Identification Procedures Research Articles

FACTORS OF INTERFERENCE IN LABORATORY INVESTIGATIONS: HEMOLYSIS, ICTERUS, LIPEMIA

The accuracy of laboratory investigation results depends on various factors, including interfer- ence factors. Additionally, it is worth noting that a significant number of samples are photometrical- ly measured in the wavelength range of 350 to 600 nm, which corresponds to the absorption peak of many interferents.
 In laboratories where the process of sample rejection is established based on serum indices (color scale) and specific cutoff points for samples showing hemolysis, icterus, and lipemia, the issuance of falsely elevated/decreased investigation results to the clinic is avoided. The declared criteria for sample rejection should comply with the recommendations of CLSI CA-56A and CLSI EP 07 standards.
 The majority of errors in the preanalytical stage are caused by hemolysis. All hemolyzed sam- ples with a concentration of free hemoglobin equal to or greater than 10.0 g/L should be rejected, and the laboratory report should indicate the reason for rejection as "Hemolyzed sample. Resampling is required." Each laboratory establishes its own cutoff point for free hemoglobin, bili- rubin and triglicerol concentration in the sample. This is because an increase in hemolysis index in serum/plasma leads to falsely increased levels of certain analytes (such as inorganic phosphorus, potassium, folic acid, magnesium, iron, urea, LDH, AST, ALT). Conversely, the concentration of albumin, bilirubin, glucose, sodium, and chloride in such samples is falsely decreased. The presence of icterus in samples is always indicative of a pathological process. The elevation of bilirubin con- centration in the sample will be reflected in the increase/decrease of analytes such as creatinine, total protein, triglycerides, cholesterol, phosphorus, magnesium, urea, hormones (free triiodothyro-nine, testosterone, estradiol), and others. An increase in triglyceride and lipoprotein concentration in the sample significantly affects the levels of analytes such as phosphorus, creatinine, total protein and calcium.
 The presence of hemolysis, icterus, and lipemia in samples affects the measurement of various analytes and, consequently, the results of laboratory investigations and patient safety. Laboratories should have a developed procedure for systematic identification of interfering agents and decision- making regarding the handling of such samples to ensure the reliability of obtained data.

Improving the accuracy of identifying objects in digital frames of one series through the procedure of preliminary identification of measurements

The object of this study is images of various objects of the Solar System on a series of digital frames. The variety and quality of shooting conditions make it difficult to identify a frame with the corresponding part of the sky. This fact significantly reduces the quality indicators of detection and estimation of the position of objects of the Solar System using already known computational methods and international astronomical astrometric and photometric catalogs. To solve this problem, a procedure for preliminary identification of measurements of digital frames of one series was devised. This procedure is based on the determination of the shift parameters between the dimensions of a frame and the forms of a catalog or another frame. Also, taking into account the possibility of forming false measurements has made it possible to increase the accuracy of identification and resistance to various kinds of destabilizing factors. Based on this, the final estimation of the shift parameters between frames was performed. Due to these features, the use of the devised preliminary identification procedure makes it possible to improve identification with reference astronomical objects and reduce the number of false detections. The study showed that when identifying frames, the fitting gives the best accuracy of binding to the starry sky. Also, the standard deviation of frame identification errors in this case is 7–10 times less than without using the devised procedure. The procedure developed for preliminary identification of measurements of digital frames of one series was tested in practice within the framework of the CoLiTec project. It has been incorporated into the Lemur software for automated detection of new and tracking of known objects. Owing to the use of the Lemur software and the proposed procedure implemented in it, more than 700,000 measurements of various astronomical objects under study were successfully identified

Words for the hearts: a corpus study of metaphors in online depression communities.

