Success Rate Of Identification Research Articles

Effects of Speleotherapy on Aerobiota: A Case Study from the Sežana Hospital Cave, Slovenia

Speleotherapy is one of the non-pharmacological methods for the treatment and rehabilitation of patients with chronic respiratory diseases, especially those with chronic obstructive pulmonary disease (COPD) and asthma. On the one hand, one of the alleged main advantages of speleotherapeutic caves is the low microbial load in the air and the absence of other aeroallergens, but on the other hand, due to the lack of comprehensive air monitoring, there is little information on the pristine and human-influenced aerobiota in such environments. The aim of this study was to assess the anthropogenic effects of speleotherapy on the air microbiota and to investigate its potential impact on human health in Sežana Hospital Cave (Slovenia). From May 2020 to January 2023, air samples were collected in the cave before and after speleotherapeutic activities using two different volumetric air sampling methods—impaction and impingement—to isolate airborne microbiota. Along with sampling, environmental data were measured (CO2, humidity, wind, and temperature) to explore the anthropogenic effects on the aerobiota. While the presence of patients increased microbial concentrations by at least 83.3%, other parameters exhibited a lower impact or were attributed to seasonal changes. The structure and dynamics of the airborne microbiota are similar to those in show caves, indicating anthropization of the cave. Locally, concentrations of culturable microorganisms above 1000 CFU/m3 were detected, which could have negative or unpredictable effects on the autochthonous microbiota and possibly on human health. A mixture of bacteria and fungi typically associated with human microbiota was found in the air and identified by MALDI-TOF MS with a 90.9% identification success rate. Micrococcus luteus, Kocuria rosea, Staphylococcus hominis, and Staphylococcus capitis were identified as reliable indicators of cave anthropization.

Rock Layer Classification and Identification in Ground-Penetrating Radar via Machine Learning

Ground-penetrating radar (GPR) faces complex challenges in identifying underground rock formations and lithological structures. The diversity, intricate shapes, and electromagnetic properties of subsurface rock formations make their accurate detection difficult. Additionally, the heterogeneity of subsurface media, signal scattering, and non-linear propagation effects contribute to the complexity of signal interpretation. To address these challenges, this study fully considers the unique advantages of convolutional neural networks (CNNs) in accurately identifying underground rock formations and lithological structures, particularly their powerful feature extraction capabilities. Deep learning models possess the ability to automatically extract complex signal features from radar data, while also demonstrating excellent generalization performance, enabling them to handle data from various geological conditions. Moreover, deep learning can efficiently process large-scale data, thereby improving the accuracy and efficiency of identification. In our research, we utilized deep neural networks to process GPR signals, using radar images as inputs and generating structure-related information associated with rock formations and lithological structures as outputs. Through training and learning, we successfully established an effective mapping relationship between radar images and lithological label signals. The results from synthetic data indicate a rock block identification success rate exceeding 88%, with a satisfactory continuity identification of lithological structures. Transferring the network to measured data, the trained model exhibits excellent performance in predicting data collected from the field, further enhancing the geological interpretation and analysis. Therefore, through the results obtained from synthetic and measured data, we can demonstrate the effectiveness and feasibility of this research method.

Abstract 3761: A modified SELEX enables the efficient identification of DNA aptamers with potential for specific cancer targeting

