V˙O2max and O2 cost did not change. These results showed that 6-week sprint training improved both sprint and long-distance running performance in highly trained distance runners without a change in aerobic capacity. Improvement in the time to exhaustion without a change in V˙O2max suggests that the enhancement of long-distance running performance could be attributable to improved anaerobic capacity.

A Comprehensive Review on Advantages of Ultra Performance Liquid Chromatography (UPLC) for Analytical Method Development, Validation, and Stability Studies

Ultra Performance Liquid Chromatography (UPLC) represents a paradigm shift in analytical separation science, offering enhanced speed, sensitivity, and resolution while retaining the foundational principles of traditional liquid chromatography. This comprehensive review explores the principles, instrumentation, advantages, and applications of UPLC in analytical method development, validation, and stability studies, with a focus on pharmaceutical analysis. UPLC leverages finer particles and intensified flow rates to achieve unprecedented performance gains, facilitating rapid and precise separations without compromising analytical rigor. The discussion delves into the intricate instrumentation involved, including sample injection, column selection, and detector operation, highlighting the critical role of each component in ensuring optimal analytical performance. Advantages of UPLC include drastically reduced run times, streamlined method optimization, and increased sample throughput, while limitations such as higher back pressures and limited column regenerability are also discussed. Furthermore, the review outlines the diverse applications of UPLC across pharmaceutical, environmental, food and beverage, clinical, forensic, bioanalytical, and polymer analysis domains, underscoring its versatility and significance in advancing research, development, and quality control in various industries. In summary, UPLC represents a transformative tool in analytical chemistry, offering unparalleled capabilities for expedited and accurate analysis of complex sample matrices, ultimately driving innovation and scientific progress. Key Words: Ultra Performance Liquid Chromatography (UPLC), analytical method development, validation, stability studies, pharmaceutical analysis, instrumentation, advantages, applications, sample injection, column chemistry, detector technology, back pressures, method optimization, sample throughput, environmental analysis, food and beverage analysis, clinical diagnostics, forensic analysis, bioanalytical research, polymer analysis, analytical chemistry.

Accurate wire rope defect MFL detection using improved Hilbert transform and LSTM neural network

ABSTRACT Wire rope is playing a significant role in various engineering fields. Although a great number of non-destructive testing methods are applied to wire rope defect detection, the results are greatly influenced by the signal noises and the quantitative defect detection of wire rope is consequently limited. Therefore, a new accurate and quantitative wire rope defect magnetic flux leakage (MFL) recognition method based on improved Hilbert transform and long short-term memory (LSTM) neural network is proposed. The theoretical background of the proposed signal processing models and preliminary results, as well as the influence of the main model parameters are analyzed first. Then, different wire rope defect classification principles through LSTM neural network and performance evaluation based on the improved signal processing and machine learning combined method are presented. Finally, the defect inspection and classification comparison results between the proposed method and other deep learning neural networks and pattern recognition strategies are given, which manifest that the proposed method has higher classification accuracy and shorter running time for five different wire rope defects under various servicing conditions, and is promising in accurate defect inspection for multi-scenario wire rope. Additionally, the main conclusions, disadvantages of the proposed method are summarized.

Paralytic Shellfish Toxins in Mollusks from Galicia Analyzed by a Fast Refined AOAC 2005.06 Method: Toxicity, Toxin Profile, and Inter-Specific, Spatial, and Seasonal Variations.

