Extensive Sampling Research Articles

Risk prediction models for feeding intolerance in patients with enteral nutrition: a systematic review and meta-analysis

BackgroundAlthough more risk prediction models are available for feeding intolerance in enteral-nourishment patients, it is still unclear how well these models will work in clinical settings. Future research faces challenges in validating model accuracy across populations, enhancing interpretability for clinical use, and overcoming dataset limitations.ObjectiveTo thoroughly examine studies that have been published on feeding intolerance risk prediction models for enteral nutrition patients.DesignConducted a systematic review and meta-analysis of observational studies.MethodsA comprehensive search of the literature was conducted using a range of databases, including China National Knowledge Infrastructure (CNKI), Wanfang Database, China Science and Technology Journal Database (VIP), SinoMed, PubMed, Web of Science, The Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Embase. The search scope was confined to articles within the database from its inception until August 12th, 2024. The data from the selected studies should be extracted, including study design, subjects, duration of follow-up, data sources, outcome measures, sample size, handling of missing data, continuous variable handling methods, variable selection, final predictors, model development and performance, and form of model presentation. The applicability and bias risk were evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist.ResultsA total of 1,472 studies were retrieved. Following the selection criteria, 18 prediction models sourced from 14 studies were incorporated into this review. In the field of model construction, only one study employed the use of multiple machine-learning techniques for the development of a model. In contrast, the remaining studies used logistic regression to construct FI risk prediction models. The incidence of FI in enteral nutrition was 32.4–63.1%. The top five predictors included in the model were APACHE II, age, albumin levels, intra-abdominal pressure, and mechanical ventilation. The reported AUC, or area under the curve, exhibited a range of values between 0.70 and 0.921. All studies were identified as having a high risk of bias, primarily due to the use of inappropriate data sources and inadequate reporting within the analysis domain.ConclusionAlthough the included studies reported a certain degree of discriminatory power in their predictive models to identify feeding intolerance in patients undergoing enteral nutrition, the PROBAST assessment tool deemed all the included studies to carry a significant risk of bias. Future research should emphasize the development of innovative predictive models. These endeavors should incorporate more extensive and diverse sample sizes, adhere to stringent methodological designs, and undergo rigorous multicenter external validation to ensure robustness and generalizability.Systematic review registrationIdentifier CRD42024585099, https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=585099.

Molecular Data Confirm the Occurrence of the Allochthonous Gambusia holbrooki (Pisces: Poeciliidae) in Sicily and the Maltese Archipelago

A major threat to biodiversity is represented by Invasive Alien Species (IAS), particularly on freshwater ecosystems, which are already heavily altered by human activities. Two of the most pernicious IAS are the eastern and western mosquitofish, i.e., Gambusia holbrooki and G. affinis. These two poeciliids are morphologically very close to each other, and soon after their formal description, G. holbrooki was considered a subspecies of G. affinis. In the following years, several studies proved that these two entities belonged to two different species; nevertheless, it was only at the end of the 1990s that their separate taxonomic status was re-established. In the 1920s and 1930s, both G. holbrooki and G. affinis were asynchronously introduced from the United States into Europe and subsequently translocated globally as biocontrol agents of the malaria vector (i.e., the larvae of the Anopheles mosquitoes), with dramatic consequences for the inland water native fauna. However, due to taxonomic uncertainties and nomenclatural instability, for years, there were doubts about which Gambusia species had been introduced in different regions. The first available molecular studies confirmed the occurrence of G. holbrooki in Europe, but no evidence confirming the occurrence of G. affinis was found. Despite this, some records report the occurrence of western mosquitofish in Italy and Malta. Considering the negative effects that the mosquitofish has on the native biota, it is of paramount importance to know the precise biological diversity of the native and non-native species to better implement environmental management strategies to properly preserve the already-fragile waterbodies. Therefore, to check for the possible occurrence of G. affinis in Italy and Malta, we conducted extensive sampling in Sicily (Italy) and in the Maltese archipelago, aiming to verify the identity of Gambusia populations occurring in the study area. Based on sequences of the mitochondrial cytochrome b gene, we consistently observed the occurrence of only G. holbrooki in the investigated area, finding, almost exclusively, the most common haplotype known for the species in the whole invaded range (i.e., “HOL1”).

