Phishing emails have experienced a rapid surge in cyber threats globally, especially following the emergence of the COVID-19 pandemic. This form of attack has led to substantial financial losses for numerous organizations. Although various models have been constructed to differentiate legitimate emails from phishing attempts, attackers continuously employ novel strategies to manipulate their targets into falling victim to their schemes. This form of attack has led to substantial financial losses for numerous organizations. While efforts are ongoing to create phishing detection models, their current level of accuracy and speed in identifying phishing emails is less than satisfactory. Additionally, there has been a concerning rise in the frequency of phished emails recently. Consequently, there is a pressing need for more efficient and high-performing phishing detection models to mitigate the adverse impact of such fraudulent messages. In the context of this research, a comprehensive analysis is conducted on both components of an email message – namely, the email header and body. Sentence-level characteristics are extracted and leveraged in the construction of a new phishing detection model. This model utilizes K Nearest Neighbor (KNN)introducing the novel dimension of sentence-level analysis. Established datasets from Kaggle was employed to train and validate the model. The evaluation of this model’s effectiveness relies on key performance metrics including accuracy of 0.97, precision, recall, and F1-measure.

Aim: Isoniazid (INH) has been used as a first-line drug to treat tuberculosis (TB) for more than 50 years. However, large inter-individual variability was found in its pharmacokinetics, and how to handle a delayed or missed dose of INH remains unclear. This study aimed to develop a population pharmacokinetics (PPK) model of INH in Chinese patients with TB to provide model-informed precision dosing and explore appropriate remedial dosing regimens for non-adherent patients. Methods: A nonlinear mixed-effects modeling was used to analyze the PPK of INH. Using Monte Carlo simulations to determine optimal dosage regimens and design remedial dosing regimens. A two-compartment model well described the PPK of INH. Results: N-acetyltransferase 2 (NAT2) genotype and body weight were identified as significant factors on INH PK. Monte Carlo simulations determined optimal dosage regimens for patients with different NAT2 genotype and body weight. For remedial dosing regimens, the missed dose should be taken as soon as possible when the delay does not exceed 12 h, and an additional dose is not needed. On delaying a INH dose exceed 12 h, only need to take the next single dose normally. Conclusion: PPK modeling and simulation provide valid evidence on the precision dosing and remedial dosing regimen of INH.

The effects of mindfulness on body awareness and interoception have been proposed as potential mechanisms for its salutary effects. However, research investigating the relationship between mindfulness and body awareness using psychophysical measures is limited. In this study, we compared 31 expert meditators with 33 matched controls on somatosensory accuracy using a somatosensory signal detection task (SSDT) alongside interoception self-report instruments. Our main hypothesis was that meditators would show increased accuracy within the SSDT, indicating higher somatosensory accuracy (body awareness) and that this would be accompanied by neural correlates of increased alpha modulation over the somatosensory cortex as measured through EEG. Participants were asked to detect a near-threshold tactile sensation, which was combined with a non-informative light in 50% of the cases. Associations between prestimulus alpha activity and responses were analysed on a trial-by-trial basis. Contrary to our expectation, instead of an increase in accuracy, we observed a decrease in the decision threshold in meditators, while we found a decrease in prestimulus alpha power in meditators. A trial-by-trial analysis revealed a negative relationship between prestimulus alpha activity and the report of touch. Meditators self-reported higher interoceptive abilities compared to readers. These findings suggest that lower prestimulus alpha activity may have increased the probability of reporting touch within the SSDT, providing a potential mechanism for the increase in response rate in meditators. Our study indicates that meditation practice alters body awareness as shown by modulated prestimulus alpha activity, potentially decreasing the filter function over the somatosensory cortex.

The extent to which external transdermal light sources illuminate the uterine environment is unknown. Recent experimental work indicates that the human fetus responds to external visual stimuli such as laser diodes, and initial modelling work suggests the fetus may not develop in a completely dark environment as previously assumed. Development of the human visual system begins within the womb, and there is motivation in fields such as developmental psychology, transabdominal oximetry, and photoacoustics to explore the extent to which light penetrates maternal abdominal tissue. In this study, we develop and adapt a Monte Carlo model that uses third trimester histological properties of maternal tissue in simulating transdermal monochromatic point light sources. We use the results of this modelling to determine approximate levels of third trimester uterine illumination from such stimuli, discuss the scope for multiple stimuli to be visibly distinct in utero, and suggest improvements for future experimental work.

