bluuebunny/crossref_metadata_2025 huggingface dataset:
Case Report: Disseminated Edwardsiella tarda infection in an immunocompromised patient
Authors: An Lucia , Chan June L. , Nguyen Margaret , Yang Shangxin , Deville Jaime G.
Published: November 2023

Human infection caused by bacteria of the Edwardsiella genus is rare and most often presents with gastroenteritis that rarely requires antibiotics. Our case report describes a medically complex patient with chronic steroid use contributing to an immunocompromised state, who presented with fever and abdominal pain. The patient was later found to have Edwardsiella tarda (E. tarda) bacteremia and underwent paracentesis confirming E. tarda bacterial peritonitis requiring a prolonged antibiotic course. This case report aims to illustrate the presentation, diagnosis, and management of an uncommon infection that can have severe complications especially among immunocompromised patients.
Estimating cardiac output from coronary CT angiography: an individualized compartment model in comparison to the Stewart–Hamilton method
Authors: Leiknes Jon Bjarne , Hiorth Aksel , Havnen Jorunn , Greve Ole Jacob , Kurz Kathinka Dæhli , Larsen Alf Inge
Published: November 2023

Background

Attenuation is correlated with the concentration of contrast medium (CM) in the arteries. The cardiac output (CO) affects the concentration of CM in the circulatory system; therefore, CO affects the time–density curve (TDC). Thus, estimating CO using TDC from test-bolus images acquired in computed tomography (CT) is possible. In this study, we compare two methods of estimating CO, namely, an individualized mathematical compartment model, integrating patient, contrast, and scanning factors with TDC, and the Stewart–Hamilton method based on the area under the curve of the TDC.

Materials and methods

Attenuation in the aorta was measured during test-bolus in 40 consecutive patients with a clinical indication for coronary CT angiography (CCTA). Each participant underwent cardiac magnetic resonance imaging following CCTA to validate the estimated CO. The individual compartment model used TDC in conjunction with scanning and patient-specific parameters to estimate the concentration of CM and CO over time. This was compared to the CO calculated from the area under the curve using the Stewart–Hamilton method.

Results

Both CO estimated with our individualized compartment model (r = 0.66, p < 0.01) and the Stewart–Hamilton method (r = 0.53, p < 0.01) were moderately correlated with CO measured with cardiac MRI. Body surface area (BSA) and time to peak (TTP) affected the accuracy of our model. Lower BSA resulted in overestimation, and lower TTP resulted in CO underestimation, respectively. We found no gender-specific difference in the accuracy of our model when correcting for BSA. The Stewart–Hamilton method performed better with a more complete TDC, whereas the compartment model performed better overall with a partial TDC.

Conclusion

The TDC acquired in CCTA allows for CO estimation. Both the Stewart–Hamilton method and our mathematical compartment model show moderate correlation when applied to our data, although each method has its strengths and limitations. If the majority of the TDC is known, the Stewart–Hamilton method may be more reliable, but an individual compartment model is preferable when there are insufficient data points in the TDC. Regardless, both methods can potentially increase the diagnostic information acquired from a CCTA, which is increasingly recommended in clinical guidelines.
Real-time arrhythmia detection using convolutional neural networks
Authors: Vu Thong , Petty Tyler , Yakut Kemal , Usman Muhammad , Xue Wei , Haas Francis M. , Hirsh Robert A. , Zhao Xinghui
Published: November 2023

Cardiovascular diseases, such as heart attack and congestive heart failure, are the leading cause of death both in the United States and worldwide. The current medical practice for diagnosing cardiovascular diseases is not suitable for long-term, out-of-hospital use. A key to long-term monitoring is the ability to detect abnormal cardiac rhythms, i.e., arrhythmia, in real-time. Most existing studies only focus on the accuracy of arrhythmia classification, instead of runtime performance of the workflow. In this paper, we present our work on supporting real-time arrhythmic detection using convolutional neural networks, which take images of electrocardiogram (ECG) segments as input, and classify the arrhythmia conditions. To support real-time processing, we have carried out extensive experiments and evaluated the computational cost of each step of the classification workflow. Our results show that it is feasible to achieve real-time arrhythmic detection using convolutional neural networks. To further demonstrate the generalizability of this approach, we used the trained model with processed data collected by a customized wearable sensor from a lab setting, and the results shown that our approach is highly accurate and efficient. This research provides the potentials to enable in-home real-time heart monitoring based on 2D image data, which opens up opportunities for integrating both machine learning and traditional diagnostic approaches.
Enhanced control strategy and energy management for a photovoltaic system with hybrid energy storage based on self-adaptive bonobo optimization
Authors: Khairalla Ahmed G. , Kotb Hossam , AboRas Kareem M. , Ragab Muhammad , ElRefaie Hesham B. , Ghadi Yazeed Yasin , Yousef Amr
Published: November 2023

