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Abstract Open science practices have been widely discussed and have been implemented with varying success in different disciplines. We argue that computational-x disciplines such as computational social science, are also susceptible to the symptoms of the crises, but in terms of reproducibility. We expand the binary definition of reproducibility into a tier system which allows increasing levels of reproducibility based on external verifiability to counteract the practice of open-washing. We provide solutions for barriers in Computational Social Science that hinder researchers from obtaining the highest level of reproducibility, including the use of alternate data sources and considering reproducibility proactively.

Rationale of the trial Although the use of engineered T cells in cancer immunotherapy has greatly advanced the treatment of hematological malignancies, reaching meaningful clinical responses in the treatment of solid tumors is still challenging. We investigated the safety and tolerability of IMA202 in a first-in-human, dose escalation basket trial in human leucocyte antigen A*02:01 positive patients with melanoma-associated antigen A1 (MAGEA1)-positive advanced solid tumors.Trial design The 2+2 trial design was an algorithmic design based on a maximally acceptable dose-limiting toxicity (DLT) rate of 25% and the sample size was driven by the algorithmic design with a maximum of 16 patients. IMA202 consists of autologous genetically modified cytotoxic CD8+ T cells expressing a T cell receptor (TCR), which is specific for a nine amino acid peptide derived from MAGEA1. Eligible patients underwent leukapheresis, T cells were isolated, transduced with lentiviral vector carrying MAGEA1-specific TCR and following lymphodepletion (fludarabine/cyclophosphamide), infused with a median of 1.4×109 specific T cells (range, 0.086×109–2.57×109) followed by interleukin 2.Safety of IMA202 No DLT was observed. The most common grade 3–4 adverse events were cytopenias, that is, neutropenia (81.3%), lymphopenia (75.0%), anemia (50.0%), thrombocytopenia (50.0%) and leukopenia (25.0%). 13 patients experienced cytokine release syndrome, including one grade 3 event. Immune effector cell-associated neurotoxicity syndrome was observed in two patients and was grade 1 in both.Efficacy of IMA202 Of the 16 patients dosed, 11 (68.8%) patients had stable disease (SD) as their best overall response (Response Evaluation Criteria in Solid Tumors V.1.1). Five patients had initial tumor shrinkage in target lesions and one patient with SD experienced continued shrinkage in target lesions for 3 months in total but had to be classified as progressive disease due to progressive non-target lesions. IMA202 T cells were persistent in peripheral blood for several weeks to months and were also detectable in tumor tissue. Peak persistence was higher in patients who received higher doses.Conclusion In conclusion, IMA202 had a manageable safety profile, and it was associated with biological and potential clinical activity of MAGEA1-targeting genetically engineered TCR-T cells in a poor prognosis, multi-indication solid tumor cohort.Trial registration numbers NCT04639245, NCT05430555.

Abstract Peptide human leukocyte antigen (pHLA) targeting therapeutics like T-cell receptor based adoptive cell therapy or bispecific T cell engaging receptor molecules hold great promise for the treatment of cancer. Comprehensive pre-clinical screening of therapeutic candidates is important to ensure patient safety but is challenging because of the size of the potential off-target space. By combining stabilized peptide-receptive HLA molecules with microarray printing and screening, we have developed an ultra-high-throughput screening platform named ValidaTe that enables large scale evaluation of pHLA-binder interactions. We demonstrate its potential by measuring and analyzing over 30.000 binding curves for a high-affinity T cell Engaging Receptor towards a large pHLA library. Compared to a dataset obtained by conventional bio-layer interferometry measurements, we illustrate that a massively increased throughput (over 650 fold) is obtained by our microarray screening, paving the way for use in pre-clinical safety screening of pHLA-targeting drugs.

Abstract Though algorithms promise many benefits including efficiency, objectivity and accuracy, they may also introduce or amplify biases. Here we study two well-known algorithms, namely PageRank and Who-to-Follow (WTF), and show to what extent their ranks produce inequality and inequity when applied to directed social networks. To this end, we propose a directed network model with preferential attachment and homophily (DPAH) and demonstrate the influence of network structure on the rank distributions of these algorithms. Our main findings suggest that (i) inequality is positively correlated with inequity, (ii) inequality is driven by the interplay between preferential attachment, homophily, node activity and edge density, and (iii) inequity is driven by the interplay between homophily and minority size. In particular, these two algorithms reduce, replicate and amplify the representation of minorities in top ranks when majorities are homophilic, neutral and heterophilic, respectively. Moreover, when this representation is reduced, minorities may improve their visibility in the rank by connecting strategically in the network. For instance, by increasing their out-degree or homophily when majorities are also homophilic. These findings shed light on the social and algorithmic mechanisms that hinder equality and equity in network-based ranking and recommendation algorithms.

