bluuebunny/crossref_metadata_2025 huggingface dataset:
In Vitro Screening of Endophytic Micromonospora Strains Associated with White Clover for Antimicrobial Activity against Phytopathogenic Fungi and Promotion of Plant Growth
Authors: Sokołowski Wojciech , Wdowiak-Wróbel Sylwia , Marek-Kozaczuk Monika , Kalita Michał
Published: May 2024

Bacteria belonging to the genus Micromonospora are recognized as microorganisms with the potential to be used in biotechnology processes, given their beneficial influence on plant growth and the biocontrol of phytopathogens. In this study, nineteen Micromonospora isolates originating from the root nodules of white clover plants were taxonomically assigned based on the phylogenetic analysis of the 16S rRNA gene and four housekeeping genes. The antifungal properties of the bacteria against phytopathogenic Botrytis cinerea, Fusarium oxysporum, Fusarium equiseti, Sclerotinia sclerotiorum, and Verticillium albo-atrum were tested with the agar plug test and the dual culture test. The ability to produce various metallophores was determined with the agar plug diffusion test on modified chrome azurol S (CAS) agar medium. International Streptomyces Project-2 medium (ISP2) broth amended with 0.2% L-tryptophan was used to indicate the bacterial ability to produce auxins. The strains belonging to M. tulbaghiae, M. inaquosa, and M. violae showed in vitro potential as antimicrobial agents against the tested fungi. M. inaquosa strain 152, M. violae strain 126, M. violae strain 66, and M. violae strain 45 were recognized as the most efficient metallophore producers. M. alfalfae strain 55 and M. lupini strain 5052 were identified as the most promising auxin compound producers and, therefore, show potential as plant-growth-promoting bacteria.
Effects of Grain Sprout Fertilizer Application Rate on Yield and Its Composition of Hybrid Middle Rice–Ratoon Rice System
Authors: Xu Fuxian , Yuan Chi , Han Dong , Xie Rong , Zhou Xingbing , Jiang Peng , Guo Xiaoyi , Xiong Hong , Zhang Lin , Guo Changchun
Published: May 2024

Enhancing yield and achieving environmental goals represent challenges for the future of agriculture. Rational nitrogen (N) management is one of the most promising ways to meet this challenge. However, complicated nitrogen management strategies and considerable input requirements still exist in rice–ratoon rice production. To address this issue, field experiments were conducted with two main high-yield rice crop genotypes and five fertilization treatments at six sites in Southwest China from 2018 to 2020. The results showed the following: (1) the yield of the main rice crop was extremely significantly affected by the year, location, and fertilization, but not by genotype; (2) the yield of the ratoon rice was extremely significantly affected by year, genotype, location, and fertilization; and (3) the total plant N content (TPN) and leaf SPAD value at the full heading stage of the main crop were significantly positively correlated with the total soil N content (TSN) and soil available N (SAN) content of the basic soil. The highly efficient N application rate of grain- and bud-promoting fertilizer for ratoon rice was 60–120 kg ha−1. The TSN, SAN, TPN, and SPAD values higher than 0.247 kg N kg−1, 298 mg N kg−1, 2.159 kg N kg−1, and 49.94 were, respectively, considered the reference values when not applying grain- and bud-promoting fertilizer. A regression equation was established to predict the amount of high-efficiency grain- and bud-promoting fertilizer based on the TSN and SPAD. Overall, the yield of rice–ratoon rice was significantly affected by year, genotype, location, fertilization, and their interactions. The use of the predicted grain- and bud-promoting fertilizer regression equation can achieve high yields under simplified and reduced N input practices in the rice–ratoon rice systems.
Machine Learning-Powered Forecasting of Climate Conditions in Smart Greenhouse Containing Netted Melons
Authors: Jeon Yu-Jin , Kim Joon Yong , Hwang Kue-Seung , Cho Woo-Jae , Kim Hak-Jin , Jung Dae-Hyun
Published: May 2024

The greenhouse environment plays a crucial role in providing favorable conditions for crop growth, significantly improving their quality and yield. Accurate prediction of greenhouse environmental factors is essential for their effective control. Although artificial intelligence technologies for predicting greenhouse environments have been researched recently, there are limitations in applying these to general greenhouse environments due to computing resources or issues with interpretability. Moreover, research on environmental prediction models specifically for melon greenhouses is also lacking. In this study, machine learning models based on MLR (Multiple Linear Regression), SVM (Support Vector Machine), ANN (Artificial Neural Network), and XGBoost were developed to predict the internal temperature, relative humidity, and CO2 conditions of melon greenhouses 30 min in advance. The XGBoost model demonstrated high accuracy and stability, with an R2 value of up to 0.9929 and an RPD (Residual Predictive Deviation) of 11.8464. Furthermore, the analysis of the XGBoost model’s feature importance and decision trees revealed that the model learned the complex relationships and impacts among greenhouse environmental factors. In conclusion, this study successfully developed a predictive model for a greenhouse environment for melon cultivation. The model developed in this study can facilitate an understanding and efficient management of the greenhouse environment, contributing to improvements in crop yield and quality.
Potential Role of Lauric Acid in Milk Fat Synthesis in Chinese Holstein Cows Based on Integrated Analysis of Ruminal Microbiome and Metabolome
Authors: Zhang Huimin , Wang Yi , Hu Liping , Cong Jiahe , Xu Zhengzhong , Chen Xiang , Rao Shengqi , Li Mingxun , Shen Ziliang , Mauck John , Loor Juan J. , Yang Zhangping , Mao Yongjiang
Published: May 2024

