bluuebunny/crossref_metadata_2025 huggingface dataset:
Intraspecific variation in antibiotic resistance potential within
E. coli
Authors: Suarez Stacy A. , Martiny Adam C.
Published: April 2024

Intraspecific genomic diversity brings the potential for an unreported and diverse reservoir of cryptic antibiotic resistance genes in pathogens, as cryptic resistance can occur without major mutations and horizontal transmission. Here, we predicted the differences in the types of antibiotics and genes that induce cryptic and latent resistance between micro-diverse
Escherichia coli
strains. For example, we hypothesize that known resistance genes will be the culprit of latent resistance within clinical strains. We used a modified functional metagenomics method to induce expression in eight
E. coli
strains. We found a total of 66 individual genes conferring phenotypic resistance to 11 out of 16 antibiotics. A total of 14 known antibiotic resistance genes comprised 21% of total identified genes, whereas the majority (52 genes) were unclassified cryptic resistance genes. Between the eight strains, 1.2% of core orthologous genes were positive (conferred resistance in at least one strain). Sixty-four percent of positive orthologous genes conferred resistance to only one strain, demonstrating high intraspecific variability of latent resistance genes. Cryptic resistance genes comprised most resistance genes among laboratory and clinical strains as well as natural, semisynthetic, and synthetic antibiotics. Known antibiotic resistance genes primarily conferred resistance to multiple antibiotics from varying origins and within multiple strains. Hence, it is uncommon for
E. coli
to develop cross-cryptic resistance to antibiotics from multiple origins or within multiple strains. We have uncovered prospective and previously unknown resistance genes as well as antibiotics that have the potential to trigger latent antibiotic resistance in
E. coli
strains from varying origins.

IMPORTANCE

Intraspecific genomic diversity may be a driving force in the emergence of adaptive antibiotic resistance. Adaptive antibiotic resistance enables sensitive bacterial cells to acquire temporary antibiotic resistance, creating an optimal window for the development of permanent mutational resistance. In this study, we investigate cryptic resistance, an adaptive resistance mechanism, and unveil novel (cryptic) antibiotic resistance genes that confer resistance when amplified within eight
E. coli
strains derived from clinical and laboratory origins. We identify the potential of cryptic resistance genes to confer cross-resistance to antibiotics from varying origins and within multiple strains. We discern antibiotic characteristics that promote latent resistance in multiple strains, considering intraspecific diversity. This study may help detect novel resistance genes and functional genes that could become responsible for cryptic resistance among diverse strains and antibiotics, thus also identifying potential novel antibiotic targets and mechanisms.
Antifibrotic treatment adherence, efficacy and outcomes for patients with idiopathic pulmonary fibrosis in Spain: a real-world evidence study
Authors: Romero Ortiz Ana Dolores , Jiménez-Rodríguez Beatriz María , López- Ramírez Cecilia , López- Bauzá Ángela , Pérez- Morales María , Delgado-Torralbo José Antonio , Villalba Moral Cristina , Alcazar-Navarrete Bernardino
Published: April 2024

Background

Idiopathic pulmonary fibrosis (IPF) is a rare disorder associated with increased mortality and morbidity. There are currently two drugs approved for IPF but their safety and efficacy profile in real-world settings in Spain is not well understood.

Methods

An observational, multicentre, prospective study was carried out among patients with IPF who started treatment with pirfenidone or nintedanib from 2015 to 2021. Data regarding clinical characteristics, drug adherence, safety profiles and clinical outcomes between these two drugs were collected.

Results

232 patients were included in the analysis. There were no meaningful differences between both groups at baseline. Patients who started pirfenidone showed a decreased risk for treatment withdrawal compared with those starting nintedanib (HR 0.65 (95% CI 0.46 to 0.94; p=0.002)). Time to first adverse event and all-cause mortality was similar between study groups. Risk factors for withdrawal were female sex, diarrhoea and photosensitivity.

Conclusions

in this real-world study, both pirfenidone and nintedanib showed similar efficacy profiles. Pirfenidone was associated with less treatment discontinuations due to side effects.
Myeloid-derived suppressor cells attenuate the antitumor efficacy of radiopharmaceutical therapy using90Y-NM600 in combination with androgen deprivation therapy in murine prostate tumors
Authors: Muralidhar Anusha , Hernandez Reinier , Morris Zachary S , Comas Rojas Hansel , Bio Idrissou Malick , Weichert Jamey P , McNeel Douglas G
Published: April 2024

Rationale

Androgen deprivation therapy (ADT) is pivotal in treating recurrent prostate cancer and is often combined with external beam radiation therapy (EBRT) for localized disease. However, for metastatic castration-resistant prostate cancer, EBRT is typically only used in the palliative setting, because of the inability to radiate all sites of disease. Systemic radiation treatments that preferentially irradiate cancer cells, known as radiopharmaceutical therapy or targeted radionuclide therapy (TRT), have demonstrable benefits for treating metastatic prostate cancer. Here, we explored the use of a novel TRT,90Y-NM600, specifically in combination with ADT, in murine prostate tumor models.

Methods

6-week-old male FVB mice were implanted subcutaneously with Myc-CaP tumor cells and given a single intravenous injection of90Y-NM600, in combination with ADT (degarelix). The combination and sequence of administration were evaluated for effect on tumor growth and infiltrating immune populations were analyzed by flow cytometry. Sera were assessed to determine treatment effects on cytokine profiles.

