bluuebunny/crossref_metadata_2025 huggingface dataset:
Pushing boundaries: mechanisms enabling bacterial pathogens to spread between cells
Authors: Raab Julie E. , Hamilton Desmond J. , Harju Tucker B. , Huynh Thao N. , Russo Brian C.
Published: April 2024

For multiple intracellular bacterial pathogens, the ability to spread directly into adjacent epithelial cells is an essential step for disease in humans. For pathogens such as
Shigella
,
Listeria
,
Rickettsia
, and
Burkholderia
, this intercellular movement frequently requires the pathogens to manipulate the host actin cytoskeleton and deform the plasma membrane into structures known as protrusions, which extend into neighboring cells. The protrusion is then typically resolved into a double-membrane vacuole (DMV) from which the pathogen quickly escapes into the cytosol, where additional rounds of intercellular spread occur. Significant progress over the last few years has begun to define the mechanisms by which intracellular bacterial pathogens spread. This review highlights the interactions of bacterial and host factors that drive mechanisms required for intercellular spread with a focus on how protrusion structures form and resolve.
Q3 lab-on-chip real-time PCR for the diagnosis of
Leishmania infantum
infection in dogs
Authors: Latrofa Maria Stefania , Cereda Marco , Louzada-Flores Viviane Noll , Dantas-Torres Filipe , Otranto Domenico
Published: April 2024

Leishmaniasis is a vector-borne disease caused by many
Leishmania
spp. which infect humans and other mammalian hosts.
Leishmania infantum
is the main agent of canine leishmaniasis (CanL) whose diagnosis is usually confirmed by serological and molecular tests. This study aimed to evaluate the clinical and analytical sensitivities of a lab-on-chip (LOC) real-time PCR applied on the portable Q3-Plus V2 platform (Q3 qPCR) in the detection of
L. infantum
. The Q3 qPCR performance was assessed by comparing the quantification cycle (Cq) values with those obtained using the qPCR run on a CFX96 Real-Time System (CFX96 qPCR). A total of 173 DNA samples (extracted from bone marrow, lymph node, blood, buffy coat, conjunctival swab, and skin) as well as 93 non-extracted samples (NES) (bone marrow, lymph node, blood, and buffy coat) collected from dogs were tested with both systems. Serial dilutions of each representative DNA and NES sample were used to assess the analytical sensitivity of the Q3 qPCR system. Overlapping Cq values were obtained with the Q3 qPCR and CFX96 qPCR, both using DNA extracted from
L. infantum
promastigotes (limit of detection, <1 promastigote per milliliter) and from biological samples as well as with NES. However, the Q3 qPCR system showed a higher sensitivity in detecting
L. infantum
in NES as compared with the CFX96 qPCR. Our data indicate that the Q3 qPCR system could be a reliable tool for detecting
L. infantum
DNA in biological samples, bypassing the DNA extraction step, which represents an advance in the point-of-care diagnostic of CanL.
The loss of the PDIM/PGL virulence lipids causes differential secretion of ESX-1 substrates in
Mycobacterium marinum
Authors: Jones Bradley S. , Hu Daniel D. , Nicholson Kathleen R. , Cronin Rachel M. , Weaver Simon D. , Champion Matthew M. , Champion Patricia A.
Published: April 2024

The mycobacterial cell envelope is a major virulence determinant in pathogenic mycobacteria. Specific outer lipids play roles in pathogenesis, modulating the immune system and promoting the secretion of virulence factors. ESX-1 (ESAT-6 system-1) is a conserved protein secretion system required for mycobacterial pathogenesis. Previous studies revealed that mycobacterial strains lacking the outer lipid PDIM have impaired ESX-1 function during laboratory growth and infection. The mechanisms underlying changes in ESX-1 function are unknown. We used a proteo-genetic approach to measure phthiocerol dimycocerosate (PDIM)- and phenolic glycolipid (PGL)-dependent protein secretion in
M. marinum,
a non-tubercular mycobacterial pathogen that causes tuberculosis-like disease in ectothermic animals. Importantly,
M. marinum
is a well-established model for mycobacterial pathogenesis. Our findings showed that
M. marinum
strains without PDIM and PGL showed specific, significant reductions in protein secretion compared to the WT and complemented strains. We recently established a hierarchy for the secretion of ESX-1 substrates in four (I–IV) groups. Loss of PDIM differentially impacted secretion of Group III and IV ESX-1 substrates, which are likely the effectors of pathogenesis. Our data suggest that the altered secretion of specific ESX-1 substrates is responsible for the observed ESX-1-related effects in PDIM-deficient strains.