Large-scale energy storage systems (ESSs) that can react quickly to energy fluctuations and store excess energy are required to increase the reliability of electricity grids that rely heavily on renewable energy sources (RESs). Hybrid systems, which combine different energy storage technologies such as batteries and supercapacitors, are becoming increasingly popular because no single technology can satisfy all requirements. In this study, a supercapacitor is used to stabilize quickly shifting bursts of power, while a battery is used to stabilize gradually fluctuating power flow. This paper proposes a robust controller for managing the direct current (DC) bus voltage to optimize the performance of ESS. The proposed controller combines a fractional-order proportional integral (FOPI) with a classical PI controller for the first time in the DC microgrid area. The hybrid (FOPI-PI) controller achieves an outstanding and superior performance in all transient and dynamic response specifications compared to other traditional controllers. The parameters of the suggested controller are incorporated with the self-adaptive bonobo optimizer (SaBO) to determine the optimal values. Furthermore, various optimization techniques are applied to the model and the SaBO’s output outperforms other techniques by minimizing the best objective function. In addition, the current study has utilized a novel power management strategy that includes two closed current loops for both batteries and supercapacitors. By using this method, batteries’ lifespans may be increased while still retaining optimal system performance. The suggested controller is implemented in MATLAB/Simulink 2022b, and the outcomes are reported for several case studies. The findings demonstrate that the control technique remarkably improves the transient response, such as transient duration, overshoot/undershoot, and the settling time. The proposed controller (FOPI-PI) with the SaBO optimizer is effective in maintaining the DC bus voltage under load and solar system variation.
An optimal dispatch schedule of EVs considering demand response using improved MACD algorithm
Authors: Bouzguenda Mounir , Hatatah Muahmmad , Sheharyar  , Ali Aamir , Abbas Ghulam , Khan Aamir , Touti Ezzeddine , Yousef Amr , Mirsaeidi Sohrab , Alshahir Ahmed
Published: November 2023

The growing popularity of electric vehicles presents a significant challenge to current electric grids since the rising number of these vehicles places additional strain on power systems inside distribution networks. A proposed paradigm is presented for electric vehicles (EVs), which is subsequently partitioned into three distinct dispatching areas to assess its practicality. This study is structured around two primary objectives. The first objective focuses on EV owners aiming to minimize their electricity consumption costs while also receiving compensation for providing services. The second objective involves using an aggregator to establish distinct tariffs for each dispatching area. Additionally, the aggregator aims to shift the charging load demand from peak to off-peak hours and distribute the charging demand to each agent. The authors of this study propose the utilization of a charging and discharging coordination method, specifically the Multi-Agents Charging and Discharging (MACD) algorithm, as a means to successfully tackle the issue of charging demand during peak hours. The objective of the proposed algorithm is to effectively handle the increased charging requirements during peak periods by using vehicle-to-grid (V2G) technologies within the context of Smart Grid systems and electric vehicle (EV) batteries. Importantly, this reduction is achieved without compromising the performance of electric vehicles (EVs) or the convenience experienced by EV owners. The algorithm under consideration demonstrates reduced power charging costs for various sectors. Specifically, it achieves a decrease of 15% for households, 14.6% for corporate buildings, and 14.5% for Industrial Park EV aggregators.
The role of WRKY transcription factors in exogenous potassium (K+) response to NaCl stress in Tamarix ramosissima
Authors: Chen Yahui , Zhang Xuanyi , Fan Yunlong , Sui Dezong , Jiang Jiang , Wang Lei
Published: November 2023

Introduction: Soil salinization poses a significant challenge to plant growth and vitality. Plants like Tamarix ramosissima Ledeb (T. ramosissima), which are halophytes, are often integrated into planting schemes tailored for saline environments. Yet, the role of WRKY transcription factors in T. ramosissima, especially under sodium chloride (NaCl) stress mitigated by exogenous K+ application, is not well-understood. This research endeavors to bridge this knowledge gap.

Methods: Using Pfam protein domain prediction and physicochemical property analysis, we delved into the WRKY genes in T. ramosissima roots that are implicated in counteracting NaCl stress when aided by exogenous K+ applications. By observing shifts in the expression levels of WRKY genes annotated to the KEGG pathway under NaCl stress at 0, 48, and 168 h, we aimed to identify potential key WRKY genes.

Results: We found that the expression of 56 WRKY genes in T. ramosissima roots responded to exogenous K+ application during NaCl stress at the indicated time points. Particularly, the expression levels of these genes were primarily upregulated within 168 h. From these, 10 WRKY genes were found to be relevant in the KEGG pathways. Moreover, six genes, namely Unigene0024962, Unigene0024963, Unigene0010090, Unigene0007135, Unigene0070215, and Unigene0077293, were annotated to the Plant-pathogen interaction pathway or the MAPK signaling pathway in plants. These genes exhibited dynamic expression regulation at 48 h with the application of exogenous K+ under NaCl stress.

Discussion: Our research highlights that WRKY transcription factors can modulate the activation or inhibition of related genes during NaCl stress with the application of exogenous K+. This regulation enhances the plant’s adaptability to saline environments and mitigates the damage induced by NaCl. These findings provide valuable gene resources for future salt-tolerant Tamarix breeding and expand our understanding of the molecular mechanisms of WRKY transcription factors in alleviating NaCl toxicity.
Molecular analysis of eight splicing variants in the hydroxymethylbilane synthase gene
Authors: Ren Yi , Wang Jiajia , Li Shuang , Lei Jiajia , Liu Yunfeng , Wang Yan , Gao Fei , Wang Jianhong , Yin Jianhong , Yang Jing
Published: November 2023

Background: Molecular genetic testing is the most sensitive and specific method to confirm acute intermittent porphyria (AIP), a rare autosomal dominant disease, caused by Hydroxymethylbilane synthase (HMBS) gene mutation. According to the Human Gene Mutation Database (HGMD), approximately 20% of the reported HMBS gene variants affect pre-RNA splicing. Thus, the ensuing challenge is how to decipher the pathogenicity of these splicing variants.