Results

ADT delivered prior to TRT (ADT→TRT) resulted in significantly greater antitumor response and overall survival than if delivered after TRT (TRT→ADT). Studies conducted in immunodeficient NRG mice failed to show a difference in treatment sequence, suggesting an immunological mechanism. Myeloid-derived suppressor cells (MDSCs) significantly accumulated in tumors following TRT→ADT treatment and retained immune suppressive function. However, CD4+ and CD8+ T cells with an activated and memory phenotype were more prevalent in the ADT→TRT group. Depletion of Gr1+MDSCs led to greater antitumor response following either treatment sequence. Chemotaxis assays suggested that tumor cells secreted chemokines that recruited MDSCs, notably CXCL1 and CXCL2. The use of a selective CXCR2 antagonist, reparixin, further improved antitumor responses and overall survival when used in tumor-bearing mice treated with TRT→ADT.

Conclusion

The combination of ADT and TRT improved antitumor responses in murine models of prostate cancer, however, this was dependent on the order of administration. This was found to be associated with one treatment sequence leading to an increase in infiltrating MDSCs. Combining treatment with a CXCR2 antagonist improved the antitumor effect of this combination, suggesting a possible approach for treating advanced human prostate cancer.
Identification of DAGLA as an autoantibody target in cerebellar ataxia
Authors: Miske Ramona , Scharf Madeleine , Borowski Kathrin , Specht Ina , Corty Merle , Loritz Monika-Johanna , Rombach Frederik , Laureys Guy , Rochow Nadine , Radzimski Christiane , Schnitter Linda , Ratuszny Dominica , Skripuletz Thomas , Wattjes Mike P , Hahn Stefanie , Denno Yvonne , Guerti Khadija , Oyaert Matthijs , Benkhadra Farid , Bien Corinna Ines , Nitsch Sophie , Wandinger Klaus-Peter , van Pesch Vincent , Probst Christian , Teegen Bianca , Komorowski Lars , Sühs Kurt-Wolfram
Published: April 2024

Background

We aimed to investigate the clinical, imaging and fluid biomarker characteristics in patients with antidiacylglycerol lipase alpha (DAGLA)-autoantibody-associated cerebellitis.

Methods

Serum and cerebrospinal fliud (CSF) samples from four index patients were subjected to comprehensive autoantibody screening by indirect immunofluorescence assay (IIFA). Immunoprecipitation, mass spectrometry and recombinant protein assays were used to identify the autoantigen. Sera from 101 patients with various neurological symptoms and a similar tissue staining pattern as the index patient samples, and 102 healthy donors were analysed in recombinant cell-based IIFA (RC-IIFA) with the identified protein. Epitope characterisation of all positive samples was performed via ELISA, immunoblot, immunoprecipitation and RC-IIFA using different DAGLA fragments.

Results

All index patients were relatively young (age: 18–34) and suffered from pronounced gait ataxia, dysarthria and visual impairments. Paraclinical hallmarks in early-stage disease were inflammatory CSF changes and cerebellar cortex hyperintensity in MRI. Severe cerebellar atrophy developed in three of four patients within 6 months. All patient samples showed the same unclassified IgG reactivity with the cerebellar molecular layer. DAGLA was identified as the target antigen and confirmed by competitive inhibition experiments and DAGLA-specific RC-IIFA. In RC-IIFA, serum reactivity against DAGLA was also found in 17/101 disease controls, including patients with different clinical phenotypes than the one of the index patients, and in 1/102 healthy donors. Epitope characterisation revealed that 17/18 anti-DAGLA-positive control sera reacted with a C-terminal intracellular DAGLA 583–1042 fragment, while the CSF samples of the index patients targeted a conformational epitope between amino acid 1 and 157.

Conclusions

We propose that anti-DAGLA autoantibodies detected in CSF, with a characteristic tissue IIFA pattern, represent novel biomarkers for rapidly progressive cerebellitis.
Pretreatment mortality risk prediction model in patients with polymyositis/dermatomyositis-associated interstitial lung disease
Authors: Gui Xianhua , Li Wangzhong , Jiang Hanyi , Wang Rujia , Yu Min , Zhao Tingting , Ma Miao , Ding Jingjing , Jin Ziyi , Qiu Yuying , Qiu Xiaohua , Zhang Yingwei , Cao Min , Huang Mei , Cao Mengshu , Dai Jinghong , Cai Hourong , Xin Xiaoyan , Xiao Yonglong
Published: April 2024

Objectives

Risk prediction for patients with polymyositis/dermatomyositis-associated interstitial lung disease (PM/DM-ILD) is challenging due to heterogeneity in the disease course. We aimed to develop a mortality risk prediction model for PM/DM-ILD.

Methods

This prognostic study analysed patients with PM/DM-ILD admitted to Nanjing Drum Hospital from 2016 to 2021. The primary outcome was mortality within 1 year. We used a least absolute shrinkage and selection operator (LASSO) logistic regression model to identify predictive laboratory indicators. These indicators were used to create a laboratory risk score, and we developed a mortality risk prediction model by incorporating clinical factors. The evaluation of model performance encompassed discrimination, calibration, clinical utility and practical application for risk prediction and prognosis.