Humans understand, think, and express themselves through metaphors. The current paper emphasizes the importance of identifying the metaphorical language used in online health communities (OHC) to understand how users frame and make sense of their experiences, which can boost the effectiveness of counseling and interventions for this population. We used a web crawler to obtain a corpus of an online depression community. We introduced a three-stage procedure for metaphor identification in a Chinese Corpus: (1) combine MIPVU to identify metaphorical expressions (ME) bottom-up and formulate preliminary working hypotheses; (2) collect more ME top-down in the corpus by performing semantic domain analysis on identified ME; and (3) analyze ME and categorize conceptual metaphors using a reference list. In this way, we have gained a greater understanding of how depression sufferers conceptualize their experience metaphorically in an under-represented language in the literature (Chinese) of a new genre (online health community). Main conceptual metaphors for depression are classified into PERSONAL LIFE, INTERPERSONAL RELATIONSHIP, TIME, and CYBERCULTURE metaphors. Identifying depression metaphors in the Chinese corpus pinpoints the sociocultural environment people with depression are experiencing: lack of offline support, social stigmatization, and substitutability of offline support with online support. We confirm a number of depression metaphors found in other languages, providing a theoretical basis for researching, identifying, and treating depression in multilingual settings. Our study also identifies new metaphors with source-target connections based on embodied, sociocultural, and idiosyncratic levels. From these three levels, we analyze metaphor research's theoretical and practical implications, finding ways to emphasize its inherent cross-disciplinarity meaningfully.

Methodical support for controlling safety of cereals for children (exemplified by furan and methylfuran)

Introduction. The study addresses issues related to developing a procedure for identification of chemicals in food products in practical instrumental investigations. We have validated the method for quantification of a heterocyclic compound (furan) and sylvan (methylfuran) in grain-based cereals for children.
 The aim of this study was to develop a precise GC-MS-method for quantifying a heterocyclic compound and sylvan in food products to control their safety for children, especially infants.
 Materials and methods. The research objects were quantification means (QM) as a certain amount of an analyzed heterocyclic compound and sylvan (the mass share of the analyzed compound is 99%) provided by Sigma Aldrich Corporation (USA), as well as milk and milk-free dry cereals for babies. The study involved using an Agilent 7890А gas chromatographer with 5975С quadrupole mass-spectrometric detector. Sample preparation involved extraction and concentration of furan and methylfuran from food samples by statistical vapour-liquid equilibrium analysis and subsequent gas-chromatographic analysis of vapour phase.
 Results. The procedure for quantification of furan and methylfuran is an original scientific and technical development based on experimental data. It gives an opportunity to identify furan and methylfuran micro-admixtures in food samples with high precision and sensitivity with a range of concentrations between 0.93 and 9.37 ng and between 0.91 and 9.13 ng accordingly. The ultimate indicators describing acceptability of the results obtained by furan and methylfuran quantification included error not higher than 24%, пintra-laboratory precision (σRл ) not higher than 10%, correctness (± σCл ) not higher than 22%.
 Conclusion. The developed technique has shown high selectivity, sensitivity, reliability and satisfactory accuracy. The methodology meets the criteria of the State Standard of the Russian Federation GOST R ISO 5725-1-6-2002 and can be used for laboratory studies of food safety by enterprises and institutions engaged in quality control and research of food products and food raw materials.

Threshold lines identification for non-Gaussian distributed diagnostic features

Machine condition monitoring systems are frequently used in the industry, especially for critical infrastructure. Decision making is still challenging due to the lack of limit values (called also thresholds). Identification of thresholds is in particular difficult for unique machines with specific Health Index (HI) data properties. Our contribution is a procedure for threshold values identification for HI data with time-varying characteristics and non-Gaussian behaviour. The proposed methodology consists of few crucial steps, such as data segmentation, data modelling, and Monte Carlo simulations for quantile lines identification based on the fitted models. The proposed methods are based on robust statistics dedicated to non-Gaussian distributed data. Novelty of the paper is related to the extension of simple ”Trend+Noise” model for HI data and model-based framework for threshold lines estimation. Thus, the method provides more accurate results for non-Gaussian, time-varying data. We demonstrate the superiority of the proposed procedure over the classical ones. The presented simulation study clearly indicates the efficiency of the approach for non-Gaussian HI data. The methodology is applied to two real datasets with different properties (level of non-Gaussianity and presence of interdependence in the random part). We believe that proposed procedure could be considered as a guideline for future research and may be implemented in commercial monitoring systems.