IMPORTANCE

Mycobacterium tuberculosis,
the cause of human tuberculosis, killed an estimated 1.3 million people in 2022. Non-tubercular mycobacterial species cause acute and chronic human infections. Understanding how these bacteria cause disease is critical. Lipids in the cell envelope are essential for mycobacteria to interact with the host and promote disease. Strains lacking outer lipids are attenuated for infection, but the reasons are unclear. Our research aims to identify a mechanism for attenuation of mycobacterial strains without the PDIM and PGL outer lipids in
M. marinum
. These findings will enhance our understanding of the importance of lipids in pathogenesis and how these lipids contribute to other established virulence mechanisms.
Colonoscopy-assisted laparoscopic wedge resection for a large symptomatic colonic lipoma
Authors: Hanevelt Julia , de Vos Tot Nederveen Cappel Wouter Hugo , ten Kate Fiebo Johannes Cornelis , van Westreenen Henderik Leendert
Published: April 2024

A colonic lipoma is an uncommon lesion that is linked with clinical symptoms in only a small portion of patients. Patients with large lipomas are often referred for major surgery, which is associated with significant morbidity and mortality. In this case, we described a female patient with recurrent episodes of gastrointestinal blood loss, abdominal pain and colocolic intussusceptions due to a large, lumen-filling, obstructive lipoma in the splenic flexure. On abdominal CT, a lesion of 3.6 cm was visualised with a fat-like density without solid components. Considering its benign nature, we intended to preserve the colon by deroofing the upper part of the lesion and then performing a colonoscopy-assisted laparoscopic wedge resection. During reassessment, auto-amputation of part of the lesion was observed, most likely as a result of long-lasting mechanical effects, which made it possible to perform solely a wedge resection with an excellent outcome.
Survival of medical treatment success in primary open-angle glaucoma and ocular hypertension
Authors: Fu Dun Jack , Ademisoye Ebenezer , Shih Vanessa , McNaught Andrew Ian , Khawaja Anthony
Published: April 2024

Background/aims

Topical agents to lower intraocular pressure (IOP) are the most common initial therapeutic measure in glaucoma prevention. This study aims to assess treatment success duration among patients initiating or intensifying topical glaucoma medication.

Methods

Medical records (2013‒2018) for adults initiating/intensifying topical glaucoma medication were extracted from five secondary-care and tertiary-care UK ophthalmology centres. Main study outcomes were time from treatment initiation/intensification to treatment failure (<20% IOP reduction or IOP >21 mm Hg at consecutive clinic visits, or intensification of glaucoma treatment) and time from treatment change to subsequent treatment intensification.

Results

Study eyes (n=6587) underwent treatment intensification 0-to-1 glaucoma drop (5358 events), 1-to-2 drops (1469 events) and 2-to-3 drops (857 events) during the observation period. Median time to treatment failure was 1.60 (95% CI 1.57 to 1.65), 1.00 (95% CI 0.94 to 1.07) and 0.92 (95% CI 0.81 to 1.02) years following escalation 0-to-1, 1-to-2 and 2-to-3 drops, respectively. Median time to treatment intensification (non-IOP–based criterion) was 4.68 (95% CI 4.50 to 5.08) years for treatment initiators, 3.83 (95% CI 3.36 to 4.08) years on escalation 1-to-2 drops and 4.35 (95% CI 3.82 to 4.88) years on escalation 2-to-3 drops. On multivariable regression, significant risk factors for both treatment failure and intensification were lower baseline visual field mean deviation, primary open-angle glaucoma and lower eyedrop count in the fellow eye; lower baseline IOP was associated with treatment failure, higher baseline IOP with treatment intensification.

Conclusion

Large-scale survival analyses provide the expected duration of treatment success from topical glaucoma medication.
Project to improve the management of the head injury patients presenting to the emergency department
Authors: Hassan Waseem , Naveed Asif , Khan Zuhair
Published: April 2024

Introduction

At Sandwell General Hospital, there was no risk stratification tool or pathway for head injury (HI) patients presenting to the emergency department (ED). This resulted in significant delays in the assessment of HI patients, compromising patient safety and quality of care.

Aims

To employ quality improvement methodology to design an effective adult HI pathway that: ensured >90% of high-risk HI patients being assessed by ED clinicians within 15 min of arrival, reduce CT turnaround times, and aiming to keep the final decision making <4 hours.

Methods

SWOT analysis was performed; driver diagrams were used to set out the aims and objectives. Plan-Do-Study-Act cycle was used to facilitate the change and monitor the outcomes. Process map was designed to identify the areas for improvement. A new HI pathway was introduced, imaging and transporting the patients was modified, and early decisions were made to meet the standards.

Results

Data were collected and monitored following the interventions. The new pathway improved the proportion of patients assessed by the ED doctors within 15 min from 31% to 63%. The average time to CT head scan was decreased from 69 min to 53 min. Average CT scan reporting time also improved from 98 min to 71 min. Overall, the average time to decision for admission or discharge decreased from 6 hours 48 min to 4 hours 24 min.

Conclusions

Following implementation of the new HI pathway, an improvement in the patient safety and quality of care was noted. High-risk HI patients were picked up earlier, assessed quicker and had CT head scans performed sooner. Decision time for admission/discharge was improved. The HI pathway continues to be used and will be reviewed and re-audited between 3 and 6 months to ensure the sustained improvement.
Lung volume measurement using chest CT in COVID-19 patients: a cohort study in Japan
Authors: Otake Shiro , Shiraishi Yusuke , Chubachi Shotaro , Tanabe Naoya , Maetani Tomoki , Asakura Takanori , Namkoong Ho , Shimada Takashi , Azekawa Shuhei , Nakagawara Kensuke , Tanaka Hiromu , Fukushima Takahiro , Watase Mayuko , Terai Hideki , Sasaki Mamoru , Ueda Soichiro , Kato Yukari , Harada Norihiro , Suzuki Shoji , Yoshida Shuichi , Tateno Hiroki , Yamada Yoshitake , Jinzaki Masahiro , Hirai Toyohiro , Okada Yukinori , Koike Ryuji , Ishii Makoto , Hasegawa Naoki , Kimura Akinori , Imoto Seiya , Miyano Satoru , Ogawa Seishi , Kanai Takanori , Fukunaga Koichi
Published: April 2024

Objective

This study aimed to investigate the utility of CT quantification of lung volume for predicting critical outcomes in COVID-19 patients.