Results

Overall, 418 patients with PM/DM-ILD were enrolled and randomly divided into development (n=282) and validation (n=136) cohorts. LASSO logistic regression identified four optimal features in the development cohort, forming a laboratory risk score: C reactive protein, lactate dehydrogenase, CD3+CD4+ T cell counts and PO2/FiO2. The final prediction model integrated age, arthralgia, anti-melanoma differentiation-associated gene 5 antibody status, high-resolution CT pattern and the laboratory risk score. The prediction model exhibited robust discrimination (area under the receiver operating characteristic: 0.869, 95% CI 0.811 to 0.910), excellent calibration and valuable clinical utility. Patients were categorised into three risk groups with distinct mortality rates. The internal validation, sensitivity analyses and comparative assessments against previous models further confirmed the robustness of the prediction model.

Conclusions

We developed and validated an evidence-based mortality risk prediction model with simple, readily accessible clinical variables in patients with PM/DM-ILD, which may inform clinical decision-making.
Optimising gout treatment: insights from a nurse-led cohort study
Authors: Rasmussen Claus , Larsen Jesper Walther , Christensen Heidi Mächler , Larsen Monica Bak , Thomsen Anna Marie , Leishmann Tinna , Kragh Jette , Nielsen Gunnar Lauge
Published: April 2024

Objectives

Currently, gout management, particularly urate-lowering therapy (ULT), is often suboptimal. Nurses successfully manage various diseases including gout. As gout prevalence is rising, and rheumatologists and general practitioners face shortages, a new approach is imperative. This real-life prospective cohort study evaluated the effectiveness of nurse-led care employing a treat-to-target strategy for gout management over a 2-year period.

Methods

All consecutively confirmed gout patients were included. The nurse-led clinic provided a structured treatment plan with consultations, patient leaflets, telephone contacts and laboratory monitoring. After a year of nurse-led care, patients transitioned to continued care in general practice. Follow-up data were complete through registries. The primary outcome was achieving target p-urate levels (<0.36 mmol/L) at 2 years after diagnosis. Secondary outcomes included treatment continuation and achievement of target p-urate levels in specific subgroups. The results were compared with patients diagnosed in the same clinic but followed up in ‘usual care’.

Results

In the nurse-led group (n=114), 83% achieved target p-urate levels and ULT was continued by 98%. This trend persisted across various patient subgroups. Only 44% of patients in usual care achieved target p-urate and with insufficient doses of allopurinol . Nurse-led care involved an average of two visits and three telephone contacts over 336 days. The 2-year mortality rate was 15%.

Conclusions

Nurse-led gout care, employing a targeted approach, was associated with a very high uptake of and adherence to ULT. The encouraging results were not achieved in usual care although a direct comparison might be influenced by selection bias.
Provenance and depositional age of metasedimentary rocks in the Frontenac terrane (Grenville Province, Ontario)
Authors: Peck William H. , Lin Henry Y.
Published: April 2024

The Mesoproterozoic Frontenac terrane in the southern Grenville Province of Ontario is separated by shear zones from the Composite Arc Belt to the west and the Adirondack Lowlands to the east. The majority of the terrane is made up of granulite-facies metasedimentary rocks that are the oldest lithologies recognized in the terrane. Five samples were selected for U–Pb geochronology to constrain (1) metamorphic age, (2) depositional age of sedimentary protoliths, and (3) source regions of detrital zircon. Two pelitic gneisses and a marble are dominated by metamorphic zircon, whereas two quartzites contain a diverse population of detrital zircon that are surrounded by metamorphic overgrowths. Metamorphic zircon have 206Pb/207Pb ages of 1.19–1.16 Ga, and a small population is 1.22 Ga. These ages correspond to the Shawinigan and Elzevirian orogenies and provide minimum ages for deposition. The youngest detrital grains with ages of 1.25–1.24 Ga provide maximum depositional ages. Quartzites (and pelitic gneisses) have a wide range of detrital zircon ages that reflect local Mesoproterozoic Grenville sources and Paleoproterzoic and Archean sources in the northern Grenville Province and elsewhere in Laurentia. Most notable is a large population of 1.9–1.8 Ga zircon, which points towards derivation from the Penokean orogen in the midcontinent or Makkovikian–Ketilidian orogen of Labrador and Greenland, indicating long sedimentary transport distances. The similarities in depositional ages and detrital zircon ages between Frontenac terrane and Adirondack metasedimentary rocks suggest a shared sedimentary history, which we interpret as deposition in the same Trans-Adirondack backarc basin at ca. 1.25 Ga.
Integration of Control Strategies for Cleaning Based on Granular Computing
Authors: Zhang Jing , Zhou Xiancun , Jia Chaochuan , Jiang Qing , Cai Cuicui , Zhou Quan , Liu Yu
Published: April 2024

Cleaning control based on changes in cleaning loss rate and impurity rate has emerged as a hot topic in the research on intelligent control for rice and wheat combined harvesters. However, numerous operation parameters can lead to deviations beyond the normal range of cleaning loss rate and impurity rate. The impact of cleaning control parameters in rice and wheat combined harvesters on cleaning loss rate and impurity rate often tends to be contradictory. How to combine different contradictory cleaning control strategies to get a more widely used and better cleaning control strategy is a hot issue in the current cleaning control research. In this paper, the granularity of quotient space is introduced into the cleaning control based on operation parameters, and a cleaning control model based on granularity synthesis theory is proposed. This method first constructs a knowledge base for intelligent control of cleaning tailored to the cleaning loss rate and impurity rate using production rule representation, and considers that these control strategies constitute different quotient spaces, and then organizes these quotient spaces according to granularity synthesis theory to get the cleaning control strategy. The experimental results verify that the validity of the cleaning control based on granular computing is better than fuzzy control.
Cost-Effective Ground-Moving Object Detection Method in Aerial Video by Change Detection of Delaunay Triangulation
Authors: Chen Chao-Ho , Huang Deng-Yuan , Su Yi-Jen , Lin Chia-En , Chen Tsong-Yi , Jiao Zai-Ci , Wang Da-Jinn , Wen Cheng-Kang
Published: April 2024

