Ice rises situated around the perimeter of Antarctica buttress ice flow and contain information about the past climate and changes in flow regime. Moreover, ice rises contain convergent and divergent flow regimes, and both floating and grounded ice over comparatively small spatial scales, meaning they are ideal locations to study ice-flow dynamics. Here, we introduce a new modelling framework that permits the comparison between modelled and observed stratigraphy. A thermo-mechanically coupled, isotropic, full Stokes ice flow model with a dynamic grounding line is used (Elmer/Ice). The result is the simulated age-depth field of a three-dimensional, steady-state ice rise which is dynamically coupled to the surrounding ice shelf. Applying the model to Derwael Ice Rise, results show a good match between observed and modelled stratigraphy over most of the ice rise and predict approximately $8000$ year old ice at a depth of $95$ \%. Differences in the prediction of age between simulations using Glen’s flow law exponents of $n=3$ and $n=4$ are generally small ($<5$ \% over most areas). In the ice rise shear zones, large differences in shear strain rates in the velocity direction are found between the $n=3$ and the $n=4$ simulations. Our simulations indicate that a Glen’s flow law exponent of $n=4$ may be better suited when modelling ice rises due to a steady-state geometry which is closer to the observed geometry. Our three-dimensional modelling framework can easily be transferred to other ice rises and has relevance for researchers interested in ice core dating and understanding ice-flow re-organisation.

The rapid accumulation of entries in protein databases makes it possible to carry out large-scale sequence analysis and to make discoveries that cannot be sufficiently supported with a small dataset. Multiple sequence alignment is a representative method for identifying positional variations in related protein sequences by working with sizable entries. Despite the convenience of operation brought about by the straightforward comparison, the widely employed multiple sequence alignment seldom considers the covariation of two or more sites in a protein sequence. Such a simplification inevitably sacrifices the chance to discover knowledge that can better reflect heritable variations in the long history of evolution. The statistical coevolution analysis method goes further by focusing on the mutation rate of two positions to identify the functional correlation, based on which the independent components (IC) were then clustered into independent sections (sector). Using this method, we analyzed the metal-binding specificity of superoxide dismutases (SOD) from different families. The results showed that the residues coordinating metal ions could be clustered into a sector. However, the SODs of different families exhibited different levels of correlation, reflected by the order of the sectors: sector 1 for SODs from families PF09055 (NiSOD, based on the Pfam database) and PF00081(MnSODs or FeSODs), and sector 2 for PF00080 (Cu/ZnSODs). Such a distribution of metal-binding residues in sectors was also consistent with the taxonomic levels of the origins of these SODs, implying the functional separation of residues responsible for catalysis and structural stabilization. This study will help to explain that mutations apart from the active site can also exert pronounced effects on metal ion binding and provide more targets for engineering the biochemical properties of SODs.

The exponential growth in data processing and resource requirements directly results from the widespread use of smart health applications. Fog computing emerges as a viable paradigm, bringing cloud capabilities to the network's edge to meet the high computational needs. In this research, we present a low-cost resource scheduling technique for smart health systems that use collaborative edge fog computing to enhance efficiency and maximize the allocation of available resources. The proposed framework uses the network's edge nodes to distribute computing and storage tasks, which decreases latency, increases scalability, and lowers infrastructure costs. Our resource allocation system dynamically assigns tasks to fog devices and servers based on job priorities, device capabilities, and resource consumption levels. This optimization guarantees consistent workload distribution, resilience in the face of errors, and swift, accurate processing of smart health data. The experimental evaluation verifies the framework's efficiency in minimizing response times and optimizing resource utilization, a major step forward in smart health. Our Low-Cost Resource Scheduling Framework for Smart Health in a Collaborative Edge Fog Environment enables healthcare providers to provide timely and affordable care. The framework uses edge devices and fog servers to process health-related data closer to data sources and end-users to improve system performance and reduce transmission latency. The framework improves service quality and reduces expenses by decreasing the need for cloud hosting. Edge fog computing's near-real-time data processing benefits users and patients, strengthening the framework's smart health application.

