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Background: Despite considerable advancements in cancer immunotherapy, advanced melanoma still presents a substantial clinical challenge. In an effort to explore treatment options, we examined the immunotherapeutic potential of effector Vγ9Vδ2-T cells in vitro in a three-dimensional (3D) human organotypic melanoma-in-skin (Mel-RhS) model. Materials and methods: Vγ9Vδ2-T cells were introduced into Mel-RhS via intradermal injection and cultured within the tissue microenvironment for up to 3 days. Results: Vγ9Vδ2-T cells remained viable for up to 3 days and were in close proximity to or within tumor nests. Upon Mel-RhS dissociation, a fraction was shown to be decorated by melanoma-associated chondroitin sulfate proteoglycan (MCSP), demonstrating their ability to actively navigate the tumor microenvironment and trogocytose cancer cells. Investigation into the apparent trogocytosis revealed an enhanced activated state of MCSP-decorated Vγ9Vδ2-T cells, evidenced by increased expression levels of 4-1BB, NKp44, programmed cell death protein-1 (PD-1), and programmed death-ligand 1 (PD-L1), compared with their MCSP− counterpart. These findings suggest that Vγ9Vδ2-T cells, upon successfully contacting melanoma cells, actively recognize and acquire MCSP from these malignant cells. Evidence of actual tumor cell elimination, although not significant, was only obtained after preincubation of Mel-RhS with pamidronate, a phosphoantigen-inducing agent, indicating the need for additional T cell receptor-mediated signaling for Vγ9Vδ2-T cells to reach their full oncolytic potential. Conclusions: This study highlights the viability and persistence of Vγ9Vδ2-T cells within the 3D microenvironment, their migratory and antitumor functionality, and the suitability of the model for testing T cell-based therapies, contributing both to the understanding of Vγ9Vδ2-T cell biology and their application in cancer immunotherapy.

We present an artificial intelligence (AI)-enhanced monitoring framework designed to assist personnel in evaluating and maintaining animal welfare using a modular architecture. This framework integrates multiple deep learning models to automatically compute metrics relevant to assessing animal well-being. Using deep learning for AI-based vision adapted from industrial applications and human behavioral analysis, the framework includes modules for markerless animal identification and health status assessment (e.g., locomotion score and body condition score). Methods for behavioral analysis are also included to evaluate how nutritional and rearing conditions impact behaviors. These models are initially trained on public datasets and then fine-tuned on original data. We demonstrate the approach through two use cases: a health monitoring system for dairy cattle and a piglet behavior analysis system. The results indicate that scalable deep learning and edge computing solutions can support precision livestock farming by automating welfare assessments and enabling timely, data-driven interventions.

Microfibers are important to several areas of human lifestyle, and the knowledge about their physicochemical characteristics allows for proposing new technological applications. The in natura microfiber of Ochroma pyramidale fruit (IN sample) and its extracted pulp (PU sample) were evaluated by X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR) and Thermogravimetry and Differential Scanning Calorimetry (TG/dTG and DSC). Microfibers were composed mainly of (68 ± 1)% holocellulose, (35.8 ± 0.1)% cellulose, (32 ± 3)% lignin and (3.7 ± 0.3)% extractives. The XRD pattern of the PU sample revealed that the mercerization process resulted in the change of the cellulose crystal structure from Iα type (triclinic) to type II (monoclinic). The SEM technique showed that the IN sample presented regular cylindrical/hollow-shaped wire-like microfibers with diameters ranging from 5 µm to 25 µm. However, the mercerization process changed their natural morphology. A significant change in the FTIR spectra after the removal of hemicellulose and lignin components was observed: weak bands at 1739 cm−1 (C=O stretching of lignin and hemicellulose fractions), 1463 cm−1 (CH3 of lignin) and 1246 cm−1 (C-O of lignin) were still observed in the PU sample, indicating that the lignin was not completely removed due to the natural difficulty of isolating pure cellulose. The TG/dTG and DSC evaluation revealed a temperature increase of the second thermal event (starting at 235 °C) in the PU sample, which was assigned to the cellulose and residual hemicellulose degradation. Then, this work aimed to disseminate and characterize a microfiber with unusual characteristics still little explored by the scientific community, as well as its cellulosic pulp, providing information that may be useful in its application in different industries, enabling the positive development of new biocompatible, renewable and sustainable materials.

