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Abstract Ubiquitin widely modifies proteins, thereby regulating most cellular functions. The complexity of ubiquitin signalling necessitates unbiased methods enabling global detection of dynamic protein ubiquitylation. Here, we describe UBIMAX (UBiquitin target Identification by Mass spectrometry in X enopus egg extracts), which enriches ubiquitin-conjugated proteins and quantifies regulation of protein ubiquitylation under precise and adaptable conditions. We benchmark UBIMAX by investigating DNA double-strand break-responsive ubiquitylation events, identifying previously known targets and revealing the actin-organizing protein Dbn1 as a major target of DNA damage-induced ubiquitylation. We find that Dbn1 is targeted for proteasomal degradation by the SCFβ-Trcp1 ubiquitin ligase, in a conserved mechanism driven by ATM-mediated phosphorylation of a previously uncharacterized β-Trcp1 degron containing an SQ motif. We further show that this degron is sufficient to induce DNA damage-dependent protein degradation of a model substrate. Collectively, we demonstrate UBIMAX’s ability to identify targets of stimulus-regulated ubiquitylation and reveal an SCFβ-Trcp1-mediated ubiquitylation mechanism controlled directly by the apical DNA damage response kinases.

Adoptive T cellular immunotherapies have emerged as relevant approaches for treating cancer patients who have relapsed or become refractory (R/R) to traditional cancer treatments. Chimeric antigen receptor (CAR) T-cell therapy has improved survival in various hematological malignancies. However, significant limitations still impede the widespread adoption of these therapies in most cancers. To advance in this field, six research groups have created the “NEXT Generation CART MAD Consortium” (NEXT CART) in Madrid’s Community, which aims to develop novel cell-based immunotherapies for R/R and poor prognosis cancers. At NEXT CART, various basic and translational research groups and hospitals in Madrid concur to share and synergize their basic expertise in immunotherapy, gene therapy, and immunological synapse, and clinical expertise in pediatric and adult oncology. NEXT CART goal is to develop new cell engineering approaches and treatments for R/R adult and pediatric neoplasms to evaluate in multicenter clinical trials. Here, we discuss the current limitations of T cell-based therapies and introduce our perspective on future developments. Advancement opportunities include developing allogeneic products, optimizing CAR signaling domains, combining cellular immunotherapies, multi-targeting strategies, and improving tumor-infiltrating lymphocytes (TILs)/T cell receptor (TCR) therapy. Furthermore, basic studies aim to identify novel tumor targets, tumor molecules in the tumor microenvironment that impact CAR efficacy, and strategies to enhance the efficiency of the immunological synapse between immune and tumor cells. Our perspective of current cellular immunotherapy underscores the potential of these treatments while acknowledging the existing hurdles that demand innovative solutions to develop their potential for cancer treatment fully.

Abstract Mean-variance portfolio optimization models are sensitive to uncertainty in risk-return estimates, which may result in poor out-of-sample performance. In particular, the estimates may suffer when the number of assets considered is high and the length of the return time series is not sufficiently long. This is precisely the case in the cryptocurrency market, where there are hundreds of crypto assets that have been traded for a few years. We propose enhancing the mean-variance (MV) model with a pre-selection stage that uses a prototype-based clustering algorithm to reduce the number of crypto assets considered at each investment period. In the pre-selection stage, we run a prototype-based clustering algorithm where the assets are described by variables representing the profit-risk duality. The prototypes of the clustering partition are automatically examined and the one that best suits our risk-aversion preference is selected. We then run the MV portfolio optimization with the crypto assets of the selected cluster. The proposed approach is tested for a period of 17 months in the whole cryptocurrency market and two selections of the cryptocurrencies with the higher market capitalization (175 and 250 cryptos). We compare the results against three methods applied to the whole market: classic MV, risk parity, and hierarchical risk parity methods. We also compare our results with those from investing in the market index CCI30. The simulation results generally favor our proposal in terms of profit and risk-profit financial indicators. This result reaffirms the convenience of using machine learning methods to guide financial investments in complex and highly-volatile environments such as the cryptocurrency market.