There is growing evidence that the humoral immune response plays a significant role in the resolution of SARS-CoV-2. Patients with inherited or acquired immunoglobulin deficiencies have a higher risk of mortality, delayed viral clearance, and prolonged symptomatic infection, and this contributes to viral evolution. Case reports/series have implied a therapeutic role of monoclonal antibodies and convalescent plasma (CCP) as well as antivirals in the management of seronegative, symptomatic patients with SARS-CoV-2. In this review, we discuss the existing evidence supporting the efficacy of the above-mentioned treatment options and provide suggestions for management.

Aims: The aim of this study was to examine the effects of sericin on diabetic cognitive impairment (DCI) in rats based on neuroinflammation. Methods: SD rats were firstly fed with high sugar and high fat diet for 4 weeks, and then injected with 50 mg/kg streptozotocin intraperitoneally to establish a diabetic model. The diabetic rats were randomly divided into 3 groups and treated with distilled water (n=10), 500 mg/kg (n =10) and 1000 mg/kg (n=20) sericin, respectively, by gavage once a day for 8 weeks. Before the end of the trail, 10 rats in the 1000 mg/kg sericin group were injected with 10 μg EX527 (a SIRT1 inhibitor) into the lateral ventricles once every other day for 5 times. Results: Treated with sericin significantly reduced the fasting blood glucose, improved DCI in rats. Sericin significantly inhibited of neuroinflammation, reduced the expression of NLRP3, TXNIP proteins and reduced cell apoptosis, while increased the expression of SIRT1 protein in the hippocampus of diabetic rats. After inhibiting SIRT1 with EX527, the above effect of sericin on DCI rats was weakened. Conclusions: These results indicated that sericin may block DCI progression in rats by inhibiting TXNIP/NLRP3 neuroinflammation and neuronal apoptosis though SIRT1.

CASE DESCRIPTION A 5-year-old girl presented to a hospital at night with intermittent vomiting and abdominal pain since the morning. She was exhausted and unable to drink or eat properly but had a normal bowel movement that day. The physician performed abdominal X-ray imaging, which revealed abnormal gas distribution (Fig. 1-a,b). However, her symptoms remained stable. Thus, she was diagnosed with early gastroenteritis, and the physician decided to discharge her. But she was transferred to our hospital the next day with worsening symptoms. She had a history of undergoing surgery for a congenital diaphragmatic hernia at birth. Her physical examination indicated a distended abdomen with periumbilical tenderness on palpation. Furthermore, abdominal X-ray revealed the absence of gas (Fig. 1-c,d), and contrast-enhanced abdominal computed tomography scan revealed an enlarged stomach with rotation and a right-sided spleen (Fig. 2). An indwelling nasogastric tube was successfully inserted under fluoroscopy, and the symptoms of the patient dramatically improved after removing >1 liter of gastric contents. Finally, she was diagnosed with acute gastric volvulus and a wandering spleen.

Mobile Edge Computing (MEC) is an emerging technology aimed at alleviating network congestion and cloud service delays by caching and processing user requests at the network edge. Despite its potential, challenges like limited caching resources and unpredictable user behavior persist. To tackle these hurdles, a new intelligent caching approach called Edge Intelligent Context-aware Caching (EICC) is introduced. EICC addresses resource limitations by considering user, video, and environmental contexts. The approach encompasses three algorithms: User Location Prediction (ULoP) for foreseeing user mobility, User Content Interest Prediction (UCIP) for anticipating user preferences, and Content Rating Algorithm (CoRa) for evaluating content. The rated content is cached at predicted user locations, allowing localized access and minimizing latency. We demonstrate that our proposed approach is more efficient in terms of cache hit rate, prediction accuracy, and overall delay.

The purpose of this study is to investigate the annual degradation rates of photovoltaic (PV) systems composed of PV modules based on recent crystalline silicon (c-Si) PV technologies. We investigated the annual degradation rates of four PV systems composed of different c-Si PV technologies. Furthermore, the effects of soiling on the annual degradation rates of these PV systems were examined. The results obtained indicate that the apparent annual degradation rates of the PV strings before surface cleaning were 0.8, 1.6, 1.4, and 1.2%/year, respectively, because of optical losses due to dust particles, although the surfaces of the PV arrays had been washed by plentiful rainfall under subtropical climatic conditions. However, the inherent annual degradation rates of the PV strings after surface cleaning were 0.1, 0.6, 0.0, and 0.3%/year, respectively. These low degradation rates indicate that the PV systems composed of the recent c-Si PV technologies all offered quite stable performance.