Deep neural networks for explainable feature extraction in orchid identification

AbstractAutomated image-based plant identification systems are black-boxes, failing to provide an explanation of a classification. Such explanations are seen as being essential by taxonomists and are part of the traditional procedure of plant identification. In this paper, we propose a different method by extracting explicit features from flower images that can be employed to generate explanations. We take the benefit of feature extraction derived from the taxonomic characteristics of plants, with the orchids as an example domain. Feature classifiers were developed using deep neural networks. Two different methods were studied: (1) a separate deep neural network was trained for every individual feature, and (2) a single, multi-label, deep neural network was trained, combining all features. The feature classifiers were tested in predicting 63 orchid species using naive Bayes (NB) and tree-augmented Bayesian networks (TAN). The results show that the accuracy of the feature classifiers is in the range 83-93%. By combining these features using NB and TAN the species can be predicted with an accuracy of 88.9%, which is better than a standard pre-trained deep neural-network architecture, but inferior to a deep learning architecture after fine-tuning of multiple layers. The proposed novel feature extraction method still performs well for identification and is explainable, as opposed to black-box solutions that only aim for the best performance. Graphical abstract

Fast Inspection of Quality of Indigo Naturalis by Multiple Light Scattering.

Quality control of Chinese herbal medicine is a crucial component of Chinese herbal medicine research and development. Faced with the challenges of modernization and internationalization of Chinese herbal medicine, it is urgent to establish thorough and effective procedures for quality identification of Chinese herbal medicine, and there is an urgent need for new analytical and testing techniques that are efficient, accurate, and environmentally friendly. Multiple light scattering is a cutting-edge and analytical method that can accurately and rapidly assess the quality of Chinese herbal medicine without altering the nature or state of the sample or using organic reagents. Indigo Naturalis is considered a good remedy for pediatric hyperthermia, psoriasis, leukemia, and ulcerative colitis. In this study, the process of addition of Indigo Naturalis powder in water was recorded precisely using a multiple light scattering instrument. The qualitative and quantitative measurements of the instrument can be used to accurately capture the overall trajectory and sinking behavior of Indigo Naturalis powder into water and to establish a rapid evaluation method for the quality of Indigo Naturalis with the transmission and backscattering spectrograms of the sample as qualitative indicators and stability index as a quantitative indicator. The analytical technique based on multiple light scattering provides a fast, accurate, green, and environmentally friendly method for the quality evaluation of Indigo Naturalis and supports the development and transformation of high-quality Indigo Naturalis.

A comparative study for optimization of MALDI-TOF MS identification of filamentous fungi.

To evaluate and compare the performance of three commercial culture media, two filamentous fungi libraries, and two different protein extraction procedures in MALDI-TOF MS fungal identification. A total of 21 quality control samples were cultured on Sabouraud dextrose agar (SDA), ID fungi plate medium (IDFP), and Sabouraud gentamicin chloramphenicol 2 agar (SGC2). For four consecutive days, fungal growths were inoculated on a MALDI target plate both by using a direct transfer technique (DT) and by using a formic acid-ethanol protein extraction procedure (EEP). The MALDI-TOF MS-generated spectra were identified by the MBT Bruker library and the MSI database. Selective culture media (IDFP and SGC2) significantly outperformed the non-selective SDA medium. IDFP was superior to the SGC2 medium for dermatophyte identification. The EEP only demonstrated a benefit over DT in the underperforming SDA medium. The MBT Bruker library outperformed the MSI database in Aspergillus identification while the MSI database outperformed the MBT library in dermatophyte identification. For non-Aspergillus fungi, the libraries performed comparably. The results of our study show the necessity of using selective culture media (IDFP and SGC2) for fungal identification with MALDI-TOF MS and demonstrate no significant benefit of the formic acid-ethanol protein extraction technique in these media. Given the relative strengths and weaknesses of the MBT library and the MSI database, it might currently be beneficial to consider these libraries as complementary and employ both databases to achieve optimal fungal identification.