Abstract Background: Aptamers have sparked significant interest in cell recognition due to their superior binding specificity and biocompatibility. Cell recognition can be mediated by targeting the major histocompatibility complex (MHC) that presents short peptides derived from intracellular antigens. Although numerous antibodies have demonstrated a specific affinity for the peptide-MHC complex, the number of aptamers that exhibit comparable characteristics is limited. However, the success rate of aptamer identification is low. This is possibly due to the presence of complementary sequences or sequences rich in quinine and cytosine that are less accessible for primers. Here, we use ovalbumin model antigen to explore whether aptamers with specificity for binding to a defined peptide-MHC complex can be identified. We developed DNA aptamers with modified SELEX by employing systemic consecutive selections with minimal PCR amplification. Furthermore, the other tumour antigen, claudin-6, was also used as a target to determine whether our modification for SELEX can be used with any selection technology. Methods: To determine whether our modified SELEX has potential for selecting aptamer capable of cancer-specific targeting, we used ovalbumin presented by H-2Kb (to represent the specific antigen presented by MHC) and claudin-6 (a cancer-associated adhesion molecule) as a target for the selection of aptamers. This modified SELEX consisted of consecutive positive and negative selections with minimal PCR amplification. Meanwhile, the selection of candidate aptamers is based on their appearance in multiple rounds rather than enrichment, and is facilitated by several computational tools. The specificity and affinity of the candidate aptamer are validated by flow cytometry, immunohistochemistry, and immunofluorescence analysis with cells with or without these molecules. Results: After high-throughput sequencing, a total of 211 candidate aptamers were selected for further analysis. The quadruplex forming G-rich sequences (QGRS) mapper, mFold modeling, and NUPACK web servers were used to predict the potential of G-quadruplex formation, resulting in only four potential aptamers. Using MTCQ1-OVA cells with FITC-conjugated aptamers, two aptamers showed selective binding to MTCQ1-OVA cells and had no binding specificity to MTCQ1-WT cells. The binding specificity was further validated by immunohistochemistry in tumour derived from mice inoculated with MTCQ1-WT or MTCQ1-OVA cells. In the pull-down assay, the binding affinity of these two candidate aptamers was 136.2 nM and 155.1 nM affinity. Conclusions: This preliminary study demonstrated that our modification for SELEX is effective in the selection of aptamers with promising potential for specific cancer targeting. The identification of aptamers may facilitate further application in diverse biomedical fields. Citation Format: Yang Lin, Cho-Yi Chen, Zhi-Qian Lin, Bing-Hong Chen, Yi-Chen Sun, Kai-Feng Hung. A modified SELEX enables the efficient identification of DNA aptamers with potential for specific cancer targeting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3761.

High-precision sensor for glucose solution using active multidimensional feature THz spectroscopy.

Terahertz waves are known for their bio-safety and spectral fingerprinting features, and terahertz spectroscopy technology holds great potential for both qualitative and quantitative identification in the biomedical field. There has been a substantial amount of research utilizing this technology in conjunction with machine learning algorithms for substance identification. However, due to the strong absorption of water for terahertz waves, the single-dimensional features of the sample become indistinct, thereby diminishing the efficiency of the algorithmic recognition. Building upon this, we propose a method that employs terahertz time-domain spectroscopy (THz-TDS) in conjunction with multidimensional feature spectrum identification for the detection of blood sugar and glucose mixtures. Our research indicates that combining THz-TDS with multidimensional feature spectrum and linear discriminant analysis (LDA) algorithms can effectively identify glucose concentrations and detect adulteration. By integrating the multidimensional feature spectrum, the identification success rate increased from 68.9% to 96.0%. This method offers an economical, rapid, and safe alternative to traditional methods and can be applied in blood sugar monitoring, sweetness assessment, and food safety.

Intelligent robotics harvesting system process for fruits grasping prediction

This paper proposes and executes an in-depth learning-based image processing approach for self-picking apples. The system includes a lightweight one-step detection network for fruit recognition. As well as computer vision to analyze the point class and anticipate a correct approach position for each fruit before grabbing. Using the raw inputs from a high-resolution camera, fruit recognition and instance segmentation are done on RGB photos. The computer vision classification and grasping systems are integrated and outcomes from tree-grown foods are provided as input information and output methodology poses for every apple and orange to robotic arm execution. Before RGB picture data is acquired from laboratory and plantation environments, the developed vision method will be evaluated. Robot harvest experiment is conducted in indoor as well as outdoor to evaluate the proposed harvesting system's performance. The research findings suggest that the proposed vision technique can control robotic harvesting effectively and precisely where the success rate of identification is increased above 95% in case of post prediction process with reattempts of less than 12%.