Preparation, Modification, Quantitation, and Dentin Biomodification Activity of Selectively Enriched Proanthocyanidins.

To date, quantitative analysis of proanthocyanidin (PAC) containing materials including plant extracts and fractions depends on colorimetric assays or phloroglucinolysis/thiolysis combined with UV-HPLC analysis. Such assays are of limited accuracy, particularly lack specificity, require extensive sample preparation and degradation, and need appropriate physical reference standards. To address this analytical challenge and toward our broader goal of developing new plant-sourced biomaterials that chemically and mechanically modulate the properties of dental tissue for clinical interventions, we have characterized 12 different PAC DESIGNER (Depletion and Enrichment of Select Ingredients Generating Normalized Extract Resources) materials. The DESIGNER approach is carried out by using either centrifugal partition chromatography (CPC) or size-exclusion chromatography (SEC) for the selective enrichment of trimeric and tetrameric PACs. Moreover, the rare but biologically interesting A-type PAC DESIGNERs can now be generated successfully from their natural AB-type PAC precursors via phenol-oxidative intramolecular coupling initiated by a mixture of the stable radicals, 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO). Furthermore, to ensure the quality and stability of PAC DESIGNER materials, we developed a quantitative analysis of the total PAC content of the DESIGNER materials in the form of a quantitative NMR (qNMR) method using a non-PAC internal calibrant combined with diol-HPLC. The total PAC content was, thus, determined to be in a range of 67.5-96.9% by qNMR. We highlight the complementarity of diol-HPLC and qNMR to accurately assess the amount of PACs across a range of concentrations and PAC stability in the DESIGNER materials. This quantitative methodology paves the way to generate standardized DESIGNER and other PAC-containing materials and to perform rigorous quality control for dental (pre)clinical studies of PACs.

Seasonal observations on the influence of groundwater discharge on meiofaunal assemblages in subterranean estuaries of southwest India.

The submarine groundwater discharge (SGD) into the sea is known to alter various biotic and abiotic properties of coastal waters. However, its influence on the lower trophic levels, namely, meiofauna, is poorly understood. This study highlights the impact of SGD on the density, distribution, and diversity of intertidal meiofaunal communities along the subterranean estuaries (STEs) of the southwest coast of India (Arabian Sea). As an outcome of extensive field sampling between 2019 and 2022, we found that groundwater discharge has a direct influence on the local meiofaunal communities. Significant decline in meiofaunal density and diversity in the SGD impacted sites with 2 to 4 times lower densities than the unimpacted sites were documented. Lower groundwater salinity, lesser organic carbon content, hypoxic sediment conditions, and lower mean grain size in STEs can be the major driving factors determining the lower meiofaunal densities. δ13C and δ15N isotopic signatures indicated that major sources of organic carbon in STEs of the Kerala coast are from C3 plants.

Toward Resolving Heterogeneous Mixtures of Nanocarriers in Drug Delivery Systems through Light Scattering and Machine Learning.

Nanocarriers (NCs) have emerged as a revolutionary approach in targeted drug delivery, promising to enhance drug efficacy and reduce toxicity through precise targeting and controlled release mechanisms. Despite their potential, the clinical adoption of NCs is hindered by challenges in their physicochemical characterization, essential for ensuring drug safety, efficacy, and quality control. Traditional characterization methods, such as dynamic light scattering and nanoparticle tracking analysis, offer limited insights, primarily focusing on particle size and concentration, while techniques like high-performance liquid chromatography and mass spectrometry are hampered by extensive sample preparation, high costs, and potential sample degradation. Addressing these limitations, this work presents a cost-effective methodology leveraging light scattering and optical forces, combined with machine learning algorithms, to characterize polydisperse nanoparticle mixtures, including lipid-based NCs. We prove that our approach provides quantification of the relative concentration of complex nanoparticle suspensions by detecting changes in refractive index and polydispersity without extensive sample preparation or destruction, offering a high-throughput solution for NC characterization in drug delivery systems. Experimental validation demonstrates the method's efficacy in characterizing commercially available synthetic nanoparticles and Doxoves, a liposomal formulation of Doxorubicin used in cancer treatment, marking a significant advancement toward reliable, noninvasive characterization techniques that can accelerate the clinical translation of nanocarrier-based therapeutics.

