Uptake Rate Research Articles

COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.

Effect of salinity on the toxicokinetics, oxidative stress, and metallothionein gene expression in Meretrix meretrix exposed to cadmium

To understand the effect of salinity on the toxicokinetics, oxidative stress, and detoxification of cadmium-exposed Meretrix meretrix, M. meretrix were acclimatized to different salinities (8, 14, 20, 26, and 32 ppt) for 14 d, exposed to 10 μg/L Cd for 7 d, followed by a 28-day depuration period. The internal Cd concentration was determined, and the activities of antioxidant enzymes (superoxide dismutase (SOD), catalase (CAT), and glutathione-S-transferase (GST)), and the malondialdehyde (MDA) content were measured. The mRNA expression levels of antioxidant enzyme (Cu/Zn SOD, CAT) and detoxification-related genes metallothionein (MT) were analyzed. The mean concentrations of Cd in M. meretrix tissues were in the order gill > digestive gland > mantle > axe foot. The Cd uptake rate in the four tissues decreased with increasing salinity (range: 14–26 ppt). The Cd elimination half-lives were the highest at 8 ppt and 14 ppt salinity. Cadmium activated the four oxidative stress-related related enzymes in the gills. At the end of accumulation period, Cd exposure at 20 ppt salinity significantly increased the expression of Cu/Zn SOD. CAT expression was significantly inhibited at 20 ppt salinity, but was induced at 32 ppt. MT mRNA expression was only induced under Cd at 20 ppt salinity. At the end of depuration period, Cu/Zn SOD expression was inhibited at salinities of 8, 14, and 26 ppt. The results indicated that SOD, CAT, GST, MDA, Cu/Zn SOD, CAT, and MT were sensitive to cadmium in a water environment, and can be used as indicators of marine heavy metal pollution.

Metabolic dysfunction induced by HFD + L-NAME preferentially affects hippocampal mitochondria, impacting spatial memory in rats.

High-fat diet-induced metabolic changes are not restricted to the onset of cardiovascular diseases, but also include effects on brain functions related to learning and memory. This study aimed to evaluate mitochondrial markers and function, as well as cognitive function, in a rat model of metabolic dysfunction. Eight-week-old male Wistar rats were subjected to either a control diet or a two-hit protocol combining a high fat diet (HFD) with the nitric oxide synthase inhibitor L-NAME in the drinking water. HFD plus L-NAME induced obesity, hypertension, and increased serum cholesterol. These rats exhibited bioenergetic dysfunction in the hippocampus, characterized by decreased oxygen (O2) consumption related to ATP production, with no changes in H2O2 production. Furthermore, OPA1 protein expression was upregulated in the hippocampus of HFD + L-NAME rats, with no alterations in other morphology-related proteins. Consistently, HFD + L-NAME rats showed disruption of performance in the Morris Water Maze Reference Memory test. The neocortex did not exhibit either bioenergetic changes or alterations in H2O2 production. Calcium uptake rate and retention capacity in the neocortex of HFD + L-NAME rats were not altered. Our results indicate that hippocampal mitochondrial bioenergetic function is disturbed in rats exposed to a HFD plus L-NAME, thus disrupting spatial learning, whereas neocortical function remains unaffected.

Laboratory Measurements of Pyrene and Acenaphthene Partition into Microplastics

Microplastics are a major environmental issue. In marine systems, these break down into small fragments that may lodge within organisms, but they behave as vectors for chemicals when pollutants such as metals, polycyclic aromatic hydrocarbons (PAHs), pesticides, and pharmaceuticals are absorbed by the particles. The rate and extent of uptake of organic compounds onto microplastics is dependent on a range of factors such as the sites available on the surface, the type of plastic, and the compound being sorbed, with hydrophobicity an important property. Laboratory experiments determined the uptake of pyrene and acenaphthene (and sometimes phenanthrene) onto pellets of polyvinyl chloride (PVC), polyethylene terephthalate (PET), polyoxymethylene (POM), polypropylene (PP) and polystyrene (PS). The kinetics of uptake in the experiments reveals half-lives ~10 h. The extent of partition onto the plastic pellets was similar for pyrene and phenanthrene, though lower for acenaphthene. This was not surprising because the octanol-water distribution coefficient of acenaphthene is about an order of magnitude smaller than that of pyrene, suggesting it is less hydrophobic. The PAHs are distributed most strongly into PP but only weakly into PVC. The fragmentation and oxidation of microplastics means that they sorb increasing amounts of organic material as they age.