Computer vision application for improved product traceability in the granite manufacturing industry

The traceability of granite blocks consists in identifying each block with a finite number of colour bands that represent a numerical code. This code has to be read several times throughout the manufacturing process, but its accuracy is subject to human errors, leading to cause faults in the traceability system. A computer vision system is presented to address this problem through colour detection and the decryption of the associated code. The system developed makes use of colour space transformations and various thresholds for the isolation of the colours. Computer vision methods are implemented, along with contour detection procedures for colour identification. Lastly, the analysis of geometrical features is used to decrypt the colour code captured. The proposed algorithm is trained on a set of 109 pictures taken in different environmental conditions and validated on a set of 21 images. The outcome shows promising results with an accuracy rate of 75.00% in the validation process. Therefore, the application presented can help employees reduce the number of mistakes in product tracking.

Experimental modal analysis of a single-link flexible robotic manipulator with curved geometry using applied system identification methods

In this paper, the principal time-domain system identification methods are studied and used to carry out the experimental modal analysis of a single-link flexible manipulator with curved geometry in a comparative fashion. The identification process carried out in this study is aimed at deriving first-order state-space dynamical models and second-order configuration-space dynamical models of the single-link flexible manipulator by using an array of fundamental system identification procedures, which includes the ARX (AutoRegressive eXogenous method), SSEST (State-Space ESTimation method), N4SID (Numerical Algorithm for Subspace State-Space System Identification), ERA/OKID (Eigensystem Realization Algorithm combined with the Observer/Kalman Filter Identification method), and TFEST (Transfer Function ESTimation method) methods. The main goal of this paper is, therefore, to perform a comparative study between the most fundamental system identification procedures applied to the numerical and experimental analysis of the structural vibrations of a flexible manipulator having a curved geometric shape. First, this paper poses the theoretical foundations for the development of the numerical study and the experimental testing to be performed for the case study. Subsequently, the flexible manipulator analyzed in this work is modeled with various levels of complexity, which range from a simple lumped parameter model to a linear finite element model developed by integrating the interconnections between the SOLIDWORKS and ANSYS software. By doing so, the computational procedures applied to the time-domain experimental measurements of the dynamic behavior of the flexible manipulator allow for reconstructing first-order and second-order mechanical models of the structural system of interest in the MATLAB simulation environment. The numerical results and the experimental tests reported in this investigation demonstrate the effectiveness of all the time-domain system identification approaches considered in the paper.

Regulation of Uncertain Markov Jump Linear Systems With Application on Automotive Powertrain Control

Modeling and control of dynamic systems that experience abrupt changes are challenging and fundamental tasks in applications of different areas of engineering. Among these, control synthesis for autonomous heavy-duty vehicles has the potential to highly benefit transportation systems. That said, we propose a robust recursive regulator for discrete-time Markov jump linear systems with polytopic uncertain data. We assume the uncertainties affect the state-space parameters as well as the transition probability matrix. We formulate an optimization problem using the penalty function method and design a recursive solution, whereby we attain the robust state feedback gains. We impose no restrictions on how fast the uncertainties can change between two consecutive iterations. Conditions for convergence and mean-square stability are well defined in terms of Riccati recursive equations. We also set up a procedure for powertrain polytopic model identification based on CAN bus data of an autonomous heavy-duty ground vehicle. We provide two numerical examples to verify the effectiveness of the robust recursive regulator. Moreover, in a third example, we validate the obtained powertrain model on a trajectory tracking problem and compute acceleration and braking inputs using the robust regulator.

