Detection Of High Levels Research Articles

Epidemiology of Fungal Colonization in Children Treated at the Department of Oncology and Hematology: Single-Center Experience.

Oncological patients are especially predisposed to fungal infections due to multiple risk factors and immunocompromising treatment. Epidemiological research regarding pediatric oncologic patients is still insufficient, and existing data are difficult to generalize on different populations. Therefore, we aimed to analyze fungal infections and fungal epidemiology in the Department of Oncology and Hematology of the University Children’s Hospital in Krakow with help from the Clinical Microbiology Department. During the chosen period of 2005 and 2015–2020, 2342 tests were performed in our ward on 847 patients. Analyzed samples were divided into five source groups. The amount of patients with positive test results was 62.5%. The year with the highest detection level was 2005. The most frequent pathogen was Candida albicans, with a significant decrease in tendency. An increase in non-albicans species was observed. Candida parapsilosis was not frequently observed compared to similar studies. We noticed an increase in positive results from the urinary tract material. Our results confirmed that fungal infections are still an issue, and they may indicate the efficacy of prophylaxis. The majority of our results are consistent with the literature, yet we managed to emphasize data unique to our patients’ population. Our findings are helpful in clinical work and for further studies in our center.

The role of PET-CT with <sup>18</sup>F-FDG in initial assessment of patients with carcinoma of unknown primary (CUP) in the head and neck

Introduction. Cancer metastases from an unknown primary origin (CUP) in the head and neck area include a large heterogeneous group of tumor formations, the primary localization of which could not be established even after a thorough diagnostic search. Combined positron emission computed tomography (PET-CT) with 18 F-FDG has a high level of detection of primary neoplasms as a result of simultaneous acquisition of precisely combined anatomical and functional images of the entire body. Objectives. 1. To evaluate the possibility of using PET-CT (positron emission tomography in combination with computed tomography) with 18 F-FDG (18-fluorodeoxyglucose) in patients with metastases from the CUP (carcinoma unknown primary) in the head and neck. To determine the effectiveness of PET-CT for the detection of primary tumor focus in patients with histologically verified diagnosis of CUP in the head and neck. To evaluate the contribution of the PET-CT technique in assessing the prevalence of the disease in patients diagnosed with head and neck cancer. Materials and methods. 134 patients diagnosed with CUP in the head and neck from September 2018 to March 2019 were included to this retrospective study: 35(26.1%) women and 99(73.9%) men, with the mean age of 61.9 ± 7.5 years. All patients underwent a biopsy from at least one metastatic lesion, and the malignant nature of the neoplasm was histologically verified. Before PET-CT with 18F-FDG, standard oncological evaluation was carried out to detect primary tumor. Results. Among 134 patients comprised the study, initial histological diagnoses were: squamous cell cancer (n = 82, 61.2%), melanoma (n = 5, 3.7%), undifferentiated carcinoma (n = 20, 15%), adenocarcinoma (n = 21, 15.6%) and undifferentiated malignant neoplasm (n = 6, 4.5%). With PET-CT with 18F-FDG, primary tumor location was revealed in 72 (54%) of the patients. New metastatic lesions were found in 60 (44.7%) of the patients. Change in the TNM stage was observed in 95(71%) of all cases after PET-CT with 18 F-FDG. It was associated both with the detection of the primary tumor and the identification of new metastatic foci. Conclusions. Use of PET-CT with 18 F-FDG allows to carry out oncological evaluation more precisely than the standard procedures in the majority of patients with CUP in the head and neck region. In a significant quota of cases, this method is in a position also to help to identify the primary tumor lesion, which, in turn, influences on strategy of treatment and also on the prognosis of disease. PET-CT with 18 F-FDG should be included to the protocol of radiological examination in patients with CUP in the head and neck anatomic area.

