The biofilm is a conglomerate of cells surrounded by an extracellular matrix, which contributes to the persistence of infections. The difficulty in removing the biofilm drives the research for new therapeutic options. In this work, the effect of terpenes (-)-trans-Caryophyllene, (S)-cis-Verbenol, (S)-(-)-Limonene, (R)-(+)-Limonene, and linalool was evaluated, individually and in combinations on bacterial growth, by assay with resazurin; the formation of biofilm, by assay with violet crystal; and the expression of associated genes, by real-time PCR, in two clinical isolates of S. aureus, ST30-t019 and ST5-t311, responsible for more than 90% of pediatric infections by this pathogen in Paraguay. All combinations of terpenes can inhibit biofilm formation in more than 50% without affecting bacterial growth. The most effective combination was (-)-trans-Caryophyllene and linalool at a 500 μg/mL concentration for each, with an inhibition percentage of 88%. This combination decreased the expression levels of the sdrD, spa, agr, and hld genes associated with the initial cell adhesion stage and quorum sensing. At the same time, it increased the expression levels of the cap5B and cap5C genes related to the production of capsular polysaccharides. The combinations of compounds tested are promising alternatives to inhibit biofilm formation in S. aureus.

Background: Handheld cameras may increase access to teleretinal diabetic retinopathy screenings (TDRS), as they are more economical and nimbler than traditional desktop cameras. However, their use in safety-net clinical settings is less understood. Methods: The Los Angeles County Department of Public Health collaborated with the Los Angeles County Department of Health Services (DHS) to pilot the use of handheld cameras in DHS’s TDRS program. This study assessed screening metrics and interviewed TDRS coordinators and photographers about their experiences using these cameras. Results: Handheld cameras are harder to operate and performed less optimally on key screening metrics (e.g., image quality) compared to desktop cameras. However, this challenge appeared to be linked to the selected model, rather than to all handheld cameras. The adoption of handheld cameras in DHS was aided by their integration into an existing agency infrastructure that already supports the use of desktop cameras; these operational supports include an established workflow, a data platform, and experienced photographers. Conclusions: The use of handheld cameras for TDRS is a promising practice for assuring equitable access to screenings. Handheld cameras are portable, smaller in size, and lower in cost than desktop cameras. Future projects should invest in and assess the performance of higher-quality models of these cameras, especially their use in safety-net clinical settings.

The COVID-19 pandemic has highlighted the critical need for telehealth and remote patient monitoring in healthcare delivery. Despite the growing use of on-body wearable sensors for continuous monitoring and predicting adverse events, such as hospitalizations or falls, their widespread adoption within the healthcare ecosystem remains a significant challenge. While the pandemic has accelerated the acceptance of these technologies, achieving widespread integration requires their sustained incorporation into routine healthcare practices beyond emergency situations. In this study, we extend the application of our previously developed remote patient monitoring system, originally designed for the geriatric frail population, to COVID-19 patients. Our objective is to assess whether the metrics obtained from this system, which includes physical activity and indoor localization sensors, along with vital sign monitoring, can provide additional insights into the recovery trajectory of individuals affected by COVID-19.

Accurate fire severity mapping is essential for understanding the impacts of wildfires on vegetation dynamics in arid savannas. The frequent wildfires in these biomes often cause topkill, where vegetation experiences above-ground combustion, but the below-ground root structures survive, allowing for subsequent regrowth post-burn. Investigating post-fire regrowth is crucial for maintaining ecological balance, elucidating fire regimes, and enhancing the knowledge base of land managers regarding vegetation response. This study examined the relationship between bush fire severity and post-burn coppicing/regeneration events of woody vegetation in the Kalahari Desert of Botswana. Utilizing UAV-derived RGB imagery combined with a Random Forest (RF) classification algorithm, we aimed to enhance the precision of burn severity mapping at a fine spatial resolution. Our research focused on a 1 km2 plot within the Modisa Wildlife Reserve, extensively burnt by the Kgalagadi Transfrontier Fire of 2021. The UAV imagery, captured at various intervals post-burn, provided detailed orthomosaics and canopy height models, facilitating precise land cover classification and burn severity assessment. The RF model achieved an overall accuracy of 79.5% and effectively identified key burn severity indicators, including green vegetation, charred grass, and ash deposits. Our analysis revealed a >50% probability of woody vegetation regrowth in high severity burn areas six months post-burn, highlighting the resilience of these ecosystems. This study demonstrates the efficacy of low-cost UAV photogrammetry for fine scale burn severity assessment and provides valuable insights into post-fire vegetation recovery, thereby aiding land management and conservation efforts in savannas.

The purpose of this study was to identify candidate novel species performing beneficial ecological functions from soil. The hypothesis was that novel culturable species with antibiotic production potential could be readily found from soil in the Nature Canyon at Los Angeles Pierce College. 137 bacterial isolates from soil were grown on nutrient agar, ISP-6 medium, or LB agar and partial 16S was ascertained using the Sanger platform for regions V1-V4. One of the potentially novel isolates, Pseudomonas sp. 21-C3-ER, was isolated on nutrient agar from fallow soil in an agricultural nature preserve in Los Angeles. The soil conditions were 3.7% moisture, clay texture by touch, 13.37% organic matter, pH=6.63, TDS=638, 150 ppm nitrogen, 114 ppm phosphate, and 42 ppm potash. Whole genome sequencing was performed on the DNBseq platform and species identification was performed with TRUEBacID; the closest relative was Pseudomonas brassicacearum subsp. neoaurantiaca which was 92.05% similar by genomic evidence. 197 virulence factors were identified including flagellar biosynthesis, motor proteins, Beta-lactamase, Fosfomycin, copper, and sulfur drug resistance. Gram stain of strain 21-C3-ER revealed gram negative rods. Rods were visualized with scanning electron microscopy, revealing individuals approximately 1 µm in length. Secondary metabolite analysis using antiSMASH showed a variety of post-translationally modified proteins and a promysalin analog. The qualitative test for pyoverdine siderophore production using UV light was positive. Metabolic analysis using RAST indicated genes were present for pyoverdine production, antimicrobial resistance, and ammonification. The nitrate reduction test was positive. Denitrifying bacteria may prevent leaching of nitrates from soil, since ammonia has a higher affinity to soil and organic matter than nitrate. We propose Pseudomonas fernandeno as a novel species. The type stain is 21-C3-ER.

Alzheimer’s disease (AD) and the therapies of AD with anti-amyloid1-42 (Aβ) monoclonal antibodies are associated with the vascular complications of brain edema, hemorrhage, and cerebral amyloid angiopathy (CAA) termed amyloid-related imaging abnormalities (ARIAs). The neuropathology of normal and AD brains demonstrates that monocyte/macrophages (MMs) are critical in successful brain clearance in healthy brain by up-loading, transporting, and degrading Aβ but fail degradation and clearance in AD patients by releasing Aβ into vessels at the blood-brain barrier, causing vasculitis and ARIAs. The naturally-formed epoxides (EpFAs) of polyunsaturated fatty acids (PUFA’s), including arachidonic, docosahexaenoic, and eicosapentaenoic fatty acids, may repair degradation of Aβ by regulating Aβ-degrading enzymes, inflammatory cytokines, and unfolded protein response (UPR) to endoplasmic reticulum stress in a homeostatic fashion. EpFAs are, however, degraded by the soluble epoxide hydrolase enzyme (sEH) but are protected by the sEH inhibitor (sEHI) TPPU. In the cultures of AD patients’ macrophages, TPPU with the epoxide EEQ, and the cGAS/STING inhibitor H-151 increased uptake of Aβ at 2 hours and degradation of Aβ at 24 hours. In conclusion, the soluble epoxide hydrolase inhibitor TPPU with omega-3 fatty acids and the cGAS/STING inhibitor H-151 increase Aβ uptake and degradation in macrophages of AD patients, and potentially enhancing Aβ clearance in the AD brain.

Manuka honey (MH) exhibits potential antitumor activity in preclinical models of a number of human cancers. Treatment in vitro with MH at concentrations ranging from 0.3-5.0% (w/v) led to significant dose-dependent inhibition of proliferation of human breast cancer MCF-7 cells, but anti-proliferative effects of MH were less pronounced in triple-negative MDA-MB-231 breast cancer cells. Effects of MH were also tested on non-malignant human mammary epithelial cells (HMEC) at 2.5% w/v, and it was found that MH reduced proliferation of MCF-7 cells but not that of HMEC. Notably, the antitumor activity of MH was in the range of that exerted by treatment of MCF-7 cells with the antiestrogen tamoxifen. Further, MH treatment stimulated apoptosis of MCF-7 cells in vitro, with most cells exhibiting acute and significant levels of apoptosis that correlated with PARP activation. Additionally, effects of MH induced activation of AMPK and inhibition of AKT/mTOR downstream signaling. Treatment of MCF7 cells with increased concentrations of MH induced AMPK phosphorylation in a dose dependent manner that was accompanied by inhibition of phosphorylation of AKT and mTOR downstream effector protein S6. In addition, MH reduced phosphorylated STAT3 levels in vitro which may correlate with MH and AMPK-mediated anti-inflammatory properties. Further, in vivo, MH administered alone significantly inhibited the growth of established MCF-7 tumors in nude mice by 84%, resulting in an observable reduction in tumor volume. Our findings highlight the need for further research into the use of natural compounds, such as MH, for antitumor efficacy and potential chemoprevention and investigation of molecular pathways underlying these actions.

Antibody-drug conjugates (ADCs), a groundbreaking advancement in cancer therapy, synergistically combine the precision of monoclonal antibodies with the potent destruction capabilities of cytotoxic drugs, signifying a new era in the targeted oncological treatments. This comprehensive paper delves into the evolving field of antibody-drug conjugates (ADCs) and their pivotal role in cancer therapy. We have conducted an in-depth exploration of the structural components of ADCs, including target antigens, monoclonal antibodies, chemical linkers, and cytotoxic payloads, each playing a crucial role in determining the efficacy and safety profile of these novel therapies. A detailed market analysis reveals the leading pharmaceutical companies shaping the ADC landscape, reflecting the growing interest and potential in this therapeutic approach. We further dissect the engineering intricacies of ADCs, focusing on their ability to target specific cancer cell antigens while minimizing off-target effects, thus offering a more focused treatment with reduced systemic side effects compared to traditional chemotherapy. This approach holds promise in a heightened therapeutic index and decreased toxicity. The mechanisms and effector functions of ADCs are explored, highlight- ing their multifaceted strategy in combating cancer. We include a comprehensive look at how ADCs operate from initial blood circulation to the induction of apoptosis in cancer cells. We also review several key ADCs currently in use or under investigation, offering insights into their unique mechanisms of action, pharmacokinetics, and clinical implications. The discussion extends to drug creation modalities, focusing on innovative conjugation techniques that enhance ADC homogeneity and therapeutic efficacy. In conclusion, the paper underscores the transformative potential of ADCs in the realm of cancer treatment, marking a shift towards more precise and targeted therapeutic approaches. By offering a thorough analysis of ADC technology, this paper sheds light on the prospects of these groundbreaking therapies in the ongoing fight against cancer.

Epilepsy is a common neurological condition, typically diagnosed using Electroencephalogram (EEG). Large scale EEG datasets have recently been made publicly available, allowing the use of advanced Machine Learning (ML) algorithms to analyze EEG patterns associated with epilepsy. While most existing studies focus on identifying seizures in the EEG, few have tried to identify preictal EEG segments. Identifying preictal EEG segments are not only useful in developing early warning systems but also helps inform the course of treatment for the patient. In this study, we propose to represent EEG segments as images, instead of time-series data, and identify preictal EEG segments using a preexisting ML algorithm (YOLOv8) designed for image processing. Multiplexed images (containing the original EEG signal represented on a 2D grid, kurtosis, and spectral entropy) achieve the best accuracy of 95.75% on the dataset while images just containing the EEG signal result in an accuracy of 91.25%. Using only spectrograms, generated from the original EEG signal, results in an accuracy of 90.15%.

Climacteric fruits undergo a characteristic ripening process regulated by ethylene, a key plant hormone. Extending the shelf life of these fruits while preserving their postharvest quality poses a significant challenge for the food industry. This review provides a comprehensive overview of physiological and molecular strategies to delay ethylene-mediated ripening in climacteric fruits and their impact on shelf life, postharvest quality, sensory attributes, and volatile compounds. Additionally, it examines the role of ethylene in fruit ripening analysing various ethylene managing strategies including ethylene inhibitors, ethylene adsorbents and ethylene scavengers by catalytic oxidation The review concludes with future research directions including integrated strategies, environmental considerations and commercial applications for improving postharvest handling and fruit quality.

Hospitalizations for patients with diabetes and dementia take a significant health and economic toll on older adults in the United States. The present study sought to examine the differences in hospitalization characteristics and outcomes associated with diabetes and dementia separately and together using discharge record data of Los Angeles County residents aged 50+ from the California Department of Health Care Access and Information for the period 2019-2021. Results from multiple linear regression analyses indicated that when compared to those with no diabetes or dementia, patients with diabetes alone exhibited the highest total charges, while those with comorbid diabetes and dementia exhibited lower charges (p≤.05). Results of a multinomial logistic regression found that patients with comorbid diabetes and dementia had the highest odds of having a length of stay of 7+ days (AOR=1.49; 95% CI=1.44-1.53). A matched-case control analysis found that comorbid diabetes and dementia was associated with significantly lower odds of hypertensive disease than diabetes alone (MOR=0.81; 95% CI=0.67-0.97). These results highlight the complexity of factors affecting variation in hospitalization outcomes across these distinct disease profiles. Findings suggest a need to carefully consider this complexity when developing health policies or strategies to improve hospitalization outcomes involving these disease conditions.