The composition and metabolic profile of the ruminal microbiome have an impact on milk composition. To unravel the ruminal microbiome and metabolome affecting milk fat synthesis in dairy cows, 16S rRNA and internal transcribed spacer (ITS) gene sequencing, as well as ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS/MS) methods were used to investigate the significant differences in ruminal bacterial and fungal communities as well as metabolome among Chinese Holstein cows with contrasting milk fat contents under the same diet (H-MF 5.82 ± 0.41% vs. L-MF 3.60 ± 0.12%). Another objective was to culture bovine mammary epithelial cells (BMECs) to assess the effect of metabolites on lipid metabolism. Results showed that the acetate-to-propionate ratio and xylanase activity in ruminal fluid were both higher in H-MF. Microbiome sequencing identified 10 types of bacteria and four types of fungi differently abundant at the genus level. Metabolomics analysis indicated 11 different ruminal metabolites between the two groups, the majority of which were lipids and organic acids. Among these, lauric acid (LA) was enriched in fatty acid biosynthesis with its concentration in milk fat of H-MF cows being greater (217 vs. 156 mg per 100 g milk), thus, it was selected for an in vitro study with BMECs. Exogenous LA led to a marked increase in intracellular triglyceride (TG) content and lipid droplet formation, and it upregulated the mRNA abundance of fatty acid uptake and activation (CD36 and ACSL1), TG synthesis (DGAT1, DGAT2 and GPAM), and transcriptional regulation (SREBP1) genes. Taken together, the greater relative abundance of xylan-fermenting bacteria and fungi, and lower abundance of bacteria suppressing short-chain fatty acid-producing bacteria or participating in fatty acid hydrogenation altered lipids and organic acids in the rumen of dairy cows. In BMECs, LA altered the expression of genes involved in lipid metabolism in mammary cells, ultimately promoting milk fat synthesis. Thus, it appears that this fatty acid plays a key role in milk fat synthesis.
Animal Feed Formulation—Connecting Technologies to Build a Resilient and Sustainable System
Authors: Akintan Oreofeoluwa , Gebremedhin Kifle G. , Uyeh Daniel Dooyum
Published: May 2024

The unprecedented challenges presented by the increase in global population have placed substantial demands on the livestock industry for human nutrition, necessitating heightened animal productivity and leading to an increased demand for natural resources to produce animal feed. Feed producers are leading the charge, consistently refining formulations to adapt to the evolving needs of livestock, driven in part by the cost of over 50% associated with feed production. This paper critically analyses the pressing issues within feed formulation, addressing the requirement for environmentally sustainable practices amidst the challenges of climate change. The exploration extends to how advanced decision support tools can enhance formulation techniques and profitability and contribute to environmental sustainability. Through an in-depth review of current feed formulation technologies, encompassing their applications and limitations, this study aims to enhance the existing knowledge base. Additionally, we examined future trends, highlighting the essential role of connecting technologies to establish a resilient and sustainable system. The emphasis is on the potential of formulation techniques to positively impact the environment and enhance the overall quality and performance of the animals. This paper provides actionable insights to improve animal production by examining feed formulation models and decision support tools. The anticipated outcome is a more informed and sustainable decision-making process, addressing the multifaceted challenges confronted by the livestock industry and making contributions to global efforts in climate change mitigation and environmental stewardship in animal production agriculture.
IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data
Authors: Maciejewska Monika , Azizah Andi , Szczurek Andrzej
Published: May 2024

This study explores the capability of machine learning techniques (MLTs) in predicting IAQ in apartments. Sensor data from kitchen air monitoring were used to determine the conditions in the living room. The analysis was based on several air parameters—temperature, relative humidity, CO2 concentration, and TVOC—recorded in five apartments. Multiple input–multiple output prediction models were built. Linear (multiple linear regression and multilayer perceptron (MLP)) and nonlinear (decision trees, random forest, k-nearest neighbors, and MLP) methods were investigated. Five-fold cross-validation was applied, where four apartments provided data for model training and the remaining one was the source of the test data. The models were compared using performance metrics (R2, MAPE, and RMSE). The naive approach was used as the benchmark. This study showed that linear MLTs performed best. In this case, the coefficients of determination were highest: R2 = 0.94 (T), R2 = 0.94 (RH), R2 = 0.63 (CO2), R2 = 0.84 (TVOC, based on the SGP30 sensor), and R2 = 0.92 (TVOC, based on the SGP30 sensor). The prediction of distinct indoor air parameters was not equally effective. Based on the lowest percentage error, best predictions were attained for indoor air temperature (MAPE = 1.57%), relative humidity (MAPE = 2.97%RH), and TVOC content (MAPE = 0.41%). Unfortunately, CO2 prediction was loaded with high error (MAPE = 20.83%). The approach was particularly effective in open-kitchen apartments, and they could be the target for its application. This research offers a method that could contribute to attaining effective IAQ control in apartments.
Characterization of Surface α-Particle Radiation, Internal Traceability and Simulation of Typical Tin Spheres
Authors: Liu Longfei , Zhang Zhangang , Zhang Hong , Li Hui , Lei Zhifeng , Luo Junyang , Peng Chao , Sun Changhao , He Yujuan
Published: May 2024

