(1) Background. At subzero temperatures, water crystallizes on ice nucleation agents. Researchers have identified ice-nucleating microbes (INMs) in insect digestive tracts that can raise the insect’s supercooling point, causing freezing at higher temperatures but slower rates. For freeze-tolerant insects, such gut microbes should allow for slower freezing away from tissues and higher survival rates. For freeze-susceptible insects, however, such microbes could cause a fatal freeze at higher temperatures, and could possibly be used as biocontrol. (2) Methods. A first-ever scoping review was carried out of research on insect-associated INMs, from observational studies attempting to isolate these microbes, to experimental studies applying them and checking for increased mortality. (3) Results. Relatively few research groups have studied insect-associated INMs in any capacity. (4) Conclusions. Several authors hypothesized that such microbes are probably abundant, and their contribution to ice nucleation activity in insects is under-reported. Biocontrol assays using ice-nucleating microbes showed promise, but a risk to non-target organisms has been experimentally confirmed. Future surveys of insect–microbe interactions using molecular tools are likely to reveal new examples, if not new microbe species capable of ice nucleation. Full article

The provision of mobility exercises through a smartphone application (app) for people undertaking neurological rehabilitation may improve mobility outcomes. However, it is difficult for clinicians and consumers to select high-quality, appropriate apps. This review aimed to identify (1) which mobile health (mHealth) apps are suitable for prescribing mobility exercises for adults with neurological health conditions, (2) how well these apps incorporate telehealth strategies, and (3) how well these apps rate in terms of quality and capacity for behaviour change. The Australian Apple iTunes Store was systematically searched, by using a search code and manually, for apps suitable for training mobility in neurological rehabilitation. Additional searches were conducted in known app repositories and for web-based apps. Trained reviewers extracted data from the included apps, including population-specific characteristics; quality, by using the Mobile App Rating Scale (MARS); and behaviour change potential, by using the App Behaviour Change Scale (ABACUS). The included apps (n = 18) provided <50 to >10,000 exercises, many incurred a subscription fee (n = 13), and half included telehealth features. App quality was moderate (mean MARS score of 3.2/5 and SD of 0.5), and potential for behaviour change was poor (mean ABACUS score of 5.7/21 and SD of 2.1). A limited number of high-quality apps are available for the prescription of mobility exercises in people with neurological conditions. Full article

This paper highlights the design of a controller established on estimated states for one-sided Lipschitz (OSL) nonlinear systems subject to output and input delays. The controller has been devised by involving Luenberger-like estimated states. The stability of the time-delayed nonlinear system is reckoned by assuming a Lyapunov functional for delayed dynamics and for which a delay-range dependent criterion is posed with a delay ranging between known upper and lower bounds. The time derivative of the functional is further exploited with linear matrix inequality (LMI) procedures, and employing Wirtinger’s inequality for the integral terms instead of the traditional and more conservative Jensen’s condition. Moreover, a sufficient and necessary solution is derived for the proposed design by involving the tedious decoupling technique to attain controller and observer gain simultaneously. The proposed methodology validates the observer error stability between observers and states asymptotically. The solution of matrix inequalities was obtained by employing cone-complementary linearization techniques to solve the tiresome constraints through simulation tools by convex optimization. Additionally, a novel scheme of an observer-based controller for the linear counterpart is also derived for one-sided Lipschitz nonlinear systems with multiple delays. Finally, the effectualness of the presented observer-based controller under input and output delays for one-sided Lipschitz nonlinear systems is validated by considering a numerical simulation of mobile systems in Cartesian coordinates. Full article