Remote sensing datasets are increasingly being used to provide spatially explicit large scale evapotranspiration (ET) estimates. The focus of this study was to estimate and thematically map on a pixel-by-pixel basis, the actual evapotranspiration (ET_a) of the Wonji Shoa Sugarcane Estate using the Surface Energy Balance Algorithm for Land (SEBAL), Simplified Surface Energy Balance (SSEB) and Operational Simplified Surface Energy Balance (SSEBop) algorithms. The results obtained revealed that the ranges of the daily ETa estimated on January 25, February 26, September 06 and October 08, 2002 using SEBAL were 0.0 - 6.85, 0.0 – 9.36, 0.0 – 3.61, 0.0 – 6.83 mm/day; using SSEB 0.0 - 6.78, 0.0 – 7.81, 0.0 – 3.65, 0.0 – 6.46 mm/day, and SSEBop were 0.05 - 8.25, 0.0 – 8.82, 0.2 – 4.0, 0.0 – 7.40 mm/day, respectively. The Root Mean Square Error (RMSE) values between SSEB and SEBAL, SSEBop and SEBAL, and SSEB and SSEBop were 0.548, 0.548, and 0.99 for January 25, 2002; 0.739, 0.753, and 0.994 for February 26, 2002;0.847, 0.846, and 0.999 for September 06, 2002; 0.573, 0.573, and 1.00 for October 08, 2002, respectively. The standard deviation of ET_a over the sugarcane estate showed high spatio-temporal variability perhaps due to soil moisture variability and surface cover. The three algorithm results showed that well watered sugarcane fields in the mid-season growing stage of the crop had higher ET_a values compared with the other dry agricultural fields confirming that they consumptively use more water. Generally during the dry season, ET_a is limited to water surplus areas only and in wet season, ET_a was high throughout the entire sugarcane estate. The evaporation fraction (ETrF) results also followed the same pattern as the daily ET_a over the sugarcane estate. The total crop and irrigation water requirement and effective rainfall estimated using the Cropwat model were 2468.8, 2061.6 and 423.8 mm/yr for January 2001 planted and 2281.9, 1851.0 and 437.8 mm/yr for March 2001 planted sugarcanes, respectively. The mean annual ET_a estimated for the whole estate were 107 Mm³, 140 Mm³, and 178 Mm³ using SEBAL, SSEB, and SSEBop, respectively. Even though the algorithms should be validated through field observation, they have potential to be used for effective estimation of ET in the sugarcane estate.

In this study we sought to investigate the impact of urbanization, presence of concrete river bottom, and nutrient pollution on microbial communities along the L.A. River. Six molecular markers were evaluated for identification of bacteria, plants, fungi, fish, and invertebrates in 90 samples. PCA (principal components analysis) was used with PAM (partitioning around medoids) clustering to reveal community structure and an NB (Negative binomial) model in DESeq2 was used for differential abundance analysis. PCA and factor analysis exposed the main axes of variation but were sensitive to outliers. Differential abundance of Proteobacteria was associated with soft bottom sites, and there was an apparent balance in the abundance of organisms responsible for nitrogen cycling. Nitrogen cycling was explained by differential abundance of ammonia oxidizing archaea, the complete ammonia oxidizers Nitrospira sp., nitrate reducing bacteria Marmoricola sp., and nitrogen fixing bacteria Devosia sp. which were differentially abundant at soft-bottom sites (p adj < 0.002). In contrast, differential abundance of several Cyanobacteria and other anoxygenic phototrophs was associated with the concrete bottom sites, which suggested the accumulation of excess nitrogen. The soft bottom sites tended to be represented by differential abundance of aerobes, whereas the concrete-associated species tended to be alkaliphilic, saliniphilic, calciphilic, sulfate dependent, and anaerobic. In Glendale Narrows, downstream from multiple water reclamation plants, there were differential abundance of cyanobacteria and algae, however indicator species for low nutrient environments and ammonia-abundance were also present. There was differential abundance of ascomycetes associated with Arroyo Seco and a differential abundance of Scenedesmaceae green algae and cyanobacteria in Maywood, in the analysis which compared suburban with urban river communities. The proportion of Ascomycota to Basidiomycota within the LA River differed from the expected proportion based on published worldwide freshwater and river 18S data; the shift in community structure was most likely associated with the extremes of urbanization. This study indicates that extreme urbanization can result in overrepresentation of cyanobacterial species that could cause reductions in water quality and safety.

RNA transcripts play a crucial role as witnesses of gene expression health. Identifying disruptive short sequences in RNA transcription and regulation is essential for potentially treating diseases. Let’s delve into the mathematical intricacies of these sequences. We’ve previously devised a mathematical approach for defining a “healthy” sequence (10.14218/GE.2023.00079). This sequence is characterized by having at most four distinct nucleotides (denoted as nt≤4). It serves as the generator of a group denoted as fp. The desired properties of this sequence are as follows: fp should be close to a free group of rank nt−1, it must be aperiodic, fp should not have isolated singularities within its SL2(C) character variety (specifically within the corresponding Groebner basis). Now, let’s explore the concept of singularities. There are cubic surfaces associated with the character variety of a four-punctured sphere denoted as S24. When we encounter these singularities, we find ourselves dealing with some algebraic solutions of a dynamical second-order differential (and transcendental) equation known as the Painlevé VI equation (10.3390/dynamics4010001). In certain cases, S24 degenerates, in the sense that two punctures collapse, resulting in a “wild” dynamics governed by the Painlevé equations of an index lower than VI. In our paper, we provide examples of these fascinating mathematical structures within the context of miRNAs. Specifically, we find a clear relationship between decorated character varieties of Painlevé equations and the character variety calculated from the seed of oncomirs. These findings should find applications in cancer research.

As urban areas expand and populations grow, environmental challenges such as the urban heat island effect, air pollution, and light pollution intensify. The urban heat island effect exacerbates extreme heat conditions, leading to prolonged periods of unhealthy and hazardous environments for both humans and other species. This study investigates the thermal environment associated with the urban heat island effect, focusing on the role of trees in mitigating this issue. Using ENVI-met simulations, the study examines various factors influencing tree cooling effectiveness, including seasonal variations, building shading, transpiration rates, tree placement, and spacing. A new tree-planting strategy is developed based on these findings, aiming to enhance thermal comfort. The study compares the thermal environment of sidewalks under the new tree-planting scheme with the existing arrangement across different months. Results indicate that the new scheme reduces UTCI temperatures by 2.2°C on the hottest day, 0.97°C on the coldest day, and 1.52°C annually in the study area of Los Angeles, demonstrating that the cooling benefits of trees in hot weather outweigh the potential drawbacks in cold weather, highlighting its potential to mitigate the urban heat island effect.

The ability of S. aureus to infect humans and cause a wide range of infections so efficiently is a consequence of the many variant virulence factors that it can produce. Superantigens (SAgs) make up a group of them and include staphylococcal enterotoxins (SE). The main objective of this study was to detect enterotoxin gene cluster (EGC) in S. aureus isolates that caused infections in Paraguayan children in the year 2017, through an observational and cross-sectional study. The detection of the EGC including the seg, sei, sem, sen, seo, and seu genes, was carried out by end-point PCR. 181 S. aureus isolates were collected, most of them caused skin and soft tissue infections (SSTI, n=140, 77%) and came from children under 6 years 50% (n=90). At least one SE gene was detected in 96% (174/181) of isolates and 24% (44/181) carried the entire EGC. An association between methicillin resistance and seg, sei, seo, and seu gene carriage (p ≤0.05) was observed. A high prevalence of EGC was detected in S. aureus isolates collected from infected children attending the main health care centers in Paraguay, its importance is due to the potential of these toxins to aggravate staphylococcal infections.

Objectives: This study assessed the associations between parent intent to have their child receive COVID-19 vaccination, and demographic factors and various child activities including attendance at in-person education or childcare. Methods: Persons undergoing COVID-19 testing residing in Minnesota and Los Angeles County, California with children aged <12 years completed anonymous internet-based surveys between May 10 and September 6, 2021 to assess factors associated with intention to vaccinate their child. Factors influencing parents’ decision to have their child attend in person school or childcare were examined. Estimated adjusted odds rations (AORs, 95% CI) were computed between parents’ intentions regarding children’s COVID-19 vaccination and participation in school and extra-curricular activities using multinomial logistic regression. Results: Compared to parents intending to vaccinate their children (n=4,686 [77.2%]), those undecided (n=874 [14.4%] or without intention to vaccinate (n=508 [8.4%]) tended to be younger, non-White, less educated and themselves not vaccinated against COVID-19. Their children more commonly participated in sports (aOR:1.51 1.17-1.95) and in-person faith or community activities (aOR:4.71 (3.62-6.11). They further indicated that additional information regarding vaccine safety and effectiveness would influence their decision. COVID-19 mitigation measures were the most common factors influencing parents’ decision to have their child attend in-person class or childcare. Conclusions: Several demographic and socioeconomic factors are associated with parents’ decision whether to vaccinate their <12-year-old children for COVID-19. Child participation in in-person activities was associated with parents’ intentions not to vaccinate. Tailored communications may be useful to inform parents’ decisions regarding safety and effectiveness of vaccination.

P-band radar remote sensing applied during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission has shown great potential for estimation of root zone soil moisture. When retrieving the soil moisture profile (SMP) from P-band radar, a mathematical function describing the vertical moisture distribution is required. Because only a limited number of observations are available, the number of free parameters of the mathematical model must not exceed the number of observed data. For example, a second order polynomial that contains 3 free parameters was presumed based on in-situ SMP data. The polynomial is currently parameterized based on 3 backscatter observations provided by AirMOSS (i.e. one frequency at three polarizations of HH, VV and HV). In this paper, a more realistic, physically-based SMP model containing 3 free parameters is derived based on a solution to Richards’ equation for unsaturated flow in soils. Evaluation of the new SMP model based on both numerical simulations and measured data revealed that it exhibits greater flexibility for fitting measured and simulated SMPs than the currently applied polynomial. It is also demonstrated that the new SMP model can be reduced to a second order polynomial at the expense of fitting accuracy.

Background: The aim was to diagnose Candida in the oral cavity of subjects with type 2 diabetes mellitus (T2DM) using a genotyping technique and to compare the results with those from con-ventional diagnosis by Papanicolau (Pap) staining. Methods: Palatal mucosa smears were per-formed in 18 dental care patients diagnosed with T2DM and grade I, II, and III prosthetic stomatitis who met the inclusion criteria; 18 healthy control subjects were also included in the study. He-moglobin A1c (HbA1c) levels were determined from total blood. Using exfoliative cytology, the Pap staining technique was used to diagnose candidiasis. Exfoliative cytology was also used for molecular diagnosis; DNA was obtained for Candida genotyping and RNA was used for gene ex-pression studies. Results: Clinical patterns indicated that all subjects were positive for Candida; however, Pap analysis revealed only three positive subjects, whereas end-point polymerase chain reaction (PCR) analysis revealed fifteen subjects with some type of Candida. The most common Candida species found were Candida guilliermondii (38.8%), Candida krusei (33.3%), Candida tropicalis, and Candida lusitaniae (22.2%). Interestingly, the coexpression of different species of Candida was found in various patients. In all patients, HbA1c levels were increased. Gene expression analysis showed a significant decrease (p ≤ 0.05) in TLR2 expression in positive subjects, whereas TLR4 expression did not differ significantly among patients. Conclusions: The end-point PCR technique showed better sensitivity for diagnosis of Candida when compared with diagnosis by Pap staining. T2DM subjects showed an increased presence of C. guilliermondii that was correlated with decreased TLR2 expression

In recent years, using pre-sprouted seedlings for sugarcane establishment has been developed in Brazil. Despite yielding promising results, the optimal irrigation management for this method during the Brazilian winter planting season has yet to be determined. Additionally, information regarding water demand and seedling behavior when subjected to different irrigation levels remains unclear. This study aimed to assess the response of pre-sprouted sugarcane seedlings to different irrigation levels during the winter in southeastern Brazil. Pre-sprouted sugarcane seedlings were subjected to different irrigation levels during the winter season in the region of Piracicaba, using a sprinkler irrigation system. Periodic evaluations were conducted, such as seedlings mortality rate, root size, root dry mass, number of sugarcane tillers sprouted per meter, total number of tillers, plant height, leaf area index, shoot dry mass, and total leaf chlorophyll index. The findings of this investigation indicate that a 10 mm irrigation level facilitated optimal fixation depth and achieved a 97.8% survival rate. Moreover, a correlation between seedling mortality rate and irrigation management was observed, emphasizing the critical role of irrigation in establishing and developing seedlings. Based on these findings, a 10 mm irrigation may represent the optimal irrigation strategy for pre-sprouted sugarcane seedlings in this scenario.

Wetlands are ecosystems that are essential to ecological balance and biodiversity, never-theless human activity is a constant threat to them. Excess nutrients are caused by inten-sive livestock and agricultural operations, pollution, and population growth, which in turn leads to uncontrolled microbiological development. This impairment in water quality can constitute a risk to animal, human, and environmental health. To thoroughly charac-terize the microbial communities, shotgun metagenomics was used to characterize the taxonomic and functional pattern of microorganisms that inhabit urban wetlands in the Los Lagos Region of Chile. The main objective was to identify microorganisms of veteri-nary relevance, assess their potential antibiotic resistance, and characterize the main vir-ulence mechanism. As expected, a high diversity of microorganisms was identified, in-cluding bacteria described as animal or human pathogens. Also, a diverse repertory of an-timicrobial-resistant genes (ARGs) was detected, which confers mainly resistance to be-ta-lactams, consistent with the families of antibiotics most used in Chile. Besides, a di-verse collection of virulence mechanisms was also identified. Given the significance of the relationship between environmental, animal, and human health—a concept known as One Health—there is a need to establish molecular surveillance programs. This work is the first report of the presence of these harmful biological elements in urban wetlands subjected to anthropogenic pressure, located in the south of Chile.