3D–3D dentition superimposition for individual identification: A study of an Eastern Chinese population

Recently, 3D dental intraoral scanning technology has been developed rapidly and applied widely in everyday dental practice. Since 3D dental scanning could provide valuable personal information, it enabled researchers to develop novel procedures for individual identification through 3D–3D dentition superimposition. This study aimed to test the applicability of this method in an Eastern Chinese population and propose a threshold for personal identification. For this purpose, 40 volunteers were recruited, and the initial 80 (upper and lower) 3D intraoral scans (IOS) were collected. After one year, 80 IOS of these volunteers were repeatedly collected. In addition, the other 120 IOS of 60 patients were extracted from the database. The 3D models were trimmed, aligned, and superimposed via Geomagic Control X software, and then the root mean square (RMS) value of point-to-point distance between the two models was calculated. The superimposition of two IOS belonging to the same individual was considered as a match, and superimposition of two IOS belonging to different individuals was considered as a mismatch. Totally, superimpositions of 80 matches and 3120 mismatches were obtained. Intra- and inter-observer errors were assessed through the calculation of relative technical error of measurement (rTEM). Mann-Whitney U test verified possible statistically significant differences between matches and mismatches (P < 0.05). The rTEM of intra- and inter-observer repeatability analyses was lower than 4.7 %. The range of RMS value was 0.05–0.18 mm in matches and 0.72–2.28 mm in mismatches without overlapping. The percentage of accurate identification reached 100 % in blind test through an arbitrary RMS threshold of 0.45 mm. The results indicated that individual identification through the 3D–3D dentition superimposition was effective in Eastern Chinese population. Successful identification could be achieved with high probability when the RMS value of the point-to-point distance of two dentitions is <0.45 mm.

Combining hyperspectral imaging techniques with deep learning to aid in early pathological diagnosis of melanoma

BackgroundCutaneous melanoma, an exceedingly aggressive form of skin cancer, holds the top rank in both malignancy and mortality among skin cancers. In early stages, distinguishing malignant melanomas from benign pigmented nevi pathologically becomes a significant challenge due to their indistinguishable traits. Traditional skin histological examination techniques, largely reliant on light microscopic imagery, offer constrained information and yield low-contrast results, underscoring the necessity for swift and effective early diagnostic methodologies. As a non-contact, non-ionizing, and label-free imaging tool, hyperspectral imaging offers potential in assisting pathologists with identification procedures sans contrast agents. MethodsThis investigation leverages hyperspectral cameras to ascertain the optical properties and to capture the spectral features of malignant melanoma and pigmented nevus tissues, intending to facilitate early pathological diagnostic applications. We further enhance the diagnostic process by integrating transfer learning with deep convolutional networks to classify melanomas and pigmented nevi in hyperspectral pathology images. The study encompasses pathological sections from 50 melanoma and 50 pigmented nevus patients. To accurately represent the spectral variances between different tissues, we employed reflectance calibration, highlighting that the most distinctive spectral differences emerged within the 500–675 nm band range. ResultsThe classification accuracy of pigmented tumors and pigmented nevi was 89% for one-dimensional sample data and 98% for two-dimensional sample data. ConclusionsOur findings have the potential to expedite pathological diagnoses, enhance diagnostic precision, and offer novel research perspectives in differentiating melanoma and nevus.

Diagnosing eyewitness identifications with reaction time‑based Concealed Information Test: the effect of viewpoint congruency between test and encoding

Mistaken eyewitness identifications continue to be a major contributor to miscarriages of justice. Previous experiments have suggested that implicit identification procedures such as the Concealed Information Test (CIT) might be a promising alternative to classic lineups when encoding conditions during the crime were favorable. We tested this idea by manipulating view congruency (frontal vs. profile view) between encoding and test. Participants witnessed a videotaped mock theft that showed the thief and victim almost exclusively from frontal or profile view. At test, viewing angle was either congruent or incongruent with the view during encoding. We tested eyewitness identification with the RT-CIT (N = 74), and with a traditional simultaneous photo lineup (N = 97). The CIT showed strong capacity to diagnose face recognition (d = 0.91 [0.64; 1.18]), but unexpectedly, view congruency did not moderate this effect. View congruency moderated lineup performance for one of the two lineups. Following these unexpected findings, we conducted a replication with a stronger congruency manipulation and larger sample size. CIT (N = 156) showed moderate capacity to diagnose face recognition (d = 0.63 [0.46; 0.80]) and now view congruency did moderate the CIT effect. For lineups (N = 156), view congruency again moderated performance for one of the two lineups. Capacity for diagnosing face recognition was similar for lineups and RT-CIT in our first comparison, but much stronger for lineups in our second comparison. Future experiments might investigate more conditions that affect performance in lineups vs. the RT-CIT differentially.