Performance Evaluation of Open-Source Endpoint Detection and Response Combining Google Rapid Response and Osquery for Threat Detection

Detecting the latest advanced persistent threats (APTs) using conventional information protection systems is a challenging task. Although various systems have been employed to detect such attacks, they are limited by their respective operating systems. Furthermore, they are developed as closed platforms and cannot be customized to meet user environments. To overcome these limitations, open-source endpoint detection and response (EDR) techniques are needed. In this study, we construct one that integrates open-source security frameworks combining GRR (Google Rapid Response) and osquery. A threat-detecting case study is conducted to validate the feasibility of the proposed open-source EDR system. Additionally, APT coverage for the proposed EDR system is analyzed using MITRE’s Adversarial Tactics, Techniques, and Common Knowledge model. The assessment result shows that APT tactics having high levels of threat detection using non-customized osquery configurations comprise 28.5 % of all detections, which is lower than the other response levels. The performance of open-source EDR can be increased by customizing osquery for specific purposes and environments. Open-source EDR combining GRR and osquery has the potential to provide the detection-coverage efficient threat detection system and has the advantage of flexible integration with other applications; it can also be developed for evolving system environments such as cloud and internet of things.

Interleukin-17A and interleukin-22 production by conventional and non-conventional lymphocytes in three different end-stage lung diseases.

ObjectivesThe contribution of adaptive vs. innate lymphocytes to IL‐17A and IL‐22 secretion at the end stage of chronic lung diseases remains largely unexplored. In order to uncover tissue‐ and disease‐specific secretion patterns, we compared production patterns of IL‐17A and IL‐22 in three different human end‐stage lung disease entities.MethodsProduction of IL‐17A, IL‐22 and associated cytokines was assessed in supernatants of re‐stimulated lymphocytes by multiplex assays and multicolour flow cytometry of conventional T cells, iNKT cells, γδ T cells and innate lymphoid cells in bronchial lymph node and lung tissue from patients with emphysema (n = 19), idiopathic pulmonary fibrosis (n = 14) and cystic fibrosis (n = 23), as well as lung donors (n = 17).ResultsWe detected secretion of IL‐17A and IL‐22 by CD4+ T cells, CD8+ T cells, innate lymphoid cells, γδ T cells and iNKT cells in all end‐stage lung disease entities. Our analyses revealed disease‐specific contributions of individual lymphocyte subpopulations to cytokine secretion patterns. We furthermore found the high levels of microbial detection in CF samples to associate with a more pronounced IL‐17A signature upon antigen‐specific and unspecific re‐stimulation compared to other disease entities and lung donors.ConclusionOur results show that both adaptive and innate lymphocyte populations contribute to IL‐17A‐dependent pathologies in different end‐stage lung disease entities, where they establish an IL‐17A‐rich microenvironment. Microbial colonisation patterns and cytokine secretion upon microbial re‐stimulation suggest that pathogens drive IL‐17A secretion patterns in end‐stage lung disease.

АДАПТИВНАЯ СИСТЕМА ЗАЩИТЫ СЕНСОРНЫХ СЕТЕЙ ОТ АКТИВНЫХ АТАК

38Адаптивная система защиты сенсорных сетей от активных атак Вопросы кибербезопасности. 2022. No 6(52) Architecture of AdAptiVe protection system for sensor netWorK Basan A.S.6 , Basan E.S.7 , Peskova O.Yu.8 , Sushkin N.A.9 , Shulika M.G.10 Purpose: Development of the adaptive protection system architecture for sensor networks and cyber-physical systems for anomaly detection based on the collection and analysis of cyber-physical parameters. Method: The method is based on the use of probability theory, mathematical statistics, and information theory. The entropy measure and normalization of the raw data make it possible to unify the data and evaluate it in terms of anomaly detection. Results: The existing solutions for the protection of cyber-physical systems from external active attacks were analyzed. The architecture of an adaptive system for a cyber-physical system protection is proposed. As part of the representation of the node data collection and analysis subsystem, a method for estimating cyber-physical parameters to detect intrusions is proposed. Three parameter changes for four behavioral scenarios were analyzed in detail in this study. Even by three parameters, you can determine the difference between attacks and normal behaviors. It is possible to evaluate not only the fact of parameter change, but also the degree of its change. At the same time, the node autonomously compared changes in its parameters with changes in the parameters of the neighboring node and could identify the impact of the attack on the neighboring node. The scientific novelty primarily consists in the fact that for the first time a method for determining the abnormal activity of a cyber-physical system based on the evaluation of system parameters using a measure of entropy and normalization of raw data has been developed, which makes it possible to achieve a high level of detection of known and unknown attacks. This method can be effectively used also for autonomous systems. The original architecture of the adaptive system for protecting the cyber-physical system is also proposed, its main components are worked out. When implementing an attack in a distributed system, this development will allow the node to detect anomalies not only autonomously, but also distributed, that is, to detect the impact on neighboring nodes.