Methods

This retrospective cohort study included 1200 hospitalised patients with COVID-19 from 4 hospitals. Lung fields were extracted using artificial intelligence-based segmentation, and the percentage of the predicted (%pred) total lung volume (TLC (%pred)) was calculated. The incidence of critical outcomes and posthospitalisation complications was compared between patients with low and high CT lung volumes classified based on the median percentage of predicted TLCct(n=600 for each). Prognostic factors for residual lung volume loss were investigated in 208 patients with COVID-19 via a follow-up CT after 3 months.

Results

The incidence of critical outcomes was higher in the low TLCct(%pred) group than in the high TLCct(%pred) group (14.2% vs 3.3%, p<0.0001). Multivariable analysis of previously reported factors (age, sex, body mass index and comorbidities) demonstrated that CT-derived lung volume was significantly associated with critical outcomes. The low TLCct(%pred) group exhibited a higher incidence of bacterial infection, heart failure, thromboembolism, liver dysfunction and renal dysfunction than the high TLCct(%pred) group. TLCct(%pred) at 3 months was similarly divided into two groups at the median (71.8%). Among patients with follow-up CT scans, lung volumes showed a recovery trend from the time of admission to 3 months but remained lower in critical cases at 3 months.

Conclusion

Lower CT lung volume was associated with critical outcomes, posthospitalisation complications and slower improvement of clinical conditions in COVID-19 patients.
Safety and effectiveness assessment of the surpass evolve (SEASE): a post-market international multicenter study
Authors: Vivanco-Suarez Juan , Dibas Mahmoud , Lopes Demetrius Klee , Hanel Ricardo A , Martínez-Galdámez Mario , Rodriguez-Calienes Aaron , Cortez Gustavo M , Fifi Johanna T , Devarajan Alex , Toth Gabor , Patterson Thomas , Altschul David , Pereira Vitor M , Liu Eileen , Puri Ajit S , Kuhn Anna Luisa , Guerrero Waldo R , Khandelwal Priyank , Bach Ivo , Kan Peter , Edhayan Gautam , Given Curtis , Narayanan Sandra , Gross Bradley A , Farooqui Mudassir , Galecio-Castillo Milagros , Derakhshani Shahram , Ortega-Gutierrez Santiago
Published: April 2024

Background

Flow diverters are the first-line treatment for specific intracranial aneurysms (iA). Surpass Evolve (SE) is a new-generation 64-wire flow diverter with a high braid angle. Current literature on the SE is limited. We aimed to report the first international real-world experience evaluating the safety and effectiveness of the SE.

Methods

The Safety and Effectiveness Assessment of the Surpass Evolve (SEASE) was a multicenter retrospective international post-marketing cohort study including consecutive patients treated with SE for iAs between 2020 and 2022. Demographic, clinical, and angiographic data were collected. Primary effectiveness was independent core lab adjudicated complete occlusion rates (Raymond-Roy Class 1) at last follow-up. Primary safety were major ischemic/hemorrhagic events and mortality.

Results

In total, 305 patients with 332 aneurysms underwent SE implantation. The patients had a median age of 59 [50-67] years, and 256 (83.9%) were female. The baseline modified Rankin scale score was 0–2 in 291 patients (96.7%). Most aneurysms were unruptured (285, 93.4%) and saccular (309, 93.1%). Previous treatment was present in 76 (22.9%) patients. The median aneurysm size was 5.1 [3.4–9.0] mm, and the median neck width was 3.6 [2.7–5.1] mm. Most aneurysms were in the internal carotid artery C6 ophthalmic segment (126, 38.0%), followed by the communicating segment (58, 17.5%). At median 10.2 [6.4–12.9] months follow-up, 233 (73.0%) aneurysms achieved complete occlusion. After adjusting for confounders, complete occlusion remained consistent. Major stroke and procedure-related mortality were reported in 6 (2%) and 2 (0.7%) cases, respectively.

Conclusion

These results demonstrate that SE has a consistently high effectiveness and favorable safety for the treatment of iAs.
Genetics impact risk of Alzheimer’s disease through mechanisms modulating structural brain morphology in late life
Authors: Korologou-Linden Roxanna , Xu Bing , Coulthard Elizabeth , Walton Esther , Wearn Alfie , Hemani Gibran , White Tonya , Cecil Charlotte , Sharp Tamsin , Tiemeier Henning , Banaschewski Tobias , Bokde Arun , Desrivières Sylvane , Flor Herta , Grigis Antoine , Garavan Hugh , Gowland Penny , Heinz Andreas , Brühl Rüdiger , Martinot Jean-Luc , Paillère Martinot Marie-Laure , Artiges Eric , Nees Frauke , Orfanos Dimitri Papadopoulos , Paus Tomáš , Poustka Luise , Millenet Sabina , Fröhner Juliane H , Smolka Michael , Walter Henrik , Winterer Jeanne , Whelan Robert , Schumann Gunter , Howe Laura D , Ben-Shlomo Yoav , Davies Neil M , Anderson Emma Louise
Published: April 2024

Background

Alzheimer’s disease (AD)-related neuropathological changes can occur decades before clinical symptoms. We aimed to investigate whether neurodevelopment and/or neurodegeneration affects the risk of AD, through reducing structural brain reserve and/or increasing brain atrophy, respectively.