5-Aminolevulinic (5-ALA) acid photodynamic therapy (PDT) is a treatment for actinic keratosis and has been studied as a treatment for non-invasive cutaneous squamous cell carcinoma (cSCC). PDT induces apoptosis and necrosis in actinic keratosis and cSCC. 5-ALA blue light PDT may modulate gene expression and pathways in surviving cells. In this study, differential gene expression and pathway analysis of cSCC and human dermal fibroblasts were compared before and after 5-ALA blue light PDT using RNA-Sequencing. No genes were differentially expressed after correcting for multiple testing (FDR <0.05). As a result, transcription factor, gene enrichment, and pathway analysis were performed with genes identified before multiple testing (p<0.05). Pathways associated with proliferation and carcinogenesis were downregulated. These findings using 5-ALA blue light PDT are similar to previously published studies using methyl-aminolevulinic and red light protocols, indicating that surviving residual cells may undergo changes consistent with a less aggressive cancerous phenotype.

Inversions are thought to play a key role in adaptation and speciation, suppressing recombination between diverging populations. Genes influencing adaptive traits cluster in inversions, and changes in inversion frequencies associate with environmental differences. However, in many organisms it is unclear if inversions are geographically and taxonomically widespread. The intertidal snail, Littorina saxatilis, is one such example. Strong associations between putative polymorphic inversions and phenotypic differences have been demonstrated between two ecotypes of L. saxatilis in Sweden and inferred elsewhere, but no direct evidence for inversion polymorphism currently exists across the species range. Using whole genome data from 107 snails, most inversion polymorphisms were found to be widespread across the species range. Frequencies of some inversion arrangements were significantly different among ecotypes, suggesting a parallel adaptive role. Many inversions were also polymorphic in the sister species L. arcana, hinting at an ancient origin. 2 Introduction:

Biotic interactions drive multitrophic species community assembly. Yet, explicitly incorporating this process in species distribution models (SDMs) is particularly challenging, even when biotic interactions are known. Here, we propose a framework that combines knowledge of trophic interactions with Bayesian structural equation models to model each species as a function of its prey or predators and environmental conditions. We tested and validated our framework on realistic simulated communities spanning different theoretical models and ecological setups. We showed that our framework improves the inference of both species’ potential and realized niches compared to single SDMs (mean performances increased by 8% and 6% respectively), especially for species with strong biotic control, thus increasing model predictive performance. Our framework can easily integrate various SDM extensions (e.g., occupancy models) and algorithms, and stands out as a novel solution for modeling multitrophic community distributions when trophic interactions are known or assumed.

Herbivores commonly vector terrestrial plant pathogens, though their role in transmitting marine plant pathogens remains unknown. Here we show that amphipods (Ampithoe lactertosa) consumed more eelgrass (Zostera marina) infected with the protist Labyrinthula zosterae (Lz), the causative agent of seagrass wasting disease, then asymptomatic control leaves. Isopods (Pentidoea wosnesenskii) and snails (Lacuna spp.) grazed more asymptomatic leaves. We isolated Lz from herbivore feces, indicating that herbivores that eat diseased eelgrass can pass the live pathogen; qPCR detected Lz in herbivores. Experiments demonstrated isopods and snails indirectly facilitated pathogen transmission by creating grazing scars that increased disease, but did not directly transmit Lz from diseased to asymptomatic eelgrass. Field surveys demonstrated a close association between disease and herbivore grazing scars. Disease risk increased by 29% on grazed leaves. This is the first report of herbivores facilitating the spread—and magnifying the impacts—of a marine plant pathogen.