Abstract Since the emergence of Bitcoin, cryptocurrencies have grown significantly, not only in terms of capitalization but also in number. Consequently, the cryptocurrency market can be a conducive arena for investors, as it offers many opportunities. However, it is difficult to understand. This study aims to describe, summarize, and segment the main trends of the entire cryptocurrency market in 2018, using data analysis tools. Accordingly, we propose a new clustering-based methodology that provides complementary views of the financial behavior of cryptocurrencies, and one that looks for associations between the clustering results, and other factors that are not involved in clustering. Particularly, the methodology involves applying three different partitional clustering algorithms, where each of them use a different representation for cryptocurrencies, namely, yearly mean, and standard deviation of the returns, distribution of returns that have not been applied to financial markets previously, and the time series of returns. Because each representation provides a different outlook of the market, we also examine the integration of the three clustering results, to obtain a fine-grained analysis of the main trends of the market. In conclusion, we analyze the association of the clustering results with other descriptive features of cryptocurrencies, including the age, technological attributes, and financial ratios derived from them. This will help to enhance the profiling of the clusters with additional descriptive insights, and to find associations with other variables. Consequently, this study describes the whole market based on graphical information, and a scalable methodology that can be reproduced by investors who want to understand the main trends in the market quickly, and those that look for cryptocurrencies with different financial performance.In our analysis of the 2018 and 2019 for extended period, we found that the market can be typically segmented in few clusters (five or less), and even considering the intersections, the 6 more populations account for 75% of the market. Regarding the associations between the clusters and descriptive features, we find associations between some clusters with volume, market capitalization, and some financial ratios, which could be explored in future research.

The growing interest in green ammonia production has spurred the development of new catalysts with the potential to carry out the Haber–Bosch process under mild pressure and temperature conditions. While there is a wide experimental background on new catalysts involving transition metals, supports and additives, the fundamentals behind ammonia synthesis performance on these catalysts remained partially unsolved. Here, we review the most important works developed to date and analyze the traditional catalysts for ammonia synthesis, as well as the influence of the electron transfer properties of the so-called 3rd-generation catalysts. Finally, the importance of metal–support interactions is highlighted as an effective pathway for the design of new materials with potential to carry out ammonia synthesis at low temperatures and pressures.

Crh proteins catalyze crosslinking of chitin and glucan polymers in fungal cell walls. Here, Bi et al. show that a Crh protein from the phytopathogenic fungus Botrytis cinerea acts as a cytoplasmic effector and elicitor of plant defense, and plants expressing this gene exhibit reduced sensitivity to the pathogen.

Optimal controllers can enhance buildings’ energy efficiency by taking forecast and uncertainties into account (e.g., weather and occupancy). This practice results in energy savings by making better use of energy systems within the buildings. Even though the benefits of advanced optimal controllers have been demonstrated in several research studies and some demonstration cases, the adoption of these techniques in the built environment remains somewhat limited. One of the main reasons is that these novel control algorithms continue to be evaluated individually. This hampers the identification of best practices to deploy optimal control widely in the building sector. This paper implements and compares variations of model predictive control (MPC), reinforcement learning (RL), and reinforced model predictive control (RL-MPC) in the same optimal control problem for building energy management. Particularly, variations of the controllers’ hyperparameters like the control step, the prediction horizon, the state-action spaces, the learning algorithm, or the network architecture of the value function are investigated. The building optimization testing (BOPTEST) framework is used as the simulation benchmark to carry out the study as it offers standardized testing scenarios. The results reveal that, contrary to what is stated in previous literature, model-free RL approaches poorly perform when tested in building environments with realistic system dynamics. Even when a model is available and simulation-based RL can be implemented, MPC outperforms RL for an equivalent formulation of the optimal control problem. The performance gap between both controllers reduces when using the RL-MPC algorithm that merges elements from both families of methods.

