Mycorrhizal fungi enhance plant nutrient acquisition and modulate nitrogen loss with variable water regimes

Climate change will alter both the amount and pattern of precipitation and soil water availability, which will directly affect plant growth and nutrient acquisition, and potentially, ecosystem functions like nutrient cycling and losses as well. Given their role in facilitating plant nutrient acquisition and water stress resistance, arbuscular mycorrhizal (AM) fungi may modulate the effects of changing water availability on plants and ecosystem functions. The well‐characterized mycorrhizal tomato (Solanum lycopersicum L.) genotype 76R (referred to as MYC+) and the mutant mycorrhiza‐defective tomato genotype rmc were grown in microcosms in a glasshouse experiment manipulating both the pattern and amount of water supply in unsterilized field soil. Following 4 weeks of differing water regimes, we tested how AM fungi affected plant productivity and nutrient acquisition, short‐term interception of a ${}^{15}{\text{NH}}_4^{+}$ pulse, and inorganic nitrogen (N) leaching from microcosms. AM fungi enhanced plant nutrient acquisition with both lower and more variable water availability, for instance increasing plant P uptake more with a pulsed water supply compared to a regular supply and increasing shoot N concentration more when lower water amounts were applied. Although uptake of the short‐term ${}^{15}{\text{NH}}_4^{+}$ pulse was higher in rmc plants, possibly due to higher N demand, AM fungi subtly modulated ${\text{NO}}_3^{?}$ leaching, decreasing losses by 54% at low and high water levels in the regular water regime, with small absolute amounts of ${\text{NO}}_3^{?}$ leached (<1 kg N/ha). Since this study shows that AM fungi will likely be an important moderator of plant and ecosystem responses to adverse effects of more variable precipitation, management strategies that bolster AM fungal communities may in turn create systems that are more resilient to these changes.     

Coherence of terrestrial vertebrate species richness with external drivers across scales and taxonomic groups

Aim

Understanding connections between environment and biodiversity is crucial for conservation, identifying causes of ecosystem stress, and predicting population responses to changing environments. Explaining biodiversity requires an understanding of how species richness and environment covary across scales. Here, we identify scales and locations at which biodiversity is generated and correlates with environment.

Location

Full latitudinal range per continent.

Time Period

Present day.

Major Taxa Studied

Terrestrial vertebrates: all mammals, carnivorans, bats, songbirds, hummingbirds, amphibians.

Methods

We describe the use of wavelet power spectra, cross-power and coherence for identifying scale-dependent trends across Earth's surface. Spectra reveal scale- and location-dependent coherence between species richness and topography (E), mean annual precipitation (Pn), temperature (Tm) and annual temperature range (ΔT).

Results

>97% of species richness of taxa studied is generated at large scales, that is, wavelengths $\gtrsim {10}^3$ km, with 30%–69% generated at scales $\gtrsim {10}^4$ km. At these scales, richness tends to be highly coherent and anti-correlated with E and ΔT, and positively correlated with Pn and Tm. Coherence between carnivoran richness and ΔT is low across scales, implying insensitivity to seasonal temperature variations. Conversely, amphibian richness is strongly anti-correlated with ΔT at large scales. At scales $\lesssim {10}^3$ km, examined taxa, except carnivorans, show highest richness within the tropics. Terrestrial plateaux exhibit high coherence between carnivorans and E at scales $\sim {10}^3$ km, consistent with contribution of large-scale tectonic processes to biodiversity. Results are similar across different continents and for global latitudinal averages. Spectral admittance permits derivation of rules-of-thumb relating long-wavelength environmental and species richness trends.

Main Conclusions

Sensitivities of mammal, bird and amphibian populations to environment are highly scale dependent. At large scales, carnivoran richness is largely independent of temperature and precipitation, whereas amphibian richness correlates strongly with precipitation and temperature, and anti-correlates with temperature range. These results pave the way for spectral-based calibration of models that predict biodiversity response to climate change scenarios.     