Surface α-particle emissivity testing and spectral characterization of two leaded tin spheres (Sn10%Pb90%, Sn63%Pb37%) and one lead-free tin sphere (Sn96.5%Ag3.0%Cu0.5%, SAC305) were carried out. The results show that Sn10%Pb90% Sn spheres have the highest α-particle emissivity; Sn63%Pb37% Sn spheres are the next highest, which is an order of magnitude lower than the α-particle emissivity of Sn10%Pb90% Sn spheres; and SAC305 Sn spheres have the lowest emissivity, which is reduced by about 55.6% compared to the emissivity of Sn63%Pb37% Sn spheres. All three types of tin spheres, after purification treatment, achieved the grade of ultra-low alpha particle emissivity (<0.002 α/(cm2·h)). The internal radionuclide traceability of the tin sphere, combined with the energy spectrum, reveals that the emission spectrum of the tin sphere exhibits an obvious “single peak” characteristic, with the peak energy in the interval of 5 MeV~5.5 MeV. Comparative analyses revealed that 210Po is the main nuclide that produces alpha particles, and 210Po originates from the decay of 210Pb. Further Monte Carlo simulations show that α-particles with energies greater than 4.1 MeV in the measured energy spectrum all come from the contribution of radionuclides within 5 μm of the surface layer of the tin sphere, which accounts for 60% of the total radioactivity. Combining the experimental and simulation results, it is found that the internal radionuclides of the tin sphere are characterized by more surface layer and less internal layer. The above results are of great significance for the establishment of α-particle mitigation methods for tin spheres.
Practical Estimation of Machine Tool Spindle Dynamics for Maintenance Decision Making
Authors: Chin Patrick , DePavia Jose M. , Veldhuis Stephen C.
Published: May 2024

Condition-based maintenance of a machine tool spindle unit is known to reduce the total cost of operation in a manufacturing environment. However, standard vibration monitoring thresholds, which are based only on average vibration energy levels, do not account for the machine’s dynamics, such as dynamic stiffness and damping ratios, which, ultimately, control the final quality of a machined workpiece. In this study, a new mechanical device is proposed for the estimation of spindle dynamics characteristics through measurement of the frequency response function (FRF) of the spindle. The device is simple, robust in a manufacturing environment, small, and requires no power source. The new device is shown to be comparable in performance to conventional impact hammer testing, with the advantage of being fully automated and capable of dynamic assessment on a rotating spindle. Such a device allows a machine tool operator to routinely monitor the stiffness and damping ratios of the spindle unit and make informed maintenance decisions based on meaningful changes in operating conditions that impact production.
Non-Linear Creep-Relaxation Constitutive Damage Model for Aging Concrete
Authors: Terán-Torres Bernardo T. , Mohammadi Jamshid , Nair Sudhakar E. , Mendoza-Rangel José M. , Flores-Vivian Ismael , Juárez-Alvarado César A.
Published: May 2024

A thermodynamic constitutive damage model for plain concrete, and other quasi-brittle aging materials, under creep relaxation is developed. The model accounts for the anisotropic damage induced through a second-order tensor damage variable. The aging viscoelasticity of the material is considered through the theory of solidification for aging solidifying materials. The material is considered a viscoelastic-damageable material. The Helmholtz free energy, utilized in the formulation, is treated based on the representation theorem of coupled damage strain tensors and Volterra integral equations. The model can analyze time-dependent damage (tertiary creep) under constant loading and can account for damage due to cyclic creep. Theoretical case studies are considered to illustrate the applicability of the model. The determination of the functions and constants, representing the material behavior, as well as any experimental companion is proposed for further research.
Micro-Gear Point Cloud Segmentation Based on Multi-Scale Point Transformer
Authors: Su Yizhou , Wang Xunwei , Qi Guanghao , Lei Baozhen
Published: May 2024

To address the challenges in industrial precision component detection posed by existing point cloud datasets, this research endeavors to amass and construct a point cloud dataset comprising 1101 models of miniature gears. The data collection and processing procedures are elaborated upon in detail. In response to the segmentation issues encountered in point clouds of small industrial components, a novel Point Transformer network incorporating a multiscale feature fusion strategy is proposed. This network extends the original Point Transformer architecture by integrating multiple global feature extraction modules and employing an upsampling module for contextual information fusion, thereby enhancing its modeling capabilities for intricate point cloud structures. The network is trained and tested on the self-constructed gear dataset, yielding promising results. Comparative analysis with the baseline Point Transformer network indicates a notable improvement of 1.1% in mean Intersection over Union (mIoU), substantiating the efficacy of the proposed approach. To further assess the method’s effectiveness, several ablation experiments are designed, demonstrating that the introduced modules contribute to varying degrees of segmentation accuracy enhancement. Additionally, a comparative evaluation is conducted against various state-of-the-art point cloud segmentation networks, revealing the superior performance of the proposed methodology. This research not only aids in quality control, structural detection, and optimization of precision industrial components but also provides a scalable network architecture design paradigm for related point cloud processing tasks.
Physiological and Biomechanical Characteristics of Olympic and World-Class Rowers—Case Study
Authors: Cardoso Ricardo , Rios Manoel , Cardoso Filipa , Fonseca Pedro , Ferreira Francisco A. , Abraldes Jose Arturo , Gomes Beatriz B. , Vilas-Boas João Paulo , Fernandes Ricardo J.
Published: May 2024