Methods: Using next-generation sequencing, we identified a novel heterozygous variant in the HMBS gene (c.160 + 5G>C) from a Chinese family with AIP. And, previously, seven HMBS variants (c.33 + 5G>A, c.88-16_88-4del, c.88-2A>G, c.161-1G>C, c.652-1G>A, c.772-2A>G and c.772-1G>C) have been reported to be linked with AIP. Herein, we performed a valid and novel in vitro minigene assay to analyze the pathogenicity of these eight splicing variants.

Results: By minigene assay in 293 T cell experiments, we demonstrated that all eight variants caused splicing defects in the pre-mRNA of the HMBS gene: c.160 + 5G>C (intron3p_141bp retention), c.33 + 5G>C(intron1p_91bp retention), c.88-16_88-4del and c.88-2A>G (Exon3p_15bp deletion), c.161-1G>C (Exon4p_18bp deletion), c.652-1G>A (Exon11p_1bp deletion), c.772-2A>G and c.772-1G>C (intron11q_104bp retention or Exon12p_4bp deletion).Encouragingly, the c.160 + 5G>C RNA sequencing from peripheral blood lymphocytes was consistent with the minigene assay result.

Conclusion: We have made a pioneering attempt to apply minigene in vitro validation to the HMBS gene to evaluate the splicing effect of eight variants, including a novel splice variant (c.160 + 5G>C). This study provides a molecular basis for future research on the pathogenesis and gene therapy of AIP.
A look at the past to draw lessons for the future: how the case of an urgent ICU transfer taught us to always be ready with a plan B
Authors: Brunelli Laura , Miotto Edoardo , Del Pin Massimo , Celotto Daniele , Moccia Adriana , Borghi Gianni , De Monte Amato , Macor Cristiana , Cocconi Roberto , Lattuada Luca , Brusaferro Silvio , Arnoldo Luca
Published: November 2023

Objective

The urgent transfer of an intensive care unit (ICU) is particularly challenging because it carries a high clinical and infectious risk and is a critical node in a hospital’s patient flow. In early 2017, exceptional rainfall damaged the roof of the tertiary hospital in Udine, necessitating the relocation of one of the three ICUs for six months. We decided to assess the impact of this transfer on quality of care and patient safety using a set of indicators, primarily considering the incidence of healthcare-associated infections (HAIs) and mortality rates.

Methods

We performed a retrospective, observational analysis of structural, process, and outcome indicators comparing the pre- and posttransfer phases. Specifically, we analyzed data between July 2016 and June 2017 for the transferred ICU and examined mortality and the incidence of HAI.

Results

Despite significant changes in structural and organizational aspects of the unit, no differences in mortality rates or cumulative incidence of HAIs were observed before/after transfer. We collected data for all 393 patients (133 women, 260 men) admitted to the ICU before (49.4%) and after transfer (50.6%). The mortality rate for 100 days in the ICU was 1.90 (34/1791) before and 2.88 (37/1258) after transfer (p = 0.063). The evaluation of the occurrence of at least one HAI included 304 patients (102 women and 202 men), as 89 of them were excluded due to a length of stay in the ICU of less than 48 h; again, there was no statistical difference between the two cumulative incidences (13.1% vs. 6.9%, p = 0.075).

Conclusion

In the case studied, no adverse effects on patient outcomes were observed after urgent transfer of the injured ICU. The indicators used in this study may be an initial suggestion for further discussion.
Fecal microbial composition associated with testosterone in the development of Meishan male pigs
Authors: Jiang Xueyuan , Deng Shaoshan , Lu Naisheng , Yao Wen , Xia Dong , Tu Weilong , Lei Hulong , Jia Peng , Gan Yeqing
Published: November 2023

Introduction

The gut microbiota closely relates to host health, whereas the relationship between gut microbiota and testosterone during the development of Meishan male pigs remains unclear. This study investigated the fecal microbiota composition and testosterone level during development in Meishan male pigs.

Methods

Fresh fecal samples of 20 healthy Meishan male pigs were individually collected at 10 and 22 weeks (wk) of age for testosterone content detection and bacteria pyrosequencing analysis. Anaerobic culture experiment of fecal bacteria in vitro was performed for bacteria pyrosequencing analysis.

Results

The fecal testosterone content increased significantly from 10 weeks (wk) to 22 wk of age (P < 0.05). Meanwhile, the boars at 22 wk had a lower abundance of phylum Bacteroidetes and Proteobacteria, and genus Alloprevotella, Prevotella_1, Prevotellaceae_NK3B31_group, and Streptococcus in the fecal microbiota composition (P < 0.05). but higher proportions of the phylum Actinobacteria, Firmicutes, Kiritimatiellaeota, and Tenericutes, and genus Clostridium_sensu_stricto_1, Muribaculaceae and Terrisporobacter than that at 10 wk (P < 0.05), and the Firmicutes to Bacteroidetes ratio was higher at 22 wk than 10 wk (P < 0.05). Moreover, the fecal testosterone level significantly correlated with the relative abundance of the phylum Actinobacteria, Firmicutes, and Tencuteseri, and genus Alloprevotella, Clostridium_sensu_stricto_1, Muribaculaceae, Prevotella_1 and Streptococcus. Furthermore, the in vitro experiments indicated that the abundance of the phylum Proteobacteria and genus Escherichia-Shigella reduced with the increase of supplemental testosterone level. In contrast, the proportion of Firmicutes phylum increased with additional testosterone levels.