Community composition and distribution of epi- and suprabenthic macrofauna in the bathyal, abyssal, and hadal zones of the northern North Pacific

The deep sea, Earth’s largest biome, harbors numerous unknown species. Prior to the AleutBio (Aleutian Trench Biodiversity Studies) expedition from July to September 2022, the Northeast (NE) Pacific at abyssal and hadal depths was virtually unexplored. Our study presents new findings from the AleutBio project on the macrofaunal composition of the Bering Sea (BS) and Aleutian Trench (AT) collected by means of an epibenthic sledge (EBS), comparing these results with data from the Kuril-Kamchatka Trench (KKT) and the Northwest (NW) Pacific. Additionally, we examine variations in macrofaunal composition and abundance across different regions and depths. A biogeographic gap analysis using data from the Ocean Biodiversity Information System (OBIS) and the Global Biodiversity Information Facility (GBIF) found that, out of 170,627 occurrence records from the North Pacific and Bering Sea, only 153 were from depths below 3,500 m. The AleutBio project addressed this gap by significantly expanding the dataset with 36,499 new records collected during the expedition using an EBS. Nearly 98 % of the specimens were from five phyla: Arthropoda, Annelida, Mollusca, Echinodermata, and Nematoda, with Polychaeta, Copepoda, and Nematoda being the most abundant taxa. Although the number of individuals varied between stations, there was no significant decrease in abundance with increasing depth, and some hadal stations had similar numbers of invertebrates as abyssal stations. Regional differences were observed, with Polychaeta and Nematoda being dominant in the BS, and Copepoda more prevalent at western abyssal stations. Depth emerged as the key factor influencing macrofaunal distribution, with distinct patterns across bathyal, abyssal, and hadal depths. Comparisons with other NW Pacific regions, like the Sea of Japan and Sea of Okhotsk, show that depth and water body isolation play crucial roles in shaping faunal communities. AleutBio’s extensive sampling below 3,500 m has vastly increased available data, aiding in the understanding and conservation of deep-sea biodiversity. While certain taxa showed patchy distributions, no significant differences in faunal composition were found between geographic areas or depth zones. These findings underscore the dynamic nature of deep-sea ecosystems and highlight the importance of depth in shaping macrofaunal communities, emphasizing the need for continued research in these fascinating environments.

Repackable microfluidic molecularly imprinted solid-phase extraction coupled with mass spectrometry (μMISPE-MS) for rapid analysis of mycotoxin in agri-foods: an example of zearalenone.

Mycotoxins are detectable in 60-80% of food crops, posing significant threats to human health and food security, and causing substantial economic losses. Most mitigation approaches focus on detecting mycotoxins with standard methods based on liquid chromatography coupled with mass spectrometry (LC-MS). Typical MS methods require extensive sample preparation and clean-up due to the matrix effect, followed by time-consuming LC separation, complicating the analysis process and limiting analytical throughput. This study reports the development of a repackable microfluidic molecularly imprinted solid-phase extraction coupled with mass spectrometry (μMISPE-MS) method for rapid detection of zearalenone in agri-food samples. Silica microspheres coated with molecularly imprinted polymers were synthesized as the sorbent for analyte enrichment and sample clean-up. A cost-effective microfluidic chip was designed and fabricated as the μMISPE platform with fully automated operation, including on-line microcolumn packing and unpacking. With optimized solvent conditions and on-chip μMISPE protocol, the entire analytical process from sample to answer was completed within 15 min and achieved high recoveries (71-94%) for corn and rice samples at residue levels of 0.05-0.5 ppm (within Canadian regulatory limits of 0.2-10 ppm). This μMISPE-MS method provides a promising tool for improving mycotoxin monitoring in agri-food systems and is generalizable to other rapid analyses of targeted chemicals in complex matrices.