Disturbance alters transience but nutrients determine equilibria during grassland succession with multiple global change drivers

Disturbance and environmental change may cause communities to converge on a steady state, diverge towards multiple alternative states or remain in long-term transience. Yet, empirical investigations of successional trajectories are rare, especially in systems experiencing multiple concurrent anthropogenic drivers of change. We examined succession in old field grassland communities subjected to disturbance and nitrogen fertilization using data from a long-term (22-year) experiment. Regardless of initial disturbance, after a decade communities converged on steady states largely determined by resource availability, where species turnover declined as communities approached dynamic equilibria. Species favoured by the disturbance were those that eventually came to dominate the highly fertilized plots. Furthermore, disturbance made successional pathways more direct revealing an important interaction effect between nutrients and disturbance as drivers of community change. Our results underscore the dynamical nature of grassland and old field succession, demonstrating how community properties such as $\beta$ diversity change through transient and equilibrium states.     

On efficient posterior inference in normalized power prior Bayesian analysis

The power prior has been widely used to discount the amount of information borrowed from historical data in the design and analysis of clinical trials. It is realized by raising the likelihood function of the historical data to a power parameter $\delta \in [0,1]$, which quantifies the heterogeneity between the historical and the new study. In a fully Bayesian approach, a natural extension is to assign a hyperprior to δ such that the posterior of δ can reflect the degree of similarity between the historical and current data. To comply with the likelihood principle, an extra normalizing factor needs to be calculated and such prior is known as the normalized power prior. However, the normalizing factor involves an integral of a prior multiplied by a fractional likelihood and needs to be computed repeatedly over different δ during the posterior sampling. This makes its use prohibitive in practice for most elaborate models. This work provides an efficient framework to implement the normalized power prior in clinical studies. It bypasses the aforementioned efforts by sampling from the power prior with $\delta =0$ and $\delta =1$ only. Such a posterior sampling procedure can facilitate the use of a random δ with adaptive borrowing capability in general models. The numerical efficiency of the proposed method is illustrated via extensive simulation studies, a toxicological study, and an oncology study.     

Contrasting responses of peak vegetation growth to asymmetric warming: Evidences from FLUXNET and satellite observations

The peak growth of plant in summer is an important indicator of the capacity of terrestrial ecosystem productivity, and ongoing studies have shown its responses to climate warming as represented in the mean temperature. However, the impacts from the asymmetrical warming, that is, different rates in the changes of daytime (T_max) and nighttime (T_min) warming were mostly ignored. Using 60 flux sites (674 site-year in total) measurements and satellite observations from two independent satellite platforms (Global Inventory Monitoring and Modeling Studies [1982–2015]; MODIS [2000–2020]) over the Northern Hemisphere (≥30°N), here we show that the peak growth, as represented by both flux-based maximum primary productivity and the maximum greenness indices (maximum normalized difference vegetation index and enhanced vegetation index), responded oppositely to daytime and nighttime warming. $T_{\max }^{-}{T}_{\min }^{+}$ (peak growth showed negative responses to T_max, but positive responses to T_min) dominated in most ecosystems and climate types, especially in water-limited ecosystems, while $T_{\max }^{+}{T}_{\min }^{-}$ (peak growth showed positive responses to T_max, but negative responses to T_min) was primarily observed in high latitude regions. These contrasting responses could be explained by the strong association between asymmetric warming and water conditions, including soil moisture, evapotranspiration/potential evapotranspiration, and the vapor pressure deficit. Our results are therefore important to the understanding of the responses of peak growth to climate change, and consequently a better representation of asymmetrical warming in future ecosystem models by differentiating the contributions between daytime and nighttime warming.     