Abstract Social networks are very important carriers of information. For instance, the political leaning of our friends can serve as a proxy to identify our own political preferences. This explanatory power is leveraged in many scenarios ranging from business decision-making to scientific research to infer missing attributes using machine learning. However, factors affecting the performance and the direction of bias of these algorithms are not well understood. To this end, we systematically study how structural properties of the network and the training sample influence the results of collective classification. Our main findings show that (i) mean classification performance can empirically and analytically be predicted by structural properties such as homophily, class balance, edge density and sample size, (ii) small training samples are enough for heterophilic networks to achieve high and unbiased classification performance, even with imperfect model estimates, (iii) homophilic networks are more prone to bias issues and low performance when group size differences increase, (iv) when sampling budgets are small, partial crawls achieve the most accurate model estimates, and degree sampling achieves the highest overall performance. Our findings help practitioners to better understand and evaluate their results when sampling budgets are small or when no ground-truth is available.

In cold regions, climate change is expected to result in warmer winter temperatures and increased temperature variability. Coupled with changing precipitation regimes, these changes can decrease soil insulation by reducing snow cover, exposing soils to colder temperatures and more frequent and extensive soil freezing and thawing. Freeze-thaw events can exert an important control over winter soil processes and the cycling of nitrogen (N), with consequences for soil health, nitrous oxide (N2O) emissions, and nearby water quality. These impacts are especially important for agricultural soils and practices in cold regions. We conducted a lysimeter experiment to assess the effects of winter pulsed warming, soil texture, and snow cover on N cycling in agricultural soils. We monitored the subsurface soil temperature, moisture, and porewater geochemistry together with air temperature, precipitation, and N2O fluxes in four agricultural field-controlled lysimeter systems (surface area of 1 m2 and depth of 1.5 m) at the University of Guelph’s Elora Research Station over one winter (December 2020 to April 2021). The lysimeters featured two soil types (loamy sand and silt loam) which were managed under a corn-soybean-wheat rotation with cover crops. Additionally, ceramic infrared heaters located above two of the lysimeters were turned on after each snowfall event to melt the snow and then turned off to mimic snow-free winter conditions with increased soil freezing. Porewater samples collected from five depths in the lysimeters were analyzed for total dissolved nitrogen (TDN), nitrate (NO3−), nitrite (NO2−), and ammonium (NH4+). N2O fluxes were measured using automated soil gas chambers installed on each lysimeter. The results from the snow removed lysimeters were compared to those of lysimeters without heaters (with snow). As expected, the removal of the insulating snow cover resulted in more intense soil freeze-thaw events, causing increased dissolved N loss from the lysimeter systems as N2O (from the silt loam system) and via NO3− leaching (from the loamy sand system). In the silt loam lysimeter, we attribute the freeze thaw-enhanced N2O fluxes to de novo processes rather than gas build up and release. In the loamy sand lysimeter, we attribute the increased NO3− leaching to the larger pore size and therefore lower water retention capacity of this soil type. Overall, our study illustrates the important role of winter snow cover dynamics and soil freezing in modulating the coupled responses of soil moisture, temperature, and N cycling.

Leaching of nitrate (NO3−)—a reactive nitrogen form with impacts on ecosystem health—increases during the non-growing season (NGS) of agricultural soils under cold climates. Cover crops are effective at reducing NGS NO3− leaching, but this benefit may be altered with less snow cover inducing more soil freezing under warmer winters. Our objective was to quantify the effect of winter warming on NO3− leaching from cover crops for a loamy sand (LS) and a silt loam (SIL) soil. This research was conducted over 2 years in Ontario, Canada, using 18 high-precision weighing lysimeters designed to study ecosystem services from agricultural soils. Infra-red heaters were used to simulate warming in lysimeters under a wheat-corn-soybean rotation planted with a cover crop mixture with (+H) and without heating (-H). Nitrate leaching determination used NO3− concentration at 90 cm (discrete sampling) and high temporal resolution drainage volume measurements. Data were analyzed for fall, overwinter, spring-thaw, post-planting, and total period (i.e., November 1 to June 30 of 2017/2018 and 2018/2019). Warming significantly affected soil temperature and soil water content—an effect that was similar for both years. As expected, experimental units under + H presented warmer soils at 5 and 10 cm, along with higher soil water content in liquid form than –H lysimeters, which translated into higher drainage values for + H than –H, especially during the overwinter period. NO3− concentrations at 90 cm were only affected by winter heating for the LS soil. The drainage and NO3− concentrations exhibited high spatial variation, which likely reduced the sensitivity to detect significant differences. Thus, although absolute differences in NO3− leaching between LS vs. SIL and +H (LS) vs. –H (LS) were large, only a trend occurred for higher leaching in LS in 2018/2019. Our research demonstrated that soil heating can influence overwinter drainage (for LS and SIL soils) and NO3− concentration at 90 cm in the LS soil—important NO3− leaching controlling factors. However, contrary to our initial hypothesis, the heating regime adopted in our study did not promote colder soils during the winter. We suggest different heating regimes such as intermittent heating to simulate extreme weather freeze/thaw events as a future research topic.