This paper is dedicated to developing a cost-effective ground-moving object detection method in aerial videos. Without limitations on types, quantity, and distribution of moving objects, which were required by the previous methods, the proposed approach can detect various ground-moving objects in aerial videos captured through diverse flying states in various ground appearances. The proposed method is mainly composed of generation of the appropriate feature points, detection of moving objects, and target tracking. In the originality of our work, a novel detection strategy designed for ground-moving objects is based on change detection of Delaunay triangulation (CDDT) and a three-step motion vector search-based tracking algorithm is further exploited for enhancing the detection rate. Experimental results show that our method can achieve the detection rate of at least 95% (roughly similar to the famous existing state-of-the-art methods) and 0.03 second/frame (far less than the famous existing methods) using test videos (containing only several moving objects distributed in a sparse space) in the previous methods compared. Besides, the average detection rate of 86.81%, average false detection rate of 9.44%, and a frame rate of about 33[Formula: see text]fps can be obtained using our test videos captured in the complicated ground appearances. This result makes the proposed method more attractive for detecting various ground-moving objects in aerial videos, when compared to other approaches, and can also achieve cost-effective performance.
ELECTROCHEMICAL SYNTHESIS OF POLYANILINE FOR ENHANCED CONGO RED REMOVAL
Authors: ZHENG DAWEI , GUO TONG , CHANG WEIXIAO , YU DEZHONG , LIU DONG
Published: April 2024

This study aimed to investigate the synthesis of polyaniline (PANI) through electrochemical cyclic voltammetry at varying pH levels and its subsequent adsorption behavior towards Congo red. The products were analyzed using Fourier Transform-infrared spectroscopy (FTIR) and Scanning Electron Microscope (SEM). Results showed that acidic conditions led to faster synthesis rates and complete planes with intercalated flakes, while neutral and alkaline conditions resulted in mostly granular aggregates. PANI synthesized with the maximum adsorption capacity is 58.07[Formula: see text]mg/g. The interaction between PANI and Congo red occurred through protonation-induced positive charges and electrostatic attraction, as indicated by UV–Vis spectroscopy results. The adsorption process followed the Langmuir model and pseudo-second-order kinetic process, indicating a chemical adsorption process. This study emphasizes the significance of pH conditions in polyaniline synthesis and its potential application in dye removal processes.
The fluctuational transition mechanism of non-hyperbolic chaotic invariant sets
Authors: Mao Yicheng , Liu Xianbin
Published: April 2024

In order to reveal the general escape mechanism of non-hyperbolic chaotic invariant sets under both weak noise limit and finite noise intensity, the simplest example of Hénon map, which represents the stretching and folding of the phase space, is taken to study the escape mechanism under two kinds of global bifurcation: the fractal boundary crisis and the attractor contact crisis. In this paper, we revealed the general exit mechanism by analyzing the escape paths derived from the shooting method and Monte Carlo simulation. Finally, to further demonstrate universality, an example of high-dimensional differential dynamical system, namely the transient chaos Duffing oscillator, is examined, which underscores the main idea of this paper analyzing specific deterministic structures not only enhances the comprehensibility of the escape process, but also allows for predictions of general escape behavior under weak noise intensity.
Dynamics and probability density function of a stochastic COVID-19 epidemic model with nonlinear incidence
Authors: Zhang Yihan , Chen Qiaoling , Teng Zhidong , Cai Yujie
Published: April 2024

In this paper, a stochastic SEIQ model with nonlinear incidence rate is proposed. The existence and uniqueness of global positive solution of the stochastic model are proved. Then, by constructing some Lyapunov functions, we derive a sufficient condition for the ergodic stationary distribution when [Formula: see text] is greater than one. By solving a four-dimensional Fokker–Planck equation, we get the exact expression of log-normal probability density function of stationary distribution for the stochastic model. Sufficient condition for the extinction of the exposed and infected population is also provided. Finally, numerical simulations are presented to verify the above theoretical results.
Well-posedness of stochastic variational inequalities with discontinuous drifts
Authors: Xiao Guangpeng , Liu Jicheng
Published: April 2024

In this paper, we investigate the well-posedness of stochastic variational inequalities on [Formula: see text] with discontinuous drift coefficients. First, a case that drift coefficients are only integrable on [Formula: see text] is considered. Utilizing Girsanov’s theorem, Zvonkin’s transform and Yamada–Watanabe’s principle, we derive the weak well-posedness if [Formula: see text] and strong well-posedness if [Formula: see text]. Second, if the drift coefficients are piecewise Lipschitz continuous and [Formula: see text], strong well-posedness is also obtained by virtue of a transform which regularizes the discontinuous drifts.
Sampling complexity of deep approximation spaces
Authors: Abdeljawad Ahmed , Grohs Philipp
Published: April 2024

