Partitioning Of Products Research Articles

Overlooked branch turnover creates a widespread bias in forest carbon accounting

Most measurements and models of forest carbon cycling neglect the carbon flux associated with the turnover of branch biomass, a physiological process quantified for other organs (fine roots, leaves, and stems). Synthesizing data from boreal, temperate, and tropical forests (184,815 trees), we found that including branch turnover increased empirical estimates of aboveground wood production by 16% (equivalent to 1.9 Pg Cy-1 globally), of similar magnitude to the observed global forest carbon sinks. In addition, reallocating carbon to branch turnover in model simulations reduced stem wood biomass, a long-lasting carbon storage, by 7 to 17%. This prevailing neglect of branch turnover suggests widespread biases in carbon flux estimates across global datasets and model simulations. Branch litterfall, sometimes used as a proxy for branch turnover, ignores carbon lost from attached dead branches, underestimating branch C turnover by 38% in a pine forest. Modifications to field measurement protocols and existing models are needed to allow a more realistic partitioning of wood production and forest carbon storage.

Oil–reservoir correlation: Unravelling the producing reservoir for the sulfur-rich Qianjiang shale oil play

The hypersaline Qianjiang shale oil play represents a promising yet challenging target for exploration due to its multiple stacked shale-carbonate layers, which complicate the identification of the most viable extraction sites. Additionally, the oil accumulated in these layers is immature to low-mature and considerably high in sulphur content. To trace the shale zones after fracturing, a newly developed oil-reservoir correlation method employing Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was utilised. This method proved effective in distinguishing between the acidic nitrogen-, sulphur-, and oxygen-containing (NSO) compounds identified by the negative mode of electrospray ionisation FT-ICR MS and the less polar NSO compounds and aromatic hydrocarbons detected by positive ion atmospheric pressure photoionisation FT-ICR MS. These compounds facilitated the deconvolution of oils produced from mixed layers in the hypersaline Qianjiang shale play. The zones of oil production identified through this method corresponded well with shale reservoirs previously characterised by anomalously high oil saturation index values (OSI >400 mg/g TOC) and a preferential enrichment of saturated hydrocarbons. A detailed analysis of C31-35 homohopane distributions in shale extracts and crude oil corroborated these findings. The variability in the producibility of NSO compounds and aromatic hydrocarbons was examined based on functional group, degree of aromatisation, and molecular weight. During production, sulphur- and nitrogen-containing compounds predominantly migrated into the produced oil, whereas oxygen-containing compounds were less affected. Nonetheless, determining the production partitioning of aromatic hydrocarbons remains challenging; if present, it may be obscured by the commingled production effects from multiple layers.

Atmospheric mercury species at Nam Co (4730m a.s.l.), a highland background site in the inland Tibetan Plateau: implications of mercury potential sources.

A field survey was conducted in the central Tibetan Plateau (Nam Co) in China for high-time resolution measurements of gaseous elemental mercury (GEM), gaseous oxidized mercury (GOM), and particle-bound mercury (PBM). Average concentrations (± 1 SD) of GEM, PBM, and GOM from November 2014 to March 2015 were 1.11 ± 0.20ngm-3, 50.8 ± 26.5pgm-3, and 3.6 ± 3.2pgm-3, respectively. During the monitoring period, both GEM and GOM exhibited relative stability in their monthly variations, whereas PBM concentrations were significantly higher in winter compared to those in later autumn and early spring. In terms of diurnal variations, the maximum concentration of GEM was typically observed after sunrise, while PBM reached its peak before sunrise, and the highest concentration of GOM was recorded in the afternoon. Vertical convection conditions, photochemical production, and gas-particle partitioning were responsible for the diurnal cycle of atmospheric mercury. Based on modeling results, it was determined that the air mass transported from South Asia significantly impacted atmospheric mercury levels at Nam Co Station. The regions of western and central Nepal, central and eastern Pakistan, and northern India were identified as potential sources of atmospheric mercury at Nam Co.