Non-specific irreversible 89Zr-mAb uptake in tumours: evidence from biopsy-proven target-negative tumours using 89Zr-immuno-PET

BackgroundDistribution of mAbs into tumour tissue may occur via different processes contributing differently to the 89Zr-mAb uptake on PET. Target-specific binding in tumours is of main interest; however, non-specific irreversible uptake may also be present, which influences quantification. The aim was to investigate the presence of non-specific irreversible uptake in tumour tissue using Patlak linearization on 89Zr-immuno-PET data of biopsy-proven target-negative tumours. Data of two studies, including target status obtained from biopsies, were retrospectively analysed, and Patlak linearization provided the net rate of irreversible uptake (Ki).ResultsTwo tumours were classified as CD20-negative and two as CD20-positive. Four tumours were classified as CEA-negative and nine as CEA-positive. Ki values of CD20-negative (0.43 µL/g/h and 0.92 µL/g/h) and CEA-negative tumours (mdn = 1.97 µL/g/h, interquartile range (IQR) = 1.50–2.39) were higher than zero. Median Ki values of target-negative tumours were lower than CD20-positive (1.87 µL/g/h and 1.90 µL/g/h) and CEA-positive tumours (mdn = 2.77 µL/g/h, IQR = 2.11–3.65).ConclusionBiopsy-proven target-negative tumours showed irreversible uptake of 89Zr-mAbs measured in vivo using 89Zr-immuno-PET data, which suggests the presence of non-specific irreversible uptake in tumours. Consequently, for 89Zr-immuno-PET, even if the target is absent, a tumour-to-plasma ratio always increases over time.

A carbonate system time series in the Eastern Mediterranean Sea. Two years of high-frequency in-situ observations and remote sensing

The rate of ocean uptake of anthropogenic CO2 has declined over the past decade, so a critical question for science and policy is whether the ocean will continue to act as a sink. Large areas of the ocean remain without observations for carbonate system variables, and oceanic CO2 observations have declined since 2017. The Mediterranean Sea is one such an area, especially its eastern part, where there is a paucity of carbonate system data, with large areas not sampled or only sampled by ship-based discrete measurements as opposed to high frequency, sensor-equipped time-series fixed stations. The aim of this study was to analyze a multi-year time-series of high-frequency (hourly) partial pressure CO2 (pCO2) and pH measurements in the Eastern Mediterranean, along with low-frequency (monthly) measurements of total dissolved inorganic carbon and total alkalinity. The pCO2 time-series was the first obtained in the Eastern Mediterranean. The study was conducted at a fixed platform of the POSEIDON system (Heraklion Coastal Buoy) located near Crete Island. Temperature was the dominant factor controlling the temporal variability of pCO2 and pH, while the remaining non-thermal variability appeared to be related to evaporation, water mixing, and biological remineralization-production. The air-sea CO2 fluxes indicated a transition from a winter-spring sink period to a summer-autumn source period. The annual air-sea CO2 flux was too low (-0.16 ± 0.02 mol m-2 yr-1) and variable to conclusively characterize the area as a net source or sink of CO2, highlighting the need for additional high frequency observation sites. Algorithms were developed using temperature, chlorophyll and salinity data to estimate pCO2 and total alkalinity, in an effort to provide tools for estimates in poorly observed areas/periods from remotely sensed products. The applicability of the algorithms was tested using Surface Ocean CO2 Atlas (SOCAT) data from the Eastern Mediterranean Sea (1999 to 2020) which showed that the algorithm pCO2 estimates were generally within ±20 μatm of the pCO2 values reported by SOCAT. Finally, the integration and analysis of the data provided directions on how to optimize the observing strategy, by readapting sensor location and using estimation algorithms with remote sensing data.

