An overview of geological originated materials as a trend for adsorption in wastewater treatment

Adsorption is a unit operation widely used for the tertiary treatment of the most diverse effluents, whose mechanism is based on removing recalcitrant compounds from the organic and inorganic origin. In this process, choosing a suitable adsorbent is a fundamental point. This review article focuses on the adsorbents with natural geological origin: minerals, clays, geopolymers, and even wastes resulted from mining activity. Therefore, over 450 articles and research papers were explored. These materials' main sources are described, and their characteristics, composition, and intrinsic properties are related to adsorption. Herein, we discuss the effects of several process parameters, such as pH, temperature, pollutant, and adsorbent concentration. Furthermore, equilibrium, kinetics, and thermodynamic aspects are also addressed, and relevant regeneration prospects and final disposal. Finally, some suggestions and perspectives on applying these adsorbents in wastewater treatment are presented as future trends

Urban Termites of Recife, Northeast Brazil (Isoptera)

This study surveyed the termite fauna in urban properties of the city of Recife, state of Pernambuco, northeastern Brazil. Six-hundred properties were inspected in search for termites or signs of their activity, such as abandoned nests, galleries on walls, and fecal pellets. Seven termite species, belonging to seven genera and three families (Kalotermitidae, Rhinotermitidae, and Termtidae) were found causing some type of damage. Nasutitermes corniger and Cryptotermes dudleyi were the most frequent species within buildings (82.4% and 9.5 %, respectively). Such species, along with Coptotermes gestroi, were responsible for the greatest damage recorded. With the exception of Cryptotermes dudleyi and Coptotermes gestroi, all of the species are native and relatively common in northeastern Brazil’s Atlantic forest

Early antenatal prediction of gestational diabetes in obese women: development of prediction tools for targeted intervention

All obese women are categorised as being of equally high risk of gestational diabetes (GDM) whereas the majority do not develop the disorder. Lifestyle and pharmacological interventions in unselected obese pregnant women have been unsuccessful in preventing GDM. Our aim was to develop a prediction tool for early identification of obese women at high risk of GDM to facilitate targeted interventions in those most likely to benefit. Clinical and anthropometric data and non-fasting blood samples were obtained at 15+0–18+6 weeks’ gestation in 1303 obese pregnant women from UPBEAT, a randomised controlled trial of a behavioural intervention. Twenty one candidate biomarkers associated with insulin resistance, and a targeted nuclear magnetic resonance (NMR) metabolome were measured. Prediction models were constructed using stepwise logistic regression. Twenty six percent of women (n = 337) developed GDM (International Association of Diabetes and Pregnancy Study Groups criteria). A model based on clinical and anthropometric variables (age, previous GDM, family history of type 2 diabetes, systolic blood pressure, sum of skinfold thicknesses, waist:height and neck:thigh ratios) provided an area under the curve of 0.71 (95%CI 0.68–0.74). This increased to 0.77 (95%CI 0.73–0.80) with addition of candidate biomarkers (random glucose, haemoglobin A1c (HbA1c), fructosamine, adiponectin, sex hormone binding globulin, triglycerides), but was not improved by addition of NMR metabolites (0.77; 95%CI 0.74–0.81). Clinically translatable models for GDM prediction including readily measurable variables e.g. mid-arm circumference, age, systolic blood pressure, HbA1c and adiponectin are described. Using a ≥35% risk threshold, all models identified a group of high risk obese women of whom approximately 50% (positive predictive value) later developed GDM, with a negative predictive value of 80%. Tools for early pregnancy identification of obese women at risk of GDM are described which could enable targeted interventions for GDM prevention in women who will benefit the most