Slow flow HD and traditional CDFI technologies in identifying pulmonary veins in the first trimester.

This study aims to assess the feasibility and effectiveness of color doppler flow imaging (CDFI) technology and the Slow Flow HD imaging technique in identifying fetal pulmonary veins (PVs) in the first trimester (11-13 + 6weeks), and further explore the factors affecting fetal pulmonary vein identification in early pregnancy. Echocardiography and scanning of PVs were performed in 240 normal singleton fetuses in early pregnancy by using CDFI and slow flow HD techniques, to compare the ability of two methods to identify the PVs. Slow Flow HD technology was used to further investigate the difference of PVs identification at different gestational ages [group I (11-11 + 6weeks), group II (12-12 + 6weeks), group III (13-13 + 6weeks)] and with different maternal body mass indices (BMI) (≥ 25 and < 25). In 31 cases of 240 fetuses, transvaginal ultrasonography was added due to maternal habitus or significant retroversion of the uterus, and the difference in PVs identification between transabdominal and transvaginal examination was analyzed. Successful PVs identification rates via CDFI and Slow Flow HD were 32.0% and 88.3%, respectively (p < 0.05). The identification rate of at least one and two pulmonary veins in Slow Flow HD was 88.3% and 76.2%, and all four pulmonary veins in 11.6% (p < 0.05). The identification rate of group I, II and III were 76.4%, 88.9% and 96.0%, respectively. The identification rate was 45.1% in the transabdominal ultrasound group and 83.8% in the transvaginal ultrasound group. The identification rate was 62.5% in the BMI ≥ 25 group and 94.7% in the BMI < 25 group (p < 0.05). Slow Flow HD can detect PVs in early pregnancy more often than using CDFI. Slow Flow HD is a feasible and effective imaging technique for evaluating PVs in early pregnancy.

A cost-effective joint multi-parameter optical performance monitoring scheme for high baud rate mode division multiplexing system

Inter-symbol interference (ISI), mode coupling (MC) and other impairments present in high baud rate mode division multiplexing (MDM) systems significantly degrade the reliability and accuracy of optical signal transmission. Therefore, performing joint multi-parameter optical performance monitoring (OPM) is crucial to ensure the efficient operation of the system. We propose a scheme that combines asynchronous single channel sampling (ASCS) images with a multi-task residual neural network (MT-ResNet) to enable cost-effective joint OPM at intermediate nodes in a 100 GBaud MDM system. The scheme requires only one photodetector to receive the signal. Based on the distinct characteristics exhibited by ASCS images for different parameters in the MDM system, the MT-ResNet is improved to capture correlations among parameters and learn residual information between inputs and outputs. This enables high-precision and rapid simultaneous implementation of modulation format identification (MFI), MC estimation, optical signal-to-noise ratio (OSNR) estimation, and chromatic dispersion identification (CDI) in the intermediate nodes of a 100 GBaud MDM system. The effectiveness of the proposed scheme is verified using a 100 GBaud QPSK/8QAM/16QAM/32QAM/64QAM MDM simulation transmission system with five spatial modes (LP01/LP11a/LP11b/LP21a/LP21b). The results show that under the influence of different linewidth (LW) of laser and differential mode group delay (DMGD), a 100 % identification success rate can be achieved for five commonly used MFs and eight different cumulative CD values in high baud rate MDM systems, across a wide range of OSNR. Additionally, the mean absolute error (MAE) of OSNR estimation corresponding to the five MFs is 0.22 dB, 0.26 dB, 0.30 dB, 0.32 dB, and 0.36 dB, respectively. The MAE of the eight different MC intensities fluctuate between 0.015 and 0.030. These results fully demonstrate the superior performance of the proposed scheme. Furthermore, the scheme exhibits favorable tolerance to large LWs and fiber nonlinear effects. Moreover, the complexity of the scheme is accurately analyzed through floating-point operations (FLOPs) and parameters (Params), which are much less than existing typical schemes, with specific values of 14.8849 M and 1.3798 M.