Rapid determination of chemical losses in a microplate bioassay using fluorescence spectroscopy.

The increase in production and innovation of chemicals that humans interface with has enhanced the need for rapid toxicity testing of new and existing chemicals. This need, along with efforts to reduce animal testing, has led to the development of high-throughput bioassays typically conducted in microplates. These bioassays offer time and resource advantages over traditional animal models; however, significant chemical losses can occur in microplates. Current methods for measuring chemical losses in microplates require extensive sample preparation and highly sensitive instruments. We propose the use of fluorescence spectroscopy to measure chemical losses in high-throughput bioassays as a low resource alternative to the existing methods. A fluorescent plate reader was used to develop methods for quantifying the aqueous concentrations of two chemicals, 2-hydroxynaphthalene and acridine, in microwells of a 96-well microplate. A high-throughput, 5 day embryonic zebrafish bioassay was used as the model bioassay for method development. Chemical losses were attributed to a combination of photodegradation, sorption, and uptake by the zebrafish embryos, kinetics of which were derived from a pseudo-first order model. Chemical uptake amount was calculated to be approximately 50% and 21% of the total chemical amount added for 2-hydroxynaphthalene and acridine, respectively. Unexpected cranial deformities were observed in the embryonic zebrafish, suggesting further investigation of potential additive toxicity of the ultraviolet radiation exposure from fluorescence measurements and chemical exposure is needed. Nonetheless, this novel method provides a rapid, low resource approach to measuring chemical losses in microplates that can be extended to a variety of autofluorescent chemicals and microplate-based bioassays.

Assessing hydrogeochemical facies and Groundwater Quality Index in rapidly urbanizing coastal region: a GIS-based approach with machine learning for enhanced management.

Groundwater is an essential freshwater source worldwide, but increasing pollution poses risks to its sustainability. This study applied a comprehensive approach to assess hydrogeochemical facies and groundwater quality in Odisha's large low-lying coastal regions. Analysis of 136 samples revealed that sodium (9.4%), potassium (40.8%), bicarbonate (2.1%), and chloride (2.1%) exceeded WHO limits. The Groundwater Quality Index (GQI) map classified 5.1% of samples as "excellent," 39.4% as "good," 31.3% as "poor," 13.8% as "very poor," and 10.2% as "unsuitable" for use. Additionally, the GQI values demonstrate a random spatial autocorrelation (- 0.06) likely due to diverse influences. The study identified the expansion of agricultural (43%) and built-up areas (13%) from the Land Use/Land Cover (LULC) map. Piper diagram and Gibbs plots suggest continued freshening, rock-water interaction, and seawater intrusion. Groundwater levels fall between 0 to 2m below ground level (mbgl), primarily due to excessive groundwater extraction. The Sodium (Na+) vs. Chloride (Cl-) cross plot shows most samples align with the mixing line, with some deviations indicating multiple contamination sources. The strong correlation (> 0.90) between total dissolved salts (TDS), electrical conductivity (EC), Na+, and Cl- signals seawater intrusion, highlighting the complex interaction between human activities and natural processes. The proposed machine learning (ML) models like random forest (RF), artificial neural network (ANN), decision tree, and linear regression (LR) offer a reliable alternative to traditional GQI methods, addressing the challenges of extensive sampling and data management. Among these, RF exhibited the highest predictive accuracy (coefficient of correlation (R2) = 95%), surpassing ANN (R2 = 82%), decision tree (R2 = 81%), and LR (R2 = 67%) as the most effective model for GQI prediction. Potassium (K+) stands out as a key indicator of contamination. GQI, LULC map, and ML methods improve understanding of contamination sources and support systematic groundwater management.

