Specific Types Of Damage Research Articles

Garden Snail Predatory Insects’ Modus Operandi Under Laboratory Conditions

Heliciculture farms are susceptible to significant biotic issues that can impact snail breeding, among them, the entomofauna predation of snails. Predatory insects can cause damage to snail shells during predation, and sometimes, the specific type of damage may be characteristic of certain insect families or species. Under laboratory conditions, we analysed the predatory activity of the species Silpha tristis Illiger, 1798 (Coleoptera: Silphidae), Ocypus olens (Müller, 1764) (Coleoptera: Staphylinidae), Carabus (Macrothorax) morbillosus constantinus Kraatz, 1899 (Coleoptera: Carabidae), and Lampyris sardiniae Geisthardt, 1987 (Coleoptera: Lampyridae) against the gastropod Cornu aspersum (Müller, 1774) reared on snail farms located in the Sardinian region. The adult and larval stages of each species were tested, except for L. sardiniae, as only the larval stage preys upon snails. This study showed that among all of the species considered, only two insects were able to damage the shell of C. aspersum: C. morbillosus constantinus and O.olens. This may prove valuable in C. aspersum breeding for the recognition of specific lesions, even in the absence of the predator.

Chemical characteristics of Salix psammophila sand barriers are accelerated degradation by ultraviolet irradiation and water.

The implementation of Salix psammophila sand barriers measures constitutes a crucial element in desertification control, providing a solid theoretical foundation for the future application and pretreatment of sand barriers in production practices. To address the specific damage types predominant in desert environments, we executed simulations of ultraviolet irradiation and rainfall phenomena on mechanical sand barriers in sandy areas and also inspected the variations in chemical properties during accelerated aging processes. The findings unequivocally demonstrate that: (1) The synergistic impact of ultraviolet irradiation and water accelerated the deformation and fracturing of the S.psammophila sand barriers, thereby causing a partial degradation of its chemical properties and conspicuous lignin oxidation; (2) The fissure of the sand barrier deepened, resulting in structural alterations. The existence of water expedites the degradation process of S.psammophila sand barriers under ultraviolet irradiation. (3)With respect to the binding form of C atoms, the carbon atoms at S.psammophila sand barriers were highly oxidized after 576 hours of accelerated aging. The components of C2 (C-O) and C3 (C=O) rising to 40.16% and 12.24% respectively, while the components of C1 (C-C) declined to 47.60%. The amount of hydroxyl (O-C-O), carbonyl (C=O), and carboxyl (O-C=O) groups increases in line with the expansion of the contact area between the sand barrier structure and ultraviolet irradiation as well as water. More free radical substances are generated, thereby causing the chemical binding properties to tend to be more stable. In summary, Ultraviolet irradiation and water change are the primary factors influencing the degradation of S.psammophila sand barriers material structure and properties. In future desertification control, it is imperative to focus on enhancing the longevity of sand barriers by ensuring their waterproofing capabilities and resistance to ultraviolet irradiation.

Carbon contrast agents for terahertz spectroscopic NDT of impacted glass fibre reinforced plastics

Impact damage poses a significant challenge for composite materials since it is often subsurface and hard to detect, necessitating effective assessment approaches. Traditional NDT methods face challenges in identifying this specific type of damage. In this study, a departure from traditional NDT approaches was taken by utilizing THz spectroscopy to detect impact damage. Three different lay-ups of GFRP laminates were studied; GFRP with non-woven carbon fibre veil, GFRP coated with MWCNTs, and plain GFRP. Impact tests were conducted ranging from 5, 12 and 20 J in energy and 2D images were then acquired It was found that the inclusion of the MWCNTs and carbon veil gave superior image contrast compared to plain GFRP. Furthermore, the damaged area observed in the images increased with impact energy across all configurations. The outcomes derived from THz spectroscopy were benchmarked against those from ultrasound testing. THz spectroscopy demonstrates greater promise as a highly effective technique for detecting impact damage using carbon materials, offering advantages over traditional NDT methods due to its distinct wavelength, portability, non-contact capability, capacity to depict fibre alignment, lack of additional calibration specimens and coupling agents, and its ability to partially uncover the scope of impact damage.

