Specific Pipeline Research Articles

A high-throughput protocol for testing heat-stress tolerance in pollen

AbstractViable pollen is crucial for fertilization, but pollen is generally highly susceptible to heat stress. A quick, reliable method for testing the heat-stress tolerance of pollen is needed to improve the heat-stress tolerance in plants, but current methods require considerable space and labor. In addition, many such methods only test tolerance to a single constant temperature, making it time-consuming to screen heat tolerance over a wide temperature range and to examine the dynamics of pollen viability at different temperatures. To address this issue, we aimed to: (1) develop an easy, reliable method for measuring pollen viability at different temperatures; and (2) identify the best temperature range for screening pollen with high heat-stress tolerance. We harvested mature pollen from wheat (Triticum aestivum) plants and transferred it to a 96-well plate filled with liquid medium containing sucrose. We placed the plate in a PCR machine operating under a gradient PCR program to simultaneously test a range of temperatures. After incubating the pollen for 4 h, at temperatures ranging from 21.9 to 47 °C, we examined the pollen grains under a light microscope and employed a specific image analysis pipeline to assess the effects of temperature on pollen morphology, germination, and tube growth. This method facilitated the high-throughput screening of many pollen samples, enabling rapid, reliable, and precise analysis of pollen viability in response to temperature. Our approach should be applicable to other plant species and could be used to identify quantitative trait loci or genes influencing heat stress tolerance in pollen for breeding programs.

Pipeline Leak Identification and Prediction of Urban Water Supply Network System with Deep Learning Artificial Neural Network

Pipeline leakage, which leads to water wastage, financial losses, and contamination, is a significant challenge in urban water supply networks. Leak detection and prediction is urgent to secure the safety of the water supply system. Relaying on deep learning artificial neural networks and a specific optimization algorithm, an intelligential detection approach in identifying the pipeline leaks is proposed. A hydraulic model is initially constructed on the simplified Net2 benchmark pipe network. The District Metering Area (DMA) algorithm and the Cuckoo Search (CS) algorithm are integrated as the DMA-CS algorithm, which is employed for the hydraulic model optimization. Attributing to the suspected leak area identification and the exact leak location, the DMA-CS algorithm possess higher accuracy for pipeline leakage (97.43%) than that of the DMA algorithm (92.67%). The identification pattern of leakage nodes is correlated to the maximum number of leakage points set with the participation of the DMA-CS algorithm, which provide a more accurate pathway for identifying and predicting the specific pipeline leaks.

Optimization of a DNA extraction protocol for improving bacterial and fungal classification based on Nanopore sequencing.

Ribosomal RNA gene amplicon sequencing is commonly used to evaluate microbiome profiles in health and disease and document the impact of interventional treatments. Nanopore sequencing is attractive since it can provide greater classification at the species level. However, optimized protocols to target marker genes for bacterial and fungal profiling are needed. To achieve an increased taxonomic resolution, we developed extraction and full-length amplicon PCR-based approaches using Nanopore sequencing. Three lysis conditions were applied to a mock microbial community, including known bacterial and fungal species: ZymoBIOMICS lysis buffer (ML) alone, incorporating bead-beating (MLB) or bead-beating plus MetaPolyzyme enzymatic treatment (MLBE). In profiling of bacteria in comparison to reference data, MLB had more statistically different bacterial phyla and genera than the other two conditions. In fungal profiling, MLB had a significant increase of Ascomycota and a decline of Basidiomycota, subsequently failing to detect Malassezia and Cryptococcus. Also, a principal coordinates analysis plot by the Bray-Curtis metric showed a significant difference among groups for bacterial (P=0.033) and fungal (P=0.012) profiles, highlighting the importance of understanding the biases present in pretreatment. Overall, microbial profiling and diversity analysis revealed that ML and MLBE are more similar than MLB for both bacteria and fungi; therefore, using this specific pipeline, bead-beating is not recommended for whole gene amplicon sequencing. However, ML alone was suggested as an optimal approach considering DNA yield, taxonomic classification, reagent cost and hands-on time. This could be an initial proof-of-concept study for simultaneous human bacterial and fungal microbiome studies.