Discussion

Testosterone could modulate the microflora structure. Meanwhile, the bacteria could degrade the testosterone in a dose testosterone-dependent manner. These results provide us with new insights into the relationship between the gut microbiome and testosterone and the contributions of the gut microbiome in physiological regulation in response to gonad development.
Does metabolic water control the phosphate oxygen isotopes of microbial cells?
Authors: Weiner Tal , Tamburini Federica , Keren Nir , Keinan Jonathan , Angert Alon
Published: November 2023

The oxygen isotopes ratio (δ18O) of microbial cell water strongly controls the δ18O of cell phosphate and of other oxygen-carrying moieties. Recently it was suggested that the isotopic ratio in cell water is controlled by metabolic water, which is the water produced by cellular respiration. This potentially has important implications for paleoclimate reconstruction, and for measuring microbial carbon use efficiency with the 18O-water method. Carbon use efficiency strongly controls soil organic matter preservation. Here, we directly tested the effect of metabolic water on microbial cells, by conducting experiments with varying the δ18O of headspace O2 and the medium water, and by measuring the δ18O of cell phosphate. The latter is usually assumed to be in isotopic equilibrium with the cell’s water. Our results showed no correlation between the δ18O of O2 and that of the cell phosphate, contradicting the hypothesis that metabolic water is an important driver of δ18O of microbial cell water. However, our labeled 18O water experiments indicated that only 43% of the oxygen in the cell’s phosphate is derived from equilibration with the medium water, during late-log to early-stationary growing phase. This could be explained by the isotopic effects of intra-and extra-cellular hydrolysis of organic compounds containing phosphate.
Movement predictability modulates sensorimotor processing
Authors: Altermatt Miriam , Thomas Felix Alexander , Wenderoth Nicole
Published: November 2023

Introduction

An important factor for optimal sensorimotor control is how well we are able to predict sensory feedback from internal and external sources during movement. If predictability decreases due to external disturbances, the brain is able to adjust muscle activation and the filtering of incoming sensory inputs. However, little is known about sensorimotor adjustments when predictability is increased by availability of additional internal feedback. In the present study we investigated how modifications of internal and external sensory feedback influence the control of muscle activation and gating of sensory input.

Methods

Co-activation of forearm muscles, somatosensory evoked potentials (SEP) and short afferent inhibition (SAI) were assessed during three object manipulation tasks designed to differ in the predictability of sensory feedback. These included manipulation of a shared object with both hands (predictable coupling), manipulation of two independent objects without (uncoupled) and with external interference on one of the objects (unpredictable coupling).

Results

We found a task-specific reduction in co-activation during the predictable coupling compared to the other tasks. Less sensory gating, reflected in larger subcortical SEP amplitudes, was observed in the unpredictable coupling task. SAI behavior was closely linked to the subcortical SEP component indicating an important function of subcortical sites in predictability related SEP gating and their direct influence on M1 inhibition.

Discussion

Together, these findings suggest that the unpredictable coupling task cannot only rely on predictive forward control and is compensated by enhancing co-activation and increasing the saliency for external stimuli by reducing sensory gating at subcortical level. This behavior might serve as a preparatory step to compensate for external disturbances and to enhance processing and integration of all incoming external stimuli to update the current sensorimotor state. In contrast, predictive forward control is accurate in the predictable coupling task due to the integrated sensory feedback from both hands where sensorimotor resources are economized by reducing muscular co-activation and increasing sensory gating.
Systematic review: clinical characteristics of anti-N-methyl-D-aspartate receptor encephalitis
Authors: Zhao Xi , Teng Yuou , Ni Jingnian , Li Ting , Shi Jing , Wei Mingqing
Published: November 2023

Background

The number of reported cases of anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis has gradually increased since its discovery in 2007, while there are no uniform treatment guidelines.

Objective

To summarize the clinical characteristics of patients with anti-NMDAR encephalitis and to analyze the factors affecting the disease prognosis.

Methods

A systematic analysis of medical records was conducted, and PubMed, Embase, and Cochrane Library were searched from January 1, 2011, to December 31, 2021. Data were extracted, analyzed, and recorded in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines.

Results

This study included 472 case reports. Most patients had prodromal symptoms of about 2 weeks, including psychiatric symptoms (53.2%), flu-like symptoms (51.5%), and seizures (23.9%), among others. Poor prognoses were associated with patients who had autonomic instability (p = 0.010), central hypoventilation (p = 0.014), and ICU support (p = 0.002). Patients with a higher age of onset were more likely to develop central hypoventilation (OR 1.024, CI 1.006–1.042, p = 0.009), cognitive impairment (OR 1.023, CI 1.009–1.037, p = 0.001), and memory impairment (OR 1.034, CI 1.017–1.050, p < 0.001), whereas patients with a lower age were more likely to have seizures (OR 0.979, CI 0.965–0.993, p = 0.003). In this study, 97.0% of patients received immunotherapy, with the most commonly used treatment regimen being intravenous methylprednisolone (IVGC) and intravenous immunoglobulin (IVIG). When compared with other treatment regimens, the IVGC+IVIG regimen (p < 0.001) resulted in better prognoses.