Structural Condition Assessment of Steel Anchorage Using Convolutional Neural Networks and Admittance Response

Structural damage in the steel bridge anchorage, if not diagnosed early, could pose a severe risk of structural collapse. Previous studies have mainly focused on diagnosing prestress loss as a specific type of damage. This study is among the first for the automated identification of multiple types of anchorage damage, including strand damage and bearing plate damage, using deep learning combined with the EMA (electromechanical admittance) technique. The proposed approach employs the 1D CNN (one-dimensional convolutional neural network) algorithm to autonomously learn optimal features from the raw EMA data without complex transformations. The proposed approach is validated using the raw EMA response of a steel bridge anchorage specimen, which contains substantial nonlinearities in damage characteristics. A K-fold cross-validation approach is used to secure a rigorous performance evaluation and generalization across different scenarios. The method demonstrates superior performance compared to established 1D CNN models in assessing multiple damage types in the anchorage specimen, offering a potential alternative paradigm for data-driven damage identification in steel bridge anchorages.

Structural damage classification in composite materials using the Wigner-Ville distribution and convolutional neural networks

Detecting damage in composite materials is crucial and has received considerable attention in the field of machine learning. However, challenges remain in addressing backbone issues and classifying specific types of damage. This paper presents a novel deep-learning approach for automatically distinguishing delamination and microcracks in Carbon Fiber-Reinforced Polymer (CFRP). The methodology utilizes signals from piezoelectric transducers transformed into time–frequency representations based on the Wigner-Ville Distribution (WVD). Backbone issues were successfully addressed by transitioning the time series classification problem into a computer vision (CV) context through Convolutional Neural Networks (CNN). The analysis included a thorough examination of delamination and microcracks datasets produced experimentally by the National Aeronautics and Space Administration (NASA), focusing on these two types of damage. The proposed methodology achieved a precision range of 98.3 % to 100 % in damage classification, demonstrating its effectiveness for structural health monitoring in composite materials.

Holistic image‐based analysis of damage on concrete surfaces—A multifaceted approach based on supervised machine learning

AbstractTraditional manual methods for inspecting damage on building structures, such as cracks or spalling on concrete surfaces, are laborious, costly, and error‐prone. Despite many attempts to automate this task using digital photographs, most studies primarily focus on detecting damage within images, neglecting the actual dimensions and their implications for structural integrity. To bridge this gap, we present a multifaceted approach for holistic damage analysis that not only detects damage within images but also determines its real‐world dimensions. To achieve this, we first distinguish between linear and areal damage, and apply two separate methods based on deep learning, each tailored to detect these specific types of damage within images. Additionally, we use a cost‐effective 3D‐printed laser projection device to project a grid of laser points onto the surface. This grid, with a known and fixed point‐to‐point distance, serves as a scale reference, facilitating true‐to‐scale measurements of the damage area. Furthermore, for depth estimation of areal damage, we employ models for monocular depth prediction trained in domains distinct from ours. We thoroughly evaluated our methods on realistic and challenging image datasets, which we captured ourselves in public space. The results show that our customized methods for damage detection achieved moderate results for linear damage and more promising results for areal damage. The quantification of damage area resulted in errors less than 10% across all evaluated images, which is suitable for most practical applications. However, estimating the depth of areal damage using models trained on distinct domains proved to be a challenge. Our research expands automated damage detection to include comprehensive, true‐to‐scale analysis of damage and underscores the need for continued refinement.

The Effects of Particle LET and Fluence on the Complexity and Frequency of Clustered DNA Damage.

Clustered DNA-lesions are predominantly induced by ionizing radiation, particularly by high-LET particles, and considered as lethal damage. Quantification of this specific type of damage as a function of radiation parameters such as LET, dose rate, dose, and particle type can be informative for the prediction of biological outcome in radiobiological studies. This study investigated the induction and complexity of clustered DNA damage for three different types of particles at an LET range of 0.5-250 keV/μm. Nanometric volumes (36.0 nm3) of 15 base-pair DNA with its hydration shell was modeled. Electron, proton, and alpha particles at various energies were simulated to irradiate the nanometric volumes. The number of ionization events, low-energy electron spectra, and chemical yields for the formation of °OH, H°, , and H2O2 were calculated for each particle as a function of LET. Single- and double-strand breaks (SSB and DSB), base release, and clustered DNA-lesions were computed from the Monte-Carlo based quantification of the reactive species and measured yields of the species responsible for the DNA lesion formation. The total amount of DNA damage depends on particle type and LET. The number of ionization events underestimates the quantity of DNA damage at LETs higher than 10 keV/μm. Minimum LETs of 9.4 and 11.5 keV/μm are required to induce clustered damage by a single track of proton and alpha particles, respectively. For a given radiation dose, an increase in LET reduces the number of particle tracks, leading to more complex clustered DNA damage, but a smaller number of separated clustered damage sites. The dependency of the number and the complexity of clustered DNA damage on LET and fluence suggests that the quantification of this damage can be a useful method for the estimation of the biological effectiveness of radiation. These results also suggest that medium-LET particles are more appropriate for the treatment of bulk targets, whereas high-LET particles can be more effective for small targets.