The Women Empowerment and Innovation Nexus: A Global Context

The aim of this study is to explain the influence of women's empowerment on innovation. To achieve this goal, we analyzed a global dataset from 1996 to 2020, covering 68 developed and developing countries and regions, which represent the most extensive sample available for the full core specification. In order to address endogeneity concerns, we apply the system GMM and Lewbel 2SLS estimators. Our results indicate a positive and statistically significant impact of women's economic empowerment on innovation. This effect is robust to alternative measures of innovation and women empowerment; the subcomponents of women empowerment; and alternative estimators that address endogeneity.

Metabolic syndrome prediction based on body composition indices

BackgroundMetabolic syndrome (MetS) is an important public health issue that has been lately linked as a growing concern worldwide.The objectiveTo find out which anthropometric and body composition indices can prognosticate MetS in Jordanian adult females.MethodsA sample of 656 Jordanian adult females was recruited (January–March 2024) in the middle of Jordan. Weight, height, waist and hip circumference, lipid profile (triglycerides and high-density lipoprotein), fasting plasma glucose, and blood pressure were measured. Fat mass index (FMI), body mass index (BMI), fat-to-muscle ratio, and waist-to-hip ratio (WHR) were calculated. The presence or absence of MetS was the outcome of interest. Receiver operating characteristic (ROC) analyses were used to examine the predictive accuracy of the indices, and the area under the curve (AUC) was measured.Results40.6% had MetS, and their mean age was 45.5 years. 90.2% of the participants with MetS were obese based on body fat percentage. The MetS participants had significantly higher means of all the anthropometric indices except the fat-to-muscle ratio. None of the MetS participants were underweight, and 70.8% and 73.8% were obese based on BMI and WHR, respectively (p < 0.001). The highest proportion of the MetS participants (35.5%) was within the Q4 of the FMI compared to those without MetS (p<0.001). The discrimination ability for all indices was almost equal in predicting the existence of MetS (fair prediction power; AUC = 0.66–0.72), except for the fat-to-muscle ratio, which had poor prediction power.ConclusionFat mass %, muscle mass %, FMI, BMI, and WHR could be used as predictors of MetS in Jordanian females, while the fat-to-muscle ratio was not. We suggested that more extensive sample size studies from both genders and different age categories are necessary to develop a superior predictor for MetS in Jordan.

Subclinical Mastitis in Lacaune Sheep: Etiologic Agents, the Effect on Milk Characteristics, and an Evaluation of Infrared Thermography and the YOLO Algorithm as a Preprocessing Tool for Advanced Analysis.

This study aimed to investigate the incidence of subclinical mastitis (SCM), the implicated pathogens, and their impact on milk quality in dairy sheep in Greece. Furthermore, we preliminarily evaluated infrared thermography and the application of AI tools for the early, non-invasive diagnosis of relevant cases. In total, 660 milk samples and over 2000 infrared thermography images were obtained from 330 phenotypically healthy ewes. Microbiological investigations, a somatic cell count (SCC), and milk chemical analyses were performed. Infrared images were analyzed using the FLIR Research Studio software (version 3.0.1). The You Only Look Once version 8 (YOLOv8) algorithm was employed for the automatic detection of the udder's region of interest. A total of 157 mammary glands with SCM were identified in 122/330 ewes (37.0%). The most prevalent pathogen was staphylococci (136/160, 86.6%). Considerable resistance was detected to tetracycline (29.7%), ampicillin (28.6%), and sulfamethoxazole-trimethoprim (23.6%). SCM correlated with high total mesophilic count (TMC) values and decreased milk fat, lactose, and protein content. A statistically significant variation (p < 0.001) was identified in the unilateral SCM cases by evaluating the mean temperatures of the udder region between the teats in the thermal images. Finally, the YOLOv8 algorithm was employed for the automatic detection of the udder's region of interest (ROI), achieving 84% accuracy in defining the ROI in this preliminary evaluation. This demonstrates the potential of infrared thermography combined with AI tools for the diagnosis of ovine SCM. Nonetheless, more extensive sampling is essential to optimize this diagnostic approach.