Methods

We used bidirectional two-sample Mendelian randomisation to estimate the effects between genetic liability to AD and global and regional cortical thickness, estimated total intracranial volume, volume of subcortical structures and total white matter in 37 680 participants aged 8–81 years across 5 independent cohorts (Adolescent Brain Cognitive Development, Generation R, IMAGEN, Avon Longitudinal Study of Parents and Children and UK Biobank). We also examined the effects of global and regional cortical thickness and subcortical volumes from the Enhancing NeuroImaging Genetics through Meta‐Analysis (ENIGMA) Consortium on AD risk in up to 37 741 participants.

Results

Our findings show that AD risk alleles have an age-dependent effect on a range of cortical and subcortical brain measures that starts in mid-life, in non-clinical populations. Evidence for such effects across childhood and young adulthood is weak. Some of the identified structures are not typically implicated in AD, such as those in the striatum (eg, thalamus), with consistent effects from childhood to late adulthood. There was little evidence to suggest brain morphology alters AD risk.

Conclusions

Genetic liability to AD is likely to affect risk of AD primarily through mechanisms affecting indicators of brain morphology in later life, rather than structural brain reserve. Future studies with repeated measures are required for a better understanding and certainty of the mechanisms at play.
Effects of genotype, weather, FHB fungicide, and pre-harvest glyphosate on grain quality of hard red spring wheat in western Canada
Authors: Dorrian Kathleen , Mkhabela Manasah , Sapirstein Harry , Bullock Paul
Published: April 2024

Diverse growing season weather in western Canada has large effects on wheat quality. Management practices, such as pesticide application, may also affect wheat quality, but are largely unknown. This study measured pesticide application effects on grain quality for six hard red spring wheat cultivars over three growing seasons at four prairie locations. Each siteyear included (i) an untreated control, (ii) fungicide applied at anthesis for Fusarium head blight (FHB) control, (iii) pre-harvest glyphosate applied at physiological maturity, and (iv) a combination of both pesticides. Generally warmer and drier conditions in 2015 and 2017 compared to 2016 resulted in wheat with higher grades, test weight, thousand-kernel weight, and grain protein content but lower Fusarium damaged kernel (FDK) content. Siteyear, reflecting weather variation by location, was the major factor affecting grain quality, contributing from 39% to 77% of total variance. Rainfall variation was greater than that for air temperature and appeared to be the main weather factor affecting quality. Genotype had a significant impact on grain quality but contributed 1%–20% of total variance. The pesticide treatments had a significant effect on several quality parameters, but they contributed only 0.2%–2% to total variance, implying that they have no detrimental effect on wheat grain quality when applied as recommended. Fungicide significantly reduced FDK level in four of ten siteyears, all with high FDK levels, but not when applied at the low FDK siteyears. Fungicide for FHB control should be used only when weather is conducive to high FHB disease pressure.
Parameter Identification Method for Collaborative Robot Joint Using a Cuckoo Search-BP Neural Network Approach
Authors: Xiong Liangping , Hu Huifeng , An Kang
Published: April 2024

Collaborative robots face challenges in achieving high accuracy using existing parameter identification methods, especially in applications like dragging, teaching, and collision detection. To address the issue of robot dynamics identification accuracy in scenarios with unknown parameters, this paper introduces a method for robot joint parameter identification based on the Cuckoo Search-backpropagation neural network (CS-BPNN). Initially, a kinetic model incorporating Coulomb viscous friction is presented. Subsequently, a BPNN is employed to approximate and fit the nonlinear function, while the CS algorithm is integrated into the BPNN model to optimize its parameters. Collected data undergo third-order Butterworth filtering, and a loss function compares predicted values with actual values to ascertain the accuracy of the proposed method. Experimental results demonstrate that the CS-BPNN approach proposed herein can converge the mean square error (MSE) to [Formula: see text] N⋅m when robot dynamics parameters are unknown. This method boasts of a lower MSE and superior discrimination accuracy compared to traditional methods like least squares (LS), genetic algorithm (GA), and BPNN. Consequently, the method presented in this study not only enhances the accuracy of parameter identification but also offers a fresh perspective for the parameter identification of robot joint dynamics models.
SterAF: A Scene Text Recognizer with Appearance-Flow Rectification
Authors: Liao Chunyan , Du Chenghu , Liu Yating , Tan Yanbao
Published: April 2024