In order to investigate freeze–thawed red sandstone failure processes under cyclic loading and unloading conditions, real-time acoustic emission (AE) and scanning electron microscopy (SEM) techniques were used to reveal the fracture process of the saturated red sandstone after cyclic loading and unloading tests using uniaxial compression. The results show that the stress–strain curves of the freeze–thawed sandstones show signs of hysteresis and exhibit a two-stage evolution of “sparse → dense”. In the cyclic loading and unloading process, the modulus of elasticity in the loading process is always larger than that in the unloading process, while the Poisson’s ratio is the opposite, and the radial irreversible strain and cumulative irreversible strain are larger than those in the axial direction. As the number of freeze–thaw cycles increases, the rock specimens need more cycles of loading and unloading to make the crack volume compressive strain $\Delta {\epsilon }_{cv}^{+}$ reach the maximum value and tend to stabilize, while the crack volume extensional strain $\Delta {\epsilon }_{cv}^{{}^{-}}$ tends to decrease gradually. This study also shows that the growth phase of the cyclic loading and unloading process has more ringing counts and a shorter duration, while the slow degradation phase has more ringing counts with loading and less with unloading. In addition, the F-T cycle gradually changes the internal microcracks of the red sandstone from shear damage, which is dominated by shear cracks, to tensile damage, which is dominated by tensile cracks. This study’s findings contribute to our knowledge of the mechanical characteristics and sandstone’s degradation process following F-T treatment, and also serve as a guide for engineering stability analyses conducted in the presence of multiphysical field coupling. Full article

Accurate measurement of coal gas permeability helps prevent coal gas safety accidents effectively. To predict permeability more accurately, we propose the IDBO-BPNN coal body gas permeability prediction model. This model combines the Improved Dung Beetle algorithm (IDBO) with the BP neural network (BPNN). First, the Sine chaotic mapping, Osprey optimization algorithm, and adaptive T-distribution dynamic selection strategy are integrated to enhance the DBO algorithm and improve its global search capability. Then, IDBO is utilized to optimize the weights and thresholds in BPNN to enhance its prediction accuracy and mitigate the risk of overfitting to some extent. Secondly, based on the influencing factors of gas permeability, effective stress, gas pressure, temperature, and compressive strength, they are chosen as the coupling indicators. The SPSS 27 software is used to analyze the correlation among the indicators using the Pearson correlation coefficient matrix. Additionally, the Kernel Principal Component Analysis (KPCA) is employed to extract the original data. Then, the original data is divided into principal component data for the model input. The prediction results of the IDBO-BPNN model are compared with those of the PSO-BPNN, PSO-LSSVM, PSO-SVM, MPA-BPNN, WOA-SVM, BES-SVM, and DPO-BPNN models. This comparison assesses the capability of KPCA to enhance the accuracy of model predictions and the performance of the IDBO-BPNN model. Finally, the IDBO-BPNN model is tested using data from a coal mine in Shanxi. The results indicate that the predicted outcome closely aligns with the actual value, confirming the reliability and stability of the model. Therefore, the IDBO-BPNN model is better suited for predicting coal gas permeability in academic research writing. Full article

BRCA1 mutations substantially elevate the risks of breast and ovarian cancer. Various modifiers, including environmental factors, can influence cancer risk. Lead, a known carcinogen, has been associated with various cancers, but its impact on BRCA1 carriers remains unexplored. A cohort of 989 BRCA1 mutation carriers underwent genetic testing at the Pomeranian Medical University, Poland. Blood lead levels were measured using inductively coupled plasma mass spectrometry. Each subject was assigned to a category based on their tertile of blood lead. Cox regression analysis was used to assess cancer risk associations. Elevated blood lead levels (>13.6 μg/L) were associated with an increased risk of ovarian cancer (univariable: HR = 3.33; 95% CI: 1.23–9.00; p = 0.02; multivariable: HR = 2.10; 95% CI: 0.73–6.01; p = 0.17). No significant correlation was found with breast cancer risk. High blood lead levels are associated with increased risk of ovarian cancer in BRCA1 carriers, suggesting priority for preventive salpingo-oophorectomy. Potential risk reduction strategies include detoxification. Validation in diverse populations and exploration of detoxification methods for lowering lead levels are required. Full article

Treating female canine mammary gland tumors is crucial owing to their propensity for rapid progression and metastasis, significantly impacting the overall health and well-being of dogs. Mitoquinone (MitoQ), an antioxidant, has shown promise in inhibiting the migration, invasion, and clonogenicity of human breast cancer cells. Thus, we investigated MitoQ’s potential anticancer properties against canine mammary gland tumor cells, CMT-U27 and CF41.Mg. MitoQ markedly suppressed the proliferation and migration of both CMT-U27 and CF41.Mg cells and induced apoptotic cell death in a dose-dependent manner. Furthermore, treatment with MitoQ led to increased levels of pro-apoptotic proteins, including cleaved-caspase3, BAX, and phospho-p53. Cell cycle analysis revealed that MitoQ hindered cell progression in the G1 and S phases in CMT-U27 and CF41.Mg cells. These findings were supported using western blot analysis, demonstrating elevated levels of cleaved caspase-3, a hallmark of apoptosis, and decreased expression of cyclin-dependent kinase (CDK) 2 and cyclin D4, pivotal regulators of the cell cycle. In conclusion, MitoQ exhibits in vitro antitumor effects by inducing apoptosis and arresting the cell cycle in canine mammary gland tumors, suggesting its potential as a preventive or therapeutic agent against canine mammary cancer. Full article

