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In search of new materials that would help to prevent microbiologically influenced corrosion (MIC), we have designed and synthetized six different copper and copper–silver nanoparticle–enzyme hybrids using a mild-conditions method carried out in water and r.t. Characterization analyses exhibited the presence of small crystalline nanoparticles with diameters from 2 to 20 nm. X-ray diffraction determined that the Cu hybrids were composed of different copper species, depending on the synthetic protocol used, while the Cu–Ag hybrids were mainly composed of copper and silver phosphate metallic species. Then, the bacterial viability of three MIC-relevant enrichments, sulfate-reducing bacteria (SRB), slime-forming bacteria (SFB), and acid-producing bacteria (APB), was studied in the presence of the bionanohybrids. The results demonstrated a notable effect of all bionanohybrids against SRB, one of the most prominent bacteria associated with MIC. In particular, Cu-2 and Cu–Ag-2 showed a reduction in bacterial cells of 94% and 98% after 48 h, respectively, at a concentration of 100 ppm. They also exhibited high efficiencies against SFB, with Cu–Ag-1 and Cu–Ag-2 hybrids being the best, with bacterial reduction percentages of 98% after 45 h of exposition at a concentration of 100 ppm. However, in the case of APB, the effect of the hybrids was lost due to the low pH level generated during the experiment. Finally, the capacity of Cu-2 and Cu–Ag-2 to inhibit the adhesion of SRB to the surface of carbon steel coupons was evaluated. Fluorescence imaging of the surface of the coupons at 24 h demonstrated that the presence of the hybrids inhibited the growth of SRB, obtaining a maximum reduction of 98% with Cu-2. Overall, the results of this study demonstrate that these novel nanomaterials have a wide-range antibacterial effect and may have a promising future in the prevention and treatment of MIC. [ABSTRACT FROM AUTHOR]

The objective was to verify the performance of spectral techniques as well as validation models in the prediction of nitrogen, total organic carbon, and humic fractions under different cultivation conditions. Chemical analyses for the determination of nitrate, total nitrogen, total organic carbon, and the chemical fractionation of soil organic matter were performed, as well as spectral analyses by Vis-NIR-SWIR and X-ray fluorescence. The results of the spectroscopy were processed using RStudio v. 4.1.3, and PLSR and support vector machine learning algorithms were applied to validate the models. The Vis-NIR-SWIR and XRF spectroscopic techniques showed high performance and are indicated for the prediction of nitrogen, total organic carbon, and humic fractions in Ferralsols of medium sandy texture. However, it is important to highlight that each technique has its own characteristic mechanism of action: Vis-NIR-SWIR detects the element based on harmonic tones, while XRF is based on the atomic number of the element or elemental association. The PLSR and SVM models showed excellent validation results, allowing them to fit the experimental data, emphasizing that they are different statistical methods. [ABSTRACT FROM AUTHOR]

Modeling spectral reflectance data using machine learning algorithms presents a promising approach for estimating soil attributes. Nevertheless, a comprehensive investigation of the most effective models, parameters, wavelengths, and data acquisition techniques is essential to ensure optimal predictive accuracy. This work aimed to (a) explore the potential of the soil spectral signature obtained in different spectral bands (VIS-NIR, SWIR, and VIS-NIR-SWIR) and, by using hyperspectral imaging and non-imaging sensors, in the predictive modeling of soil attributes; and (b) analyze the accuracy of different ML models in predicting particle size and soil organic carbon (SOC) applied to the spectral signature of different spectral bands. Six soil monoliths, located in the central north region of Parana, Brazil, were collected and scanned via hyperspectral cameras (VIS-NIR camera and SWIR camera) and spectroradiometer (VIS-NIR-SWIR) in the laboratory. The spectral signature of the soils was analyzed and subsequently applied to ML models to predict particle size and SOC. Each set of data obtained by the different sensors was evaluated separately. The algorithms used were k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), linear regression (LR), artificial neural network (NN), and partial least square regression (PLSR). The most promising predictive performance was observed for the complete VIS-NIR-SWIR spectrum, followed by SWIR and VIS-NIR. Meanwhile, KNN, RF, and NN models were the most promising algorithms in estimating soil attributes for the dataset obtained from both sensors. The general mean R2 (determination coefficient) values obtained using these models, considering the different spectral bands evaluated, were around 0.99, 0.98, and 0.97 for sand prediction, and around 0.99, 0.98, and 0.96 for clay prediction. The lower performances, obtained for the datasets from both sensors, were observed for silt and SOC, with R2 results between 0.40 and 0.59 for these models. KNN demonstrated the best predictive performance. Integrating effective ML models with robust sample databases, obtained by advanced hyperspectral imaging and spectroradiometers, can enhance the accuracy and efficiency of soil attribute prediction. [ABSTRACT FROM AUTHOR]

Wild vegetables (WVs) have been an essential source of human nutrition since ancient times. Foraging is a millennia-old practice that has gained more attention recently and is becoming fashionable, especially in restaurants in urban areas, as they attract customers who see WVs as an innovative sensory element and specialty food. Some cooks have used very few WVs for decades, but most chefs have only recently introduced them in their modern restaurants. Our study aims to have a deeper understanding of the diversity of WVs used by restaurants in the Lombardy region in Northern Italy and to know how they are introduced onto different menus, as well as the source of knowledge and the innovation paths related to the use/introduction of WVs in the selected sample of restaurants. Semi-structured interviews were conducted with 15 restaurant managers, chefs, and their professional foragers in the Lombardy region in Northern Italy in 2022; fifty-four wild plant taxa were recorded to be used in the considered restaurants. The collected data were analyzed to understand the current situation and the potential developments of this practice by exploring the reasons/motivations that underpin the inclusion of WVs in restaurants. A broad spectrum of restaurants was considered to evaluate the potential differences in handling and sourcing these ingredients. The results demonstrated that this trend has mainly been driven by attempts to revitalize traditional cuisines and to generate a positive impact on health, but the actual culinary preparations based upon WVs are often original and remarkably diverge from the Italian food ethnobotanical heritage. Moreover, concerns related to the environmental sustainability of these practices have been addressed. [ABSTRACT FROM AUTHOR]

Breast milk (BM) is a unique food due to its nutritional composition and anti-inflammatory characteristics. Evidence has emerged on the role of Presepsin (PSEP) as a reliable marker of early sepsis diagnosis. In the present study, we aimed to investigate the measurability of PSEP in BM according to different maturation stages (colostrum, C; transition, Tr; and mature milks, Mt) and corrected for delivery mode and gender. We conducted a multicenter prospective case–control study in women who had delivered 22 term (T) and 22 preterm (PT) infants. A total of 44 human milk samples were collected and stored at −80 °C. BM PSEP (pg/mL) levels were measured by using a rapid chemiluminescent enzyme immunoassay. PSEP was detected in all samples analyzed. Higher (p < 0.05) BM PSEP concentrations were observed in the PT compared to the T infants. According to the grade of maturation, higher (p < 0.05) levels of PSEP in C compared to Tr and Mt milks were observed in the whole study population. The BM subtypes' degrees of maturation were delivery mode and gender dependent. We found that PSEP at high concentrations supports its antimicrobial action both in PT and T infants. These results open the door to further studies investigating the role of PSEP. [ABSTRACT FROM AUTHOR]

Infrared thermography is a contactless imaging technique, useful for measuring surface temperature at distance, which has been widely applied in animal production. The possible applications within the pig husbandry include sow heat detection, the reduction in the incidence of neonatal mortality, the recognition of the febrile state caused by pathogens, the study of metabolic efficiency, the evaluation of stress caused by heat, by constraints, or by aggressive interaction, and meat quality prediction. Even if this technique could be easily influenced by environmental factors, the continuous improvement in the procedures and the decrease in the cost of the equipment will allow for new and more applications in swine farming in the future. [ABSTRACT FROM AUTHOR]