The challenges of developing a textile-based device for recognizing swallowing movements during different swallowing tasks

Swallowing difficulty, also known as dysphagia, is a fairly common symptom that may lead to malnutrition, dehydration, pulmonary complications, and a reduced quality of life. Currently, a swallowing evaluation requires a hospital environment, skilled personnel, and expensive devices. Thus, it is important to develop a successful method for evaluating swallowing in an everyday environment. This study developed a textile-based solution that resembles a neck gaiter. Wireless accelerometer and gyroscope sensors embedded inside the gaiter were used to detect swallowing movements. The recognition of swallows was achieved through cervical auscultation and video footage, and this data was subsequently compared with the information obtained from wireless accelerometer and gyroscope sensors. Based on the measurement results for 17 adults without dysphagia, the developed neck gaiter can identify individual swallows of 5 sips of water (with a 97 % identification success rate) and consecutive swallows of 100 ml of water (98 % identification success rate). However, we were not able to recognize swallowing movements from speaking, chewing and head movements. Future research is now needed to develop a prototype to differentiate swallowing movements from other movements, such as chewing and speaking.

Improving electrotactile communication with a multi-pad electrode under cognitive load.

Electrotactile systems are compact interfaces that can be used to convey information through the skin by producing a range of haptic sensations. In many applications, however, the user needs to perceive and interpret haptic stimulation while being engaged in parallel activities. Developing methods that ensure reliable recognition of electrotactile messages despite additional cognitive load is, therefore, an important step for the practical application of electrotactile displays. This study investigated if a simple strategy of repeating electrotactile messages can improve message identification during multitasking. Ten participants identified 36 spatiotemporal electrotactile messages delivered through a 3 × 2 pad-matrix electrode placed on the torso while performing a concomitant cognitive task in three conditions: the messages were presented once (No-REP), and each message was repeated three (REP3) and five (REP5) times. The main outcome measure was the success rate (SR) of message identification. During multitasking, in the No-REP condition, the SR (median (IQR)) dropped to 56.25% (22.62%), demonstrating that the cognitive task decreased performance. However, the SR significantly improved with message repetitions, reaching 72.92% (21.87%) and 81.25% (18.66%) in REP3 and REP5 conditions respectively, without a statistically significant difference between REP3 and REP5. Multitasking affected the efficacy of haptic communication, but message repetition was shown to be an effective strategy for improving performance. Additionally, only three repetitions were enough, as an additional increase in the duration of message transmission (5 repetitions) did not lead to further improvement. This study is an important step toward delivering electrotactile communication that can cope with the demands of real-world applications.

The Rapid Phenotypic Susceptibility Testing in Real-Life Experience: How the MIC Values Impact on Sepsis Fast Diagnostic Workflow.

The MIC value definition faithfully reflects antimicrobial sensitivity, profoundly impacting the infection's clinical outcome. Our study aimed to evaluate the Accelerate PhenoTM System in defining the importance of fast phenotypic susceptibility data. A number of 270 monomicrobial samples simultaneously underwent standard procedures and fast protocols after a contemporary Gram stain. Finally, we provided Turn-around Time (TAT) and statistical evaluations. The fast technology required a medium value of 7 h to complete ID and AST profiles. Although there were some spectrum limitations, it revealed an optimal success rate in microbial identification directly from positive blood cultures. The Gram-negative AST reached a 98.9% agreement between the Accelerate Pheno™ System and the standard method. In addition, the Gram-positive AST gathered a 98.7% agreement comparing the same systems. The chance to rapidly provide precise MIC values is one of the last frontiers in clinical microbiology, especially in high-prevalence antimicrobial resistance areas.

