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The taxonomic treatment of the species of Melastomataceae occurring in the Parque Estadual de Utinga, Pará, is presented. Twenty-seven species distributed in 13 genera were recorded, with five new records for the study area: Leandra micropetala, M. affinis, M. argyrophylla, M. punctata, and Pterolepis glomerata; the latter is also a new record for the state of Pará. Miconia was the largest genus with 9 species, followed by Clidemia (four species), Aciotis (three species), and Tococa (two species). The remaining genera were all represented by a single species (Adelobotrys, Bellucia, Henriettea, Leandra, Maieta, Mouriri, Nepsera, Pterolepis, and Tibouchina). Identification keys, morphological descriptions, information on phenology, habitat, geographical distribution, taxonomic comments, and illustrations of the species are provided. The Melastomataceae figure among the largest plant families in the area of the state capital, Belém, the data presented herein may be useful for habitat restoration.

A novel approach for the quantification of recycled polyethylene terephthalate (r-PET) in commercial bottles is presented. Fifty-eight bottle samples from several brands and producers containing different percentages of r-PET were purchased from the market. Samples were analyzed by two spectroscopic methods: near-infrared (NIR) spectroscopy and attenuated total reflection (ATR) spectroscopy in the mid-infrared (MIR) region. No chemical pre-treatment was applied before analyses. The spectra were analyzed by partial-least squares (PLS) regression, and two models for NIR and MIR data were computed. Then, a multi-block regression was applied to join the two datasets. All models were validated by cross-validation and by excluding and projecting onto the model the replicated spectra of one sample at a time. Results demonstrated the potential of this approach, especially considering the variability of commercial samples in terms of additives, shape, or thickness of the bottles: for samples close to the centroids of the models (i.e., from 10 to 50% r-PET), the predictions of multi-block method seldom departed from the expected values of ±10%. Only for samples with 0% declared r-PET, the models showed poor prediction abilities.

Pediatric traumatic brain injury (pTBI) is a major risk factor associated with adulthood incarceration. Most research into the link between pTBI and adulthood incarceration has focused on incarcerated males, who comprise the vast majority of incarcerated adults, particularly in industrialized nations. In this review, we sought to identify sex-related differences in the incidence and pathophysiology of pTBI and subsequent risk of adulthood incarceration. A scoping review was undertaken using PubMed, Scopus, Ovid, and the Cochrane Library. Articles analyzing sex-related differences in pTBI and adult incarceration rates, studies conducted on an incarcerated population, and cohort studies, cross-sectional studies, clinical trials, systematic reviews, or meta-analyses were included in this review. Of the 85 unique results, 25 articles met our inclusion criteria. Male children are 1.5 times more likely to suffer a TBI than females; however, the prevalence of incarcerated adults with a history of pTBI is ?35?45% for both sexes. Neurophysiologically, female sex hormones are implicated in neuroprotective roles, mitigating central nervous system (CNS) damage post-TBI, although this role may be more complex, given that injury severity and sequelae have been correlated with male sex whereas increased mortality has been correlated with female sex. Further investigation into the relationship between estrogen and subsequent clinical measurements of CNS function is needed to develop interventions that may alleviate the pathophysiological consequences of pTBI.

Purpose: This study explored the impact of in-person oral presentations on stuttering as a means of improving attitudes toward stuttering among preservice teachers. The educational intervention was tested under three conditions, which varied according to who presented the information. The research question sought to determine if differences in group change scores on the Public Opinion Survey Human Attributes--Stuttering (POSHA-S) occurred after training. Method: University classes containing 48 preservice teachers were randomly assigned to either one of two experimental groups or to a control group. The two experimental groups received a presentation on stuttering given by a speech-language pathologist (SLP), with or without a child who stutters. The control group received no training. The change in the Overall Stuttering Score of the POSHA-S was compared between the groups. Conclusions: The results indicated that the SLP-only group had a statistically significant improvement on the POSHA-S compared with the control group. Education significantly improved the attitudes of both experimental groups. This research supports the potential benefit of training future educators on stuttering using in-person oral presentations. Clinical and research implications of this study are discussed.

This study computationally evaluates the molecular docking affinity of various perfluoroalkyl and polyfluoroalkyl substances (PFAs) towards blood proteins using a generative machine-learning algorithm, DiffDock, specialized in protein–ligand blind-docking learning and prediction. Concerns about the chemical pathways and accumulation of PFAs in the environment and eventually in the human body has been rising due to empirical findings that levels of PFAs in human blood has been rising. DiffDock may offer a fast approach in determining the fate and potential molecular pathways of PFAs in human body.