France is Europe’s leading producer of flaxseed. This seed is rich in omega-3, energy, and protein for animals, but it also contains anti-nutritional factors such as mucilage. Thus, mucilage must be removed and could be used as a bio-admixture in cementitious materials development, reducing the environmental impact of cementitious materials. This study aims to valorize the usage of flaxseed mucilage (FM) in ordinary Portland cement. FM caused macroscopic and microscopic changes in the materials studied. The higher the concentration, the greater the changes were. The admixed samples showed an exponentially concentration-dependent delay in setting. FM degradation products induced by the cementitious conditions accentuated the delay. However, this delay in setting did not affect the hydrates’ growth in the material. In fact, FM showed a “delay accelerator” behavior, meaning that once hydration began, it was accelerated as compared to a reference. Macroscopically, FM induced significant flocculation, increasing material porosity and carbonation. Consequently, bulk density and thermal conductivity were reduced. At the highest amount of FM admixture (0.75% w/w), FM allowed bridge formation between Ca(OH)₂ crystals, which can improve the mechanical properties of mortars. Because FM is highly hygroscopic, it has the capability to absorb water and subsequently release it gradually and under controlled conditions into the cement matrix. Therefore, regulation of water diffusion from the mucilage may induce the self-healing properties responsible for mechanical properties similar to that of the reference in the medium to long term. Full article

Ciprofloxacin is a widely used antibiotic in the fluoroquinolone class. It is widely acknowledged by various researchers worldwide, and it has been documented to have a broad range of other pharmacological activities, such as anticancer, antiviral, antimalarial activities, etc. Researchers have been exploring the synthesis of ciprofloxacin derivatives with enhanced biological activities or tailored capability to target specific pathogens. The various biological activities of some of the most potent and promising ciprofloxacin derivatives, as well as the synthetic strategies used to develop them, are thoroughly reviewed in this paper. Modification of ciprofloxacin via 4-oxo-3-carboxylic acid resulted in derivatives with reduced efficacy against bacterial strains. Hybrid molecules containing ciprofloxacin scaffolds displayed promising biological effects. The current review paper provides reported findings on the development of novel ciprofloxacin-based molecules with enhanced potency and intended therapeutic activities which will be of great interest to medicinal chemists. Full article

Microcystins (MCs), toxins generated by cyanobacteria, feature microcystin-LR (MC-LR) as one of the most prevalent and toxic variants in aquatic environments. MC-LR not only causes environmental problems but also presents a substantial risk to human health. This study aimed to investigate the impact of MC-LR on APC^min/+ mice, considered as an ideal animal model for intestinal tumors. We administered 40 µg/kg MC-LR to mice by gavage for 8 weeks, followed by histopathological examination, microbial diversity and metabolomics analysis. The mice exposed to MC-LR exhibited a significant promotion in colorectal cancer progression and impaired intestinal barrier function in the APC^min/+ mice compared with the control. Gut microbial dysbiosis was observed in the MC-LR-exposed mice, manifesting a notable alteration in the structure of the gut microbiota. This included the enrichment of Marvinbryantia, Gordonibacter and Family_XIII_AD3011_group and reductions in Faecalibaculum and Lachnoclostridium. Metabolomics analysis revealed increased bile acid (BA) metabolites in the intestinal contents of the mice exposed to MC-LR, particularly taurocholic acid (TCA), alpha-muricholic acid (α-MCA), 3-dehydrocholic acid (3-DHCA), 7-ketodeoxycholic acid (7-KDCA) and 12-ketodeoxycholic acid (12-KDCA). Moreover, we found that Marvinbryantia and Family_XIII_AD3011_group showed the strongest positive correlation with taurocholic acid (TCA) in the mice exposed to MC-LR. These findings provide new insights into the roles and mechanisms of MC-LR in susceptible populations, providing a basis for guiding values of MC-LR in drinking water. Full article