Adaptive Risk Management in Road Construction: Oyon-Ambo Highway Insights, El Niño 2019 Case Study

This research provides an in-depth analysis of the challenges faced during the construction of the Oyon-Ambo highway in Peru, particularly during the extreme rainfall events associated with the 2019 El Niño phenomenon. Situated in the high Andean area of the Pasco region, this rigid pavement road project encountered significant disruptions due to the extraordinary weather conditions. The adjacent Chaupihuaranga River experienced increased flow rates, causing extensive damage to the road and necessitating substantial modifications to the engineering plans. Focusing on 14 critical sectors identified in the project's technical file as prone to instability, the study details the accelerated degradation and eventual destruction of these sectors due to the intense rainfall. Emphasizing the importance of adaptive risk management in road construction, especially in regions susceptible to natural phenomena like El Niño, the paper underscores the need for responsive reengineering approaches based on specific damage types. It also highlights the critical role of continuous monitoring and evaluation in adapting to environmental changes. The insights from the Oyon-Ambo highway case offer valuable lessons for future road construction projects, underscoring the importance of proactive and adaptive risk management strategies to minimize the impacts of extraordinary natural events.

Detection and Classification of Uniform and Concentrated Wall-Thinning Defects Using High-Order Circumferential Guided Waves and Artificial Neural Networks.

Pipeline structures are susceptible to corrosion, leading to significant safety, environmental, and economic implications. Existing long range guided wave inspection systems often fail to detect footprints of the concentrated defects, which can lead to leakage. One way to tackle this issue is the utilization of circumferential guided waves that inspect the pipe's cross section. However, achieving the necessary detection resolution typically necessitates the use of high-order modes hindering the inspection data interpretation. This study presents the implementation of an ultrasonic technique capable of detecting and classifying wall thinning and concentrated defects using high-order guided wave modes. The technique is based on a proposed phase velocity mapping approach, which generates a set of isolated wave modes within a specified phase velocity range. By referencing phase velocity maps obtained from defect-free stages of the pipe, it becomes possible to observe changes resulting from the presence of defects and assign those changes to the specific type of damage using artificial neural networks (ANN). The paper outlines the fundamental principles of the proposed phase velocity mapping technique and the ANN models employed for classification tasks that use synthetic data as an input. The presented results are meticulously verified using samples with artificial defects and appropriate numerical models. Through numerical modeling, experimental verification, and analysis using ANN, the proposed method demonstrates promising outcomes in defect detection and classification, providing a more comprehensive assessment of wall thinning and concentrated defects. The model achieved an average prediction accuracy of 92% for localized defects, 99% for defect-free cases, and 98% for uniform defects.

Automated Quantification of Wind Turbine Blade Leading Edge Erosion from Field Images

Wind turbine blade leading edge erosion is a major source of power production loss and early detection benefits optimization of repair strategies. Two machine learning (ML) models are developed and evaluated for automated quantification of the areal extent, morphology and nature (deep, shallow) of damage from field images. The supervised ML model employs convolutional neural networks (CNN) and learns features (specific types of damage) present in an annotated set of training images. The unsupervised approach aggregates pixel intensity thresholding with calculation of pixel-by-pixel shadow ratio (PTS) to independently identify features within images. The models are developed and tested using a dataset of 140 field images. The images sample across a range of blade orientation, aspect ratio, lighting and resolution. Each model (CNN v PTS) is applied to quantify the percent area of the visible blade that is damaged and classifies the damage into deep or shallow using only the images as input. Both models successfully identify approximately 65% of total damage area in the independent images, and both perform better at quantifying deep damage. The CNN is more successful at identifying shallow damage and exhibits better performance when applied to the images after they are preprocessed to a common blade orientation.

Detection and Quantitation of DNA Damage on a Genome-wide Scale Using RADAR-seq.

