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Packaging materials mainly serve the function of protecting products. The most common representative of this group is poly(ethylene terephthalate) (PET), which is not biodegradable and therefore, its waste might be burdensome to the environment. Thus, this work aims to develop outlines for obtaining polyester-based systems, preferably biobased ones, intended for the packaging industry and their detailed characterization. The obtained multilayer systems based on two biobased thermoplastic polyesters, i.e., poly(ethylene 2,5-furandicarboxylate) (PEF) and the "double green" polylactide (PLA), were subjected to various analyses, i.e., UV-Vis spectrophotometry, microscopic evaluation, tensile tests, differential scanning calorimetry (DSC), oxygen transmission rate (OTR), water absorption tests, surface analyses, and biofilm formation. The best possible arrangement was selected in terms of the packaging industry. It was proven that the elastic modulus was significantly higher for multilayer systems, whilst higher-strength parameters were observed for PLA single foils. Regardless of thickness, PLA and PEF foils exhibit similar absorption values in cold water. Moreover, PEF foils demonstrated significantly better barrier properties towards oxygen gas compared to PLA foils of the same thickness. However, a multilayer system composed of two PLA foils with a single inner PEF foil had an OTR value only slightly higher than that of the PEF foil alone. PEF was also found to be a material that exhibited a limited formation of bacterial biofilm, particularly strains of S. aureus and E. coli. All of the above findings clearly confirm the sensibility of the research topic undertaken in this work on the application of biobased thermoplastic polyesters in the packaging industry. [ABSTRACT FROM AUTHOR]

Simple Summary: This multicenter study assessed the incidence of pneumonia and its impact on survival in 322 patients with chronic lymphocytic leukemia (CLL) who were treated with venetoclax-based regimens. In real-world settings, patients with CLL who are treated with venetoclax-based regimens are at a higher risk of pneumonia, especially patients with R/R CLL who are treated with venetoclax in combination with rituximab compared to registration trials. Pneumonia has a negative impact on overall survival (OS) in patients with CLL who are receiving venetoclax-based therapy. However, in patients treated with first-line venetoclax and obinutuzumab, despite the high burden of comorbidities, the occurrence of pneumonia does not affect OS. Key risk factors for shorter event-free survival (EFS) in patients with CLL who were treated with venetoclax-based regimens included chronic obstructive pulmonary disease/asthma, spleen enlargement, elevated creatinine levels, and severe anemia; we identified a subgroup of patients at a high risk of pneumonia who particularly required anti-infective prophylaxis, appropriate treatment of underlying diseases, and close monitoring. Neutropenia, although common (59%), was not linked to an increased risk of pneumonia. These findings highlight the important issue of pneumonia and its impact on OS in the CLL patient population treated with venetoclax-based regimens, especially in patients following earlier lines of treatment, with R/R disease. Background/Objectives: Patients with chronic lymphocytic leukemia (CLL) are susceptible to infections that can affect their clinical outcomes. Aims: The aims of this study were to assess the following: (1) the incidence of pneumonia in CLL patients treated with venetoclax-based regimens in a real-world setting, (2) the risk factors for event-free survival (EFS), and (3) overall survival (OS). Methods: This multicenter study included 322 patients from eight centers. Univariable and multivariable analyses (MVA) were performed, with the development of pneumonia during venetoclax-based treatment and OS as outcomes. Results: The most common complication was neutropenia (59%). During treatment with venetoclax-based regimens, 66 (20%) patients developed pneumonia—50 (23%) patients in the rituximab-plus-venetoclax (R-VEN) group and 13 (16%) patients in the obinutuzumab-plus-venetoclax (O-VEN) group (p = 0.15). Chronic obstructive pulmonary disease (COPD)/asthma, splenomegaly, elevated creatinine, and anemia < 8 g/dL were the risk factors for EFS in MVA (HR = 2.08, 95%CI 1.16–3.74, p = 0.014; HR 1.73, 95%CI 1.08–2.78, p = 0.02; HR 2.13, 95%CI 1.10–4.11, p = 0.03, HR 3.58, 95%CI 2.18–5.89, p < 0.001, respectively). Relapsed/refractory (R/R) CLL patients treated with R-VEN with pneumonia had worse OS than those without (p < 0.001). In patients treated with O-VEN, median OS did not differ between patients with and without pneumonia (p = 0.45). Conclusions: Our real-world study showed that pneumonia during venetoclax treatment occurs more frequently than reported in registration trials and has a negative impact on OS, especially in patients with R/R CLL who are treated with R-VEN. Neutropenia is not a risk factor for pneumonia. [ABSTRACT FROM AUTHOR]

Year by year, more and more plastic is used worldwide. A large part of post-consumer waste is still stored in landfills instead of being reused. The solution to this problem may be recycled materials (recyclates) or biodegradable materials. The method of 3D printing, regarded as a clean processing technology, can significantly contribute to addressing global plastic pollution by utilizing post-consumer recycled polymers to create new components and parts. Therefore, this study focuses on the assessment of various properties and characteristics of 3D-printed compositions based on post-consumer polypropylene (PP) and rubber crumbs, recycled from packages foils and car tires, respectively. Moreover, within this study, we compared the mechanical performance of the injection molding material with the one obtained from 3D printing. A characterization was made considering the thermal and mechanical properties as well as the "print quality" through the microscopic and tomographic analysis of subsequent print passes, the number of free spaces, and imperfections in the polymer melt. Samples obtained using the FDM and injection methods exhibited comparable melting temperatures, while the samples obtained by injection molding exhibited slightly better mechanical performance, higher hardness, and impact strength. [ABSTRACT FROM AUTHOR]

Nitrides are the leading semiconductor material used for the fabrication of high electron mobility transistors (HEMTs). They exhibit piezoelectric properties, which, coupled with their high mechanical stiffness, expand their versatile applications into the fabrication of piezoelectric devices. Today, due to advances in device technology that result in a reduction in the size of individual transistor elements and due to increased structural complexity (e.g., multi-gate transistors), the integration of piezoelectric materials into HEMTs leads to an interesting occurrence, namely acoustic emission from the transistor gate due to piezoelectric effects. This could affect the device's performance, reliability, and durability. However, this phenomenon has not yet been comprehensively described. This paper aims to examine this overlooked aspect of AlGaN/GaN HEMT operation, that is, the acoustic emission from the gate region of the device induced by piezoelectric effects. For this purpose, dedicated test structures were designed, consisting of two narrow 1.7 μm-wide metallization strips placed at distances ranging from 5 μm to 200 μm fabricated in AlGaN/GaN heterostructures to simulate and examine the gate behavior of the HEMT transistor. For comparison, the test device structures were also fabricated on sapphire, which is not a piezoelectric material. Measurements of acoustic and electrical interactions in the microwave range were carried out using the "on wafer" method with Picoprobe's signal–ground–signal (SGS)-type microwave probes. The dependence of reflectance |S11| and transmittance |S21| vs. frequency was investigated, and the coupling capacitance was determined. An equivalent circuit model of the test structure was developed, and finite element method simulation was performed to study the distribution of the acoustic wave in the nitride layers and substrate for different frequencies using Comsol Multiphysics software. At frequencies up to 2–3 GHz, the formation of volume waves and a surface wave, capable of propagating over long distances (in the order of tens of micrometers) was observed. At higher frequencies, the resulting distribution of displacements as a result of numerous reflections and interferences was more complicated. However, there was always the possibility of a surface wave occurrence, even at large distances from the excitation source. At small gate distances, electrical interactions dominate. Above 100 µm, electrical interactions are comparable to acoustic ones. With further increases in distance, weakly attenuated surface waves will dominate. [ABSTRACT FROM AUTHOR]