Concentrations, Partitioning and Ecological Risk of Pharmaceuticals and Personal Care Products in UK Freshwater Sediment

Pharmaceuticals and personal care products (PPCPs) have received increasing attention in recent years as emerging contaminants that could adversely affect human health and the environment. While previous studies focused mainly on water, little is known about the occurrence and risk of PPCPs in sediment. The present study investigated 30 widely used PPCPs in sediment samples from two rivers and two canals in the UK. Average Σ30PPCPs concentrations in sediments were 129, 79, 62 and 110ng/g dry weight in the River Tame, Coventry Canal, River Severn, and Birmingham & Worcester canal, respectively. Amoxicillin, gabapentin, caffeine, propranolol, DEET, naproxen, diclofenac Na, meclofenamic acid and β-estradiol were detected in all studied locations. The highest concentration measured was 43.6ng/g of diclofenac (Coventry Canal). The octanol-water partition coefficient (Log KOW) of target PPCPs, and their experimentally measured sediment–water distribution coefficient (Log KP), showed a significant positive correlation, indicating the partitioning of PPCPs between water and sediment may be attributed, at least partially, to their lipophilicity. The ecological risk of target PPCPs in sediment to aquatic biota was assessed using a risk quotient (RQ) approach. The estimated RQ values for amoxicillin on Cyanobacteria were > 1 in all studied locations, indicating high risk to freshwater Cyanobacteria, while 17-α-ethinyl estradiol and caffeine posed high ecological risk (RQ > 1) to fish. These results highlight the need for better removal efficiency of PPCPs by wastewater treatment plants to prevent their accumulation in sediment and call for further development of effect-based sediment quality standards.

No existence of a linear algorithm for the one-dimensional Fourier phase retrieval

Fourier phase retrieval, which aims to reconstruct a signal from its Fourier magnitude, is of fundamental importance in fields of engineering and science. In this paper, we provide a theoretical understanding of algorithms for the one-dimensional Fourier phase retrieval problem. Specifically, we demonstrate that if an algorithm exists which can reconstruct an arbitrary signal x∈CN in Poly(N)log⁡(1/ϵ) time to reach ϵ-precision from its magnitude of discrete Fourier transform and its initial value x(0), then P=NP. This partially elucidates the phenomenon that, despite the fact that almost all signals are uniquely determined by their Fourier magnitude and the absolute value of their initial value |x(0)|, no algorithm with theoretical guarantees has been proposed in the last few decades. Our proofs employ the result in computational complexity theory that the Product Partition problem is NP-complete in the strong sense.

Changes in productivity partitioning induced by precipitation extremes increase inaccuracy of grassland carbon estimation.

The fraction of net primary productivity (NPP) allocated to belowground organs (fBNPP) in grasslands is a critical parameter in global carbon cycle models; moreover, understanding the effect of precipitation changes on this parameter is vital to accurately estimating carbon sequestration in grassland ecosystems. However, how fBNPP responds to temporal precipitation changes along a gradient from extreme drought to extreme wetness, remains unclear, mainly due to the lack of long-term data of belowground net primary productivity (BNPP) and the fact that most precipitation experiments did not have a gradient from extreme drought to extreme wetness. Here, by conducting both a precipitation gradient experiment (100-500 mm) and a long-term observational study (34 years) in the Inner Mongolia grassland, we showed that fBNPP decreased linearly along the precipitation gradient from extreme drought to extreme wetness due to stronger responses in aboveground NPP to drought and wet conditions than those of BNPP. Our further meta-analysis in grasslands worldwide also indicated that fBNPP increased when precipitation decreased, and the vice versa. Such a consistent pattern of fBNPP response suggests that plants increase the belowground allocation with decreasing precipitation, while increase the aboveground allocation with increasing precipitation. Thus, the linearly decreasing response pattern in fBNPP should be incorporated into models that forecast carbon sequestration in grassland ecosystems; failure to do so will lead to underestimation of the carbon stock in drought years and overestimation of the carbon stock in wet years in grasslands.

The role of red and white light in optimizing growth and accumulation of plant specialized metabolites at two light intensities in medical cannabis (Cannabis sativa L.).