An integrated approach that utilises hyperspectral and chlorophyll a fluorescence sensors to predict biochemical and biophysical parameters represents a new generation of remote-sensing research. The main objective of this study was to obtain a detailed spectral profile that correlates with plant physiology, thereby enhancing our understanding and management of plant health, pigment profiles, and compound fingerprints. Leveraging datasets using non-imaging or passive hyperspectral and chlorophyll fluorescence sensors to collect data in Tradescantia species demonstrated significant differences in leaf characteristics with pigment concentrations and structural components. The main goal was to use principal component analysis (PCA) and partial least squares regression (PLS) methods to analyse the variations in their spectra. Our findings demonstrate a strong correlation between hyperspectral data and chlorophyll fluorescence, which is further supported by the development of hyperspectral vegetation indices (HVIs) that can accurately evaluate fingerprints and predict many compounds in variegated leaves. The higher the integrated analytical approach and its potential application in HVIs and fingerprints, the better the selection of wavelengths and sensor positions for rapid and accurate analysis of many different compounds in leaves. Nonetheless, limitations arose from the specificity of the data for the Tradescantia species, warranting further research across diverse plant types and compounds in the leaves. Overall, this study paves the way for more sustainable and informed agricultural practices through breakthroughs in the application of sensors to remote-sensing technologies. [ABSTRACT FROM AUTHOR]

Identifying potassium (K+) deficiency in plants has traditionally been a difficult and expensive process. Traditional methods involve inspecting leaves for symptoms and conducting a laboratory analysis. These methods are not only time-consuming but also use toxic reagents. Additionally, the analysis is performed during the reproductive stage of growth, which does not allow enough time for corrective fertilization. Moreover, soybean growers do not have other tools to analyze the nutrition status during the earlier stages of development. Thus, this study proposes a quick approach for monitoring K+ in soybean crops using hyperspectral data through principal component analysis (PCA) and linear discriminant analysis (LDA) with a wavelength selection algorithm. The experiment was carried out at the Brazilian National Soybean Research Center in the 2017–2018, 2018–2019, and 2019–2020 soybean crop seasons, at the stages of development V4–V5, R1–R2, R3–R4, and R5.1–R5.3. Three treatments were evaluated that varied in K+ availability: severe potassium deficiency (SPD), moderate potassium deficiency (MPD), and an adequate supply of potassium (ASP). Spectral data were collected using an ASD Fieldspec 3 Jr. hyperspectral sensor. The results showed a variation in the leaf spectral signature based on the K+ availability, with SPD having higher reflectance in the visible region due to a lower concentration of pigments. PCA explained 100% of the variance across all stages and seasons, making it possible to distinguish SPD at an early development stage. LDA showed over 70% and 59% classification accuracies for discriminating a K+ deficiency in the simulation and validation stages. This study demonstrates the potential of the method as a rapid nondestructive and accurate tool for identifying K+ deficiency in soybean leaves. [ABSTRACT FROM AUTHOR]

The differential effects of cellular and ultrastructural characteristics on the optical properties of adaxial and abaxial leaf surfaces in the genus Tradescantia highlight the intricate relationships between cellular arrangement and pigment distribution in the plant cells. We examined hyperspectral and chlorophyll a fluorescence (ChlF) kinetics using spectroradiometers and optical and electron microscopy techniques. The leaves were analysed for their spectral properties and cellular makeup. The biochemical compounds were measured and correlated with the biophysical and ultrastructural features. The main findings showed that the top and bottom leaf surfaces had different amounts and patterns of pigments, especially anthocyanins, flavonoids, total phenolics, chlorophyll-carotenoids, and cell and organelle structures, as revealed by the hyperspectral vegetation index (HVI). These differences were further elucidated by the correlation coefficients, which influence the optical signatures of the leaves. Additionally, ChlF kinetics varied between leaf surfaces, correlating with VIS-NIR-SWIR bands through distinct cellular structures and pigment concentrations in the hypodermis cells. We confirmed that the unique optical properties of each leaf surface arise not only from pigmentation but also from complex cellular arrangements and structural adaptations. Some of the factors that affect how leaves reflect light are the arrangement of chloroplasts, thylakoid membranes, vacuoles, and the relative size of the cells themselves. These findings improve our knowledge of the biophysical and biochemical reasons for leaf optical diversity, and indicate possible implications for photosynthetic efficiency and stress adaptation under different environmental conditions in the mesophyll cells of Tradescantia plants. [ABSTRACT FROM AUTHOR]

Background: Angiocentric glioma (AG) is a rare, benign, and slow-growing tumor. First described in 2005, it is now gaining attention with respect to the possibility of being diagnosed. Even with no statistical differences between sex, it has been reported both in children and the elderly. A total of 120 cases have been described in the literature. The aim of this study is to provide new data for a new statistical assessment of the prevalence and incidence of AG in populations. Case report: An 8-year-old male patient with no history of epilepsy and no need for antiepileptic therapy underwent surgery for a left-parietal brain lesion, revealed through MRI. Imaging was acquired after his first absence episode. The lesion was completely resected. Histological findings indicated angiocentric glioma. No signs of recurrency after two years of follow-up. Conclusion: AG is usually an epilepsy-related low-grade glioma. Few cases exhibit disease progression and exitus. Surgical management should aim for a gross total resection to avoid recurrence and persisting epilepsy. Surgery represents the gold standard in diagnosis and treatment and must be performed as soon as possible in consideration of its healing properties and its useful diagnosis. [ABSTRACT FROM AUTHOR]

Simple Summary: Pembrolizumab monotherapy remains the first-line standard of care for patients with metastatic NSCLC and a PD-L1 tumor proportion score ≥ 50%. However, although other real-world studies have been published, long-term follow-up data on progression-free survival, overall survival, treatment response, and safety in a large cohort of patients are still lacking. We evaluated these outcomes in a cohort of 880 patients treated with first-line pembrolizumab monotherapy in 16 Italian centers. We also analyzed the prognostic impact of variables such as age, sex, histology, PD-L1 expression, ECOG, habitual smoking, and the presence of brain metastases. Median PFS and OS were 8.6 and 25.5 months, respectively, consistent with the results of Keynote-024. According to univariate analysis, it was determined that PD-L1 expression, ECOG, and habitual smoking had an impact on PFS, while age, sex, ECOG, histology, and habitual smoking had an impact on OS. However, results from univariate analysis should be considered with caution. Results from the phase III Keynote-024 clinical trial established pembrolizumab monotherapy as the first-line standard of care for patients with metastatic NSCLC who have PD-L1 expression ≥ 50%, EGFR, and ALK wild-type tumors. However, given the differences between patients treated in routine clinical practice and those treated in a clinical trial, real-world data are needed to confirm the treatment benefit in standard practice. Given the lack of data on large cohorts of patients with long follow-ups, we designed an observational retrospective study of patients with metastatic NSCLC who were treated with pembrolizumab, starting from its reimbursement eligibility until December 2020. The primary endpoints were PFS and OS, determined using the Kaplan–Meier method. Response and safety were also evaluated. We followed 880 patients (median follow-up: 35.1 months) until February 2022. Median PFS and OS were 8.6 months (95% CI: 7.6–10.0) and 25.5 months (95% CI: 21.8–31.6), respectively. We also found that ECOG PS, PD-L1 expression, and habitual smoking were prognostic factors for PFS, while age, sex, ECOG PS, habitual smoking and histology had an impact on OS. Multivariable analysis confirms the prognostic role of PD-L1 for PFS and of ECOG for both PFS and OS. 39.9% of patients reported an adverse event, but only 6.3% of patients discontinued therapy due to toxicity. Our results suggest a long-term benefit of pembrolizumab in the first-line setting, as well as a safety profile consistent with the results of Keynote-024. Many collected variables appear to influence clinical outcome, but results from these exploratory unadjusted analyses should be interpreted with caution. [ABSTRACT FROM AUTHOR]