Paralytic shellfish poisoning is an important concern for mollusk fisheries, aquaculture, and public health. In Galicia, NW Iberian Peninsula, such toxicity has been monitored for a long time using mouse bioassay. Therefore, little information exists about the precise toxin analogues and their possible transformations in diverse mollusk species and environments. After the change in the European PSP reference method, a refinement of the Lawrence method was developed, achieving a 75% reduction in chromatogram run time. Since the beginning of 2021, when this refinement Lawrence method was accredited under the norm UNE-EN ISO/IEC 17025, it has been used in the area to determine the toxin profiles and to estimate PSP toxicity in more than 4500 samples. In this study, we have summarized three years of monitoring results, including interspecific, seasonal, and geographical variability of PSP toxicity and toxin profile. PSP was detected in more than half of the samples analyzed (55%), but only 4.4% of the determinations were above the EU regulatory limit. GTX1,4 was the pair of STX analogs that produced the highest toxicities, but GTX2,3 was found in most samples, mainly due to the reduction of GTX1,4 but also by the higher sensitivity of the method for this pair of analogs. STX seems to be mainly a product of biotransformation from GTX2,3. The studied species (twelve bivalves and one gastropod) accumulated and transformed PSP toxins to a different extent, with most of them showing similar profiles except for Spisula solida and Haliotis tuberculata. Two seasonal peaks of toxicity were found: one in spring-early summer and another in autumn, with slightly different toxin profiles during outbreaks in relation to the toxicity during valleys. In general, both the total toxicity and toxin profiles of the southernmost locations were different from those in the northern part of the Atlantic coast and the Cantabrian Sea, but this general pattern is modified by the PSP history of some specific locations.

STABILITY BASED HPLC METHOD FOR CYCLOPHOSPHAMIDE RELATED SUBSTANCES IN FINISHED DRUG PRODUCTS: DEVELOPMENT AND VALIDATION

Objective: The current study aimed to develop a simple, sensitive, and precise high-performance liquid chromatographic (HPLC) method for estimating cyclophosphamide and its related substances, as well as to implement the developed method in a capsule product. Methods: Method development was performed using various solvent and buffer-solvent ratios at different flow rates for better resolution and to decrease the run time. The developed method was validated in accordance with the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines. The developed method was implemented to estimate the amount of cyclophosphamide and its related substances. Results: Chromatographical conditions were optimised, and the best chromatographical conditions with adequate resolution for cyclophosphamide and its related substances were achieved using the enable X-Bridge C18 column, using a mobile phase combination of phosphate buffer pH 7.0, water, methanol, and acetonitrile at a flow rate of 0.5 ml/min. The detection was monitored at a wavelength of 200 nm. The developed method was validated for system suitability, specificity, limit of detection (LOD), limit of quantitation (LOQ), linearity, precision, accuracy and robustness. The results indicate that the method was sensitive and could detect and quantify lower levels of cyclophosphamide and its related substances. The linearity range was from LOQ to 150 %, and a correlation coefficient ranging from 0.9987 to 0.9999 indicates that at this concentration range, substances were highly linear. This was further supported by satisfactory forced degradation studies. Conclusion: The developed analytical method is simple, precise, and reproducible and thus can be used for the stability-indicating analysis of cyclophosphamide and its related substances in pharmaceutical formulations.

Detail-preserving Joint Image Upsampling

Image operators can be instrumental to computational imaging and photography. However, many of them are computationally intensive. In this paper, we propose an effective yet efficient joint upsampling method to accelerate various image operators. We show that edge-preserving filtering can be facilitated with a downsampling-and-upsampling process. Moreover, when the extent of smoothing is mild, the process is detail-preserving, i.e., the fine details lost in the low-resolution (LR) images can be accurately restored in the high-resolution (HR) images. Given an HR input and an LR output of an operator, we downsample the HR input and calculate its affinities to the HR input. By applying the affinities to the LR output, we promote its resolution. Due to the strong detail-preserving property, the HR output derived in the previous step may exhibit aliasing artifacts around the salient edges. We further refine it based on the linear relations in a small neighborhood to rid the artifacts. Experiments on various image operators show that our method achieves superior quality over the state-of-the-art joint upsampling methods. Furthermore, the running time of our method is linear to the number of pixels. Our naive implementation derives 1080P images in real time (24 fps) on an NVIDIA GTX 3070 GPU.