Listeriosis is one of the most serious foodborne diseases under surveillance, with an overall mortality rate in the EU currently being high at 18.1%. Therefore, this study aims to investigate Listeria monocytogenes strains isolated from clinical and food samples for susceptibility to antimicrobials, presence of virulence factors, and genetic diversity. Species were identified using the MALDI-TOF, resistance to 11 antibiotics was determined according to EUCAST guidelines, and multiplex PCR was used for serotyping and detecting virulence genes. Strains were genotyped using the PFGE method. Clinical strains showed full sensitivity to all tested antibiotics. In total, 33.3% of strains from food products were found to be resistant to ciprofloxacin and 4.2% to tetracycline. Most of the tested isolates (79.2%) belonged to serotype 1/2a-3a, and the rest (20.8%) belonged to serotype 4ab-4b,4d-4e. Five virulence genes (prfA, hlyA, plcB, inlA, and lmo2672) were detected in all strains studied. The llsX gene was the least common, in 37.5% of clinical strains and 18.75% of strains isolated from food products. Among the analyzed strains, 13 strains displayed unique PFGE profiles. The other 11 strains belong to 3 clusters of pulsotypes: cluster 1 (2 strains), cluster 2 (6 strains), and cluster 3 (2 strains). The percentage of hospitalizations and deaths of Polish patients with listeriosis indicates the seriousness of this disease, especially in an aging society, while the molecular testing of clinical strains has been rarely performed, which makes it difficult to determine the source of infection. [ABSTRACT FROM AUTHOR]

The key association between gut dysbiosis and cancer is already known. Here, we used whole-genome shotgun sequencing (WGS) and gas chromatography/mass spectrometry (GC/MS) to conduct metagenomic and metabolomic analyses to identify common and distinct taxonomic configurations among 40, 45, 71, 34, 50, 60, and 40 patients with colorectal cancer, stomach cancer, breast cancer, lung cancer, melanoma, lymphoid neoplasms and acute myeloid leukemia (AML), respectively, and compared the data with those from sex- and age-matched healthy controls (HC). α-diversity differed only between the lymphoid neoplasm and AML groups and their respective HC, while β-diversity differed between all groups and their HC. Of 203 unique species, 179 and 24 were under- and over-represented, respectively, in the case groups compared with HC. Of these, Faecalibacillus intestinalis was under-represented in each of the seven groups studied, Anaerostipes hadrus was under-represented in all but the stomach cancer group, and 22 species were under-represented in the remaining five case groups. There was a marked reduction in the gut microbiome cancer index in all case groups except the AML group. Of the short-chain fatty acids and amino acids tested, the relative concentration of formic acid was significantly higher in each of the case groups than in HC, and the abundance of seven species of Faecalibacterium correlated negatively with most amino acids and formic acid, and positively with the levels of acetic, propanoic, and butanoic acid. We found more differences than similarities between the studied malignancy groups, with large variations in diversity, taxonomic/metabolomic profiles, and functional assignments. While the results obtained may demonstrate trends rather than objective differences that correlate with different types of malignancy, the newly developed gut microbiota cancer index did distinguish most of the cancer cases from HC. We believe that these data are a promising step forward in the search for new diagnostic and predictive tests to assess intestinal dysbiosis among cancer patients. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of this study is to evaluate the performance of our deep learning algorithm in calculating cardiothoracic ratio (CTR) and thus in the assessment of cardiomegaly or pericardial effusion occurrences on chest radiography (CXR). Methods: From a database of 8000 CXRs, 13 folders with a comparable number of images were created. Then, 1020 images were chosen randomly, in proportion to the number of images in each folder. Afterward, CTR was calculated using RadiAnt Digital Imaging and Communications in Medicine (DICOM) Viewer software (2023.1). Next, heart and lung anatomical areas were marked in 3D Slicer. From these data, we trained an AI model which segmented heart and lung anatomy and determined the CTR value. Results: Our model achieved an Intersection over Union metric of 88.28% for the augmented training subset and 83.06% for the validation subset. F1-score for subsets were accordingly 90.22% and 90.67%. In the comparative analysis of artificial intelligence (AI) vs. humans, significantly lower transverse thoracic diameter (TTD) (p < 0.001), transverse cardiac diameter (TCD) (p < 0.001), and CTR (p < 0.001) values obtained using the neural network were observed. Conclusions: Results confirm that there is a significant correlation between the measurements made by human observers and the neural network. After validation in clinical conditions, our method may be used as a screening test or advisory tool when a specialist is not available, especially on Intensive Care Units (ICUs) or Emergency Departments (ERs) where time plays a key role. [ABSTRACT FROM AUTHOR]

The replacement of polymers derived from petrochemical resources has been a prominent area of focus in recent decades. Polymers used in engineering materials must exhibit mechanical strength and stiffness while maintaining performance through a broad temperature range. Most of the polyesters used as engineering materials are based on terephthalic acid (TPA) and its derivatives, which provide necessary rigidity to molecular chains due to an aromatic ring. Bio-based alternatives for TPA-based polyesters that are gaining popularity are the polyesters derived from 2,5-furandicarboxylic acid (FDCA). To broaden applicational possibilities, one effective way to achieve specific properties in targeted applications is to adjust the composition and structure of polymers using advanced polymer chemistry techniques. The incorporation of rigid diols such as isosorbide, 1,4-cyclohexanedimethanol (CHDM), and 2,2,4,4-tetramethyl-1,3-cyclobutanediol (CBDO) should result in a greater stiffness of the molecular chains. This review extensively explores the effect of incorporating rigid diols on material properties through a review of research articles as well as patents. Moreover, this review mainly focuses on the polyesters and copolyesters synthesized via two-step melt polycondensation and its alterations due to the industrial importance of this method. Innovative synthesis strategies and the resulting material properties are presented. [ABSTRACT FROM AUTHOR]

