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Abstract Open Science Partnerships (OSPs) are gaining attention as alternatives to university–industry collaborations with restrictive IPR and knowledge sharing policies. OSPs have different expected outcomes and deploy varying means to reach them. Appreciating these differences is crucial to understanding their scientific and socio-economic impact, and yet these differences have never been systematically investigated. This exploratory study draws on qualitative case studies of five biomedical OSPs involving academic partners and pharmaceutical companies. It identifies key elements—purpose, activities and structure—that can be used to describe how OSPs are designed. We identify two key aspects of purpose—predominant intent and research aims—which we argue affect the activities and structure of an OSP. Based on these two aspects, we propose four ideal types of OSPs that are designed to provide a starting point for researchers who explore the nature and impact of OSPs and for practitioners who are developing OSPs and wish to ensure that they deploy appropriate means to meet the intended outcomes of their partnership.

The treatment of relapsed and refractory multiple myeloma has improved substantially in the last 5–10 years based on the development and use of several novel classes of drugs and drug combinations. These advances have led to improvements in progression-free and overall survival as well as quality of life. The general tendency has been to advance drugs/combinations that have performed well in advanced disease to the earlier line settings (frontline, first/early relapse). There are several triplet drug combinations that, when used as part of first or early relapse, can provide remission durations of 3 years or longer. More recently, impressive responses have been seen with the use of targeted immunotherapeutics (chimeric antigen receptor T-cells and bispecific antibodies) in heavily pretreated patients with MM. These treatments, however, have been associated with some new and occasionally severe toxicities, including cytokine release syndrome, immune effector cell-associated neurotoxicity syndrome, and severe infections, including opportunistic infections and profound cytopenias. These potential toxicities bring into question whether these immune-targeting drugs should remain as late-line therapeutics or whether the high single-agent overall response rates mandate that these agents be used in earlier line settings. Herein, the authors provide a point and counterpoint about the future use of these agents.

\textbf{Background:}Rhizomania counts as the most important disease in sugar beet (\textit{Beta vulgaris} L.) for which no plant protection is available, leaving plant breeding as the only defence strategy at the moment. Five resistance genes have been detected on the same chromosome and further studies suggested that these might be different alleles at two resistance clusters. Nevertheless, it was postulated that rhizomania resistance might be a quantitative trait with multiple unknown minor resistance genes. Here, we present a first attempt at genomic prediction of rhizomania resistance in a population that was genotyped using single nucleotide polymorphism (SNP) markers. \newline \textbf{Methods:} First, genomic prediction was performed using all SNPs. Next, we calculated the variable importance for each SNP using machine learning and performed genomic prediction by including the SNPs incrementally in the prediction model based on their variable importance. Using this method, we selected the optimal number of SNPs that maximised the prediction accuracy. Furthermore, we performed genomic prediction with SNP pairs. We also performed feature selection with SNP pairs using the information about the variable importance of the single SNPs. \newline \textbf{Results:} From the four methods under investigation, the latter led to the highest prediction accuracy. These results lead to the following conclusions: (I) The genotypes that were resistant at all known resistance genes, provided the highest possible variation of virus concentrations that the machine can measure. Thus, it can be assumed that more genes must be involved in the resistance towards rhizomania. (II) We show that prediction models that include SNP interactions increased the prediction accuracy. \newline \textbf{Conclusions:} Altogether, our findings suggest that rhizomania resistance is a complex quantitative trait that is affected by multiple genes as well as their interaction.

Abstract Artificial intelligence (AI) and machine learning (ML) pose a challenge for achieving science that is both reproducible and replicable. The challenge is compounded in supervised models that depend on manually labeled training data, as they introduce additional decision‐making and processes that require thorough documentation and reporting. We address these limitations by providing an approach to hand labeling training data for supervised ML that integrates quantitative content analysis (QCA)—a method from social science research. The QCA approach provides a rigorous and well‐documented hand labeling procedure to improve the replicability and reproducibility of supervised ML applications in Earth systems science (ESS), as well as the ability to evaluate them. Specifically, the approach requires (a) the articulation and documentation of the exact decision‐making process used for assigning hand labels in a “codebook” and (b) an empirical evaluation of the reliability” of the hand labelers. In this paper, we outline the contributions of QCA to the field, along with an overview of the general approach. We then provide a case study to further demonstrate how this framework has and can be applied when developing supervised ML models for applications in ESS. With this approach, we provide an actionable path forward for addressing ethical considerations and goals outlined by recent AGU work on ML ethics in ESS.