[Conventional tillage](https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/conventional-tillage) is still widely used in mollisols sloping farmland, which leads to serious [soil erosion](https://www.sciencedirect.com/topics/earth-and-planetary-sciences/soil-erosion) . Effective farming methods have positive significance for alleviating soil erosion .To prevent and control soil and water loss in mollisols sloping farmland,field experiments were carried out in 2022 with three tillage methods and their combination modes(Subsoiling tillage (SF),Ridge tillage and pitting field (RF),Ridge tillage and pitting field + subsoiling tillage (RF-S),Contour tillage (TP),Contour tillage + ridge tillage and pitting field (TP-R),Contour tillage + subsoiling tillage (TP-S)) to evaluate the effects of soil and water conservation under different tillage methods. The results showed that the contour tillage + ridge field treatment significantly reduced surface runoff and soil loss ( P < 0.05), and there was no nutrient loss. Compared with other treatments, this method significantly reduced runoff, erosion, and nutrient loss. The contour tillage + subsoiling tillage treatment significantly reduced the soil three-phase R value and improved the soil texture, and its yield, harvest index, and water use efficiency were higher than those of other treatments. Considered comprehensively, the results show that contour tillage + subsoiling tillage can reduce soil erosion and improve soil quality and water use efficiency while ensuring high production capacity. Furthermore, the results of this study provide a theoretical basis for conserving soil and water resources, improving soil fertility, and increasing farmland yield in the mollisols area of northeast China.

Polycyclic N-heterocycles are very important scaffolds in biomedicinal chemistry and materials science. Intramolecular alkyne hy-droamination is a powerful method for the construction of N-heterocycles. In the last two decades, copper-catalyzed domino reac-tions based on intramolecular alkyne hydroamination has emerged as a robust strategy for assembling various polycyclic N-heterocycles. Great progress has been achieved in this area. This short review covers the advances made in copper-catalyzed domino synthesis of polycyclic N-heterocycles based on this strategy from 2008 to 2023, and will hopefully serve as an inspiration towards the exploration of new copper-catalyzed versions of the transformation. The domino transformations are introduced and discussed from five aspects according to the different key processes involved in these reactions.

Both P3b and Inspection Time Task (ITT) are related with intelligence, yet their relationships have not been well addressed. This event-related potential study sought to address this question with four levels of discriminative stimulus duration, and further tried to shed lights on the long controversial subject about what psychological process is associated with P3b. Participants (N=25) were asked to perform this ITT while undergoing 64-channel EEG recording. A Tapping task and four Elementary Cognitive Tests (ECTs), including a Simple Reaction Time (SRT), 2 Choice Reaction Time task (CRT), and a Pattern Discrimination task (PD), were included. N1, P1, and P3b at Pz were analyzed. Results showed that the P3b latency significantly prolonged with the increase of ITT duration. More intriguingly, the P3b latencies was negatively correlated with ITT accuracies but positively correlated with the RTs of the SRT, but only in the 34 ms condition. No significant correlation was found between the P3b latencies and the RTs of other ECTs or the Tapping task. Our results indicate that those who are able to accurately perceive and process very briefly presented stimuli have a higher information processing speed reflected by P3b latency, which is likely a reflection of mainly bottom-up process to form a perceptual memory and a decision. We conclude that these results support Verleger et al.’s view that P3b elicited by ITT, at least in the early stage, is more related with tactical processing that marks the closure of a perceptual epoch, rather than strategic processing or metacognition.

The aim of this study was to investigate the production, stability and applicability of colorants produced by filamentous fungi isolated from soil samples from the Amazon. Initially, the isolates were evaluated in a screening for the production of colorants. The influences of cultivation and nutritional conditions on the production of colorants by fungal isolates were investigated. The colorants produced by selected fungal isolates were chemically characterized using the LC-MS technique. The antimicrobial and cytotoxic activities, stability evaluation and applicability of the colorants were investigated. As results, we observed that the isolates Penicillium sclerotiorum P3SO224, Clonostachys rosea P2SO329 and Penicillium gravinicasei P3SO332 stood out since they produced the most intense colorants. Compounds produced by Penicillium sclerotiorum P3SO224 and Clonostachys rosea P2SO329 were identified as sclerotiorin and penicillic acid. The colorant fraction (EtOAc) produced by these species has antimicrobial activity, stability at temperature and at different pHs, stability when exposure to light and UV, and when exposed to different concentrations of salts, as well as being non-toxic and having the ability to dye fabrics and be used as a pigment in creams and soap. Considering the results found in this study, it was concluded that fungi from the soil in the Amazon have the potential to produce colorants with applications in the textile and pharmaceutical industries.