In this study, we quantified relevant biophysical characteristics of two elite rowers across a wide range of intensities. Two <40-year-old male and female Olympic and World Championship finalists performed a 7 × 3 min protocol plus 1 min maximal effort on a rowing ergometer. The intensity increase resulted in maximum values of 79.4 ± 2.4 and 69.7 ± 1.5 mL/min/kg for oxygen uptake, 179.3 ± 5.7 and 152.5 ± 2.9 L/min for ventilation, 170 ± 1 and 173 ± 0 bpm for heart rate, 10.6 and 15.8 mmol/L for blood lactate concentration, and 38.1 ± 0.03 and 38.8 ± 0.03 °C for core temperature for the male and female rowers. The percentage of power corresponding to a previously conducted maximum 2000 m rowing ergometer test and the work at each step increased from 49 to 127 and 42 to 103% and from 226.8 to 398.9 J and 174.0 to 250.0 J, from low to extreme intensities, for the male and female. Concurrently, there was a decrease in cycle length and propulsive time, followed by an increase in maximal handle drive velocity, with the rise in rowing intensity. These world-class rowers seem capable of maintaining physiological and technical profiles (and a remarkable capacity to generate substantial power) at this phase of their careers possibly due to long-term engagement in elite-level training. Biophysical data provide valuable referential information for guiding rowers to improve their performance.
New Bivariate Copulas via Lomax Distribution Generated Distortions
Authors: Aldhufairi Fadal Abdullah Ali , Sepanski Jungsywan H.
Published: May 2024

We develop a framework for creating distortion functions that are used to construct new bivariate copulas. It is achieved by transforming non-negative random variables with Lomax-related distributions. In this paper, we apply the distortions to the base copulas of independence, Clayton, Frank, and Gumbel copulas. The properties of the tail dependence coefficient, tail order, and concordance ordering are explored for the new families of distorted copulas. We conducted an empirical study using the daily net returns of Amazon and Google stocks from January 2014 to December 2023. We compared the popular Clayton, Gumbel, Frank, and Gaussian copula models to their corresponding distorted copula models induced by the unit-Lomax and unit-inverse Pareto distortions. The new families of distortion copulas are equipped with additional parameters inherent in the distortion function, providing more flexibility, and are demonstrated to perform better than the base copulas. After analyzing the data, we have found that the joint extremes of Amazon and Google stocks are more likely for high daily net returns than for low daily net returns.
XplAInable: Explainable AI Smoke Detection at the Edge
Authors: Lehnert Alexander , Gawantka Falko , During Jonas , Just Franz , Reichenbach Marc
Published: May 2024

Wild and forest fires pose a threat to forests and thereby, in extension, to wild life and humanity. Recent history shows an increase in devastating damages caused by fires. Traditional fire detection systems, such as video surveillance, fail in the early stages of a rural forest fire. Such systems would see the fire only when the damage is immense. Novel low-power smoke detection units based on gas sensors can detect smoke fumes in the early development stages of fires. The required proximity is only achieved using a distributed network of sensors interconnected via 5G. In the context of battery-powered sensor nodes, energy efficiency becomes a key metric. Using AI classification combined with XAI enables improved confidence regarding measurements. In this work, we present both a low-power gas sensor for smoke detection and a system elaboration regarding energy-efficient communication schemes and XAI-based evaluation. We show that leveraging edge processing in a smart way combined with buffered data samples in a 5G communication network yields optimal energy efficiency and rating results.
Cash Rules Everything around Me: Investigating Craft Beer Drinkers Purchase Decisions during Inflationary Period
Authors: Taylor Scott , Taylor D. Christopher
Published: May 2024

Since March 2020, there have been multiple outside influences negatively impacting the U.S. craft beer industry from COVID-19 to inflation all leading up to increased prices for consumers. However, to date, research has not investigated consumer sentiment toward increased prices and their willingness to continue paying increased prices for beer from various outlets. The current study provides an initial assessment of craft beer consumers’ purchase behaviors for on(own)- and off-premise consumption given the impacts of COVID-19 and the recent economic situation. Past research has indicated that increased prices tend to lead to decreased purchases of beer (and alcohol in general). Results of the current online survey of craft beer drinkers mostly align with past findings, as nearly half of the 350 participants in this study indicated that they had decreased their consumption of craft beer away from home. Relatedly, respondents indicated a relatively low willingness to pay price increases for craft beer consumed on-premise (own-premise) or off-premise.
Adherence-Promoting Design Features in Pediatric Neurostimulators for ADHD Patients
Authors: Delatte William , Camp Allyson , Kreider Richard B. , Guiseppi-Elie Anthony
Published: May 2024