The cultivation of medical cannabis (Cannabis sativa L.) is expanding in controlled environments, driven by evolving governmental regulations for healthcare supply. Increasing inflorescence weight and plant specialized metabolite (PSM) concentrations is critical, alongside maintaining product consistency. Medical cannabis is grown under different spectra and photosynthetic photon flux densities (PPFD), the interaction between spectrum and PPFD on inflorescence weight and PSM attracts attention by both industrialists and scientists. Plants were grown in climate-controlled rooms without solar light, where four spectra were applied: two low-white spectra (7B-20G-73R/Narrow and 6B-19G-75R/2Peaks), and two high-white (15B-42G-43R/Narrow and 17B-40G-43R/Broad) spectra. The low-white spectra differed in red wavelength peaks (100% 660 nm, versus 50:50% of 640:660 nm), the high-white spectra differed in spectrum broadness. All four spectra were applied at 600 and 1200 μmol m-2 s-1. Irrespective of PPFD, white light with a dual red peak of 640 and 660 nm (6B-19G-75R/2Peaks) increased inflorescence weight, compared to white light with a single red peak of 660 nm (7B-20G-73R/Narrow) (tested at P = 0.1); this was associated with higher total plant dry matter production and a more open plant architecture, which likely enhanced light capture. At high PPFD, increasing white fraction and spectrum broadness (17B-40G-43R/Broad) produced similar inflorescence weights compared to white light with a dual red peak of 640 and 660 nm (6B-19G-75R/2Peaks). This was caused by an increase of both plant dry matter production and dry matter partitioning to the inflorescences. No spectrum or PPFD effects on cannabinoid concentrations were observed, although at high PPFD white light with a dual red peak of 640 and 660 nm (6B-19G-75R/2Peaks) increased terpenoid concentrations compared to the other spectra. At low PPFD, the combination of white light with 640 and 660 nm increased photosynthetic efficiency compared with white light with a single red peak of 660nm, indicating potential benefits in light use efficiency and promoting plant dry matter production. These results indicate that the interaction between spectrum and PPFD influences plant dry matter production. Dividing the light energy in the red waveband over both 640 and 660 nm equally shows potential in enhancing photosynthesis and plant dry matter production.

Observation-constrained kinetic modeling of isoprene SOA formation in the atmosphere

Abstract. Isoprene has the largest global non-methane hydrocarbon emission, and the oxidation of isoprene plays a crucial role in the formation of secondary organic aerosol (SOA). Two primary processes are known to contribute to SOA formation from isoprene oxidation: (1) the reactive uptake of isoprene-derived epoxides on acidic or aqueous particle surfaces and (2) the absorptive gas–particle partitioning of low-volatility oxidation products. In this study, we developed a new multiphase condensed isoprene oxidation mechanism that includes these processes with key molecular intermediates and products. The new mechanism was applied to simulate isoprene gas-phase oxidation products and SOA formation from previously published chamber experiments under a variety of conditions and atmospheric observations during the Southern Oxidant and Aerosol Studies (SOAS) field campaign. Our results show that SOA formation from most of the chamber experiments is reasonably reproduced using our mechanism, except when the concentration ratios of initial nitric oxide to isoprene exceed ∼ 2, the formed SOA is significantly underpredicted. The SOAS simulations also reasonably agree with the measurements regarding the diurnal pattern and concentrations of different product categories, while the total isoprene SOA remains underestimated. The molecular compositions of the modeled SOA indicate that multifunctional low-volatility products contribute to isoprene SOA more significantly than previously thought, with a median mass contribution of ∼ 57 % to the total modeled isoprene SOA. However, this contribution is intricately intertwined with IEPOX-derived SOA (IEPOX: isoprene-derived epoxydiols), posing challenges for their differentiation using bulk aerosol composition analysis (e.g., the aerosol mass spectrometer with positive matrix factorization). Furthermore, the SOA from these pathways may vary greatly, mainly dependent on the volatility estimation and treatment of particle-phase processes (i.e., photolysis and hydrolysis). Our findings emphasize that the various pathways to produce these low-volatility species should be considered in models to more accurately predict isoprene SOA formation. The new condensed isoprene chemical mechanism can be further incorporated into regional-scale air quality models, such as the Community Multiscale Air Quality Modelling System (CMAQ), to assess isoprene SOA formation on a larger scale.