Salmon aquaculture generates a substantial volume of waste material, offering potential for biomaterial production. This industry produces significant waste, which can be repurposed for various biological applications, particularly in biomaterial production for tissue engineering with pertinent applications in osteo-conduction, osteo-induction, and clinical/surgical bone regeneration and repair. The process involves a standardized pre-treatment stage aimed at minimizing biological waste content. Subsequently, the treatment stage, contingent on the chosen methodology, facilitates the removal of proteins, lipids, molecules, and other compounds, leaving the mineral phase as a valuable substrate. The selection of the optimal method, whether alkaline hydrolysis, calcination, or NaOH hydrolysis, for obtaining this substrate necessitates thorough examination through chemical, physical, and biological assessments, including Raman Spectroscopy and X-Ray Diffraction. This study aims to identify the most efficient laboratory process for biofunctional nano-sized hydroxyapatite mineral production derived from Chilean salmon fish backbone waste.

Dengue (DENV) and Zika (ZIKV) virus continue to pose significant challenges globally due to their widespread prevalence and severe health implications. Given the absence of effective vaccines and specific therapeutics, targeting of the highly conserved NS5 RNA-dependent RNA polymerase (RdRp) domain has emerged as a promising strategy. However, limited efforts have been made to develop inhibitors for this crucial target. In this study, we employed an integrated in silico approach utilizing combinatorial chemistry, docking, molecular dynamics simulations, MM/GBSA, and ADMET studies to target the allosteric N-pocket of DENV3-RdRp and ZIKV-RdRp. Using this methodology, we designed lycorine analogs with natural S-enantiomers (LYCS) and R-enantiomers (LYCR) as potential inhibitors of non-structural protein 5 (NS5) in DENV3 and ZIKV. Notably, 12 lycorine analogs displayed a robust binding free energy (&lt;-9.00 kcal/mol), surpassing that of RdRp-Ribavirin (&lt;-7.00 kcal/mol) along with promising ADMET score predictions (&lt;4.00), of which (LYCR728-210, LYCS728-210, LYCR728-212, LYCS505-214) displayed binding properties to both DENV3 and ZIKV targets. This study highlights the potential of non-nucleoside lycorine-based analogs with different enantiomers that may present different or even completely opposite metabolic, toxicological, and pharmacological profiles as promising candidates for inhibiting NS5-RdRp in ZIKV and DENV3, paving the way for further exploration for the development of effective antiviral agents.

Our study examines how dengue incidence is associated with spatial (demographic and socioeconomic) alongside temporal (environmental) factors at multiple scales. We used the incidence of dengue in Ibagué, Colombia, from 2013 to 2018, to examine the associations with climate, socioeconomic and demographic factors from the census and satellite imagery at various levels of local spatial aggregation, including Manzanas, Secciones, Sectores, and Comunas. Our findings show a significant effect of spatial variables at finer levels of aggregation, showing varying degrees of correlation with dengue incidence. Temporal variables such as temperature and precipitation displayed consistent patterns across all spatial levels, with notable variations in Relative Risk (RR). Our study employs Geographically Weighted Regression (GWR) to identify relevant socioeconomic and demographic predictors. Then, these predictors were integrated into hierarchical models implemented in Integrated Nested Laplace Approximation (INLA) at each spatial level to assess spatiotemporal interactions. We comprehensively analyzed the three distinct models developed for each level: spatial, temporal, and spatiotemporal. A comparative evaluation of the models reveals that while higher aggregation levels often yield a better overall model fit, finer levels offer more detailed insights into the localized impacts of socioeconomic and demographic variables on dengue incidence. Our results underscore the importance of considering macro and micro-level factors in epidemiological modeling and highlight the potential for targeted public health interventions based on localized risk factor analysis. Notably, the intermediate level Secciones emerged as the most informative, balancing spatial heterogeneity and case distribution density, thereby providing a robust framework for understanding the spatial determinants of dengue.

Artificial Intelligence (AI) is poised to revolutionize the architectural design and energy management of green buildings, offering significant advancements in sustainability and efficiency. This paper explores the transformative impact of AI on improving energy efficiency and reducing carbon emissions in commercial buildings. By leveraging AI algorithms, architects can optimize building performance through advanced environmental analysis, automation of repetitive tasks, and real-time data-driven decision-making. AI facilitates precise energy consumption forecasting and integration of renewable energy sources, enhancing the overall sustainability of buildings. Our study demonstrates that AI can reduce energy consumption and CO2 emissions by approximately 8% and 19%, respectively, in typical mid-size office buildings by 2050 compared to conventional methods. Further, the combination of AI with energy efficiency policies and low-emission energy production is projected to yield reductions of up to 40% in energy consumption and 90% in CO2 emissions. This paper provides a systematic approach for quantifying AI's benefits across various building types and climate zones, offering valuable insights for decision-makers in the construction industry.

The construction industry is an important part of the national economic market of various countries; since 2013, the construction industry's added value in the gross domestic product has been more than 6%, reaching 6.89% in 2022, and is a pillar industry of the national economy. Intelligent construction is the realistic demand to promote the high-quality development of China's construction industry. It is the key focus of transforming and upgrading the traditional construction industry to information, digital, and intelligent. As a new production factor, construction robots have become the key to promoting intelligent construction. Under the guidance of various national and industry policies, thanks to China's huge construction market volume and rich application scenarios, many innovative and entrepreneurial entities have entered the field of construction robots. Architectural visualization is a crucial aspect of architectural design and communication. With the development of science and technology, artificial intelligence (AI) technology is increasingly becoming an essential tool for architectural visualization and communication. The emergence of AI technology has provided architects with more flexible and creative ways to present design ideas. All along, designers who love architecture have been passionate about exploring better solutions for architecture, but there is never an optimal solution for architectural design; AI is the fire of the future era; it brings us more opportunities but also forces us to face new challenges from the future.

The Chikungunya virus (CHIKV) presents global health challenges, with Brazil witnessing out-breaks since its introduction in 2014. In 2023, Minas Gerais (MG) experienced a CHIKV outbreak, prompting entomological investigations to identify vectors, viral lineages, and assess insecticide resistance. This study utilized social media platforms and citizen science concept to conduct a comprehensive entomological survey. Following Instagram posts, residents with CHIKV sus-pected cases were able to schedule mosquito aspirations. During the aspirations, 421 mosquitoes (165 Aedes aegypti and 256 Culex quinquefasciatus) were captured from 40 households in Salinas city (MG) and tested for Dengue, Zika and Chikungunya viruses through RT-qPCR. Twelve out 57 pools (10 of Ae. aegypti and 2 of Cx. quinquefasciatus) tested positive for CHIKV RNA. Viral RNA was also detected in the heads of 9 Ae. aegypti individuals (but not in Cx. quinquefasciatus). Genome sequencing revealed that CHIKV belonged to the East-Central-South-Africa (ECSA) genotype. Additionally, genetic analyses revealed high frequencies of kdr alleles, suggesting resistant to pyrethroids insecticides in this Ae. aegypti population. These findings emphasize the urgent need for innovative vector study and control strategies to combat arbovirus transmission, and inter-disciplinary approaches in public health interventions.

Diabetic foot ulcers (DFUs) significantly affect the lives of patients and increase the risk of hospital stay and amputation. We suggest a remote-monitoring platform for better DFU care. This system uses digital health metrics (scaled from 0 to 10, where higher scores indicate a greater risk of slow healing) to provide a comprehensive overview through a visual interface. The platform features smart offloading devices that capture behavioral metrics such as offloading adherence, daily steps, and cadence. Coupled with remotely measurable frailty and phenotypic metrics, it offers an in-depth patient profile. Additional demographic data, wound characteristics, and clinical parameters, such as cognitive function, were integrated, contributing to a comprehensive risk factor profile. We evaluated the feasibility of this platform with 124 DFU patients over 12 weeks; 39% experienced unfavorable outcomes such as dropout, adverse events, or non-healing. Digital biomarkers were benchmarked (0-10), categorized into low, medium, and high risk for unfavorable outcomes, and visually represented using color-coded radar plots. The initial results of case reports illustrate the value of this holistic visualization to pinpoint the underlying risk factors for unfavorable outcomes, including a high number of steps, poor adherence, and cognitive impairment. Although future studies are needed to validate the effectiveness of this visualization in personalizing care and improving wound outcomes, early results in identifying risk factors for unfavorable outcomes are promising.

We present 4 cases of patients diagnosed with multiple myeloma initially treated without monoclonal antibodies. It was decided to provide the benefit of the drug after maintenance with lenalidomide was initiated to consolidate the post-maintenance response followed by monitoring without any kind of treatment. In this series of 4 cases 100% of the patients achieved a strict complete response with undetectable Minimal Residual Disease (MRD) for flow cytometry after post-maintenance consolidation with daratumumab regardless of the scheme used as induction/consolidation, as well as whether they had received HSCT. Until the last follow-up the patients are under surveillance with no evidence of progression. It should be noted that 3 of 4 of these patients debuted with high-risk disease.

This paper evaluates the potential of solar concentration technologies—compound parabolic collector (CPC), linear Fresnel collector (LFC) and parabolic trough collector (PTC)—as an alternative to conventional sources of energy for industrial processes in Latin America, where high levels of solar radiation and isolated areas without energy supply exist. The analysis is addressed from energy, economic and environmental perspective. A specific application for Argentina in which fourteen locations are analyzed is considered. Results show that solar concentration technologies can be an economically and environmentally viable alternative. Levelized cost of energy (LCOE) ranges between 2.5 and 16.9 c€/kWh/m² and greenhouse gas (GHG) emissions avoided range between 33 and 348 kgCO₂/(m²·year). CPC technology stands out as the most recommendable technology when the working fluid temperature ranges from 373K to 423K. As the working fluid temperature increases the differences between the LCOE values of the CPC and LFC technologies decrease. When 523K is reached LFC technology is the one which presents the lowest LCOE values for all analyzed sites, while the LCOE values of PTC technology are close to CPC technology values. Results show that solar concentration technologies have reached economic and environmental competitiveness levels under certain scenarios, mainly linked to solar resource available, thermal level requirements and solar technology cost.

The application of AI technology in urban planning covers multiple levels, such as data analysis, decision support, and automated planning. Urban research relies on AI technology to understand and summarize the law of urban growth and improve the analysis of the evolution trend of urban space. Planning and design use AI technology to explore the relevant factors affecting urban development and their weights and discuss the critical role of green building technology in the sustainable development of the construction industry. With the increase in global energy consumption and carbon emissions, traditional building methods can no longer meet environmental protection requirements and efficient use of resources. As a sustainable development solution, green building technology has been paid more and more attention to and adopted by people. These technologies focus not only on the energy efficiency and environmental impact of buildings but also on the resource utilization and environmental load of green buildings over their entire life cycle driven by machine learning. This paper details the basic principles and applications of green building technologies, including AI-driven reduction of negative environmental impacts, improvement of occupant health, efficient use of resources, and optimization of indoor environmental quality. This paper focuses on the critical role of the LEED assessment system developed by the U.S. Green Building Council in advancing green building practices. In addition, the paper analyzes vital points such as water use in green building design, machine learning-driven wind environment optimization, solar technology application, and practical application cases of these technologies on a global scale.

Larus michahellis increased its population throughout the 20th century in its worldwide distribution area. The increase in the Salinas de San Pedro del Pinatar has produced changes in the habitats due to the contribution of guano, predation on birds, incidents with workers, and the production of salt. The objective is to analyze the impacts of L. michahellis on the landscape, habitats, waterfowl, salt production, and workers, as well as to evaluate the effectiveness of control activities. Censuses of wintering L. michahellis have been carried out between 1990-2021, of nesting aquatic birds between 1994-2021, and nests and eggs of L. michahellis have been eliminated between 2000-2021. The result has been a decrease in pairs of L. michahellis, recovery of waterfowl populations, colonization of others, absence of incidents with workers, and reduction of damage to salt production. However, to reach a definitive solution, measures should be adopted to prevent L. michahellis from accessing the main sources of human food: urban solid waste dumps, aquaculture farms, and fish discards.

The aim of this work was to classify the business incubators in Spain according to the four phases of the startup’s incubation process. Considering that the graduation rate implies greater survival and business success of the incubated companies, they have been identified at each stage of the incubation (spread of entrepreneurship, pre-incubation, advanced incubation, and graduation). The activities that present higher impacts on the success of the incubated companies and the activities carried out by the business incubator that have a greater relevance on the graduation of the companies have concretely been considered. Principal component (PC) cluster analysis has been applied. All the incubation variables were used simultaneously, reducing their number, and grouping them into factors. Finally, the cases were grouped according to these latent variables. Principal components analysis reduced dimensionality to 8 factors with a 74 % of explained variance. Factor 1 was positively related to pre-incubation variables, factor 2 was linked to training and collaboration variables within the entrepreneurship diffusion phase. Factor 3, named activity monitoring and control, was related to phase 3 or basic incubation variables. Cluster analysis facilitates the grouping of business incubators into three clusters: Group 1 (16 % of the total), incubators with strong deficits in incubation phases 1, 2 and 3. They are small sized business incubators, often located in rural areas or cities and low graduation rate. Group 2 (30 %), business incubators with very high graduation rate, and strongly positive values in factors 1 and 2. Factor 3, although positive, it is susceptible to improvement. They are the largest group of business incubators and usually located in industrial and technological parks. Group 3 (54 %) is the majority, with values close to cluster 2 and 3.