The formation and persistence of DNA damage can impact biological processes such as DNA replication and transcription. To maintain genome stability and integrity, organisms rely on robust DNA damage repair pathways. Techniques to detect and locate DNA damage sites across a genome enable an understanding of the consequences of DNA damage as well as how damage is repaired, which can have key diagnostic and therapeutic implications. Importantly, advancements in technology have enabled the development of high-throughput sequencing-based DNA damage detection methods. These methods require DNA enrichment or amplification steps that limit the ability to quantitate the DNA damage sites. Further, each of these methods is typically tailored to detect only a specific type of damage. RAre DAmage and Repair (RADAR) sequencing is a DNA sequencing workflow that overcomes these limitations and enables detection and quantitation of DNA damage sites in any organism on a genome-wide scale. RADAR-seq works by replacing DNA damage sites with a patch of modified bases that can be directly detected by Pacific Biosciences Single-Molecule Real Time sequencing. Here, we present three protocols that enable detection of thymine dimers and ribonucleotides in bacterial and archaeal genomes. Basic Protocol 1 enables construction of a reference genome required for RADAR-seq analyses. Basic Protocol 2 describes how to locate, quantitate, and compare thymine dimer levels in Escherichia coli exposed to varying amounts of UV light. Basic Protocol 3 describes how to locate, quantitate, and compare ribonucleotide levels in wild-type and ΔRNaseH2 Thermococcus kodakarensis. Importantly, all three protocols provide in-depth steps for data analysis. Together they serve as proof-of-principle experiments that will allow users to adapt the protocols to locate and quantitate a wide variety of DNA damage sites in any organism. © 2022 New England Biolabs. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Constructing a reference genome utilizing SMRT sequencing Basic Protocol 2: Mapping and quantitating genomic thymine dimer formation in untreated versus UV-irradiated E. coli using RADAR-seq Basic Protocol 3: Mapping and quantitating genomic ribonucleotide incorporation in wildtype versus ΔRNaseH2 T. kodakarensis using RADAR-seq.

Decomposition methods for impact‐based fault detection algorithms in railway inspection applications

Railway track defects must be detected and repaired to ensure the safe operation of the railway. Signals generated by wheelset contact with rail discontinuities contain information about railway track structure and health. Each signal has a characteristic pattern, which is modified in the presence of defects. Signal decomposition is commonly used to extract characteristics from impact response signals. It is necessary to select an appropriate decomposition method for the wheel/rail impact signal. This paper presents a study of the effectiveness of decomposition methods to identify the most appropriate technique for classification of rail conditions. Vibration signals have been generated in experiments and decomposed. The signal characteristics are presented and energy-based methods are used in an evaluation of the different approaches. Standard deviation is used to assess the similarity of parameter value from different intrinsic mode functions (IMFs); maximum and minimum differences from the mean value are used to consider the effectiveness of each technique in identifying specific damage types. The evaluation shows that variational mode decomposition (VMD) is suitable for the rail application. Classifiers are applied to the outputs of the selected decomposition method. The results demonstrate the suitability of the approach for processing wheel/rail impact response signals to detect faults and improve safety.

Inductive Thermography as Non-Destructive Testing for Railway Rails

Inductive thermography is a non-destructive testing method, whereby the specimen is slightly heated with a short heating pulse (0.1–1 s) and the temperature change on the surface is recorded with an infrared (IR) camera. Eddy current is induced by means of high frequency (HF) magnetic field in the surface ‘skin’ of the specimen. Since surface cracks disturb the eddy current distribution and the heat diffusion, they become visible in the IR images. Head checks and squats are specific types of damage in railway rails related to rolling contact fatigue (RCF). Inductive thermography can be excellently used to detect head checks and squats on rails, and the method is also applicable for characterizing individual cracks as well as crack networks. Several rail pieces with head checks, with artificial electrical discharge-machining (EDM)-cuts and with a squat defect were inspected using inductive thermography. Aiming towards rail inspection of the track, 1 m long rail pieces were inspected in two different ways: first via a ‘stop-and-go’ technique, through which their subsequent images are merged together into a panorama image, and secondly via scanning during a continuous movement of the rail. The advantages and disadvantages of both methods are compared and analyzed. Special image processing tools were developed to automatically fully characterize the rail defects (average crack angle, distance between cracks and average crack length) in the recorded IR images. Additionally, finite element simulations were used to investigate the effect of the measurement setup and of the crack parameters, in order to optimize the experiments.