This study presents a relatively low-cost method for modifying TiO2-based materials for photocatalytic bacterial inactivation. The photocatalytic inactivation of Gram-negative (Escherichia coli) and Gram-positive (Staphylococcus epidermidis) bacteria using modified sulphated TiO2 was studied. The modification focused on the reduction of TiO2 by ammonia agents and hydrogen at 400–450 °C. The results showed a high impact of sulphate species on the inactivation of E. coli. The presence of these species generated acid sites on TiO2, which shifted the pH of the reacted titania slurry solution to lower values, around 4.6. At such a low pH, TiO2 was positively charged. The ammonia solution caused the removal of sulphate species from TiO2. On the other hand, hydrogen and ammonia molecules accelerated the removal of sulphur species from TiO2, as did heating it to 450 °C. Total inactivation of E. coli was obtained within 30 min of simulated solar light irradiation on TiO2 heat-treated at 400 °C in an atmosphere of Ar or NH3. The S. epidermidis strain was more resistant to photocatalytic oxidation. The contact of these bacteria with the active titania surface is important, but a higher oxidation force is necessary to destroy their cell membrane walls because of their thicker cell wall than E. coli. Therefore, the ability of a photocatalyst to produce ROS (reactive oxidative species) will determine its ability to inactivate S. epidermidis. An additional advantage of the studies presented is the inactivation of bacteria after a relatively short irradiation time (30 min), which does not often happen with photocatalysts not modified with noble metals. The modification methods presented represent a robust and inexpensive alternative to photocatalytic inactivation of bacteria. [ABSTRACT FROM AUTHOR]

IoE components are becoming an integral part of our lives and support the operation of systems such as smart homes, smart cities, or Industry 4.0. The large number and variety of IoE components force the creation of flexible systems for data acquisition, processing, and analysis. The work presents a proposal for a new flexible architecture model and technology stack designed for the diagnostics and monitoring of industrial components and processes in an IoE device environment. The proposed solutions allow creating custom flexible systems for managing a distributed IoT environment, including the implementation of innovative mechanisms like, for example: predictive maintenance, anomaly detection, business intelligence, optimization of energy consumption, or supervision of the manufacturing process. In the present study, two detailed system architectures are proposed, and one of them was implemented. The developed system was tested in near-production conditions using a real IoT device infrastructure including industrial systems, drones, and sensor networks. The results showed that the proposed model of a central data-acquisition and -processing system allows the flexible integration of various IoE solutions and has a very high implementation potential wherever there is a need to integrate data from different sources and systems. [ABSTRACT FROM AUTHOR]

The article presents an attempt to identify an appropriate regression model for the estimation of cutting tool lifespan in the milling process based on the analysis of the R2 parameters of these models. The work is based on our own experiments and the accumulated database (which we make available for further use). The study uses a Haas VF-1 milling machine equipped with vibration sensors and based on a Beckhoff PLC data collector. As the acquired sensor data are continuous, and in order to account for dependencies between them, regression models were used. Support Vector Regression (SVR), decision trees and neural networks were tested during the work. The results obtained show that the best prediction results with the lowest error values were obtained for two-dimensional neural networks using the LBFGS solver (93.9%). Very similar results were also obtained for SVR (93.4%). The research carried out is related to the realisation of intelligent manufacturing dedicated to Industry 4.0 in the field of monitoring production processes, planning service downtime and reducing the level of losses resulting from damage to materials, semi-finished products and tools. [ABSTRACT FROM AUTHOR]

This article presents an integrated system that uses the capabilities of unmanned aerial vehicles (UAVs) to perform a comprehensive crop analysis, combining qualitative and quantitative evaluations for efficient agricultural management. A convolutional neural network-based model, Detectron2, serves as the foundation for detecting and segmenting objects of interest in acquired aerial images. This model was trained on a dataset prepared using the COCO format, which features a variety of annotated objects. The system architecture comprises a frontend and a backend component. The frontend facilitates user interaction and annotation of objects on multispectral images. The backend involves image loading, project management, polygon handling, and multispectral image processing. For qualitative analysis, users can delineate regions of interest using polygons, which are then subjected to analysis using the Normalized Difference Vegetation Index (NDVI) or Optimized Soil Adjusted Vegetation Index (OSAVI). For quantitative analysis, the system deploys a pre-trained model capable of object detection, allowing for the counting and localization of specific objects, with a focus on young lettuce crops. The prediction quality of the model has been calculated using the AP (Average Precision) metric. The trained neural network exhibited robust performance in detecting objects, even within small images. [ABSTRACT FROM AUTHOR]

In recent years, deep neural networks have enabled countless innovations in the field of image classification. Encouraged by success in this field, researchers worldwide have demonstrated how to use Convolutional Neural Network techniques in medical imaging problems. In this article, the results were obtained through the use of the EfficientNet in the task of classifying 14 different diseases based on chest X-ray images coming from the NIH (National Institutes of Health) ChestX-ray14 dataset. The approach addresses dataset imbalances by introducing a custom split to ensure fair representation. Binary cross entropy loss is utilized to handle the multi-label difficulty. The model architecture comprises an EfficientNet backbone for feature extraction, succeeded by sequential layers including GlobalAveragePooling, Dense, and BatchNormalization. The main contribution of this paper is a proposed solution that outperforms previous state-of-the-art deep learning models average area under the receiver operating characteristic curve—AUC-ROC (score: 84.28%). The usage of the transfer-learning technique and traditional deep learning engineering techniques was shown to enable us to obtain such results on consumer-class GPUs (graphics processing units). [ABSTRACT FROM AUTHOR]

Biochar, a fine-grained porous material, exhibits properties that improve soil quality on agricultural land. Biochar, in combination with mineral fertilizers in perennial mixed crops, has so far not been studied for its effect on biomass production and feed value. The study, conducted in 2021 and 2022, aims to investigate the impact of different biochar application rates (alone and in combination with high and low NPK (nitrogen, phosphorus, potassium) fertilizer dosages) on the yield and structural carbohydrate content in grass–legume mixtures. Thus, a two-factorial pot study consisting of 36 pots was established in 2021. The study was set up in a randomized block design with nine fertilization treatments in four replicates. The factors studied were the rate of biochar (0, 5, and 10 Mg ha−1) and the rate of NPK mineral fertilizer application (0, 105, and 185 kg ha−1). First, biochar was applied and then the grass–legume mixture was sown, consisting of Lolium perenne L., Festuca arundinacea Schreb., Dectylis glomerata L., Lolium multiflorum Lam., Phleum pratense L., Festuca rubra L., Festuca ovina L., Poa pratensis L., Trifolium repens L., and Medicago sativa L. In both years of the study, during the growing season, plant biomass was harvested three times to assess biomass production and structural carbohydrate content. In the collected samples, neutral detergent fiber (NDF), acid detergent fiber (ADF), and acid detergent lignin (ADL) were examined, and, based on these, dry matter digestibility (DDM), dry matter intake (DMI), and relative feed value (RFV) were calculated. Compared to the control, the biochar addition increased the biomass production by 6.7–14.4% in the first year and by 49–59% in the second year after application. The addition of biochar in combination with NPK fertilization caused an increase in biomass production—22–45% in the first year and 71–136% in the second year after application. The structural carbohydrate content in the mixture depended neither on the biochar dose nor on fertilization. On the other hand, the most significant differences for the studied parameters were observed due to different harvest dates. [ABSTRACT FROM AUTHOR]