What counts as a multimodal metaphor and metonymy? Evolution of inter-rater reliability across rounds of annotation

AbstractAn open question in research on multimodal figuration is how to mitigate the analyst’s bias in identifying and interpreting metaphor and metonymy; an issue that determines the generalizability of the findings. Little is known about the causes that motivate different annotations. Inter-rater reliability tests are useful to investigate the sources of variation in annotations by independent researchers that can help inform and refine protocols.Inspired by existing procedures for verbal, visual, and filmic metaphor identification, we formulated instructions to identify multimodal metaphor and metonymy and tested it against a corpus of 21 generic advertisements and 21 genre-specific advertisements (mobile phones). Two independent researchers annotated the advertisements in six rounds. A joint discussion followed each round to consider conflicting annotations and refine the protocol for the ensuing round.By examining the evolution of inter-rater reliability results, we found that (1) we reached similar levels of agreement for the identification of metaphor and metonymy, although converging on the interpretation of metonymy was more difficult; (2) some genre specificities made it easier to agree on the annotations for mobile advertisements than for the general advertisements; and (3) there was a consistent increase in the kappa scores reaching substantial agreement by the sixth round.

Early Detection of Avian Diseases Based on Thermography and Artificial Intelligence.

Non-invasive measures have a critical role in precision livestock and poultry farming as they can reduce animal stress and provide continuous monitoring. Animal activity can reflect physical and mental states as well as health conditions. If any problems are detected, an early warning will be provided for necessary actions. The objective of this study was to identify avian diseases by using thermal-image processing and machine learning. Four groups of 14-day-old Ross 308 Broilers (20 birds per group) were used. Two groups were infected with one of the following diseases: Newcastle Disease (ND) and Avian Influenza (AI), and the other two were considered control groups. Thermal images were captured every 8 h and processed with MATLAB. After de-noising and removing the background, 23 statistical features were extracted, and the best features were selected using the improved distance evaluation method. Support vector machine (SVM) and artificial neural networks (ANN) were developed as classifiers. Results indicated that the former classifier outperformed the latter for disease classification. The Dempster-Shafer evidence theory was used as the data fusion stage if neither ANN nor SVM detected the diseases with acceptable accuracy. The final SVM-based framework achieved 97.2% and 100% accuracy for classifying AI and ND, respectively, within 24 h after virus infection. The proposed method is an innovative procedure for the timely identification of avian diseases to support early intervention.

A procedure for the stiffness identification of parallel robots under measurement limitations

This paper introduces a procedure to obtain reliable stiffness model for parallel robots from experimental data and identify its parameters considering measurement limitations. The efficiency of the proposed identification procedure validated via simulation and experimental studies on a 3-DOF Delta parallel robot. Simulation results showed that the proposed simplification and model reduction keeps more than 95% of entire stiffness properties (for the worst-case analysis). The experimental results proved that the obtained model on average describes 95% of compliance errors and for the worst case the error does not overcome 9.8%.

Enhancing Precision and Stability: A Cognitive Approach for Pesticide Image Segmentation in Crop Leaves

The precise delineation of pests from crop foliage is a crucial stage in the field of intelligent pest identification. This study presents a novel cognitive segmentation methodology that aims to enhance the accuracy and reliability of this crucial procedure. The technique comprises a number of essential stages: To begin with, the proposed method utilises sophisticated image block processing techniques to partition the pest image into smaller, more manageable parts. Additionally, an adaptive learning technique is utilised to carefully choose the initial cluster centres, hence guaranteeing the precision of the segmentation procedure. Subsequently, the use of K-means clustering facilitates the acquisition of initial segmentation outcomes, hence augmenting the procedure of identification. In order to mitigate the impact of leaf veins, the proposed approach utilises three digital morphological characteristics that are closely linked to ellipses. The study involved conducting experimental segmentation trials on crop photos that contained whiteflies. The findings of the study provide compelling evidence that the suggested cognitive segmentation method outperforms existing techniques in terms of accuracy and robustness. This technological development provides a robust basis for future advancements in pest identification and crop management

Development of PCR based SSR markers for Wilsonomyces carpophilus and a PCR based diagnosis protocol for the early detection of shot hole disease in stone fruit crops.