Response of nitrate leaching to no-tillage is dependent on soil,climate, and management factors: A global meta-analysis

No tillage (NT) has been proposed as a practice to reduce the adverse effects of tillage on contaminant (e.g., sediment and nutrient) losses to waterways. Nonetheless, previous reports on impacts of NT on nitrate (${\mathrm{NO}}_3^{-}$) leaching are inconsistent. A global meta-analysis was conducted to test the hypothesis that the response of ${\mathrm{NO}}_3^{-}$ leaching under NT, relative to tillage, is associated with tillage type (inversion vs non-inversion tillage), soil properties (e.g., soil organic carbon [SOC]), climate factors (i.e., water input), and management practices (e.g., NT duration and nitrogen fertilizer inputs). Overall, compared with all forms of tillage combined, NT had 4% and 14% greater area-scaled and yield-scaled ${\mathrm{NO}}_3^{-}$ leaching losses, respectively. The ${\mathrm{NO}}_3^{-}$ leaching under NT tended to be 7% greater than that of inversion tillage but comparable to non-inversion tillage. Greater ${\mathrm{NO}}_3^{-}$ leaching under NT, compared with inversion tillage, was most evident under short-duration NT (<5 years), where water inputs were low (<2 mm day⁻¹), in medium texture and low SOC (<1%) soils, and at both higher (>200 kg ha⁻¹) and lower (0–100 kg ha⁻¹) rates of nitrogen addition. Of these, SOC was the most important factor affecting the risk of NO₃⁻ leaching under NT compared with inversion tillage. Globally, on average, the greater amount of NO₃⁻ leached under NT, compared with inversion tillage, was mainly attributed to corresponding increases in drainage. The percentage of global cropping land with lower risk of NO₃⁻ leaching under NT, relative to inversion tillage, increased with NT duration from 3 years (31%) to 15 years (54%). This study highlighted that the benefits of NT adoption for mitigating ${\mathrm{NO}}_3^{-}$ leaching are most likely in long-term NT cropping systems on high-SOC soils.     

The role of migration in mutant dynamics in fragmented populations

Mutant dynamics in fragmented populations have been studied extensively in evolutionary biology. Yet, open questions remain, both experimentally and theoretically. Some of the fundamental properties predicted by models still need to be addressed experimentally. We contribute to this by using a combination of experiments and theory to investigate the role of migration in mutant distribution. In the case of neutral mutants, while the mean frequency of mutants is not influenced by migration, the probability distribution is. To address this empirically, we performed in vitro experiments, where mixtures of GFP-labelled (“mutant”) and non-labelled (“wid-type”) murine cells were grown in wells (demes), and migration was mimicked via cell transfer from well to well. In the presence of migration, we observed a change in the skewedness of the distribution of the mutant frequencies in the wells, consistent with previous and our own model predictions. In the presence of de novo mutant production, we used modelling to investigate the level at which disadvantageous mutants are predicted to exist, which has implications for the adaptive potential of the population in case of an environmental change. In panmictic populations, disadvantageous mutants can persist around a steady state, determined by the rate of mutant production and the selective disadvantage (selection-mutation balance). In a fragmented system that consists of demes connected by migration, a steady-state persistence of disadvantageous mutants is also observed, which, however, is fundamentally different from the mutation-selection balance and characterized by higher mutant levels. The increase in mutant frequencies above the selection-mutation balance can be maintained in small ($N<N_c$) demes as long as the migration rate is sufficiently small. The migration rate above which the mutants approach the selection-mutation balance decays exponentially with $N/N_c$. The observed increase in the mutant numbers is not explained by the change in the effective population size. Implications for evolutionary processes in diseases are discussed, where the pre-existence of disadvantageous drug-resistant mutant cells or pathogens drives the response of the disease to treatments.     

Biogeographic pattern of living vegetation carbon turnover time in mature forests across continents

Aim

Theoretically, woody biomass turnover time ($\tau$) quantified using outflux (i.e. tree mortality) predicts biomass dynamics better than using influx (i.e. productivity). This study aims at using forest inventory data to empirically test the outflux approach and generate a spatially explicit understanding of woody $\tau$ in mature forests. We further compared woody $\tau$ estimates with dynamic global vegetation models (DGVMs) and with a data assimilation product of C stocks and fluxes—CARDAMOM.