Regarding the dynamic development of 3D printing technology, as well as its application in a growing part of industries, i.e., in the automotive industry, construction industry, medical industry, etc., there is a notable opportunity for its application in producing dental implants, which presents a promising alternative to traditional implant manufacturing methods. The medical industry is very restrictive regarding the applied materials, and it is necessary to use materials that exhibit very good mechanical and thermal parameters, show clinical indifference and biocompatibility, are non-allergenic and non-cancerous, and are likely to sterilize. Such materials are poly(aryl-ether-ketone)s (PAEK)s, mainly poly(ether-ether-ketone) (PEEK) and poly(ether-ketone-ketone) (PEKK), that are found to be high-performance polymers and can be defined as materials that retain their functionality even in extreme conditions. In the present paper, two types of PEEKs and PEKK were compared regarding their structural, mechanical, and thermal properties along with the biological activity toward selected strains. The tested samples were obtained with Fused Deposition Modeling (FDM) technology. The PEKK, after heat treatment, exhibits the most promising mechanical properties as well as less bacterial adhesion on its surface when compared to both PEEKs. Consequently, among the evaluated materials, PEKK after heat treatment stands out as the optimal selection for a dental prosthesis. [ABSTRACT FROM AUTHOR]

Diagnostic imaging has become an integral part of the healthcare system. In recent years, scientists around the world have been working on artificial intelligence-based tools that help in achieving better and faster diagnoses. Their accuracy is crucial for successful treatment, especially for imaging diagnostics. This study used a deep convolutional neural network to detect four categories of objects on digital chest X-ray images. The data were obtained from the publicly available National Institutes of Health (NIH) Chest X-ray (CXR) Dataset. In total, 112,120 CXRs from 30,805 patients were manually checked for foreign objects: vascular port, shoulder endoprosthesis, necklace, and implantable cardioverter-defibrillator (ICD). Then, they were annotated with the use of a computer program, and the necessary image preprocessing was performed, such as resizing, normalization, and cropping. The object detection model was trained using the You Only Look Once v8 architecture and the Ultralytics framework. The results showed not only that the obtained average precision of foreign object detection on the CXR was 0.815 but also that the model can be useful in detecting foreign objects on the CXR images. Models of this type may be used as a tool for specialists, in particular, with the growing popularity of radiology comes an increasing workload. We are optimistic that it could accelerate and facilitate the work to provide a faster diagnosis. [ABSTRACT FROM AUTHOR]

The MCC family of genes plays a role in colorectal cancer development through various immunological pathways, including the Th17/Treg axis. We have previously shown that MCC1 but not MCC2 plays a role in Treg differentiation. Our understanding of the genetic divergence patterns and evolutionary history of the MCC family in relation to its function, in general, and the Th17/Treg axis, in particular, remains incomplete. In this investigation, we explored 12 species' genomes to study the phylogenetic origin, structure, and functional specificity of this family. In vertebrates, both MCC1 and MCC2 homologs have been discovered, while invertebrates have a single MCC homolog. We found MCC homologs as early as Cnidarians and Trichoplax, suggesting that the MCC family first appeared 741 million years ago (Ma), whereas MCC divergence into the MCC1 and MCC2 families occurred at 540 Ma. In general, we did not detect significant positive selection regulating MCC evolution. Our investigation, based on MCC1 structural similarity, suggests that they may play a role in the evolutionary changes in Tregs' emergence towards complexity, including the ability to utilize calcium for differentiation through the use of the EFH calcium-binding domain. We also found that the motif NPSTGE was highly conserved in MCC1, but not in MCC2. The NPSTGE motif binds KEAP1 with high affinity, suggesting an Nrf2-mediated function for MCC1. In the case of MCC2, we found that the "modifier of rudimentary" motif is highly conserved. This motif contributes to the regulation of alternative splicing. Overall, our study sheds light on how the evolution of the MCC family is connected to its function in regulating the Th17/Treg axis. [ABSTRACT FROM AUTHOR]

Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years. ML involves the application of algorithms to automate decision-making processes using models that have not been manually programmed but have been trained on data. ANNs that are a part of ML aim to simulate the structure and function of the human brain. DL, on the other hand, uses multiple layers of interconnected neurons. This enables the processing and analysis of large and complex databases. In medicine, these techniques are being introduced to improve the speed and efficiency of disease diagnosis and treatment. Each of the AI techniques presented in the paper is supported with an example of a possible medical application. Given the rapid development of technology, the use of AI in medicine shows promising results in the context of patient care. It is particularly important to keep a close eye on this issue and conduct further research in order to fully explore the potential of ML, ANNs, and DL, and bring further applications into clinical use in the future. [ABSTRACT FROM AUTHOR]

Melica uniflora Retz., commonly known as wood melick, is a grass species that is native to most of Europe. Melica uniflora grows in hardwood forests near Fagus species, providing vital food and shelter for forest wildlife. The nutritional status of wood melick is not sufficiently recognized. The study aims to identify the intrapopulation variability of Melica uniflora plants collected from natural forest habitats in Poland in terms of nutritional status variability in relation to stage development and the course of weather conditions. The research was conducted for two consecutive years: 2021 and 2022, in the area of the Ślęża Massif, near the town of Sobótka in Lower Silesia voivodeship (Poland). The material for analyses was collected from 10 natural forest sites twice: in July (I) and October (II). The content of nutritive components: crude protein (CP), crude ash (CA), neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), and water-soluble carbohydrates (WSC) was evaluated. Relative feed value (RFV), dry matter digestibility (DDM), dry matter intake (DMI), cellulose (CL), and hemicellulose (HCL) content were calculated based on ADF and NDF. Melica uniflora plants contained 75.2 g·kg−1 DM of CP, 290.6 g·kg−1 DM of CF, 120.0 g·kg−1 DM of CA, and 25.9 g·kg−1 DM of WSC. The content of NDF was 637.6 g·kg−1 DM, ADF 407.5 g·kg−1 DM, and ADL 58.0 g·kg−1 DM. The nutritional status of wood melick depended on the course of weather conditions in the following years and its location, which changed during the growing season. Plants collected in the first year of the study contained more CL, NDF, and ADF fractions and less CA, ADL, and WSC. Melica uniflora plants harvested in June were characterized by higher CP (102.1 g·kg−1 DM) and WSC (30.1 g·kg−1 DM) content and lower content of remaining nutrients. It can be concluded that Melica uniflora plants can be a valuable source of these nutrients in the forage of forest animals. [ABSTRACT FROM AUTHOR]

