Search Results (76)

The Marburg virus (MARV) is an extremely contagious zoonotic virus that leads to severe hemorrhagic fever in humans, with a fatality rate as high as 90%. It is known for causing nosocomial outbreaks in hospitals and laboratories globally. The recent outbreak of MARV in Rwanda highlighted significant challenges to infection prevention and control (IPC) protocols in two major hospitals, leading to outbreaks in intensive care units (ICUs) where the majority of infections occurred among healthcare providers. In contrast, the Rwandan National Reference Laboratory (NRL) demonstrated remarkable preparedness and resilience due to systematic investments and capacity building, which resulted in zero contamination, exposure, or infection, despite handling thousands of samples from across the country. This stark difference in infection dynamics between laboratory personnel at the NRL and healthcare providers underscores the effectiveness of the strict biosafety and biosecurity measures in place. Consequently, this situation underscores the urgent need for cross-facility training, the sharing of best practices, and the role of the NRL in reinforcing IPC measures throughout the country. This report delves into the preparedness and resilience of the NRL by examining its exemplary laboratory biosafety and biosecurity practices, emphasizing the crucial need for ongoing training, supervision, adherence to safety protocols, and improvements in the structure and operations of healthcare settings to prevent future outbreaks. Full article

Various Higher Education Institutions (HEIs) set themselves goals to become carbon neutral through the implementation of different reduction strategies such as the replacement of fossil-fueled vehicles with electric cars. However, even if all reduction measures are taken, residual GHG emissions will still remain. Therefore, most HEIs have to compensate for the remaining emissions by, for example, buying carbon credits. However, due to growing criticism of carbon credit purchases, HEIs need to explore options for establishing carbon sinks on their own premises to offset their remaining, unavoidable emissions. This study aimed to assess the CO₂ footprint of Hochschule Geisenheim University (HGU) as an exemplary HEI, identify emission hot-spots, and investigate the potential of biomass utilization for achieving carbon neutrality or even negative emissions. The analysis found that HGU’s main emissions were scope 1 emissions, primarily caused by on-site heat supply. The research determined that conversion to a wood chip-based heating system alone was insufficient to achieve climate neutrality, but this goal could be achieved through additional carbon dioxide removal (CDR). By operating a pyrolysis-based bivalent heating system, the study demonstrated that heat demand could be covered while producing sufficient C-sink certificates to transform HGU into the first carbon-negative HEI, at a comparable price to conventional combustion systems. Surplus C-sink certificates could be made available to other authorities or ministries. The results showed that bivalent heating systems can play an important role in HEI transitions to CO₂ neutrality by contributing significantly to the most urgent challenge of the coming decades: removing CO₂ from the atmosphere to limit global warming to as far below 2 °C as possible at nearly no extra costs. Full article

The genus Alternaria, functioning as a saprobe, endophyte, and plant pathogen, is widely distributed across various natural and human-impacted environments. Leaf spot and black spot diseases, caused by Alternaria species, are the most prevalent plant diseases within this genus, leading to significant reductions in crop yields and substantial economic losses. To facilitate the timely detection of Alternaria species during the early stages of infection, enable targeted treatments, and mitigate associated damages, we employed a species identification method based on Analysis of whole-GEnome (AGE). We downloaded 148 genomes, including 31 Alternaria species, from the NCBI GenBank database. Through bioinformatics analysis, we constructed a specific-target sequence library and selected a representative sequence per species. The specific target sequences of the seven exemplary Alternaria species were subsequently used for validation and rapid detection, utilizing Sanger sequencing and CRISPR-Cas12a technology, respectively. The results demonstrated that our method accurately identified the target species. Additionally, by combining Enzymatic Recombinase Amplification (ERA) with CRISPR-Cas12a, we achieved rapid and precise identification of genomic DNA samples, with a detection limit as low as 0.01 ng/µL within 30 min. Therefore, AGE proves to be a highly robust and efficient method for the detection of Alternaria species, offering broad potential for various applications. Full article