Possibilistic picture fuzzy product partition C-means clustering incorporating rich local information for medical image segmentation

Possibilistic picture fuzzy product partition C-means clustering incorporating rich local information for medical image segmentation

Modeling Novel Aqueous Particle and Cloud Chemistry Processes of Biomass Burning Phenols and Their Potential to Form Secondary Organic Aerosols.

Phenols emitted from biomass burning contribute significantly to secondary organic aerosol (SOA) formation through the partitioning of semivolatile products formed from gas-phase chemistry and multiphase chemistry in aerosol liquid water and clouds. The aqueous-phase SOA (aqSOA) formed via hydroxyl radical (•OH), singlet molecular oxygen (1O2*), and triplet excited states of organic compounds (3C*), which oxidize dissolved phenols in the aqueous phase, might play a significant role in the evolution of organic aerosol (OA). However, a quantitative and predictive understanding of aqSOA has been challenging. Here, we develop a stand-alone box model to investigate the formation of SOA from gas-phase •OH chemistry and aqSOA formed by the dissolution of phenols followed by their aqueous-phase reactions with •OH, 1O2*, and 3C* in cloud droplets and aerosol liquid water. We investigate four phenolic compounds, i.e., phenol, guaiacol, syringol, and guaiacyl acetone (GA), which represent some of the key potential sources of aqSOA from biomass burning in clouds. For the same initial precursor organic gas that dissolves in aerosol/cloud liquid water and subsequently reacts with aqueous phase oxidants, we predict that the aqSOA formation potential (defined as aqSOA formed per unit dissolved organic gas concentration) of these phenols is higher than that of isoprene-epoxydiol (IEPOX), a well-known aqSOA precursor. Cloud droplets can dissolve a broader range of soluble phenols compared to aqueous aerosols, since the liquid water contents of aerosols are orders of magnitude smaller than cloud droplets. Our simulations suggest that highly soluble and reactive multifunctional phenols like GA would predominantly undergo cloud chemistry within cloud layers, while gas-phase chemistry is likely to be more important for less soluble phenols. But in the absence of clouds, the condensation of low-volatility products from gas-phase oxidation followed by their reversible partitioning to organic aerosols dominates SOA formation, while the SOA formed through aqueous aerosol chemistry increases with relative humidity (RH), approaching 40% of the sum of gas and aqueous aerosol chemistry at 95% RH for GA. Our model developments of biomass-burning phenols and their aqueous chemistry can be readily implemented in regional and global atmospheric chemistry models to investigate the aqueous aerosol and cloud chemistry of biomass-burning organic gases in the atmosphere.

Gas-to-Particle Partitioning of Products from Ozonolysis of Δ3-Carene and the Effect of Temperature and Relative Humidity.

Formation of oxidized products from Δ3-carene (C10H16) ozonolysis and their gas-to-particle partitioning at three temperatures (0, 10, and 20 °C) under dry conditions (<2% RH) and also at 10 °C under humid (78% RH) conditions were studied using a time-of-flight chemical ionization mass spectrometer (ToF-CIMS) combined with a filter inlet for gases and aerosols (FIGAERO). The Δ3-carene ozonolysis products detected by the FIGAERO-ToF-CIMS were dominated by semivolatile organic compounds (SVOCs). The main effect of increasing temperature or RH on the product distribution was an increase in fragmentation of monomer compounds (from C10 to C7 compounds), potentially via alkoxy scission losing a C3 group. The equilibrium partitioning coefficient estimated according to equilibrium partitioning theory shows that the measured SVOC products distribute more into the SOA phase as the temperature decreases from 20 to 10 and 0 °C and for most products as the RH increases from <2 to 78%. The temperature dependency of the saturation vapor pressure (above an assumed liquid state), derived from the partitioning method, also allows for a direct way to obtain enthalpy of vaporization for the detected species without accessibility of authentic standards of the pure substances. This method can provide physical properties, beneficial for, e.g., atmospheric modeling, of complex multifunctional oxidation products.