The paper presents the results of testing the performance of lightweight structural concrete containing hemp shives as an aggregate. It has been analysed how the higher binder content and use of the Portland cement affect the thermal and microbiological properties of the lightweight concrete. The aggregates of the plant origin and cement are incompatible because the plant chemical compounds, dissolved in water or an alkaline environment, inhibit cement hydration. To avoid this, mineralisation of the aggregates of plant origin is necessary. The most often used binder in hemp concrete is hydrated lime, a mineraliser. An addition of hydrated lime and sodium trisilicate was used for hemp shiv mineralisation in the tested materials with a cement binder. Concrete containing hemp shiv and cement binder, of which volume share in the concrete was at most 15%, was prepared as a reference concrete. In the remaining three concretes, the total content of the binder in relation to hemp shiv (by mass) was increased 2.5 times. It was shown that lime-binder hemp concrete offers a promising antimicrobial strategy, as it can inhibit bacterial and fungal growth on their surface with superior efficacy. The best results were obtained for tested concretes with the cement–lime binder regarding compressive strength; the average compressive strength was 9.56 MPa. [ABSTRACT FROM AUTHOR]

Continuous, real-time monitoring of occupational health and safety in high-risk workplaces such as construction sites can substantially improve the safety of workers. However, introducing such systems in practice is associated with a number of challenges, such as scaling up the solution while keeping its cost low. In this context, this work investigates the use of an off-the-shelf, low-cost smartwatch to detect health issues based on heart rate monitoring in a privacy-preserving manner. To improve the smartwatch's low measurement quality, a novel, frugal machine learning method is proposed that corrects measurement errors, along with a new dataset for this task. This method's integration with the smartwatch and the remaining parts of the health and safety monitoring system (built on the ASSIST-IoT reference architecture) are presented. This method was evaluated in a laboratory environment in terms of its accuracy, computational requirements, and frugality. With an experimentally established mean absolute error of 8.19 BPM, only 880 bytes of required memory, and a negligible impact on the performance of the device, this method meets all relevant requirements and is expected to be field-tested in the coming months. To support reproducibility and to encourage alternative approaches, the dataset, the trained model, and its implementation on the smartwatch were published under free licenses. [ABSTRACT FROM AUTHOR]

An overview was made of dry matter (DM) and quality losses that occur during the ensiling process. The aim was to review the current knowledge on the course of the fermentation pathways in various raw materials and the loss of DM accompanying this process. This review discusses the main groups of microorganisms involved in the ensiling process, the accompanying fermentation patterns, and the resulting DM losses. The possibility of reducing DM and quality losses during the ensiling process in practice is presented. The paper concludes with future perspectives and recommended management practices to reduce losses over the whole ensiling process. [ABSTRACT FROM AUTHOR]

Preparation of TiO2 using the hydrothermal treatment in NH4OH solution and subsequent thermal heating at 500–700 °C in Ar was performed in order to introduce some titania surface defects. The highest amount of oxygen vacancies and Ti3+ surface defects were observed for a sample heat-treated at 500 °C. The presence of these surface defects enhanced photocatalytic properties of titania towards the deactivation of two bacteria species, E. coli and S. epidermidis, under artificial solar lamp irradiation. Further modification of TiO2 was targeted towards the doping of Cu species. Cu doping was realized through the impregnation of the titania surface by Cu species supplied from various copper salts in an aqueous solution and the subsequent heating at 500 °C in Ar. The following precursors were used as a source of Cu: CuSO4, CuNO3 or Cu(CH3COO)2. Cu doping was performed for raw TiO2 after a hydrothermal process with and without NH4OH addition. The obtained results indicate that Cu species were deposited on the titania surface defects in the case of reduced TiO2, but on the TiO2 without NH4OH modification, Cu species were attached through the titania adsorbed hydroxyl groups. Cu doping on TiO2 increased the absorption of light in the visible range. Rapid inactivation of E. coli within 30 min was obtained for the ammonia-reduced TiO2 heated at 500 °C and TiO2 doped with Cu from CuSO4 solution. Photocatalytic deactivation of S. epidermidis was greatly enhanced through Cu doping on TiO2. Impregnation of TiO2 with CuSO4 was the most effective for inactivation of both E. coli and S. epidermidis. [ABSTRACT FROM AUTHOR]

The challenge of improving the activity of TiO2 by modifying it with metals and using it for targeted applications in microreactor environments is an active area of research. Recently, microreactors have emerged as successful candidates for many photocatalytic reactions, especially for the selective oxidation process. The current work introduces ultrasound-assisted catalyst deposition on the inner walls of a perfluoro-alkoxy alkane (PFA) microtube under mild conditions. We report Cu-Au/TiO2 and Fe-Au/TiO2 nanoparticles synthesized using the sol–gel method. The obtained photocatalysts were thoroughly characterized by UV–Vis diffuse-reflectance spectroscopy (DRS), high-resolution scanning electron microscopy (HR-SEM), high-resolution transmission electron microscopy (HR-TEM), X-ray diffraction analysis (XRD), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and N2 physisorption. The photocatalytic activity under UV (375 nm) and visible light (515 nm) was estimated by the oxidation of lignin-based model aromatic alcohols in batch and fluoropolymer-based flow systems. The bimetallic catalyst exhibited improved photocatalytic selective oxidation. Herein, four aromatic alcohols were individually investigated and compared. In our experiments, the alcohols containing hydroxy and methoxy groups (coniferyl and vanillin alcohol) showed high conversion (93% and 52%, respectively) with 8% and 17% selectivity towards their respective aldehydes, with the formation of other side products. The results offer an insight into ligand-to-metal charge transfer (LMCT) complex formation, which was found to be the main reason for the activity of synthesized catalysts under visible light. [ABSTRACT FROM AUTHOR]