The anxiety of doctoral students in school has consistently been a major concern in society and the medical community, stemming from pressures, such as the pursuit of identity within highly professional and fiercely competitive academic communities, the urgent drive for high-level original scientific research output, and the need to break through the limits of personal ability to complete in-depth academic training. Current research has focused on the prevalence of, causes of, and intervention strategies for anxiety among doctoral students, but it still exhibits deficiencies, such as overly generalized analytical methods, neglect of the diversity within the doctoral student population, and the incomplete theoretical framework for the mechanisms of influence. Therefore, our study aims to examine the anxiety status of different categories of doctoral students and to explore their anxiety intervention mechanisms. Specifically, we employ a mixed research method combining quantitative and qualitative approaches to address issues, such as the influencing factors of anxiety in different types of doctoral students, the mechanisms of action of each factor, and the formation of an influence mechanism framework. The results indicate that anxiety is prevalent among doctoral students, with the key influencing factors being gender, age, love and marriage pressure, and self-perception. Full-time and part-time doctoral students exhibit different anxiety states and causes on account of varying pressures related to graduation, employment, and family factors. Our research distinguishes the heterogeneity of anxiety among different types of doctoral students in China, innovatively constructing a set of anxiety intervention mechanisms for doctoral students. It aims to provide policy insights for the adjustment of their anxiety and hopes to offer novel perspectives and exemplary references for the theoretical research and practical exploration of doctoral students’ anxiety in other countries around the world. Full article

Macrostomum lignano is gaining increasing recognition as a model organism for toxicological studies in marine ecosystems and expands the range of simple animal models currently used. Water pollution caused by human activities not only endangers environmental integrity but also affects human health, underlining the need to monitor water pollution effectively. This review describes the distinctive characteristics of M. lignano, including its rapid reproductive cycle, increased sensitivity to environmental variability, and remarkable regenerative abilities. Over the last thirty years, M. lignano has been used in various research areas, particularly molecular biology and toxicology. This endeavor has benefited from significant advances in genome and transcriptome technologies. Recent investigations have revealed its sensitivity to various pollutants and highlighted its potential for assessing toxicological effects at the physiological and molecular levels. Furthermore, the ecological versatility and stable microbiome of M. lignano make it an exemplary model for research into pollutant interactions in marine ecosystems. Despite challenges associated with its complex genomic architecture, ongoing genomic efforts are promising to significantly enhance its utility in toxicological research. This review underscores the pivotal role of M. lignano in advancing environmental health studies and outlines future research directions to maximize its potential as a model organism. Full article

Pinhole defects in thin films can significantly degrade their physical and chemical properties and act as sites for electrochemical corrosion. Therefore, the development of methods for the preparation of pinhole-free films is crucial. Electrodeposition, recognised for its efficiency and cost-effectiveness, shows great potential for applications in electrochemistry, biosensors, solar cells and electronic device fabrication. This review aims to elucidate the role of nucleation and growth models in understanding and optimising the electrodeposition process. Key parameters, such as crystal structure, orientation, surface morphology and defect control, are highlighted. In addition, the causes of pinhole defects, the effects of impurities and the potential and electrolyte composition on the deposited films are discussed. In particular, methods for minimising pinhole defects and two exemplary cases for a compact layer in relatively large-scale perovskite solar cells and nano-scale ultramicroelectrodes are discussed, exploring the influence of surface morphology, thickness and fabrication size under current common film preparation experiments. Finally, the critical aspects of controlled preparation, theoretical and technological advances, and the ongoing challenges in the field are provided. Full article