The emergence of remote health monitoring and increased at-home care emphasizes the importance of patient adherence outside the clinical setting. This is particularly pertinent in the treatment of Attention Deficit Hyperactivity Disorder (ADHD) in pediatric patients, as the population inherently has difficulty remembering and initiating treatment tasks. Neurostimulation is an emerging treatment modality for pediatric ADHD and requires strict adherence to a treatment regimen to be followed in an at-home setting. Thus, to achieve the desired therapeutic effect, careful attention must be paid to design features that can passively promote and effectively monitor therapeutic adherence. This work describes instrumentation designed to support a clinical trial protocol that tests whether choice of color, or color itself, can statistically significantly increase adherence rates in pediatric ADHD patients in an extraclinical environment. This is made possible through the development and application of an internet-of-things approach in a remote adherence monitoring technology that can be implemented in forthcoming neurostimulation devices for pediatric patient use. This instrumentation requires minimal input from the user, is durable and resistant to physical damage, and provides accurate adherence data to parents and physicians, increasing assurance that neurostimulation devices are effective for at-home care.
Antibacterial and Antibiofilm Potential of Bacterial Cellulose Hydrogel Containing Vancomycin against Multidrug-Resistant Staphylococcus aureus and Staphylococcus epidermidis
Authors: Correia Ana Alice Venancio , de Almeida Campos Luís André , de Queiroz Macêdo Hanne Lazla Rafael , de Lacerda Coriolano Davi , Agreles Maria Anndressa Alves , Xavier Danilo Elias , de Siqueira Ferraz-Carvalho Rafaela , de Andrade Aguiar José Lamartine , Cavalcanti Isabella Macário Ferro
Published: May 2024

The present study aimed to evaluate the in vitro antibacterial and antibiofilm activity of bacterial cellulose hydrogel produced by Zoogloea sp. (HYDROGEL) containing vancomycin (VAN) against bacterial strains that cause wound infections, such as multidrug-resistant (MDR) Staphylococcus aureus and Staphylococcus epidermidis. Initially, HYDROGEL was obtained from sugar cane molasses, and scanning electron microscopy (SEM) was performed to determine morphological characteristics. Then, VAN was incorporated into HYDROGEL (VAN-HYDROGEL). The antibacterial activity of VAN, HYDROGEL, and VAN-HYDROGEL was assessed using the broth microdilution method to determine the minimum inhibitory concentration (MIC) and the minimum bactericidal concentration (MBC) against methicillin-sensitive S. aureus (MSSA) ATCC 25923, methicillin-resistant S. aureus (MRSA) ATCC 33591, S. epidermidis INCQS 00016 (ATCC 12228), five clinical isolates of MRSA, and nine clinical isolates of methicillin-resistant S. epidermidis, following the Clinical and Laboratory Standards Institute (CLSI) guidelines. Additionally, the antibacterial activity of VAN, HYDROGEL, and VAN-HYDROGEL was studied using the time-kill assay. Subsequently, the antibiofilm activity of VAN, HYDROGEL, and VAN-HYDROGEL was evaluated using crystal violet and Congo red methods, as well as SEM analysis. VAN and VAN-HYDROGEL showed bacteriostatic and bactericidal activity against MRSA and methicillin-resistant S. epidermidis strains. HYDROGEL did not show any antibacterial activity. Analysis of the time-kill assay indicated that HYDROGEL maintained the antibacterial efficacy of VAN, highlighting its efficiency as a promising carrier. Regarding antibiofilm activity, VAN and HYDROGEL inhibited biofilm formation but did not demonstrate biofilm eradication activity against methicillin-resistant S. aureus and S. epidermidis strains. However, it was observed that the biofilm eradication potential of VAN was enhanced after incorporation into HYDROGEL, a result also proven through images obtained by SEM. From the methods carried out in this study, it was possible to observe that HYDROGEL preserved the antibacterial activity of vancomycin, aside from exhibiting antibiofilm activity and enhancing the antibiofilm effect of VAN. In conclusion, this study demonstrated the potential of HYDROGEL as a candidate and/or vehicle for antibiotics against MDR bacteria that cause wound infections.
Correction: Kramer et al. A Simple, Efficient Method for an Automatic Adjustment of the Lumbar Curvature Alignment in an MBS Model of the Spine. Biomechanics 2023, 3, 166–180
Authors: Kramer Ivanna , Bauer Sabine , Keppler Valentin
Published: May 2024

In Section 2 [...]
Clinical Developments and Challenges in Treating FGFR2-Driven Gastric Cancer
Authors: Lau David K. , Collin Jack P. , Mariadason John M.
Published: May 2024