As the demand for recognizing irregular text in natural scenes increases, people are increasingly realizing the value of such applications, such as license plate recognition systems, image search, handwriting recognition, and autonomous driving, which are profoundly changing our lives in the field of text recognition. Recent studies have shown that the recognition of curved text and perspective text has become an important challenge in the field of text recognition, and the correction of curved text is a key step to achieve accurate recognition. However, current methods use strained text image correction methods, resulting in poor recognition accuracy when recognizing curved text. Therefore, we propose an end-to-end framework called Scene Text Recognizer with Appearance-Flow rectification (SterAF), which includes a correction network and a recognition network. Specifically, the framework’s steps are as follows: first, the input text image is deformed through an appearance flow-based correction network to adaptively warp the text image, to prevent irregular and unnatural deformations of the text image. Second, a sequence-to-sequence recognition network predicts the sequence of characters in the corrected text image to accurately recognize the text in the image. Through subjective and objective experiments, our SterAF model has shown excellent performance in both qualitative and quantitative experiments.
A Fine-Grained Image Classification Method Built on MobileViT
Authors: Lu Zhengqiu , Wang Haiying
Published: April 2024

With a rapid development of artificial intelligence technology, fine-grained image classification has gained widespread application. For mobile terminals, this paper introduces an image classification method built on MobileViT, and it can apply into fine-grained image classification. The original MobileViT model has been optimized in three ways. Initially, the h-swish activation function is used to enhance the network performance. Second, the cross-entropy loss function is used to further realize the parameter optimization and model accuracy improvement. Finally, a dropout layer is joined before the fully connected layer can effectively decrease the model recognition time and prevent over-fitting. Experimental data on public tomato disease datasets demonstrate that the improved fine-grained image classification method put forward in this paper exhibits higher classification accuracy, better stability and network generalization ability than other models.
TSAD: Two-Stage Separable Adversarial Distortion-Based Robust Watermarking Framework for Diffusion Tensor Imaging
Authors: Zheng Long , Li Zhi , Liu Zhangyu , Li Dandan , Zhang Li , Yue Hong , Cheng Fei , Mao Qin , Wei Xuekai , Zhou Mingliang
Published: April 2024

Recent deep learning-based watermarking methods have achieved impressive results. However, they struggle with unknown distortions and often suffer from poor generalization, slow convergence, unstable training, and degraded visual quality in watermarked images. To address the above problems, this paper proposes a two-stage separable adversarial distortion (TSAD)-based robust watermarking algorithm for diffusion tensor imaging (DTI). The algorithm uses a noise-free end-to-end network in the first stage for learning and training DTI images. In the second stage, it fixes the watermark embedding network trained in the first stage, interacts the noise distortion network with the watermark extraction network to perform adversarial training for improving robustness. Experimental results show that our method achieves comparable or better robustness to seen distortions and better robustness to unseen distortions, along with enhanced stability, faster convergence, and improved visual quality in watermarked DTI images.
Flaky Test Detection Based on Adaptive Latest Position Execution for Concurrent Android Applications
Authors: Zhang Weixi , Wang Weiwei , Zhao Ruilian
Published: April 2024

Tests may pass or fail under the same conditions. These tests are commonly known as flaky tests. In Android applications, the primary reason for flaky tests is attributed to its event-driven programming paradigm and multi-threading concurrency mechanism. It may activate an unexpected event order when a test is executed, causing test flakiness. The later the execution of asynchronous events, the more likely it is to result in test flakiness. Inspired by this deduction, this paper puts forward a flaky test detection method for concurrent Android applications based on adaptive latest position execution. In more detail, the latest execution positions of each asynchronous event are identified by analyzing the sequential dependencies between events. On this basis, the asynchronous event is scheduled at the corresponding position, thereby trying to change the test results and detecting flaky tests. To validate the effectiveness and efficiency of our approach, a series of experiments are conducted on 16 known flaky test cases across 7 Android applications. The experimental results show that compared with the state-of-the-art tool FlakeScanner, the flaky test detection rate of our approach improves by 18.75%.
Charged anisotropic conformal star model with a quadratic equation of state
Authors: Christopher Joseph , Jape Jonas W. , Sunzu Jefta M.
Published: April 2024

A new solution to the Einstein–Maxwell field equations for a charged anisotropic compact star is generated. The presence of both a conformal Killing vector and a quadratic equation of state give rise to a new solution with astrophysical significance. The incorporation of the conformal Killing vector leads to an equation relating the gravitational potentials. By combining the quadratic equation of state with the Einstein–Maxwell field equations, an equation for electric field intensity is obtained. Subsequently, one of the gravitational potentials is chosen on physical merits to obtain the second gravitational potential and other matter variables. The generated solution undergoes a thorough analysis to ensure acceptance of physical conditions for realistic compact star models. A deep analysis of the physical aspects reveals the well-behaved nature of the gravitational potentials and matter variables. A smooth matching of the interior and exterior metrics is achieved at the boundary. The gravitational potentials are free from physical and geometric singularities. Also, the energy conditions are satisfied, and the ratio of mass to radius and the surface redshifts remain within reasonable ranges. The equilibrium is attained by the balanced natural interior physical forces. The stability of the model against gravitational collapse is satisfied. Interestingly, the use of quadratic equation of state in conformal stars is missing in treatments performed in the past.
MODELING AND ANALYSIS OF OPTIMAL IMPLEMENTATION OF STERILE INSECT TECHNIQUE TO SUPPRESS MOSQUITO POPULATION
Authors: SARKAR SUDDHYASHIL , BHATTACHARYYA JOYDEB , PAL SAMARES
Published: April 2024