Absolute and Relative Binding Free Energy Calculations of Nucleotides to Multiple Protein Classes.

Polyphosphate nucleotides, such as ATP, ADP, GTP, and GDP, play a crucial role in modulating protein functions through binding and/or catalytically activating proteins (enzymes). However, accurately calculating the binding free energies for these charged and flexible ligands poses challenges due to slow conformational relaxation and the limitations of force fields. In this study, we examine the accuracy and reliability of alchemical free energy simulations with fixed-charge force fields for the binding of four nucleotides to nine proteins of various classes, including kinases, ATPases, and GTPases. Our results indicate that the alchemical simulations effectively reproduce experimental binding free energies for all proteins that do not undergo significant conformational changes between their triphosphate nucleotide-bound and diphosphate nucleotide-bound states, with 87.5% (7 out of 8) of the absolute binding free energy results for 4 proteins within ±2 kcal/mol of experimental values and 88.9% (8 out of 9) of the relative binding free energy results for 9 proteins within ±3 kcal/mol of experimental values. However, our calculations show significant inaccuracies when divalent ions are included, suggesting that nonpolarizable force fields may not accurately capture interactions involving these ions. Additionally, the presence of highly charged and flexible ligands necessitates extensive conformational sampling to account for the long relaxation times associated with long-range electrostatic interactions. The simulation strategy presented here, along with its demonstrated accuracy across multiple protein classes, will be valuable for predicting the binding of nucleotides or their analogs to protein targets.

Macroalgae of the Campeche Bank, Gulf of Mexico.

The coastal habitats in the southern Gulf of Mexico face multiple threats, such as rising water temperatures, acidification, increased turbidity, invasive species and pollutants. This imperils the biodiversity of beaches, wetlands and coral reefs. To address this, there is a need for comprehensive baseline information on marine biodiversity. Several reefs in the Gulf of Mexico have been extensively studied, yet smaller reefs on the Yucatan continental shelf lack thorough exploration despite their ecological significance. These reefs serve as crucial biodiversity hotspots influenced by environmental characteristics, receiving diverse taxa from the Gulf of Mexico.The macroalgae study at the Bank of Campeche dates back to the 1950s, but comprehensive investigations have been sporadic. The recent study aims to fill this gap, contributing to the taxonomic inventory of the area's benthic macroflora. Methodologically, extensive sampling across eight reefs was conducted, collecting and preserving macroalgae samples for identification in the laboratory.The study documented 90 infrageneric taxa across the reefs, with Cayo Arenas exhibiting the highest species count. Additionally, three new distribution reports for Mexico were identified in the region. The distribution of species varied amongst locations, with few species in common even amongst geographically proximate reefs. The diversity found in these reefs slightly trails behind other studied regions, but surpasses previous reports for the Campeche Bank.It is important to emphasise that the significance of this study lies in its focus on remote reefs with complicated and costly access logistics. Additionally, it is one of the first publicly available datasets published for this region.The study aligns with existing literature on prevalent families in reef environments and highlights differences in species distribution, based on depth variations amongst reefs. The findings of new distribution records in the region and the distinctiveness of localities despite their proximity underscore the unique ecological dynamics of these reefs. These reefs are located in remote and difficult-to-access areas, highlighting the importance of the data obtained on their biodiversity and conservation status. This work presents for the first time a list of macroalgae for the reefs of Banco Nuevo, Banco Obispo, Banco Pera and Serpientes Reef. Three new distribution reports for Yucatan were identified at the Banco Obispo reef: Botryocladiashanksii E. Y. Dawson, Ceratodictyonscoparium (Montagne & Millardet) R. E. Norris and Asteromeniapeltata (W. R. Taylor) Huisman & Millar and a new report for the Mexican Atlantic, Herposiphoniaparca Setchell at the Triángulo Oeste reef. This results in distinct algal communities compared to other reefs in the region, emphasising their ecological significance and the need for continued research and conservation efforts. To our knowledge, this is one of the first interoperable datasets being published on the marine algae of the southern Gulf of Mexico reef ecosystems.