The aim of this work was to classify the business incubators in Spain according to the four phases of startups incubation. Taking into account that the graduation rate implies greater survival and business success of the incubated companies, they have been identified at each stage of the incubation (spread of entrepreneurship, pre-incubation, advanced incubation, and graduation). The activities that present higher impacts on the success of the incubated companies and the activities carried out by the incubator that have a greater relevance on the graduation of the companies have concretely been considered. Principal component (PC) cluster analysis have been applied. All the incubation variables were used simultaneously, reducing their number, and grouping them into factors, finally the cases were grouped according to these latent variables. Principal components analysis reduced dimensionality to 8 factors with a 74 % of explained variance. Factor 1 was positively related to pre-incubation variables, factor 2 was linked to training and collaboration variables within the entrepreneurship diffusion phase. Factor 3 called activity monitoring and control and was related to phase 3 or basic incubation variables. Clusters analysis facilitates the grouping of incubators into three clusters: Group 1 (16 % of the total), incubators with strong deficits in incubation phases 1, 2 and 3. They are small Business Incubators, often located in rural areas or cities and low graduation rate. Group 2 (30 %), business incubators with very high graduation rate, and strongly positive values in factor 1 and 2. Factor 3 although positive is susceptible to improvement. They are the largest incubators and usually located in industrial and technological parks. Group 3 (54 %) is the majority, with values close to cluster 2 and 3.

A single water pollution by dyes is a huge environmental problem; there is a necessity to produce new decolorization methods that are effective, cost-attractive, and suitable in industrial use. Magnetic chitosan polymers offer the advantage of easy separation from the dye solution. In this work, a Chitosan-Fe polymer was synthesized, characterized, and tested for removal of the azo dye Direct Red 83:1 from water. The polymer was characterized in terms of surface morphology (SEM), elemental analysis (EA), differential scanning calorimetry (DSC), thermal gravimetric analysis (TGA), infrared spectrophotometry (IR), X-ray powder diffraction (XRD) and density functional theory (DFT). The reported results hint that 1 g and pH 7.0 were the best conditions to carry out both kinetic and isotherm models. To understand the adsorption behavior of Direct Red 83:1 onto the polymers, adsorption rate and maximum adsorption capacity were calculated using kinetic tests and isotherm curves respectively. The kinetic data and mechanism of the adsorption process were analyzed by three models and the equilibrium data by three adsorption isotherms. The results indicated that the adsorption process follows a pseudo-second-order kinetics (R2= 0.999) and fits the Temkin isotherm (R2= 0.946), indicating that the adsorption occurred on heterogeneous surfaces. The newly synthesized Chitosan-Fe polymer exhibited good adsorption properties and easy separation of cleaned water.

Zirconia-reinforced lithium silicate (ZLS) is utilized as a material for prosthetic tooth crowns, offering enhanced strength compared to other dental glass-ceramics. In this study, we investigate a commercial ZLS material, provided in a fully crystallized form. We examine the effects of an optional post-processing heat treatment on micro-contact damage using controlled indentation tests simulating the primary modes of contact during chewing: axial and sliding. Our findings indicate that the heat treatment does not affect mechanical properties such as the elastic modulus, hardness and indentation fracture toughness. However, it does enhance the resistance to contact damage by fracture and chipping in both axial and sliding modes, as well as the resistance to crack initiation measured from sliding tests. This improvement is attributed to the refinement of the flaw population achieved through the heat treatment. The results are analysed within the framework of contact and fracture mechanics, and the implications for prosthetic dentistry are briefly discussed.

This paper addresses the study of synoptic-scale meteorological conditions that favor long-range pollen transport in southern South America combining airborne pollen counts, modeled three-dimensional backward trajectories, and synoptic and surface meteorological data. Alnus pollen transport trajectories indicate origins predominantly in Montane forests of the Yungas between 1500 and 2800 m altitude. The South American Low-Level Jet is the main meteorological feature that explains 64\% of the detected pollen arrival at the target site. Podocarpus and Nothofagus pollen instead are linked primarily to the widespread Subantartic forests in southern Patagonia. Their transport patterns are consistent with previous studies which show an association with synoptic patterns related to cold front passages carrying pollen in the free atmosphere (27% for Nothofagus and 25% for Podocarpus). These results show the significance of understanding long-distance pollen transport for disciplines such as climate change reconstruction and agriculture, emphasizing the need for further research to refine atmospheric circulation models and refine interpretations of past vegetation and climate dynamics.

Gastric cancer (GC) remains a significant global health challenge, with high mortality rates, especially in developing countries. Current treatments are invasive and have considerable risks, necessitating the exploration of safer alternatives. Quercetin (QRC), a flavonoid present in various plants and foods, has demonstrated multiple health benefits, including anticancer properties. This study investigates the therapeutic potential of QRC in the treatment of GC. We utilized advanced molecular techniques to assess the impact of QRC on GC cells, examining its effects on cellular pathways and gene expression. Our findings indicate that QRC significantly inhibits GC cell proliferation and induces apoptosis, suggesting its potential as a safer therapeutic option for GC treatment. Further research is required to validate these results and explore the clinical applications of QRC in cancer therapy

To mitigate the COVID-19 infection, many world governments endorsed the cessation of non-essential activities, such as the school attendance. Thereby, forcing the evolution of the teaching model to the virtual classroom. In the present work we show the application of a modified version of the adapted COVID-19 stress scales (ACSS) which also included teaching anxiety and preparedness, and resilience for academic professionals in Mexico, during the unprecedented transformation of the education system undergone in the COVID-19 quarantine. Most of the studied variables: gender, age, academic degree, household occupants, having a disease, teaching level, teaching mode, work hours, resilience, teaching anxiety and preparedness, and fear of being an asymptomatic patient (FOBAP), showed significant statistical correlation between each other (p<0.050) and to the 6 areas of the ACSS (danger, contamination, social economical, xenophobia, traumatic stress and compulsive checking). Our results further showed that the perceived stress and anxiety, fell into the category of absent to mild with only the danger section of the ACSS falling into the moderate category. Finally, resilience generated throughout the quarantine, seems to be a predictor of the adaptation the academic professional has undergone to cope with stress.

Background: Bibliometric analysis provides insight into knowledge gaps of a specific field. We want to know what part of medical care has been the subject of research in a group of Mexican physicians. The Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado (ISSSTE) cares for a wide spectrum of disease and it provides a unique vision of what specialists has investigated about health. Methods: Papers belonging to “ISSSTE” affiliation were harvested and downloaded to .csv Excel files from the Scopus database including most bibliometric variables. VOSviewer, biblioshiny and bibliometric were used to conduct the bibliometric. Results: 2,063 papers were found and re-trieved; internal medicine had the greatest number of papers with 831; nine institutions were listed claiming “ISSSTE” as their mother affiliation; original papers represent 82% of the total and 52% of them were written in Spanish. Research production in Mexico City and the most productive center is Centro Médico Nacional 20 de Noviembre. Discussion: We identified main institutions, prolific authors, top-cited researchers and their affiliations, however, our paper is a call to action for the medical community in Latin America to join our efforts in building a solid group of researchers for the future of science.

Traditional painting artefacts study and documentation face the limitations of well-known recording tools and methods, such as handwritten or digital descriptions, scaled 2D drawings in situ, measurements with calipers, rulers or tape measures, sketches, tracings, as well as conventional or technical photographs. This paper advocates Close-Range Photogrammetry (SfM-MVS) and Reflectance Transformation Imaging (RTI) for recording vital morphological and 2.5D geometric features of painted surfaces with high accuracy. These digital tools, widely accessible due to increased computing power, enhance art observation, material study and conservation assessment. Despite specialized alternatives like 3D laser scanners, the focus here is on the cost-effective SfM-MVS and RTI, which offer advanced pixel colour accuracy. The ability to reproduce photorealistic textures proves essential for accurate documentation of painted artefacts. The paper showcases promising results from applying these methods to a mural from the Palace of Tiryns, justifying their fundamental role in an integrated scientific art study methodology.

This article introduces a novel two-step fifth-order Jacobian-free iterative method aimed at efficiently solving systems of nonlinear equations. The method leverages the benefits of Jacobian-free approaches, utilizing divided differences to circumvent the computationally intensive calculation of Jacobian matrices. This adaptation significantly reduces computational overhead and simplifies the implementation process while maintaining high convergence rates. We demonstrate that this method achieves fifth-order convergence under specific parameter settings, with a broad applicability across various types of nonlinear systems. The effectiveness of the proposed method is validated through a series of numerical experiments which confirm its superior performance in terms of accuracy and computational efficiency compared to existing methods.

In this study, Cu2O nanoparticles were synthesized using the sol-gel technique and subsequently functionalized with extracts from plants of the Rauvolfioideae subfamily and citrus fruits. Com-prehensive characterization techniques, including UV-Vis spectroscopy, FT-IR, XRD, BET, SEM, and TEM, were employed to evaluate the structural and surface properties of the synthesized nanoparticles. The results demonstrated that both functionalized Cu2O nanoparticles exhibit mesoporous structures, as confirmed by nitrogen adsorption-desorption isotherms and pore size distribution analysis. The Green Extract functionalized nanoparticles displayed a more uniform pore size distribution compared to those functionalized with the Orange Extract. The study un-derscores the potential of these functionalized Cu2O nanoparticles for applications in drug de-livery, catalysis, and adsorption processes, highlighting the influence of the functionalization method on their textural properties and performance in antibacterial efficacy.

A new sustainable polypropylene terephthalate (PPT) coating was synthesized from recycled polyethylene terephthalate (PET) and applied on hydraulic concrete substrate to improve its durability. First step, PET bottles wastes were grinded and depolymerized by glycolysis using propylene glycol (PG), in a Vessel-type reactor (20–180 °C) to synthesize bis(2-hydroxypropyl)-terephthalate (BHPT), which was applied as coating to one-three layers on hydraulic concrete substrate, by brushing technique and polymerized (150 °C for 15 h) to obtain PPT. PET, BHPT and PPT were characterized by FT-IR, PET and PPT by TGA, and the PPT coatings by SEM (thickness), by ASTM-D3359-17 (adhesion) and by water contact angle (wettability). The durability of hydraulic concrete coated with PPT was studied by resist chloride ion penetration (ASTM-C1202-17), carbonation depth at 28 days (RILEM-CPC-18), and absorption water ratio (ASTM-C1585-20). The results demonstrated that the BHPT and PPT were synthetized (FT-IR), PPT had a similar thermal behaviour to PET (TGA); the PPT coatings had good adhesion to the substrate, with thicknesses of micrometric units; hydrophilic (similar to PET coatings), and the durability of hydraulic concrete coated with PPT (2-3 layers) improved (migration of chloride ions decreased, carbonation depth was negligible, and the absorption water ratio decreased).

Maize mosaic virus (MMV) is a plant virus that is transmitted by the corn planthopper, Peregrinus maidis Ashmead (Hemiptera: Delphacidae) in a persistent and propagative manner. MMV is the causal agent for mosaic disease that can cause significant loss in corn yield. Management is focused on controlling insect vector population. The molecular mechanisms of virus transmission by the insect vector are not fully understood. Previously, we identified a receptor for MMV, the P. maidis syntaxin 18 (PmStx18). PmStx18 interacts with the viral glycoprotein and potentially facilitates MMV recognition and entry into the vector, enabling virus proliferation. Stx18 is a key component of the vesicular trafficking machinery and also plays a critical role in neurotransmitter release in insects. In this work, we investigated the expression of PmStx18 in different developmental stages and major parts of the insect vector. Primers were designed to generate templates for synthesis of double-stranded RNA fragments. A 374-bp amplicon was selected and gene-specific sequences were flanked by T7 promoter sequences for in vitro transcription. Results showed a significant difference in the expression of Stx18 in the different developmental stages of P. maidis but not between male and female adults and major body parts. In vitro transcription products indicated optimal reaction conditions and protocol suitability. The findings establish the initial steps for developing RNAi-based approach in controlling MMV transmission by targeting Syntaxin-18 in P. maidis. Further investigations are needed to evaluate the efficacy of this approach.

In this work, numerical and experimental implementation of a state observer applied to an Erbium-Doped Fiber Laser (EDFL) has been developed. The state observer is designed through the mathematical model of the EDFL to estimate the non-measurable variable; however, in numerical estimation, the state variables can be measurable given the mathematical model. Only the laser intensity variable was experimentally measured. The state observer estimated the population inversion through the obtained experimental laser intensity time series fitted with their numerical laser intensity using the mean square error (MSE) tool. A bifurcation diagram of the population inversion time series local maximum was built from the state observer. The state space of the experimental laser intensity versus observed population inversion was built.

Cuba is struggling with a growing environmental problem: the uncontrolled spread of the allochthonous weed species marabou (Dichrostachys cinerea) throughout the country. Over the last 70 years, marabou has become a formidable invasive species that poses a threat to Cuban biodiversity and agricultural productivity. In this paper, we present a free and affordable method for regularly mapping the spatial distribution of the marabou based on the Google Earth Engine platform and ecological surveys. To test its accuracy, we develop an 18-year analysis (2000–2018) of marabou dynamics using the Valle de los Ingenios, a Cuban UNESCO World Heritage Site, as an experimental model. Our spatial analysis reveals clear patterns of marabou distribution and highlights areas of concentrated growth. Temporal trends illustrate the dynamic nature of the species, identifying periods of expansion and decline. In addition, our system is able to detect specific, large-scale human interventions against the marabou plague in the area. The results highlight the urgent need for remedial strategies to maintain the fragile balance in the region.

Virtual tours with 360 photos present a quick, easy and accessible way to create immersive experiences. The visualization in diverse devices, provide a user experience that can be different. A virtual tour with 360 photos of the Chapel of Dolores (Tenerife), has been created. Whose access is limited to specific days, so the creation of a virtual tour allows you to visit it at any time, in addition, a pilot test has been carried out with students of the degree of restoration and conservation of cultural heritage in order to measure the user experience of the virtual tour depending on the device used. The participants took a virtual tour of the Dolores Chapel, first with a cell phone and then with the virtual reality headset, and then completed a user experience questionnaire. The results obtained comparing both visualization technologies are detailed, highlighting that when using the virtual reality headset no better results are obtained in the immersion subscale, in addition, the visualization on a Smartphone is more comfortable and obtains better results in consequences such as dizziness and vertigo. Therefore, it may be an interesting strategy to have simple versions that work quickly on the cell phones.