The search for natural food additives makes propolis an exciting alternative due to its known antimicrobial activity. This work aims to investigate propolis’ behavior as a nitrite substitute ingredient in cooked ham (a ready-to-eat product) when confronted with pathogenic microorganisms of food interest. The microbial evolution of Listeria monocytogenes, Staphylococcus aureus, Bacillus cereus, and Clostridium sporogenes inoculated at known doses was examined in different batches of cooked ham. The design of a challenge test according to their shelf life (45 days), pH values, and water activity allowed the determination of the mesophilic aerobic flora, psychotropic, and acid lactic bacteria viability. The test was completed with an organoleptic analysis of the samples, considering possible alterations in color and texture. The cooked ham formulation containing propolis instead of nitrites limited the potential growth (δ < 0.5 log₁₀) of all the inoculated microorganisms until day 45, except for L. monocytogenes, which in turn exhibited a bacteriostatic effect between day 7 and 30 of the storage time. The sensory analysis revealed the consumer’s acceptance of cooked ham batches including propolis as a natural additive. These findings suggest the functionality of propolis as a promising alternative to artificial preservatives for ensuring food safety and reducing the proliferation risk of foodborne pathogens in ready-to-eat products. Full article

Label-free measurement and analysis of single bacterial cells are essential for food safety monitoring and microbial disease diagnosis. We report a microwave flow cytometric sensor with a microstrip sensing device with reduced channel height for bacterial cell measurement. Escherichia coli B and Escherichia coli K-12 were measured with the sensor at frequencies between 500 MHz and 8 GHz. The results show microwave properties of E. coli cells are frequency-dependent. A LightGBM model was developed to classify cell types at a high accuracy of 0.96 at 1 GHz. Thus, the sensor provides a promising label-free method to rapidly detect and differentiate bacterial cells. Nevertheless, the method needs to be further developed by comprehensively measuring different types of cells and demonstrating accurate cell classification with improved machine-learning techniques. Full article

Numerical models can be powerful tools for evaluating the best scenarios for the construction of artificial nourishments to mitigate coastal erosion. Until recent decades, when looking at medium- to long-term simulations, cross-shore and alongshore processes have been studied separately. Accounting for both processes in a shoreline evolution numerical model would improve the understanding and predictive capacity of future changes in coastline evolution. The AX-COAST project aims to develop new capacities in modeling cross-shore sediment transport processes by adding the CS-Model, a cross-shore numerical model, into the existing LTC (Long-Term Configuration) model. The LTC model is a shoreline evolution numerical model which is a module of the cost–benefit assessment tool COAST. This work presents the first steps of the CS-Model implementation, which involve evaluating its performance by calibrating the model with extensive measured datasets of wave climate, beach profiles, tide levels, etc., from coastal areas in IJmuiden and Sand Motor in the Netherlands. The results show good agreement between modeled and observed values. Additionally, wave climate datasets derived from global and regional wave models were considered to evaluate modeling performance at IJmuiden. Using derived timeseries from the wave models did not significantly lead to different results compared to using measured data. The obtained mean absolute and relative errors for each profile were low for both types of datasets. Calibration processes with consistent data are important in modeling simulations to accurately represent the study area and ensure the credibility of future simulations. Full article

MortalityMinder enables healthcare researchers, providers, payers, and policy makers to gain actionable insights into where and why premature mortality rates due to all causes, cancer, cardiovascular disease, and deaths of despair rose between 2000 and 2017 for adults aged 25–64. MortalityMinder is designed as an open-source web-based visualization tool that enables interactive analysis and exploration of social, economic, and geographic factors associated with mortality at the county level. We provide case studies to illustrate how MortalityMinder finds interesting relationships between health determinants and deaths of despair. We also demonstrate how GPT-4 can help translate statistical results from MortalityMinder into actionable insights to improve population health. When combined with MortalityMinder results, GPT-4 provides hypotheses on why socio-economic risk factors are associated with mortality, how they might be causal, and what actions could be taken related to the risk factors to improve outcomes with supporting citations. We find that GPT-4 provided plausible and insightful answers about the relationship between social determinants and mortality. Our work is a first step towards enabling public health stakeholders to automatically discover and visualize relationships between social determinants of health and mortality based on available data and explain and transform these into meaningful results using artificial intelligence. Full article