Recent advances in the treatment of gastric cancer (GC) with chemotherapy, immunotherapy, anti-angiogenic therapy and targeted therapies have yielded some improvement in survival outcomes; however, metastatic GC remains a lethal malignancy and amongst the leading causes of cancer-related mortality worldwide. Importantly, the ongoing molecular characterisation of GCs continues to uncover potentially actionable molecular targets. Among these, aberrant FGFR2-driven signalling, predominantly arising from FGFR2 amplification, occurs in approximately 3–11% of GCs. However, whilst several inhibitors of FGFR have been clinically tested to-date, there are currently no approved FGFR-directed therapies for GC. In this review, we summarise the significance of FGFR2 as an actionable therapeutic target in GC, examine the recent pre-clinical and clinical data supporting the use of small-molecule inhibitors, antibody-based therapies, as well as novel approaches such as proteolysis-targeting chimeras (PROTACs) for targeting FGFR2 in these tumours, and discuss the ongoing challenges and opportunities associated with their clinical development.
A Novel Bifunctional Fusion Protein (Anti-IL-17A-sST2) Protects against Acute Liver Failure, Modulating the TLR4/MyD88 Pathway and NLRP3 Inflammasome Activation
Authors: Bai Yu , Zhou Rongrui , Xie Xinlei , Zhu An , Nan Yanyang , Wu Tao , Hu Xiaozhi , Cao Zhonglian , Ju Dianwen , Fan Jiajun
Published: May 2024

Acute liver failure (ALF) is a serious inflammatory disorder with high mortality rates, which poses a significant threat to human health. The IL-33/ST2 signal is a crucial regulator in inflammation responses associated with lipopolysaccharide (LPS)-induced macrophages. The IL-17A signaling pathway promotes the release of chemokines and inflammatory cytokines, recruiting neutrophils and T cells under LPS stimulation, thus facilitating inflammatory responses. Here, the potential therapeutic benefits of neutralizing the IL-17A signal and modulating the IL-33/ST2 signal in ALF were investigated. A novel dual-functional fusion protein, anti-IL-17A-sST2, was constructed, which displayed high purity and biological activities. The administration of anti-IL-17A-sST2 resulted in significant anti-inflammatory benefits in ALF mice, amelioration of hepatocyte necrosis and interstitial congestion, and reduction in TNF-α and IL-6. Furthermore, anti-IL-17A-sST2 injection downregulated the expression of TLR4 and NLRP3 as well as important molecules such as MyD88, caspase-1, and IL-1β. The results suggest that anti-IL-17A-sST2 reduced the secretion of inflammatory factors, attenuated the inflammatory response, and protected hepatic function by regulating the TLR4/MyD88 pathway and inhibiting the NLRP3 inflammasome, providing a new therapeutic approach for ALF.
Remission Factors for Ustekinumab Treatment of Ulcerative Colitis: A Multicenter Retrospective Study of Real-World Data in Japan
Authors: Omori Masashi , Shibuya Tomoyoshi , Ishino Hirotaka , Fukuo Yuka , Odakura Rina , Koma Masao , Maruyama Takafumi , Ito Kentaro , Haraikawa Mayuko , Nomura Kei , Yano Shintaro , Nomura Osamu , Ishikawa Dai , Hojo Mariko , Osada Taro , Nagahara Akihito
Published: May 2024

Ustekinumab (UST) is an anti–IL-12/23p40 monoclonal antibody used to treat inflammatory bowel disease. The aim of this retrospective, multicenter study was to investigate the effectiveness of UST administration in achieving remission in patients with ulcerative colitis (UC) and to determine patient characteristics that influence its effectiveness. Of 88 UC patients who received UST from March 2020 to August 2023, 47 with traceable data and for whom 56 weeks had elapsed since the start of treatment received UST to induce remission. The remission rates at 8 weeks were 66% overall, 73.7% for Bio Naïve (never used biologics/JAK inhibitors), and 60.7% for Bio Failure (used biologics/JAK inhibitors) groups. Remission rates at 56 weeks were 70.2% overall, 73.7% for Bio Naïve, and 67.9% for Bio Failure groups. Ustekinumab showed good mid-to-long-term results in the induction of remission of UC in both Bio Naïve and Bio Failure groups. The group showing remission at 8 weeks had a significantly higher non-relapse or continuation rate (proportion of patients with no worsened symptoms necessitating surgery/drug change) at 56 weeks. Predictive factors for achieving remission after UST in UC were female gender, low body mass index, and low lymphocyte-to-monocyte ratio. Thus, UST is effective for moderate-to-severe UC.
Colostrum Lactoferrin Following Active and Recovered SARS-CoV-2 Infections during Pregnancy
Authors: Gaweł Paulina , Łukianowski Błażej , Kościelska-Kasprzak Katarzyna , Bartoszek Dorota , Krajewska Magdalena , Królak-Olejnik Barbara
Published: May 2024

Lactoferrin (Lf), which is particularly abundant in human breast milk during the early stages of lactation, provides protection against a variety of infections, including viral infections, and has demonstrated activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The objective of this study was to measure the concentrations of Lf in the colostrum of mothers with active coronavirus disease 2019 (COVID-19) infections during delivery, in mothers with a history of COVID-19 during pregnancy, and in non-infected controls. In this cross-sectional study, colostrum samples from 41 lactating mothers with a confirmed history of SARS-CoV-2 infection (asymptomatic or symptomatic) (both active and past infections) were collected. Twenty-eight colostrum samples collected during the pre-pandemic period served as a control group. An enzyme-linked immunosorbent assay was performed to analyze the Lf concentrations. Concentrations of Lf in the colostrum samples were closely related to virus infection. Colostrum samples from mothers with confirmed SARS-CoV-2 infections contained higher concentrations of lactoferrin compared with samples from mothers from the control group. The highest concentrations of Lf were found in the colostrum samples of mothers with active SARS-CoV-2 infection during delivery when compared with the post-infection and control samples. This observed increase in lactoferrin suggests that it may be an important protective factor for breastfed infants, a finding which was particularly relevant during the pandemic period and remains relevant whenever a breastfeeding mother is infected.
The Crucial Role of Interdisciplinary Conferences in Advancing Explainable AI in Healthcare
Authors: Patel Ankush U. , Gu Qiangqiang , Esper Ronda , Maeser Danielle , Maeser Nicole
Published: May 2024