This study harnesses the potential of artificial intelligence (AI) focusing on machine learning (ML) to improve decision-making in advanced engineering education. The onset of the COVID-19 pandemic compelled a swift transformation in higher education methodologies, particularly in the domain of course modality. This study aims to align institutional decisions with student and faculty preferences in the face of rapid changes in instructional approaches, particularly learning styles, theoretical, practical, and theoretical-practical, prompted by the COVID-19 pandemic. To ascertain the preferences of students and instructors regarding class modalities across various courses, we utilized the Cognitive Process-Embedded Systems and E-learning conceptual framework. This framework effectively delineates the task execution process within the scope of technology-enhanced learning environments for both students and instructors. This study was conducted in seven Iranian universities and their STEM departments, examining their preferences for different learning styles. After analyzing the variables by different feature selection methods, we used three ML methods—decision trees (DT), support vector machines (SVM), and random forest (RF)—for comparative analysis. The results demonstrated the high performance of the RF model in predicting curriculum style preferences, making it a powerful decision-making tool in the evolving post-COVID educational landscape. This study not only demonstrates the effectiveness of ML in predicting educational preferences but also contributes to understanding the role of self-regulated learning in educational policy and decision-making in higher education.

Abscisic acid (ABA) is a conserved “stress hormone” in unicellular organisms, plants and animals. In mammals, ABA and its receptors LANCL1 and LANCL2 stimulate insulin-independent cell glucose uptake and oxidative metabolism: overexpression of LANCL1/2 increases, and their si-lencing conversely reduces, mitochondrial number, respiration and proton gradient dissipation in muscle cells and in brown adipocytes. We hypothesized that the ABA/LANCL hor-mone/receptors system could be involved in thermogenesis. Heat production by LANCL1/2-overexpressing vs. double-silenced cells was compared in rat H9c2 cardiomyocytes with two different methods: differential temperature measurements using sensitive thermistor probes, and differential isothermal calorimetry. Results indicate that overexpressing cells generate an approx. double amount of thermal power compared with double-silenced cells, and that addition of ABA further doubles heat production in overexpressing cells. With the temperature probes, we find a time-scale of approx. 4 min for thermogenesis to “turn on” after nutrient addition. These results identify the ABA/LANCL hormone receptors system as a hitherto unknown regu-lator of cell thermogenesis. Pharmacologic means of increasing metabolic energy dissipation are considered a promising strategy to reduce oxidative stress and lipid accumulation at a cellular and organismic level: from this study, LANCL1-2 proteins emerge as targetable controllers of ther-mogenesis through their natural ligand ABA.

Este estudio empleó el algoritmo MaxEnt para evaluar la distribución potencial de la serpiente de cascabel de cola larga, Crotalus stejnegeri , una especie amenazada endémica de México. Los resultados demuestran una buena fiabilidad del modelo, alcanzando un AUC de 0,879. Destacan una distribución potencial concentrada en las estribaciones de la Sierra Madre Occidental, principalmente en el centro-sur de Sinaloa, suroeste de Durango y norte de Nayarit, caracterizada por bosque seco tropical. La temperatura durante el trimestre más seco y la estacionalidad de la precipitación se identifican como factores determinantes, mientras que la baja idoneidad en Chihuahua sugiere una afinidad tropical de la especie. La variabilidad en las precipitaciones durante el trimestre más frío enfatiza la importancia de ampliar el número de registros de ocurrencia de la especie mediante una mayor exploración de campo dentro de su área de distribución. Estos hallazgos proporcionan información valiosa para los esfuerzos de conservación y subrayan áreas clave para futuras investigaciones.

The contribution of academics and researchers to the discussion around sustainable rural development planning and its impact on rural communities has grown exponentially in recent years. Understanding trends in sustainable rural development research requires considering the different factors involved and affecting people from a holistic approach. This review examines, through bibliometric studies, the scientific knowledge generated on sustainable rural development planning in the last 50 years, analyzing 6,895 articles published in journals between 1970 and 2020. The results reveal the existence of three clusters and an important growth is observed to respond to the continuous needs in relation to sustainable rural development. This research shows the evolution of a new approach for the planning of sustainable rural development projects in postmodernity: Working with People (WWP). This WWP model, as a conceptual framework from social learning has been validated as a novel proposal in numerous contexts. The bibliometric analysis shows an evolution "From Putting the Last first" to "Working with People in Rural Development" research and the contributions of influential teachers such as Chambers and Cernea. These bibliometric analyses demonstrate the correct approach of WWP model, and open new fields of research in the planning of sustainable rural development projects.

Opuntia ficus-indica has always interacted with many phytophagous insects at its center of origin; two of them are Dactylopius coccus and D. opuntiae. D. coccus, or true cochineal, is produced to extract carminic acid, and D. opuntiae or wild cochineal is a key invasive pest of O. ficus-indica in more than 20 countries around the world. Despite the economic and environmental relevance of this cactus and, D. opuntiae and D. coccus, there are few studies that have explored volatile organic compounds (VOCs) derived from the plant-insect interaction. The aim of this work was to determine VOCs of D. coccus and D. opuntiae feeding on cultivars suitable for their development and to identify different VOCs in cladodes infested by each Dactylopius species. Volatiles were identified through their essential oils, which were obtained by hydrodistillation. A total of 66 VOCs from both Dactylopius species were identified, and 125 from the Esmeralda and Rojo Pelón cultivars infested by D. coccus and D. opuntiae, respectively, were determined. Differential VOCs production due to infestation by each Dactylopius species was also found. Some changes in methyl salicylate, terpenes such as linalool or the alcohol p-vinylguaiacol were related to Dactylopius feeding on cladodes of their respective cultivars. Changes in these VOCs and their probable role in plant defense mechanisms should receive more attention because this knowledge could improve D. coccus rearing, or to its inclusion in breeding programs for D. opuntiae control in regions where it is a key pest of O. ficus-indica.

“De-[Constructing] Growth” is offered as a deeper and more useful conceptualization that avoids the negative connotations of, and resistance to, “degrowth” by decoupling profit from unsustainable consumption, production, and inequality.

Oral bone defects occur as a result of trauma, cancer, infections, periodontal diseases, and caries. Autogenic and allogenic grafts are the gold standard used to treat and regenerate damaged or defective bone segments. However, these materials do not possess the antimicrobial properties necessary to inhibit the invasion of the numerous deleterious pathogens present in the oral microbiota. In the present study poly(ε-caprolactone) (PCL), nano-hydroxyapatite (nHAp), and a commercial extract of Humulus lupulus L. (hops) were electrospun into polymeric matrices to assess their po-tential for drug delivery and bone regeneration. The fabricated matrices were analyzed by scanning electron microscopy (SEM), tensile analysis, thermogravimetric analysis (TGA), FT-IR assay, and in vitro hydrolytic degradation. The antimi-crobial properties were evaluated against the oral pathogens Streptococcus mutans, Porphyromonas gingivalis, and Aggregatibacter actinomycetemcomitans. The cytocompatibility was proved by the MTT assay. SEM analysis proved the nanostructured matrices have suitable fiber size. The present research provides new information about the interaction of natural compounds with ceramic and polymeric biomaterials. The hop extract and other natural or synthetic medicinal agents can be effectively loaded into PCL fibers and has the potential to be used in biomedical applications.

In this study, we have designed a promising platform based on metal-organic frameworks (MOFs) to develop paper-based analytical devices (PADs) to immobilize biomolecules for application in biosensors. PADs have become increasingly popular in sensing applications in recent years due to their portability, low cost, simplicity, efficiency, fast detection capability, excellent sensitivity, and selectivity. In addition, MOFs are an excellent choice for developing highly sensitive and selective sensors. Their ability to reversibly and reproducibly adsorb and desorb specific molecules allows using these materials as sensors for various applications. They also offer the possibility to modify their structure and properties, making them highly versatile and adaptable to different environments and sensing needs. In this research, we synthesized and characterized three different amino-functionalized MOFs: UiO66-NH2 (Zr), MIL125-NH2 (Ti), and MIL101-NH2 (Fe). These MOFs were used to fabricate PADs capable of sensitive and portable monitoring of alkaline phosphatase (ALP) enzyme activity by laser-induced fluorescence (LIF). Overall, amino-derivated MOF platforms demonstrate significant potential for integration into biosensor PADs, offering key properties that enhance their performance and applicability in analytical chemistry and diagnostics.

Fall armyworm (FAW), Spodoptera frugiperda, has been causing significant crop losses and has now become the most economically important pest of corn globally. With the challenges arising from the extensive use of synthetic chemicals such as reports that FAW gained resistance to various insecticides, researchers are developing environmentally sound control measures to address these issues. An example is the application of the RNA interference in managing FAW populations. RNA interference is a biological process where dsRNA molecules silence specific gene of interest. Here, dsRNA fragments were generated to target Cytochrome P450 monooxygenases CYP321A7 and CYP321A8 in FAW. Mortality and phenotypic effects of RNAi-mediated knockdown of essential genes of target CYP450s through RNAi in FAW were analyzed. Results showed that RNA in vitro transcription yielded ~14 μg/μL of dsCYP321A7 and dsCYP321A8. The mortality rate of dsCYP321A7- and dsCYP321A8-fed larvae following exposure to insecticide with dual active ingredient of Chlorantraniliprole and Thiamethoxam increased by 90% and 80%, respectively as compared to dsGFP-fed larvae with 40% at 2 days post insecticide exposure. This study provided evidence for the potential of RNAi for FAW management strategies particularly for insecticide-resistant populations, including the potential for biopesticide development.

With the advent of Web 3.0, the swift advancement of technology confronts an imminent threat from quantum computing. Security protocols safeguarding the integrity of Web 2.0 and Web 3.0 are growing more susceptible to both quantum attacks and sophisticated classical threats. The article introduces our novel long-distance free-space quantum secure direct communication (LF QSDC) as a method to safeguard against security breaches in both quantum and classical contexts. Differing from techniques like quantum key distribution (QKD), LF QSDC surpasses constraints by facilitating encrypted data transmission sans key exchanges, thus diminishing the inherent weaknesses of key-based systems. The distinctiveness of this attribute, coupled with its quantum mechanics base, protects against quantum computer assaults and advanced non-quantum dangers, harmonizing seamlessly with the untrustworthy tenets of the Web 3.0 age. The focus of our study is the technical design and incorporation of LF QSDC into web 3.0 network infrastructures, highlighting its efficacy for extended-range communication. LF QSDC is based on the memory DL04 protocol and enhanced with our novel Quantum-Aware Low-Density Parity Check (LDPC), Pointing, Acquisition, and Tracking (PAT) technologies, and Atmospheric Quantum Correction Algorithm (AQCA). Utilizing this method not only bolsters the security of worldwide Web 3.0 networks but also guarantees their endurance in a time when quantum and sophisticated classical threats exist simultaneously. Consequently, LF QSDC stands out as a robust security solution, well-suited for Web 3.0 systems amidst the constantly evolving digital environment.

The symptoms of multiple sclerosis (MS) are determined by the location of demyelinating lesions in the white matter of the brain and spinal cord. Currently, magnetic resonance imaging (MRI) is the most common tool for diagnosing MS, understanding the course of the disease, and analyzing the effects of treatments. However, undesirable components may appear during the generation of MRI, such as noise or intensity variations. Mathematical morphology (MM) is a powerful image analysis technique that helps to filter the image and extract relevant structures. Granulometry is an image measurement tool of MM that determines the size distribution of objects in an image without explicitly segmenting each object. While, several methods have been proposed for the automatic segmentation of MS lesions in MRI, in some cases only simple data preprocessing, such as image resizing to standardize the input dimensions, has been performed before the algorithm training. Therefore, this paper proposes an MRI preprocessing algorithm performing elementary morphological transformations in brain images of MS patients and healthy individuals to delete undesirable components and extract the relevant structures such as MS lesions. Also, the algorithm computes the granulometry in MRI to describe the size qualities of lesions and trains two artificial neural networks (ANN) to predict MS diagnosis. The computing of differences in granulometry measurements of an image with MS lesions and a reference image (without lesions) can determine the size characterization of the lesions. Then, the ANNs were evaluated with the validation set and the performance results (test accuracy = 0.9753, and cross-entropy loss = 0.0247) show the proposed algorithm can support the decision of specialists for diagnosing MS and estimating the disease progress, based on granulometry values.

Transforming growth factor beta (TGF-β) is a determinant for inflammation and fibrosis in cardiac and skeletal muscle in Chagas disease. To determine its regulatory mechanisms, we investigated the response of T. cruzi-infected cardiomyocytes (CM), cardiac fibroblasts (CF) and L6E9 skeletal myoblasts to TGF-β. Cultures of CM, CF and L6E9 were infected with T. cruzi (Y strain) and treated with TGF-β (1–10 ng/mL, 1h or 48 h). Fibronectin (FN) distribution was analyzed by immunofluorescence and Western blot (WB). Phosphorylated SMAD2 (PS2), phospho-p38 (p-p38), and phospho-c-Jun (p-c-Jun) signaling were evaluated by WB. CF and L6E9 showed an increase in FN from 1 ng/mL of TGF-β, while CM displayed FN modulation only after 10 ng/mL treatment. CF and L6E9 showed higher PS2 levels than CM, while p38 is less stimulated in CF than CM and L6E9. After T. cruzi infection, localized FN disorganization was observed in infected CF and L6E9. T. cruzi induced an increase in FN in CF cultures, mainly in uninfected cells. Infected CF cultures treated with TGF-β showed a reduction in PS2 and an increase in p-p38 and p-c-Jun levels. Our data suggest that p38 and c-Jun pathways may be participating in the fibrosis regulatory process mediated by TGF-β after T. cruzi infection.