Conclusion

When encountering patients with fever, headache, and initial psychiatric symptoms of unknown etiology, clinicians should test their CSF for antibodies to distinguish autoimmune encephalitis. Patients with autonomic instability, central hypoventilation, and ICU support had poorer prognoses. Clinicians should be aware that older patients are more likely to develop central hypoventilation, cognitive impairment, and memory impairment, while younger patients are more likely to develop seizures. The IVGC+IVIG treatment regimen has better prognoses than others. This study includes case reports, which have obvious selection bias, and there are no unified standards to measure the severity of the disease. Therefore, in the future, larger samples and randomized controlled trials are needed to evaluate the efficacy of different treatment regimens.
A novel splicing mutation identified in a DMD patient: a case report
Authors: Wen Yuting , Yang Luo , Shen Gan , Dai Siyu , Wang Jing , Wang Xiang
Published: November 2023

Background

Duchenne muscular dystrophy (DMD, ORPHA:98896) is a lethal X-linked recessive disease that manifests as progressive muscular weakness and wasting. Mutations in the dystrophy gene (DMD) are the main cause of Duchenne muscular dystrophy.

Case presentation

This study aims to determine novel mutations of DMD and help preimplantation genetic diagnosis (PGD) for family planning. Here present a 4-year-old Chinses boy with DMD, whole-exome sequencing (WES) was performed to identify the molecular basis of the disease. It was confirmed that the boy carried a novel hemizygous mutation of NC_000023.11(NM_004006.3): c.5912_5922 + 19delinsATGTATG in DMD which inherited from his mother. This led to the aberrant splicing of DMD which demonstrated by a minigene splicing assay and further resulted in the impairment of the dystrophy protein.

Conclusions

Our study discovered a novel splicing mutation of DMD in a DMD patient, which expands the variant spectrum of this gene and provide precise genetic diagnosis of DMD for timely therapy. Meanwhile, this finding will supply valuable information for preimplantation genetic diagnosis.
Fiber bundle model applied to slope stability assessment: co-detection multi-threshold analysis for early warning
Authors: Faillettaz Jerome
Published: November 2023

Forecasting the imminent failure of natural slopes is crucial for effective Disaster Risk Reduction. However, the nonlinear nature of geological material failure makes predictability challenging. Recent advancements in seismic wave monitoring and analysis offer promising solutions. In this study, we investigated the co-detection method, which involves real-time processing of micro-seismic events detected concurrently by multiple sensors, to provide easy access to their initial magnitude and approximate location. By studying the Fiber Bundle Model and considering the attenuation of seismic waves, we demonstrated disparities in the statistical behavior of various rupture types before global catastrophic failure. Comparing avalanches with attenuated seismic wave amplitudes directly measured at sensor locations, we observed differences in their evolution towards catastrophic rupture. Leveraging a network of seismic wave sensors, we showed that the co-detection method was effective in detecting precursory seismic events, even with weak signals, making it a valuable tool for monitoring and predicting unstable slopes. Additionally, we demonstrated that a multi-threshold analysis of co-detection activity allowed for instantaneous capture of the seismic activity structure on unstable slopes. These findings contribute to our understanding of slope stability and offer insights for improved hazard assessment and risk management.
Cataloging SCN resistance loci in North American public soybean breeding programs
Authors: Mahmood Anser , Bilyeu Kristin D. , Škrabišová Mária , Biová Jana , De Meyer Elizabeth J. , Meinhardt Clinton G. , Usovsky Mariola , Song Qijian , Lorenz Aaron J. , Mitchum Melissa G. , Shannon Grover , Scaboo Andrew M.
Published: November 2023

Soybean cyst nematode (SCN) is a destructive pathogen of soybeans responsible for annual yield loss exceeding $1.5 billion in the United States. Here, we conducted a series of genome-wide association studies (GWASs) to understand the genetic landscape of SCN resistance in the University of Missouri soybean breeding programs (Missouri panel), as well as germplasm and cultivars within the United States Department of Agriculture (USDA) Uniform Soybean Tests—Northern Region (NUST). For the Missouri panel, we evaluated the resistance of breeding lines to SCN populations HG 2.5.7 (Race 1), HG 1.2.5.7 (Race 2), HG 0 (Race 3), HG 2.5.7 (Race 5), and HG 1.3.6.7 (Race 14) and identified seven quantitative trait nucleotides (QTNs) associated with SCN resistance on chromosomes 2, 8, 11, 14, 17, and 18. Additionally, we evaluated breeding lines in the NUST panel for resistance to SCN populations HG 2.5.7 (Race 1) and HG 0 (Race 3), and we found three SCN resistance-associated QTNs on chromosomes 7 and 18. Through these analyses, we were able to decipher the impact of seven major genetic loci, including three novel loci, on resistance to several SCN populations and identified candidate genes within each locus. Further, we identified favorable allelic combinations for resistance to individual SCN HG types and provided a list of available germplasm for integration of these unique alleles into soybean breeding programs. Overall, this study offers valuable insight into the landscape of SCN resistance loci in U.S. public soybean breeding programs and provides a framework to develop new and improved soybean cultivars with diverse plant genetic modes of SCN resistance.
Determinants of photochemical characteristics of the photosynthetic electron transport chain of maize
Authors: Liu Xiuping , Qiao Yunzhou , Zhou Wangming , Dong Wenxu , Gu Lianhong
Published: November 2023

Introduction

The photosynthetic electron transport chain (ETC) is the bridge that links energy harvesting during the photophysical reactions at one end and energy consumption during the biochemical reactions at the other. Its functioning is thus fundamental for the proper balance between energy supply and demand in photosynthesis. Currently, there is a lack of understanding regarding how the structural properties of the ETC are affected by nutrient availability and plant developmental stages, which is a major roadblock to comprehensive modeling of photosynthesis.