Quality control in pharmaceutical manufacturing necessitates rigorous testing and approval, adhering to Current Good Manufacturing Practices before commercialization. The production of solid drugs presents significant industrial challenges regarding uniformity, homogeneity, and consistency. Traditional quality guidelines rely on classical analytical methods such as liquid chromatography coupled with mass spectrometry. However, the emergence of Process Analytical Technology introduced non-destructive, rapid, and cost-effective methods like UV-Visible Diffuse Reflectance Spectroscopy. The present study aimed to develop a chemometric method for quantifying Active Pharmaceutical Ingredients (APIs) in Neo Nisidine®, a solid mixture drug, using spectrophotometric data. The Net Analyte Signal (NAS) method, combined with standard additions, allowed the creation of a pseudo-univariate standard addition model, overcoming some challenges in solid-phase analysis. Successful quantifications of APIs in ideal laboratory samples and real pharmaceutical tablets were obtained. NAS-based chemometric models showed high precision and reliability, whose results were validated by comparisons with HPLC ones. The study revealed that solid-phase spectrophotometric analyses can be considered a valid alternative to API analyses. Solid-phase analysis offers non-destructive, cost-effective, and environmentally friendly benefits, enabling its integration into pharmaceutical production to improve quality control.

Wastewater (WW) analysis is a critical step in various operations, such as the control of a WW treatment facility, and speeding up the analysis of WW quality can significantly improve such operations. This work demonstrates the capability of neural network (NN) regression models to estimate WW characteristic properties such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammonia (NH3-N), total dissolved substances (TDS), total alkalinity (TA), and total hardness (TH) by training on WW spectral reflectance in the visible to near-infrared spectrum (400–2000 nm). The dataset contains samples of spectral reflectance intensity, which were the inputs, and the WW parameter levels (BOD, COD, NH3-N, TDS, TA, and TH), which were the outputs. Various NN model configurations were evaluated in terms of regression model fitness. The mean-absolute-error (MAE) was used as the metric for training and testing the NN models, and the coefficient of determination (R2) between the model predictions and true values was also computed to measure how well the NN models predict the true values. The highest R2 (0.994 for training set and 0.973 for testing set) and lowest MAE (0.573 mg/L BOD, 6.258 mg/L COD, 0.369 mg/L NH3-N, 6.98 mg/L TDS, 2.586 m/L TA, and 0.014 mmol/L TH) were achieved when NN models were configured for single-variable output compared to multiple-variables output. Hyperparameter grid-search and k-fold cross-validation improved the NN model prediction performance. With online spectral measurements, the trained neural network model can provide non-contact and real-time estimation of WW quality at minimum estimation error.

Biomass is an excellent sustainable carbon neutral energy source, however its use as a coal/petroleum coke substitute in thermal applications poses several challenges. Several inherent properties of biomass including higher heating value (HHV), bulk density, and its hydrophilic and fibrous nature, all contribute to challenges for it to be used as a solid fuel. Torrefaction or mild pyrolysis is a well-accepted thermal pretreatment technology that solves most of the above-mentioned challenges and results in a product with superior coal-like properties. Torrefaction involves the heating of biomass to moderate temperatures typically between 200 °C and 300 °C in a non-oxidizing atmosphere. This study focused on evaluating the influence of torrefaction operating temperature (204–304 °C) and residence time (10–40 min) on properties of pine. Tests were performed on a continuous 0.3 ton/day indirectly heated rotary reactor. The influence of torrefaction operational conditions on pine was evaluated in terms of the composition of torrefied solids, mass yield, energy yield, and HHV using a simulated model developed in Aspen Plus™ software. A kinetic model was established based on the experimental data generated. An increase in torrefaction severity (increasing temperature and residence time) resulted in an increase in carbon content, accompanied with a decrease in oxygen and hydrogen. Results from the simulated model suggest that the solid and energy yields decreased with an increase in temperature and residence time. Solid yield varied from 80% at 204 °C to 68% at 304 °C, and energy yield varied from 99% at 204 °C to 70% at 304 °C, respectively. On the other hand, HHV improved from 22.8 to 25.1 MJ/kg with an increase in temperature at 20 min residence time. Over the range of 10 to 40 min residence time at 260 °C, solid and energy yields varied from 77% to 59% and 79% to 63%, respectively; however the HHV increased by only 3%. Solid yield, energy yield, and HHV simulated data were within the 5% error margin when compared to the experimental data. Validation of the simulation parameters was achieved by the conformance of the experimental and simulation data obtained under the same testing conditions. These simulated parameters can be utilized to study other operating conditions fundamental for the commercialization of these processes. Desirable torrefaction temperature to achieve the highest solid fuel yield can be determined using the energy yield and mass loss data.