As artificial intelligence (AI) integrates within the intersecting domains of healthcare and computational biology, developing interpretable models tailored to medical contexts is met with significant challenges. Explainable AI (XAI) is vital for fostering trust and enabling effective use of AI in healthcare, particularly in image-based specialties such as pathology and radiology where adjunctive AI solutions for diagnostic image analysis are increasingly utilized. Overcoming these challenges necessitates interdisciplinary collaboration, essential for advancing XAI to enhance patient care. This commentary underscores the critical role of interdisciplinary conferences in promoting the necessary cross-disciplinary exchange for XAI innovation. A literature review was conducted to identify key challenges, best practices, and case studies related to interdisciplinary collaboration for XAI in healthcare. The distinctive contributions of specialized conferences in fostering dialogue, driving innovation, and influencing research directions were scrutinized. Best practices and recommendations for fostering collaboration, organizing conferences, and achieving targeted XAI solutions were adapted from the literature. By enabling crucial collaborative junctures that drive XAI progress, interdisciplinary conferences integrate diverse insights to produce new ideas, identify knowledge gaps, crystallize solutions, and spur long-term partnerships that generate high-impact research. Thoughtful structuring of these events, such as including sessions focused on theoretical foundations, real-world applications, and standardized evaluation, along with ample networking opportunities, is key to directing varied expertise toward overcoming core challenges. Successful collaborations depend on building mutual understanding and respect, clear communication, defined roles, and a shared commitment to the ethical development of robust, interpretable models. Specialized conferences are essential to shape the future of explainable AI and computational biology, contributing to improved patient outcomes and healthcare innovations. Recognizing the catalytic power of this collaborative model is key to accelerating the innovation and implementation of interpretable AI in medicine.
Detection of Glutamate Decarboxylase Antibodies and Simultaneous Multi-Molecular Translocation Exploration by Glass Nanopores
Authors: Tao Chongxin , Bai Yun , Chen Jiang , Lu Jing , Bi Yan , Li Jian
Published: May 2024

Glutamic acid decarboxylase antibody (GADAb) has emerged as a significant biomarker for clinical diagnosis and prognosis in type 1 diabetes (T1D). In this study, we investigated the potential utilization of glass capillary solid-state nanopores as a cost-effective and easily preparable platform for the detection of individual antigens, antibodies, and antigen-antibody complexes without necessitating any modifications to the nanopores. Our findings revealed notable characteristic variations in the translocation events of glutamic acid decarboxylase (GAD65) through nanopores under different voltage conditions, discovered that anomalous phenomenon of protein translocation events increasing with voltage may potentially be caused by the crowding of multiple proteins in the nanopores, and demonstrated that there are multiple components in the polyclonal antibodies (GADAb-poly). Furthermore, we achieved successful differentiation between GAD65, GADAb, and GADAb-GAD65 complexes. These results offer promising prospects for the development of a rapid and reliable GADAb detection method, which holds the potential to be applied in patient serum samples, thereby facilitating a label-free, cost-effective, and early diagnosis of type I diabetes.
Microfluidic Mechanoporation: Current Progress and Applications in Stem Cells
Authors: Wang Rubing , Wang Ziqi , Tong Lingling , Wang Ruoming , Yao Shuo , Chen Di , Hu Huan
Published: May 2024

Intracellular delivery, the process of transporting substances into cells, is crucial for various applications, such as drug delivery, gene therapy, cell imaging, and regenerative medicine. Among the different approaches of intracellular delivery, mechanoporation stands out by utilizing mechanical forces to create temporary pores on cell membranes, enabling the entry of substances into cells. This method is promising due to its minimal contamination and is especially vital for stem cells intended for clinical therapy. In this review, we explore various mechanoporation technologies, including microinjection, micro–nano needle arrays, cell squeezing through physical confinement, and cell squeezing using hydrodynamic forces. Additionally, we highlight recent research efforts utilizing mechanoporation for stem cell studies. Furthermore, we discuss the integration of mechanoporation techniques into microfluidic platforms for high-throughput intracellular delivery with enhanced transfection efficiency. This advancement holds potential in addressing the challenge of low transfection efficiency, benefiting both basic research and clinical applications of stem cells. Ultimately, the combination of microfluidics and mechanoporation presents new opportunities for creating comprehensive systems for stem cell processing.
Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study
Authors: Qian Shiting , Yang Qinqin , Cai Congbo , Dong Jiyang , Cai Shuhui
Published: May 2024