Recurrent pregnancy loss (RPL) affects around 2 % of women of reproductive age. Primary RPL is defined by ≥2 pregnancy losses and no normal birth delivery. In secondary RPL, the losses are after a normal pregnancy and delivery. Most cases have no clear aetiology, although primary cases are the most complex. Several gene polymorphisms have been associated with RPL. Some polymorphisms are increased in women of Asian or Caucasian race. In admixed RPL populations, the information on possible genetic links is scarce and contradictory. The study aimed to assess the frequency of two SNP in different enzymes involved in medical conditions observed during pregnancy. Microsomal epoxy hydrolase (mEPH) is involved in detoxifying xenobiotics. It is present in ovaries, and it is hormonally regulated. The endothelial nitric oxide synthase forms nitric oxide from arginine and is involved in vascular tone. Two SNPs, rs1051740 and rs1799983, were assessed. The study included 26 controls and 63 primary RPL patients. Significant differences were observed in the frequencies of both SNP (p<0.05). The frequency of mutated alleles was higher in patients. Double mutated homozygotes were encountered only in RLP patients (p<0.05). Genetic polymorphisms rs1051740 and rs1799983 may be involved in primary RPL in the Venezuelan admix population. Genetic studies could provide crucial information on the aetiology of primary RPL.

Recurrent Pregnancy Loss (RPL) affects 1-2% of women, and its triggering factors are unclear. Several studies have shown the vaginal, endometrial, and gut microbiota may play a role in RPL. A decrease in the quantity of Lactobacillus crispatus in local microbiota has been associated with an increase in local (vaginal and endometrial) inflammatory response and immune cell activation that leads to pregnancy loss. The inflammatory response may be triggered by gram-negative bacteria, lipopolysaccharides (LPS), viral infections, mycosis, or atypia (tumor growth). Bacterial structures and metabolites produced by microbiota could be involved in immune cell modulation and may be responsible for immune cell activation and molecular mimicry. Gut microbiota metabolic products may increase the amount of circulating pro-inflammatory lymphocytes, which, in turn, will migrate into vaginal or endometrial tissues. Local proinflammatory Th1 and Th17 subpopulations along with a decrease in Treg and tolerogenic NK cells are accountable for the increase in pregnancy loss. Local microbiota may modulate the local inflammatory response increasing pregnancy success. Analyzing local and gut microbiota may be necessary to characterize some RPL patients. Although oral supplementation of probiotics has not been shown to modify vaginal or endometrial microbiota, the metabolites produced by it may benefit patients. Lactobacillus crispatus transplantation into the vagina may enhance the required immune tolerogenic response to achieve a normal pregnancy. The effect of hormone stimulation and progesterone to maintain early pregnancy on microbiota has not been adequately studied, and more research is needed in this area. Well-designed clinical trials are needed to ascertain the benefit of microbiota modulation in RPL.

Coenzyme Q (CoQ) is a lipidic compound widely distributed in nature with crucial functions in metabolism, protection against oxidative damage and ferroptosis, and other processes. CoQ biosynthesis is a conserved and complex pathway involving several proteins. COQ2 is a member of the UbiA family of transmembrane prenyltransferases that catalyzes the condensation of the head and tail precursors of CoQ, a key step in the process because its product is the first intermediate that will be modified in the head by the next component of the synthesis process. Mutations in this protein have been linked to primary CoQ deficiency in humans, a rare disease predominantly affecting organs with a high energy demand. The reaction catalyzed by COQ2 and its mechanism are still unknown. Here we aimed at clarifying COQ2 reaction by exploring possible substrate binding sites using a strategy based on homology, comprising the identification of ligand-bound homologs with solved structures available in the Protein Data Bank (PDB) and their subsequent structural superposition to the AlphaFold predicted model for COQ2. The results highlight some residues located on the central cavity or the matrix loops that may be involved in substrate interaction, some of them mutated in primary CoQ deficiency patients. Furthermore, we analyze the structural modifications introduced by the pathogenic mutations found in humans. These findings shed new light on the understanding of COQ2 function and, thus, CoQ biosynthesis and pathogenicity of primary CoQ deficiency.

Endophytic fungi (EF) have emerged as promising modulators of plant growth and stress tolerance in agricultural ecosystems. This review synthesizes current knowledge on the role of EF in en-hancing crop adaptation to abiotic stress. Abiotic stresses, such as drought, salinity, and extreme temperatures, pose significant challenges to crop productivity worldwide. EF have shown re-markable potential in alleviating the adverse effects of these stresses. Through various mechanisms including synthesis of osmolytes, production of stress-related enzymes, and induction of plant de-fense mechanisms, EF enhance plant resilience to abiotic stressors. Moreover, EF promote nutrient uptake and modulate hormonal balance in plants, further enhancing stress tolerance. Recent ad-vancements in molecular techniques have facilitated the identification and characterization of stress-tolerant endophytic fungal strains, paving the way for their utilization in agricultural prac-tices. Furthermore, the symbiotic relationship between EF and plants offers ecological benefits, such as improved soil health and reduced dependence on chemical inputs. However, challenges remain in understanding the complex interactions between EF and host plants, as well as in scaling up their application in diverse agricultural systems. Future research should focus on elucidating the mechanisms underlying endophytic fungal-mediated stress tolerance and developing sustainable strategies for harnessing their potential in crop production.

Bacterial endophytes (120) were isolated from six halophytes (Distichlis spicata, Cynodon dactylon, Eragrostis obtusiflora, Suaeda torreyana, Kochia scoparia, and Baccharis salicifolia). These halophiles were molecularly identified and characterized with or without NaCl conditions. Characterization was based on tests such as indole acetic acid (IAA), exopolysaccharides (EPS), and siderophores (SID) production; solubilization of phosphate (P), potassium (K), zinc (Zn), and manganese (Mn); mineralization of phytate; enzymatic activity (acid and alkaline phosphatase, phytases, xylanases, and chitinases) and mineralization/solubilization mechanisms involved (organic acids and sugars). Eleven halophiles (tolerated NaCl 2.5 M) were identified as Oceanobacillus sp. (1), Bacillus sp. (2), Nesterenkonia sp. (3), Bacillus velezensis (4), Halobacillus sp. (5), Oceanobacillus sp. (6), B. velezensis (7) Halomonas sp. (8), B. pumilus (9), Pseudomonas sp. (10) and Oceanobacillus sp. (11). Biochemical responses of nutrient solubilization and enzymatic activity varied between bacteria and the presence of NaCl. Organic acids were involved in P-mineralization and nutrient solubilization. Tartaric acid was common in the solubilization of P, Zn, and K. Maleic and vanillic acid were only detected in Zn and K solubilization, respectively. Moreover, sugars appeared to be involved in the solubilization of nutrients; fructose was detected in solubilization tests. Therefore, these bacteria should be tested as a consortium to mitigate saline stress in glycophytes under a global climate change scheme that threatens to exacerbate soil salinity.

Seeing atomic configuration of single organic nanoparticles at sub-Å spatial resolution by transmission electron microscopy has been so far prevented by the high sensitivity of soft matter to radiation damage. The difficulty is related to the need to irradiate the particle with a total dose of few electrons/Å2, not compatible with the electron beam density necessary to search the low-contrast nanoparticle, to control its drift, to finely adjust the electro-optical conditions and particle orientation, and to finally acquire an effective atomic resolution image. On the other hand, the capability to study individual pristine nanoparticles, such as proteins, active pharmaceutical ingredients, and polymers, with peculiar sensitivity to the variation of the local structure, defects, and strain, would provide advancements in many fields, including materials science, medicine, biology, and pharmacology. Here we report direct sub-ångström resolution imaging at room temperature of pristine unstained crystalline polymer-based nanoparticles. This result is obtained by combining low-dose in-line electron holography and phase-contrast imaging on state-of-the-art equipment, providing an effective tool for the quantitative sub-angstrom imaging of soft matter.

The c-Jun N-terminal Kinase (JNK) signalling pathway is a conserved response to a wide range of internal and external cellular stress signals. Besides the stress response, the JNK pathway is involved in a series of vital regulatory mechanisms during development and adulthood that are critical to maintain tissue homeostasis. These mechanisms include the regulation of apoptosis, growth, proliferation, differentiation, migration and invasion. The JNK pathway has such a diverse functionality and cell-tissue specificity, that it has emerged as a key player in regeneration, tumorigenesis and other pathologies such as neurodegenerative diseases. The JNK pathway is highly active in the central nervous system (CNS), and plays a central role for the cells to cope with pathophysiological insults during both development and adulthood. Among the many mechanisms described in the literature, in this review we focus on the JNK pathway functions in pathologies of the CNS. More specifically, we discuss some newly identified examples and mechanisms of JNK-driven tumor progression in glioblastoma, regeneration/repair after an injury in the CNS, neurodegeneration, and neuronal cell death. Recent studies have shown that the JNK pathway regulates matrix metalloproteinases (MMPs) production in response to cytoneme/tumor microtubes formation and Wingless (Wg)/WNT pathway activation in glioblastoma cells. Thus, JNK pathway is essential for glioblastoma progression, infiltration and non-autonomous induction of neurodegeneration. In regeneration, the JNK pathway controls Draper (Drpr) expression in glial cells that mediate engulfment and regeneration of the CNS upon injury.

This paper explores the application of machine learning in financial time series analysis, focusing on predicting trends in financial enterprise stocks and economic data. It begins by distinguishing stocks from stocks and elucidates risk management strategies in the stock market. Traditional statistical methods such as ARIMA and exponential smoothing are discussed in terms of their advantages and limitations in economic forecasting. Subsequently, the effectiveness of machine learning techniques, particularly LSTM and CNN-BiLSTM hybrid models, in financial market prediction is detailed, highlighting their capability to capture nonlinear patterns in dynamic markets. The study demonstrates the advancements in predictive accuracy and robustness achieved by deep learning methods through empirical analysis and model validation. The findings contribute significantly to academic discourse and offer practical insights for investors, financial analysts, and policymakers navigating market volatility and optimizing investment strategies. Finally, the paper outlines prospects for machine learning in financial forecasting, laying a theoretical foundation and methodological framework for achieving more precise and reliable economic predictions.

This paper, based on the "Unified Theory of Forces," rigorously proves the formula of de Broglie's wave function. This helps us better understand the following important issues: 1) Theoretically prove and understand the essence of de Broglie's wave function; 2) Prove that when the particle's momentum P is determined, the greater the mass of the particle, the more concentrated its wave function distribution, and the weaker the interference effect; 3) Correct the spatial distribution of the wave function of free particles, proving that the wave function of free particles is strictly constrained within a finite spatial range, which is consistent with the facts we observe.

Over the last two decades, a considerable amount of equipment has been acquired (spreaders, seeders, sprayers, among others) to respond to the challenges of Precision Agriculture (PA) concept. Most of this equipment has been purchased at a high cost. However, many of them, despite still being functional and equipped with sensors, actuators, and electronic processing units capable of adjusting to variations in speed, have become obsolete in terms of communication, and incompatible with new monitoring and control systems based on the “Isobus” protocol. This work aims to present a solution for updating the control system (“Ferticontrol”) of a “Vicon RS-EDW” spreader with variable rate application (VRA), making it compatible with the “InCommand” system from “Ag Leader”. The solution includes low-cost “Arduino” and “Raspberry Pi” microcontrollers and open-source software. The development shows that it is possible to implement a solution that is accessible to farmers in general. It also provides a niche business opportunity for young researchers to set up small technology-based enterprises associated with universities and research centers. These partnerships guarantee permanent innovation and represent a decisive step towards modern, technological, competitive, and sustainable agriculture.

A constant effort is made in the physical metallurgy of transformation induced plasticity (TRIP) steels. These are multiphase steels, whose mechanical behavior depends on the different phases present, the intrinsic strength of each phase, the work hardening of each constituent and the austenite to martensite transformation, among others. Hard and soft phases can be distinguished in these steels and both stress and strain are distributed between them. The objective of this work was to simulate the stress-strain behavior for two TRIP steels. Bouquerel’s model was modified to account for the initial martensite, a moderate hard austenite and adjustable strain hardening parameters. The austenite fraction that transforms into martensite was computed through an Olson and Cohen model. The Mecking-Kocks model was used to evaluate the increment in the dislocation density and the Rodriguez-Gutierrez model, to compute the hardening of martensite. Ferrite is considered a soft phase, martensite and bainite, hard phases and austenite, a phase with a moderate hardness. The routine was implemented in MATLAB, and results were compared with those obtained experimentally in a tension test of two TRIP steels of the same composition but different heat treatments, obtaining in both cases a good agreement for the stress-strain-curves.

Amuyo Ponds (AP) are a group of three hydrothermal lagoons located at 3,700 m above sea level, in a pre-Andean setting at the Atacama Desert. Each pond shows a conspicuous green, red, or yellow coloration, and discharge waters rich in arsenic and boron into the Caritaya River (Camarones Basin, Arica and Parinacota Region, in northern Chile). Microorganisms at these ponds are subjected to harsh environmental conditions, and microbial composition and diversity in Amuyo Ponds sediments are unknown. Then, microbial life colonizing AP sediments was explored by metagenomics analyses. This approach showed a highly diverse microbial life dominated by members of the Bacterial Domain (nearly 800 bacterial genome sequences) and includes sequences associated to Archaea, Eukarya, and viruses. We also report putative functional capabilities related to virulence and defense genes, and biosynthesis of secondary metabolites. Thirteen bacterial and fourteen virus metagenome-assembled genomes were reconstructed and are here informed. This work expands our knowledge on the richness of microorganisms in AP and open further studies on the ecology and genomics of this striking Andean geosite.

De novo metastatic hormone-sensitive PC (mHSPC) accounts for 5-10% of all prostate cancer (PC) diagnoses but it is responsible for nearly 50% of PC-related deaths. Since 2015, the prognosis of mHSPC has slightly improved thanks to the introduction of new hormonal agents and chemotherapy combined with androgen deprivation therapy from the first-line setting. This review describes the current therapeutic opportunities in de novo mHSPC, focusing on potential molecular biomarkers identified in the main clinical trials that have changed the standard of care, the genomic features of de novo mHSPC, and the principal ongoing trials that are investigating new therapeutic approaches and the efficacy of a biomarker-guided treatment in this setting. The road towards a personalized treatment for de novo mHSPC is still long considering that the randomized clinical trials, which have furnished the basis of the current therapeutic options, stratified patients according to clinical criteria that not necessarily reflect the biological rationale of the chosen therapy. The role of transcriptomic profiling of mHSPC as predictive biomarker requires further validation, as well as it remains to ascertain how the genomic alterations detected in mHSPC, that are considered predictive in the castration-resistant disease, can be exploited in the mHSPC setting.