Pulmonary rehabilitation improves exercise capacity, health-related quality of life, and cardiopulmonary function in patients with non-small cell lung cancer

BackgroundLung cancer significantly impairs exercise capacity and health-related quality of life (HRQL). Pulmonary rehabilitation (PR) has demonstrated positive effects on exercise capacity and HRQL in lung cancer patients. However, its impact on cardiopulmonary function needs further exploration. The aim of this study was to explore the effects of PR on cardiopulmonary function, exercise capacity and HRQL in patients with lung cancer.MethodsPatients with lung cancer were enrolled in a 12-week PR program. Each participant underwent a thorough evaluation, which included spirometry, cardiopulmonary exercise testing, respiratory muscle strength test, and evaluation of HRQL using the Chronic Obstructive Pulmonary Disease Assessment Test (CAT).ResultsFifty-six patients completed the PR program. Following PR, exercise capacity significantly improved, as evidenced by increased peak oxygen uptake and work rate (both p < 0.05). Exertional symptoms were notably reduced, including leg soreness and dyspnea at peak exercise, accompanied by a decrease in the CAT score (all p < 0.05). Furthermore, improvements in cardiopulmonary function were observed, encompassing respiratory muscle strength, ventilatory equivalent, tidal volume, stroke volume index, and cardiac index at peak exercise (all p < 0.05).ConclusionsPR demonstrated notable enhancements in cardiopulmonary function, exertional symptoms, exercise capacity, and HRQL in patients with lung cancer.

Minimizing acetate formation from overflow metabolism in Escherichia coli: comparison of genetic engineering strategies to improve robustness toward sugar gradients in large-scale fermentation processes.

Introduction: Escherichia coli, a well characterized workhorse in biotechnology, has been used to produce many recombinant proteins and metabolites, but have a major drawback in its tendency to revert to overflow metabolism. This phenomenon occurs when excess sugar triggers the production of mainly acetate under aerobic conditions, a detrimental by-product that reduces carbon efficiency, increases cell maintenance, and ultimately inhibits growth. Although this can be prevented by controlled feeding of the sugar carbon source to limit its availability, gradients in commercial-scale bioreactors can still induce it in otherwise carbon-limited cells. While the underlying mechanisms have been extensively studied, these have mostly used non-limited cultures. In contrast, industrial production typically employs carbon-limited processes, which results in a substantially different cell physiology. Objective: The objective of this study was to evaluate and compare the efficiency of different metabolic engineering strategies with the aim to reduce overflow metabolism and increase the robustness of an industrial 2'-O-fucosyllactose producing strain under industrially relevant conditions. Methods: Three distinct metabolic engineering strategies were compared: i) alterations to pathways leading to and from acetate, ii) increased flux towards the tricarboxylic acid (TCA) cycle, and iii) reduced glucose uptake rate. The engineered strains were evaluated for growth, acetate formation, and product yield under non-limiting batch conditions, carbon limited fed-batch conditions, and after a glucose pulse in fed-batch mode. Results and Discussion: The findings demonstrated that blockage of the major acetate production pathways by deletion of the pta and poxB genes or increased carbon flux into the TCA cycle by overexpression of the gltA and deletion of the iclR genes, were efficient ways to reduce acetate accumulation. Surprisingly, a reduced glucose uptake rate did not reduce acetate formation despite it having previously been shown as a very effective strategy. Interestingly, overexpression of gltA was the most efficient way to reduce acetate accumulation in non-limited cultures, whereas disruption of the poxB and pta genes was more effective for carbon-limited cultures exposed to a sudden glucose shock. Strains from both strategies showed increased tolerance towards a glucose pulse during carbon-limited growth indicating feasible ways to engineer industrial E. coli strains with enhanced robustness.

Eliciting parental preferences and values for the return of additional findings from genomic sequencing

Health economic evidence is needed to inform the design of high-value and cost-effective processes for returning genomic results from analyses for additional findings (AF). This study reports the results of a discrete-choice experiment designed to elicit preferences for the process of returning AF results from the perspective of parents of children with rare conditions and to estimate the value placed on AF analysis. Overall, 94 parents recruited within the Australian Genomics and Melbourne Genomics programmes participated in the survey, providing preferences in a total of 1128 choice scenarios. Statistically significant preferences were identified for the opportunity to change the choices made about AF; receiving positive AF in person from a genetic counsellor; timely access to a medical specialist and high-quality online resources; receiving automatic updates through a secure online portal if new information becomes available; and lower costs. For AF uptake rates ranging between 50–95%, the mean per person value from AF analysis was estimated at AU$450–$1700 (US$300–$1140). The findings enable the design of a value-maximising process of analysis for AF in rare-disease genomic sequencing.