Personalized treatment selection via product partition models with covariates.

Precision medicine is an approach for disease treatment that defines treatment strategies based on the individual characteristics of the patients. Motivated by an open problem in cancer genomics, we develop a novel model that flexibly clusters patients with similar predictive characteristics and similar treatment responses; this approach identifies, via predictive inference, which one among a set of treatments is better suited for a new patient. The proposed method is fully model based, avoiding uncertainty underestimation attained when treatment assignment is performed by adopting heuristic clustering procedures, and belongs to the class of product partition models with covariates, here extended to include the cohesion induced by the normalized generalized gamma process. The method performs particularly well in scenarios characterized by considerable heterogeneity of the predictive covariates in simulation studies. A cancer genomics case study illustrates the potential benefits in terms of treatment response yielded by the proposed approach. Finally, being model based, the approach allows estimating clusters' specific response probabilities and then identifying patients more likely to benefit from personalized treatment.

Clustering blood donors via mixtures of product partition models with covariates.

Motivated by the problem of accurately predicting gap times between successive blood donations, we present here a general class of Bayesian nonparametric models for clustering. These models allow for the prediction of new recurrences, accommodating covariate information that describes the personal characteristics of the sample individuals. We introduce a prior for the random partition of the sample individuals, which encourages two individuals to be co-clustered if they have similar covariate values. Our prior generalizes product partition models with covariates (PPMx) models in the literature, which are defined in terms of cohesion and similarity functions. We assume cohesion functions that yield mixtures of PPMx models, while our similarity functions represent the denseness of a cluster. We show that including covariate information in the prior specification improves the posterior predictive performance and helps interpret the estimated clusters in terms of covariates in the blood donation application.

Identifying Latent Groups in Spatial Panel Data Using a Markov Random Field Constrained Product Partition Model

Understanding the heterogeneity over spatial locations is an important problem that has been widely studied in many applications such as economics and environmental science. In this paper, we focus on regression models for spatial panel data analysis, where repeated measurements are collected over time at various spatial locations. We propose a novel class of nonparametric priors that combines Markov random field (MRF) with the product partition model (PPM), and show that the resulting prior, called by MRF-PPM, is capable of identifying the latent group structure among the spatial locations while efficiently utilizing the spatial dependence information. We derive a closed-form conditional distribution for the proposed prior and introduce a new way to compute the marginal likelihood that renders efficient Bayesian inference. We further study the theoretical properties of the proposed MRF-PPM prior and show a clustering consistency result for the posterior distribution. We demonstrate the excellent empirical performance of our method via extensive simulation studies and applications to a US precipitation data and a California median household income data study.

Gas–particle partitioning of toluene oxidation products: an experimental and modeling study

Abstract. Toluene represents a large fraction of anthropogenic emissions and significantly contributes to tropospheric ozone and secondary organic aerosol (SOA) formation. Despite the fact that toluene is one of the most studied aromatic compounds, detailed chemical mechanisms still fail to correctly reproduce the speciation of toluene gaseous and condensed oxidation products. This study aims to elucidate the role of initial experimental conditions in toluene SOA mass loadings and to investigate gas–particle partitioning of its reaction products at different relevant temperatures. Gaseous and particulate reaction products were identified and quantified using a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) coupled to a CHemical Analysis of aeRosol ONline (CHARON) inlet. The chemical system exhibited a volatility distribution mostly in the semi-volatile regime. Temperature decrease caused a shift of saturation concentration towards lower values. The CHARON–PTR-ToF-MS instrument identified and quantified approximately 60 %–80 % of the total organic mass measured by an aerosol mass spectrometer. A detailed mechanism for toluene gaseous oxidation was developed based on the Master Chemical Mechanism (MCM) and Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) deterministic mechanisms, modified following the literature. The new mechanism showed improvements in modeling oxidation product speciation with more observed species represented and more representative concentrations compared to the MCM–GECKO-A reference. Tests on partitioning processes, nonideality, and wall losses highlighted the high dependency of SOA formation on the considered processes. Our results underline the fact that volatility is not sufficient to explain the gas–particle partitioning: the organic and the aqueous phases need to be considered as well as the interactions between compounds in the particle phase.