Fairness-enhancing classification methods for non-binary sensitive features—How to fairly detect leakages in water distribution systems

Especially if artificial intelligence (AI)-supported decisions affect the society, the fairness of such AI-based methodologies constitutes an important area of research. In this contribution, we investigate the applications of AI to the socioeconomically relevant infrastructure of water distribution systems (WDSs). We propose an appropriate definition of protected groups in WDSs and generalized definitions of group fairness, applicable even to multiple non-binary sensitive features, that provably coincide with existing definitions for a single binary sensitive feature. We demonstrate that typical methods for the detection of leakages in WDSs are unfair in this sense. Further, we thus propose a general fairness-enhancing framework as an extension of the specific leakage detection pipeline, but also for an arbitrary learning scheme, to increase the fairness of the AI-based algorithm. Finally, we evaluate and compare several specific instantiations of this framework on a toy and on a realistic WDS to show their utility.

Numerical Study on Explosion Risk and Building Structure Dynamics of Long-Distance Oil and Gas Tunnels

To comprehensively understand the explosion risk in underground energy transportation tunnels, this study employed computational fluid dynamics technology and finite element simulation to numerically analyze the potential impact of an accidental explosion for a specific oil and gas pipeline in China and the potential damage risk to nearby buildings. Furthermore, the study investigated the effects of tunnel inner diameter (d = 4.25 m, 6.5 m), tunnel length (L = 4 km, 8 km, 16 km), and soil depth (primarily Lsoil = 20 m, 30 m, 40 m) on explosion dynamics and on structural response characteristics. The findings indicated that as the tunnel length and inner diameter increased, the maximum explosion overpressure gradually rose and the peak arrival time was delayed, especially when d = 4.25 m; with the increase in L, the maximum explosion overpressure rapidly increased from 1.03 MPa to 2.12 MPa. However, when d = 6.5 m, the maximum explosion overpressure increased significantly by 72.8% from 1.25 MPa. Evidently, compared to the change in tunnel inner diameter, tunnel length has a more significant effect on the increase in explosion risk. According to the principle of maximum explosion risk, based on the peak explosion overpressure of 2.16 MPa under various conditions and the TNT equivalent calculation formula, the TNT explosion equivalent of a single section of the tunnel was determined to be 1.52 kg. This theoretical result is further supported by the AUTODYN 15.0 software simulation result of 2.39 MPa (error < 10%). As the soil depth increased, the distance between the building and the explosion source also increased. Consequently, the vibration peak acceleration and velocity gradually decreased, and the peak arrival time was delayed. In comparison to a soil depth of 10 m, the vibration acceleration at soil depths of 20 m and 30 m decreased by 81.3% and 91.7%, respectively. When the soil depth was 10 m, the building was at critical risk of vibration damage.

Modelling and optimization of an existing onshore gas gathering network using PIPESIM

Pakistan has limited natural gas reserves, and most are found onshore. This article reports on the problems of an onshore gas gathering network (GGN) analysed through steady-state simulation modelling using PIPESIM software. The research methodology incorporates a comprehensive steady-state hydraulic analysis considering fluid flowing velocity limitations, liquid holdup and slugging along with other issues faced by gas gathering networks. The steady-state hydraulic analysis has led us to pinpoint specific GGN pipelines facing critically low gas velocities and consequent liquid holdup. Addressing these issues involved application of PIPESIM software for modelling, considering various operating schemes of gas-producing wells and their associated pipelines. To select an optimal operating scheme, the study utilized the Analytic Hierarchy Process (AHP) for operational optimization, to identify the most effective solution for reduced liquid holdup, improving production, and ensuring the safe operation among available alternatives. Findings from our hydraulic analysis highlight the importance of reducing GGN outlet pressure to mitigate challenges associated with liquid holdup which causes slugging and back pressure effect at source leading to low production and poor performance of the GGN. Study of three alternative cases reveals that decreasing outlet pressure lowers the liquid holdup, improve gas flowing velocities, and enhanced overall production. These findings validate our hypothesis that reducing GGN outlet pressure is a viable strategy to lower the liquid holdup in pipelines. This research offers significant value by providing a comprehensive solution to GGN liquid holdup, low flowing velocities, back pressure and low production challenges. The integration of steady-state hydraulic analysis, simulation modelling with PIPESIM, and the application of AHP for optimization contributes novel insights into the optimization of operation of gas gathering networks. Emphasizing the reduction of liquid holdup and enhancing production through outlet pressure adjustments offers a practical framework for optimizing the functionality of gas gathering networks.