Antibiotic-metal complexes in wastewaters: fate and treatment trajectory.

Unregulated usage, improper disposal, and leakage from pharmaceutical use and manufacturing sites have led to high detection levels of antibiotic residues in wastewater and surface water. The existing water treatment technologies are insufficient for removing trace antibiotics and these residual antibiotics tend to interact with co-existing metal ions and form antibiotic-metal complexes (AMCs) with altered bioactivity profile and physicochemical properties. Typically, antibiotics, including tetracyclines, fluoroquinolones, and sulphonamides, interact with heavy metals such as Fe2+, Co2+, Cu2+, Ni2+, to form AMCs which are more persistent and toxic than parent compounds. Although many studies have reported antibiotics detection, determination, distribution and risks associated with their environmental persistence, very few investigations are published on understanding the chemistry of these complexes in the wastewater and sludge matrix. This review, therefore, summarizes the structural features of both antibiotics and metals that facilitate complexation in wastewater. Further, this work critically appraises the treatment methods employed for antibiotic removal, individually and combined with metals, highlights the knowledge gaps, and delineates future perspectives for their treatment.

Occurrence, Profile, and Potential Risks of Novel and Legacy Polyfluoroalkyl Substances in Bullfrogs: Pilot Study in an Intensive Aquaculture Region, China

Per- and polyfluoroalkyl substances (PFASs) have captured worldwide attention due to their persistent and bioaccumulative characteristics. The bullfrog is a highly nutritious and edible aquatic product, but the PFAS accumulation characteristics of bullfrogs had not been paid attention. The present study analyzed the PFAS concentrations in different tissues of cultured bullfrogs and relevant water and sediments collected from the aquaculture region in the coastal area of South China. Results showed that total PFAS concentrations were 50.26–364.25 ng/L in water, with a mean of 135.58 ng/L; concentrations in effluent water were significantly higher than those in influent water, which means bullfrog farming could increase contaminants into the surroundings. The total PFAS concentrations varied from 2.89 to 162.26 ng/g·dw in sediment. The distribution profile of PFASs in sediment was similar to that of water, with short chain playing a significant role. Total PFASs in bullfrog tissues were 3.36–84.07 ng/g·dw, with the highest values in the digestive system (intestines and stomach). As for one novel PFAS alternative, chlorinated polyfluorinated ether sulfonate (F-53B) was found at high detectable levels in all habitat samples and had a higher value of logarithmic bioaccumulation factors. The estimated dietary intake (EDI) for residents in the study area was generally higher than that in other regions; however, the hazard ratios (HRs) for most detected PFASs were far below the safety threshold value “1.” These results suggest that frequent consumption of these bullfrogs would basically not cause severe health effects on local residents in terms of PFASs.

Trace precious metals in major sulfide minerals from the Federova Tundra platinum group element deposit in the Fedorova-Pana layered intrusion, central Kola Peninsula, Russia

ABSTRACTDrill-core samples from the basal Cu-Ni-platinum-group element mineralization of the Early Proterozoic Fedorova Tundra intrusion in the Fedorova-Pana layered intrusion, central Kola Peninsula, Russia, were studied in two separate projects in Canada and Russia. In Canada, trace precious metal analyses by laser ablation inductively coupled mass spectrometry of 323 base metal sulfide particles [pentlandite (101), pyrrhotite (98), chalcopyrite (25), and pyrite (99)] show that Pd is highly concentrated in pentlandite. Most of the analyses (71%) were done using two master composite samples of comminuted drill core representative of the West Pit and East Pit mineralization, FWMC and FEMC, respectively. Fewer analyses were made of three other comminuted drill core samples from the West Pit referred to as “lithology” samples: OLFW (olivine-bearing rocks), ANFW (leucocratic rocks), and GNFW (gabbronorite). In Russia, 120 polished sections sliced from drill core from the West and East Pits and from four other Fedorova Tundra intrusion deposits (Kievey, Northern Kamennik, Eastern Chuarvy, and Southern Kievey) were studied mineralogically. Platinum group mineral characterization and trace Pd electron probe microanalyses of pentlandite were done using polished sections from all six locations (n = 95). The trace electron probe microanalysis data for Pd in pentlandite from the West (n = 35) and East (n = 19) Pit samples, though at much higher detection levels, are considered to be comparable to the laser ablation inductively coupled mass spectrometry data. The Eastern Chuarvy samples show particularly high Pd concentrations averaging 0.49 wt.% Pd (n = 11) and as high as 1.64 wt.% Pd. The combined data from these studies guides our estimate that pentlandite accounts for 30 to 50% of the Pd in these ores and that Rh solid solution in sulfides may account for >98% of the total Rh.