Soil aggregate stability governs field greenhouse gas fluxes in agricultural soils

Agriculture is responsible for 30–50% of the yearly CO2, CH4, and N2O emissions. Soils have an important role in the production and consumption of these greenhouse gases (GHGs), with soil aggregates and the inhabiting microbes proposed to function as biogeochemical reactors, processing these gases. Here we studied, for the first time, the relationship between GHG fluxes and aggregate stability as determined via laser diffraction analysis (LDA) of agricultural soils, as well as the effect of sustainable agricultural management strategies thereon. Using the static chamber method, all soils were found to be sinks for CH4 and sources for CO2 and N2O. The application of organic amendments did not have a conclusive effect on soil GHG fluxes, but tilled soils emitted more CO2. LDA was a useful and improved method for assessing soil aggregate stability, as it allows for the determination of multiple classes of aggregates and their structural composition, thereby overcoming limitations of traditional wet sieving. Organic matter content was the main steering factor of aggregate stability. The presence of persistent stable aggregates and the disintegration coefficient of stable aggregates were improved in organic-amended and no-tilled soils. Predictive modelling showed that, especially in these soils, aggregate stability was a governing factor of GHG fluxes. Higher soil CH4 uptake rates were associated with higher aggregate stability, while CO2 and N2O emissions increased with higher aggregate stability. Altogether, it was shown that sustainable agricultural management strategies can be used to steer the soil's aggregate stability and, both consequently and outright, the soil GHG fluxes, thereby creating a potential to contribute to the mitigation of agricultural GHG emissions.

Biomimetic mercury immobilization by selenium functionalized polyphenylene sulfide fabric

Highly efficient decontamination of elemental mercury (Hg0) remains an enormous challenge for public health and ecosystem protection. The artificial conversion of Hg0 into mercury chalcogenides could achieve Hg0 detoxification and close the global mercury cycle. Herein, taking inspiration from the bio-detoxification of mercury, in which selenium preferentially converts mercury from sulfoproteins to HgSe, we propose a biomimetic approach to enhance the conversion of Hg0 into mercury chalcogenides. In this proof-of-concept design, we use sulfur-rich polyphenylene sulfide (PPS) as the Hg0 transporter. The relatively stable, sulfur-linked aromatic rings result in weak adsorption of Hg0 on the PPS rather than the formation of metastable HgS. The weakly adsorbed mercury subsequently migrates to the adjacent selenium sites for permanent immobilization. The sulfur-selenium pair affords an unprecedented Hg0 adsorption capacity and uptake rate of 1621.9 mg g−1 and 1005.6 μg g−1 min−1, respectively, which are the highest recorded values among various benchmark materials. This work presents an intriguing concept for preparing Hg0 adsorbents and could pave the way for the biomimetic remediation of diverse pollutants.

Toxicokinetics of the Antidepressant Fluoxetine and Its Active Metabolite Norfluoxetine in Caenorhabditis elegans and Their Comparative Potency.

The nematode Caenorhabditis elegans is a valuable model for ecotoxicological research, yet limited attention has been given to understanding how it absorbs, distributes, metabolizes, and excretes chemicals. This is crucial for C. elegans because the organism is known to have strong uptake barriers that are known to be susceptible to potential confounding effects of the presence of Escherichia coli as a food source. One frequently studied compound in C. elegans is the antidepressant fluoxetine, which has an active metabolite norfluoxetine. In this study, we evaluated the toxicokinetics and relative potency of norfluoxetine and fluoxetine in chemotaxis and activity tests. Toxicokinetics experiments were conducted with varying times, concentrations of fluoxetine, and in the absence or presence of E. coli, simulated with a one-compartment model. Our findings demonstrate that C. elegans can take up fluoxetine and convert it into norfluoxetine. Norfluoxetine proved slightly more potent and had a longer elimination half-life. The bioconcentration factor, uptake, and elimination rate constants depended on exposure levels, duration, and the presence of E. coli in the exposure medium. These findings expand our understanding of toxicokinetic modeling in C. elegans for different exposure scenarios, underlining the importance of considering norfluoxetine formation in exposure and bioactivity assessments of fluoxetine.