Sterile Insect Technique (SIT) is a biological insect (or pest) control tool aiming to reduce or eliminate wild insect (or pest) populations by releasing sterile insects (or pests). In this paper, we propose and study a stage- and sex-structured entomological model describing the dynamics of wild-type mosquito population and observed that the extinction equilibrium of the model is globally asymptotically stable when the basic offspring number is less than unity. However, when the basic offspring number is greater than unity, the extinction equilibrium becomes unstable, followed by the emergence of the stable interior equilibrium. We extend the model by introducing sterile male mosquitoes as a biological control agent against wild-type mosquito species. We have considered the Allee effect in the fertile female mosquito population due to the presence of non-egg-laying females in the mosquito population. While the wild mosquito-free equilibrium of the SIT model is always locally asymptotically stable, there exists either no interior equilibrium or a pair of interior equilibria, among which one is always unstable, and the other is always locally asymptotically stable. We observed that the wild mosquito population of the SIT system goes to extinction, followed by a saddle-node bifurcation when the supply rate of sterile males increases through some critical threshold value. As an alternative to the eradication policy, we formulated an optimal control problem to suppress the wild mosquito population, which suggests increasing the investment in awareness campaigns to suppress the mosquito population.
A Hybrid Method for Multiple Sclerosis Lesion Segmentation Using Wavelet and Dense U-Net
Authors: Alijamaat Ali , Mirhosseini Seyed Mohsen , Aliakbari Reyhaneh
Published: April 2024

Multiple Sclerosis (MS) is one of the debilitating disorders of the central nervous system. This disease causes lesions in the white matter of the brain tissue. It can also lead to many physical and psychological disorders in movement, vision, and memory. Lesion segmentation in MRI images to determine the number and size of lesions is one of the diagnostic problems for specialists. Using automated diagnostic tools as an aid can help professionals. Traditional image processing and deep learning methods are used to automate lesion segmentation. The U-Net is one of the most widely used deep learning architectures for MS lesion segmentation. The images are used in the Fourier domain in the U-Net network, which does not include all its features. Our proposed method combines the HAR wavelet transform and the Dense net-based U-Net. This makes local features and lesions of different sizes more prominent and leads to higher quality segmentation. The proposed method had a better Dice value than the compared methods in the experiments.
Context Aggregation Network for Remote Sensing Image Semantic Segmentation
Authors: Zhang Changxing , Bai Xiangyu , Wang Dapeng , Zhou KeXin
Published: April 2024

In recent years, remote sensing technology has been widely applied in various industries, and semantic segmentation of remote sensing images has attracted much attention. Due to the complexity and special characteristics of remote sensing images, multi-scale object detection and accurate object localization are important challenges in remote sensing image semantic segmentation. Therefore, this paper proposes a context aggregation network (CANet). The design of CANet is influenced by advanced technologies such as attention mechanisms and feature fusion and enhancement. This network first introduces nested dilated residual module (NDRM), which can fully utilize the features extracted by the backbone network. Then, improved integrated successive dilation module (IISD) is proposed to effectively aggregate a series of contextual information scales. Next, Swim Transformer module is embedded to provide global contextual information. Finally, multi-resolution fusion module (MRFM) is proposed, allowing the comprehensive fusion of feature layers from different stages of the encoder, preserving more semantic and detailed information. The experimental results show that CANet outperforms other advanced models on the Potsdam and Vaihingen datasets.
Person Image Generation Guided by Posture, Expression and Illumination
Authors: Wang Kai , Lu Hongming , Guo Jing , Ke Yongzhen , Yang Shuai , Tian Kai
Published: April 2024

Person pose transfer has shown great application prospects and potential in recent years. However, this research mainly focuses on posture transfer and does not pay much attention to other human body attributes. In addition, there are problems such as blurred facial expressions and inconsistent facial texture. In order to solve these problems and improve user experience, this paper proposes a multi-feature transfer person image generation model, which combines facial expression transfer and pose transfer and completes the lighting reconstruction of person images. Our model can simultaneously migrate and supplement facial expressions while migrating a person’s posture, ensuring the consistency of characters’ overall migration. Our model can freely switch personal body lighting to adapt to different scenes, greatly improving the user experience. The experimental results show that our method’s preservation rate for face identity is improved by nearly 35% compared with the benchmark, and the similarity index for the overall person body is improved by nearly 14%. The evaluation results indicate that our method performs well in person posture transfer. The experimental results show that the proposed method is superior to the existing algorithms in person posture transformation.
A Hybrid Approach for Binary Classification of Imbalanced Data
Authors: Tsai Hsinhan , Yang Ta-Wei , Wong Wai-Man , Kao Han-Yi , Chou Cheng-Fu
Published: April 2024

Binary classification with an imbalanced dataset is challenging. Models tend to consider all samples as belonging to the majority class. Although existing solutions such as sampling methods, cost-sensitive methods, and ensemble learning methods improve the poor accuracy of the minority class, these methods are limited by overfitting or cost parameters that are difficult to decide. This paper proposes a hybrid approach with dimension reduction that consists of data block construction, dimensionality reduction, and ensemble learning with deep neural network classifiers. The performance is evaluated on eight imbalanced public datasets in terms of recall, G-mean, AUC, F-measure, and balanced accuracy. The results show that the proposed model outperforms state-of-the-art methods.
Long-Term Inhibiting Salt Crystallization on Photothermal Sheet by a Superhydrophobic CNT Composite Coating
Authors: Wang Shanlin , Cheng Xiaohong , Tan Xue , Cheng Yi , Zhen Xueting
Published: April 2024