People differ in intelligence, cognitive ability, personality traits, motivation, and similar valued and, to a large degree, inherited characteristics that determine success and achievements. When does individual heterogeneity lead to a fair distribution of rewards and outcomes? Here, we develop this question theoretically and then test it experimentally for a set of structural conditions in a specific interaction situation. We first catalogue the functional relationship between individual endowments and outcomes to distinguish between fairness concepts such as meritocracy, equality of opportunity, equality of outcomes, and Rawl’s theory of justice. We then use an online experiment to study which of these fairness patterns emerge when differently endowed individuals can share their resources with others, depending on whether information about others’ endowments and outcomes is available. We find that while visible outcomes lessen inequality by decreasing the statistical dispersion of outcomes across the group, endowments need to be visible for better equality of opportunity for the most disadvantaged.

Abstract During the past decades the importance of soft skills for labour market outcomes has grown substantially. This carries implications for labour market inequality, since previous research shows that soft skills are not valued equally across race and gender. This work explores the role of soft skills in job advertisements by drawing on methods from computational science as well as on theoretical and empirical insights from economics, sociology and psychology. We present a semi-automatic approach based on crowdsourcing and text mining for extracting a list of soft skills. We find that soft skills are a crucial component of job ads, especially of low-paid jobs and jobs in female-dominated professions. Our work shows that soft skills can serve as partial predictors of the gender composition in job categories and that not all soft skills receive equal wage returns at the labour market. Especially “female” skills are frequently associated with wage penalties. Our results expand the growing literature on the association of soft skills on wage inequality and highlight their importance for occupational gender segregation at labour markets.

Addition of sulfuric acid (H2SO4) to liquid dairy manure (slurry) reduces methane (CH4), nitrous oxide (N2O), and ammonia (NH3) emissions. There is interest in understanding how gaseous emissions respond to decreasing rates of acidification, to determine economically optimum application rates. Acidification rates were tested ranging from 0 to 2 g sulfuric acid (H2SO4) L−1 slurry in six meso-scale outdoor storage tanks, each filled with 10.6 m3 slurry and stored for 114 d. Results showed that the rate of acidification for maximum inhibition of CH4 and NH3 emissions varied markedly, whereas N2O reductions were modest. Reductions of CH4 increased with acid rate from 0 to 1.2 g L−1, with no additional response beyond >1.2 g L−1. In contrast to CH4, inhibitions of NH3 showed a linear response across all rates, although reductions were ≤ 30%. Thus, higher acidification rates would be required to achieve greater NH3 emission reductions. Our findings indicate that achieving >85% NH3 emissions reductions would require 4 × more acid than achieving >85% CH4 reductions. Decisions on optimum H2SO4 rates will depend on the need to mitigate CH4 emissions (the primary greenhouse gas emitted from stored liquid manure) or reduce NH3 emissions (which is regulated in some regions). These results will help develop guidelines related to the potential costs and benefits of reducing emissions through acidification.

Liquid manure storages are an important source of greenhouse gases (GHG) on dairy farms. Methane (CH4) and nitrous oxide (N2O) are the predominant GHGs, while ammonia (NH3) is an indirect source of N2O. Addition of acid to manure has shown promising emission reductions, however, cost of acidification may be unfeasible for farmers. Fully cleaning storages has also shown to reduce CH4, due to removal of inoculating effects of residual manure (“inoculum”) on fresh manure (FM). However, complete removal of inoculum is practically impossible on large farms, thus acidifying only the inoculum may reduce GHGs without requiring acidification of all FM. This study aimed to quantify the effect of acidified inoculum on CH4, N2O, and NH3 emissions from stored manure and quantify the changes in methanogen abundance and activity. Emissions were measured from six 10.6 m3 storages filled with 20% inoculum (1-year-old manure) and 80% FM. Inoculum was treated in three ways: untreated (control); previously acidified (1-year prior); and newly acidified with 70% H2SO4 (1.1 L m−3 manure). The CH4 and N2O emissions were continuously measured from June—November using tunable diode trace gas analyzers coupled with venturi air flow systems. The NH3 emissions were measured at 24-h intervals 3 × weekly using acid traps. The activity and abundance of methanogens were quantified by targeting the Methyl Coenzyme M Reductase A (mcrA) gene and transcript which encodes a subunit of the key enzyme that catalyzes the final step of methanogenesis. Bacterial abundance was quantified by targeting the bacterial 16S rRNA gene. Quantifications were performed using quantitative real-time PCR. CH4 emissions were reduced by 77% using newly acidified inoculum and 38% using previously acidified inoculum, compared to the control with untreated inoculum (36.1 g CH4 m−2). Significant treatment reductions in mcrA gene and transcript abundance suggest that CH4 reductions were caused by disruption of methanogen activity. NH3 and N2O emissions were reduced by 33 and 73% using acidified inoculum and 23 and 50% using previously acidified inoculum, respectively, compared to the control. Results suggest that lower acid rates and acidifying less frequently may still have good treatment effects while minimizing cost.