Abstract PO-022: Distinct intratumoral microbiome signatures enrichment in patients with different Anaplaplastic Large Cell Lymphoma (ALCL) subtypes

Abstract Background: The intratumoral microbiome is a tumor-specific component of the tumor microenvironment (TME), including microorganisms localized inside and around cancer cells and immune cells (Nejman Science2020), which was recently added to the “Hallmarks of Cancer” (Hanahan, Cancer Discovery 2022). Specific intratumoral microbiome signatures (IMS) can be detected using our targeted sequencing platform Memorial Sloan Kettering Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) (Prashkin Nat Comm 2023) data through the implementation of a specific bioinformatic pipeline (Vanderbilt J Mol Diagn 2022, Abate Ann Surg 2022). In lymphoma, where antigen stimulation is known to promote lymphomagenesis, studying the intratumoral microbiome might inform lymphoma biology and potentially new treatment strategies. In anaplastic large cell lymphoma (ALCL), a specific histologic phenotype can be found in a very diverse spectrum of diseases, ranging from smoldering lymphomas such as breast-implant associated (BIA-ALCL) and primary cutaneous (pcALCL) to systemic aggressive lymphomas such as ALK-positive or ALK-negative ALCL. We hypothesized that the different IMS might be present in different ALCL clinical presentations. Methods: We used metagenomic techniques to analyze IMS on MSK-IMPACT-HEME as previously described (Vanderbilt J Mol Diagn 2022) of patients with ALCL in our lymphoma database. All patients signed informed consent. We calculated the odds ratio (OR) of each intra-lymphoma IMS in one ALCL subtype compared to that of the same IMS in the other ALCL subtypes. We used several different analyses to process the data for commensals and contaminants. In this regard, we built a database of potentially pathogenic or commensal microorganisms integrated in the decontam (R package) pipeline dataset. For BIA-ALCL, we compared the IMS results with those of known components of explanted textured implant biofilm. Results: We re-analyzed the MSK-IMPACT-HEME BAM files of 58 patients with ALCL, including 12 BIA-ALCL, 11 pcALCL, and 35 systemic ALK+/- ALCL. After reviewing the data for commensals and contaminants, we could identify more than one potentially lymphoma-specific or site-specific microorganism in pcALCL (n=2) and in BIA-ALCL (n=14) (slow-growing subtypes) compared with systemic ALCL. Fewer differences were noted between pcALCL and BIA-ALCL; however, Achromobacter and Rothia were significantly enriched in BIA-ALCL compared to systemic ALCL and pcALCL (OR 14.9 p=0.001; OR=5, p=0.025) and Micromonospora in pcALCL compared to BIA-ALCL and systemic ALCL (OR=5.5, p=0.05). No specific IMS was found in systemic ALCL, as the only potentially enriched IMS (Delftiae) is likely a contaminant. Conclusions: This analysis of IMS using targeted sequencing data, highlighted some possible associations between intratumoral microorganisms and lymphoma-specific subtypes, especially in indolent subtypes of ALCL (BIA- and pc-) compared to systemic ALCL. Further analyses with more cases and confirmatory techniques might confirm these initial findings. Citation Format: Paola Ghione, Horwitz Steven, Peter Cordeiro, Maria Arcila, Natasha Galasso, Ahmet Dogan, Gilles Salles, Chad Vanderbilt. Distinct intratumoral microbiome signatures enrichment in patients with different Anaplaplastic Large Cell Lymphoma (ALCL) subtypes [abstract]. In: Proceedings of the Fourth AACR International Meeting on Advances in Malignant Lymphoma: Maximizing the Basic-Translational Interface for Clinical Application; 2024 Jun 19-22; Philadelphia, PA. Philadelphia (PA): AACR; Blood Cancer Discov 2024;5(3_Suppl):Abstract nr PO-022.