Location

Continents.

Time Period

Historic from 1951 to 2018.

Major Taxa Studied

Trees and forests.

Methods

We compared the approaches of using outflux versus influx for estimating woody $\tau$ and predicting biomass accumulation rates. We investigated abiotic and biotic drivers of spatial woody $\tau$ and generated a spatially explicit map of woody $\tau$ at a 0.25-degree resolution across continents using machine learning. We further examined whether six DGVMs and CARDAMOM generally captured the observational pattern of woody $\tau$.

Results

Woody $\tau$ quantified by the outflux approach better (with R² 0.4–0.5) predicted the biomass accumulation rates than the influx approach (with R² 0.1–0.4) across continents. We found large spatial variations of woody $\tau$ for mature forests, with highest values in temperate forests (98.8 ± 2.6 y) followed by boreal forests (73.9 ± 3.6 y) and tropical forests. The map of woody $\tau$ extrapolated from plot data showed higher values in wetter eastern and pacific coast USA, Africa and eastern Amazon. Climate (temperature and aridity index) and vegetation structure (tree density and forest age) were the dominant drivers of woody $\tau$ across continents. The highest woody $\tau$ in temperate forests was not captured by either DGVMs or CARDAMOM.

Main Conclusions

Our study empirically demonstrated the preference of using outflux over influx to estimate woody $\tau$ for predicting biomass accumulation rates. The spatially explicit map of woody $\tau$ and the underlying drivers provide valuable information to improve the representation of forest demography and carbon turnover processes in DGVMs.     

Climate impact of diverting residual biomass to cement production

Co-firing residual lignocellulosic biomass with fossil fuels is often used to reduce greenhouse gas (GHG) emissions, especially in processes like cement production where fuel costs are critical and residual biomass can be obtained at a low cost. Since plants remove CO₂ from the atmosphere, CO₂ emissions from biomass combustion are often assumed to have zero global warming potential (${\mathrm{GWP}}_{{\mathrm{bCO}}_2}$= 0) and do not contribute to climate forcing. However, diverting residual biomass to energy use has recently been shown to increase the atmospheric CO₂ load when compared to business-as-usual (BAU) practices, resulting in ${\mathrm{GWP}}_{{\mathrm{bCO}}_2}$ values between 0 and 1. A detailed process model for a natural gas-fired cement plant producing 4200 megagrams of clinker per day was used to calculate the material and energy flows, as well as the lifecycle emissions associated with cement production without and with diverted biomass (supplying 50% of precalciner energy demand) from forestry and landfill sources. Biomass co-firing reduced natural gas demand in the precalciner of the cement plant by 39% relative to the reference scenario (100% natural gas), but the total demands for thermal, electrical, and diesel (transportation) energy increased by at least 14%. Assuming ${\mathrm{GWP}}_{{\mathrm{bCO}}_2}$ values of zero for biomass combustion, cement's lifecycle GHG intensity changed from the reference (natural gas only) plant by −40, −23, and − 89 kg CO₂/Mg clinker for diverted biomass from slash burning, forest floor and landfill biomass, respectively. However, using the calculated ${\mathrm{GWP}}_{{\mathrm{bCO}}_2}$ values for diverted biomass from these same fuel sources, the lifecycle GHG intensities changes were −37, +20 and +28 kg CO₂/Mg clinker, respectively. The switch from decreasing to increasing cement plant GHG emissions (i.e., forest floor or landfill feedstocks scenarios) highlights the importance of calculating and using the ${\mathrm{GWP}}_{{\mathrm{bCO}}_2}$ factor when quantifying lifecycle GHG impacts associated with diverting residual biomass to bioenergy use.     