Voltage notches are steady-state sub-cycle waveform distortions caused by the normal operation of line-commutated power converters, significantly impacting power quality in industrial low-voltage (LV) networks. Despite their common occurrence, research on this phenomenon is still incipient, and realistic simulation platforms are lacking. This paper introduces a detailed MATLAB (R2024a)/Simulink-based simulation platform that models a benchmark low-voltage industrial installation, including a six-pulse controlled rectifier, linear loads, and a capacitor bank for power factor correction. Systematic simulations are performed with the platform to examine the sensitivity of notch characteristics to key parameters within plausible ranges, such as short-circuit power at the point of common coupling, commutation reactance, firing angle, snubber circuits, and rated power of the rectifier. In addition, parameters such as the rated power of linear loads and the compensation power of the capacitor bank are examined. Other influencing parameters including background voltage unbalance and distortion are also modeled and considered. A comparative analysis with field measurements from German industrial LV networks validates the plausibility and suitability of the simulations. Building upon this platform, a Monte Carlo simulation approach is adopted to generate extensive datasets of realistic voltage notch waveforms by randomly varying these key parameters. A case study conducted under conditions typical of German LV networks demonstrates the applicability of the simulations. To support further research, the simulation platform and exemplary synthetic waveforms are provided alongside the paper, serving as a valuable tool for testing and designing strategies for analysis, detection, and monitoring of voltage notches. Full article

Traditionally, precious metals are processed by either lost-wax casting or the casting of semi-finished products followed by cold or hot working, machining, and surface finishing. Long process chains usually conclude in a high material input factor and a significant amount of new scrap to be refined. The maturing of Additive Manufacturing (AM) technologies is advantageous with regard to resources among other criteria by opening up new processing techniques like laser-based powder bed fusion (LPBF) for the production of near net shape metal products. This paper gives an insight into major advantages of the powder-based manufacturing of precious metal components over conventional methods focusing on product carbon footprints (PCF). Material Flow Cost Accounting (MFCA) for selected applications show energy and mass flows and inefficient recoverable losses in detail. An extended MFCA approach also shows the greenhouse gas (GHG) savings from avoiding recoverable material losses and provides PCF for the products. The PCF of the precious metals used is based on a detailed Life Cycle Assessment (LCA) of the refining process of end-of-use precious metals. In the best case, the refining of platinum from end-of-life recycling, for example, causes 60 kg CO_2e per kg of platinum. This study reveals recommended actions for improvements in efficiency and gives guidance for a more sustainable production of luxury or technical goods made from precious metals. This exemplary study on the basis of an industrial application shows that the use of AM leads to a carbon footprint of 2.23 kg CO_2e per piece in comparison with 3.17 kg CO_2e by conventional manufacturing, which means about a 30 percent reduction in GHG emissions and also in energy, respectively. Full article

Hyperbolae in radargrams are caused by a variety of small subsurface objects. The analysis of their curvature enables the determination of propagation velocity in the subsurface, which is important for exact time-to-depth conversion and migration and also yields information on the water content of the soil. Using deep learning methods and fitting (DLF) algorithms, it is possible to automatically detect and analyze large numbers of hyperbola in 3D Ground-Penetrating Radar (GPR) datasets. As a result, a 3D velocity model can be established. Combining the hyperbola locations and the 3D velocity model with reflection depth sections and timeslices leads to improved archaeological interpretation due to (1) correct time-to-depth conversion through migration with the 3D velocity model, (2) creation of depthslices following the topography, (3) evaluation of the spatial distribution of hyperbolae, and (4) derivation of a 3D water content model of the site. In an exemplary study, we applied DLF to a 3D GPR dataset from the multi-phased (2nd to 12th century CE) archaeological site of Goting on the island of Föhr, Northern Germany. Using RetinaNet, we detected 38,490 hyperbolae in an area of 1.76 ha and created a 3D velocity model. The velocities ranged from approximately 0.12 m/ns at the surface to 0.07 m/ns at approx. 3 m depth in the vertical direction; in the lateral direction, the maximum velocity variation was ±0.048 m/ns. The 2D-migrated radargrams and subsequently created depthslices revealed the remains of a longhouse, which was not known beforehand and had not been visible in the unmigrated timeslices. We found hyperbola apex points aligned along linear strong reflections. They can be interpreted as stones contained in ditch fills. The hyperbola points help to differentiate between ditches and processing artifacts that have a similar appearance as the ditches in time-/depthslices. From the derived 3D water content model, we could identify the thickness of the archaeologically relevant layer across the whole site. The layer contains a lot of humus and has a high water retention capability, leading to a higher water content compared to the underlying glacial moraine sand, which is well-drained. Full article