A novel partitioning of gross primary production and water use efficiency for sustaining water and food security using Budyko hypothesis

This study coupled the green water and blue water accounting with the existing standard Budyko framework and Fu's 1-parameter Budyko framework to diagnose the basin hydrological behavior. Both Budyko frameworks were employed to determine green water consumption (ETGreen) and blue water consumption (ETBlue) which, in turn, were used to map the blue water index (BWI) hotspots and green water index (GWI) bright spots. The relative contributions of green water and blue water were quantified for sustaining water and food security. A new methodology is proposed using BWI and GWI for partitioning the Gross Primary Production (GPP) and Water Use Efficiency (WUE) into GPPBlue, GPPGreen and WUEBlue and WUEGreen. The methodology was applied to five sub-basins of the Central Godavari River Basin (CGRB): Purna, Dhalegaon, GR Bridge, Yeli and Delta. Results showed that all five basins exhibited larger deviations from the theoretical Budyko curve of Fu's 1-parameter Budyko framework than did the standard Budyko framework and the Dhalegaon basin showed the largest deviations. The partitioning of GPP and WUE by the proposed methodology showed that the proportion of GPPGreen to the total GPP was much higher than that of the GPPBlue. Similarly, the proportion of WUEGreen to WUE was more than that of WUEBlue. The mapping of GPPBlue and GPPGreen, and WUEBlue and WUEGreen showed that the Delta and Yeli basins had the highest values of both GPPGreen & GPPBlue and WUEBlue and WUEGreen (bright spot basins) and the Dhalegaon and parts of GR Bridge basin had the lowest values (hot spot basins). The proposed partitioning of GPP and WUE will help identify the relative contributions of green water and blue water (for managing agricultural waters) and formulate agronomical and engineering practices for stakeholders and policy makers for increasing the overall WUE and GPP to sustain water and food security.

Continuous microfluidic platform combining cell lysis and protein extraction for screening overall process conditions

AbstractBACKGROUNDMicrofluidic devices have been increasingly used as tools to accelerate the development of new biomanufacturing methods, given their ability to test a large set of variables in a short amount of time with minimal reagent consumption. However, while being able to expedite a bioprocess design, the modular aspect of these devices has not been systematically explored, with most applications focusing on single unitary operations. This paper explores the modular design of these devices through the development of an integrated microfluidic chip.RESULTSTwo separate unitary operations were included into a single device for continuous operation: product release by chemical cell lysis and product concentration by aqueous two‐phase system. In this way, it is possible to screen multiple conditions for each operation and evaluate their combined effect on the final product, making this a great tool in the development and optimization of a process. A recombinant Escherichia coli (E. coli) strain producing green fluorescent protein (GFP) was used as model system, which allows for lysis efficiency and partition coefficient to be evaluated by fluorescence microscopy. A chemical lysis solutions was used in the lysis step, presenting lysis efficiencies of about 100%, while a polyethylene glycol (PEG)/phosphate system was screened for the separation stage, presenting partition coefficients of about 4.CONCLUSIONSThe integrated device allowed for the continuous screening of multiple combinations of operation conditions, highlighting, for example, conditions where improvements on the lysis efficiency subsequently impaired the separation, decreasing the partition of the target product, thus demonstrating the need for the evaluation of a process in its entirety. In this way, the microfluidic device delivered a rapid process screening and optimization, with very low reagent consumption, and using a simple fluorescence microscope for data collection. © 2023 Society of Chemical Industry (SCI).