Intelligent joint multi-parameter optical performance monitoring scheme based on HT images and MT-ResNet for elastic optical network

In elastic optical network (EON), joint multi-parameter optical performance monitoring (OPM) can effectively manage, diagnose, and reduce operational costs for transmission optical links. In this paper, we propose a novel joint monitoring scheme utilizing Hough transform (HT) images combined with multi-task residual neural network (MT-ResNet) for EON. The scheme can realize baud rate identification (BRI), modulation format identification (MFI), residual chromatic dispersion identification (CDI), optical signal-to-noise ratio (OSNR) and residual differential group delay (DGD) estimation at the same time. The HT image is obtained by preprocessing original constellation diagram, which is a key feature of parameter monitoring for EON signals and presents obvious differentiation in parameter space. By optimizing the skip connections in MT-ResNet, we effectively resolve the issue of details information loss or incompleteness caused by the transmission of impaired optical signals in the neural network. The simulation results demonstrate that the identification success rate can reach 100 % for two common BRs, five mainstream MFs, and seven residual CD values with different impairment degrees. The mean absolute errors (MAEs) of OSNR and residual DGD estimates are 0.42 dB and 0.014 times symbol period respectively. The scheme has excellent tolerance for fiber nonlinear effects. In experimental verification, the accuracies of BRI and MFI are 100 %, and the MAEs of corresponding OSNR estimation for PDM-QPSK/16QAM/32QAM are 0.25 dB, 0.36 dB, and 0.40 dB, respectively. Compared with the existing typical schemes, our scheme significantly improves performance and reduces complexity while simultaneously monitoring a large number of parameters.

Characterization of Functional Coatings on Cork Stoppers with Laser-Induced Breakdown Spectroscopy Imaging.

Evaluating the efficiency of surface treatments is a problem of paramount importance for the cork stopper industry. Generically, these treatments create coatings that aim to enhance the impermeability and lubrification of cork stoppers. Yet, current methods of surface analysis are typically time-consuming, destructive, have poor representativity or rely on indirect approaches. In this work, the use of a laser-induced breakdown spectroscopy (LIBS) imaging solution is explored for evaluating the presence of coating along the cylindrical surface and in depth. To test it, several cork stoppers with different shaped areas of untreated surface were analyzed by LIBS, making a rectangular grid of spots with multiple shots per spot, to try to identify the correspondent shape. Results show that this technique can detect the untreated area along with other features, such as leakage and holes, allowing for a high success rate of identification and for its performance at different depths, paving the way for future industry-grade quality control solutions with more complex surface analysis.

Identification and diversity assessment of Ziziphus species using DNA barcoding

The genus Ziziphus, belonging to the family Rhamnaceae, is of great economic and medicinal importance worldwide due to the value of its fruits in household income and food security. However, many species within this genus are phenotypically identical, leading to imprecise identification and hindering their conservation, variation in the gene pool, and exploitation. To address this issue, we collected 26 individuals from different regions of Khyber Pakhtunkhwa, Pakistan and Shanxi province, China, to assess the efficiency of DNA barcoding regions in identifying Ziziphus species. We tested the ability of five DNA barcodes regions, namely ITS, matK, rbcL, trnH-psbA, and trnL-UAA, to distinguish between Ziziphus species as an alternative approach for species identification and the phylogenetic reconstruction. The results of the study, as the success rate of PCR amplification of matK, trnL-UAA, and ITS, provided the highest species resolution with 100 % accuracy, except for trnH-psbA (90.78 %). ITS and matK were identified as the most effective single barcode, with a species identification success rate of 87.4 %±0.98 % and 84.89 %±0.66 %. These results suggest that matK, trnL-UAA, and ITS are universal barcode regions for identification of all Ziziphus species identification and their discrimination power to differentiate. In contrast, the combination of trnL-UAA+ITS region displayed the highest species identification rate (92.1 %), the five barcodes region combination, and superior to any barcodes regions combination that included rbcL+trnH-psbA. Additionally, the study found high haplotypes and nucleotide diversity in Pakistani Ziziphus species, mainly in the ITS barcode, with values of 0.84 and 0.0086, respectively. In conclusion, the study establishes that the DNA fragments ITS, trnL-UAA, and matK are effective in identifying and distinguishing Ziziphus species. The approach can be considered as an accurate tool for identifying these species and will be used as a universal barcode's region for Ziziphus species. DNA barcoding is a useful approach and provides a reliable and effective means for Ziziphus discrimination, and in combination with morphology-based taxonomy will be a robust approach for tackling taxonomically complex species. Additionally, this study provides valuable insights into the genomic architecture of Ziziphus species, which has implications for their classification, conservation, and germplasm resource improvement. This research provides a novel approach to the problem of incorrectly identifying economically and medicinally important species, particularly those in the genus Ziziphus.