Towards efficient N cycling in intensive maize: role of cover crops and application methods of digestate liquid fraction

Digestate, a by-product of biogas production, is widely recognized as a promising renewable nitrogen (N) source with high potential to replace synthetic fertilizers. Yet, inefficient digestate use can lead to pollutant N losses as ammonia (NH₃) volatilization, nitrous oxide (N₂O) emissions and nitrate (${\mathrm{NO}}_3^{-}$) leaching. Cover crops (CCs) may reduce some of these losses and recycle the N back into the soil after incorporation, but the effect on the N balance depends on the CC species. In a one-year field study, we tested two application methods (i.e., surface broadcasting, BDC; and shallow injection, INJ) of the liquid fraction of separated co-digested cattle slurry (digestate liquid fraction [DLF]), combined with different winter cover crop (CC) options (i.e., rye, white mustard or bare fallow), as starter fertilizer for maize. Later, side-dressing with urea was required to fulfil maize N-requirements. We tested treatment effects on yield, N-uptake, N-use efficiency parameters, and N-losses in the form of N₂O emissions and ${\mathrm{NO}}_3^{-}$ leaching. CC development and biomass production were strongly affected by their contrasting frost tolerance, with spring-regrowth for rye, while mustard was winter killed. After the CCs, injection of DLF increased N₂O emissions significantly compared with BDC (emission factor of 2.69% vs. 1.66%). Nitrous oxide emissions accounted for a small part (11%–13%) of the overall yield-scaled N losses (0.46–0.97 kg N Mg grain⁻¹). The adoption of CCs reduced fall ${\mathrm{NO}}_3^{-}$ leaching, being 51% and 64% lower for mustard and rye than under bare soil. In addition, rye reduced ${\mathrm{NO}}_3^{-}$ leaching during spring and summer after termination by promoting N immobilization, thus leading to −57% lower annual leaching losses compared with mustard. DLF application method modified N-loss pathways, but not the cumulative yield-scaled N losses. Overall, these insights contribute to inform an evidence-based design of cropping systems in which nutrients are recycled more efficiently.     

Multi-objective Bayesian algorithm automatically discovers low-cost high-growth serum-free media for cellular agriculture application

In this work, we applied a multi-information source modeling technique to solve a multi-objective Bayesian optimization problem involving the simultaneous minimization of cost and maximization of growth for serum-free C2C12 cells using a hyper-volume improvement acquisition function. In sequential batches of custom media experiments designed using our Bayesian criteria, collected using multiple assays targeting different cellular growth dynamics, the algorithm learned to identify the trade-off relationship between long-term growth and cost. We were able to identify several media with $>100\%$ more growth of C2C12 cells than the control, as well as a medium with 23% more growth at only 62.5% of the cost of the control. These algorithmically generated media also maintained growth far past the study period, indicating the modeling approach approximates the cell growth well from an extremely limited data set.     

CO2 fertilization contributed more than half of the observed forest biomass increase in northern extra-tropical land

The existence of a large-biomass carbon (C) sink in Northern Hemisphere extra-tropical ecosystems (NHee) is well-established, but the relative contribution of different potential drivers remains highly uncertain. Here we isolated the historical role of carbon dioxide (CO₂) fertilization by integrating estimates from 24 CO₂-enrichment experiments, an ensemble of 10 dynamic global vegetation models (DGVMs) and two observation-based biomass datasets. Application of the emergent constraint technique revealed that DGVMs underestimated the historical response of plant biomass to increasing [CO₂] in forests (${\beta }_{\text{Forest}}^{\mathrm{Mod}}$) but overestimated the response in grasslands (${\beta }_{\text{Grass}}^{\mathrm{Mod}}$) since the 1850s. Combining the constrained ${\beta }_{\text{Forest}}^{\mathrm{Mod}}$ (0.86 ± 0.28 kg C m⁻² [100 ppm]⁻¹) with observed forest biomass changes derived from inventories and satellites, we identified that CO₂ fertilization alone accounted for more than half (54 ± 18% and 64 ± 21%, respectively) of the increase in biomass C storage since the 1990s. Our results indicate that CO₂ fertilization dominated the forest biomass C sink over the past decades, and provide an essential step toward better understanding the key role of forests in land-based policies for mitigating climate change.     