Methods

Redox parameters reflect the structural controls of ETC on the photochemical reactions and electron transport. We conducted joint measurements of chlorophyll fluorescence (ChlF) and gas exchange under systematically varying environmental conditions and growth stages of maize and sampled foliar nutrient contents. We utilized the recently developed steady-state photochemical model to infer redox parameters of electron transport from these measurements.

Results and discussion

We found that the inferred values of these photochemical redox parameters varied with leaf macronutrient content. These variations may be caused either directly by these nutrients being components of protein complexes on the ETC or indirectly by their impacts on the structural integrity of the thylakoid and feedback from the biochemical reactions. Also, the redox parameters varied with plant morphology and developmental stage, reflecting seasonal changes in the structural properties of the ETC. Our findings will facilitate the parameterization and simulation of complete models of photosynthesis.
Tomato brown rot disease detection using improved YOLOv5 with attention mechanism
Authors: Liu Jun , Wang Xuewei , Zhu Qianyu , Miao Wenqing
Published: November 2023

Brown rot disease poses a severe threat to tomato plants, resulting in reduced yields. Therefore, the accurate and efficient detection of tomato brown rot disease through deep learning technology holds immense importance for enhancing productivity. However, intelligent disease detection in complex scenarios remains a formidable challenge. Current object detection methods often fall short in practical applications and struggle to capture features from small objects. To overcome these limitations, we present an enhanced algorithm in this study, building upon YOLOv5s with an integrated attention mechanism for tomato brown rot detection. We introduce a hybrid attention module into the feature prediction structure of YOLOv5s to improve the model’s ability to discern tomato brown rot objects in complex contexts. Additionally, we employ the CIOU loss function for precise border regression. Our experiments are conducted using a custom tomato disease dataset, and the results demonstrate the superiority of our enhanced algorithm over other models. It achieves an impressive average accuracy rate of 94.6% while maintaining a rapid detection speed of 112 frames per second. This innovation marks a significant step toward robust and efficient disease detection in tomato plants.
Ambivalent identification mediates the relationship between organizational justice and stress
Authors: Ciampa Valeria
Published: November 2023

The present study aims to examine the relationship between organizational justice and employee stress through the lenses of social identity theory and the ambivalent identification process. The research hypotheses assume that employees working in organizational environments with low levels of justice could experience more stress, and this relationship is also mediated by ambivalent identification. In other words, the mediating mechanism of this relation posited that low levels of organizational justice were associated with high levels of ambivalent identification, which in turn increased levels of work-related stress. Across a field study in several organizations from healthcare sectors, results confirmed that employees treated with less fairness experienced high ambivalence toward their organization, which increased their perception of stress, i.e., work-related burnout, client-related burnout, physical symptoms, and interpersonal strain at work. Furthermore, results supported only a full mediation model, in which the direct relationship between organizational justice and stress was not significant. The present results make an important contribution to the research literature on justice: the inclusion of the mediator variable, namely, ambivalent identification, drops the expected direct effect of organizational justice on stress, suggesting a call for action in adopting the social identity perspective in addition to organizational justice models, and specifically introducing the study of a detrimental form of identification, such as ambivalent identification. Limitations and practical implications of the study were discussed.
Assessment, patient selection, and rehabilitation of nerve transfers
Authors: Bateman Emma A. , Larocerie-Salgado Juliana , Ross Douglas C. , Miller Thomas A. , Pripotnev Stahs
Published: November 2023

Peripheral nerve injuries are common and can have a devastating effect on physical, psychological, and socioeconomic wellbeing. Peripheral nerve transfers have become the standard of care for many types of peripheral nerve injury due to their superior outcomes relative to conventional techniques. As the indications for, and use of, nerve transfers expand, the importance of pre-operative assessment and post-operative optimization increases. There are two principal advantages of nerve transfers: (1) their ability to shorten the time to reinnervation of muscles undergoing denervation because of peripheral nerve injury; and (2) their specificity in ensuring proximal motor and sensory axons are directed towards appropriate motor and sensory targets. Compared to conventional nerve grafting, nerve transfers offer opportunities to reinnervate muscles affected by cervical spinal cord injury and to augment natural reinnervation potential for very proximal injuries. This article provides a narrative review of the current scientific knowledge and clinical understanding of nerve transfers including peripheral nerve injury assessment and pre- and post-operative electrodiagnostic testing, adjuvant therapies, and post-operative rehabilitation for optimizing nerve transfer outcomes.
Retrieving BRDFs from UAV-based radiometers for fiducial reference measurements: caveats and recommendations
Authors: Schunke Sebastian , Leroy Vincent , Govaerts Yves
Published: November 2023