Drug repurposing in the context of neuroimmunological (NI) investigations is still in its primary stages. Drug repurposing is an important method that bypasses lengthy drug discovery procedures and focuses on discovering new usages for known medications. Neuroimmunological diseases, such as Alzheimer's, Parkinson's, multiple sclerosis, and depression, include various pathologies that result from the interaction between the central nervous system and the immune system. However, the repurposing of NI medications is hindered by the vast amount of information that needs mining. We previously presented Adera1.0, which was capable of text mining PubMed for answering query-based questions. However, Adera1.0 was not able to automatically identify chemical compounds within relevant sentences. To challenge the need for repurposing known medications for neuroimmunological diseases, we built a deep neural network named Adera2.0 to perform drug repurposing. The workflow uses three deep learning networks. The first network is an encoder and its main task is to embed text into matrices. The second network uses a mean squared error (MSE) loss function to predict answers in the form of embedded matrices. The third network, which constitutes the main novelty in our updated workflow, also uses a MSE loss function. Its main usage is to extract compound names from relevant sentences resulting from the previous network. To optimize the network function, we compared eight different designs. We found that a deep neural network consisting of an RNN neural network and a leaky ReLU could achieve 0.0001 loss and 67% sensitivity. Additionally, we validated Adera2.0's ability to predict NI drug usage against the DRUG Repurposing Hub database. These results establish the ability of Adera2.0 to repurpose drug candidates that can shorten the development of the drug cycle. The workflow could be download online. [ABSTRACT FROM AUTHOR]

Artisanal cheeses can be sources of Listeria monocytogenes and cause disease in humans. This bacterial pathogen is a species of diverse genotypic and phenotypic characteristics. The aim of the study was to characterize 32 isolates of L. monocytogenes isolated in 2014–2018 from artisanal cheeses. The isolates were characterized using whole genome sequencing and bioinformatics analysis. The artisanal cheese isolates resolved to four molecular groups: 46.9% of them to IIa (1/2a-3a), 31.2% to IVb (4ab-4b-4d-4e), 12.5% to IIc (1/2c-3c), and 9.4% to IIb (1/2b-3b-7). Two evolutionary lineages emerged: lineage II having 59.4% of the isolates and lineage I having 40.6%. The sequence types (ST) totaled 18: ST6 (15.6% of the isolates), ST2, ST20, ST26, and ST199 (each 9.4%), ST7 and ST9 (each 6.3%), and ST1, ST3, ST8, ST16, ST87, ST91, ST121, ST122, ST195, ST217, and ST580 (each 3.1%). There were 15 detected clonal complexes (CC): CC6 (15.6% of isolates), CC9 (12.5%), CC2, CC20, CC26, and CC199 (each 9.4%), CC7 and CC8 (each 6.3%), and CC1, CC3, CC14, CC87, CC121, CC195, and CC217 (each 3.1%). The isolates were varied in their virulence genes and the differences concerned: inl, actA, LIPI-3, ami, gtcA, aut, vip, and lntA. [ABSTRACT FROM AUTHOR]

Cross-linked polyethylene (XLPE) is one of the most popular insulation materials used in the production of medium and high voltage cables (MV, HV). This article presents the results of research carried out on two types of commercially used insulation materials, modified with the addition of organophilic phyllosilicate (CLOISITE C20A)and halloysite nanotubes (HNTs). The influence of fillers on the mechanical properties of insulating materials is discussed as a potential mechanism for increasing their resistance to the phenomenon of water-tree. SEM and XRD analyses were performed to investigate the morphology and DSC for comparing phase transitions. Mechanical and functional properties for different concentrations of nanofillers, such as their hybrids, were also investigated. [ABSTRACT FROM AUTHOR]

Simple Summary: Our goal was to analyze the financial needs of patients with urological cancer on the basis of online crowdfunding campaigns. In our report, we included 2126 individual campaigns fundraising for prostate, bladder, kidney and testicular cancer. We specified the detailed financial needs of uro-oncologic patients, campaigns' characteristics and the factors associated with campaigns' success and high fundraising. Our work showed that, with the constantly increasing costs of medical care, patients are looking for funding from online communities including seeking funds for alternative therapies. Overall, these data point to a wide spectrum of patient needs for urologic cancer care as well as identifiable factors influencing campaign success. Background: we aimed to characterize the financial needs expressed through online crowdfunding for urologic cancers. Methods: the data used in this study came from the online crowdfunding platform GoFundMe.com. Using an automated software method, we extracted data for campaigns related to urologic cancers. Subsequently, four independent investigators reviewed all extracted data on prostate, bladder, kidney and testicular cancer. We analyzed campaigns' basic characteristics, goals, fundraising, type of treatment and factors associated with successful campaigns. Results: in total, we identified 2126 individual campaigns, which were related to direct treatment costs (34%), living expenses (17%) or both (48%). Median fundraising amounts were greatest for testicular cancer. Campaigns for both complementary and alternative medicine (CAM) (median $11,000) or CAM alone (median $8527) achieved higher fundraising totals compared with those for conventional treatments alone (median $5362) (p < 0.01). The number of social media shares was independently associated with campaign success and highest quartile of fundraising. Conclusions: using an automated web-based approach, we identified and characterized online crowdfunding for urologic cancer care. These findings indicated a diverse range of patient needs related to urologic care and factors related to campaigns' success. [ABSTRACT FROM AUTHOR]

A series of poly(ester amide)s based on dimethyl furan 2,5-dicarboxylate (DMFDC), 1,3-propanediol (PDO), 1,6-hexylene glycol (HDO), and 1,3-diaminopropane (DAP) were synthesized via two-step melt polycondensation. The phase transition temperatures and structure of the polymers were studied by differential scanning calorimetry (DSC). The positron annihilation lifetime spectroscopy (PALS) measurement was carried out to investigate the free volume. In addition, the mechanical properties of two series of poly(ester amide)s were analyzed. The increase in the number of methylene groups in the polymer backbone resulted in a decrease in the values of the transition temperatures. Depending on the number of methylene groups and the content of the poly(propylene furanamide) (PPAF), both semi-crystalline and amorphous copolymers were obtained. The free volume value increased with a greater number of methylene groups in the polymer backbone. Moreover, with a lower number of methylene groups, the value of the Young modulus and stress at break increased. [ABSTRACT FROM AUTHOR]