ABSTRACT Background The COVID-19 pandemic has brought substantial challenges for current practices in treating hereditary neuromuscular disorders (hNMDs). However, this infection has not been the only concern for these patients. Social distancing has compromised multidisciplinary assistance and physical activity, and has brought about several mental health issues. We presented a follow-up on 363 patients with hNMDs at a Brazilian tertiary center during the peak of the COVID-19 pandemic. Objective We aimed to show the frequency and severity of SARS-CoV-2 infection among hNMD patients and to demonstrate the effects of the pandemic on life habits, disease progression and multidisciplinary supportive care status. Methods Three hundred and sixty-three patients (58% male and 42% female) were followed for three months through three teleconsultations during the peak of the COVID-19 pandemic in Brazil. Results There were decreases in the numbers of patients who underwent physical, respiratory and speech therapies. For several patients, their appetite (33%) and sleep habits (25%) changed. Physical exercises and therapies were interrupted for most of the patients. They reported new onset/worsening of fatigue (17%), pain (17%), contractions (14%) and scoliosis (7%). Irritability and sleep, weight and appetite changes, and especially diminished appetite and weight loss, were more frequent in the group that reported disease worsening. There was a low COVID-19 contamination rate (0.8%), and all infected patients had a mild presentation. Conclusion The isolation by itself was protective from a COVID-19 infection perspective. However, this isolation might also trigger a complex scenario with life habit changes that are associated with an unfavorable course for the NMD.

Conjugated polymers are essential materials for the organic optoelectronic industry, serving a pivotal role in cutting‐edge technologies. In this study, we conducted an integrated characterization approach, including spectroscopic techniques coupled with X‐ray diffraction analysis to explore the structure–property relationship of poly(4‐methoxyaniline), commonly referred to as poly(p‐anisidine) or PPA, along with two distinct ceramic composites: PPA/α‐Al2O3 and PPA/Eu2O3. From powder X‐ray diffraction analysis, a triclinic unit cell in space group P1 is proposed, after the whole powder pattern decomposition (WPPD) refinement is employed for the semicrystalline regions of the polymeric phases. Fractal‐like structures are observed, following analysis of small‐angle X‐ray scattering (SAXS) data and scanning electron microscopy (SEM) from which we could infer the approximate sizes of the fractal clusters. Pure PPA displays a glass transition temperature (Tg) of approximately 80°C and an electrical conductivity slightly above 10−5 S/cm. In contrast, the composite materials do not exhibit a glass transition temperature but perform better in terms of crystallinity and thermal stability. PPA/Eu2O3 present conductivity enhancement exceeding tenfold, surpassing 10−4 S/cm. These findings provide the baseline for further explorations on the development of organic electronic devices and sensors. [ABSTRACT FROM AUTHOR]

We present an artificial intelligence (AI)-enhanced monitoring framework designed to assist personnel in evaluating and maintaining animal welfare using a modular architecture. This framework integrates multiple deep learning models to automatically compute metrics relevant to assessing animal well-being. Using deep learning for AI-based vision adapted from industrial applications and human behavioral analysis, the framework includes modules for markerless animal identification and health status assessment (e.g., locomotion score and body condition score). Methods for behavioral analysis are also included to evaluate how nutritional and rearing conditions impact behaviors. These models are initially trained on public datasets and then fine-tuned on original data. We demonstrate the approach through two use cases: a health monitoring system for dairy cattle and a piglet behavior analysis system. The results indicate that scalable deep learning and edge computing solutions can support precision livestock farming by automating welfare assessments and enabling timely, data-driven interventions. [ABSTRACT FROM AUTHOR]