Surface Bidirectional reflectance distribution function (BRDF) is a key intrinsic geophysical variable depending only on the characteristics of the observed medium. It is therefore the most suitable measurand to support the definition of fiducial reference measurements (FRM). Field acquisition of surface reflectance data relies on substantial assumptions and simplifications, often without accounting for their impact. For example, the BRDF is a theoretical concept and can never be measured in the field. In contrast, the hemispherical conical reflectance factor (HCRF), which is the measurand obtained during field campaigns, is impacted by all scene elements and is not intrinsic to the surface. This study analyses the impact of four parameters (atmospheric scattering, measurement device field of view cropping, acquisition duration, non-Lambertian reference panels) on HCRF estimation. Simulations are performed on a 3D vegetation scene, using the new radiative transfer model Eradiate. It is found that among the aforementioned parameters, atmospheric scattering alone leads to a relative root-mean-square error (RRMSE) of more than 10% between HCRF and reference Bidirectional reflectance factor (BRF).
A Novel RUL Prognosis Model Based on Counterpropagating Learning Approach
Authors: Baz Mohammed
Published: November 2023

The aviation industry is one of the fastest-growing sectors and is crucial for both passenger transport and logistics. However, the high costs associated with maintenance, refurbishment, and overhaul (MRO) constitute one of the biggest challenges facing this industry. Motivated by the significant role that remaining useful life (RUL) prognostics can play in optimising MRO operations and saving lives, this paper proposes a novel data-driven RUL prognosis model based on counter propagation network principles. The proposed model introduces the recursive growing hierarchical self-organisation map (ReGHSOM) as a variant of SOM that can cluster multivariate time series with high correlations and hierarchical dependencies typically found in RUL datasets. Moreover, ReGHSOM is designed to allow this clustering to evolve dynamically at runtime without imposing constraints or prior assumptions on the hypothesis spaces of the architectures. The output of ReGHSOM is fed into the supervised learning layers of Grossberg to make the RUL prediction. The performance of the proposed model is comprehensively evaluated by measuring its learnability, evolution, and comparison with related work using standard statistical metrics. The results of this evaluation show that the model can achieve an average mean square error of 5.24 and an average score of 293 for the C-MPASS dataset, which are better results than most of the comparable works.
Advancing OCR Accuracy in Image-to-LaTeX Conversion—A Critical and Creative Exploration
Authors: Orji Everistus Zeluwa , Haydar Ali , Erşan İbrahim , Mwambe Othmar Othmar
Published: November 2023

This paper comprehensively assesses the application of active learning strategies to enhance natural language processing-based optical character recognition (OCR) models for image-to-LaTeX conversion. It addresses the existing limitations of OCR models and proposes innovative practices to strengthen their accuracy. Key components of this study include the augmentation of training data with LaTeX syntax constraints, the integration of active learning strategies, and the employment of active learning feedback loops. This paper first examines the current weaknesses of OCR models with a particular focus on symbol recognition, complex equation handling, and noise moderation. These limitations serve as a framework against which the subsequent research methodologies are assessed. Augmenting the training data with LaTeX syntax constraints is a crucial strategy for improving model precision. Incorporating symbol relationships, wherein contextual information is considered during recognition, further enriches the error correction. This paper critically examines the application of active learning strategies. The active learning feedback loop leads to progressive improvements in accuracy. This article underlines the importance of uncertainty and diversity sampling in sample selection, ensuring that the dynamic learning process remains efficient and effective. Appropriate evaluation metrics and ensemble techniques are used to improve the operational learning effectiveness of the OCR model. These techniques allow the model to adapt and perform more effectively in diverse application domains, further extending its utility.
Soil Bacteriome Resilience and Reduced Nitrogen Toxicity in Tomato by Controlled Release Nitrogen Fertilizer Compared to Urea
Authors: Rohrbaugh Carley R. , Dixon Mary M. , Delgado Jorge A. , Manter Daniel K. , Vivanco Jorge M.
Published: November 2023

Controlled release fertilizers (CRFs) mitigate negative effects of high nitrogen (N) fertilization rates, such as N toxicity and soil N loss. However, it is unknown if potentially toxic rates of CRF and quick release fertilizer differentially affect soil bacterial communities. To examine potential N toxicity effects on soil microbial communities, we grew tomato (Solanum lycopersicum “Rutgers”) for eight weeks in soils that were fertilized with high levels of quick release or controlled release urea and in soils with either low or high initial microbial N competitor populations. In both soils, we observed N toxicity in urea-fertilized tomatoes, but toxicity was ameliorated with CRF application. Controlled release fertilization increased soil N retention, thereby reducing soil N loss. While N toxicity symptoms manifested in the plant, the soil microbiome was only minorly affected. There were subtle differences in soil bacterial populations, in which nitrifying bacteria accumulated in soils fertilized at high N rates, regardless of the type of N fertilizer used. Ultimately, CRF reduced plant N toxicity symptoms but did not change the soil microbiome compared to quick release urea. These results show that while there are clear benefits of CRF regarding N toxicity tolerance on crops, the soil microbiome is resilient to this abiotic stressor.
SpineHRformer: A Transformer-Based Deep Learning Model for Automatic Spine Deformity Assessment with Prospective Validation
Authors: Zhao Moxin , Meng Nan , Cheung Jason Pui Yin , Yu Chenxi , Lu Pengyu , Zhang Teng
Published: November 2023