A novel approach to the development of Distributed Temperature-Sensing (DTS) systems based on Raman Scattering in Multimode optical fibers operating at around 800 nm is presented, focusing on applications requiring temperature profile measurement in the range of a few hundreds of meters. In contrast to the standard Raman DTS systems, which aim to shorten the pulse space width as much as possible to improve the precision of measurement, the novel approach studied in this work is based on the use of pulses with a space width that is approximately equal to the distance covered by the fiber under test. The proposed technique relies on numerical post-processing to obtain the temperature profile measurement with a precision of about ± 3   ° C and a spatial resolution of 8 m, due to the transaction phases of the optical pulses. This solution simplifies the electronic circuit development, also minimizing the required laser peak power needed compared to the typical narrow pulse techniques. [ABSTRACT FROM AUTHOR]

Background: Activated phosphoinositide 3-kinase-δ syndrome 2 (APDS2) is characterized by lymphoproliferation and increased risk of malignancy. FDG-PET/CT may represent a helpful diagnostic tool for differentiating these clinical features and correctly diagnosing inborn errors of immunity (IEI). Case report: We present the case of a female patient diagnosed with Hodgkin's lymphoma at 19 years of age, although atypical imaging aspects emerged: baseline FDG-PET/CT revealed several hot lymph nodes with a symmetrical distribution, and increased tracer uptake in spleen, axial, and appendicular bone marrow. Imaging repeated after chemotherapy and autologous stem cell transplantation showed persistent increased FDG uptake at multiple supradiaphragmatic nodes and in bone marrow. After the diagnosis of APDS2 and rapamycin treatment, FDG-PET/CT confirmed complete metabolic normalization of all sites. Conclusions: In the IEI scenario, FDG-PET/CT plays an effective role in differentiating malignant proliferation and immune dysregulation phenotypes. Atypical patterns at FDG-PET/CT should be interpreted as a red flag for the need of an early immunological evaluation. [ABSTRACT FROM AUTHOR]

Isthmocele is a gynecological condition characterized by a disruption in the uterine scar, often associated with prior cesarean sections. This anatomical anomaly can be attributed to inadequate or insufficient healing of the uterine wall following a cesarean incision. It appears that isthmocele may impact a woman's quality of life as well as her reproductive capacity. The incidence of isthmocele can range from 20% to 70% in women who have undergone a cesarean section. This review aims to sum up the current knowledge about the effect of isthmocele on fertility and the possible therapeutic strategies to achieve pregnancy. However, currently, there is not sufficiently robust evidence to indicate the need for surgical correction in all asymptomatic patients seeking fertility. In cases where surgical correction of isthmocele is deemed necessary, it is advisable to evaluate residual myometrial thickness (RMT). For patients with RMT >2.5–3 mm, hysteroscopy appears to be the technique of choice. In cases where the residual tissue is lower, recourse to laparotomic, laparoscopic, or vaginal approaches is warranted. [ABSTRACT FROM AUTHOR]

Chitinase 3-like 1 (also known as CHI3L1 or YKL-40) is a mammalian chitinase that has no enzymatic activity, but has the ability to bind to chitin, the polymer of N-acetylglucosamine (GlcNAc). Chitin is a component of fungi, crustaceans, arthropods including insects and mites, and parasites, but it is completely absent from mammals, including humans and mice. In general, chitin-containing organisms produce mammalian chitinases, such as CHI3L1, to protect the body from exogenous pathogens as well as hostile environments, and it was thought that it had a similar effect in mammals. However, recent studies have revealed that CHI3L1 plays a pathophysiological role by inducing anti-apoptotic activity in epithelial cells and macrophages. Under chronic inflammatory conditions such as inflammatory bowel disease and chronic obstructive pulmonary disease, many groups already confirmed that the expression of CHI3L1 is significantly induced on the apical side of epithelial cells, and activates many downstream pathways involved in inflammation and carcinogenesis. In this review article, we summarize the expression of CHI3L1 under chronic inflammatory conditions in various disorders and discuss the potential roles of CHI3L1 in those disorders on various cell types. [ABSTRACT FROM AUTHOR]

The Standard Model is an up-to-date theory that best summarizes current knowledge in particle physics. Although some problems still remain open, it represents the leading model which all physicists refer to. One of the pillars which underpin the Standard Model is represented by the Lorentz invariance of the equations that form its backbone. These equations made it possible to predict the existence of particles and phenomena that experimental physics had not yet been able to detect. The first hint of formulating a fundamental theory of particles can be found in the 1932 Majorana equation, formulated when electrons and protons were the only known particles. Today we know that parts of the hypotheses set by Majorana were not correct, but his equation hid concepts that are found in the Standard Model. In this study, the Majorana equation is revisited and solved for free particles. The time-like, light-like and space-like solutions, represented by infinite-component wave functions, are discussed. [ABSTRACT FROM AUTHOR]

The manufacturing of active RF devices like klystrons is dominated by expensive and time-consuming cycles of machining and brazing. In this article, we characterize the RF properties of X-band klystron cavities and an integrated circuit manufactured with a novel additive manufacturing process. Parts are 3D printed in 316 L stainless steel with direct metal laser sintering, electroplated in copper, and brazed in one simple braze cycle. Stand-alone test cavities and integrated circuit cavities were measured throughout the manufacturing process. The un-tuned cavity frequency varies by less than 5% of the intended frequency, and Q factors reach above 1200. A tuning study was performed, and unoptimized tuning pins achieved a tuning range of 138 MHz without compromising Q. Klystron system performance was simulated with as-built cavity parameters and realistic tuning. Together, these results show promise that this process can be used to cheaply and quickly manufacture a new generation of highly integrated high power vacuum devices. [ABSTRACT FROM AUTHOR]

Fiber-Reinforced Polymer (FRP) composites have emerged as a promising alternative to conventional steel reinforcements in concrete structures owing to their benefits of corrosion resistance, higher strength-to-weight ratio, reduced maintenance cost, extended service life, and superior durability. However, there has been limited research on non-destructive testing (NDT) methods applicable for identifying damage in FRP-reinforced concrete (FRP-RC) elements. This knowledge gap has often limited its application in the construction industry. Engineers and owners often lack confidence in utilizing this relatively new construction material due to the challenge of assessing its condition. Thus, the main objective of this study is to determine the applicability of two of the most common NDT methods: the Ground-Penetrating Radar (GPR) and Phased Array Ultrasonic (PAU) methods for the detection of damage in FRP-RC elements. Three slab specimens with variations in FRP type (glass-, carbon- and basalt-FRP, i.e., GFRP, CFRP, and BFRP, respectively), bar diameter, bar depths, and defect types were investigated to determine the limitations and detection capabilities of these two NDT methods. The results show that GPR could detect damage in GFRP bars and CFRP strands, but PAU was limited to damage detection in CFRP strands. The findings of this study show the applicability of conventional NDT methods to FRP-RC and at the same time identify the areas with a need for further research. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate psychometric properties of the Italian version of the Short-Form Supportive Care Needs Survey Questionnaire (SCNS-SF34) in a cancer population. A multicenter prospective observational study was carried out in outpatient and inpatient settings. The evaluated psychometric properties were as follows: the five-domain structure, the internal consistency, the convergent validity with the Edmond Symptom Assessment System (ESAS) questionnaire, the discriminant validity and test–retest reliability. A total of 714 patients with different types, stages and treatment settings of cancer were recruited. A total of 56% of participants were women, the median age 59 years (range 18–88). The prevalence of patients reporting at least one unmet need was 78.7%. The factor analysis explained 71.3% of the total variance, confirming the five-domain structure of the original model. Internal consistency was good, with Cronbach's alpha values ranging from 0.87 ("psychosocial need", "patient support and health system", "information") to 0.90 ("sexuality"). The convergent validity of the SCNS-SF34-It with the ESAS scale was low, suggesting that these questionnaires cover different concepts. The SCNS-SF34-It was able to discriminate differences between groups, and the test–retest reliability was good (ICC 0.72–0.84). The SCNS-SF34-It proved to be a reliable instrument for use in clinical practice for evaluating unmet needs in the Italian population of cancer patients. This study was not registered. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has attracted huge attention for manufacturing metals, ceramics, highly filled composites, or virgin polymers. Of all the AM methods, material extrusion (MEX) stands out as one of the most widely employed AM methods on a global scale, specifically when dealing with thermoplastic polymers and composites, as this technique requires a very low initial investment and usage simplicity. This review extensively addresses the latest advancements in the field of MEX of feedstock made of polymers highly filled with metal particles. After developing a 3D model, the polymeric binder is removed from the 3D-printed component in a process called debinding. Furthermore, sintering is conducted at a temperature below the melting temperature of the metallic powder to obtain the fully densified solid component. The stages of MEX-based processing, which comprise the choice of powder, development of binder system, compounding, 3D printing, and post-treatment, i.e., debinding and sintering, are discussed. It is shown that both 3D printing and post-processing parameters are interconnected and interdependent factors, concurring in determining the resulting mechanical properties of the sintered metal. In particular, the polymeric binder, along with its removal, results to be one of the most critical factors in the success of the entire process. The mechanical properties of sintered components produced through MEX are generally inferior, compared with traditional techniques, as final MEX products are more porous. [ABSTRACT FROM AUTHOR]