Research on Data Security Detection Algorithm in IoT Based on K-means

The development of the Internet of Things has prominently expanded the perception of human beings, but ensuing security issues have attracted people's attention. From the perspective of the relatively weak sensor network in the Internet of Things. Proposed method is aiming at the characteristics of diversification and heterogeneity of collected data in sensor networks; the data set is clustered and analyzed from the aspects of network delay and data flow to extract data characteristics. Then, according to the characteristics of different types of network attacks, a hybrid detection method for network attacks is established. An efficient data intrusion detection algorithm based on K-means clustering is proposed. This paper proposes a network node control method based on traffic constraints to improve the security level of the network. Simulation experiments show that compared with traditional password-based intrusion detection methods; the proposed method has a higher detection level and is suitable for data security protection in the Internet of Things. This paper proposes an efficient intrusion detection method for applications with Internet of Things.

Magnet integrated fabric phase sorptive extraction of selected endocrine disrupting chemicals from human urine followed by high-performance liquid chromatography – photodiode array analysis

In current paper, a new advanced modification of fabric phase sorptive extraction is introduced for the first time. This advantageous configuration that integrates the stirring and extraction mechanism into a single sample preparation device was originated by equally considering the beneficial role of the increase of extraction kinetics and more specifically of diffusion on the extraction efficiency of the equilibrium based microextraction techniques and the need for integrating and unite processes for better promotion and implementation of the principles of Green Analytical Chemistry.The resulted magnet integrated fabric phase sorptive extraction (MI-FPSE) device was the spearhead to develop a new analytical methodology for the determination of selected very common endocrine disrupting chemicals as model analytes in human urine by high-performance liquid chromatography-photodiode array analysis. More specifically, the sol-gel Carbowax 20 M coated on hydrophilic cellulose fabric substrate, MI-FPSE device was efficiently employed for the establishment of a new extraction protocol before the chromatographic determination. The sample preparation workflow was methodically optimized in terms of the elution solvent mixture, the volume of the sample, the extraction and the elution time, the stirring speed during the extraction, the ionic strength, and the pH of the sample matrix. The chromatographic separation was performed on a Spherisorb C18 column and a gradient elution program within 14 minutes. Mobile phase consisted of 0.05 ammonium acetate aqueous solution and acetonitrile. The method was validated towards linearity, sensitivity, selectivity, precision, accuracy, and stability. LOD and LOQ ranged between 1.05-1.80 and 3.5-6.0 ng/mL, while %RSD values were found lower than 9.0% in all cases. The method was efficiently applied to the bioanalysis of real samples. All the chosen EDCs were measured at high detection levels. The new MI-FPSE device has demonstrated its performance superiority as a magnet integrated stand-alone extraction device and could be considered as a significant improvement in the field of analytical/bioanalytical sample preparation.

Developing a Recognition System for Diagnosing Melanoma Skin Lesions Using Artificial Intelligence Algorithms.