There may be significant histopathological overlap between Epstein–Barr virus (EBV) -positive diffuse large B cell lymphoma (DLBCL) and other diagnoses, including angioimmunoblastic T-cell lymphoma (AITL). Herein, we report a rare case of EBV-positive AITL developing two years after the initial diagnosis of EBV-positive DLBCL. Histone deacetylase (HDAC) inhibitor, chidamide, in combination with COEP (cyclophosphamide, vindesine, etoposide, prednisone) were administrated to treat AITL, which appeared to prolong the survival of patient. Next-generation sequencing (NGS) was used to study the possible mechanisms by which this patient developed AITL after DLBCL. NGS revealed that TET2 mutated in both DLBCL and AITL. When EBV-positive DLBCL patients with the TET2 mutation, it is necessary to beware of lymphoma recurrence and note that it may be completely different from the previous type. Our case suggests that chidamide plus COEP may be a treatment option for AITL after DLBCL and may prolong patient survival, but this requires a larger sample size to confirm.

Aim: As the non-vitamin K antagonist oral anticoagulant (NOAC) most recently approved in China, data pertaining to clinical edoxaban use are still scarce. This study investigated the prevalence of and contemporary trends in edoxaban prescription among Chinese patients as well as factors associated with its inappropriate use in a multi-center registry of patients treated in real-world clinical practice. Methods: This real-world, prospective, multicenter, and non-interventional study included 1005 inpatients treated with edoxaban. According to National Medical Products Administration and European Heart Rhythm Association guidelines, edoxaban therapy was determined to be appropriate or inappropriate in each case. Results: The median patient age was 70.0 years (interquartile range, 61.0–78.0 years), and 46.3% were women. Overall, 456 (45.4%) patients received inappropriate edoxaban therapy, and common issues included an inappropriately low (183, 18.2%) or high (73, 7.3%) dosage, wrong drug selection (109, 10.8%), unreasonable off-label use (49, 4.9%), incorrect administration timing (16, 1.6%), and contraindication due to other medications (27, 2.7%). Several factors (e.g., age, weight, kidney function, anemia, and bleeding history) were associated with an increased risk of inappropriate edoxaban therapy, whereas factors associated with cardiovascular specialties (e.g., hospitalized in cardiovascular department and dronedarone or amiodarone use) decreased this risk. Conclusion: In this real-world study, 45.4% of patients received an inappropriate treatment with edoxaban. Multiple clinical characteristics can help identify patients who should receive edoxaban. Further development and implantation of educational activities and management strategies are needed to ensure the correct use of edoxaban.

Unexpected equipment failure in machines interrupts production schedules and creates costly downtime. Therefore, the importance of timely equipment maintenance is to extend the machine lifespan, prevent unplanned downtime, and reduce the need to buy equipment. Textile factories tend to have overcapacity of looms with inconsistent maintenance time schedules. The main objective of the research was to establish a suitable maintenance schedule time and parameters by assessing the state of maintenance practices of the critical equipment in the weaving section. The maintenance time schedules of rapier, and air-jet looms were studied. Weibull distribution, and Monte Carlo simulation were undertaken followed by regression analysis of the data. The setup of the Monte Carlo simulation entailed 1000 instances of the random values from the systems in the critical equipment. The data were optimized through Monte Carlos regression modeling and Weibull distribution analysis to get shape parameter and the scale parameter of 1.47 and 1683.46 hours. In conclusion, the findings indicated that weaving looms were the critical equipment. The model's shape parameter of 1.47 described a steady increase in the risk of wear-out failure during the early life of the machines. Also, the value of the shape parameter suggested early wear-out failure and premature failures after installation. The optimal time interval for maintenance operations was 1683.46 hours from the scale parameter. The findings indicated that looms had an inconsistent and incoherent maintenance time scheduling approach. According to the results, it is recommended that preventive maintenance schedules be done once every 1683.46 hours.