Wheat bran inclusion level impacts its net energy by shaping gut microbiota and regulating heat production in gestating sows

An accurate estimation of net energy (NE) of wheat bran is essential for precision feeding of sows. However, the effects of inclusion level on NE of wheat bran have not been reported. Inclusion level was hypothesized to impact NE of wheat bran by regulating gut microbiota and partitioning of heat production. Therefore, twelve multiparous sows (Yorkshire × Landrace; 2 to 4 parity) were assigned to a replicated 3 × 6 Youden square with 3 successive periods and 6 diets in each square. The experiment included a corn-soybean meal diet (WB0) and five diets including 9.8% (WB10), 19.5% (WB20), 29.2% (WB30), 39.0% (WB40) and 48.7% wheat bran (WB50), respectively. Each period included 6 d of adaptation to diets followed by 6 d for heat production measurement using open-circuit respiration chambers. Compared with other groups, WB30, WB40, and WB50 enriched different fiber-degrading bacteria genera (P < 0.05). Apparent total tract digestibility of neutral detergent fiber and acid detergent fiber of wheat bran were greater in WB30 and WB40 (P < 0.05). Physical activity (standing and sitting) decreased as inclusion level increased (P = 0.04), which tended to decrease related heat production (P = 0.07). Thermic effect of feeding (TEF) was higher in WB50 than other treatments (P < 0.01). Metabolizable energy of wheat bran was similar among treatment groups (except for WB10). NE of wheat bran conformed to a quadratic regression equation with inclusion level (R2 = 0.99, P < 0.01) and peaked at an inclusion level of 35.3%. In conclusion, increasing inclusion level decreased energy expenditure of sows on physical activity and promoted growth of fiber-degrading bacteria, which improved energy utilization of fiber. Fermentation of wheat bran fiber by Prevotellaceae_UCG-003 and norank_f__Paludibacteraceae might increase TEF. Consequently, sows utilized energy in wheat bran most efficiently at an inclusion level of 35.3%.

The different structure of economic ecosystems at the scales of companies and countries

A key element to understand complex systems is the relationship between the spatial scale of investigation and the structure of the interrelation among its elements. When it comes to economic systems, it is now well-known that the country-product bipartite network exhibits a nested structure, which is the foundation of different algorithms that have been used to scientifically investigate countries’ development and forecast national economic growth. Changing the subject from countries to companies, a significantly different scenario emerges. Through the analysis of a unique dataset of Italian firms’ exports and a worldwide dataset comprising countries’ exports, here we find that, while a globally nested structure is observed at the country level, a local, in-block nested structure emerges at the level of firms. This in-block nestedness is statistically significant with respect to suitable null models and the algorithmic partitions of products into blocks correspond well with the UN-COMTRADE product classification. These findings lay a solid foundation for developing a scientific approach based on the physics of complex systems to the analysis of companies, which has been lacking until now.

In situ recovery of taxadiene using solid adsorption in cultivations with Saccharomyces cerevisiae

In this study, an engineered strain of Saccharomyces cerevisiae was used to produce taxadiene, a precursor in the biosynthetic pathway of the anticancer drug paclitaxel. Taxadiene was recovered in situ with the polymeric adsorbent Diaion © HP-20. Here we tested two bioreactor configurations and adsorbent concentrations to maximize the production and recovery of taxadiene. An external recovery configuration (ERC) was performed with the integration of an expanded bed adsorption column, whereas the internal recovery configuration (IRC) consisted in dispersed beads inside the bioreactor vessel. Taxadiene titers recovered in IRC were higher to ERC by 3.4 and 3.5 fold by using 3% and 12% (w/v) adsorbent concentration respectively. On the other hand, cell growth kinetics were faster in ERC which represents an advantage in productivity (mg of taxadiene/L*h). High resin bead concentration (12% w/v) improved the partition of taxadiene onto the beads up to 98%. This result represents an advantage over previous studies using a 3% resin concentration where the partition of taxadiene on the beads was around 50%. This work highlights the potential of in situ product recovery to improve product partition, reduce processing steps and promote cell growth. Nevertheless, a careful design of bioreactor configuration and process conditions is critical.