Global soil nitrogen cycle pattern and nitrogen enrichment effects: Tropical versus subtropical forests

Tropical and subtropical forest biomes are a main hotspot for the global nitrogen (N) cycle. Yet, our understanding of global soil N cycle patterns and drivers and their response to N deposition in these biomes remains elusive. By a meta-analysis of 2426-single and 161-paired observations from 89 published ¹⁵ N pool dilution and tracing studies, we found that gross N mineralization (GNM), immobilization of ammonium ($I_{{\mathrm{NH}}_4}$) and nitrate ($I_{{\mathrm{NO}}_3}$), and dissimilatory nitrate reduction to ammonium (DNRA) were significantly higher in tropical forests than in subtropical forests. Soil N cycle was conservative in tropical forests with ratios of gross nitrification (GN) to $I_{{\mathrm{NH}}_4}$ (GN/$I_{{\mathrm{NH}}_4}$) and of soil nitrate to ammonium (NO₃⁻/NH₄⁺) less than one, but was leaky in subtropical forests with GN/$I_{{\mathrm{NH}}_4}$ and NO₃⁻/NH₄⁺ higher than one. Soil NH₄⁺ dynamics were mainly controlled by soil substrate (e.g., total N), but climatic factors (e.g., precipitation and/or temperature) were more important in controlling soil NO₃⁻ dynamics. Soil texture played a role, as GNM and $I_{{\mathrm{NH}}_4}$ were positively correlated with silt and clay contents, while $I_{{\mathrm{NO}}_3}$ and DNRA were positively correlated with sand and clay contents, respectively. The soil N cycle was more sensitive to N deposition in tropical forests than in subtropical forests. Nitrogen deposition leads to a leaky N cycle in tropical forests, as evidenced by the increase in GN/$I_{{\mathrm{NH}}_4}$, NO₃⁻/NH₄⁺, and nitrous oxide emissions and the decrease in $I_{{\mathrm{NO}}_3}$ and DNRA, mainly due to the decrease in soil microbial biomass and pH. Dominant tree species can also influence soil N cycle pattern, which has changed from conservative in deciduous forests to leaky in coniferous forests. We provide global evidence that tropical, but not subtropical, forests are characterized by soil N dynamics sustaining N availability and that N deposition inhibits soil N retention and stimulates N losses in these biomes.     

Dryness limits vegetation pace to cope with temperature change in warm regions

Climate change leads to increasing temperature and more extreme hot and drought events. Ecosystem capability to cope with climate warming depends on vegetation's adjusting pace with temperature change. How environmental stresses impair such a vegetation pace has not been carefully investigated. Here we show that dryness substantially dampens vegetation pace in warm regions to adjust the optimal temperature of gross primary production (GPP) ($T_{\mathrm{opt}}^{\mathrm{GPP}}$) in response to change in temperature over space and time. $T_{\mathrm{opt}}^{\mathrm{GPP}}$ spatially converges to an increase of 1.01°C (95% CI: 0.97, 1.05) per 1°C increase in the yearly maximum temperature (T_max) across humid or cold sites worldwide (37^oS–79^oN) but only 0.59°C (95% CI: 0.46, 0.74) per 1°C increase in T_max across dry and warm sites. $T_{\mathrm{opt}}^{\mathrm{GPP}}$ temporally changes by 0.81°C (95% CI: 0.75, 0.87) per 1°C interannual variation in T_max at humid or cold sites and 0.42°C (95% CI: 0.17, 0.66) at dry and warm sites. Regardless of the water limitation, the maximum GPP (GPP_max) similarly increases by 0.23 g C m⁻² day⁻¹ per 1°C increase in $T_{\mathrm{opt}}^{\mathrm{GPP}}$ in either humid or dry areas. Our results indicate that the future climate warming likely stimulates vegetation productivity more substantially in humid than water-limited regions.     