Heat stress is an abiotic factor that affects the photosynthetic parameters of plants. In this study, we examined the photosynthetic mechanisms underlying the rapid response of tobacco plants to heat stress in a controlled environment. To evaluate transient heat stress conditions, changes in photochemical, carboxylative, and fluorescence efficiencies were measured using an infrared gas analyser (IRGA Licor 6800) coupled with chlorophyll a fluorescence measurements. Our findings indicated that significant disruptions in the photosynthetic machinery occurred at 45 °C for 6 h following transient heat treatment, as explained by 76.2% in the principal component analysis. The photosynthetic mechanism analysis revealed that the dark respiration rate (Rd and Rd*CO2) increased, indicating a reduced potential for carbon fixation during plant growth and development. When the light compensation point (LCP) increased as the light saturation point (LSP) decreased, this indicated potential damage to the photosystem membrane of the thylakoids. Other photosynthetic parameters, such as AMAX, VCMAX, JMAX, and ΦCO2, also decreased, compromising both photochemical and carboxylative efficiencies in the Calvin–Benson cycle. The energy dissipation mechanism, as indicated by the NPQ, qN, and thermal values, suggested that a photoprotective strategy may have been employed. However, the observed transitory damage was a result of disruption of the electron transport rate (ETR) between the PSII and PSI photosystems, which was initially caused by high temperatures. Our study highlights the impact of rapid temperature changes on plant physiology and the potential acclimatisation mechanisms under rapid heat stress. Future research should focus on exploring the adaptive mechanisms involved in distinguishing mutants to improve crop resilience against environmental stressors. [ABSTRACT FROM AUTHOR]

The use of biostimulants and biofilms in agriculture is constantly increasing, as they may support plant growth and productivity by improving nutrient absorption, increasing stress resilience and providing sustainable alternatives to chemical management practices. In this work, two commercial products based on Trichoderma afroharzianum strain T22 (Trianum P®) and a seaweed extract from Ascophyllum nodosum (Phylgreen®) were tested on industrial tomato plants (Solanum lycopersicum var. Heinz 5108F1) in a field experiment. The effects of single and combined applications of microbial and plant biostimulants on plants grown on two different biodegradable mulch films were evaluated in terms of changes in the metabolic profiles of leaves and berries. Untargeted metabolomics analysis by LC-MS Q-TOF revealed the presence of several significantly accumulated compounds, depending on the biostimulant treatment, the mulch biofilm and the tissue examined. Among the differential compounds identified, some metabolites, belonging to alkaloids, flavonoids and their derivatives, were more abundant in tomato berries and leaves upon application of Trichoderma-based product. Interestingly, the biostimulants, when applied alone, similarly affected the plant metabolome compared to control or combined treatments, while significant differences were observed according to the mulch biofilm applied. [ABSTRACT FROM AUTHOR]

In an intercropping system, the interplay between cereals and legumes, which is strongly driven by the complementarity of below-ground structures and their interactions with the soil microbiome, raises a fundamental query: Can different genotypes alter the configuration of the rhizosphere microbial communities? To address this issue, we conducted a field study, probing the effects of intercropping and diverse maize (Zea mays L.) and bean (Phaseolus vulgaris L., Phaseolus coccineus L.) genotype combinations. Through amplicon sequencing of bacterial 16S rRNA genes from rhizosphere samples, our results unveil that the intercropping condition alters the rhizosphere bacterial communities, but that the degree of this impact is substantially affected by specific genotype combinations. Overall, intercropping allows the recruitment of exclusive bacterial species and enhances community complexity. Nevertheless, combinations of maize and bean genotypes determine two distinct groups characterized by higher or lower bacterial community diversity and complexity, which are influenced by the specific bean line associated. Moreover, intercropped maize lines exhibit varying propensities in recruiting bacterial members with more responsive lines showing preferential interactions with specific microorganisms. Our study conclusively shows that genotype has an impact on the rhizosphere microbiome and that a careful selection of genotype combinations for both species involved is essential to achieve compatibility optimization in intercropping. [ABSTRACT FROM AUTHOR]

Most tablets put on the market are coated with polymers soluble in water. The Opadry II 85 series from Colorcon Inc., is a family of PVA-based products marketed since the 1990s. Despite numerous publications on the properties of PVA, to date, limited work has been undertaken to determine the physico-chemical parameters (i.e., UV light, high temperature, and relative humidity) that could affect the performance of PVA-based coatings. To this end, we performed artificial ageing processes on samples made of Opadry Orange II or of some selected components of this coating and analysed them by means of a multidisciplinary approach, using, for example, FTIR, NMR, rheology, and DMTA measurements. In this way, we analysed the influence of the critical components of the Opadry Orange II formula, such as titanium dioxide and aluminium hydroxide, on the coating characteristics under ageing conditions. [ABSTRACT FROM AUTHOR]

Fiber-reinforced polymer (FRP) composites offer a corrosion-resistant, lightweight, and durable alternative to traditional steel material in concrete structures. However, the lack of established inspection methods for assessing reinforced concrete elements with externally bonded FRP (EB-FRP) composites hinders industry-wide confidence in their adoption. This study addresses this gap by investigating non-destructive testing (NDT) techniques for detecting damage and defects in EB-FRP concrete elements. As such, this study first identified and categorized potential damage in EB-FRP concrete elements considering where and why they occur. The most promising NDT methods for detecting this damage were then analyzed. And lastly, experiments were carried out to assess the feasibility of the selected NDT methods for detecting these defects. The result of this study introduces infrared thermography (IR) as a proper method for identifying defects underneath the FRP system (wet lay-up). The IR was capable of highlighting defects as small as 625 mm2 (1 in.2) whether between layers (debonding) or between the substrate and FRP (delamination). It also indicates the inability of GPR to detect damage below the FRP laminates, while indicating the capability of PAU to detect concrete delamination and qualitatively identify bond damage in the FRP system. The outcome of this research can be used to provide guidance for choosing effective on-site NDT techniques, saving considerable time and cost for inspection. Importantly, this study also paves the way for further innovation in damage detection techniques addressing the current limitations. [ABSTRACT FROM AUTHOR]