A Cell-Type Selective Stimulation and Recording System for Retinal Ganglion Cells.

Future retinal implants will require a stimulation selectivity between different sub-types of Retinal Ganglion Cells (RGCs) to evoke natural perceptions rather than phosphenes in patients. To achieve this, a cell-type specific stimulation pipeline is required that identifies target RGC sub-types from recorded input images and extracts the specific stimulation parameters to activate this cell-type selectively. Promising biological experiments showed that ON-/OFF- sustained/transient RGCs could be selectively activated by modulating repetition rate and amplitude of an electrical stimulation current in the kilohertz range. This research presents a 42 channel current controlled stimulation and recording system on chip (SoC) with parameter input from a real time target RGC selection algorithm. The SoC is able to stimulate retinal tissue with sinusoidal frequencies higher than 1 kHz at amplitudes of up to 200 μA at a supply voltage of 1.8 V. It also includes tunable recording units with an integrated action potential detection pipeline that are able to amplify signals between 1 Hz and 50 kHz. The required area of one stimulator is 0.0071 mm2, while one recording unit consumes an area of 0.0092 mm2. The application of sinusoidal stimulation currents in the kilohertz range towards retinal tissue leads to a suppressive response of only certain RGC sub-types that has not been oberved before, using electrical stimulation. Because this response is very similar to the natural light response of RGCs, this stimulation approach can lead to a more genuine visual perception for patients using retinal implants.

Three-layer and robust planning models to evaluate the strategies of defense layer, attack layer, and operation layer for optimal protection in natural gas pipeline network

Using game theory techniques, a three-layer planning model (THLPM) for the defense, attack, and operational layers has been established to address malicious damage to pipelines. A robust planning model was developed for situations with uncertain attack resources. In this model, the defense layer protects critical infrastructure within the network to minimize losses. The attack layer targets unprotected facilities based on the defensive strategy. The operational layer formulates the optimal pipeline transmission strategy based on the decisions of the defense and attack layers. By utilizing the duality theorem, the THLPM is transformed into a two-layer planning problem, which is then linearized and solved using a decomposition algorithm. Taking a specific ring-shaped pipeline network as an example, the study analyzes the significant role of backup pipelines in enhancing system resilience and evaluates the offensive and defensive strategies of THLPM. The effectiveness of THLPM in protecting the network was assessed, showing that when defense and attack resources are invested at levels 3 and 10, respectively, operational costs of the network are reduced by 54.2 % compared to a scenario without any defenses. The methods proposed in this paper are applicable to other pipeline systems.

Comprehensive empirical investigation for prioritizing the pipeline of using feature selection and data resampling techniques

The contemporary real-world datasets often suffer from the problem of class imbalance as well as high dimensionality. For combating class imbalance, data resampling is a commonly used approach whereas for tackling high dimensionality feature selection is used. The aforesaid problems have been studied extensively as independent problems in the literature but the possible synergy between them is still not clear. This paper studies the effects of addressing both the issues in conjunction by using a combination of resampling and feature selection techniques on binary-class imbalance classification. In particular, the primary goal of this study is to prioritize the sequence or pipeline of using these techniques and to analyze the performance of the two opposite pipelines that apply feature selection before or after resampling techniques i.e., F + S or S + F. For this, a comprehensive empirical study is carried out by conducting a total of 34,560 tests on 30 publicly available datasets using a combination of 12 resampling techniques for class imbalance and 12 feature selection methods, evaluating the performance on 4 different classifiers. Through the experiments we conclude that there is no specific pipeline that proves better than the other and both the pipelines should be considered for obtaining the best classification results on high dimensional imbalanced data. Additionally, while using Decision Tree (DT) or Random Forest (RF) as base learner the predominance of S + F over F + S is observed whereas in case of Support Vector Machine (SVM) and Logistic Regression (LR), F + S outperforms S + F in most cases. According to the mean ranking obtained from Friedman test the best combination of resampling and feature selection techniques for DT, SVM, LR and RF are SMOTE + RFE (Synthetic Minority Oversampling Technique and Recursive Feature Elimination), Least Absolute Shrinkage and Selection Operator (LASSO) + SMOTE, SMOTE + Embedded feature selection using RF and SMOTE + RFE respectively.