In recent years, computerized biomedical imaging and analysis have become extremely promising, more interesting, and highly beneficial. They provide remarkable information in the diagnoses of skin lesions. There have been developments in modern diagnostic systems that can help detect melanoma in its early stages to save the lives of many people. There is also a significant growth in the design of computer-aided diagnosis (CAD) systems using advanced artificial intelligence. The purpose of the present research is to develop a system to diagnose skin cancer, one that will lead to a high level of detection of the skin cancer. The proposed system was developed using deep learning and traditional artificial intelligence machine learning algorithms. The dermoscopy images were collected from the PH2 and ISIC 2018 in order to examine the diagnose system. The developed system is divided into feature-based and deep leaning. The feature-based system was developed based on feature-extracting methods. In order to segment the lesion from dermoscopy images, the active contour method was proposed. These skin lesions were processed using hybrid feature extractions, namely, the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) methods to extract the texture features. The obtained features were then processed using the artificial neural network (ANNs) algorithm. In the second system, the convolutional neural network (CNNs) algorithm was applied for the efficient classification of skin diseases; the CNNs were pretrained using large AlexNet and ResNet50 transfer learning models. The experimental results show that the proposed method outperformed the state-of-art methods for HP2 and ISIC 2018 datasets. Standard evaluation metrics like accuracy, specificity, sensitivity, precision, recall, and F-score were employed to evaluate the results of the two proposed systems. The ANN model achieved the highest accuracy for PH2 (97.50%) and ISIC 2018 (98.35%) compared with the CNN model. The evaluation and comparison, proposed systems for classification and detection of melanoma are presented.

Evaluation of 10 Cone-beam Computed Tomographic Devices for Endodontic Assessment of Fine Anatomic Structures

IntroductionThe purpose of this study was to classify 10 cone-beam computed tomographic (CBCT) devices using a ranking model according to the detection of fine endodontic structures. MethodsA dedicated dentate anthropomorphic phantom was scanned 2 times using 10 CBCT devices without any metal (metal-free condition) and with an endodontically treated tooth containing a metallic post (metal condition). A reference image acquired on an industrial micro-CT scanner was used to register all CBCT images, yielding corresponding anatomic slices. Afterward, 3 experienced observers assessed all acquired CBCT images for their ability to assess a narrow canal, isthmus, and apical delta ramification using a categoric rank from 1 (best) to 10 (worst). Fleiss kappa statistics were used to calculate intra- and interobserver agreements for each CBCT device separately. Based on the observers’ scores, general linear mixed models were applied to compare image quality among different CBCT devices for performing endodontic diagnostic tasks (α = .05). ResultsThe 10 CBCT devices performed differently for the evaluated endodontic tasks (P < .05), with 3 devices performing better for endodontic feature detection. Yet, in the presence of metal, only 2 devices were able to keep a high level of endodontic feature detection. ConclusionsThe evaluated endodontic tasks were CBCT device dependent, and their detection was influenced by the presence of metal.

TileNET: Hardware accelerator for ternary Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are popular in Advanced Driver Assistance Systems (ADAS) for camera perception. The versatility of the algorithm makes it applicable in multiple applications like object detection, lane detection and semantic segmentation. For image processing to be viable in driver assistance systems, the throughput requirement ranges in the order of a few tens of TeraMACs per second (TMACs). In addition, high accuracy levels of image detection and recognition cannot be compromised for the need for high throughput.In this paper, we present TileNET, a novel tiled architecture for ternary-weighted CNNs. TileNET is modular and scalable across variations in network organization and device configurations. Two modes of the implementation are presented, viz., systolic and streaming. A high-level estimation technique has been developed that facilitates fast performance evaluation through design space exploration among a range of target devices and varying CNN models.Performance has been verified for area and throughput estimation for Xilinx Virtex, Artix, Kintex and Zynq devices. TileNET implemented on Virtex-7 (XC7VX1140T) results in a throughput of about 16 Tera-operations per second (TOPs) for LeNet, AlexNet, ResNet-50 and VGG-16. In addition, the 45nm standard cell implementation of TileNet shows a throughput of about 30 TOPs respectively.

Control of grey mould disease on strawberry using the effective agent, Bacillus amyloliquefaciens Y1

ABSTRACT This study was conducted to investigate the effect of Bacillus amyloliquefaciens Y1 on the control of grey mould disease caused by Botrytis cinerea and subsequent growth promotion of strawberry. Strain Y1 strongly inhibited mycelial growth of B. cinerea on different media with the highest inhibition of 61.22% on V8 juice agar medium. The higher the bacterial culture filtrate (BCF) concentration of strain Y1 blended in the PDA medium, the stronger the rate of inhibition on mycelial growth of B. cinerea. Microscopic observation revealed that the hyphal structure of the fungal pathogen was severely affected by the BCF indicating abnormal growth with excessive branching and swelling. Strain Y1 showed the promising ability to suppress grey mould disease under in vivo conditions and significantly lessened disease severity on flowers and fruits leading to an increase in disease control value when compared to fungicide treatment. Moreover, application of strain Y1 resulted in a significant promotion of strawberry plants with higher values in most growth characteristics compared to other treatments. The detection of higher levels of the defence-related enzyme, β-1,3-glucanase in the Y1-treated plants indicated that induction of resistance by this strain was at least partly involved in disease suppression. Even though there were reductions in the overall numbers of the soil bacterial population in the Y1-treated plants, no significant impact on the growth of strawberry was observed. Based on our results, strain Y1 is a promising candidate for environment-friendly disease control strategies.