The immune response is crucial in Pregnancy Loss (RPL), which affects 1-2% of women. In RPL, peripheral blood lymphocytes and local immune cells are activated with a concomitant decrease in the leukocyte tolerogenic response in the uterus and decidua. IL-17 plays an essential role in T cell activation, weakening tolerogenic responses of the T cells. The report aims to analyze the IL-17 genetic polymorphism, circulating IL-17 lymphocyte populations, the reaction upon lymphocyte priming, the amount of cytokine in serum, and the number of T-reg cells in a group of 50 RPL and 50 normal women from the admixed Venezuelan population. No differences were observed in the genetic polymorphisms rs2275913 and rs763780. A significant decrease (P&lt;0.001) was observed in RPL patients compared to controls in the IL-17+ cell populations CD3+, CD8+, CD56+, and CD4CD25 FoxP3, but not in the CD4IL17+ cells. The response upon cell priming was significantly decreased (P&lt;0.001) in all subpopulations of RPL as compared to controls. It is concluded that despite the increase in IL-17A in the serum of patients, the number of circulating IL-17 cells and the lymphocyte response to stimulation was significantly decreased (p&lt;0.001). No differences in the genetic polymorphism compared to controls suggest that biological factors influence IL-17 levels in RPL patients, which must be carefully studied.

Single nucleotide polymorphisms in the mitochondrial genome have been investigated in relation to breast cancer. Certain variants could be associated with an increased risk of this malignant disease. These associations underline the importance of mitochondrial function in cancer biology and highlight the potential for mtDNA-based biomarkers and future therapies. The general objective of this research was to develop a model which is capable of identifying nonlinear patterns in breast cancer mitochondrial sequences. Association rules were used to explore mtDNA variant positions. The analysis included 41 breast cancer patients and 28 control samples. An a priori algorithm identified significant association rules with lift > 1 and confidence > 0.5. A total of 5562 rules for cancer and 157,140 for control sequences were refined after redundancy removal. A total of 150 associations were identified, of which only 78 showed significant support and confidence, while 315 and 1438 positions showed strong associations with breast cancer. Association rules analysis revealed significant patterns, especially in sequences associated with the control region and a specific locus around genes coding for tRNAs and NADH dehydrogenase subunits. However, further research is necessary to establish causality, clinical relevance, and to confirm these findings.

Nowadays, the transparency of governments with respect to the sustainability of public services is a very interesting issue for stakeholders and academics. It has led to previous research and international organisations (EU, IMF, OECD, United Nations, IFAC, G-20, World Bank) to recommend promotion of the online dissemination of economic, social and environmental information. Based on previous studies about e-government and the influence of administrative cultures on governmental accountability, this paper seeks to identify political actions useful to improve the practices of transparency on economic, social and environmental sustainability in European local governments. We perform a comparative analysis of sustainability information published on the websites of 72 local governments in 10 European countries grouped into main three cultural contexts (Anglo-Saxon, Southern European and Nordic). Using international sustainability reporting guidelines, our results reveal significant differences in local government transparency in each context. The most transparent local governments are the Anglo-Saxon ones, followed by Southern European and Nordic governments. Based on individualized empirical results for each administrative style, our conclusions propose useful policy interventions to enhance sustainability transparency within each cultural tradition, such as development of legal rules on transparency and sustainability, tools to motivate local managers for online diffusion of sustainability information and analysis of information needs of stakeholders.

The cultural and historical heritage of a city is a major resource to attract tourists. Therefore, reaching out to perceive and identify the characteristics of persons visiting a locality attracted by their property is relevant for the purpose of better meeting their expectations and needs. This research focuses on knowing the perceptions, motivations and satisfaction of women visiting Cordoba (Spain). Thus, on the basis of the questionnaire carried out on women visiting Cordoba during the months of June and November 2019, a statistical analysis has been made. Using various multivalent case-grouping techniques, tourist segments with similar perceptions of cultural heritage have been detected. Moreover, non-parametric statistical techniques have showed up significant differences between the valuations obtained in the various segments. This analysis has exposed a segment of women who, known as absorbent tourists, are characterized by the high cultural motivation to visit the city of Cordoba linked to the heritage they have visited. The conclusions obtained should be useful in adjusting the city's tourist supply to the specific demand of a population sector such as women visiting the city.

This article explores the application of machine learning techniques, specifically focusing on ensemble methods like Random Forests, for detecting fraudulent activities in digital financial transactions. Highlighting the evolution from traditional statistical approaches to modern machine learning models, it underscores the effectiveness of Random Forests in handling the inherent challenges of imbalanced datasets typical in fraud detection scenarios. Using a Kaggle dataset of credit card transactions, the study optimizes Random Forest parameters through rigorous parameter tuning, achieving significant improvements in model performance metrics such as Area Under the Curve (AUC). The findings underscore the critical role of machine learning in enhancing fraud detection capabilities, emphasizing the ongoing evolution and future potential of these methodologies in financial risk management.

In this paper we describe a method which combines sentiment analysis with machine learning techniques and/or multivariate statistical analysis. By applying this methodology it is possible to classify a collection of texts into two or more groups or clusters. On the basis of a number of previously defined clusters, the novelty of the outlined approach is the use of the sentiment analysis results as input to the machine learning model or multivariate statistical analysis. Once the classifier has been obtained, we can assign a given text into one of the pre-established clusters. The groups or clusters can represent different time periods, classes of texts transcribed from different conversations, etc. The method is illustrated through an example taken from one of the two studies in which we have applied this methodology. In one of the studies, the method was used to classify press news of a volcanic eruption, while in the other study it was used to classify the conversations recorded between a chatbot with different kinds of speakers (humans or chatbots). This last study was the seminal work in which we introduced this methodology.

(1) Background: To determine the diagnostic accuracy that optical coherence tomography (OCT) can add to fundus photography (FP) in assessing the condition of the retinal posterior pole; (2) Methods: Two blocks of analysis: at first, the posterior pole of each eye was examined using FP non-mydriatic imaging device. Secondly: OCT was used in addition to FP. After consolidating specific diagnostic criteria, the assessments were evaluated by two blinded independent investigation groups (by optometrists, and by ophthalmologists who was considered the gold standard); (3) Results: We calculated the diagnostic accuracy of FP compared to OCT and found that both had similar sensitivity, FP had slightly higher specificity, (p-value: 0.01) and OCT had a higher kappa coefficient 0.50 (95%CI: 0.46-0.55) vs 0.39 (95%CI: 0.34-0.45) of FP; (4) Conclusions: Based on the premise that the role of the optometrist in Spain is not to diagnose but to detect lesions and refer patients to an ophthalmologist, the results of this study support the use of OCT, that it provided gradable images in almost all examined eyes (97.5%), compared to FP (73.5%). However, the optometrist need a detailed and standardized guide in order to evaluate according to the ophthalmologist's criteria.

The Southern king crab, Lithodes santolla, is an important fishery resource in the inner seas of Chilean Patagonia. The fishing gear used to catch Southern king crab (bait trap) and its operation at different depths over several months provide the opportunity to gather information about relative abundance. The aim of the present study was to analyze the information collected from the catches of Southern king crab and its associated fauna in the Magdalena Fjord to study the biodiversity and ecology of scavenging decapod crustaceans. Specifically, the study aimed to determine the bathymetric patterns of abundance by species, to model the abundance by season (winter and spring) and to determine the structuring pattern of scavenging decapods assemblage. Samples were collected between June and November 2019 by deploying traps in four bathymetric strata: 1 = 0–50 m; 2 = 51–100 m; 3 =101–150 m; 4 = 151–200 m. We registered a total of 673 decapod specimens associated with the Southern king crab fishery, representing five species: Lithodes santolla (n = 96), Libidoclaea smithii (n = 10), Metacarcinus edwardsii (n = 58), Munida gregaria (n = 507) and Peltarion spinulosum (n = 2). The abundance of decapod species varied by month and bathymetric strata. We found bathymetric patterns for L. santolla abundance, M. gregaria, M. edwardsii, and total species abundance. The most informative model to explain scavenging decapod abundance by GLM included species and season. The null model of species co-occurrence analysis revealed that all species associations during the sampled months were random. Our study is the first that provides evidence about patterns of bathymetrical and temporal variation of scavenging decapod abundance and expands the body of knowledge regarding the biodiversity of scavenger decapods in the Magdalena Sound, which currently is affected by multiple stressors such as climate change and anthropic activities (aquaculture and fisheries).

Lettuce (Lactuca sativa) is a leafy vegetable that provides a valuable source of phytonutrients for a healthy human diet. Assessment of plant growth and composition is vital for determining crop yield and overall quality, however, classical laboratory analyses are slow and costly. Therefore, new, less expensive, more rapid, and non-destructive approaches are being developed, including those based on (hyper)spectral reflectance. Additionally, it is also important to determine how plant phenotypes respond to fertilizer treatments and whether these differences in response can be detected from analyses of hyperspectral image data. In the current study, we demonstrate the suitability of hyperspectral imaging in combination with machine learning models to estimate the content of chlorophyll (SPAD), anthocyanins (ACI), glucose, fructose, sucrose, vitamin C, β-carotene, N, P, K, dry matter content, and plant fresh weight. The implemented five classification and regression machine learning models showed high accuracy in classifying the lettuces by the applied fertilizers treatments and estimating nutrient concentrations. To reduce the input (predictor data, i.e., hyperspectral data) dimension, 13 principal components were found and applied in models. The implemented artificial neural network models of the machine learning algorithm demonstrated high accuracy (r = 0.85 ... 0.99) in estimating fresh leaf weight, and contents of chlorophyll, anthocyanins, N, P, K, and β-carotene. The four applied classification models of machine learning demonstrated 100% accuracy in classifying the studied baby leaf lettuces by phenotype when certain fertilizer treatments were applied.

Keywords: Biomass pyrolysis, Chemical Looping Combustion, Life Cycle Assessment, Environmental Impacts, Carbon Footprint.

The adoption of health information technology (HIT) has increased considerably, contributing to better communication between physicians and patients and providing technological bases for learning and institutional improvement. This type of technology brings many challenges; therefore, understanding its adoption and assimilation is important to assess its potential for engendering desirable outcomes in health management. The assimilation of health information systems should be highlighted as their importance in health organisations is now recognised as a key facilitator assisting in providing better health outcomes. Thus, this study aimed to analyse HIT adoption based on models such as Technology, Organisation and Environment (TOE), which analyses at the organisational level, with other models, such as the Technology Acceptance Model (TAM), which analyses at the individual level, and the assimilation of the adopted technologies.

Given the landscape of intricate and constantly evolving cyber threats, organizations demand refined strategies to deploy a Security Information and Event Management to support the management of a Cyber Security Operations Center. The dynamic nature of cyber threats complicates the efficient allocation of the location of network intrusion detection sensors, a critical component of a robust cybersecurity framework. Our research introduces an approach that integrates the precision of biomimetic optimization algorithms with the adaptability of Deep Q–Learning. By employing different biomimetic algorithms enhanced with deep learning, we aim to refine the deployment of sensors in network infrastructures, balancing the network security imperative against deployment costs. The results of computational tests demonstrate that the improved iterations leveraging Deep Q–Learning have outperformed their native counterparts. These findings underscore the importance of reinforcement learning, specifically through Deep Q–Learning, as a powerful tool to enhance the effectiveness of metaheuristics in addressing optimization challenges.

Renewable energy technologies (RET) are critical to the sustainable development of modern societies. Hong Kong has set up a RET theme park so as to fulfill the needs of and impart knowledge on RET through entertainment and educational activities. Such understanding aids in formulating appropriate future park designs and neuromarketing strategies to maximize the attractiveness of the park. Therefore, this study attempts to investigate potential visitors’ preferences with regard to cartoon characters in the RET theme park, especially for student groups to meet the “edutainment” theme of the park. A total of 400 people have participated in this study. Findings indicate that Snoopy, Mickey Mouse, and Donald Duck show a high degree of likeness of presence in the RET theme park.

BackgroundThe present review summarizes the state-of-the-art of in silico methods and techniques that are the most useful in drug discovery, their relationship with data science, as well as the successful application of data science, machine learning (ML) and artificial intelligence (AI) applications. A meta-analysis of the various technologies available is furthermore proposed as a guideline for the non-expert, reader relative to the several subject areas is also discussed in this article. The scope of this meta-analysis is to rank the enlisted technologies by their field of applications and to depict the latter according to knowledge accessibility, from students to experts.Method The search strategy utilized for this review first produced a general collection of 900 papers without duplications, which were subsequently streamlined and divided into two independent collections: the top 300 most-cited papers of all time (since 2000) and the papers with the highest interest for a systematic review analysis (high-impact exciting papers). Results In Part 1, we discuss the most cited and quality 97 articles in these top 300 papers most relevant to the field of in silico drug discovery. The different disciplines are listed according to their industrial and economic incurred to society, independently from the “metric” results of how many new drug approvals (NDAs) each discipline has generated to date.ConclusionBig data, the ensemble of known items stored in publicly available databases, has improved our understanding of the many fates of a potential drug candidate during its development and even after its commercialization. Moreover, the combination of new screening techniques and “omics” with old drugs has led to a new paradigm in which the unknown knowledge of any biological molecule and its cellular structure, now plays an important role as a target for a series of yet-to-be-developed drugs: the chemical space. Furthermore, leveraging big data, data science, ML, and AI can revolutionize drug discovery by swiftly analyzing massive datasets, predicting efficacy and safety profiles, streamlining development, cutting costs, and boosting success rates for new drugs. AI also speeds up the search for promising drug candidates, advancing innovative therapies.