INFLUENCE OF IHSĀN (BENEVOLENCE) ON SUSTAINING INDUSTRIAL RELATIONS PRACTICES AT ISLAMIC UNIVERSITIES IN SOUTHEAST ASIA

Ihsān (benevolence) is frequently overlooked in industrial relations. Conventional methods of industrial relations emphasise compliance with legal and organisational mandates via a tripartite framework. Conversely, ihsān underscores the cultivation of a peaceful and ethical connection between employers and employees. This study explores the influence of ihsān on maintaining industrial connections among employees of Islamic universities in Southeast Asia. The study employed focused group discussions (FGDs) with five faculty members from Islamic universities. Participants contended that ihsān, manifested as trust, mutual cooperation, and a spirit of generosity, possesses superior worth. The participants recounted experiences from Islamic universities that have implemented ihsān for nearly a century, illustrating the viability of these institutions in fostering a harmonious and humane work environment. Participants also advocated for an ihsān-driven industrial relations. Thus, this study contributes in terms of ihsān in sustaining harmonious industrial relations practices. However, the study is confined to a limited number of Islamic universities in Southeast Asia. Future research should encompass a more extensive sample of Islamic universities around the region to yield comprehensive findings.

Sampling-free investigation of microbial carbon source preferences on renewable feedstocks via online monitoring of oxygen transfer rate.

The transition towards sustainable bioprocesses requires renewable feedstocks to reduce dependency on finite resources. While plant-based feedstocks offer significant potential, their complex composition poses new challenges. The microorganisms often exhibit polyauxic growth when presented with multiple carbon sources simultaneously, consuming them in a distinct order according to their carbon source preferences. The traditional investigation of polyauxic growth involves laborious sampling and offline analysis, hindering high-throughput screenings. This study introduces an efficient method for identifying carbon source consumption and their order of metabolization by various microorganisms using the respiration activity monitoring system (RAMOS) in shake flasks. As aerobic carbon metabolization and oxygen consumption are strictly correlated, the characteristic phases of polyauxic growth are visible in the oxygen transfer rate (OTR) and can be assigned to the respective carbon sources. An extended 16-flask RAMOS enables real-time monitoring of microbial respiration on up to seven carbon sources and one reference cultivation simultaneously, thus providing crucial insights into their metabolization without extensive sampling and offline analysis. The method's accuracy was validated against traditional high-performance liquid chromatography (HPLC). Its applicability to both fast-growing Escherichia coli (investigated carbon sources: glucose, arabinose, sorbitol, xylose, and glycerol) and slow-growing Ustilago trichophora (glucose, glycerol, xylose, sorbitol, rhamnose, galacturonic acid, and lactic acid) was demonstrated. Additionally, it was successfully applied to the plant-based second-generation feedstock corn leaf hydrolysate, revealing the bioavailability of the included carbon sources (glucose, sucrose, arabinose, xylose, and galactose) and their order of metabolization by Ustilago maydis.

Predicting solvation free energies with an implicit solvent machine learning potential.