Increasing evidence shows that climate change promotes the development of harmful cyanobacterial blooms, and affects the competition of toxic and non-toxic strains. Previous studies have found low CO2 favors toxic strains, but how changing dissolved CO2 (CO2 [aq]) in water body influences the succession of toxic and non-toxic strains remains uncertain. Here, we combined laboratory competition experiments, field observations, and a machine learning model to reveal the links between CO2 changes and the succession. Laboratory experiments showed under low CO2 conditions (100–150 ppm), toxic strains better utilized CO2 (aq) and dominated. Non-toxic strains demonstrated a growth advantage as CO2 concentration increased (400–1000 ppm). Field observations from May to November in Taihu Lake showed the percentage of toxic strains increased as CO2 (aq) decreased. Machine learning highlighted links between the inorganic carbon concentration and the proportion of advantageous strains. Our findings provide new insights for cyanoHABs prediction and prevention.

Key points Recurrent or persistent ear discharge may occur when hearing aids are not cleaned, if a perforated tympanic membrane is present or due to traumatic insertion or removal of the hearing aid. 16 of 20 consented adult hearing aid users with discharging ear(s) had swabs taken from the discharging ear(s), their hearing aid worn in the discharging ear before and after their routine cleaning, and then after using HOCl*. 5 had no growth/commensal organisms but Pseudomonas sp., candida, and mixed growth were the most frequently identified pathogens. Patients’ questionnaires completed at the initial appointment and 4 weeks later, provided positive feedback regarding relief of pain and discharge. There were no adverse reactions. All 20 patients in this study reported less, or no discharge when HOCl* compared to routine cleaning methods, presumably through HOCl*’s antimicrobial and anti-inflammatory properties and this enabled greater use of their hearing aid(s).

A document by * Harshvardhan. Click on the document to view its contents.

To solve the problem that the detection performance of over the horizon radar (OTHR) is seriously affected by the transient interference, an adaptive transient interference suppression method is proposed based on time-frequency double sparsity (TFDS). The TFDS takes the double sparsity of transient interference in the slow-time domain and sea clutter in the frequency domain as the priori information to construct the objective function. Then, optimizing the objective function by the alternating direction multiplier method (ADMM) to suppress the transient interference. The TFDS can adaptively separate interference and recover pure spectrum with a low computational complexity. Experimental results show that the TFDS has better interference suppression performance and noise robustness than the existing methods.

Panel count data refers to the research objects that are only observed at discrete time intervals, and frequently appear in recurrent events. When discrete events occur repeatedly, it is of great significance to investigate the influence of covariates on recurrent events. Parameter estimation with the smallest deviation is essential to explain the authenticity of semiparametric regression model, as well as the harm of recurrent events could be decreased by receiving effective regression prediction. The affect of covariates on recurrent events is surveyed using a semiparametric regression model in this study. It has been assumed that the recurrent events, observation process and follow-up process are related to each other, and two latent variables are applied to represent the relationship among the three processes, with supposing the two latent variables following Dirichlet process, in order to establish a joint model. To solve the parameter estimation in common, the EM and MCMC algorithms are utilized, and the accuracy of parameter estimation with respect to our model is compared to that of other models. Lastly, the data of COVID-NET has been used for exploring the effect of four factors, including age, gender, race, and whether patients have potential diseases, as covariates on repeated hospitalization.

This paper develops a technical framework for the next-generation power grid transformer (NGPGT) for grid renewables. This framework is structured to overcome the environmen-tal challenges produced due to the explosive use of non-renewable base energy generation sources. The use of these sources cannot meet the required electricity for the world’s growing community due to their availability, cost, and lack of flexibility. However, modern energy systems focus on the use of renewable energy sources where the grid transformer’s interaction acts the essential role in their generation, transmission, and distribution. The lack of central-ization, local monitoring, interoperability, authenticity, and precise bi-directional flow may limit the application of current framework power grid transformers in grid renewables. In this paper, a new technical framework, called NGPGT, is developed by introducing some ex-tended features for addressing the challenges shown in the current-generation transformers. This is structured by enabling some advanced technical features with the existing frame-work which includes automatic condition monitoring, intelligent inverters, edge computing, automatic controlling, and intelligent management. This paper also illustrates the benefits and scopes of the NGPGT compared to the existing transformer by assembling essential re-quirements and obligatory components. Additionally, this paper highlights a few difficulties of implementing NGPGT in terms of operational, communication, energy management, and economic point of view which may enable further research scopes for the researchers.