Currently, medicine uses typical industrial structure techniques, including reverse engineering, data processing, 3D-CAD modeling, 3D printing, and coordinate measurement techniques. Taking this into account, one can notice the applications of procedures used in the aviation or automotive industries based on the structure of Industry 4.0 in the planning of operations and the production of medical models with high geometric accuracy. The procedure presented in the publication shortens the processing time of tomographic data and increases the reconstruction accuracy within the hip and knee joints. The procedure allows for the partial removal of metallic artifacts from the diagnostic image. Additionally, numerical models of anatomical structures, implants, and bone cement were developed in more detail by averaging the values of local segmentation thresholds. Before the model manufacturing process, additional tests of the PLA material were conducted in terms of its strength and thermal properties. Their goal was to select the appropriate type of PLA material for manufacturing models of anatomical structures. The numerical models were divided into parts before being manufactured using the Fused Filament Fabrication technique. The use of the modifier made it possible to change the density, type of filling, number of counters, and the type of supporting structure. These treatments allowed us to reduce costs and production time and increase the accuracy of the printout. The accuracy of the manufactured model geometry was verified using the MCA-II measuring arm with the MMDx100 laser head and surface roughness using a 3D Talyscan 150 profilometer. Using the procedure, a decrease in geometric deviations and amplitude parameters of the surface roughness were noticed. The models based on the presented approach allowed for detailed and meticulous treatment planning. [ABSTRACT FROM AUTHOR]

The following paper presents the key design and test activities associated with the development of POLON—a green microsatellite propulsion module using 98% Hydrogen Peroxide (HTP). POLON, which stands for "Polish Propulsion Module", is the first step toward the development of a full, ready-to-be-commercialized satellite propulsion system at the Łukasiewicz—Institute of Aviation (Ł-IoA). The development of an entire microsatellite propulsion system within the frame of the POLON project effort is the natural milestone on the Ł-IoA green propulsion roadmap, which so far embodied research on fundamental HTP chemistry, work on elementary propulsion technologies, as well as the development of individual propulsion components. Within this article, POLON propulsion development logic is introduced first, and the major challenges associated with utilizing HTP for an orbital propulsion system are described. Consequently, the specific R&D activities aimed at mitigating the identified issues and risks are discussed. Those cover analytical as well as experimental work, including, but not limited to, HTP compatibility studies with candidate construction materials, waterhammer effect studies, HTP catalyst testing and evaluation, and propellant tank manufacturing studies. The initial results for those activities are presented and, finally, further development plans are discussed. [ABSTRACT FROM AUTHOR]

Progressive climate changes are the most important challenges for modern agriculture. Permanent grassland represents around 70% of all agricultural land. In comparison with other agroecosystems, grasslands are more sensitive to climate change. The aim of this study was to create deterministic models based on artificial neural networks to identify highly significant factors influencing the yield and digestibility of grassland sward in the climatic conditions of central Poland. The models were based on data from a grassland experiment conducted between 2014 and 2016. Phytophenological data (harvest date and botanical composition of sward) and meteorological data (average temperatures, total rainfall, and total effective temperatures) were used as independent variables, whereas qualitative and quantitative parameters of the feed made from the grassland sward (dry matter digestibility, dry matter yield, and protein yield) were used as dependent variables. Nine deterministic models were proposed Y_G, DIG_G, P_G, Y_GB, DIG_GB, P_GB, Y_GC, DIG_GC, and P_GC, which differed in the input variable and the main factor from the grassland experiment. The analysis of the sensitivity of the neural networks in the models enabled the identification of the independent variables with the greatest influence on the yield of dry matter and protein as well as the digestibility of the dry matter of the first regrowth of grassland sward, taking its diverse botanical composition into account. The results showed that the following factors were the most significant (rank 1): the average daily air temperature, total rainfall, and the percentage of legume plants. This research will be continued on a larger group of factors influencing the output variables and it will involve an attempt to optimise these factors. [ABSTRACT FROM AUTHOR]

One of the problems of sustainable agricultural land management (SALM) is the competition between food production and biomass production. For this reason, marginal lands with unfavorable agrotechnical conditions have been proposed for non-food crops in recent years. To this end, a better understanding of the impact of environmental factors on crop development and yield is needed. The objective of the study was to investigate the effects of soil water availability on selected morphological, physiological and growth characteristics of four C4 grass species (Miscanthus × giganteus, Miscanthus sacchariflorus, Miscanthus sinensis and Spartina pectinate) growing under different water and fertilizer conditions. A pot experiment was conducted under greenhouse conditions with four grass species, three different water rates (100, 85 and 70%) and three fertilizer rates (270, 180 and 90 kg NPK ha−1). The study showed that water stress, regardless of plant species, increased the chlorophyll content index without affecting the photosynthetic efficiency of the plants. Water stress significantly decreased plant fresh and dry mass, shoot number and length, and shoot/leaf ratio. The response to water deficit depended on the plant species. Miscanthus sinensis was the most sensitive to water deficit and Spartina pectinate the most tolerant (reduction in dry mass of 41.5% and 18%, respectively). Water stress (85% and 70%) reduced the number and the length of shoots without affecting the average diameter of shoots of the tested grasses, resulting in a significant reduction in biomass production of plants grown under optimal conditions with mineral NPK fertilization (180 kg NPK ha−1). Miscanthus sacchariflorus showed the highest dry matter under the worst growing conditions (70% and 90 NPK) and therefore could be recommended for cultivation on marginal lands with unfavorable agrotechnical conditions. It should be emphasized that the high yield of this species was not due to the photosynthetic efficiency, but better growth stem parameters (length and number). It appears that, for long-term agricultural land management, it is preferable to determine fertilizer rates for each crop species based on soil water availability. It should also be emphasized that increasing the yield of potential lignocellulosic crops for energy purposes while reducing environmental impact appears to be one of the viable answers to the difficulties of conventional energy production. [ABSTRACT FROM AUTHOR]

This study analyzes the possibility of using Go (Golang) in the context of Java and Python in decision-making processes, with particular emphasis on their use in industry-specific solutions for Industry 4.0. The authors intentionally compared Go with Java and Python, which have been widely used for many years for data analysis in many areas. The research work was based on decision trees data mining algorithms, and especially on classification trees, in which the measure of entropy as a heuristics to choose an attribute was taken into account. The tests were carried out on various parameters describing calculation time, RAM usage, and CPU usage. The source data, which were the basis for the computing of the decision tree algorithm implemented using these three languages, were obtained from a commercial remote prototyping system and were related to the target customers' choice of methods and means of the full design-creation process. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed model and architecture can be used to build virtual prototyping laboratories, taking into account geographically distributed human, manufacturing, and measurement resources, which will be seen as one coherent cyber-human system. This will accelerate the time needed to develop a prototype and reduce the cost of its production. In this paper, the authors present a completely new approach to the remote prototyping process, taking into account the distributed nature of design and manufacturing resources. A new model is suggested, taking into account the conditions of the Industry 4.0 concept, along with a component of remote implementation and coordination of operations. On the basis of this model, the architecture of the target system is developed, which is further built and implemented in the actual productive environment. The system's functionality additionally enables the implementation of the design and production process in critical conditions resulting from natural disasters or epidemic states. The practical application of the developed solutions is presented on the design of a ventilator, which is dedicated to help in the fight against epidemic states, e.g., coronavirus. [ABSTRACT FROM AUTHOR]