While it is well-known that neural networks enjoy excellent approximation capabilities, it remains a big challenge to compute such approximations from point samples. Based on tools from Information-based complexity, recent work by Grohs and Voigtlaender [Proof of the theory-to-practice gap in deep learning via sampling complexity bounds for neural network approximation spaces, preprint (2021), arXiv:2104.02746] developed a rigorous framework for assessing this so-called ”theory-to-practice gap”. More precisely, in that work it is shown that there exist functions that can be approximated by neural networks with ReLU activation function at an arbitrary rate while requiring an exponentially growing (in the input dimension) number of samples for their numerical computation. This study extends these findings by showing analogous results for the ReQU activation function.
Image Denoise Model Based on Structural Re-Parameterized Uniformer Transformer and UNet
Authors: Lu Zhengwei , Zhang Duzhen , Wang Tao , Jiang Hengchang , Pu Yingkai
Published: April 2024

Image denoising is a fundamental problem in computer vision (CV). Vision Transformer (ViT) is an improvement in CV after convolutional neural networks (CNNs). Recently, it has been demonstrated that the RepVGG network of the structural re-parameterization performs well in image tasks. Experiments indicate that the ViT-based uniformer transformer network has successfully balanced local and global information. As image denoising tasks require more local and global information about the image, a novel image denoising model named structural Re-parameterization Uniformer Transformer-UNet (Rep-UUNet) is proposed in this paper. The model is structural and re-parameterized the structure of the Uniformer Transformer network and uses the UNet skip connections to reconstruct the output image. To complete image downstream tasks, the RepVGG model which utilizes the image local information is used. Indicators such as PSNR, SSIM and others are used to assess the performance of image denoising. Experimental results demonstrate that our Rep-UUNet network model outperforms other five models.
An Effective Intrusion Detection System for Edge Computing Using ConvNeXt and ResNet152V2
Authors: Balusa Vasavi Sravanthi , Srinivas K.
Published: April 2024

The proliferation of edge computing, driven by network applications and wireless devices, increases the vulnerability of confidential information to security risks. In this environment, existing intrusion detection algorithms fail to satisfy the requirements of prompt responses, heavy network load management, inadequate extraction of features, and imprecise model classification. In this work, the imbalanced data problem in the input dataset is mitigated using the Data Augmentation Generative Adversarial Network (DAGAN). Next, an efficient ConvNeXt-based feature extraction method is created to retrieve the key characteristics from the dataset for every class. Last, multi-attack intrusion detection is achieved through the deployment of an optimized deep learning classifier based on ResNet152V2. Furthermore, simulation experiments are carried out on the ToN-IoT and BoT-IoT datasets, and the outcomes demonstrate that our suggested model performs better than the existing models, with accuracy levels of 99.20% and 99.31%, respectively. These findings show that this approach is successful in building and refining large-scale IDS in the edge computing framework.
Fuzzy Deep Learning Recurrent Neural Network Algorithm to Detect Corn Leaf Disease
Authors: Irianto Suhendro Y. , Findley Enrico
Published: April 2024

Corn is a major commodity after rice in supporting food self-sufficiency in Indonesia. However, due to leaf disease, the quality and quantity of corn plants are greatly reduced. The problem with detecting corn leaf diseases is that the detection method is still manual, making it inefficient and ineffective. Therefore, in this study, disease detection on corn leaves was performed using the Fuzzy C-Means (FCM) and Long Short-Term Memory (LSTM) methods. First, oversampling was carried out to ensure an equal amount of data in all classes, then the corn leaf images were pre-processed before being input into the LSTM algorithm. After completing clustering process in the [Formula: see text] algorithm, the next step involved extracting texture features using the Gray Level Co-occurrence Matrix (GLCM) technique, followed by classification using LSTM. To assess their performance, both algorithms underwent evaluation using the k-fold cross-validation method, and their accuracy and speed were compared. The results of the k-fold cross-validation demonstrated that the [Formula: see text] algorithm achieved an accuracy of 63.53%, whereas the LSTM algorithm achieved an accuracy of 80.24%. In terms of the time required for training and prediction, the LSTM algorithm took 13[Formula: see text]min and 18[Formula: see text]s for training on corn leaf disease images, while the prediction process only took 1.59[Formula: see text]s. The training and prediction time required for the [Formula: see text] algorithm were 65[Formula: see text]min and 24[Formula: see text]s and 5[Formula: see text]min and 44[Formula: see text]s, respectively. The conclusion of this study is that the LSTM algorithm has better accuracy and time compared to [Formula: see text] on the dataset used in this study in terms of corn leaf disease detection.
Green Synthesis of Hollow and Spiny Gold and Silver Bimetallic Nanotubes for Catalytic Reduction of p-Nitrophenol
Authors: Ma Yuanqing , Ou Quanhong , Tang Junqi
Published: April 2024