Heterogeneous crystallization resulted in low conversion efficiency and short service life has become one of the important challenges of desalination. Here, a fluoro-modified CNT@epoxy composite coating is developed to durably inhibit salt crystallization of photothermal sheet even in highly concentrated salt water. Typically, it can have stable evaporation efficiency of [Formula: see text] without salt crystallization after continuously being operated for 40 cycles under one solar intensity in the 20 wt.% NaCl solution. That is mainly due to the Cassie state superhydrophobicity that can large increase the heterogeneous nucleation barrier quite close to the homogeneous one in salt water. This study expects to solve the existing problems of complex structure and short service time of seawater desalination units.
CLIMATE CHANGE CHALLENGES IN BANGLADESH
Authors: DAS LIPON CHANDRA , MOHIUL ISLAM A. S. M. , AKTAR SHOMI , SULTANA FARZANA
Published: April 2024

Climate change is a pervasive global challenge with far-reaching implications for both the environment and human society. A thorough comprehension of these repercussions is essential in crafting effective strategies to mitigate their effects. Climate change, a major threat to sustainable development, impacts the environment, economy, natural resources, food security, human health, and physical infrastructure globally. Bangladesh stands out among affected nations due to geographical and socio-economic vulnerabilities. Despite its vulnerability, Bangladesh has successfully mitigated the impact of extreme events over the years. This study focuses on examining climate change, its outcomes, and mitigation initiatives in Bangladesh. It endeavors to offer a comprehensive analysis of the current climate scenario in Bangladesh, delving into pivotal trends, impacts, adaptation strategies, and evaluating the effectiveness of national policies, legal frameworks, involvement of local governments, non-governmental organizations, and international collaborative efforts in developing an efficient disaster risk reduction and anticipatory action system. Despite achievements, safeguarding the livelihoods and properties of vulnerable communities from disaster-induced loss and damage remains a challenge. Additionally, addressing past adaptation failures is crucial. The paper highlights the necessity to enhance disaster risk reduction, green economy, promote economic development, implement government policies, and bolster anticipatory action systems, emphasizing the pivotal role of establishing a locally led climate-resilient system in addressing these challenges.
Look-ahead-while-drilling resistivity tools and their role in drilling sedimentary basins containing complex volcanic geology
Authors: Maguire Alistair , Smart Leanne , Layfield Lucinda K. , Bower Mike , Schofield Nick
Published: April 2024

Developments in ultradeep azimuthal resistivity (UDAR) technology has recently progressed from their application in high-angle and horizontal wells to low-angle and vertical wells. This has presented the opportunity to assess their suitability as derisking tools because of their ability to look ahead of the bit, up to
c.
100 ft (
c.
30.5 m).

Dominated by a complex interplay of structure, sedimentation, and volcanic activity, the NE Atlantic Margin presents a challenging environment to plan and drill wells safely and within planned budget, with many wells drilled within the Faroe–Shetland Basin (FSB) currently the most expensive to-date within the United Kingdom continental shelf (UKCS). The limited number of wells drilled in the FSB and their geographical sparsity relative to those of other areas of the UKCS, make offset analysis and derisking a challenge. The often-unpredictable distribution of igneous rocks throughout the basin presents significant challenges in the planning and drilling of oil and gas wells. Up to 88% of igneous intrusions within the FSB are estimated to be below seismic resolution, which alongside variation in composition, presents significant issues related to drilling safely, efficiently and successfully.

Within this paper, we investigate the use of UDAR technology and how they could be deployed in areas of complex volcanic geology such as the West of Shetland (WoS). This paper reviews the nature of the drilling experiences within the FSB to date, the impact it has on exploration, and how improvements in drilling technology could help decrease nonproductive time (NPT).
“Jumping Out from the Pressure of Work and into the Game”: Curating Immersive Digital Game Experiences for Post-Work Recovery
Authors: Mella Jonathan , Iacovides Ioanna , Cox Anna
Published: April 2024

Extant research demonstrates that playing digital games after work can have a psychologically restorative effect. This article focuses on understanding how players can maximise this effect by strategically leveraging the immersive potential of digital games. For one week, eleven participants played games after work with the aim of developing strategies to support their post-work recovery. A follow-up laddering interview identified various “immersion optimisation” strategies that fulfilled components of successful recovery, such as mental disengagement from work and relaxation. These strategies predominantly focused on cognitive involvement and challenge, neglecting other dimensions of immersion. Based on these findings, we contribute an initial framework of immersion optimisation strategies which can be used to enhance the recovery potential of digital games. We also suggest exploring potential boundary conditions of the immersion optimisation phenomenon and offer methodological reflections on the use of the laddering methodology in this study.
M
3
Rec: A Context-Aware Offline Meta-Level Model-Based Reinforcement Learning Approach for Cold-Start Recommendation
Authors: Wang Yanan , Ge Yong , Li Zhepeng , Li Li , Chen Rui
Published: April 2024