A multi-camera and multimodal dataset for posture and gait analysis

Monitoring gait and posture while using assisting robotic devices is relevant to attain effective assistance and assess the user’s progression throughout time. This work presents a multi-camera, multimodal, and detailed dataset involving 14 healthy participants walking with a wheeled robotic walker equipped with a pair of affordable cameras. Depth data were acquired at 30 fps and synchronized with inertial data from Xsens MTw Awinda sensors and kinematic data from the segments of the Xsens biomechanical model, acquired at 60 Hz. Participants walked with the robotic walker at 3 different gait speeds, across 3 different walking scenarios/paths at 3 different locations. In total, this dataset provides approximately 92 minutes of total recording time, which corresponds to nearly 166.000 samples of synchronized data. This dataset may contribute to the scientific research by allowing the development and evaluation of: (i) vision-based pose estimation algorithms, exploring classic or deep learning approaches; (ii) human detection and tracking algorithms; (iii) movement forecasting; and (iv) biomechanical analysis of gait/posture when using a rehabilitation device.This work has been supported by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under Grant 2020.05708.BD and under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020

Metabolic phenotyping by treatment modality in obese women with gestational diabetes suggests diverse pathophysiology: An exploratory study

Background and purpose: Excess insulin resistance is considered the predominant pathophysiological mechanism in obese women who develop gestational diabetes (GDM). We hypothesised that obese women requiring differing treatment modalities for GDM may have diverse underlying metabolic pathways. Methods: In this secondary analysis of the UK pregnancies Better Eating and Activity Trial (UPBEAT) we studied women from the control arm with complete biochemical data at three gestational time points; at 15–18+6 and 27–28+6 weeks (before treatment), and 34–36+0 weeks (after treatment). A total of 89 analytes were measured (plasma/serum) using a targeted nuclear magnetic resonance (NMR) platform and conventional assays. We used linear regression with appropriate adjustment to model metabolite concentration, stratified by treatment group. Main findings: 300 women (median BMI 35kg/m2; inter quartile range 32.8–38.2) were studied. 71 developed GDM; 28 received dietary treatment only, 20 metformin, and 23 received insulin. Prior to the initiation of treatment, multiple metabolites differed (p<0.05) between the diet and insulin-treated groups, especially very large density lipoprotein (VLDL) and high density lipoprotein (HDL) subclasses and constituents, with some differences maintained at 34–36 weeks’ gestation despite treatment. Gestational lipid profiles of the diet treatment group were indicative of a lower insulin resistance profile, when compared to both insulin-treated women and those without GDM. At 28 weeks’ the diet treatment group had lower plasma fasting glucose and insulin than women treated with insulin, yet similar to those without GDM, consistent with a glycaemic mechanism independent of insulin resistance. Conclusions/Interpretation: This exploratory study suggests that GDM pathophysiological processes may differ amongst obese women who require different treatment modalities to achieve glucose control and can be revealed using metabolic profiling

Chemical constituents and antifungal potential of Attalea geraensis Barb. Rodr. (Arecaceae) palm leaves, a species native to the Cerrado of Brazil

Abstract Fungal diseases, especially those that affect the root systems of plants, caused by Rhizoctonia and Macrophomina are limiting factors for achieving high crop yields. Alternatives to controlling fungi with chemical products drive the search for new options for bioactive compounds from plants. Attalea geraensis, a palm tree from the Brazilian Cerrado, is rich in flavonoids with antifungal actions. The objective of this work is to identify the chemical classes present in the ethanolic extract of green leaves of A. geraensis and determine the antifungal potential of the extract against isolates of Macrophomina phaseolina (Tassi) Goid. and Rhizoctonia solani JG Kühn. Phytochemical prospection, flavonoid dereplication, and antifungal activity were carried out of the ethanolic extract of the green leaves of A. geraensis harvested in the Cerrado area of Brazil. Steroids, triterpenes, saponins, and anthraquinones are described here for the first time for the leaves of A. geraensis. The flavonoids quercetin, isorhamnetin, 3,7-dimethylquercetin, quercetin 3-galactoside, 5,7-dihydroxy-2-(4-hydroxy-3-methoxyphenyl)-3-{[3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxy}-4H-chromen-4-one, rhamnazin 3-galactoside, keioside, and rhamnazin 3-rutinoside were identified. Of these, only quercetin and isorhamnetin had already been identified in the leaves of A. geraensis. The results show a fungistatic potential for the species. The diversity of flavonoids present in the leaves of A. geraensis may be the result of a synergistic action between fungus and plant or there could be an antagonistic effect between flavonoids and the other identified chemical classes