Peer production online communities are groups of people that collaboratively engage in the building of common resources such as wikis and open source projects. In such communities, participation is highly unequal: few people concentrate the majority of the workload, while the rest provide irregular and sporadic contributions. The distribution of participation is typically characterized as a power law distribution. However, recent statistical studies on empirical data have challenged the power law dominance in other domains. This work critically examines the assumption that the distribution of participation in wikis follows such distribution. We use statistical tools to analyse over 6,000 wikis from Wikia/Fandom, the largest wiki repository. We study the empirical distribution of each wiki comparing it with different well-known skewed distributions. The results show that the power law performs poorly, surpassed by three others with a more moderated heavy-tail behavior. In particular, the truncated power law is superior to all competing distributions, or superior to some and as good as the rest, in 99.3% of the cases. These findings have implications that can inform a better modeling of participation in peer production, and help to produce more accurate predictions of the tail behavior, which represents the activity and frequency of the core contributors. Thus, we propose to consider the truncated power law as the distribution to characterize participation distribution in wiki communities. Furthermore, the truncated power law parameters provide a meaningful interpretation to characterize the community in terms of the frequency of participation of occasional contributors and how unequal are the group of core contributors. Finally, we found a relationship between the parameters and the productivity of the community and its size. These results open research venues for the characterization of communities in wikis and in online peer production.

In this study, we describe SARS-CoV-2 infection dynamics in one cat and three dogs from households with confirmed human cases of COVID-19 living in the Madrid Community (Spain) at the time of expansion (December 2020 through June 2021) of the alpha variant (lineage B.1.1.7). A thorough physical exam and nasopharyngeal, oropharyngeal, and rectal swabs were collected for real-time reverse-transcription PCR (RT-qPCR) SARS-CoV-2 testing on day 0 and in successive samplings on days 7, 14, 21, and 47 during monitoring. Blood was also drawn to determine complete blood counts, biochemical profiles, and serology of the IgG response against SARS-CoV-2. On day 0, the cat case 1 presented with dyspnea and fever associated with a mild bronchoalveolar pattern. The dog cases 2, 3, and 4 were healthy, but case 2 presented with coughing, dyspnea, and weakness, and case 4 exhibited coughing and bilateral nasal discharge 3 and 6 days before the clinical exam. Case 3 (from the same household as case 2) remained asymptomatic. SARS-CoV-2 detection by RT-qPCR showed that the cat case 1 and the dog case 2 exhibited the lowest cycle threshold (Ct) (Ct < 30) when they presented clinical signs. Viral detection failed in successive samplings. Serological analyses revealed a positive IgG response in cat case 1 and dog cases 3 and 4 shortly after or simultaneously to virus shedding. Dog case 2 was seronegative, but seroconverted 21 days after SARS-CoV-2 detection. SARS-CoV-2 genome sequencing was attempted, and genomes were classified as belonging to the B.1.1.7 lineage.

Peer-to-peer (P2P) lending demands effective and explainable credit risk models. Typical machine learning algorithms offer high prediction performance, but most of them lack explanatory power. However, this deficiency can be solved with the help of the explainability tools proposed in the last few years, such as the SHAP values. In this work, we assess the well-known logistic regression model and several machine learning algorithms for granting scoring in P2P lending. The comparison reveals that the machine learning alternative is superior in terms of not only classification performance but also explainability. More precisely, the SHAP values reveal that machine learning algorithms can reflect dispersion, nonlinearity and structural breaks in the relationships between each feature and the target variable. Our results demonstrate that is possible to have machine learning credit scoring models be both accurate and transparent. Such models provide the trust that the industry, regulators and end-users demand in P2P lending and may lead to a wider adoption of machine learning in this and other risk assessment applications where explainability is required.

Transglycosylases strengthen the fungal cell wall by forming a rigid network of crosslinks. Here, Fang et al. show that the five Crh transglycosylases of Aspergillus fumigatus are dispensable for cell wall integrity in vitro, and solve the crystal structure of Crh5 in complex with chitooligosaccharides.