Machine learning (ML) potentials are a powerful tool in molecular modeling, enabling abinitio accuracy for comparably small computational costs. Nevertheless, all-atom simulations employing best-performing graph neural network architectures are still too expensive for applications requiring extensive sampling, such as free energy computations. Implicit solvent models could provide the necessary speed-up due to reduced degrees of freedom and faster dynamics. Here, we introduce a Solvation Free Energy Path Reweighting (ReSolv) framework to parameterize an implicit solvent ML potential for small organic molecules that accurately predicts the hydration free energy, an essential parameter in drug design and pollutant modeling. Learning on a combination of experimental hydration free energy data and abinitio data of molecules in vacuum, ReSolv bypasses the need for intractable abinitio data of molecules in an explicit bulk solvent and does not have to resort to less accurate data-generating models. On the FreeSolv dataset, ReSolv achieves a mean absolute error close to average experimental uncertainty, significantly outperforming standard explicit solvent force fields. Compared to the explicit solvent ML potential, ReSolv offers a computational speedup of four orders of magnitude and attains closer agreement with experiments. The presented framework paves the way for deep molecular models that are more accurate yet computationally more cost-effective than classical atomistic models.

Concomitant benign serous cystadenoma with struma ovarii - An unusual combination of tumours from different origin, a case report and review of literature

Struma ovarii is a rare ovarian tumour of germ cell origin which accounts for less than 5% of all ovarian neoplasms. It can occur alone or in association with other non-germ cell epithelial ovarian neoplasms. The coexistence of struma ovarii with serous cystadenoma is extremely uncommon with only 6 cases reported so far until 2017. Herein, we report an extremely rare case of a 39-year-old woman who presented with an ovarian mass. Clinicoradiologically possibility of a right ovarian cyst with torsion was made. Histopathological examination revealed an unusual dual pathology – benign serous cystadenoma and struma ovarii. This case is reported to highlight the importance of extensive sampling and careful histopathological examination even in a grossly benign looking cystic lesion of the ovary. This case is presented due to its extreme rarity which can cause diagnostic dilemmas and to provide insight into rare entities and its prognostic significance.

Extended Sampling of Macromolecular Conformations from Uniformly Distributed Points on Multidimensional Normal Mode Hyperspheres.

Proteins are dynamic entities that adopt diverse conformations, which play a pivotal role in their function. Understanding these conformations is essential, and protein collective motions, particularly those captured by normal mode (NM) and their linear combinations, provide a robust means for conformational sampling. This work introduces a novel approach to obtaining a uniformly oriented set of a given number of lowest frequency NM combined vectors and generating harmonically equidistant restrained structures along them. They are all thus uniformly located on concentric hyperspheres, systematically covering the defined NM space fully. The generated structures are further relaxed with standard molecular dynamics (MD) simulations to explore the conformational space. The efficiency of the approach we termed "distributed points Molecular Dynamics using Normal Modes" (dpMDNM) was assessed by applying it to hen egg-white lysozyme and human cytochrome P450 3A4 (CYP3A4). To this purpose, we compared our new approach with other methods and analyzed the sampling of existing experimental structures. Our results demonstrate the efficacy of dpMDNM in extensive conformational sampling, particularly as more NMs are considered. Ensembles generated by dpMDNM exhibited a broad coverage of the experimental structures, providing valuable insights into the functional aspects of lysozyme and CYP3A4. Furthermore, dpMDNM also covered lysozyme structures with relatively elevated energies corresponding to transient states not easily obtained by standard MD simulations, in conformity with nuclear magnetic resonance structural indications. This method offers an efficient and rational framework for comprehensive protein conformational sampling, contributing significantly to our understanding of protein dynamics and function.

A reassessment of the taxonomic status and distribution of the subspecies of Lyroderma lyra (Chiroptera: Megadermatidae)

Abstract Lyroderma lyra is one of the six currently recognised species of the family Megadermatidae. Previous studies have suggested subspecific division within L. lyra, wherein the nominate subspecies is predominantly distributed in South Asia, and the other subspecies, L. lyra sinense is occurring in southern China and Southeast Asia. Based on the congruent results of comparative morphology of skulls and bacula, as well as phylogenetic tree reconstructions – the first ones including both subspecies and incorporating the most extensive geographic sampling of “sinense” to date – using COI and cytb sequences, we have elevated the Southeast Asian subspecies to a distinct species status, Lyroderma sinense.