Socio-Demographic Factors Associated with the Uptake of Community Led Total Sanitation: An Empirical Study among the Residents of Isiolo County, Kenya

Despite Kenya aiming to achieve 100% ODF status by 2030 and adopting CLTS, about 5.6 million (14%) of the population practice OD. Thus, this study was guided by the research question: What is the level of uptake of community led total sanitation among residents of Isiolo County? The study used a descriptive cross-sectional methodology and conducted among residents of Isiolo Sub-County, Kenya. The study sampled 373 household heads from population of about 21,870 household heads for the study. Systematic random sampling was used to select the participants. An inclusion and exclusion criteria-based period lived in the county and consent to participate in the study were used. Data collection instruments entailed structured questionnaires for households’ heads, key informant interview (KII) guides for opinion leaders, and an observation checklist. The content validity of the tools was assessed by expert from the department of Environmental Health at Kenyatta University. The tools were piloted in Longopito Sub-location with 37 households. Reliability of the tools was assessed using the piloted data through the test-retest technique. The paired data sets were correlated, giving R=0.75. The study found an association between demographic factors and the uptake of CLTS in Isiolo county. The uptake of CLTS was found to be low with almost half of the sampled residents lacking latrines. The study concludes there were a number of demographic factors that were significantly associated with the utilization of CLTS including the highest level of education (χ2=66.866, p = 0.001), Occupation (χ2=12.690, p = 0.002), Average monthly family income (KShs) (χ2=43.373, p = 0.012) and number of household members (χ2=6.948, p = 0.044). The study also found that Isiolo County had a low uptake rate for community-led total sanitation. Only three out of ten households in the county uptake CLTS. The study recommends that the County Government of Isiolo should scale up health education programs on community led total sanitation

Assortative mixing of opinions about COVID-19 vaccination in personal networks

Many countries worldwide had difficulties reaching a sufficiently high vaccination uptake during the COVID-19 pandemic. Given this context, we collected data from a panel of 30,000 individuals, which were representative of the population of Romania (a country in Eastern Europe with a low 42.6% vaccination rate) to determine whether people are more likely to be connected to peers displaying similar opinions about COVID-19 vaccination. We extracted 443 personal networks, amounting to 4430 alters. We estimated multilevel logistic regression models with random-ego-level intercepts to predict individual opinions about COVID-19 vaccination. Our evidence indicates positive opinions about the COVID-19 vaccination cluster. Namely, the likelihood of having a positive opinion about COVID-19 vaccination increases when peers have, on average, a more positive attitude than the rest of the nodes in the network (OR 1.31, p < 0.001). We also found that individuals with higher education and age are more likely to hold a positive opinion about COVID-19 vaccination. With the given empirical data, our study cannot reveal whether this assortative mixing of opinions is due to social influence or social selection. However, it may nevertheless have implications for public health interventions, especially in countries that strive to reach higher uptake rates. Understanding opinions about vaccination can act as an early warning system for potential outbreaks, inform predictions about vaccination uptake, or help supply chain management for vaccine distribution.

Progression from the mean: Cultivating instructors' unique trajectories of practice using educational technology

AbstractBackgroundIn taking up educational technology tools and student‐centered instructional practice, there is consensus that instructors consider the unique aspects of their instructional context. However, tool adoption success is often framed narrowly by numerical uptake rates or by conformity with non‐negotiable components.PurposeWe pursue an alternative ecosystems framing which posits that variability among contexts is fundamental to understanding instructors' uptake of instructional tools and the ways their teaching trajectories develop over time.Design/MethodThrough a multiple‐case study approach using interviews, usage data, surveys, and records of community meetings, we examine 12 instructors' trajectories to illustrate the dynamic uptake of a technology tool.ResultsCross‐case analysis found that instructors' trajectories are tool‐mediated and community‐mediated. We present five cases in detail. Two foreground ways that instructors gained insight into student learning from student responses in the tool. Two illustrate the role played by the project's Community of Practice (CoP), an extra‐institutional support for deepening practice. The final case illustrates the complexity of an evolving instructional ecosystem and its role in instructors' satisfaction and continued use.ConclusionsUse of the educational technology tool perturbed ecosystems and supported instructors' evolving trajectories through mediation of instructor and student activity. Instructors' goals guided initial uptake, but both goals and practice were adapted using information from interactions with the tool and the CoP and changes in instructional contexts. The study confirms the need to understand the complexity of the uptake of innovations and illustrates opportunities for educators, developers, and administrators to enhance uptake and support diversity goals.