The Cobb angle (CA) serves as the principal method for assessing spinal deformity, but manual measurements of the CA are time-consuming and susceptible to inter- and intra-observer variability. While learning-based methods, such as SpineHRNet+, have demonstrated potential in automating CA measurement, their accuracy can be influenced by the severity of spinal deformity, image quality, relative position of rib and vertebrae, etc. Our aim is to create a reliable learning-based approach that provides consistent and highly accurate measurements of the CA from posteroanterior (PA) X-rays, surpassing the state-of-the-art method. To accomplish this, we introduce SpineHRformer, which identifies anatomical landmarks, including the vertices of endplates from the 7th cervical vertebra (C7) to the 5th lumbar vertebra (L5) and the end vertebrae with different output heads, enabling the calculation of CAs. Within our SpineHRformer, a backbone HRNet first extracts multi-scale features from the input X-ray, while transformer blocks extract local and global features from the HRNet outputs. Subsequently, an output head to generate heatmaps of the endplate landmarks or end vertebra landmarks facilitates the computation of CAs. We used a dataset of 1934 PA X-rays with diverse degrees of spinal deformity and image quality, following an 8:2 ratio to train and test the model. The experimental results indicate that SpineHRformer outperforms SpineHRNet+ in landmark detection (Mean Euclidean Distance: 2.47 pixels vs. 2.74 pixels), CA prediction (Pearson correlation coefficient: 0.86 vs. 0.83), and severity grading (sensitivity: normal-mild; 0.93 vs. 0.74, moderate; 0.74 vs. 0.77, severe; 0.74 vs. 0.7). Our approach demonstrates greater robustness and accuracy compared to SpineHRNet+, offering substantial potential for improving the efficiency and reliability of CA measurements in clinical settings.
Polyherbal and Multimodal Treatments: Kaempferol- and Quercetin-Rich Herbs Alleviate Symptoms of Alzheimer’s Disease
Authors: Alexander Claire , Parsaee Ali , Vasefi Maryam
Published: November 2023

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder impairing cognition and memory in the elderly. This disorder has a complex etiology, including senile plaque and neurofibrillary tangle formation, neuroinflammation, oxidative stress, and damaged neuroplasticity. Current treatment options are limited, so alternative treatments such as herbal medicine could suppress symptoms while slowing cognitive decline. We followed PRISMA guidelines to identify potential herbal treatments, their associated medicinal phytochemicals, and the potential mechanisms of these treatments. Common herbs, including Ginkgo biloba, Camellia sinensis, Glycyrrhiza uralensis, Cyperus rotundus, and Buplerum falcatum, produced promising pre-clinical results. These herbs are rich in kaempferol and quercetin, flavonoids with a polyphenolic structure that facilitate multiple mechanisms of action. These mechanisms include the inhibition of Aβ plaque formation, a reduction in tau hyperphosphorylation, the suppression of oxidative stress, and the modulation of BDNF and PI3K/AKT pathways. Using pre-clinical findings from quercetin research and the comparatively limited data on kaempferol, we proposed that kaempferol ameliorates the neuroinflammatory state, maintains proper cellular function, and restores pro-neuroplastic signaling. In this review, we discuss the anti-AD mechanisms of quercetin and kaempferol and their limitations, and we suggest a potential alternative treatment for AD. Our findings lead us to conclude that a polyherbal kaempferol- and quercetin-rich cocktail could treat AD-related brain damage.
AI-Based Homology Modelling of Fatty Acid Transport Protein 1 Using AlphaFold: Structural Elucidation and Molecular Dynamics Exploration
Authors: Acharya Ranjitha , Shetty Shilpa S. , Pavan Gollapalli , Monteiro Flama , Munikumar Manne , Naresh Sriram , Kumari Nalilu Suchetha
Published: November 2023

Fatty acid transport protein 1 (FATP1) is an integral transmembrane protein that is involved in facilitating the translocation of long-chain fatty acids (LCFA) across the plasma membrane, thereby orchestrating the importation of LCFA into the cell. FATP1 also functions as an acyl-CoA ligase, catalyzing the ATP-dependent formation of fatty acyl-CoA using LCFA and VLCFA (very-long-chain fatty acids) as substrates. It is expressed in various types of tissues and is involved in the regulation of crucial signalling pathways, thus playing a vital role in numerous physiological and pathological conditions. Structural insight about FATP1 is, thus, extremely important for understanding the mechanism of action of this protein and developing efficient treatments against its anomalous expression and dysregulation, which are often associated with pathological conditions such as breast cancer. As of now, there has been no prior prediction or evaluation of the 3D configuration of the human FATP1 protein, hindering a comprehensive understanding of the distinct functional roles of its individual domains. In our pursuit to unravel the structure of the most commonly expressed isoforms of FATP1, we employed the cutting-edge ALPHAFOLD 2 model for an initial prediction of the entire protein’s structure. This prediction was complemented by molecular dynamics simulations, focusing on the most promising model. We predicted the structure of FATP1 in silico and thoroughly refined and validated it using coarse and molecular dynamics in the absence of the complete crystal structure. Their relative dynamics revealed the different properties of the characteristic FATP1.