We present a numerical approach to compute bid and ask prices of European options within the conic finance paradigm. In particular, we show how discretizing option bid and ask pricing formulae, where the latter are expressed in terms of Choquet integrals, allows to perform efficient pricing and calibration by means of the COS method and the fast Fourier transform technique. Additionally, the approach introduced provides alternative expressions for risk-neutral European option prices by leveraging the relationship between Choquet and Riemann-Stieltjes integrals. These alternative formulations, requiring only the discretization of the cumulative distribution function of the (transformed) terminal value of the underlying asset, enhance computational efficiency under risk-neutral settings. We provide practical illustrations of the methodology when the underlying asset process follows Heston or Variance Gamma dynamics for both pricing and implied liquidity calculations. [ABSTRACT FROM AUTHOR]

The growing interest in sustainable biopolymers has highlighted cocoyam starch as a promising material for functional biodegradable packaging film development. This study evaluates its physicochemical, structural, thermal, rheological, and mechanical properties to determine its suitability for packaging. Cocoyam starch contains moisture (9.15%), protein (0.68%), lipid (0.57%), ash (2.82%), and resistant starch (5.61%), offering moderate digestibility. Total soluble phenolic compounds are quantified at 50.56 mg GAE·100 g−1 of starch, which influences the physicochemical properties. Cocoyam starch presented a semi‐crystalline structure comprising both Type A and B starches, along with monohydrated calcium oxalate crystals. The gelatinization temperature is determined to be 65.81 °C, with an associated gelatinization enthalpy of 15.38 J g−1. Gel retrogradation exhibits a rate of 38.52%. Rheological properties reveal a high peak viscosity and breakdown, suggestive of a high amylopectin content in the starch. Cocoyam starch films demonstrate variations in thickness and neutral instrumental color parameters. Mechanical analysis reveals that these films have lower tensile strength but higher elongation to rupture compared to cassava starch films, likely due to the intermolecular connections formed by cocoyam starch. Crystallography patterns of films indicate structural transformations, including calcium oxalate phases and semi‐crystalline structures, underscoring their potential applications in biodegradable packaging materials. [ABSTRACT FROM AUTHOR]

El presente estudio pretende arrojar luces sobre las etapas de la construcción de un espacio del Real Alcázar de Madrid que ha permanecido en las sombras hasta el día de hoy: el Cuarto bajo de Verano de la reina y sus bóvedas.

In humans, the phosphorylated pathway (PP) converts the glycolytic intermediate D-3-phosphoglycerate (3-PG) into L-serine through the enzymes 3-phosphoglycerate dehydrogenase, phosphoserine aminotransferase (PSAT) and phosphoserine phosphatase. From the pathogenic point of view, the PP in the brain is particularly relevant, as genetic defects of any of the three enzymes are associated with a group of neurometabolic disorders known as serine deficiency disorders (SDDs). We recombinantly expressed and characterized eight variants of PSAT associated with SDDs and two non-SDD associated variants. We show that the pathogenetic mechanisms in SDDs are extremely diverse, including low affinity of the cofactor pyridoxal 5′-phosphate and thermal instability for S179L and G79W PSAT, loss of activity of the holo form for R342W PSAT, aggregation for D100A PSAT, increased Km for one of the substrates with invariant kcats for S43R PSAT, and a combination of increased Km and decreased kcat for C245R PSAT. Finally, we show that the flux through the in vitro reconstructed PP at physiological concentrations of substrates and enzymes is extremely sensitive to alterations of the functional properties of PSAT variants, confirming PSAT dysfunctions as a cause of SSDs.

In the article at hand neural networks are used to model liquidity in financial markets, under conic finance settings, in two different contexts. That is, on the one hand this paper illustrates how the use of neural networks within a two-price economy allows to obtain accurate pricing and Greeks of financial derivatives, enhancing computational performances compared to classical approaches such as (conic) Monte Carlo. The methodology proposed for this purpose is agnostic of the underlying valuation model, and it easily adapts to all models suitable for pricing in conic financial markets. On the other hand, this article also investigates the possibility of valuing contingent claims under conic assumptions, using local stochastic volatility models, where the local volatility is approximated by means of a (combination of) neural network(s). Moreover, we also show how it is possible to generate hybrid families of distortion functions to better fit the implied liquidity of the market, as well as we introduce a conic version of the SABR model, based on the Wang transform, that still allows for analytical bid and ask pricing formulae. [ABSTRACT FROM AUTHOR]