Reinforcement learning (RL) has shown great promise in optimizing long-term user interest in recommender systems. However, existing RL-based recommendation methods need a large number of interactions for each user to learn the recommendation policy. The challenge becomes more critical when recommending to new users who have a limited number of interactions. To that end, in this article, we address the cold-start challenge in the RL-based recommender systems by proposing a novel context-aware offline meta-level model-based RL approach for user adaptation. Our proposed approach learns to infer each user's preference with a user context variable that enables recommendation systems to better adapt to new users with limited contextual information. To improve adaptation efficiency, our approach learns to recover the user choice function and reward from limited contextual information through an inverse RL method, which is used to assist the training of a meta-level recommendation agent. To avoid the need for online interaction, the proposed method is trained using historically collected offline data. Moreover, to tackle the challenge of offline policy training, we introduce a mutual information constraint between the user model and recommendation agent. Evaluation results show the superiority of our developed offline policy learning method when adapting to new users with limited contextual information. In addition, we provide a theoretical analysis of the recommendation performance bound.
Unsupervised Social Bot Detection via Structural Information Theory
Authors: Peng Hao , Zhang Jingyun , Huang Xiang , Hao Zhifeng , Li Angsheng , Yu Zhengtao , Yu Philip S.
Published: April 2024

Research on social bot detection plays a crucial role in maintaining the order and reliability of information dissemination while increasing trust in social interactions. The current mainstream social bot detection models rely on black-box neural network technology, for example, Graph Neural Network, Transformer, and so on, which lacks interpretability. In this work, we present UnDBot, a novel unsupervised, interpretable, yet effective, and practical framework for detecting social bots. This framework is built upon structural information theory. We begin by designing three social relationship metrics that capture various aspects of social bot behaviors:
posting type distribution
,
posting influence
, and
follow-to-follower ratio
. Three new relationships are utilized to construct a new, unified, and weighted social multi-relational graph, aiming to model the relevance of social user behaviors and discover long-distance correlations between users. Second, we introduce a novel method for optimizing heterogeneous structural entropy. This method involves the personalized aggregation of edge information from the social multi-relational graph to generate a two-dimensional encoding tree. The heterogeneous structural entropy facilitates decoding of the substantial structure of the social bots network and enables hierarchical clustering of social bots. Third, a new community labeling method is presented to distinguish social bot communities by computing the user’s stationary distribution, measuring user contributions to network structure, and counting the intensity of user aggregation within the community. Compared with 10 representative social bot detection approaches, comprehensive experiments demonstrate the advantages of effectiveness and interpretability of UnDBot on 4 real social network datasets.
Generating Clinical-Grade Gene–Disease Validity Classifications Through the ClinGen Data Platforms
Authors: Wright Matt W. , Thaxton Courtney L. , Nelson Tristan , DiStefano Marina T. , Savatt Juliann M. , Brush Matthew H. , Cheung Gloria , Mandell Mark E. , Wulf Bryan , Ward TJ , Goehringer Scott , O'Neill Terry , Weller Phil , Preston Christine G. , Keseler Ingrid M. , Goldstein Jennifer L. , Strande Natasha T. , McGlaughon Jennifer , Azzariti Danielle R. , Cordova Ineke , Dziadzio Hannah , Babb Lawrence , Riehle Kevin , Milosavljevic Aleksandar , Martin Christa Lese , Rehm Heidi L. , Plon Sharon E. , Berg Jonathan S. , Riggs Erin R. , Klein Teri E.
Published: April 2024

Clinical genetic laboratories must have access to clinically validated biomedical data for precision medicine. A lack of accessibility, normalized structure, and consistency in evaluation complicates interpretation of disease causality, resulting in confusion in assessing the clinical validity of genes and genetic variants for diagnosis. A key goal of the Clinical Genome Resource (ClinGen) is to fill the knowledge gap concerning the strength of evidence supporting the role of a gene in a monogenic disease, which is achieved through a process known as Gene–Disease Validity curation. Here we review the work of ClinGen in developing a curation infrastructure that supports the standardization, harmonization, and dissemination of Gene–Disease Validity data through the creation of frameworks and the utilization of common data standards. This infrastructure is based on several applications, including the ClinGen GeneTracker, Gene Curation Interface, Data Exchange, GeneGraph, and website.
The Visual Systems of Zebrafish
Authors: Baier Herwig , Scott Ethan K.
Published: April 2024

The zebrafish visual system has become a paradigmatic preparation for behavioral and systems neuroscience. Around 40 types of retinal ganglion cells (RGCs) serve as matched filters for stimulus features, including light, optic flow, prey, and objects on a collision course. RGCs distribute their signals via axon collaterals to 12 retinorecipient areas in forebrain and midbrain. The major visuomotor hub, the optic tectum, harbors nine RGC input layers that combine information on multiple features. The retinotopic map in the tectum is locally adapted to visual scene statistics and visual subfield–specific behavioral demands. Tectal projections to premotor centers are topographically organized according to behavioral commands. The known connectivity in more than 20 processing streams allows us to dissect the cellular basis of elementary perceptual and cognitive functions. Visually evoked responses, such as prey capture or loom avoidance, are controlled by dedicated multistation pathways that—at least in the larva—resemble labeled lines. This architecture serves the neuronal code's purpose of driving adaptive behavior.