Validating maintenance strategies is crucial for industrial equipment reliability. Regression analysis establishes correlations between plans and Mean Time Between Failures (MTBF). This study validates maintenance schedules and parameters for critical equipment in a textile factory using regression analysis of system data and maintenance intervals. Employing the Monte Carlo Simulation technique, it analyzes relationships between input variables (maintenance activities, equipment age, operating conditions) and MTBF. An R-squared value of over 0.70 confirms the significance of the regression model. Survey design identifies critical departments, and real-time equipment failure data supports the methodology. Regression analysis yields a significant model (R-squared = 85.56%) with 18 input variables contributing to MTBF variance. Sensitivity analysis reveals their hierarchical impact. Conclusions emphasize regression analysis’s efficacy in validating maintenance strategies, showcasing the input variables’ significance. Findings underscore tailored maintenance plans and suggest predictive analytics expansion. Recommendations include adaptive strategies, predictive analytics integration, optimal maintenance intervals determination, cost-benefit analyses, and spare parts inventory optimization.

In the context of a textile industry, where inconsistent maintenance scheduling and disjointed maintenance strategies could lead to breakdowns, reduced efficiency, and safety concerns, the need for reliable maintenance schedules and coherent strategies became paramount. This study endeavored to address this challenge by harnessing the power of the Weibull distribution. Its application involved scrutinizing system data and the time intervals between maintenance operations for critical equipment, with the overarching goal of deriving maintenance schedules and parameters that amplified both reliability and performance. To realize this objective, a methodological approach rooted in the Weibull distribution was employed. The analysis encompassed not only failure data examination but also the calculation of the Mean Time Between Failures (MTBF), offering insights into the system’s reliability. The study delved into the intricate connections among Weibull distribution parameters, hazard functions, and reliability functions. To validate the derived models, an array of techniques such as data fitting, probability plots, and regression analysis were systematically undertaken. Consequently, the study unveiled a spectrum of failure patterns contingent upon the shape parameters identified. These patterns encompassed premature, random, and wear-out failure modes, each necessitating specific maintenance strategies tailored to optimize equipment performance and ensure safety. The calculated MTBF values shed light on the equipment’s reliability, while the derived probability density functions, survival functions, and hazard functions enriched the comprehensive understanding of the system’s behavior. It was established that a shape of 1.46503 implies that most of the failures are associated with early wear-out failure. By pinpointing the failure modes and aligning corresponding maintenance approaches, the study not only enhanced equipment performance but also elevated safety standards.The study also proposed avenues for improving analysis accuracy through diverse data collection, real-time monitoring, and exploring dynamic parameter adjustments to accommodate evolving operational conditions.

The aim of this study was to investigate the survival, distribution and reaction of different cell types on a monolayer disk, as well as their behavior under bioreactor treatment. Specifically, porcine EEC and porcine fibroblasts (PCF) were labeled with GFT and Texas Red, respectively, to track their viability and distribution. The experiments involved monitoring the cells using various microscopy techniques and comparing the results with controls. These findings have important implications for understanding cell behavior and potential applications for Discrete Subaortic Stenosis. This paper aims to discuss the implications of the findings in the context of existing literature and future research directions.

Objective To establish and evaluate an intestinal microbiota dysbiosis-induced obesity mouse model. Methods 50 C57BL/6J male healthy mice were randomly divided into an obesity model group and the control group. The body weight, body length, and Lee’s index of the two groups of mice at week 1 and week 10 were compared. Serum glucose (GLU), total cholesterol (TC) and triglyceride (TG) were measured by enzyme-labeled colorimetric methods. Illumina HiSeq 16S rDNA high-throughput sequencing technology was used to characterize intestinal microbiota in feces. Results The success rate of model establishment in obese mice was 52%. The body weight, body length, Lee’s index, and abdominal fat (wet weight) in the obese model group were all higher than those in the control group, and the differences were statistically significant (P＜0.01). Serum GLU and TC levels in the obesity model group were higher than those in the control group (P＜0.05) , and there was no difference in TG levels between the two groups（P>0.05）. The control group contained more abundant intestinal microbiota phylum and genera than did the obesity model group; the differences between the two groups were significant（FDR≤0.05，P≤0.05）. Conclusion Intestinal microbiota dysbiosis can be used to generate an obesity model in mice.