Deformation, as the most intuitive index, can reflect the operation status of hydraulic structures comprehensively, and reasonable analysis of deformation behavior has important guiding significance for structural long-term service. Currently, the health evaluation of dam deformation behavior has attracted widespread attention and extensive research from scholars due to its great importance. However, given that the sluice is a low-head hydraulic structure, the consequences of its failure are easily overlooked without sufficient attention. While the influencing factors of the sluice’s deformation are almost identical to those of a concrete dam, nonuniform deformation is the key issue in the sluice’s case because of the uneven property of the external load and soil foundation, and referencing the traditional deformation statistical model of a concrete dam cannot directly represent the nonuniform deformation behavior of a sluice. In this paper, we assume that the deformation at various positions of the sluice consist of both overall and individual effects, where overall effect values describe the deformation response trend of the sluice structure under external loads, and individual effect values represent the degree to which the deformation of a single point deviates from the overall deformation. Then, the random coefficient model of panel data is introduced into the analysis of sluice deformation to handle the unobservable overall and individual effects. Furthermore, the maximum entropy principle is applied, both to approximate the probability distribution function of individual effect extreme values and to determine the early warning indicators, completing the assessment and analysis of the nonuniform deformation state. Finally, taking a project as an example, we show that the method proposed can effectively identify the overall deformation trend of the sluice and the deviation degree of each measuring point from the overall deformation, which provides a novel approach for sluice deformation behavior research. Full article

This research explores the association between ABO blood groups and susceptibility to SARS-CoV-2 infection, analyzing Convalescent COVID-19 plasma (CCP) donors (n = 500) and healthy whole blood donors (BDs) (n = 9678) during the pandemic (1 May 2020 to 30 April 2021). A comparison is made with pre-pandemic BDs (n = 11,892) from 1 May 2018 to 30 April 2019. Significant differences in blood group distribution are observed, with blood group A individuals being three times more likely to be CCP donors. Conversely, blood groups B, O, and AB are less associated with CCP donation. Notably, blood group O is more prevalent among regular BDs, suggesting potential resistance to SARS-CoV-2 infection. This study underscores variations in blood group distribution during the pandemic compared to pre-pandemic periods. The findings support previous research indicating a link between blood group antigens and viral susceptibility, including SARS-CoV-2. Understanding these associations has implications for public health strategies, with potential for predicting COVID-19 outcomes and transmission patterns. Further research is crucial to explore molecular and immunological mechanisms, providing valuable insights for targeted preventive strategies and personalized healthcare in managing the impact of COVID-19. Full article

Tumor necrosis factor-α-induced protein 8-like 3 (TNFAIP8L3 or TIPE3) functions as a transfer protein for lipid second messengers. TIPE3 is highly upregulated in several human cancers and has been established to significantly promote tumor cell proliferation, migration, and invasion and inhibit the apoptosis of cancer cells. Thus, inhibiting the function of TIPE3 is expected to be an effective strategy against cancer. The advancement of artificial intelligence (AI)-driven drug development has recently invigorated research in anti-cancer drug development. In this work, we incorporated DFCNN, Autodock Vina docking, DeepBindBC, MD, and metadynamics to efficiently identify inhibitors of TIPE3 from a ZINC compound dataset. Six potential candidates were selected for further experimental study to validate their anti-tumor activity. Among these, three small-molecule compounds (K784-8160, E745-0011, and 7238-1516) showed significant anti-tumor activity in vitro, leading to reduced tumor cell viability, proliferation, and migration and enhanced apoptotic tumor cell death. Notably, E745-0011 and 7238-1516 exhibited selective cytotoxicity toward tumor cells with high TIPE3 expression while having little or no effect on normal human cells or tumor cells with low TIPE3 expression. A molecular docking analysis further supported their interactions with TIPE3, highlighting hydrophobic interactions and their shared interaction residues and offering insights for designing more effective inhibitors. Taken together, this work demonstrates the feasibility of incorporating deep learning and MD simulations in virtual drug screening and provides inhibitors with significant potential for anti-cancer drug development against TIPE3−. Full article