APPRAISAL OF SNOW IN CONTEXT OF TEMPERATURE VARIATIONS IN GILGIT BASIN

The study on Appraisal of Snow in Context of Temperature Variations in Gilgit Basin was conducted to find the snow cover change over Gilgit and Hunza Rivers. Hypsometric curve for this catchment was convex upward between normalized area and elevation. Using the MODIS snow cover product classification of snow cover was done which indicate the present of snow in the catchment. Change detection technique indicates there was high level of snow cover area change during the melting period of 2011. This area of snow which melted during melting period of 2011 was 9358.762km2. There was high level of snow cover change detection appeared in the melting period of 2011, 2012 and 2015 along the Gilgit-Hunza Rivers. The minimum snow area change was observed in 2014 during the melting period. During this year snow melting was observed with low level of change detection. Besides there was also low level of snow cover change detection observed in 2013 melting period. There was decreasing trend in snow cover change appeared in this basin.

Assessing the impact of varying levels of case detection and contact tracing on COVID-19 transmission in Canada during lifting of restrictive closures using a dynamic compartmental model.

The coronavirus disease 2019 (COVID-19) pandemic began with a detected cluster of pneumonia cases in Wuhan, China in December 2019. Endemic transmission was recognized in Canada in early February 2020, making it urgent for public health stakeholders to have access to robust and reliable tools to support decision-making for epidemic management. The objectives of this paper are to present one of these tools-an aged-stratified dynamic compartmental model developed by the Public Health Agency of Canada in collaboration with Statistics Canada-and to model the impact of non-pharmaceutical interventions on the attack rate of COVID-19 infection in Canada. This model simulates the impact of different levels of non-pharmaceutical interventions, including case detection/isolation, contact tracing/quarantine and changes in the level of physical distancing in Canada, as restrictive closures began to be lifted in May 2020. This model allows us to highlight the importance of a relatively high level of detection and isolation of cases, as well as tracing and quarantine of individuals in contact with those cases, in order to avoid a resurgence of the epidemic in Canada as restrictive closures are lifted. Some level of physical distancing by the public will also likely need to be maintained. This study underlines the importance of a cautious approach to lifting restrictive closures in this second phase of the epidemic. This approach includes efforts by public health to identify cases and trace contacts, and to encourage Canadians to get tested if they are at risk of having been infected and to maintain physical distancing in public areas.

Determination of veterinary pharmaceutical runoffs from a swine manure pile using LC\u2013MS/MS

The mass usage of veterinary pharmaceuticals in farms has contributed to environmental pollution in vicinity waters, soils, and sediments from farms and composting facilities. In the present study, we investigated the usage of four antibiotics (viz., lincomycin, sulfamethazine, sulfamethoxazole, and trimethoprim) to understand their contamination routes from livestock manure piles. Residual levels of these antibiotics in a nearby reservoir were set as a positive control (Site 1), and a swine manure pile in a farm (Site 2) and a soil sample around the manure pile (Site 3) were selected for this study. Artificial rainwater was flowed into the manure sample (Site 2), the soil sample around the manure pile (Site 3), and a soil sample around the vicinity river (Site 4). A stream sample (Site 5) around the manure pile and river water near the manure pile (Site 6) were also collected. For qualitative and quantitative analyses, analytical validation was performed, and all the four antibiotics were detected at Site 1 in the concentration range of 0.03–1.6 µg/L. Lincomycin was the antibiotic with the highest detection level. At Site 2, the detection level of all antibiotics remained at 0.3–17.3 µg/L, and their residual amounts were continuously detected in subsequent samples with approximately 30-fold decrease. The migration of antibiotics was confirmed to be independent of pH value. Therefore, this study indicates that farm manure pile should be thoroughly managed for antibiotic contamination in vicinity areas with periodical monitoring, especially waterways.