Sensitivity analysis and adaptive mutation strategy differential evolution algorithm for optimizing enzymes' turnover numbers in metabolic models

Genome-scale metabolic network model (GSMM) based on enzyme constraints greatly improves general metabolic models. The turnover number ($k_{\mathrm{cat}}$) of enzymes is used as a parameter to limit the reaction when extending GSMM. Therefore, turnover number plays a crucial role in the prediction accuracy of cell metabolism. In this work, we proposed an enzyme-constrained GSMM parameter optimization method. First, sensitivity analysis of the parameters was carried out to select the parameters with the greatest influence on predicting the specific growth rate. Then, differential evolution (DE) algorithm with adaptive mutation strategy was adopted to optimize the parameters. This algorithm can dynamically select five different mutation strategies. Finally, the specific growth rate prediction, flux variability, and phase plane of the optimized model were analyzed to further evaluate the model. The enzyme-constrained GSMM of Saccharomyces cerevisiae, ecYeast8.3.4, was optimized. Results of the sensitivity analysis showed that the optimization variables can be divided into three groups based on sensitivity: most sensitive (149 $k_{\mathrm{cat}}$c), highly sensitive (1759 $k_{\mathrm{cat}}$), and nonsensitive (2502 $k_{\mathrm{cat}}$) groups. Six optimization strategies were developed based on the results of the sensitivity analysis. The results showed that the DE with adaptive mutation strategy can indeed improve the model by optimizing highly sensitive parameters. Retaining all parameters and optimizing the highly sensitive parameters are the recommended optimization strategy.     

Data implementation matters: Effect of software choice and LCI database evolution on a comparative LCA study of permanent magnets

Life cycle assessment (LCA) databases and software evolve. We analyzed to which extent software and evolving life cycle inventory databases affect the comparison of technology alternatives, using a comparative LCA on permanent magnets as a case study, with two selected software tools: CMLCA and Brightway LCA. We migrated the system models from the CMLCA to Brightway LCA software and alternated between the ecoinvent database versions 2.2 and 3.1 to 3.6 in the system background. When using ecoinvent v3.6 instead of v2.2, the change of the indicator results ranged from $-34\%$ to 283%. The evolution of the ecoinvent database impacted the absolute amounts of the characterized results and the relative performance between alternatives. The impact category with the highest variability was ionizing radiation, which even showed a ranking inversion with ecoinvent v3.4. In contrast, the impact of using CMLCA or Brightway was negligible because the same data and modeling assumptions caused percentage differences below 0.4%. During the semi-automated data migration to Brightway, we identified 23 environmental flows in the CMLCA model that were not paired with their corresponding characterization factors in the published study of reference. This error had led to an underestimation of 63% in the photochemical oxidation indicator of one of the alternatives. This underestimation relates to an interoperability issue regarding the nomenclature of environmental flows in software alternatives and is a matter of data implementation rather than an issue intrinsic to the selected software. Finally, we identified improvement opportunities for the transparency and reusability of LCA models. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/badges .      

Center-augmented ℓ2-type regularization for subgroup learning

The existing methods for subgroup analysis can be roughly divided into two categories: finite mixture models (FMM) and regularization methods with an ℓ₁-type penalty. In this paper, by introducing the group centers and ℓ₂-type penalty in the loss function, we propose a novel center-augmented regularization (CAR) method; this method can be regarded as a unification of the regularization method and FMM and hence exhibits higher efficiency and robustness and simpler computations than the existing methods. In particular, its computational complexity is reduced from the $O(n^2)$ of the conventional pairwise-penalty method to only $O(nK)$, where n is the sample size and K is the number of subgroups. The asymptotic normality of CAR is established, and the convergence of the algorithm is proven. CAR is applied to a dataset from a multicenter clinical trial, Buprenorphine in the Treatment of Opiate Dependence; a larger R² is produced and three additional significant variables are identified compared to those of the existing methods.     