The growing adoption of behind-the-meter (BTM) photovoltaic (PV) systems, electric vehicle (EV) home chargers, and heat pumps (HPs) is causing increased grid congestion issues, particularly in power distribution grids. Leveraging BTM prosumer flexibility offers a cost-effective and readily available solution to address these issues without resorting to expensive and time-consuming infrastructure upgrades. This work evaluated the effectiveness of this solution by introducing a novel modeling framework that combines a rolling horizon (RH) optimal power flow (OPF) algorithm with a customized piecewise linear cost function. This framework allows for the individual control of flexible BTM assets through various control measures, while modeling the power flow (PF) and accounting for grid constraints. We demonstrated the practical utility of the proposed framework in an exemplary residential region in Schutterwald, Germany. To this end, we constructed a PF-ready grid model for the region, geographically allocated a future BTM asset mix, and generated tailored load and generation profiles for each household. We found that BTM storage systems optimized for self-consumption can fully resolve feed-in violations at HV/MV stations but only mitigate 35% of the future load violations. Implementing additional control measures is key for addressing the remaining load violations. While curative measures, e.g., temporarily limiting EV charging or HP usage, have minimal impacts, proactive measures that control both the charging and discharging of BTM storage systems can effectively address the remaining load violations, even for grids that are already operating at or near full capacity. Full article

The COVID-19 (coronavirus disease) pandemic had a severe impact on public health worldwide. A rare but serious complication after administration of adenoviral vaccines against SARS-CoV-2 (AstraZeneca–Oxford and Johnson & Johnson) is vaccine-induced immune thrombotic thrombocytopenia and thrombosis (VITT), which can lead to serious complications such as cerebral venous sinus thrombosis (CVST). CVST itself can cause subarachnoid hemorrhage (SAH) and/or intracerebral hemorrhage (ICH), leading to high mortality due to herniation of brain parenchyma. In those patients, an emergent decompressive hemicraniectomy (DC) is regularly performed. Herein, the authors want to focus on the patients who survive DC following VITT-associated CVST and shed light on the neurosurgical considerations in those patients. We herein propose a treatment algorithm regarding the timing and the perioperative management of cranioplasty. We describe an exemplary case highlighting that special circumstances may result in a more urgent need for autologous cranioplasty than usual, based on individual risk assessment. Full article

Wearable sensors, specifically microneedle sensors based on electrochemical methods, have expanded extensively with recent technological advances. Today’s wearable electrochemical sensors present specific challenges: they show significant modulus disparities with skin tissue, implying possible discomfort in vivo, especially over extended wear periods or on sensitive skin areas. The sensors, primarily based on polyethylene terephthalate (PET) or polyimide (PI) substrates, might also cause pressure or unease during insertion due to the skin’s irregular deformation. To address these constraints, we developed an innovative, wearable, all-fiber-structured electrochemical sensor. Our composite sensor incorporates polyurethane (PU) fibers prepared via electrospinning as electrode substrates to achieve excellent adaptability. Electrospun PU nanofiber films with gold layers shaped via thermal evaporation are used as base electrodes with exemplary conductivity and electrochemical catalytic attributes. To achieve glucose monitoring, gold nanofibers functionalized by gold nanoflakes (AuNFs) and glucose oxidase (GOx) serve as the working electrode, while Pt nanofibers and Ag/AgCl nanofibers serve as the counter and reference electrode. The acrylamide-sodium alginate double-network hydrogel synthesized on electrospun PU fibers serves as the adhesive and substance-transferring layer between the electrodes. The all-fiber electrochemical sensor is assembled layer-by-layer to form a robust structure. Given the stretchability of PU nanofibers coupled with a high specific surface area, the manufactured porous microneedle glucose sensor exhibits enhanced stretchability, superior sensitivity at 31.94 μA (lg(mM))⁻¹ cm⁻², a broad detection range (1–30 mM), and a significantly low detection limit (1 mM, S/N = 3), as well as satisfactory biocompatibility. Therefore, the novel electrochemical microneedle design is well-suited for wearable or even implantable continuous monitoring applications, thereby showing promising significant potential within the global arena of wearable medical technology. Full article