The positive effect of soil organic matter (SOM) on crop yield has historically been attributed to the ability of SOM to supply crops with nitrogen and water. Whether management-induced increases in SOM meaningfully supplement water supply has received recent scrutiny, introducing uncertainty to the mechanisms by which SOM benefits crops. Here, we posit that to benefit crops SOM does not need to increase the supply of a growth-limiting resource; it only needs to facilitate root access to extant resource stocks. We highlight evidence for the ability of SOM to alleviate negative impacts of inadequate aeration (mainly waterlogging) and compaction on roots. Waterlogging, even if transient, can permanently downregulate root biosynthesis and call for expensive growth of new roots. Management practices that promote SOM reduce waterlogging by accelerating water infiltration and may promote aeration in non-saturated soils. Compaction as a restriction to root development manifests in drying soils, when mechanical impedance (MI) inflates photosynthate required to extend root tips, leading to short, thick, and shallow roots. SOM reduces MI in dry soils and is associated with root channels to subsoil, granting crops access to deep soil water. Both waterlogging and compaction necessitate additional belowground investment per unit resource uptake. In this framework, crop response to SOM depends on interactions of crop susceptibility to inadequate aeration or compaction, soil moisture, and “baseline” soil aeration and compaction status. By exploring the proposition that SOM catalyzes resource uptake by permitting root development, future research may constrain crop yield improvements expected from SOM management.

The soil water retention curve (SWRC) shows the relationship between soil water (θ) and water potential (ψ) and provides fundamental information for quantifying and modeling soil water entry, storage, flow, and groundwater recharge processes. While traditionally it is measured in a laboratory through cumbersome and time-intensive methods, soil sensors measuring in-situ θ and ψ show strong potential to estimate in-situ SWRC. The objective of this study was to estimate in-situ SWRC at different depths under two different soil types by integrating measured θ and ψ using two commercial sensors: time-domain reflectometer (TDR) and dielectric field water potential (e.g., MPS-6) principles. Parametric models were used to quantify θ—ψ relationships at various depths and were compared to laboratory-measured SWRC. The results of the study show that combining TDR and MPS-6 sensors can be used to estimate plant-available water and SWRC, with a mean difference of −0.03 to 0.23 m3m−3 between the modeled data and laboratory data, which could be caused by the sensors’ lack of site-specific calibration or possible air entrapment of field soil. However, consistent trends (with magnitude differences) indicated the potential to use these sensors in estimating in-situ and dynamic SWRC at depths and provided a way forward in overcoming resource-intensive laboratory measurements.

Crops can uptake only a fraction of nitrogen from nitrogenous fertilizer, while losing the remainder through volatilization, leaching, immobilization and emissions from soils. The emissions of nitrogen in the form of nitrous oxide (N2O) have a strong potency for global warming and depletion of stratospheric ozone. N2O gets released due to nitrification and denitrification processes, which are aided by different environmental, management and soil variables. In recent years, researchers have focused on understanding and simulating the N2O emission processes from agricultural farms and/or watersheds by using process-based models like Daily CENTURY (DAYCENT), Denitrification-Decomposition (DNDC) and Soil and Water Assessment Tool (SWAT). While the former two have been predominantly used in understanding the science of N2O emission and its execution within the model structure, as visible from a multitude of research articles summarizing their strengths and limitations, the later one is relatively unexplored. The SWAT is a promising candidate for modeling N2O emission, as it includes variables and processes that are widely reported in the literature as controlling N2O fluxes from soil, including nitrification and denitrification. SWAT also includes three-dimensional lateral movement of water within the soil, like in real-world conditions, unlike the two-dimensional biogeochemical models mentioned above. This article aims to summarize the N2O emission processes, variables affecting N2O emission and recent advances in N2O emission modeling techniques in SWAT, while discussing their applications, strengths, limitations and further recommendations.