Kinetic Modeling of Brain [18-F]FDG Positron Emission Tomography Time Activity Curves with Input Function Recovery (IR) Method.

Accurate positron emission tomography (PET) data quantification relies on high-quality input plasma curves, but venous blood sampling may yield poor-quality data, jeopardizing modeling outcomes. In this study, we aimed to recover sub-optimal input functions by using information from the tail (5th-100th min) of curves obtained through the frequent sampling protocol and an input recovery (IR) model trained with reference curves of optimal shape. Initially, we included 170 plasma input curves from eight published studies with clamp [18F]-fluorodeoxyglucose PET exams. Model validation involved 78 brain PET studies for which compartmental model (CM) analysis was feasible (reference (ref) + training sets). Recovered curves were compared with original curves using area under curve (AUC), max peak standardized uptake value (maxSUV). CM parameters (ref + training sets) and fractional uptake rate (FUR) (all sets) were computed. Original and recovered curves from the ref set had comparable AUC (d = 0.02, not significant (NS)), maxSUV (d = 0.05, NS) and comparable brain CM results (NS). Recovered curves from the training set were different from the original according to maxSUV (d = 3) and biologically plausible according to the max theoretical K1 (53//56). Brain CM results were different in the training set (p < 0.05 for all CM parameters and brain regions) but not in the ref set. FUR showed reductions similarly in the recovered curves of the training and test sets compared to the original curves (p < 0.05 for all regions for both sets). The IR method successfully recovered the plasma inputs of poor quality, rescuing cases otherwise excluded from the kinetic modeling results. The validation approach proved useful and can be applied to different tracers and metabolic conditions.

Estimating the influence of field inventory sampling intensity on forest landscape model performance for determining high-severity wildfire risk

Historically, fire has been essential in Southwestern US forests. However, a century of fire-exclusion and changing climate created forests which are more susceptible to uncharacteristically severe wildfires. Forest managers use a combination of thinning and prescribed burning to reduce forest density to help mitigate the risk of high-severity fires. These treatments are laborious and expensive, therefore optimizing their impact is crucial. Landscape simulation models can be useful in identifying high risk areas and assessing treatment effects, but uncertainties in these models can limit their utility in decision making. In this study we examined underlying uncertainties in the initial vegetation layer by leveraging a previous study from the Santa Fe fireshed and using new inventory plots from 111 stands to interpolate the initial forest conditions. We found that more inventory plots resulted in a different geographic distribution and wider range of the modelled biomass. This changed the location of areas with high probability of high-severity fires, shifting the optimal location for management. The increased range of biomass variability from using a larger number of plots to interpolate the initial vegetation layer also influenced ecosystem carbon dynamics, resulting in simulated forest conditions that had higher rates of carbon uptake. We conclude that the initial forest layer significantly affects fire and carbon dynamics and is dependent on both number of plots, and sufficient representation of the range of forest types and biomass density.

Fatty acid unsaturation improves germination of upland cotton (Gossypium hirsutum) under cold stress.