Summary: Background: Both dabrafenib/trametinib (D/T) and anti-PD-1 monotherapy (PD-1) are approved adjuvant therapies for patients with stage III BRAF V600-mutant melanoma. However, there is still a lack of head-to-head comparative data. We aimed to describe efficacy and toxicity outcomes for these two standard therapies across melanoma centers. Methods: This multicenter, retrospective cohort study was conducted in 15 melanoma centers in Australia, China, Germany, Italy, Japan, UK, and US. We included adult patients with resected stage III BRAF V600-mutant melanoma who received either adjuvant D/T or PD-1 between Jul 2015 and Oct 2022. The primary endpoint was relapse-free survival (RFS). Secondary endpoints included overall survival (OS), recurrence pattern and toxicity. Findings: We included 598 patients with stage III BRAF V600-mutant melanoma who received either adjuvant D/T (n = 393 [66%]) or PD-1 (n = 205 [34%]) post definitive surgery between Jul 2015 and Oct 2022. At a median follow-up of 33 months (IQR 21–43), the median RFS was 51.0 months (95% CI 41.0-not reached [NR]) in the D/T group, significantly longer than PD-1 (44.8 months [95% CI 28.5-NR]) (univariate: HR 0.66, 95% CI 0.50–0.87, P = 0.003; multivariate: HR 0.58, 95% CI 0.39–0.86, P = 0.007), with comparable OS with PD-1 (multivariate, HR 0.90, 95% CI 0.48–1.70, P = 0.75). Similar findings were observed using a restricted-mean-survival-time model. Among those who experienced recurrence, the proportion of distant metastases was higher in the D/T cohort. D/T had a higher incidence of treatment modification due to adverse events (AEs) than PD-1, but fewer persistent AEs. Interpretation: In patients with stage III BRAF V600-mutant melanoma post definitive surgery, D/T yielded better RFS than PD-1, with higher transient but lower persistent toxicity, and comparable OS. D/T seems to provide a better outcome compared with PD-1, but a longer follow-up and ideally a large prospective trial are needed. Funding: Dr. Xue Bai was supported by the Beijing Hospitals Authority Youth Programme (QMS20211101) for her efforts devoted to this study. Dr. Keith T. Flaherty was funded by Adelson Medical Research Foundation for the efforts devoted to this study.

This work focused on demonstrating the capability of unsupervised machine learning techniques in detecting impending anomalies by extracting hidden trends in the datasets of fuel economy and emissions of light-duty vehicles (LDVs), which consist of cars and light-duty trucks. This case study used the vehicles’ fuel economy and emissions testing datasets for vehicle model years 2015 to 2023 with a total of 34,602 data samples on LDVs of major vehicle manufacturers. Three unsupervised techniques were used: principal components analysis (PCA), K-Means clustering, and self-organizing maps (SOM). Results show that there are clusters of data that exhibit trends not represented by the dataset as a whole. Fuel CO vs. Fuel Economy has a negative correlation in the whole dataset (r = −0.355 for LDVs model year 2022), but it has positive correlations in certain sample clusters (e.g., LDVs model year 2022: r = +0.62 in a K-Means cluster where the slope is around 0.347 g−CO/mi/MPG). A time series analysis of the results of clustering indicates that Test Procedure and Fuel Type, specifically Test Procedure 11 and Fuel Type 26 as defined by the US EPA, could be the contributors to the positive correlation of CO and Fuel Economy. This detected peculiar trend of CO-vs.-Fuel Economy is an impending anomaly, as the use of Fuel 26 in emissions testing with Test Procedure 11 of US-EPA has been increasing through the years. With the finding that the clustered data samples with positive CO-vs.-Fuel Economy correlation all came from vehicle manufacturers that independently conduct the standard testing procedures and not data from US-EPA testing centers, it was concluded that the chemistry of using Fuel 26 in performing Test Procedure 11 should be re-evaluated by US-EPA.