A document by Marius G. Floriancic. Click on the document to view its contents.

In this paper, we use the generalized notions of Riemann-Liouville (fractional calculus with respect to a regular function σ) to extend the definitions of fractional integration and derivative from the functional sense to the distributional sense. First, we give some properties of fractional integral and derivative for the functions infinitely differentiable with compact support. Then, we define the weak derivative, as well as the integral and derivative of a distribution with compact support, the integral and derivative of a distribution using the convolution product. Then, we generalize those concepts from the unidimensional to the multidimensional case. Finally, we propose the definitions of some usual differential operators.

The Fractions Skill Score (FSS) is a neighbourhood verification method originally designed to verify deterministic forecasts of binary events. Previous studies employed different approaches for computing an ensemble-based FSS for probabilistic forecast verification. We show that the formulation of an ensemble-based FSS substantially affects verification results. Comparing four possible approaches, we determine how different ensemble-based FSS variants depend on ensemble size, neighbourhood size, and forecast event frequency of occurrence. We demonstrate that only one ensemble-based FSS, which we call the probabilistic FSS (pFSS), is well-behaved and reasonably dependent on ensemble size. Furthermore, we derive a relationship that allows one to predict how the pFSS behaves with ensemble size. The proposed relationship is very similar to a known result for the Brier Skill Score. Our study uses high-resolution 1000-member ensemble precipitation forecasts from a high-impact weather period. The large ensemble enables us to study the influence of ensemble and neighbourhood size on forecast skill by deriving probabilistic skilful spatial scales.

Transparent Ce:Lu3Al5O12 (Ce:LuAG) phosphor ceramics are promising for high-power white lighting due to their high quantum efficiency, thermal stability and low thermal quenching. However, the shortage of red spectral composition and expensive price are preventing the application of Ce:LuAG phosphor ceramics in high-quality white lighting. In this work, (Lu,Gd)3Al5O12-Al2O3 (LuGAG-Al2O3) nanoceramics were prepared by partially replacing Lu3+ with Gd3+ through glass crystallization. These ceramics exhibit outstanding transparency and excellent mechanical properties. Compared with Ce:LuAG-Al2O3 transparent nanoceramics, the emission spectrum of transparent Ce:LuGAG-Al2O3 nanoceramics shows a substantial red shift (505 nm→570 nm), which effectively supplement the red light lacking in Ce:LuAG ceramics. With a maximum quantum efficiency of 81.4% and excellent thermal stability (87.6% @423 K), these nanoceramics have potential for high-power white lighting. When used in high-power LED and LD lighting, Ce:LuGAG-Al2O3 transparent nanoceramics achieve continuous adjustable changes from green light to orange-yellow light, and furthermore provide high quality warm white lighting with low color temperature, high color rendering index, and excellent luminous efficiency. Combining the facile and moderate elaboration process through full glass crystallization, the transparent Ce:LuGAG-Al2O3 nanoceramics reported in this paper are therefore regarded as promising color converters candidates for high-power warm white LED/LD lighting.

Bi2WO6 flower-like materials (FMs) were prepared by hydrothermal method and reduced by in situ reduction reaction to obtain Au@Bi2WO6 FMs. The samples were characterized by X-ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HRTEM), and X-ray photoelectron spectroscopy (XPS). It is found that the calculated OV content of Au@Bi2WO6 FMs is 25.16% whereas that of Bi2WO6 FMs is 20.81%, which provided potential active sites for gas adsorption and then enhanced excellent sensing property. In addition, the performance of Au@Bi2WO6 FMs sensors was evaluated by detecting volatile and toxic gases such as formaldehyde, methanol, acetone, benzene, toluene, and xylene. It revealed that the optimal operating temperatures for the Bi2WO6 FMs and Au@Bi2WO6 FMs sensors were 290°C and 260°C, respectively. Compared Au@ Bi2WO6 FMs sensor and Bi2WO6 FMs one, the best response of the front was 250 (900) to 100 (800) ppm formaldehyde whereas that of the latter was 90 (230). Therefore, Au@ Bi2WO6 FMs have good response and selectivity, which are promising candidates for formaldehyde detection.