Effects of Anthropogenic Disturbance and Seasonal Variation on Aerobiota in Highly Visited Show Caves in Slovenia.

Aerosols in caves are natural tracers and, together with climatic parameters, provide a detailed insight into atmospheric conditions, responses to climatic changes and anthropogenic influences in caves. Microbiological air monitoring in show caves is becoming increasingly useful to understand changes in cave ecosystems and to implement and review measures for sustainable cave use and tourism development. In 2017 and 2018, air along tourist trails in caves Postojnska jama and Škocjanske jame (Slovenia) was sampled before and after tourist visits. Samples were analysed using culture-dependent methods, flow cytometry, detection of β-D-glucan and lipopolysaccharide and compared with CO2 and temperature data to measure anthropogenic influences and seasonality on aerobiota. While the presence of tourists significantly increased concentrations of airborne microorganisms (p < 0.05), β-D-glucan and CO2 did not show such a trend and were more dependent on seasonal changes. Locally, concentrations of cultivable microorganisms above 1000 CFU/m3 were detected, which could have negative effects on the autochthonous microbiota and possibly on human health. A mixture of bacteria typically associated with humans was found in the air and identified with MALDI-TOF MS. Using MALDI-TOF MS, we achieved a 69.6% success rate in identification. Micrococcus luteus, Streptococcus mitis, Staphylococcus epidermidis and Moraxella spp. were recognized as good indicators of cave anthropisation.

Tumescent anesthesia use for cutaneous malignancies does not impact sentinel node identification but decreases estimated blood loss

Sentinel node biopsy is performed to stage clinically and radiographically occult regional disease in cases of malignancy, including cutaneous cancer such as melanoma. The use of tumescent anesthesia with sentinel node biopsy for cutaneous malignancies has not been well studied. Therefore, we aimed to compare the rate of successful sentinel node identification and estimated blood loss between patients who underwent sentinel node biopsy with and without the use of tumescent anesthesia. A retrospective review was conducted of a prospectively maintained single-institution database of all patients who underwent reconstruction after the extirpation of a cutaneous malignancy over an 18-month period. Patient demographics, tumor histology, and characteristics, indication for and success of sentinel node biopsy, use of tumescent anesthesia, and total estimated blood loss were examined. Sentinel node biopsy was performed in 15 of 39 patients (38.5%) receiving tumescent anesthesia compared with 6 of 26 patients (23.1%) not receiving it (p=0.19). Sentinel node biopsy had a success rate of 100% in the tumescent and nontumescent anesthesia groups. The mean estimated blood loss in the tumescent anesthesia group was 36.7mL versus 59.6mL in the nontumescent anesthesia group (p<0.001). Complication rates were comparable between the tumescent anesthesia (12.8%) and nontumescent anesthesia (19.2%) groups (p=0.48). The use of tumescent anesthesia in cutaneous malignancy extirpation and immediate reconstruction was not associated with a decreased sentinel node identification rate or change in complication rates. However, tumescent anesthesia was associated with a decrease in the estimated blood loss.