Machine Learning for failure prediction: A cost-oriented model selection

Industry 4.0 technologies are changing industrial maintenance management. In particular, Machine Learning (ML) techniques have been increasingly used for failure prediction. In this context, the ML model performance influences maintenance costs, as it defines, for example, whether interventions will be carried out before or after the failure. Several studies describe the ML model process design, considering the model evaluation based on standard performance metrics, such as accuracy, recall, precision, and F1-Score. However, when the ML process is applied to multiple models with specific ML pipelines, choosing the ideal one based on these metrics does not always represent the lowest maintenance cost. Therefore, this work proposes an ML process for failure prediction based on binary classification models with a cost-oriented ML model selection phase. Finally, an application example of the proposed process was provided, exemplifying the potential of the proposed model for reducing maintenance costs.

Guideline for the Decommissioning/Abandonment of Subsea Pipelines

The operating lifespan of pipelines is limited, defined by their specific design codes and specifications, with the economic justification for this being determined primarily by the pipeline owner. During its operational lifespan, a pipeline’s integrity is affected mainly by the quality of the hydrocarbons being transported. The integrity of a pipeline can be maintained with regular inspections and maintenance/cleaning programmes followed from installation to commissioning. As production matures and declines, operators face several decisions concerning the pipeline’s future. There are several potential scenarios, and each should be assessed on a case-by-case basis for any specific pipeline in question. The industry best practices outline the minimum requirements for the safe decommissioning of pipelines. However, there currently need to be international specifications to be followed for the decommissioning of disused offshore pipelines. This paper aims to provide insight into the decommissioning and abandonment of offshore pipelines. Also, this article provides case studies for the decommissioning of subsea pipelines.

Leakage Risk Assessment of Urban Water Distribution Network Based on Unascertained Measure Theory and Game Theory Weighting Method

Assessing the risk of water leakage within urban water distribution networks (UWDN) is crucial prior to implementing any control measures. Conducting a risk assessment facilitates the development of effective water leakage management plans. By comprehensively analyzing the probability and loss factors that contribute to the risk of leakage in UWDN, this paper presents an evaluation index system for pipeline leakage risk. This index system utilized both quantitative and qualitative data on influencing factors derived from an actual pipeline network. In order to determine the precise level of pipeline leakage risk, an index theory-based pipeline leakage risk evaluation model was established. This model consisted of a single-index measure function and a multi-index comprehensive measure vector. The combined weight of evaluation indices through game theory was used to determine the weight of each index, thereby minimizing the negative effects of a single weight determination method. A risk assessment model that evaluated the leakage risk of specific pipelines was constructed based on actual data from the water distribution network in a certain area of China. The analysis showed that the risk of pipeline leakage in this area was mainly classified as a third-level risk, which is consistent with the actual evaluation results obtained from field visits.

How Climate and Data Quality Impact Photovoltaic Performance Loss Rate Estimations

Different data pipelines and statistical methods are applied to photovoltaic (PV) performance datasets to quantify the performance loss rate (PLR). Since the real values of PLR are unknown, a variety of unvalidated values are reported. As such, the PV industry commonly assumes PLR based on statistically extracted ranges from the literature. However, the accuracy and uncertainty of PLR depend on several parameters including seasonality, local climatic conditions, and the response of a particular PV technology. In addition, the specific data pipeline and statistical method used affect the accuracy and uncertainty. To provide insights, a framework of (≈200 million) synthetic simulations of PV performance datasets using data from different climates is developed. Time series with known PLR and data quality are synthesized, and large parametric studies are conducted to examine the accuracy and uncertainty of different statistical approaches over the contiguous US, with an emphasis on the publicly available and “standardized” library, RdTools. In the results, it is confirmed that PLRs from RdTools are unbiased on average, but the accuracy and uncertainty of individual PLR estimates vary with climate zone, data quality, PV technology, and choice of analysis workflow. Best practices and improvement recommendations based on the findings of this study are provided.