The use of metaproteomics for studying the human gut microbiota can shed light on the taxonomic profile and the functional role of the microbial community. Nevertheless, methods for extracting proteins from stool samples continue to evolve, in the pursuit of optimal protocols for moistening and dispersing the stool sample and for disrupting microbial cells, which are two critical steps for ensuring good protein recovery. Here, we evaluated different stool sample processing (SSP) and microbial cell disruption methods (CDMs). The combination of a longer disintegration period of the stool sample in a tube rotator with sonication increased the overall number of identified peptides and proteins. Proteobacteria, Bacteroidetes, Planctomycetes, and Euryarchaeota identification was favored by mechanical cell disruption with glass beads. In contrast, the relative abundance of Firmicutes, Actinobacteria, and Fusobacteria was improved when sonication was performed before bead beating. Tenericutes and Apicomplexa identification was enhanced by moistening the stool samples during processing and by disrupting cells with medium-sized glass beads combined with or without sonication. Human protein identifications were affected by sonication. To test the reproducibility of these gut metaproteomic analyses, we examined samples from six healthy individuals using a protocol that had shown a good taxonomic diversity and identification of proteins from Proteobacteria and humans. We also detected proteins involved in microbial functions relevant to the host and related mostly to specific taxa, such as B12 biosynthesis and short chain fatty acid (SCFA) production carried out mainly by members in the Prevotella genus and the Firmicutes phylum, respectively. The taxonomic and functional profiles obtained with the different protocols described in this work provides the researcher with valuable information when choosing the most adequate protocol for the study of certain pathologies under suspicion of being related to a specific taxon from the gut microbiota.

Conditions altering the yeast cell wall lead to the activation of an adaptive transcriptional response mainly governed by the cell wall integrity (CWI) mitogen-activated protein kinase (MAPK) pathway. Two high-throughput screenings were developed using the yTHC collection of yeast conditional mutant strains to systematically identify essential genes related to cell wall integrity, and those required for the transcriptional program elicited by cell wall stress. Depleted expression of 52 essential genes resulted in hypersensitivity to the dye Calcofluor white, with chromatin organization, Golgi vesicle transport, rRNA processing, and protein glycosylation processes, as the most highly representative functional groups. Via a flow cytometry-based quantitative assay using a CWI reporter plasmid, 97 strains exhibiting reduced gene-reporter expression levels upon stress were uncovered, highlighting genes associated with RNA metabolism, transcription/translation, protein degradation, and chromatin organization. This screening also led to the discovery of 41 strains displaying a basal increase in CWI-associated gene expression, including mainly putative cell wall-related genes. Interestingly, several members of the RSC chromatin remodelling complex were uncovered in both screenings. Notably, Rsc9 was necessary to regulate the gene expression of CWI-related genes both under stress and non-stress conditions, suggesting distinct requirements of the RSC complex for remodelling particular genes.

A la par del auge de los movimientos sociales en la postdictadura chilena, el Estado y su institucionalidad ha desplegado una serie de técnicas de respuesta que, contrariamente a lo demandado, refuerzan el modelo actual. Dentro de estas, la impunidad de los agentes estatales se posiciona como un fenómeno vigente y duramente criticado por la sociedad civil. A través de la revisión de prensa, en diálogo con la literatura científica y empírica existente, buscamos aproximarnos a la problematización sobre la impunidad en la postdictadura, entregando elementos, desde una lógica reflexiva, que propicien y contribuyan al debate público sobre este fenómeno.

The venture capital (VC) industry offers opportunities for investment in early-stage companies where uncertainty is very high. Unfortunately, the tools investors currently have available are not robust enough to reduce risk and help them managing uncertainty better. Machine learning data-driven approaches can bridge this gap, as they already do in the hedge fund industry. These approaches are now possible because data from thousands of companies over the world is available through platforms such as Crunchbase. Previous academic efforts have focused only on predicting two classes of exits, i.e., being acquired by other company or offering shares to the public, using only one or a few subsets of explanatory variables. These events are typically related to high returns, but also higher risk, making hard for a venture fund to get repeatable and sustainable returns. On the contrary, we will try to predict more possible outcomes including a subsequent funding round or the closure of the company using a large set of signals. In this way, our approach would provide VC investors with more information to set up a portfolio with lower risk that may eventually achieve higher returns than those based on finding unicorns (i.e., companies with a valuation higher than one billion dollars). We will analyze the performance of several machine learning methods in a dataset of over 120,000 early-stage companies in a realistic setting that tries to predict their progress in a 3-year time window. Results show that machine learning can support venture investors in their decision-making processes to find opportunities and better assessing the risk of potential investments.