Seasonal forecasting of spring floods in snow-covered basins is challenging due to the ambiguity in the driving processes, uncertain estimations of antecedent catchment conditions and the choice of predictor variables. In this study we attempt to improve the prediction of spring flow peaks in southern Quebec, Canada, by studying the preconditioning mechanisms of runoff generation and their impact on inter-annual variations in the timing and magnitude of spring peak flow. Historical observations and simulated data from a hydrological and snowmelt model were used to study the antecedent conditions that control flood characteristics in twelve snow-dominated catchments. Maximum snow accumulation (peak SWE), snowmelt and rainfall volume, snowmelt and rainfall intensity, and soil moisture were estimated during the pre-flood period. Stepwise multivariate linear regression analysis was used to identify the most relevant predictors and assess their relative contribution to the interannual variability of flood characteristics. Results show that interannual variations in spring peak flow are controlled differently between basins. Overall, interannual variations in peak flow were mainly governed, in order of importance, by snowmelt intensity, rainfall intensity, snowmelt volume, rainfall volume, peak SWE, and soil moisture. Variations in the timing of peak flow were controlled in most basins by rainfall volume and rainfall and snowmelt intensity. In the northernmost, snow-dominated basins, pre-flood rainfall amount and intensity mostly controlled peak flow variability, whereas in the southern, rainier basins snowpack conditions and melt dynamics controlled this variability. Snowpack interannual variations were found to be less important than variations in rainfall in forested basins, where snowmelt is more gradual. Conversely, peak flow was more sensitive to snowpack conditions in agricultural basins where snowmelt occurs faster. These results highlight the impact of land cover and use on spring flood generation mechanism, and the limited predictability potential of spring floods using simple methods and antecedent hydrological factors.

Introduction Tropical seasonality of respiratory syncytial virus (RSV) is one of the leading causes of hospitalization and mortality among children with respiratory tract infections. The non-pharmaceutical preventive measures implemented worldwide against Covid-19 may have also reduced the transmission of RSV, hence altering its epidemiological seasonality. However, with easing of Covid-19 restrictions, studies reported a drastic rebound of cases due to poor protective immunity from long-term reduced exposure to RSV. Thus, this is the first study to evaluate the changes of RSV epidemiology before, during and after Covid-19 pandemic in Malaysia. Methods We analyzed collated data from 2017 to 2022, involving 4084 children aged <12 years who were admitted for severe respiratory infections requiring non-invasive ventilation (NIV). Results Generally, we reported significant inverse relationship between RSV and Covid-19 infections during the pandemic (2020-2021) (p<0.05). In 2020, RSV positivity rate declined sharply to 8.3% and 5.9%, respectively in the 2 prominent seasons. Time series analysis showed a tremendous decrease in cases compared to the expected values, with the first season (98.3%, CI 95%) and second season (95.7%, CI 95%). However, with the lifting of the restriction order in 2022, RSV infections rose sharply with a positivity rate of 36.3% which was higher than before Covid-19 pandemic. Conclusion This study provides evidence of increasing RSV cases after Covid-19 pandemic due to immunity debt. Hence, the healthcare system must be prepared to deal with future RSV outbreaks with appropriate implementation of prophylaxis and public health measures.

Habitat fragmentation due to human encroachment have significantly impacted the genetic diversity and population dynamics of red deer (Cervus elaphus) in Western Europe. Recognizing the urgent need for conservation and management measures, this study sought to identify markers for the lifelong identification of male red deer. Traditional markers, such as antlers, proved unsuitable for long-term recognition due to their variability. This project therefore aimed to identify markers for the lifelong recognition and identification of male red deer, focusing on facial features as potential markers. The author analysed a dataset of 12,662 images of red deer, emphasizing characteristics that could facilitate long-term recognition. A total of 49 male red deer were tracked over several years, based on a subset of 7,252 images. From this dataset, image-series for 25 different male red deer were created, spanning from 2 to 9 years (average 5.5 years, median 6.0 years). All images in a series were cropped to focus the attention on the face, removing antlers and other distracting body features. The results showed that muzzle patterns were a distinctive feature to discriminate individual male, but they were not suitable for the long-term identification of individual male red deer, as they changed over time in some cases. However, the shape and features of the nose-lip-mirror of male red deer remained consistent throughout the documented lifespans of the 25 indviduals. The nose-lip-mirror has proven to be a reliable and age-independent marker for the lifelong identification of male red deer, offering valuable insights for conservation efforts and population monitoring.