P-nitrophenol (4-nitrophenol, 4-NP) is mostly from domestic sewage and industrial waste. 4-NP has been listed as a “priority pollutant” by USEPA. Research on noble metal nanomaterials to degrade 4-NP to p-aminophenol (4-aminophenol, 4-AP) is very important. Hollow and spiny gold and silver (Au–Ag) bimetallic nanotube is a kind of noble metal nanomaterial with a high aspect ratio, large specific surface area and good quantum size effect, they show great potential for applications in catalysis, surface-enhanced Raman spectroscopy (SERS), sensors, optoelectronics and biomedicine. Therefore, it is essential to synthesize Au–Ag bimetallic nanotubes with multispike-like shape and apply them in the field of catalysis. In this study, silver nanowires were used as sacrificial templates, Galvanic replacement reaction was carried out in chloroauric acid solution in the presence of polyvinylpyrrolidone (PVP), and hollow and spiny Au–Ag bimetallic nanotubes (HS AuAgNTs) were synthesized. It was shown that the reaction time and the amount of chloroauric acid had a significant effect on the morphology and size of the nanotubes. The catalytic reduction of 4-NP to 4-AP was carried using HS AuAgNTs as catalysts in the presence of excess sodium borohydride, the results suggest that the HS AuAgNTs have a promising potential for catalytic applications.
Construction and Evaluation of Paclitaxel Mn-Doped Dendritic Mesoporous Silica Coated with N-Succinyl Chitosan
Authors: Zhang Fuyuan , Zhao Yang , Shang Xin , Lu Wenhui , Miao Yanping , Yang Jing
Published: April 2024

Mesoporous silica, as a drug carrier, has become the new research focus in the field of nanodrug delivery system in recent years. In this study, Mn-doped mesoporous silicas are synthesized by template and in-situ doping method and physically characterized. The drug-loading performance of silica and its impact on drug release are studied. The Mn-doped mesoporous silicas show dendritic morphology (MDMS) with a loose wrinkle structure on the surface and a large number of pores. MDMS is used as a carrier to solve the problem of low water solubility of paclitaxel. In order to avoid leakage during drug transportation, the surface of Mn-doped mesoporous silica loaded with paclitaxel is coated with N-succinyl chitosan to construct a new type of nanodrug delivery system (MDMS-PTX-NSC). Compared to MDMS, MDMS-PTX-NSC shows an increase in particle size and smooth surface. The dissolution characteristics of manganese ions and swelling behavior of N-succinyl chitosan make MDMS-PTX-NSC delivery system exhibit a good pH-responsive release. And MDMS-PTX-NSC release curve can be well fitted by Ritger-Peppas equation. The cytotoxicity test shows that the MDMS-PTX-NSC has significant biocompatibility and enhanced cytotoxicity, which reveals that the MDMS-PTX-NSC is a promising nanodrug delivery system.
The Application of Spectroscopic Parameters for Computing the Energy Spectrum of Selected Diatomic Molecules (VH, LiH, TiC and CuLi)
Authors: Reggab Khalid
Published: April 2024

A basic theoretical model known as the Schrodinger equation is utilized to explain the physics of the waveform, a phenomenon in quantum mechanics. The whole characterization of a technique’s components is included within the wave function. There are several aspects of the Schrodinger equation’s numerical solution. In this work, we determined mathematically the eigenvalues and characteristic functions of the Eckart-Hellmann potential. This study adopted an estimating strategy for solving the problem recommended by the Nikiforov–Uvarov approach and employed the Greene–Aldrich approximation. The aim was determining bound energies and applying the findings to particular diatomic molecules and their spectroscopic parameters. By contrasting our eigenvalue data with extra numerical data collected by other scholars, the good outcomes of our technique were validated.
Monte Carlo Investigations of Magnetization Plateaus in Mixed Nano-Kagome Bilayer Lattices
Authors: Fadil Z. , Sabbah Hussein , El Fdil R. , Ifseisi Ahmad A. , Rosaiah P. , Raorane Chaitany Jayprakash , Kim Seong Cheol
Published: April 2024

The Monte Carlo studies presented in this paper provide insight into stable phases, magnetic hysteresis cycles and magnetization plateaus in mixed nano-Kagome bilayer lattices. In particular, exchange coupling reduction affects coercive and saturation fields, and temperature variations impact hysteresis loop regions. The role of the crystal field in the design of magnetization plateaus was highlighted. The study explores the interaction of magnetization plates and their response to critical and saturation fields, depending on the crystal field and ferrielectric coupling. Extremely low temperatures ([Formula: see text]) play a crucial role in the manifestation of magnetization plateaus. These results suggest possible applications in nanotechnology, particularly in the design of storage media.
Geochemistry and magmatic petrology of meta-ophiolites from the Bajgan Complex (Makran Accretionary Prism, SE Iran): new insights on the nature of the Early Cretaceous Middle East Neotethys
Authors: Saccani Emilio , Barbero Edoardo , Delavari Morteza , Dolati Asghar , Brombin Valentina , Marroni Michele , Pandolfi Luca
Published: April 2024

The Bajgan Complex in the North Makran Domain (Makran Accretionary Prism) comprises disrupted meta-ophiolitic sequences originating from oceanic crust protoliths. They include ultramafic and mafic cumulates, isotropic gabbros, plagiogranites and basalts. Ultramafic–mafic cumulates and plagiogranites exhibit compositions akin to rocks formed in mid-ocean ridge settings. Isotropic gabbro and basalt protoliths can be subdivided into three distinct geochemical types. Type-1 rocks are sub-alkaline (Nb/Y < 0.1) with low Th, Nb and Ta contents and La
N
/Yb
N
ratios <1, resembling those of normal mid-ocean ridge basalts (N-MORB). Type-2 rocks display slight enrichment in Th, Ta, Nb (Nb/Y = 0.36–0.45) and La
N
/Yb
N
 = 2.12–3.20, resembling the chemistry of enriched (E-) MORB. Type-3 basalts show an alkaline nature (Nb/Y = 0.88–1.82), significant Th, Ta and Nb enrichment, and high La
N
/Yb
N
ratios (7.01–20.08), resembling the chemistry of alkaline basalts (ocean island basalts, OIB). Petrogenetic modelling indicates that N-MORB protoliths originated from a depleted MORB mantle source, whereas E-MORB and OIB protoliths were generated from partial melting of sub-oceanic depleted sources that underwent varying degrees of OIB-type enrichment. The Bajgan meta-ophiolitic protoliths were formed within a Late Jurassic to Cretaceous oceanic basin influenced by mantle plume activity and plume–ridge interaction.