Featured Application: The results of the work may find a potential application in creating effective wireless communication environments with high probability connections; for example, for IoT. The paper focuses on the phenomenon of phase transitions related to changes taking place directly in a network structure; i.e., in physical topology. Although these changes are related to the physical layer, they directly affect the functioning of higher layers, and thus the quality and efficiency of network transmission. The research presented herein was focused in particular on phase transformations in MESH type wireless topologies. The dependence between the values of radius (transmitter power) nodes and the coherence of the MESH network is presented. These considerations are important for understanding the dynamics of processes occurring in topological structures, and for optimizing the use of power sources for individual nodes. The paper also presents the use of phase transition analysis for early detection of network coherence loss at the physical layer level. A new mechanism for assessing the level of network communication and an active method of preventing its failure using the arbiter and a set of limited diagnostic information are described. The solution presented can be used both in Internet of Things (IoT) systems and Industry 4.0 to create a high level of reliability wireless connection infrastructure between mobile and fixed devices. [ABSTRACT FROM AUTHOR]

We present a high-scale method to produce few-layer graphene (FLG) based on the mechanical exfoliation of graphite and compare the obtained FLG with the one reported earlier arising from pencil lead ablation. Several elements were modified and improved in the new approach. The purification and the ablation set-up were simplified, and the morphology of the FLG was modified and improved in view of some applications. The morphology-dependent properties of FLGs, lead-FLG, and graphite-FLG as conductive layers and in nanocomposites were investigated. The newly obtained FLG had a higher aspect ratio (high lateral size vs thickness/higher 2D aspect), which is reflected by enhanced transparency-conductivity features of the layer (film) and elongation-at-break behavior of the polymer composites. On the contrary, the nanocomposite containing lead-FLG showed, for instance, excellent gas barrier properties due to the multi-step structure of the lead-FLG flakes. Such structure exhibited less 2D and more 3D character, which can be highly suitable for applications where the presence of active/reactive edges is beneficial, e.g., in catalysis or supercapacitors' electrodes. Nuclear reaction analysis was employed to investigate the morphology of graphite-FLG film. [ABSTRACT FROM AUTHOR]

Featured Application: The paper presents a recent study on the characterization of the composite materials with improved functional properties that can be used for dental prostheses. The study involved research related to the selection of the material with improved functional properties that can be used for dental prostheses. An innovative system of nanofillers, that differ in shape, by means of gelatin-modified halloysite nanotubes (HNTs-g) along with silane-coupled aluminum trihydrate (ATH-sil) was prepared, in order to observe a synergistic improvement of acrylic material (methyl methacrylate with methyl methacrylate monomer (MM/mMM)). Selected mechanical properties of manufactured nanocomposites, along with utilitarian properties, like hardness, buffer solution absorption, and abrasion resistance, along with a fall test from the height of finished products have been discussed. Moreover, the study of the biofilm formation on the surface of dental prostheses confirmed the occurrence of a synergistic improvement of properties and the legitimacy of using modified mineral nanofillers in the form of a hybrid system. [ABSTRACT FROM AUTHOR]

The aquatic microbiome is composed of a multi-phylotype community of microbes, ranging from the numerically dominant viruses to the phylogenetically diverse unicellular phytoplankton. They influence key biogeochemical processes and form the base of marine food webs, becoming food for secondary consumers. Due to recent advances in next-generation sequencing, this previously overlooked component of our hydrosphere is starting to reveal its true diversity and biological complexity. We report here that 250 mL of seawater is sufficient to provide a comprehensive description of the microbial diversity in an oceanic environment. We found that there was a dominance of the order Caudovirales (59%), with the family Myoviridae being the most prevalent. The families Phycodnaviridae and Mimiviridae made up the remainder of pelagic double-stranded DNA (dsDNA) virome. Consistent with this analysis, the Cyanobacteria dominate (52%) the prokaryotic diversity. While the dinoflagellates and their endosymbionts, the superphylum Alveolata dominates (92%) the microbial eukaryotic diversity. A total of 834 prokaryotic, 346 eukaryotic and 254 unique virus phylotypes were recorded in this relatively small sample of water. We also provide evidence, through a metagenomic-barcoding comparative analysis, that viruses are the likely source of microbial environmental DNA (meDNA). This study opens the door to a more integrated approach to oceanographic sampling and data analysis. [ABSTRACT FROM AUTHOR]

A series of poly(trimethylene terephthalate)-block-poly(ethylene oxide) (PTT-b-PEOT) copolymers with different compositions of rigid PTT and flexible PEOT segments were synthesized via condensation in the melt. The influence of the block length and the block ratio on the micro-separated phase structure and elastic properties of the synthesized multiblock copolymers was studied. The PEOT segments in these copolymers were kept constant at 1130, 2130 or 3130 g/mol, whereas the PTT content varied from 30 up to 50 wt %. The phase separation was assessed using differential scanning calorimetry (DSC) and dynamic mechanical thermal analysis (DMTA). The crystal structure of the synthesised block copolymers and their microstructure on the manometer scale was evaluated by using WAXS and SAXS analysis. Depending on the PTT/PEOT ratio, but also on the rigid and flexible segment length in PTT-b-PEO copolymers, four different domains were observed i.e.,: a crystalline PTT phase, a crystalline PEO phase (which exists for the whole series based on three types of PEOT segments), an amorphous PTT phase (only at 50 wt % content of PTT rigid segments) and an amorphous PEO phase. Moreover, the elastic deformability and reversibility of PTT-b-PEOT block copolymers were studied during a cyclic tensile test. Determined values of permanent set resultant from maximum attained stain (100% and 200%) for copolymers were used to evaluate their elastic properties. [ABSTRACT FROM AUTHOR]

The bacterium Xanthomonas campestris pathovar musacearum (Xcm) is the causal agent of banana Xanthomonas wilt (BXW). This disease has devastated economies based on banana and plantain crops (Musa species) in East Africa. Here we use genome-wide sequencing to discover a set of single-nucleotide polymorphisms (SNPs) among East African isolates of Xcm. These SNPs have potential as molecular markers for phylogeographic studies of the epidemiology and spread of the pathogen. Our analysis reveals two major sub-lineages of the pathogen, suggesting that the current outbreaks of BXW on Musa species in the region may have more than one introductory event, perhaps from Ethiopia. Also, based on comparisons of genome-wide sequence data from multiple isolates of Xcm and multiple strains of X. vasicola pathovar vasculorum, we identify genes specific to Xcm that could be used to specifically detect Xcm by PCR-based methods. [ABSTRACT FROM AUTHOR]