Whole mitogenome sequencing uncovers a relation between mitochondrial heteroplasmy and leprosy severity

BackgroundIn recent years, the mitochondria/immune system interaction has been proposed, so that variants of mitochondrial genome and levels of heteroplasmy might deregulate important metabolic processes in fighting infections, such as leprosy.MethodsWe sequenced the whole mitochondrial genome to investigate variants and heteroplasmy levels, considering patients with different clinical forms of leprosy and household contacts. After sequencing, a specific pipeline was used for preparation and bioinformatics analysis to select heteroplasmic variants.ResultsWe found 116 variants in at least two of the subtypes of the case group (Borderline Tuberculoid, Borderline Lepromatous, Lepromatous), suggesting a possible clinical significance to these variants. Notably, 15 variants were exclusively found in these three clinical forms, of which five variants stand out for being missense (m.3791T > C in MT-ND1, m.5317C > A in MT-ND2, m.8545G > A in MT-ATP8, m.9044T > C in MT-ATP6 and m.15837T > C in MT-CYB). In addition, we found 26 variants shared only by leprosy poles, of which two are characterized as missense (m.4248T > C in MT-ND1 and m.8027G > A in MT-CO2).ConclusionWe found a significant number of variants and heteroplasmy levels in the leprosy patients from our cohort, as well as six genes that may influence leprosy susceptibility, suggesting for the first time that the mitogenome might be involved with the leprosy process, distinction of clinical forms and severity. Thus, future studies are needed to help understand the genetic consequences of these variants.

Real-time, adaptive microphone subset selection for a 128-elementarray

Some advantages of high-sensor count microphone arrays include gain over random noise, increased multiple-source resolution, and potential increases in effective frequency bandwidth. But in real-time applications, computational capabilities of embedded computing hardware might limit the number data channels that can be processed without data loss. For example, given a specific processing pipeline and sampling rate, perhaps only 24 of 128 channels can be supported in real-time. Therefore, the question of which 24-element subset of the 128 possible elements should be being used to detect and track multiple moving acoustic sources. In this presentation, principles of genetic search algorithms are used to develop a method to adaptively select optimal subsets of sensors as data is being processed. Discussion concerning optimality criteria, types of genetic algorithms, and algorithm performance will be presented as well as results corresponding to a 3-D 128-element array of digital MEMS microphones when multiple types of moving acoustic sources were nearby.

RNxQuest: An Extension to the xQuest Pipeline Enabling Analysis of Protein-RNA Cross-Linking/Mass Spectrometry Data.

Cross-linking and mass spectrometry (XL-MS) workflows are increasingly popular techniques for generating low-resolution structural information about interacting biomolecules. xQuest is an established software package for analysis of protein-protein XL-MS data, supporting stable isotope-labeled cross-linking reagents. Resultant paired peaks in mass spectra aid sensitivity and specificity of data analysis. The recently developed cross-linking of isotope-labeled RNA and mass spectrometry (CLIR-MS) approach extends the XL-MS concept to protein-RNA interactions, also employing isotope-labeled cross-link (XL) species to facilitate data analysis. Data from CLIR-MS experiments are broadly compatible with core xQuest functionality, but the required analysis approach for this novel data type presents several technical challenges not optimally served by the original xQuest package. Here we introduce RNxQuest, a Python package extension for xQuest, which automates the analysis approach required for CLIR-MS data, providing bespoke, state-of-the-art processing and visualization functionality for this novel data type. Using functions included with RNxQuest, we evaluate three false discovery rate control approaches for CLIR-MS data. We demonstrate the versatility of the RNxQuest-enabled data analysis pipeline by also reanalyzing published protein-RNA XL-MS data sets that lack isotope-labeled RNA. This study demonstrates that RNxQuest provides a sensitive and specific data analysis pipeline for detection of isotope-labeled XLs in protein-RNA XL-MS experiments.