6 months of radioxenon detection in western Europe with the SPALAX-New generation system - Part1: Metrological capabilities

The SPALAX-NG is a new-generation system that is designed to detect radioactive xenon at trace levels in the atmosphere following a nuclear explosion or civilian source release. This new system formed part of a validation program led by the Provisional Technical Secretary of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) Organization. In this study, the first SPALAX-NG unit was tested for six months between October 2018 and April 2019 at the CEA/DIF premises near Paris, France. This test period provided an outstanding opportunity to illustrate the high level of detectability and reliability of the system. The data availability obtained over this period was approximately 99%, which was well above the CTBT Data Availability criteria of 95%. The data reliability was demonstrated by a comparison with a collocated SPALAX-1 unit (former version of SPALAX) and by re-measuring several samples at the CTBT-certified French laboratory FRL08. The high sensitivity to the detection of the four relevant radioxenon isotopes was fully demonstrated and enabled the recording of a major dataset for western Europe. A large set of isotopic ratios was measured, which enabled the discrimination criteria between civilian sources and nuclear test signatures to be refined.

A comparison between different anti-retroviral therapy regimes on soluble inflammation markers: a pilot study

BackgroundAlthough HIV-related deaths have decreased dramatically following the introduction of antiretroviral therapy (ART), HIV infection itself causes increased morbidity and mortality for both non-AIDS-related events or chronic inflammation and immune activation. The use of certain antiretroviral drugs can contribute to this process.MethodsWe investigated 26 potential biomarkers in serum samples from HIV-1 infected patients virologically suppressed under ART. The main objective of our study was to evaluate if virological suppression achieved with a triple drug regimen containing tenofovir disoproxil fumarate co-formulated with emtricitabine (TDF/FTC) as backbone, could correlate with a better immunological and inflammatory profile in relation to the third class of antiretroviral drug administered. The eligible patients were then divided into 3 groups in relation to the third drug associated with TDF/FTC: nucleoside reverse transcriptase inhibitors (NNRTI) (Group 1, n = 16), protease inhibitors (PI) (Group 2, n = 17) and integrase inhibitors (INI) (Group 3, n = 16).ResultsInflammatory cytokines and chemokines were more represented in Group 2 than in Group 3 (IL-1Ra, p = 0.013; IL-12p70 p = 0.039; TNF-α p = 0.041; IL-8, p = 0.027; MIP1 β, p = 0.033). Eotaxin showed lower levels in Group 1 compared to Group 2 (p = 0.010), while IP-10 was significantly lower in Group 1 compared to both Group 2 and Group 3 (p = 0.003 and p = 0.007, respectively).ConclusionsOur results seem to discourage the administration of PI as a third drug in a virologically effective antiretroviral regimen, as its use is linked to the detection of higher levels of pro-inflammatory mediators in comparison with INI and NNRTI.

Nanostructure ITO and Get More of It. Better Performance at Lower Cost.

In this paper, we investigated how different growth conditions (i.e., temperature, growth time, and composition) allows for trading off cost (i.e., In content) and performance of nanostructured indium tin oxide (ITO) for biosensing applications. Next, we compared the behavior of these functionalized nanostructured surfaces obtained in different growth conditions between each other and with a standard thin film as a reference, observing improvements in effective detection area up to two orders of magnitude. This enhanced the biosensor’s sensitivity, with higher detection level, better accuracy and higher reproducibility. Results show that below 150 °C, the growth of ITO over the substrate forms a homogenous layer without any kind of nanostructuration. In contrast, at temperatures higher than 150 °C, a two-phase temperature-dependent growth was observed. We concluded that (i) nanowire length grows exponentially with temperature (activation energy 356 meV) and leads to optimal conditions in terms of both electroactive surface area and sensitivity at around 300 °C, (ii) longer times of growth than 30 min lead to larger active areas and (iii) the In content in a nanostructured film can be reduced by 10%, obtaining performances equivalent to those found in commercial flat-film ITO electrodes. In summary, this work shows how to produce appropriate materials with optimized cost and performances for different applications in biosensing.