Who peeled the bones? An actualistic and taphonomic study of axial elements from the Toll Cave Level 4, Barcelona, Spain

Equifinality constitutes a challenge when interpreting agency in archaeological sites. The fact that a specific type of damage frequently cannot be linked to a single actor, behavior, or ecological context, handicaps correct interpretations of site formation processes. Actualistic studies have been used to address this type of problem by creating models and analogies to infer the processes that occurred in the past and explain the formation processes of fossil faunas found at archaeological sites. Here, we apply this approach using observational data from Arilla et al. (2014) describing the consumption of ungulate carcasses by wild brown bears (Ursus arctos arctos). We focus on a specific type of damage, peeling, which was observed to be one of the most significant modifications of axial skeletal elements of carcasses eaten by bears. This fact was especially relevant because the peeling damage was initially attributed to the feeding activities of primates (humans and chimpanzees—Pan troglodytes) and only anecdotally to other taphonomic agents. The observational data are then applied to Level 4 of Toll Cave (Moià, Barcelona, Spain), dated to >49,000 14C BP, which has been interpreted to be a hibernation lair with significant activity by carnivores and sporadic human presence. Rib and vertebral peeling have also been identified at Level 4 bone assemblage, casting doubt on the agent responsible for this damage (effector) in the cave. The aim of our study is to address the equifinality problems that involve peeling as a taphonomical signature in archaeological sites, taking the Pleistocene site of Toll Cave as a case study.

Structural Health Monitoring using deep learning with optimal finite element model generated data

Identifying damage through Structural Health Monitoring (SHM) methods is increasingly attracting attention due to multiple maintenance and failure prevention applications. In order to create reliable SHM systems for structural damage identification (DI) tasks, access to large amounts of data containing measured structural responses is usually necessary. The data acquisition is mostly based on direct experimental responses up to now and requires time consuming measurements in various working and ambient conditions of the structure. In the present work, a novel SHM method is tested where all data is solely derived from FE calculated responses, after an initial experimental cost for FE model updating on the healthy structure state. The proposed method can be especially applied in cases where specific damage types are expected or anomalies are adequately defined so they can be effectively simulated by FE models. Origin of such models may then be the healthy experimental status. To test the proposed SHM system, the optimal FE model of an experimental benchmark linear beam structure is constructed, simulating an undamaged condition. In order to check the robustness of the proposed method the damage magnitudes imposed on the benchmark are kept small and combined with random excitations. Next, the optimal FE model is used for generating labeled SHM vibration data through a repetitive load case scheme which also includes uncertainties simulation. The data derived from the optimal FE model is finally used to train a Deep Learning (DL) Convolutional Neural Network (CNN) classifier which is after experimentally validated on the benchmark structure. The optimal FE generated data proves to be able to train an accurate CNN that can predict adequately the experimental benchmark states. A comparison is also given with a CNN trained by the corresponding nominal FE model data which is found not reliable on the experimental validations. The presented combination of optimal FE and DL is a potential solution for future SHM tools and further investigation is encouraged.

MAP‑1B, PACS‑2 and AHCYL1 are regulated by miR‑34A/B/C and miR‑449 in neuroplasticity following traumatic spinal cord injury in rats: Preliminary explorative results from microarray data.

Spinal cord injury (SCI) is a specific type of damage to the central nervous system causing temporary or permanent changes in its function. The present aimed to identify the genetic changes in neuroplasticity following SCI in rats. The GSE52763 microarray dataset, which included 15 samples [3 sham (1 week), 4 injury only (1 week), 4 injury only (3 weeks), 4 injury + treadmill (3 weeks)] was downloaded from the Gene Expression Omnibus database. An empirical Bayes linear regression model in limma package was used to identify the differentially expressed genes (DEGs) in injury vs. sham and treadmill vs. non-treadmill comparison groups. Subsequently, time series and enrichment analyses were performed using pheatmap and clusterProfile packages, respectively. Additionally, protein-protein interaction (PPI) and transcription factor (TF)-microRNA (miRNA)-target regulatory networks were constructed using Cytoscape software. In total, 159 and 105 DEGs were identified in injury vs. sham groups and treadmill vs. non-treadmill groups, respectively. There were 40 genes in cluster 1 that presented increased expression levels in the injury (1 week/3 weeks) groups compared with the sham group, and decreased expression levels in the injury + treadmill group compared with the injury only groups; conversely, 52 genes in cluster 2 exhibited decreased expression levels in the injury (1 week/3 weeks) groups compared with the sham group, and increased expression levels in the injury + treadmill group compared with the injury only groups. Enrichment analysis indicated that clusters 1 and 2 were associated with immune response and signal transduction, respectively. Furthermore, microtubule associated protein 1B, phosphofurin acidic cluster sorting protein 2 and adenosylhomocysteinase-like 1 exhibited the highest degrees in the regulatory network, and were regulated by miRNAs including miR-34A, miR-34B, miR-34C and miR-449. These miRNAs and their target genes may serve important roles in neuroplasticity following traumatic SCI in rats. Nevertheless, additional in-depth studies are required to confirm these data.