SimpiTB - a pipeline designed to extract meaningful information from whole genome sequencing data of Mycobacterium tuberculosis complex, allows to combine genomic, phylogenetic and clustering analyses in existing SITVIT databases.

Data obtained from new sequencing technologies are evolving rapidly, leading to the development of specific bioinformatic tools, pipelines and softwares. Several algorithms and tools are today available allowing a better identification and description of Mycobacterium tuberculosis complex (MTBC) isolates worldwide. Our approach consists in applying existing methods to analyze DNA sequencing data (from FASTA or FASTQ files), and tentatively extract meaningful information that would facilitate identification as well as a better understanding and management of MTBC isolates (taking into account whole genome sequencing and classical genotyping data). The aim of this study is to propose a pipeline analysis allowing to potentially simplify MTBC data analysis by providing different ways to interpret genomic or genotyping information based on existing tools. Furthermore, we propose a "reconciledTB" list making a link with results directly obtained from whole genome sequencing (WGS) data and results obtained from classical genotyping analysis (data inferred from SpoTyping and MIRUReader). Data visualization graphics and trees generated provide additional elements to better understand and confer associations among information overlap analyses. Additionally, comparison between data entered in an international genotyping database (SITVITEXTEND) and ensuing data obtained from the pipeline not only provide meaningful information, but further suggest that simpiTB could also be suitable for new data integration in specific TB genotyping databases.

TEMPERATURE PREDICTION OIL PIPELINE IN FROST AREAS

Pipeline operation in frost areas is essential thing in the present days. Changes of low-temperature profile of frozen soil along the length of a pipeline lead to changing conditions of the pipeline. That’s why it is vital to properly forecast and model thermo-hydraulics regimes and evaluate pipeline conditions to sustain correct positioning of the pipeline and to prevent incidents. That’s why this paper describes a simple forecasting method. It is based on heat balance equation, where coefficient k describing heat transfer from a pipeline to the environment considers that soil gets to technogenic state. Actual efficiency and temperature data from an active non-isothermal pipeline over 2 years period after its commissioning are used to derive actual value of the coefficient k. Pipeline type – underground, insulated, initial petrol temperature is up to 45 °С. Using Shukhov’s formula it is possible to calculate average value of the coefficient k. Based on this coefficient we can then derive forecasted values and get the following data: temperature profile, temperature value at specific points and cross sections of a pipeline, thawing area, critical locations of a pipeline. This method allows to timely evaluate conditions of a pipeline in permanent frost soil. Results can be used to forecast and predict thermo-hydraulics regimes not only for a specific pipeline but also for other Northern ones. It also allows to evaluate conditions of critical parts of a pipeline when taking in account actual size of thawing areas.

New variable sources revealed by DECam toward the LMC: The first 15 deg2

The Dark Energy Camera (DECam) is a sensitive, wide field instrument mounted at the prime focus of the 4 mV. Blanco Telescope in Chile. Beside its main objectives, i.e. understanding the growth and evolution of structures in the Universe, the camera offers the opportunity to observe a ∼3deg2 field of view in one single pointing and, with an adequate cadence, to identify the variable sources contained. In this paper, we present the result of a DECam observational campaign toward the LMC and give a catalogue of the observed variable sources. We considered all the available DECam observations of the LMC, acquired during 32 nights over a period of two years (from February 2018 to January 2020), and set up a specific pipeline for detecting and characterizing variable sources in the observed fields. Here, we report on the first 15deg2 in and around the LMC as observed by DECam, testing the capabilities of our pipeline. Since many of the observed fields cover a rather crowded region of the sky, we adopted the ISIS subtraction package which, even in these conditions, can detect variables at a very low signal to noise ratio. All the potentially identified variable sources were then analysed and each light curve tested for periodicity by using the Lomb–Scargle and Schwarzenberg-Czerny algorithms. Furthermore, we classified the identified sources by using the UPSILoN neural network. This analysis allowed us to find 70 981 variable stars, 1266 of which were previously unknown. We estimated the period of the variables and compared it with the available values in the catalogues. Moreover, for the 1266 newly detected objects, an attempted classification based on light curve analysis is presented.