In Mexico, the evaluations of environmental flows are regulated by the Mexican Norm NMX-AA-159-SCFI-2012 and they warrant the establishment of water reserves for the environment. Yet, the pressure for water use limits the establishment of said reserves. This research aimed to evaluate the changes through time of the variables that serve as a basis for the implementation strategy by the Mexican government. A geographical information system was built with updated variables, the desired conservation status was analyzed, and the potential reserves were estimated based on the reference values for all basins nationwide. The results were examined according to their historical, current, and potential contribution to the conservation of natural protected areas and their connectivity. The outcomes point towards an administrative implementation strategy with positive outcomes despite the growing pressure for water use, with a change rate higher than the one for the creation of new protected areas. Currently, basins with low pressure and high conservation value have the potential to contribute to 86–88% of the goal set by the present administration without affecting water availability. Finally, reserving water in the priority basins would guarantee the legal protection of the flow regime in 48–50% of the hydrographic network in a desired conservation status, 45–49% of wetlands of international importance, and other natural protected areas in 85–89% of the global ecoregions represented in Mexico.

This paper presents a new methodology that provides the analysis of surface texture changes in areas adjacent to the volcano and its impact product of volcanic activity. To do this, algorithms from digital image processing such as the co-occurrence matrix and the wavelet transform are used. These methods are working on images taken by the Landsat satellite platform sensor 5 TM and Landsat 7 ETM + sensor, and implemented with the purpose of evaluating superficial changes that can warn of surface movements of the volcano. The results were evaluated by similarity metrics for grayscale images, and validated in two different scenarios that have the same type of eruption, but differ, essentially, in climate and vegetation. Finally, the proposed algorithm is presented, setting the parameters and constraints for implementation and use.

Background: The presence of a leadership role in the nursing manager appears to be closely related to a positive and productive work environment within health settings. In some contexts, such as Mexico, the profile of the nursing manager has not been formally defined, and, sometimes those who occupy these positions do not always have sufficient training, and therefore this vocation, which is related to the social image of the nursing profession. This study aims to understand the influence of the vocation for leadership that the nursing manager has and how it can influence the work environment, through the analysis of experiences lived by health professionals with a management profile of different health environments; Methods: Qualitative study of an approximation to grounded theory using Corbin &amp; Strauss as reference, using semi-structured individual interviews as a technique, and with the participation of 10 Mexican nursing managers from second-level care hospitals; Results: The meaning of the vocation for the interviewed managers is based in the first instance on the love of the profession and the vocation to be a nurse, then on the development of leadership for decision-making in practice and is fed back with the positive effects that it generates in the work environment, helping them to better organize human resources and professional activities, and strengthening themselves with specific training on the subject and the experience acquired. ; Conclusions: An understanding model is provided about the phenomenon where two central categories were identified, the first central category is the "Management vocation and natural nursing leadership" which is the basis; The second central category is the "Explanation and influence of the managerial vocation, natural leadership and work environment in nursing". Two connection categories were also created; the connection category one "Motives, meaning, and practical vocation of managerial nursing" and the connection category two "Description of the managerial vocation, natural leadership, and work environment", said connection categories emanate from the central category one, to connect with the central category two, which contains the interaction of the managerial vocation, natural leadership, and work environment in nursing.

A The guinea pig value chain in Jauja, Peru, has been developed through the application of productive projects, institutional strengthening and implementation of the principles of responsible investment in agriculture and food systems (CFS-RAI). The actors in this chain have been showing economic improvements, representativeness and participation, characteristics that make them the key human capital for development. To improve the performance of the actors in the sustainable rural development of the territory of Jauja, Peru, the research carried out an evaluation of the competencies and capacities for project management in 46 actors linked to the business programme on the CFS-RAI Principles, representing 1,094 people in the guinea pig value chain in Jauja, using the Working With People (WWP) model and empirical instruments based on the Octagon method, International Project Management Association (IPMA) competencies, performance evaluation carried out by experts, attitudinal evaluation and the Business Model Canvas. The results have helped to structure the actors of the value chain, who show an intermediate mastery of project management competencies (2.73/5), institutional capacities in development (2.89/7) and a growing organizational management performance (26.2/100). In addition, trust and proactivity have been generated among the actors in the chain, with an interest in continuing with the implementation of the CFS-RAI Principles in order to achieve sustainability in their productive projects with commercial innovation. The results have allowed us to generate the design of an innovative programme for the development of competencies, which balances the three dimensions of IPMA competencies (perspective, person and practice) in connection with the CFS-RAI Principles. Both processes complement the strengthening of human capital in agrifood value chains through sustainable project management and contributing to sustainable development in the region.

The paradigm that assumes the autonomous management of universities involves them in the redefinition of their policies and processes and the training of their staff, designing new formulas that allow them to adapt to a changing environment. In this context, research and sustainable universities can link with society to solve its problems and influence a responsible and sustainable development. Through a Delphi panel, importance to acquire and improve project management (PM) competences by teaching and research staff (TRS) into innovating education and research projects is measured, from the standard of individual competences (ICB4) of the International Project Management Association (IPMA). Also, internal data sources from the flexible structures of two Spanish universities (the University of Cadiz (UCA) and the Technical University of Madrid (UPM)), are investigated, in order to analyze how they are organized. Thanks to the study of cases, an increasing tendency to work by projects is observed, empowering teams, managing properly stakeholders and facilitating their functions towards society. Likewise, after two rounds of experts’ consultation, consensus is reached with an acceptable and stable level of responses, resulting in confirmation that there is alignment between IPMA competences and TRS’ needs for sustained success in education and research, contributing to universities’ development, improvement and sustainability.

Lentil (Lens culinaris Medik) is an annual herb that belongs to family Fabaceae, it is one of the most ancient food crops that has been grown in the world, and originated from southwestern Asia as early as 7000 BC. In Spain lentil acreage raised 42,200 ha in 2022, two authonomous regions Castilla-León and Castilla-La Mancha are traditionally the large producers. Lentils have the capacity to fix atmospheric N2 in symbiosis with rhizobia and, additionally it is considered moderately tolerant to drought which in turn makes this pulse a low water and carbon fingerprint crop. The aim of this study was to assess the symbiotic responsiveness to rhizobia strains of several lentil accessions from the Spanish germplasm bank, to determine the nutritional value and mineral seed composition and, to evaluate the agronomic performance of six selected accessions in two regions under rainfed conditions and different management systems. The protein content of all genotypes was higher than 25%, with two outstanding seeds from accessions: BGE025596 and BGE026702 which account for >30% of protein and <2% of fat. Accessions BGE016362 and, BGE026702 had an exceptional Fe content, > 1 g/100 g of seed flour. The agronomic performance varied depending upon the cropping región.

Invasive species are one of the five main causes of biodiversity loss, along with habitat destruction, overexploitation, pollution, and climate change. Represent one of the first barriers to overcome in ecological conservation programs, since are difficult to control and eradicate. Due to the lack of records of invasive exotic species in Panama, this research is necessary to identify and register the documented groups of invasive species of the Chordates and Arthropod groups in Panama. This exhaustive search of invasive species was carried out in different bibliographic databases, electronic portals and scientific journals which addressed the topic at a global level. The results indicate that approximately 133 invasive exotic species of the Arthropoda and Chordata Phylum have been reported in Panama. Of the 133 species, 49 species belong to the Arthropoda Phylum and 84 species belong to the Chordate Phylum. The Panamanian economic activity could promote the introduction of alien species in this country. The study provides the first list of invasive exotic chordate and arthropod species reported for the Republic of Panama.

The management of a cultural change in higher education is becoming necessary and unavoidable: changes in teaching, changes in research and changes in governance. Digital transformation is an essential part of many countries’ modernisation agendas. The aim of this non-experimental, descriptive, survey-based study was to explore the perceptions of university students toward the digital transformation that took place in university teaching as a consequence of COVID-19. The specific objectives proposed were to: a) analyse the perceptions of students toward digital transformation in university teaching; b) determine the valuation of students about the digital transformation that occurred in university teaching as a result of COVID-19; and c) explore the resources (hardware-software), professional collaboration, digital pedagogy and student empowerment (motivation) with respect to digital education and the recent changes in university teaching due to the pandemic. The results show that a large number of items are positively correlated. In conclusion, further research should delve into motivation, collaboration, reflective experience sharing, self-learning and initiatives that promote the development of competences in future teachers.

Escherichia coli O157:H7, a Shiga toxin-producing E. coli (STEC) is an important pathogen related to foodborne disease that is responsible for a growing number of outbreaks worldwide and has been detected in processed meats, dairy, and fresh vegetables. Although culturing is the gold standard method for detection of this bacteria, molecular methods based on nucleic acid amplification techniques such as PCR are becoming more common because of their rapidity, sensitivity, and specificity. However, to ensure reliable results among the several alternative PCR protocols (e.g., commercial kits and reference methods), different measurement assurance tools, including validated methods, reference materials, and proficiency tests among others, are required. Herein, we present a digital PCR method validation for E. coli O157:H7 detection and quantification using seven gene sequences; this method quantified nucleic acids from different E. coli serotypes, with a detection range between 6.6 to 7,900 copies/µL and a repeatability standard deviation over the concentration range of between 1% to 13.6%. The relative standard uncertainty was 3.5%–14.6%, and the detection limit was 0.27 copies/µL. Subsequently two batches of a candidate reference material based on E. coli O157:H7 genomic DNA were then produced and characterized for evaluation of copy number concentration with the validated dPCR method, with assigned values of 164,770 ± 9,251 and 172 ± 9 copies/μL. Thus, this study demonstrated the development a validated method and reference material for dPCR and qPCR detection of E. coli O157:H7, a key STEC responsible for food poisoning.

The goal was to determine the personal, interpersonal, and sociocultural predictors of condom use among rural indigenous adolescents. Predictor variables were selected from Bandura's Social Cognitive Theory and Leininger's Transcultural Theory. The sample consisted of 419 Nahuas adolescents randomly selected from the total number of neighborhood blocks in a rural community in Puebla, Mexico. The instruments had acceptable psychometric characteristics (Cronbach alpha and validity scores). Multiple linear regression models were used. Results: 56.8% of participants were female, and 50.40 % were students. The mean age was M = 17.5 (SD = 0.97), and the majority (63%) identified as Catholic. Age at menarche/first ejaculation (β = -1.2, p = .038), attitude toward condom use (β = .13, p < .001), ethnic identity (β = .21, p < .001), and ability to negotiate condom use (β = .13, p < .003), predicted (R2 = 22.3) condom use. This study provides a basis for the integration of the cultural values of indigenous adolescents within interventions for sexual health promotion.

The decapod fauna in intertidal rocky shore in continental Chile has high species diversity mainly in northern and central Chile due high productivity of these coast due influence of cold Humboldt stream. One of the abundant species in these ecosystems is decapod Cyclograpsus cinereus Dana, 1851. The aim of the present study was determine the spatial distribution pattern of C. cinereus along rocky shore in Antofagasta bay, north of Chile between spring and summer 2018-2020. Data was obtained counting individuals from random quadrants in intertidal zones, to the obtained data was applied the variance mean ratio for determine if the specimens have random, aggregate or uniform distribution, that are associated to Poisson, negative binomial or positive binomial distributions respectively. Data obtained revealed the existence of associated distribution with respective negative binomial distribution pattern in 16 observations, and uniform distribution for 3 observations. The sites corresponds to rocky shores in urban zones, and in a protected zone, and the density does not have significant differences for both kind of sites. The observed data about aggregated pattern agree with observations for decapods for rocky shore in central and southern Chile, specifically in interpretative probabilistic models.

Integrated nutrient management (INM) is an approach that optimizes the benefits derived from various nutrient sources and beneficial soil microorganisms in an integrated manner to increase crop productivity while maintaining soil fertility. An open-field experiment was conducted at Cabuyao, Laguna, Philippines to investigate the effects of combining chemical fertilizers, vermicompost, arbuscular mycorrhizal fungi (AMF), and nitrogen-fixing bacteria (Azospirillum spp.) on the growth, nutrient uptake and yield of tomato. The experiment was arranged in a randomized complete block design replicated four times. The treatments were recommended rate of chemical fertilizer (RRC), combined 50% RRC and 4 t ha-1 vermicompost (INM 1), AMF inoculation with combined nutrient sources (INM 2), and dual inoculation with AMF and Azospirillum in synergy with combined nutrient sources (INM 3). Results revealed that the growth parameters were not significantly affected by the treatments, but the highest data were observed in the treatments under INM. AMF inoculation significantly increased phosphorus, potassium, calcium, and magnesium uptake of tomato while INM 3 recorded the highest nitrogen uptake. Treatments with vermicompost application recorded significantly higher uptake of copper. INM 1, INM 2, and INM 3 gave a significantly higher fruit yield than the RRC with an increase of 7.98%, 13.40%, and 13.65%, respectively. The percentage of mycorrhizal root colonization and number of rhizosphere spores were higher in mycorrhizal plants. Fruit yield and AMF root colonization are positively correlated with uptake of various nutrients. The results imply that these INM strategies can improve growth of tomato, increase fruit yield, reduce the amount of chemical fertilizers applied, and potentially lead to economic, soil health and environmental benefits.

In this study, the physical properties of briquettes produced from two different biomass feedstocks (sawdust and date palm trunk) and different plastic wastes were investigated, without using any external binding agent. The biomass feedstocks were blended with different ratios of two WEEE plastics (halogen-free wire and print circuit boards (PCB)) and automotive shredder residues (ASR). The briquettes production is studied at different waste proportions (10-30%), pressures (22-67 MPa) and temperatures (room-130 ˚C). Physical properties as density and durability rating were measured, usually increasing with temperature. Palm trunk gave better results than sawdust in most cases, due to its moisture content and the extremely fine particles that are easily obtained.