This work is part of a research project aimed at developing a bio-based binder, composed mainly of polylactic acid (PLA), to produce Ti6Al4V feedstock suitable for use in MAM (Metal Additive Manufacturing) via mFFF (metal Fused Filament Fabrication), in order to manufacture a titanium alloy specimen. While in Bragaglia et al. the mechanical characteristics of this sample were analyzed, the aim used of this study is to compare the mentioned mFFF process with one of the most used MAM processes in aerospace applications, known as Selective Laser Sintering (SLS), based on the Life Cycle Assessment (LCA) method. Despite the excellent properties of the products manufactured via SLS, this 3D printing technology involves high upfront capital costs while mFFF is a cheaper process. Moreover, the mFFF process has the advantage of potentially being exported for production in microgravity or weightless environments for in-space use. Nevertheless, most scientific literature shows comparisons of the Fused Filament Fabrication (FFF) printing stage with other AM technologies, and there are no comparative LCA "Candle to Gate" studies with mFFF processes to manufacture the same metal sample. Therefore, both MAM processes are analyzed with the LCA "Candle to Gate" method, from the extraction of raw materials to the production of the finished titanium alloy sample. The main results demonstrate a higher impact (+50%) process for mFFF and higher electrical energy consumption (7.31 kWh) compared to SLS (0.32 kWh). After power consumption, the use of titanium becomes the main contributor of Global Warming Potential (GWP) and Abiotic Depletion Potential (ADP) for both processes. Finally, an alternative scenario is evaluated in which the electrical energy is exclusively generated through photovoltaics. In this case, the results show how the mFFF process develops a more sustainable outcome than SLS. [ABSTRACT FROM AUTHOR]

Wood vinegar is a by-product of the pyrolysis of organic raw materials. In this study, we investigated the chemical composition and biological activity of industrial wood vinegar derived from the pyrolysis of wood pruning waste. The composition of wood vinegar was characterized using liquid chromatography (LC) and gas chromatography–mass spectrometry (GC-MS). Wood vinegar bioactivity was tested against Bactrocera oleae under field conditions in an olive grove for two years. Furthermore, wood vinegar was applied in a greenhouse experiment with strawberry plants and in a strawberry field infested with the nematode Meloidogyne incognita. Finally, a seed root length bioassay was performed to evaluate the phytotoxicity or biostimulation of wood vinegar on Eruca sativa, Lactuca sativa, Lens culinaryis, Lolium multiflorum, and Solanum lycopersicum. Our results showed that wood vinegar had a pH of 3.2, with high concentrations of acetic acid (27,840.16 mg L−1) and phenols (54.00 mg L−1). No repellent effect against B. oleae was observed when wood vinegar was applied as an aerosol in olive groves. On strawberry plants in greenhouse conditions, wood vinegar showed phytotoxic effects at high concentrations, resulting in a decrease in the total yield of the plants. In the field, at a 1% concentration, wood vinegar led to a significant 15% reduction in the infection caused by M. incognita in strawberry plants. Finally, in the in vitro crop bioassay, wood vinegar demonstrated remarkable phytotoxicity effects at high concentrations while promoting root growth when diluted. The efficacy of wood vinegar displayed considerable variability based on concentration and delivery system, emphasizing the need for careful evaluation when considering its application, particularly in diverse crops and production systems. [ABSTRACT FROM AUTHOR]

To identify objects in images, a complex set of skills is needed that includes understanding the context and being able to determine the borders of objects. In computer vision, this task is known as semantic segmentation and it involves categorizing each pixel in an image. It is crucial in many real-world situations: for autonomous vehicles, it enables the identification of objects in the surrounding area; in medical diagnosis, it enhances the ability to detect dangerous pathologies early, thereby reducing the risk of serious consequences. In this study, we compare the performance of various ensembles of convolutional and transformer neural networks. Ensembles can be created, e.g., by varying the loss function, the data augmentation method, or the learning rate strategy. Our proposed ensemble, which uses a simple averaging rule, demonstrates exceptional performance across multiple datasets. Notably, compared to prior state-of-the-art methods, our ensemble consistently shows improvements in the well-studied polyp segmentation problem. This problem involves the precise delineation and identification of polyps within medical images, and our approach showcases noteworthy advancements in this domain, obtaining an average Dice of 0.887, which outperforms the current SOTA with an average Dice of 0.885. [ABSTRACT FROM AUTHOR]

Muscle injuries and subsequent reinjuries significantly impact athletes, especially in football. These injuries lead to time loss, performance impairment, and long-term health concerns. This review aims to provide a comprehensive overview of the current understanding of muscle reinjuries, delving into their epidemiology, risk factors, clinical management, and prevention strategies. Despite advancements in rehabilitation programs and return-to-play criteria, reinjury rates remain alarmingly high. Age and previous muscle injuries are nonmodifiable risk factors contributing to a high reinjury rate. Clinical management, which involves accurate diagnosis, individualized rehabilitation plans, and the establishment of return-to-training and return-to-play criteria, plays a pivotal role during the sports season. Eccentric exercises, optimal loading, and training load monitoring are key elements in preventing reinjuries. The potential of artificial intelligence (AI) in predicting and preventing reinjuries offers a promising avenue, emphasizing the need for a multidisciplinary approach to managing these injuries. While current strategies offer some mitigation, there is a pressing need for innovative solutions, possibly leveraging AI, to reduce the incidence of muscle reinjuries in football players. Future research should focus on this direction, aiming to enhance athletes' well-being and performance. [ABSTRACT FROM AUTHOR]

This paper explores the potential of using the SAM (Segment-Anything Model) segmentator to enhance the segmentation capability of known methods. SAM is a promptable segmentation system that offers zero-shot generalization to unfamiliar objects and images, eliminating the need for additional training. The open-source nature of SAM allows for easy access and implementation. In our experiments, we aim to improve the segmentation performance by providing SAM with checkpoints extracted from the masks produced by mainstream segmentators, and then merging the segmentation masks provided by these two networks. We examine the "oracle" method (as upper bound baseline performance), where segmentation masks are inferred only by SAM with checkpoints extracted from the ground truth. One of the main contributions of this work is the combination (fusion) of the logit segmentation masks produced by the SAM model with the ones provided by specialized segmentation models such as DeepLabv3+ and PVTv2. This combination allows for a consistent improvement in segmentation performance in most of the tested datasets. We exhaustively tested our approach on seven heterogeneous public datasets, obtaining state-of-the-art results in two of them (CAMO and Butterfly) with respect to the current best-performing method with a combination of an ensemble of mainstream segmentator transformers and the SAM segmentator. The results of our study provide valuable insights into the potential of incorporating the SAM segmentator into existing segmentation techniques. We release with this paper the open-source implementation of our method. [ABSTRACT FROM AUTHOR]

Pultruded FRP composites have emerged as a promising alternative to traditional materials like concrete, steel, and timber, especially in corrosive environmental conditions. However, the unique properties of these composites necessitate careful consideration during their implementation, as they differ significantly from conventional materials. Proper testing and characterization of FRP pultruded materials is key for their efficient and safe implementation. However, the existing specifications are not unified, resulting in ambiguity among stakeholders. This paper aims to bridge this gap by thoroughly reviewing current destructive and non-destructive test methods for FRP pultruded materials, specifications, quality control, and health monitoring of FRP structures. Each subsection is further divided into subtopics, providing a comprehensive overview of the subject. By shedding light on these crucial aspects, this article aims to accelerate the adoption and utilization of these innovative materials in practical applications. [ABSTRACT FROM AUTHOR]