Optimized catalytic properties and reactant adsorption energy play a crucial role in promoting CO2 electrocatalysis. Herein, Cu7S4/Cu undergoes in situ dynamic restructuring to generate S-Cu2O/Cu hybrid catalyst for effective electrochemical CO2 reduction to formate that outperforms Cu2O/Cu and Cu7S4. Thermodynamic and experimental analyses reveal that the optimized adsorption of the HCOO* intermediate on S-Cu2O/Cu is regulated and the H2 pathway (surface H) is suppressed by S-doping. Meanwhile, Cu7S4/Cu nanoflowers create abundant grain boundaries for ECR and strengthen the CO2 adsorption by inducing Cu. These findings provide a new perspective on synthetic methods for various electrocatalytic reduction processes.

The physical phenomena with uncontrollable singularities pose challenges in solving related differential equations. In this work, we intend to investigate the quantitative and qualitative aspects of a multi-singular integro-differential equation with the help of quantum fractional operators by presenting numerical algorithms. Quantum calculus enables us to use numerical algorithms and software. The α- ψ-contraction, a new technique of fixed point theory, plays a significant role in proving the existence of the solution. To interpret tables with quantum values quickly and easily, we use heatmaps. We also presented three numerical examples to illustrate the accuracy and efficiency of our main results.

A document by Riley Hodgson. Click on the document to view its contents.

The development of high-performance organic solar cells (OSCs) with high operational stability is essential to accelerate their commercialization. Unfortunately, there is currently a lack of detailed understanding of the origin of instabilities in state-of-the-art OSCs based on bulk heterojunction (BHJ) featuring non-fullerene acceptors (NFAs). Herein, we developed NFA-based OSCs using different charge extraction interlayer materials and studied their storage, thermal, and operational stabilities. Despite the high power conversion efficiency (PCE) of the OSCs (17.54%), we found that cells featuring self-assembled monolayers (SAMs) as hole-extraction interlayers exhibited poor stability. The time required for these OSCs to reach 80% of their initial performance (T80) was only 6 h under continuous thermal stress at 85 °C in a nitrogen atmosphere and 1 h under maximum power point tracking (MPPT) in a vacuum. Inserting MoOx between ITO and SAM enhanced the T80 to 50 h and ~15 h after the thermal and operational stability tests, respectively, while maintaining a PCE of 16.9%. Replacing the organic PDINN electron transport layer with ZnO NPs further enhances the cells’ thermal and operational stability, boosting the T80 to 1000 and 170 h, respectively. Our work reveals the synergistic role of charge interlayers and device architecture in developing efficient and stable OSCs.

Cancer has become a global public health problem and its harmful effects have received widespread attention. Conventional treatments such as surgical resection, radiotherapy and other techniques are applicable to clinical practice, but new drugs are constantly being developed and other therapeutic approaches such as immunotherapy are being applied. In addition to studying the effects on individual tumor cells, it is important to explore the role of tumor microenvironment (TME) on tumor cell development since tumor cells do not exist alone but in the tumor microenvironment. In the TME, tumor cells are interconnected with other stromal cells and influence each other, among which tumor-associated macrophages (TAMs) are the most numerous immune cells. At the same time, it was found that cancer cells have different levels of autophagy from normal cells. In cancer therapy, the occurrence of autophagy plays an important role in promoting tumor cell death or inhibiting tumor cell death, and is closely related to the environment. Therefore, elucidating the regulatory role of autophagy between TAMs and tumor cells is an important breakthrough, providing new perspectives for further research on anti-tumor immune mechanisms and understanding the efficacy of cancer immunotherapy.

A document by Baraka Alphonce. Click on the document to view its contents.

A document by Qingquan Wang. Click on the document to view its contents.