Development of a Cloud-Based Image Processing Health Checkup System for Multi-Item Urine Analysis.

With the busy pace of modern life, an increasing number of people are afflicted by lifestyle diseases. Going directly to the hospital for medical checks is not only time-consuming but also costly. Fortunately, the emergence of rapid tests has alleviated this burden. Accurately interpreting test results is extremely important; misinterpreting the results of rapid tests could lead to delayed medical treatment. Given that URS-10 serve as a rapid test capable of detecting 10 distinct parameters in urine samples, the results of assessing these parameters can offer insights into the subject's physiological condition. These parameters encompass aspects such as metabolism, renal function, diabetes, urinary tract disorders, hemolytic diseases, and acid-base balance, among others. Although the operational procedure is straightforward, the variegated color changes exhibited in the outcomes of individual parameters render it challenging for lay users to deduce causal factors solely from color variations. Moreover, potential misinterpretations could arise due to visual discrepancies. In this study, we successfully developed a cloud-based health checkup system that can be used in an indoor environment. The system is used by placing a URS-10 test strip on a colorimetric board developed for this study, then using a smartphone application to take images which are uploaded to a server for cloud computing. Finally, the interpretation results are stored in the cloud and sent back to the smartphone to be checked by the user. Furthermore, to confirm whether the color calibration technology can eliminate color differences between different cameras, and also whether the colorimetric board and the urine test strips can perform color comparisons correctly in different light intensity environments, indoor environments that could simulate a specific light intensity were established for testing purposes. When comparing the experimental results to real test strips, only two groups failed to reach an identification success rate of 100%, and in both of these cases the success rate reached 95%. The experimental results confirmed that the system developed in this study was able to eliminate color differences between camera devices and could be used without special technical requirements or training.

From bites to barcodes: uncovering the hidden diversity of black flies (Diptera: Simuliidae) in Vietnam

BackgroundPrompt and precise identification of black flies (Simuliidae) is crucial, given their biting behaviour and significant impact on human and animal health. To address the challenges presented by morphology and chromosomes in black fly taxonomy, along with the limited availability of molecular data pertaining to the black fly fauna in Vietnam, this study employed DNA-based approaches. Specifically, we used mitochondrial and nuclear-encoded genes to distinguish nominal species of black flies in Vietnam.MethodsIn this study, 135 mitochondrial cytochrome c oxidase subunit I (COI) sequences were established for 45 species in the genus Simulium in Vietnam, encompassing three subgenera (Gomphostilbia, Nevermannia, and Simulium), with 64 paratypes of 27 species and 16 topotypes of six species. Of these COI sequences, 71, representing 27 species, are reported for the first time.ResultsCombined with GenBank sequences of specimens from Malaysia, Myanmar, Thailand, and Vietnam, a total of 234 DNA barcodes of 53 nominal species resulted in a 71% success rate for species identification. Species from the non-monophyletic Simulium asakoae, S. feuerborni, S. multistriatum, S. striatum, S. tuberosum, and S. variegatum species groups were associated with ambiguous or incorrect identifications. Pairwise distances, phylogenetics, and species delimitation analyses revealed a high level of cryptic diversity, with discovery of 15 cryptic taxa. The current study also revealed the limited utility of a fast-evolving nuclear gene, big zinc finger (BZF), in discriminating closely related, morphologically similar nominal species of the S. asakoae species group.ConclusionThis study represents the first comprehensive molecular genetic analysis of the black fly fauna in Vietnam to our knowledge, providing a foundation for future research. DNA barcoding exhibits varying levels of differentiating efficiency across species groups but is valuable in the discovery of cryptic diversity.Graphical abstract