In computer vision and image analysis, Convolutional Neural Networks (CNNs) and other deep-learning models are at the forefront of research and development. These advanced models have proven to be highly effective in tasks related to computer vision. One technique that has gained prominence in recent years is the construction of ensembles using deep CNNs. These ensembles typically involve combining multiple pretrained CNNs to create a more powerful and robust network. The purpose of this study is to evaluate the effectiveness of building CNN ensembles by combining several advanced techniques. Tested here are CNN ensembles constructed by replacing ReLU layers with different activation functions, employing various data-augmentation techniques, and utilizing several algorithms, including some novel ones, that perturb network weights. Experimental results performed across many datasets representing different tasks demonstrate that our proposed methods for building deep ensembles produces superior results. [ABSTRACT FROM AUTHOR]

Recently, an increase in the incidence of brain tumors has been observed in the most industrialized countries. This event triggered considerable interest in the study of heavy metals and their presence in the environment (air, water, soil, and food). It is probable that their accumulation in the body could lead to a high risk of the onset of numerous pathologies, including brain tumors, in humans. Heavy metals are capable of generating reactive oxygen, which plays a key role in various pathological mechanisms. Alteration of the homeostasis of heavy metals could cause the overproduction of reactive oxygen species and induce DNA damage, lipid peroxidation, and the alteration of proteins. A large number of studies have shown that iron, cadmium, lead, nickel, chromium, and mercury levels were significantly elevated in patients affected by gliomas. In this study, we try to highlight a possible correlation between the most frequently encountered heavy metals, their presence in the environment, their sources, and glioma tumorigenesis. We also report on the review of the relevant literature. [ABSTRACT FROM AUTHOR]

Photosynthesis is a key process in plant physiology. Understanding its mechanisms is crucial for optimizing crop yields and for environmental monitoring across a diverse range of plants. In this study, we employed reflectance, transmittance, and absorbance hyperspectral sensors and utilized multivariate statistical techniques to improve the predictive models for chlorophyll a fluorescence (ChlF) parameters in Hibiscus and Geranium model plants. Our objective was to identify spectral bands within hyperspectral data that correlate with ChlF indicators using high-resolution data spanning the electromagnetic spectrum from ultraviolet to shortwave infrared (UV–VIS–NIR–SWIR). Utilizing the hyperspectral vegetation indices (HVIs) tool to align importance projection for wavelength preselection and select the most responsive wavelength by variable importance projection (VIP), we optimized partial least squares regression (PLSR) models to enhance predictive accuracy. Our findings revealed a strong relationship between hyperspectral sensor data and ChlF parameters. Employing principal component analysis, kappa coefficients (k), and accuracy (Acc) evaluations, we achieved values exceeding 86% of the predicted ChlF parameters for both Hibiscus and Geranium plants. Regression models for parameters such as Ψ(EO), ϕ(PO), ϕ(EO), ϕ(DO), δRo, ρRo, Kn, Kp, SFI(abs), PI(abs), and D.F. demonstrated model accuracies close to 0.84 for R2 and approximately 1.96 for RPD. The spectral regions linked with these parameters included blue, green, red, infrared, SWIR1, and SWIR2, emphasizing their relevance for noninvasive evaluations. This research demonstrates the ability of hyperspectral sensors to noninvasively predict chlorophyll a fluorescence (ChlF) parameters, which are essential for assessing photosynthetic efficiency in plants. Notably, hyperspectral absorbance data were more accurate in predicting JIP-test-based chlorophyll a kinetic parameters. In conclusion, this study underscores the potential of hyperspectral sensors for deepening our understanding of plant photosynthesis and monitoring plant health. [ABSTRACT FROM AUTHOR]

Paleogenomics focuses on the recovery, manipulation, and analysis of ancient DNA (aDNA) from historical or long-dead organisms to reconstruct and analyze their genomes. The aDNA is commonly obtained from remains found in paleontological and archaeological sites, conserved in museums, and in other archival collections. Herbarium collections represent a great source of phenotypic and genotypic information, and their exploitation has allowed for inference and clarification of previously unsolved taxonomic and systematic relationships. Moreover, herbarium specimens offered a new source for studying phenological traits in plants and for disentangling biogeography and evolutionary scenarios of species. More recently, advances in molecular technologies went in parallel with the decreasing costs of next-generation sequencing (NGS) approaches, which paved the way to the utilization of aDNA for whole-genome studies. Although many studies have been carried out combining modern analytic techniques and ancient samples, such as herbarium specimens, this research field is still relatively unexplored due to the need for improving strategies for aDNA manipulation and exploitation from ancient samples. The higher susceptibility of aDNA to degradation and contamination during herbarium conservation and manipulation and the occurrence of biochemical postmortem damage can result in a more challenging reconstruction of the original DNA sequence. Here, we review the methodological approaches that have been developed for the exploitation of historical herbarium plant materials, such as best practices for aDNA extraction, amplification, and genotyping. We also focus on some strategies to overcome the main problems related to the utilization of herbarium specimens for their exploitation in plant evolutionary studies. [ABSTRACT FROM AUTHOR]

Reflectance hyperspectroscopy is recognised for its potential to elucidate biochemical changes, thereby enhancing the understanding of plant biochemistry. This study used the UV-VIS-NIR-SWIR spectral range to identify the different biochemical constituents in Hibiscus and Geranium plants. Hyperspectral vegetation indices (HVIs), principal component analysis (PCA), and correlation matrices provided in-depth insights into spectral differences. Through the application of advanced algorithms—such as PLS, VIP, iPLS-VIP, GA, RF, and CARS—the most responsive wavelengths were discerned. PLSR models consistently achieved R2 values above 0.75, presenting noteworthy predictions of 0.86 for DPPH and 0.89 for lignin. The red-edge and SWIR bands displayed strong associations with pivotal plant pigments and structural molecules, thus expanding the perspectives on leaf spectral dynamics. These findings highlight the efficacy of spectroscopy coupled with multivariate analysis in evaluating the management of biochemical compounds. A technique was introduced to measure the photosynthetic pigments and structural compounds via hyperspectroscopy across UV-VIS-NIR-SWIR, underpinned by rapid multivariate PLSR. Collectively, our results underscore the burgeoning potential of hyperspectroscopy in precision agriculture. This indicates a promising paradigm shift in plant phenotyping and biochemical evaluation. [ABSTRACT FROM AUTHOR]

In an effort to improve the efficiency of soil classification, traditional methods are being combined with analytical and computational techniques. This integration has strengthened the connection between conventional classification and the application of machine-learning (ML) models to interpret soil spectral reflectance data. Due to the time and computational cost, several studies are limited to testing the classification performance of a few algorithms and do not always explore the best parameters for model optimization. The study aims to assess the efficiency of combining soil spectral reflectance with prevalent ML models for classifying pedogenetic horizons and soil suborders, enhancing traditional classification methods. We collected seven soil monoliths, previously classified according to the Brazilian Soil Classification System (SiBCS) and soil taxonomy. Using the ASD Fieldspec spectroradiometer, we obtained spectral reflectance samples along each monolith (n = 800 per monolith) to classify horizons and n = 5600 for suborder classification. Spectral fingerprints were obtained and explored by Principal Component Analysis (PCA). The spectral data were subdivided into training (70%) and test (30%) sets and submitted to the Logistic Regression (LR), Artificial Neural Network (NN), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB) models for the classification of horizons and suborders, considering the model's adjustment parameters. Accuracy and F-Score were used to verify the performance of the models. There was a significant influence of particle size and soil organic carbon on the spectral fingerprint of the soils. The PCA indicated that topsoil horizons clustered in most of the monoliths analyzed, while most of the subsoil horizons showed data overlap. The NN model showed the highest accuracy in the classification of horizons (97%), while the SVM showed the lowest performance (52% accuracy). The classification of soil suborders presented accuracies between 95% and 98%. Therefore, our study concludes that spectral data combined with ML models can enhance the discrimination and classification of soil horizons and suborders, improving upon traditional methods. [ABSTRACT FROM AUTHOR]