The field of materials science has experienced a transformative shift with the emergence of high-entropy materials (HEMs), which possess a unique combination of properties that traditional single-phase materials lack. Among these, high-entropy nitrides (HENs) stand out for their exceptional mechanical strength, thermal stability, and resistance to extreme environments, making them highly sought after for applications in aerospace, defense, and energy sectors. Central to the design of these materials is their entropy forming ability (EFA), a measure of a material’s propensity to form a single-phase, disordered structure. This study introduces the application of the sure independence screening and sparsifying operator (SISSO), a machine learning technique, to predict the EFA of HEN ceramics. By utilizing a rich dataset curated from theoretical computational data, SISSO has been trained to identify the most critical features contributing to EFA. The model’s strong interpretability allows for the extraction of complex mathematical expressions, providing deep insights into the material’s composition and its impact on EFA. The predictive performance of the SISSO model is meticulously validated against theoretical benchmarks and compared with other machine learning methodologies, demonstrating its superior accuracy and reliability. This research not only contributes to the growing body of knowledge on HEMs but also paves the way for the efficient discovery and development of new HEN materials with tailored properties for advanced technological applications. Full article

Extracellular vesicles represent a group of structures with the capacity to communicate with different cells and organs. This complex network of interactions can regulate multiple physiological processes in the organism. Very importantly, these processes can be altered during the appearance of different diseases including cancer, metabolic diseases, etc. In addition, these extracellular vesicles can transport different cargoes, altering the initiation of the disease, driving the progression, or even accelerating the pathogenesis. Then, we have explored the implication of these structures in different alterations such as pancreatic cancer, and in different metabolic alterations such as diabetes and its complications and non-alcoholic fatty liver disease. Finally, we have explored in more detail the communication between the liver and the pancreas. In summary, extracellular vesicles represent a very efficient system for the communication among different tissues and permit an efficient system as biomarkers of the disease, as well as being involved in the extracellular-vesicle-mediated transport of molecules, serving as a potential therapy for different diseases. Full article

This study utilizes provincial panel data from China spanning the period from 2011 to 2020 to assess the coupled and coordinated development of spatial functions related to production, life, and ecology (PLE) in rural areas. The assessment is based on quantifying the spatial function indices for PLE in China’s rural regions. Additionally, it examines the characteristics of their spatial and temporal evolution, spatial correlation, and driving factors. The findings indicate a modest upward trend in the spatial coupling and coordination levels of these functions across rural China, although a significant proportion of provinces still exhibit a near-disordered decline. Exploratory spatial data analysis reveals a geographical disparity, with higher levels of coupled and coordinated development observed in the eastern regions, lower levels in the west, and noticeable spatial clustering. By employing the spatial Durbin model to investigate the determinants of coupling degrees, we discovered that factors such as regional economic development, urbanization, the urban–rural income gap, financial support for agriculture, science and technology investment level, and agricultural structural adjustments significantly influence the spatial coupling of rural PLE functions. Furthermore, using the geographic detector model, the analysis identifies science and technology investment level, economic development, and financial support for agriculture as key drivers influencing the spatial coupling and coordination of these functions. These findings provide valuable reference points for policies and strategies related to rural management. Full article

Unlike business process diagrams, where ISO/IEC 19510 (BPMN 2.0) prevails, high-level process landscape diagrams are being designed using a variety of standard- or semi-standard-based notations. Consequently, landscape diagrams differ among organizations, domains, and modeling tools. As (process landscape) diagrams need to be understandable in order to communicate effectively and thus form the basis for valid business decisions, this study aims to empirically validate the cognitive effectiveness of common landscape designs, including those BPMN-L-based, which represent a standardized extension of BPMN 2.0 specifically aimed at landscape modeling. Empirical research with 298 participants was conducted in which cognitive effectiveness was investigated by observing the speed, ease, accuracy, and efficiency of answering questions related to semantically equivalent process landscape diagrams modeled in three different notations: value chains, ArchiMate, and BPMN-L. The results demonstrate that BPMN-L-based diagrams performed better than value chain- and ArchiMate-based diagrams concerning speed, accuracy, and efficiency; however, subjects perceived BPMN-L-based diagrams as being less easy to use when compared to their counterparts. The results indicate that differences in cognitive effectiveness measures may result from the design principles of the underlying notations, specifically the complexity of the visual vocabulary and semiotic clarity, which states that modeling concepts should have unique visualizations. Full article