Weld line defects, commonly occurring during the plastic product manufacturing process, are caused by the merging of two opposing streams of molten plastic. The presence of weld lines harms the product’s aesthetic appeal and durability. This study uses artificial neural networks to forecast the ultimate tensile strength of a PA6 composite incorporating 30% glass fibers (GFs). Data were collected from tensile strength tests and the technical parameters of injection molding. The packing pressure factor is the one that significantly affects the tensile strength value. The melt temperature has a significant impact on the product’s strength as well. In contrast, the filling time factor has less impact than other factors. According to the scanning electron microscope result, the smooth fracture surface indicates the weld line area’s high brittleness. Fiber bridging across the weld line area is evident in numerous fractured GF pieces on the fracture surface, which enhances this area. Tensile strength values vary based on the injection parameters, from 65.51 MPa to 73.19 MPa. In addition, the experimental data comprise the outcomes of the artificial neural networks (ANNs), with the maximum relative variation being only 4.63%. The results could improve the PA6 reinforced with 30% GF injection molding procedure with weld lines. In further research, mold temperature improvement should be considered an exemplary method for enhancing the weld line strength. Full article

Inducing apoptosis in cancer cells is a primary goal in anti-cancer therapy, but curing cancer with a single drug is unattainable due to drug resistance. The complex molecular network in cancer cells causes heterogeneous responses to single-target drugs, thereby inducing an adaptive drug response. Here, we showed that targeted drug perturbations can trigger state conflicts between multi-stable motifs within a molecular regulatory network, resulting in heterogeneous drug responses. However, we revealed that properly regulating an interconnecting molecule between these motifs can synergistically minimize the heterogeneous responses and overcome drug resistance. We extracted the essential cellular response dynamics of the Boolean network driven by the target node perturbation and developed an algorithm to identify a synergistic combinatorial target that can reduce heterogeneous drug responses. We validated the proposed approach using exemplary network models and a gastric cancer model from a previous study by showing that the targets identified with our algorithm can better drive the networks to desired states than those with other control theories. Of note, our approach suggests a new synergistic pair of control targets that can increase cancer drug efficacy to overcome adaptive drug resistance. Full article

This article presents a comprehensive comparative evaluation of a three-phase Three-Level (3L) Flying Capacitor Converter (FCC) and a Stacked Polyphase Bridge Inverter (SPBI), specifically a converter system formed by two Series-Stacked Two-Level three-phase Converters (2L-SSC), for the realization of a 7.5 kW Integrated Motor Drive (IMD) with a high short-term overload capability. The 2L-SSC requires a motor with two three-phase windings and a split DC-link, but uses standard six-switch, two-level transistor configurations. In contrast, the bridge legs of the 3L-FCC feature flying capacitors whose voltages must be actively balanced. Despite the 800 V DC-link voltage, both topologies employ the same set of 650 V GaN power transistors, i.e., the same total chip area, and if operated at the same switching frequency, show identical semiconductor losses. Electric Discharge Machining (EDM) damage of the motor bearings is a relevant issue caused by the common-mode (CM) voltages of the inverter stage. The high effective switching frequency of the 3L-FCC and the possibility of CM voltage canceling in the 2L-SSC facilitate mitigation of EDM by means of CM chokes, whereby a substantially smaller CM choke with lower losses suffices for the 2L-SSC; based on exemplary designs, the 2L-SSC features only about 75% of the total volume and 85% of the nominal losses of the 3L-FCC. If, alternatively, motor-friendliness is maximized by including DC-referenced sine-wave output filters, the 3L-FCC’s higher effective switching frequency and the 2L-SSC’s need for two sets of filters due to the dual-winding-set motor change the outcome. In this case, the 3L-FCC features only about 60% of the volume and only about 55% of the 2L-SSC’s nominal losses. Full article