The quantification of soil organic matter (SOM) has increased over the years, especially in the Brazilian Cerrado region, one of the most important areas for grain production in the country. In this area, SOM content tends to be low, which directly impacts the physical, chemical and biological quality of soils. Thus, the use of spectroradiometry has been widely evaluated to investigate whether it can be used as a faster, more reliable and cheaper solution to meet the SOM estimation. In this context, the objective of the present paper was to evaluate the performance of a local spectral model for SOM prediction generated through the spiking strategy. The research was developed in the municipality of Passos, Minas Gerais State, located in the Brazilian Cerrado. Soil samples (0–0.2 m and 0.2–0.4 m depths) were collected in a zigzag pattern and split in calibration of the local models from a test area (90 soil samples) and recalibration and validation from a target area (46 soil samples). After this stage, the SOM contents were determined in a laboratory, and the spectral responses (350–2500 nm) of each soil sample were collected. From the target area, 10, 25 and 50% of soil spectra were selected for recalibration of the local models generated for the test area. Although median results were observed in the post-recalibration, due to the type of sample selected and the relative similarity among the spectral curves of both areas, improvement was observed for all statistical indices, especially when using 50% (23) of samples for recalibration of the local models, reaching r2 = 0.43, RMSEP = 2.34 gdm−3 and RPIQ = 4.58. These results are important for the SOM estimation in the Brazilian Cerrado considering its importance to the food security and socioeconomic activities. However, considering the lack of similar research in the study area, it is necessary to further investigate the development of spectral models on a local scale and their contribution to improve the identification of SOM spatial variability. [ABSTRACT FROM AUTHOR]

Usually, energy and structural improvements for historic masonry buildings are addressed separately using distinct methods and protocols. This paper covers an integrated assessment of new composite materials to reduce the seismic vulnerability of historic masonry buildings while complying with sustainable conservation requirements, emissions' reduction, and energy savings. Firstly, this study focused on selecting suitable thermal mortars that could serve as the base material for the innovative composite. Subsequently, the mechanical characteristics of these mortars were examined by subjecting them to compressive and three-point bending tests. Dynamic thermo-hygrometric simulations were conducted using commercially available software to check the energy performance of the composite material when used on walls of existing masonry buildings. The thermal mortar that exhibited the most favorable mechanical and thermal properties was subsequently reinforced with a basalt fabric. A composite sample was assembled and subjected to direct tensile testing to determine its stress–strain behavior. [ABSTRACT FROM AUTHOR]

Additive manufacturing is a versatile technique that shows promise in providing quick and dynamic manufacturing for complex engineering problems. Research has been ongoing into the use of additive manufacturing for potential applications in radiofrequency (RF) component technologies. Here, we present a method for developing an effective prototype load produced from 316L stainless steel on a direct metal laser sintering machine. The model was tested using simulation software to verify the validity of the design. The load structure was manufactured by an online digital manufacturing company, showing the viability of using easily accessible tools to manufacture RF structures. The produced load was able to produce an S 11 value of −22.8 dB at a C-band frequency of 5.712 GHz while under a vacuum. In a high-power test, the load was able to terminate a peak power of 8.1 MW. The discussion includes future applications of the present method and how it will help to improve the implementation of future accelerator concepts. [ABSTRACT FROM AUTHOR]

Simple Summary: The survival of advanced melanoma patients has been improved in recent years due to immunotherapy. However, not all patients respond to this treatment. For this reason, it is necessary to know the mechanisms of the response and resistance to immunotherapy. In this work, clinical samples from advanced melanoma patients treated with immunotherapy were analyzed. The obtained results suggested that the proteins involved in protein processing in the endoplasmic reticulum and antigen presentation, as well as the immune and inflammatory responses, play a role in the response to immunotherapy. Additionally, we built a prognostic signature capable of identifying those patients that will respond to immunotherapy. The study of the mechanisms of the resistance and response to immunotherapy could help in the definition of new therapies for these patients that do not respond to immunotherapy. Immunotherapy improves the survival of patients with advanced melanoma, 40% of whom become long-term responders. However, not all patients respond to immunotherapy. Further knowledge of the processes involved in the response and resistance to immunotherapy is still needed. In this study, clinical paraffin samples from fifty-two advanced melanoma patients treated with anti-PD-1 inhibitors were assessed via high-throughput proteomics and RNA-seq. The obtained proteomics and transcriptomics data were analyzed using multi-omics network analyses based on probabilistic graphical models to identify those biological processes involved in the response to immunotherapy. Additionally, proteins related to overall survival were studied. The activity of the node formed by the proteins involved in protein processing in the endoplasmic reticulum and antigen presentation machinery was higher in responders compared to non-responders; the activity of the immune and inflammatory response node was also higher in those with complete or partial responses. A predictor for overall survival based on two proteins (AMBP and PDSM5) was defined. In summary, the response to anti-PD-1 therapy in advanced melanoma is related to protein processing in the endoplasmic reticulum, and also to genes involved in the immune and inflammatory responses. Finally, a two-protein predictor can define survival in advanced disease. The molecular characterization of the mechanisms involved in the response and resistance to immunotherapy in melanoma leads the way to establishing therapeutic alternatives for patients who will not respond to this treatment. [ABSTRACT FROM AUTHOR]

For robust classification, selecting a proper classifier is of primary importance. However, selecting the best classifiers depends on the problem, as some classifiers work better at some tasks than on others. Despite the many results collected in the literature, the support vector machine (SVM) remains the leading adopted solution in many domains, thanks to its ease of use. In this paper, we propose a new method based on convolutional neural networks (CNNs) as an alternative to SVM. CNNs are specialized in processing data in a grid-like topology that usually represents images. To enable CNNs to work on different data types, we investigate reshaping one-dimensional vector representations into two-dimensional matrices and compared different approaches for feeding standard CNNs using two-dimensional feature vector representations. We evaluate the different techniques proposing a heterogeneous ensemble based on three classifiers: an SVM, a model based on random subspace of rotation boosting (RB), and a CNN. The robustness of our approach is tested across a set of benchmark datasets that represent a wide range of medical classification tasks. The proposed ensembles provide promising performance on all datasets. [ABSTRACT FROM AUTHOR]

Approximately 80–96% of people with amyotrophic lateral sclerosis (ALS) become unable to speak during the disease progression. Assessing upper and lower motor neuron impairment in bulbar regions of ALS patients remains challenging, particularly in distinguishing spastic and flaccid dysarthria. This study aimed to evaluate acoustic voice parameters as useful biomarkers to discriminate ALS clinical phenotypes. Triangular vowel space area (tVSA), alternating motion rates (AMRs), and sequential motion rates (SMRs) were analyzed in 36 ALS patients and 20 sex/age-matched healthy controls (HCs). tVSA, AMR, and SMR values significantly differed between ALS and HCs, and between ALS with prevalent upper (pUMN) and lower motor neuron (pLMN) impairment. tVSA showed higher accuracy in discriminating pUMN from pLMN patients. AMR and SMR were significantly lower in patients with bulbar onset than those with spinal onset, both with and without bulbar symptoms. Furthermore, these values were also lower in patients with spinal onset associated with bulbar symptoms than in those with spinal onset alone. Additionally, AMR and SMR values correlated with the degree of dysphagia. Acoustic voice analysis may be considered a useful prognostic tool to differentiate spastic and flaccid dysarthria and to assess the degree of bulbar involvement in ALS. [ABSTRACT FROM AUTHOR]