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain’s intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD.
From Connectivity to Controllability: Unraveling the Brain Biomarkers of Major Depressive Disorder
Authors: Pan Chunyu , Ma Ying , Wang Lifei , Zhang Yan , Wang Fei , Zhang Xizhe
Published: May 2024

Major Depressive Disorder (MDD) is a significant neurological condition associated with aberrations in brain functional networks. Traditional studies have predominantly analyzed these from a network topology perspective. However, given the brain’s dynamic and complex nature, exploring its mechanisms from a network control standpoint provides a fresh and insightful framework. This research investigates the integration of network controllability and machine learning to pinpoint essential biomarkers for MDD using functional magnetic resonance imaging (fMRI) data. By employing network controllability methods, we identify crucial brain regions that are instrumental in facilitating transitions between brain states. These regions demonstrate the brain’s ability to navigate various functional states, emphasizing the utility of network controllability metrics as potential biomarkers. Furthermore, these metrics elucidate the complex dynamics of MDD and support the development of precision medicine strategies that incorporate machine learning to improve the precision of diagnostics and the efficacy of treatments. This study underscores the value of merging machine learning with network neuroscience to craft personalized interventions that align with the unique pathological profiles of individuals, ultimately enhancing the management and treatment of MDD.
Experimental Assessment of Mechanical Properties of Corroded Low–Alloy Structural Steel
Authors: Chen Yao , Ma Boshi , Lu Ruihua
Published: May 2024

This study investigates the mechanical properties of corroded Q355B structural steel subjected to a simulated marine atmosphere and an industrial atmosphere. The micro-morphology of corroded steel in two different environments was analyzed by SEM (scanning electron microscopy). Tension tests were performed to determine the degradation laws of the mechanical properties of corroded steel, including its yield strength, ultimate strength, elastic modulus, ultimate strain and elongation after fracture. The test results indicate that the elongation after fracture of the steel is the most severely deteriorated property after corrosion. The recommended empirical formula for limiting the maximum corrosion rate is established. It is found that when the initial elongation is 30%, the maximum allowable corrosion rate is 19.2%. Based on the achieved results, a simplified time-dependent stress–strain model of Q355B structural steel is established considering the coupling effects of corrosive environments and applied stress, which is also evaluated using relevant research. In addition, axial compression tests were conducted on corroded square stud columns to verify the effectiveness of the established model. It is indicated that the model can be used for fitness-for-purpose analyses in structural integrity assessments.
Effects of Niobium Addition on the Mechanical Properties and Corrosion Resistance of Microalloyed Steels: A Review
Authors: Benedito André Vitor , Benedetty Torres Carlos Alberto , Silva Rebecca Mansur de Castro , Krahl Pablo Augusto , Cardoso Daniel Carlos Taissum , Silva Flávio de Andrade , Martins Carlos Humberto
Published: May 2024

Steel structures are prone to corrosion, a chemical reaction between steel and the atmosphere that gradually weakens the material. Over time, this reaction can significantly reduce the structural integrity and lifespan of steel elements. Without intervention, corrosion can cause structures to fail, leading to financial, environmental, and potential human losses. Enhancing steel’s corrosion resistance is crucial, and one method involves adding niobium (Nb). Niobium microalloyed steels are known for their increased strength, and some research indicates that Nb may also improve corrosion resistance by making the grain structure of the steel finer. However, the complete potential of Nb in corrosion prevention remains underexplored, with significant research gaps across various scales, from microstructural impacts on durability to macroscopic effects on mechanical properties. The research community has utilized numerous experimental approaches to test corrosion resistance under different conditions, but there is a lack of comprehensive studies that aggregate and analyze these findings. This paper seeks to fill that void by reviewing the impact of Nb on the strength and corrosion resistance of structural steels, examining how steel beams’ ultimate capacity degrades over time and identifying key areas where further research is needed to understand Nb’s role in mitigating corrosion.
Hyaluronic Acid Interacting Molecules Mediated Crosstalk between Cancer Cells and Microenvironment from Primary Tumour to Distant Metastasis
Authors: Xu Yali , Benedikt Johannes , Ye Lin
Published: May 2024

Hyaluronic acid (HA) is a prominent component of the extracellular matrix, and its interactions with HA-interacting molecules (HAIMs) play a critical role in cancer development and disease progression. This review explores the multifaceted role of HAIMs in the context of cancer, focusing on their influence on disease progression by dissecting relevant cellular and molecular mechanisms in tumour cells and the tumour microenvironment. Cancer progression can be profoundly affected by the interactions between HA and HAIMs. They modulate critical processes such as cell adhesion, migration, invasion, and proliferation. The TME serves as a dynamic platform in which HAIMs contribute to the formation of a unique niche. The resulting changes in HA composition profoundly influence the biophysical properties of the TME. These modifications in the TME, in conjunction with HAIMs, impact angiogenesis, immune cell recruitment, and immune evasion. Therefore, understanding the intricate interplay between HAIMs and HA within the cancer context is essential for developing novel therapeutic strategies. Targeting these interactions offers promising avenues for cancer treatment, as they hold the potential to disrupt critical aspects of disease progression and the TME. Further research in this field is imperative for advancing our knowledge and the treatment of cancer.