The conidial Ascomycota fungus Wilsonomyces carpophilus causing shot hole in stone fruits is a major constraint in the production of stone fruits worldwide. Shothole disease symptoms appear on leaves, fruits, and twigs. Successful isolation of the pathogen from different hosts on synthetic culture medium is a time consuming and tedious procedure for identification of the pathogen based on morpho-cultural characterization. The present research was carried out to develop a successful PCR based early detection protocol for the shot hole disease of stone fruits, viz., peach, plum, apricot, cherry, and almond using the pathogen specific SSR markers developed from the Wilsonomyces carpophilus genome using Genome-wide Microsatellite Analysing Tool package (GMATA) software. Diseased leaf samples of different stone fruits were collected from the SKUAST-K orchard and the pathogen was isolated on potato dextrose agar (PDA) medium and maintained on Asthana and Hawkers' medium with a total of 50 pathogen isolates comprised of 10 isolates each from peach, plum, apricot, cherry and almond. The DNA was extracted from both healthy and infected leaf samples of different stone fruits. The DNA was also extracted from the isolated pathogen cultures (50 isolates). Out of 2851 SSR markers developed, 30 SSRs were used for the successful amplification of DNA extracted from all the 50 pathogen isolates. These SSRs were used for the amplification DNA from shot hole infected leaf samples of different stone fruits, but the amplification was not observed in the control samples (DNA from healthy leaves), thus confirming the detection of this disease directly from the shot hole infected samples using PCR based SSR markers. To our knowledge, this forms the first report of SSR development for the Wilsonomyces carpophilus and their validation for the detection of shot hole disease directly from infected leaves. PCR based SSR makers were successfully developed and used for the detection of Wilsonomyces carpophilus causing shot hole disease in stone fruits including almond in nuts for the first time. These SSR markers could successfully detect the pathogen directly from the infected leaves of stone fruits namely peach, plum, apricot and cherry including almond from the nuts.

Gendered impacts of the war in Ukraine: identifying potential, presumed or actual women victims of trafficking at the Italian borders

According to the United Nations High Commissioner for Refugee (UNHCR) data, approximately 8 million people, mostly women and children, entered and registered in Europe since the beginning of the war on the 24th of February, 2022. It is well-known that human trafficking tends to increase during conflicts and humanitarian crises due to the spread of impunity, destruction of institutions, and breakdown of law. The EU and many international organizations have warned those fleeing Ukraine about the high risk of trafficking and adopted policies to prevent and detect trafficking. The EU anti-trafficking coordinator along with other EU agencies have developed the Common Anti-Trafficking Plan to specifically address this risk and support the potential victims fleeing the war in Ukraine meeting one of the goals set in the 10 Point Action Plan issued by the European Council in March to better coordinate EU actions for people leaving Ukraine. Among other goals, the 10 Point Action Plan aims to improve early identification of the victims of human trafficking as provided by the 2021–2025 Strategy on Combatting Trafficking and the 2011 Anti-trafficking Directive. This paper investigates the actions which the Italian government has put in place to detect and prevent the trafficking of women among the Ukrainians fleeing the war from the beginning of the conflict until September 2022, with a focus on the identification process. More specifically we explored the perceptions of front-line workers and street-level bureaucrats at the Slovenian and Austrian borders, where Ukrainians cross to enter Italy. Through interviews we collected data to shed light on the identification procedures adopted. The need to learn more about the current war in Ukraine corroborates this study. The proposed qualitative analysis has identified three main findings: (1) conflicts create increased opportunities for trafficking that in the case of Ukraine are greater due to the scale of those fleeing; (2) temporary Italian residence permit reveals some criticalities that could lead to greater vulnerability; (3) gaps in the identification process can increase the risks of trafficking, especially in the absence of longer-term assistance programs.