A Fast Star-Detection Algorithm under Stray-Light Interference

The interference caused by stray light leads to the invalid attitude of star sensors in orbit, thus affecting the attitude control of satellites. In order to overcome this problem, this paper proposes a fast star-detection algorithm with strong stray-light suppression ability. The first step in the proposed method is stray-light suppression. The highlighted pixels are unified and then erosion and dilation operations based on a large template are performed. Using the background image only, which is filled with stray light, the cleaner star image is obtained by subtracting the background from the unified image. The second step in the proposed method is binarization. The binary star image is obtained by using a line-segment strategy combined with a local threshold. The third step in the proposed method is star labeling. It comprises connected-domain labeling based on the preordering of pixels and the calculation of centroid coordinates of stars in each connected domain. The experimental results show that the proposed algorithm extracts the stars stably under the interference of different stray lights. The proposed method consumes less resources, and the output delay is only 18.256 us. Moreover, the successful identification rate is 98% and the attitude accuracy of the X and Y axes is better than 5″(3σ) when the star sensor works at the speed of zero.

Radial velocities and stellar population properties of 56 MATLAS dwarf galaxies observed with MUSE

Dwarf galaxies have been extensively studied in the Local Group, in nearby groups, and selected clusters, giving us a robust picture of their global stellar and dynamical properties, such as their circular velocity, stellar mass, surface brightness, age, and metallicity in particular locations in the Universe. Intense study of these properties has revealed correlations between them, called the scaling relations, including the well-known universal stellar mass-metallicity relation. However, since dwarfs play a role in a vast range of different environments, much can be learned about galaxy formation and evolution through extending the study of these objects to various locations. We present MUSE spectroscopy of a sample of 56 dwarf galaxies as a follow-up to the MATLAS survey in low- to moderate-density environments beyond the Local Volume. The dwarfs have stellar masses in the range of M*/M⊙ = 106.1–109.4 and show a distance range of D = 14–148 Mpc, the majority of which (75%) are located in the range targeted by the MATLAS survey (10–45 Mpc). We thus report a 75% success rate for the semi-automatic identification of dwarf galaxies (79% for dwarf ellipticals) in the MATLAS survey on the subsample presented here. Using pPXF full spectrum fitting, we determine their line-of-sight velocity and can match the majority of them with their massive host galaxy. Due to the observational setup of the MATLAS survey, the dwarfs are located in the vicinity of massive galaxies. Therefore, we are able to confirm their association through recessional velocity measurements. Close inspection of their spectra reveals that ∼30% show clear emission lines, and thus star formation activity. We estimate their stellar population properties (age and metallicity) and compare our results with other works investigating Local Volume and cluster dwarf galaxies. We find that the dwarf galaxies presented in this work show a systematic offset from the universal stellar mass-metallicity relation toward lower metallicities at the same stellar mass. A similar deviation is present in other works in the stellar mass range probed in this work and might be attributed to the use of different methodologies for deriving the metallicity.

Geometric morphometric analysis of Pleuronectiformes vertebrae: A new tool to identify archaeological fish remains?

Flatfish (Pleuronectiformes) vertebrae are difficult to identify to species due to the lack of diagnostic features. This has resulted in a lack of understanding of the species abundances across archaeological sites, hindering interpretations of historical fisheries in the North Sea area. We use a new approach, utilising a combined 2D landmark-based geometric morphometric analysis as an objective and non-destructive method for species identification of flatfish vertebrae from the North Sea area. Modern specimens were used as a reference to describe the morphological variation between taxa using principal component analysis (PCA) and to trial an automated classification using linear discriminant analysis. Although there is limited distinction between taxa using PCAs, the classification shows high accuracies, indicating that flatfish species identifications using geometric morphometrics are possible. Bone samples (n = 105) from two archaeological sites in the United Kingdom and France were analysed using this approach and their identifications were verified using collagen peptide mass fingerprinting. The success rate of species identification was usually less than 50%, indicating that this technique has limited applicability due to preservation/fragmentation of archaeological fish bone. Nonetheless, this could prove a valuable tool for modern and non-fragmented samples. Furthermore, the technique applied in this study can be easily adapted to work on other landmark datasets.