Very human bears: Wild brown bear neo-taphonomic signature and its equifinality problems in archaeological contexts

Different agents can lead to similar damage patterns, and different causes can result in the same type of modification. This phenomenon was defined by Lyman (1987) as a problem of equifinality, with which the researcher warned about the risks of making direct systematic correlations. The fact that a specific type of damage cannot be linked to a single actor, behaviour or ecological context is primarily applicable to damage associated with the direct consumption of carcasses. Some carnivores show physical and dental characteristics that could lead to bone modifications potentially like those generated by humans. For example, bears have a bunodont dentition and plantigrade locomotion –the latter allows them to frequently release and use their claws as “hands”. Here, we present the neo-taphonomical study of 17 ungulate carcasses eaten by wild brown bears (Ursus arctos arctos) in the Spanish Pyrenees. Our observations express this equifinal problem due to the similarities between tooth marks and peeling generated by both bears and humans. This fact is especially significant, given that peeling and the combination of this damage with visible tooth marks were primarily associated with the feeding activities of humans and chimpanzees (Pan troglodytes) and only anecdotally with other taphonomic agents, such as spotted hyenas. With this research, we try to show some equifinal phenomena that could occur in Pleistocene faunal assemblages as well, where the presence of both hominids and bears is documented.

Genomic and Proteomic Signatures of Replication Fork Collapse

Transient obstruction of DNA polymerase progression activates the ATR checkpoint kinase, which in turn suppresses fork breakage and DNA repair‐factor recruitment. To characterize the causes of and responses to replication fork collapse driven by inhibition of ATR, two unbiased, information‐rich approaches were applied: genome‐wide identification of replication fork collapse sites and quantification of protein recruitment to such sites. Over 500 murine and human sites in the genome were identified as dependent on ATR for stability. The vast majority of these sites were characterized by repetitive DNA, including quasipalindromic AT‐rich repeats, inverted retrotransposable elements and purine‐rich ministatelites. While the prevalence of these types of break‐associated repeats in human and mouse cell lines differed, the formation of stable intrastrand structures was a common feature. These data indicate that intrastrand structure formation is a primary driver of fork collapse upon ATR inhibition. Using a novel two‐step replication fork retrieval approach (iPOND2), we have gone on to show that the response to replication fork collapse involves both the rapid recruitment of various repair factors and subsequent large‐scale ubiquitin‐driven turnover of these same factors. We speculate that the establishment of this dynamic equilibrium of repair factors may allow the best repair mechanism for the specific type of damage and intermediate generated to gain access and be favored. These findings and how distinct types of DNA repeats could drive differing repair responses will be discussed.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

A Mammalian Genetic Complementation Assay for Assessing Cellular Resistance to Genotoxic Compounds.

A complementation assay was developed to determine whether alleles of DNA repair genes are necessary for repairing specific types of damage. The assay was established by measuring the resistance capacity of Rad51d-deficient mouse embryonic fibroblasts (MEFs) transfected with mammalian expression constructs. Here, we describe the methods used to assess colony survival following the treatment of transfected cells with genotoxic compounds. This approach provides a time-efficient and stringent strategy to screen genetic alleles for identifying regions or specific amino acid residues critical for function or regulation of DNA repair pathways.

Metoda badań porównawczych zbiorów sprawdzeń do oceny stanu technicznego ciągników

In this paper the authors propose an original method of the numerical evaluation of checkup sets for the technical condition of an agricultural tractor. Information entropy required in diagnostic tests for a specific checkup set was used as the evaluation criterion. A formal description is given for the technical condition diagnostics of a tractor in its operation period, which is characterised by a high rate of average damage. The structural model was constructed using information entropy. This model accumulates the number of checkups, probability of specific damage types and assigns them a common numerical measure. The conducted logic analysis of the proposed method and the results obtained in experiments on its practical applicability in service stations indicate that the method adequately describes this area of machine operation and thus may be a measure of information effectiveness for checkup sets determining the machine technical condition.