The level of fatty acid unsaturation in seeds is one of the major determinants of cold germination ability, particularly in oilseeds. The presence of cis double bonds in unsaturated fatty acids creates bends that lowers their melting temperatures compared to saturated fatty acids. Unsaturated fatty acids with low melting points mobilize faster at low temperatures providing seeds with sufficient energy for germination. To investigate the effects of fatty acid unsaturation on the ability of cotton seeds to germinate under cold conditions, four recombinant inbred lines (RILs) of cotton with unique fatty acid profiles were evaluated using a set of developmental and biochemical assays at 12°C (critically low temperature), 15°C (cardinal minimum temperature) and 30°C (optimum temperature). Furthermore, whole seed lipidome profiling using liquid chromatography with mass spectrometry was done to compare the lipid compositional changes at 12°C and 30°C after imbibing cotton seeds of all the six genotypes for 0 hours, 3 hours and 6 hours. The RILs with higher unsaturation/saturation ratios registered robust germination performance, lower solute leakage, and optimum water uptake rates under cold stress. Imbibition at 30°C for 8 hours before cold exposure significantly improved the germination of cold sensitive genotypes, indicating that the first few hours of water uptake are critical for cold stress. Whole seed lipidome profiling of all the genotypes specifically associated cold germination ability with higher unsaturation levels of phospholipids during early imbibition. The presence of cis double bonds in phospholipids creates kinks that maintain the fluidity of cell membranes under low temperature. Membrane flexibility under cold conditions is essential for facilitating key germination events including membrane organization and respiration. The current results highlight the importance of fatty acid composition in cold germination ability of upland cotton.

OptMSP: A toolbox for designing optimal multi-stage (bio)processes

One central goal of bioprocess engineering is to maximize the production of specific chemicals using microbial cell factories. Many bioprocesses are one-stage (batch) processes (OSPs), in which growth and product synthesis are coupled. However, OSPs often exhibit low volumetric productivities due to the competition for substrate for biomass and product synthesis implying trade-offs between biomass and product yields. Two-stage or, more generally, multi-stage processes (MSPs) offer the potential to tackle this trade-off for improved efficiency of bioprocesses, for example, by separating growth and production. MSPs have recently gained much attention, also because of a rapidly growing toolbox for the dynamic control of metabolic fluxes. Despite these promising advancements, computational tools specifically tailored for the optimal design of MSPs in the field of biotechnology are still lacking.Here, we present OptMSP, a new Python-based toolbox for identifying optimal MSPs maximizing a user-defined process metrics (such as volumetric productivity, yield, and titer or combinations thereof) under given constraints. In contrast to other methods, our framework starts with a set of well-defined modules representing relevant stages or sub-processes. Experimentally determined parameters (such as growth rates, substrate uptake and product formation rates) are used to build suitable ODE models describing the dynamic behavior of each module. OptMSP finds then the optimal combination of those modules, which, together with the optimal switching time points, maximize a given objective function. We demonstrate the applicability and relevance of the approach with three different case studies, including the example of lactate production by E. coli in a batch setup, where an aerobic growth phase can be combined with anaerobic production phases with or without growth and with or without enhanced ATP turnover.

Comparison of Initial Thoracic CT Images of COVID-19 Patients with Non-Variant, Alpha, Delta, and Omicron Variants: A Retrospective Study

Background:: CT findings and Ground glass opacity (GGO) volumes may differ between SARS CoV-2 non-variant, alpha, delta, and omicron variants. Objective:: To compare the thoracic CT findings, GGO volumes, and GGOs’ lung uptake rates among patients with COVID-19 variants. Methods:: Thoracic CT images of 83 patients with non-variant, 78 patients with alpha variant, 93 patients with delta variant, and 73 patients with omicron variant having positive Real-Time Polymerase Chain Reaction test results were analyzed retrospectively. GGO volumes and lung volumes were calculated by using the Cavalieri Principle. Differences in CT findings, ground-glass opacity volumes, and lung involvement rates between non-variant and variant groups were evaluated. Results:: There were significant differences found in the incidence of GGOs (p &lt; 0.001), air bronchogram (p = 0.007), reticulation (p = 0.002) and subpleural lines, and linear opacities (p = 0.034) between non-variant and variant groups. GGO uptake rates (ground glass opacity volumes × 100 ÷ lung volume) were 8.88% in the non-variant, 4.83% in the alpha variant, 3.50% in the delta variant, and 2.02% in the omicron variant. In estimating variant groups, it was determined that the increase in the rate of GGOs in the right lung increased the probability of having an omicron variant, whereas the presence of nodules decreased it. The possibility of the delta variant increased with an increase in the rate of ground glass opacities in the left lung. Conclusion:: Thoracic CT findings solely can be helpful in distinguishing COVID-19 variants. Decreased frequency of uptake rates of GGOs suggested that the severity of COVID-19 disease was gradually decreasing.