Supplementary material
: Field photographs, micrographs, rocks sampled, analytical methods and results, and supplementary tables are available at
https://doi.org/10.6084/m9.figshare.c.7193937

Thematic collection:
This article is part of the Ophiolites, melanges and blueschists collection available at:
https://www.lyellcollection.org/topic/collections/ophiolites-melanges-and-blueschists
Translating Test Responses to Images for Test-termination Prediction via Multiple Machine Learning Strategies
Authors: Wang Hongfei , Li Jingyao , Wang Jiayi , Ping Zijun , Xiong Hongcan , Liu Wei , Zou Dongmian
Published: April 2024

Failure diagnosis is a software-based, data-driven procedure. Collecting an excessive amount of fail data not only increases the overall test cost but can also potentially reduce diagnostic resolution. Thus, test-termination prediction is proposed to dynamically determine the appropriate failing test pattern to terminate testing, producing an amount of test data that is sufficient for an accurate diagnosis analysis. In this work, we describe a set of novel methods utilizing advanced machine learning techniques for efficient test-termination prediction. To implement this approach, we first generate images representing failing test responses from failure-log files. These images are then used to train a multi-layer convolutional neural network (CNN) incorporating a residual block. The trained CNN model leverages the images and known diagnostic results to determine the optimal test-termination strategy within the testing process, ensuring efficient and high-quality diagnosis. In addition to the integration of test response-to-image translation, our approach harnesses two cutting-edge learning strategies to enhance fail data and boost performance in subsequent tasks. The first strategy is transfer learning, which utilizes sample-label information from one circuit to guide the decision of whether to continue or stop testing for another circuit lacking labels. The second strategy involves the use of a generative deep model to generate fail data in the form of synthetic images. This technique increases the modeling effectiveness by expanding the volume of training samples. Experimental results conducted on actual failing chips and standard benchmarks validate that our proposed method surpasses existing approaches. Our method creates opportunities to harness the power of recent advances in machine learning for improving test and diagnosis efficiency.
Local Proofs Approaching the Witness Length
Authors: Ron-Zewi Noga , Rothblum Ron
Published: April 2024

Interactive oracle proofs (IOPs) are a hybrid between interactive proofs and PCPs. In an IOP, the prover is allowed to interact with a verifier (like in an interactive proof) by sending relatively long messages to the verifier, who in turn is only allowed to query a few of the bits that were sent (like in a PCP). Efficient IOPs are currently at the core of leading practical implementations of highly efficient proof-systems.

In this work we construct, for a large class of NP relations, IOPs in which the communication complexity approaches the witness length. More precisely, for any NP relation for which membership can be decided in polynomial-time with bounded polynomial space (i.e., space
n
ξ
for some sufficiently small constant ξ > 0; e.g., SAT, Hamiltonicity, Clique, Vertex-Cover) and for any constant γ > 0, we construct an IOP with communication complexity (1 + γ) ⋅
n
, where
n
is the original witness length. The number of rounds, as well as the number of queries made by the IOP verifier, are constant.

This result improves over prior works on short IOPs/PCPs in two ways. First, the communication complexity in these short IOPs is proportional to the complexity of
verifying
the NP witness, which can be polynomially larger than the witness size. Second, even ignoring the difference between witness length and non-deterministic verification time, prior works incur (at the very least) a large constant multiplicative overhead to the communication complexity.

In particular, as a special case, we also obtain an IOP for CircuitSAT with communication complexity (1 + γ) ⋅
t
, for circuits of size
t
and any constant γ > 0. This improves upon the prior state-of-the-art work of Ben Sasson et al. (ICALP, 2017) who construct an IOP for CircuitSAT with communication length
c ⋅ t
for a large (unspecified) constant
c
≥ 1.

Our proof leverages the local testability and (relaxed) local correctability of high-rate tensor codes, as well as their support of a sumcheck-like procedure. In particular, we bypass the barrier imposed by the low rate of
multiplication codes
(e.g., Reed–Solomon, Reed–Muller, or AG codes)—a key building block of all known short PCP/IOP constructions.
A Meta-Study of Software-Change Intentions
Authors: Krüger Jacob , Li Yi , Lossev Kirill , Zhu Chenguang , Chechik Marsha , Berger Thorsten , Rubin Julia
Published: April 2024