Concave likelihood-based regression with finite-support response variables

We propose a unified framework for likelihood-based regression modeling when the response variable has finite support. Our work is motivated by the fact that, in practice, observed data are discrete and bounded. The proposed methods assume a model which includes models previously considered for interval-censored variables with log-concave distributions as special cases. The resulting log-likelihood is concave, which we use to establish asymptotic normality of its maximizer as the number of observations n tends to infinity with the number of parameters d fixed, and rates of convergence of L₁-regularized estimators when the true parameter vector is sparse and d and n both tend to infinity with $\log (d)/n\to 0$. We consider an inexact proximal Newton algorithm for computing estimates and give theoretical guarantees for its convergence. The range of possible applications is wide, including but not limited to survival analysis in discrete time, the modeling of outcomes on scored surveys and questionnaires, and, more generally, interval-censored regression. The applicability and usefulness of the proposed methods are illustrated in simulations and data examples.     

Time-dependent sorption behavior of lentiviral vectors during anion-exchange chromatography

Use of lentiviral vectors (LVs) in clinical Cell and Gene Therapy applications is growing. However, functional product loss during capture chromatography, typically anion-exchange (AIEX), remains a significant unresolved challenge for the design of economic processes. Despite AIEX's extensive use, variable performance and generally low recovery is reported. This poor understanding of product loss mechanisms highlights a significant gap in our knowledge of LV adsorption and other types of vector delivery systems. This work demonstrates HIV-1-LV recovery over quaternary-amine membrane adsorbents is a function of time in the adsorbed state. Kinetic data for product loss in the column bound state was generated. Fitting a second order-like rate model, we observed a rapid drop in functional recovery due to increased irreversible binding for vectors encoding two separate transgenes ($t_{Y_{1/2}}$ = 12.7 and 18.7 min). Upon gradient elution, a two-peak elution profile implicating the presence of two distinct binding subpopulations is observed. Characterizing the loss kinetics of these two subpopulations showed a higher rate of vector loss in the weaker binding peak. This work highlights time spent in the adsorbed state as a critical factor impacting LV product loss and the need for consideration in LV AIEX process development workflows.     

A comprehensive review on bio-mimicked multimolecular frameworks and supramolecules as scaffolds for enzyme immobilization

Immobilization depicts a propitious route to optimize the catalytic performances, efficient recovery, minimizing autocatalysis, and also augment the stabilities of enzymes, particularly in unnatural environments. In this opinion, supramolecules and multimolecular frameworks have captivated immense attention to achieve profound controllable interactions between enzyme molecules and well-defined natural or synthetic architectures to yield protein bioconjugates with high accessibility for substrate binding and enhanced enantioselectivities. This scholastic review emphasizes the possibilities of associating multimolecular complexes with biological entities via several types of interactions, namely covalent interactions, host–guest complexation, $\mathrm{\pi }-\mathrm{\pi }$ interactions, intra/inter hydrogen bondings, electrostatic interactions, and so forth offers remarkable applications for the modulations of enzymes. The potential synergies between artificial supramolecular structures and biological systems are the primary concern of this pedagogical review. The majority of the research primarily focused on the dynamic biomolecule-responsive supramolecular assemblages and multimolecular architectures as ideal platforms for the recognition and modulation of proteins and cells. Embracing sustainable green demeanors of enzyme immobilizations in a quest to reinforce site-selectivity, catalytic efficiency, and structural integrality of enzymes are the contemporary requirements of the biotechnological sectors that instigate the development of novel biocatalytic systems.