Abstract Air‐sea interaction in late boreal winter is studied over the extratropical North Atlantic (NA) during 1960–2020 by examining the relationship between sea‐surface temperature (SST) and total turbulent heat flux (THF). The two quantities are positively correlated on interannual timescales over the central‐midlatitude and subpolar NA, suggesting the atmosphere on average drives SST and THF variability is independent of SST. On decadal timescales and over the central‐midlatitude NA the correlation is negative, suggesting ocean processes on average drive SST and THF variability is sensitive to SST. The correlation is positive over the subpolar NA. There, interannual and decadal THF variability is governed by the North Atlantic Oscillation (NAO). During the major late 20th and early 21st century SST increase in the subpolar NA diminishing oceanic heat loss associated with a weakening NAO was observed. This study suggests that the atmosphere is more sensitive to SST over the central‐midlatitude than subpolar NA.

Abstract Background The ever-increasing availability of high-density genomic markers in the form of single nucleotide polymorphisms (SNPs) enables genomic prediction, i.e. the inference of phenotypes based solely on genomic data, in the field of animal and plant breeding, where it has become an important tool. However, given the limited number of individuals, the abundance of variables (SNPs) can reduce the accuracy of prediction models due to overfitting or irrelevant SNPs. Feature selection can help to reduce the number of irrelevant SNPs and increase the model performance. In this study, we investigated an incremental feature selection approach based on ranking the SNPs according to the results of a genome-wide association study that we combined with random forest as a prediction model, and we applied it on several animal and plant datasets. Results Applying our approach to different datasets yielded a wide range of outcomes, i.e. from a substantial increase in prediction accuracy in a few cases to minor improvements when only a fraction of the available SNPs were used. Compared with models using all available SNPs, our approach was able to achieve comparable performances with a considerably reduced number of SNPs in several cases. Our approach showcased state-of-the-art efficiency and performance while having a faster computation time. Conclusions The results of our study suggest that our incremental feature selection approach has the potential to improve prediction accuracy substantially. However, this gain seems to depend on the genomic data used. Even for datasets where the number of markers is smaller than the number of individuals, feature selection may still increase the performance of the genomic prediction. Our approach is implemented in R and is available at https://github.com/FelixHeinrich/GP_with_IFS/ .

Research indicates that student achievement enhances if coupled with information and communication technology (ICT). However, regardless of its extreme importance, there are examples of ICT underuse across all levels of classroom teaching and learning in Pakistan. Evidence from previous research studies suggests that teachers' perceptions, attitudes and motivation towards ICT determine the practical use of ICT in classroom teaching. The aim of this study was to find the effect of university teachers' perceptions, attitude, and motivation towards their readiness for the integration of ICT in their classroom teaching. A sample of 377 university teachers from 51 higher education institutions responded to the survey instrument. The data were analyzed by using Exploratory Factor Analysis (EFA) and Multiple Regression Analysis. The analysis of data revealed a statistically significant effect of university teachers' perceptions, attitude and motivation towards teachers' readiness for the integration of ICT in their classroom teaching. Based on the findings, the study recommends that university teachers with basic ICT literacy should be encouraged to have a positive attitude toward technology use for discharging professional responsibilities.

Summary: Cytoplasmic mislocalization and aggregation of the RNA-binding protein TDP-43 is a pathological hallmark of the motor neuron (MN) disease amyotrophic lateral sclerosis (ALS). Furthermore, while mutations in TARDBP (encoding TDP-43) have been associated with ALS, the pathogenic consequences of these mutations remain poorly understood. Using CRISPR-Cas9, we engineered two homozygous knock-in induced pluripotent stem cell lines carrying mutations in TARDBP encoding TDP-43A382T and TDP-43G348C, two common yet understudied ALS TDP-43 variants. Motor neurons (MNs) differentiated from knock-in iPSCs had normal viability and displayed no significant changes in TDP-43 subcellular localization, phosphorylation, solubility, or aggregation compared with isogenic control MNs. However, our results highlight synaptic impairments in both TDP-43A382T and TDP-43G348C MN cultures, as reflected in synapse abnormalities and alterations in spontaneous neuronal activity. Collectively, our findings suggest that MN dysfunction may precede the occurrence of TDP-43 pathology and neurodegeneration in ALS and further implicate synaptic and excitability defects in the pathobiology of this disease.