Cancer vaccines are increasingly being studied as a possible strategy to prevent and treat cancers. While several prophylactic vaccines for virus-caused cancers are approved and efficiently used worldwide, the development of therapeutic cancer vaccines needs to be further implemented. Virus-like particles (VLPs) are self-assembled protein structures that mimic native viruses or bacteriophages but lack the replicative material. VLP platforms are designed to display single or multiple antigens with a high-density pattern, which can trigger both cellular and humoral responses. The aim of this review is to provide a comprehensive overview of preventive VLP-based vaccines currently approved worldwide against HBV and HPV infections or under evaluation to prevent virus-caused cancers. Furthermore, preclinical and early clinical data on prophylactic and therapeutic VLP-based cancer vaccines were summarized with a focus on HER-2-positive breast cancer. [ABSTRACT FROM AUTHOR]

This paper reports on a combined experimental and numerical modeling investigation of cracking of concrete slabs with GFRP reinforcement. At this stage of the project, attention is given to early-age cracking driven by plastic shrinkage, preceding longer term considerations of cracking resistance over the service life of field applications. Of interest is the effectiveness of GFRP reinforcement in restricting plastic shrinkage cracking. Nine small-scale slab specimens were subjected to controlled evaporation rates. Images of crack development were acquired periodically, from which crack width estimations were made. Comparisons were made between slabs reinforced with conventional steel and those reinforced with GFRP, along with control specimens lacking reinforcement. During the period of plastic shrinkage, the time of crack initiation and subsequent crack openings do not appear to be influenced by the presence of the reinforcing bars. To understand this behavior, six early-age bond tests were conducted for both types of the bars after 1, 2, and 3 h exposure to the controlled evaporation rate. In addition, concrete strength development and time of settings were measured using penetration resistance tests on a representative mortar. The numerical modeling component of this research is based on a Voronoi cell lattice model; in this approach, the relative humidity, temperature, and displacement fields are discretized in three-dimensions, allowing for a comprehensive investigation of material behavior within the controlled environment. Based on the measured bond properties, our simulations confirm that the reinforcing bars restrict crack development, though they do not prevent it entirely. [ABSTRACT FROM AUTHOR]

Simple Summary: House-soiling is one of the commonest behavioral problems in cats and one of the main reasons why cats are abandoned at shelters. This study aimed to document the litter management practices and the recalled prevalence of elimination problems in a representative sample of the Italian pet cat population. An online survey collected data for a total of 3106 cats. Cats were mostly European adult-aged and living in apartments with other pets. They were mainly provided with covered litter boxes filled with clumping substrates, and the cleaning of the litter box and its full replacement took place daily and weekly, respectively. Professionals and amateurs owned cats with characteristics oriented toward breeding and companionship, respectively. Professionals provided fewer square meters per cat to their cats but were more diligent in litter box cleaning compared to amateurs. House-soiling was reported by 16.7% of the respondents. It was mainly related to urine elimination on objects and was lower than in other investigated populations. The demographic information collected has increased our knowledge and may be useful to enhance cat management in Italy. There are about 10.1 million domestic cats in Italy, but information on cats' litter management and house-soiling prevalence is scant. This study described cats' and cat owners' profiles, litter management practices, and whether cats show house-soiling, also comparing between professionals (i.e., breeders) and amateurs (i.e., pet owners). A cross-sectional online survey sought respondents' housing, family, and cat details, as well as other pet details, litter details, and whether the cats showed house-soiling. Data for a total of 3106 cats were obtained. Italian cats lived mainly in apartments, along with other cats or dogs. Italians owned mostly adult European breed cats, to whom they provided covered litter boxes filled with clumping substrates, scooped daily, and completely replaced weekly. Litter cleaning was more frequent when cats were owned for financial purposes (i.e., breeders) rather than for companionship, but more space was provided for pets than for breeding cats. The recalled prevalence of elimination problems (16.7%) was lower compared to other studies, with cats mainly eliminating urine (54.6%) on objects in squatting posture (35.2%). Overall, this research increased our understanding of cat litter management in Italy. These findings could fill a gap in the knowledge regarding litter management and house-soiling incidences in Italy. Further studies to investigate possible risk factors for house-soiling are needed. [ABSTRACT FROM AUTHOR]

Ecoacoustics is arguably the best method for monitoring marine environments, but analyzing and interpreting acoustic data has traditionally demanded substantial human supervision and resources. These bottlenecks can be addressed by harnessing contemporary methods for automated audio signal analysis. This paper focuses on the problem of assessing dolphin whistles using state-of-the-art deep learning methods. Our system utilizes a fusion of various resnet50 networks integrated with data augmentation (DA) techniques applied not to the training data but to the test set. We also present training speeds and classification results using DA to the training set. Through extensive experiments conducted on a publicly available benchmark, our findings demonstrate that our ensemble yields significant performance enhancements across several commonly used metrics. For example, our approach obtained an accuracy of 0.949 compared to 0.923, the best reported in the literature. We also provide training and testing sets that other researchers can use for comparison purposes, as well as all the MATLAB/PyTorch source code used in this study. [ABSTRACT FROM AUTHOR]

Object classification is a crucial task in deep learning, which involves the identification and categorization of objects in images or videos. Although humans can easily recognize common objects, such as cars, animals, or plants, performing this task on a large scale can be time-consuming and error-prone. Therefore, automating this process using neural networks can save time and effort while achieving higher accuracy. Our study focuses on the classification step of human chromosome karyotyping, an important medical procedure that helps diagnose genetic disorders. Traditionally, this task is performed manually by expert cytologists, which is a time-consuming process that requires specialized medical skills. Therefore, automating it through deep learning can be immensely useful. To accomplish this, we implemented and adapted existing preprocessing and data augmentation techniques to prepare the chromosome images for classification. We used ResNet-50 convolutional neural network and Swin Transformer, coupled with an ensemble approach to classify the chromosomes, obtaining state-of-the-art performance in the tested dataset. [ABSTRACT FROM AUTHOR]

Hyperspectral technology offers significant potential for non-invasive monitoring and prediction of morphological parameters in plants. In this study, UV−VIS−NIR−SWIR reflectance hyperspectral data were collected from Nicotiana tabacum L. plants using a spectroradiometer. These plants were grown under different light and gibberellic acid (GA3) concentrations. Through spectroscopy and multivariate analyses, key growth parameters, such as height, leaf area, energy yield, and biomass, were effectively evaluated based on the interaction of light with leaf structures. The shortwave infrared (SWIR) bands, specifically SWIR1 and SWIR2, showed the strongest correlations with these growth parameters. When classifying tobacco plants grown under different GA3 concentrations in greenhouses, artificial intelligence (AI) and machine learning (ML) algorithms were employed, achieving an average accuracy of over 99.1% using neural network (NN) and gradient boosting (GB) algorithms. Among the 34 tested vegetation indices, the photochemical reflectance index (PRI) demonstrated the strongest correlations with all evaluated plant phenotypes. Partial least squares regression (PLSR) models effectively predicted morphological attributes, with R2CV values ranging from 0.81 to 0.87 and RPDP values exceeding 2.09 for all parameters. Based on Pearson's coefficient XYZ interpolations and HVI algorithms, the NIR−SWIR band combination proved the most effective for predicting height and leaf area, while VIS−NIR was optimal for optimal energy yield, and VIS−VIS was best for predicting biomass. To further corroborate these findings, the SWIR bands for certain morphological characteristic wavelengths selected with s−PLS were most significant for SWIR1 and SWIR2, while i−PLS showed a more uniform distribution in VIS−NIR−SWIR bands. Therefore, SWIR hyperspectral bands provide valuable insights into developing alternative bands for remote sensing measurements to estimate plant morphological parameters. These findings underscore the potential of remote sensing technology for rapid, accurate, and non-invasive monitoring within stationary high-throughput phenotyping systems in greenhouses. These insights align with advancements in digital and precision technology, indicating a promising future for research and innovation in this field. [ABSTRACT FROM AUTHOR]