Liquid dairy manure treated with sulfuric acid was stored in duplicate pilot-scale storage tanks for 120 days with continuous monitoring of CH4 emissions and concurrent examination of changes in the structure of bacterial and methanogenic communities. Methane emissions were monitored at the site using laser-based Trace Gas Analyzer whereas quantitative real-time polymerase chain reaction and massively parallel sequencing were employed to study bacterial and methanogenic communities using 16S rRNA and methyl-coenzyme M Reductase A (mcrA) genes/transcripts, respectively. When compared with untreated slurries, acidification resulted in 69–84% reductions of cumulative CH4 emissions. The abundance, activity, and proportion of bacterial communities did not vary with manure acidification. However, the abundance and activity of methanogens (as estimated from mcrA gene and transcript copies, respectively) in acidified slurries were reduced by 6 and 20%, respectively. Up to 21% reduction in mcrA transcript/gene ratios were also detected in acidified slurries. Regardless of treatment, Methanocorpusculum predominated archaeal 16S rRNA and mcrA gene and transcript libraries. The proportion of Methanosarcina, which is the most metabolically-diverse methanogen, was the significant discriminant feature between acidified and untreated slurries. In acidified slurries, the relative proportions of Methanosarcina were ≤ 10%, whereas in untreated slurries, it represented up to 24 and 53% of the mcrA gene and transcript libraries, respectively. The low proportions of Methanosarcina in acidified slurries coincided with the reductions in CH4 emissions. The results suggest that reduction of CH4 missions achieved by acidification was due to an inhibition of the growth and activity of Methanosarcina species.

From small communities to entire nations and society at large, inequality in wealth, social status, and power is one of the most pervasive and tenacious features of the social world. What causes inequality to emerge and persist? In this study, we investigate how the structure and rules of our interactions can increase inequality in social groups. Specifically, we look into the effects of four structural conditions-network structure, network fluidity, reputation tracking, and punishment institutions-on the distribution of earnings in network cooperation games. We analyze 33 experiments comprising 96 experimental conditions altogether. We find that there is more inequality in clustered networks compared to random networks, in fixed networks compared to randomly rewired and strategically updated networks, and in groups with punishment institutions compared to groups without. Secondary analyses suggest that the reasons inequality emerges under these conditions may have to do with the fact that fixed networks allow exploitation of the poor by the wealthy and clustered networks foster segregation between the poor and the wealthy, while the burden of costly punishment falls onto the poor, leaving them poorer. Surprisingly, we do not find evidence that inequality is affected by reputation in a systematic way but this could be because reputation needs to play out in a particular network environment in order to have an effect. Overall, our findings suggest possible strategies and interventions to decrease inequality and mitigate its negative impact, particularly in the context of mid- and large-sized organizations and online communities.

The accurate measurement of greenhouse gas emissions is a challenge for atmospheric science. Long-range open-path sensors are flexible enough to be applied to a variety of complex emission sources, and single devices are often used to measure both high and low path-integrated concentrations. As this technology develops, it is important to examine potential sources of inaccuracy. A GasFinder3 open-path laser was tested with a range of path-integrated concentrations from 11.7 to 182 ppm∙m CH4 using certified standard gases. The measured path-integrated concentrations had a positive bias which was higher than 10% at low path-integrated concentrations (

Rubber tree plantations may improve the soil’s physical and chemical properties, and they may sequester atmospheric carbon in the biomass or the soil. However, the potential role of these plantations in sequestering carbon in the soil and plant biomass has not been fully evaluated. This study evaluated rubber tree plantations at Paranapoema, which is located in the northwestern region of the Paraná state of Brazil, to measure the biomass in plantations of different ages and to determine the organic carbon content and d13C in the soils. Biomass accumulation was evaluated using the destructive method in plantations of different ages. The total carbon stock in the top 60 cm of the soil was 63.4 Mg C ha-1 for the pasture adjacent to the plantations and 66.8 and 79.3 Mg C ha-1 for the 4- and 15-year-old rubber tree plantations, respectively. These values are equivalent to an annual increase in soil carbon stocks of 0.85 and 1.06 Mg ha-1, respectively, and they do not include the accumulation of carbon as tree woody biomass. The soil d13C indicated a relatively fast conversion from the previous C4-C (pasture; Brachiaria-Urochloa brizantha) to C3-C (rubber tree). The results from this study suggest that rubber tree plantations have untapped potential to sequester carbon over a relatively short time period.