Every software system undergoes changes, for example, to add new features, fix bugs, or refactor code. The importance of understanding software changes has been widely recognized, resulting in various techniques and studies, for instance, on change-impact analysis or classifying developers’ activities. Since changes are triggered by developers’ intentions—something they plan or want to change in the system—many researchers have studied intentions behind changes. While there appears to be a consensus among software-engineering researchers and practitioners that knowing the intentions behind software changes is important, it is not clear how developers can actually benefit from this knowledge. In fact, there is no consolidated, recent overview of the state of the art on software-change intentions (SCIs) and their relevance for software engineering. We present a meta-study of 122 publications, which we used to derive a categorization of SCIs and to discuss motivations, evidence, and techniques relating to SCIs. Unfortunately, we found that individual pieces of research are often disconnected from each other, because a common understanding is missing. Similarly, some publications showcase the potential of knowing SCIs, but more substantial research to understand the practical benefits of knowing SCIs is needed. Our contributions can help researchers and practitioners improve their understanding of SCIs and how SCIs can aid software engineering tasks.
Prospects for Antiferromagnetic Spintronic Devices
Authors: Khalili Amiri Pedram , Phatak Charudatta , Finocchio Giovanni
Published: April 2024

This article examines recent advances in the field of antiferromagnetic spintronics from the perspective of potential device realization and applications. We discuss advances in the electrical control of antiferromagnetic order by current-induced spin–orbit torques, particularly in antiferromagnetic thin films interfaced with heavy metals. We also review possible scenarios for using voltage-controlled magnetic anisotropy as a more efficient mechanism to control antiferromagnetic order in thin films with perpendicular magnetic anisotropy. Next, we discuss the problem of electrical detection (i.e., readout) of antiferromagnetic order and highlight recent experimental advances in realizing anomalous Hall and tunneling magnetoresistance effects in thin films and tunnel junctions, respectively, which are based on noncollinear antiferromagnets. Understanding the domain structure and dynamics of antiferromagnetic materials is essential for engineering their properties for applications. For this reason, we then provide an overview of imaging techniques as well as micromagnetic simulation approaches for antiferromagnets. Finally, we present a perspective on potential applications of antiferromagnets for magnetic memory devices, terahertz sources, and detectors.
The Economics of Drought
Authors: Edwards Eric , Sanchez Leslie , Sekhri Sheetal
Published: April 2024

Water scarcity and drought have determined the structure, location, and fate of civilizations throughout history. Drought remains an important factor in the performance of developed and developing economies, especially in the agricultural sector. While significant attention has been paid to drought as a meteorological phenomenon and on its economic impact, comparative institutional analysis of the economics of drought is limited. In this review, we focus on how economic institutions, the humanly devised constraints that shape the allocation and use of water, impact the severity and incidence of droughts. Water property rights in developed countries encourage infrastructure investments and reallocations that mitigate drought impacts, although such institutions may codify inequitable water access during drought. Developing economies rely more on informal strategies for mitigating drought and remain more vulnerable, experiencing economic losses, conflict, and violence.
A Novel State-of-Health Prediction and Assessment Strategies for High-Capacity Mining Lithium-Ion Batteries Based on Multi-Indicator
Authors: Feng Juqiang , Cai Feng , Zhan Xinju , Zhang Xing , Zhao Yang , Wang Shunli
Published: April 2024

Accurately assessing battery state of health (SOH) is crucial for ensuring the safety of lithium-ion batteries. However, current SOH evaluation methods suffer from inconsistent criteria and limited accuracy in prediction models. This paper introduces a novel SOH prediction and assessment strategy that relies on multiple indicators to address these challenges. First, multifaceted health factors are extracted based on charge cycle data, including battery charging time, incremental capacity, and d

V
/d

t
curve. Subsequently, a support vector regression model optimized by the sparrow search algorithm is proposed to predict SOH. The results show that MAE, RMSE, and MAPE are less than 0.037%, 0.047%, and 0.04%, respectively. Meanwhile, the Kalman filtering method is used to identify the second-order RC model online, and the relative SOH curves are obtained by defining the SOH through the internal resistance. Finally, by analyzing the effects of capacity and internal resistance changes on SOH, a new strategy for SOH assessment is proposed, which considers various factors and selects an appropriate judgment mechanism according to the characteristics exhibited by the battery at different life stages. The strategy is more conservative and reliable, providing a solid guarantee for the safe operation of mining equipment.
Extracting and Interpreting Electrochemical Impedance Spectra (EIS) from Physics-Based Models of Lithium-Ion Batteries
Authors: Zhu Huayang , Evans Tyler A. P. , Weddle Peter J. , Colclasure Andrew M. , Chen Bor-Rong , Tanim Tanvir R. , Vincent Tyrone L. , Kee Robert J.
Published: April 2024

This paper implements a highly efficient algorithm to extract electrochemical impedance spectra (EIS) from physics-based battery models (e.g., a P2D model). The mathematical approach is different from how EIS is practiced experimentally. Experimentally, the voltage (current) is harmonically perturbed over a wide range of frequencies and the amplitude and phase shift of the corresponding current (voltage) is measured. The experimental approach can be implemented in simulation software, but is computationally expensive. The approach here is to determine locally linear state-space models from the full physical model. The four Jacobian matrices that are the basis of the state-space models can be derived by numerical differentiation of the physical model. The EIS is then extracted from the state-space model using computationally efficient matrix-manipulation techniques. The algorithm can evaluate the full EIS at an instant in time during a transient, independent of whether the battery is in a stationary state. The approach is also able to separate the full-cell impedance to evaluate partial EIS, such as for a battery anode alone. Although such partial EIS is difficult to measure experimentally, the partial EIS provides valuable insights in interpreting the full-cell EIS.