Antenatal detection of large-for-gestational-age fetuses following implementation of the Growth Assessment Protocol: secondary analysis of a randomised control trial

OBJECTIVE: To determine whether the Growth Assessment Protocol (GAP) affects the antenatal detection of large for gestational age (LGA) or maternal and perinatal outcomes amongst LGA babies. DESIGN: Secondary analysis of a pragmatic open randomised cluster control trial comparing the GAP with standard care. SETTING: Eleven UK maternity units. POPULATION: Pregnant women and their LGA babies born at ≥36+0  weeks of gestation. METHODS: Clusters were randomly allocated to GAP implementation or standard care. Data were collected from electronic patient records. Trial arms were compared using summary statistics, with unadjusted and adjusted (two-stage cluster summary approach) differences. MAIN OUTCOME MEASURES: Rate of detection of LGA (estimated fetal weight on ultrasound scan above the 90th centile after 34+0  weeks of gestation, defined by either population or customised growth charts), maternal and perinatal outcomes (e.g. mode of birth, postpartum haemorrhage, severe perineal tears, birthweight and gestational age, neonatal unit admission, perinatal mortality, and neonatal morbidity and mortality). RESULTS: A total of 506 LGA babies were exposed to GAP and 618 babies received standard care. There were no significant differences in the rate of LGA detection (GAP 38.0% vs standard care 48.0%; adjusted effect size -4.9%; 95% CI -20.5, 10.7; p = 0.54), nor in any of the maternal or perinatal outcomes. CONCLUSIONS: The use of GAP did not change the rate of antenatal ultrasound detection of LGA when compared with standard care

Prediction of uncomplicated pregnancies in obese women:a prospective multicentre study

Background: All obese pregnant women are considered at equal high risk with respect to complications in pregnancy and birth, and are commonly managed through resource-intensive care pathways. However, the identification of maternal characteristics associated with normal pregnancy outcomes could assist in the management of these pregnancies. The present study aims to identify the factors associated with uncomplicated pregnancy and birth in obese women, and to assess their predictive performance. Methods: Data form obese women (BMI ≥ 30 kg/m2) with singleton pregnancies included in the UPBEAT trial were used in this analysis. Multivariable logistic regression was used to identify sociodemographic, clinical and biochemical factors at 15+0 to 18+6 weeks’ gestation associated with uncomplicated pregnancy and birth, defined as delivery of a term live-born infant without antenatal or labour complications. Predictive performance was assessed using area under the receiver operating characteristic curve (AUROC). Internal validation and calibration were also performed. Women were divided into fifths of risk and pregnancy outcomes were compared between groups. Sensitivity, specificity, and positive and negative predictive values were calculated using the upper fifth as the positive screening group. Results: Amongst 1409 participants (BMI 36.4, SD 4.8 kg/m2), the prevalence of uncomplicated pregnancy and birth was 36% (505/1409). Multiparity and increased plasma adiponectin, maternal age, systolic blood pressure and HbA1c were independently associated with uncomplicated pregnancy and birth. These factors achieved an AUROC of 0.72 (0.68–0.76) and the model was well calibrated. Prevalence of gestational diabetes, preeclampsia and other hypertensive disorders, preterm birth, and postpartum haemorrhage decreased whereas spontaneous vaginal delivery increased across the fifths of increasing predicted risk of uncomplicated pregnancy and birth. Sensitivity, specificity, and positive and negative predictive values were 38%, 89%, 63% and 74%, respectively. A simpler model including clinical factors only (no biomarkers) achieved an AUROC of 0.68 (0.65–0.71), with sensitivity, specificity, and positive and negative predictive values of 31%, 86%, 56% and 69%, respectively. Conclusion: Clinical factors and biomarkers can be used to help stratify pregnancy and delivery risk amongst obese pregnant women. Further studies are needed to explore alternative pathways of care for obese women demonstrating different risk profiles for uncomplicated pregnancy and birth