Building predictive control has proven to achieve energy savings and higher comfort levels than classical rule-based controllers. The choice of the model complexity needed to be used in model-based optimal control is not trivial, and a wide variety of model types is implemented in the scientific literature. This paper shares practical aspects of implementing different control-oriented models for model predictive control in a building. A real thermally activated test building is used to compare the white-, grey-, and black-box modeling paradigms in prediction and control performance. The experimental results obtained in our particular case reveal that there is not a significant correlation between prediction and control performance and highlight the importance of modeling the heat emission system based on physics. It is also observed that most of the complexity of the physics-based model arises from the building envelope while this part of the building is the most sensitive to weather forecast uncertainty.

Abstract Fungal cells trigger adaptive mechanisms to survive in situations that compromise cell wall integrity. We show here that the global transcriptional response elicited by inhibition of the synthesis of β-1,3-glucan by caspofungin, encompasses a set of genes that are dependent on Slt2, the MAPK of the Cell Wall Integrity (CWI) pathway, and a broad group of genes regulated independently of Slt2. Genes negatively regulated by the cyclic AMP/Protein Kinase A (PKA) signaling pathway were overrepresented in the latter group. Moreover, cell wall stress mediated by inhibition of β-1,3-glucan synthesis, but not by other cell wall interfering compounds, negatively regulated PKA signaling as indicated by the nuclear localisation of Msn2, cellular glycogen accumulation, a decrease of intracellular cAMP levels and a severe decrease in both the activation of the small GTPase Ras2 and the phosphorylation of known substrates of PKA. All these effects relied on the plasma membrane-spanning sensor of the CWI pathway Wsc1. In addition, caspofungin induced a reduction in the cytosolic pH, which was dependent on the extracellular region of Wsc1. Therefore, alterations of the β-1,3-glucan network in the fungal cell wall, induce, through Wsc1, the activation of the CWI pathway and parallel inhibition of PKA signaling.

Today, it is common for software projects to collect measurement data through development processes. With these data, defect prediction software can try to estimate the defect proneness of a software module, with the objective of assisting and guiding software practitioners. With timely and accurate defect predictions, practitioners can focus their limited testing resources on higher risk areas. This paper reports the results of three empirical studies that uses an automated genetic defect prediction framework. This framework generates and compares different learning schemes (preprocessing + attribute selection + learning algorithms) and selects the best one using a genetic algorithm, with the objective to estimate the defect proneness of a software module. The first empirical study is a performance comparison of our framework with the most important framework of the literature. The second empirical study is a performance and runtime comparison between our framework and an exhaustive framework. The third empirical study is a sensitivity analysis. The last empirical study, is our main contribution in this paper. Performance of the software development defect prediction models (using AUC, Area Under the Curve) was validated using NASA-MDP and PROMISE data sets. Seventeen data sets from NASA-MDP (13) and PROMISE (4) projects were analyzed running a NxM-fold cross-validation. A genetic algorithm was used to select the components of the learning schemes automatically, and to assess and report the results. Our results reported similar performance between frameworks. Our framework reported better runtime than exhaustive framework. Finally, we reported the best configuration according to sensitivity analysis.

Aspergillus fumigatus is an airborne opportunistic fungal pathogen responsible for severe infections. Among them, invasive pulmonary aspergillosis has become a major concern as mortality rates exceed 50% in immunocompromised hosts. In parallel, allergic bronchopulmonary aspergillosis frequently encountered in cystic fibrosis patients, is also a comorbidity factor. Current treatments suffer from high toxicity which prevents their use in weakened subjects, resulting in impaired prognostic. Because of their low toxicity and high specificity, anti-infectious therapeutic antibodies could be a new alternative to conventional therapeutics. In this study, we investigated the potential of Chitin Ring Formation cell wall transglycosylases of A. fumigatus to be therapeutic targets for therapeutic antibodies. We demonstrated that the Crf target was highly conserved, regardless of the pathophysiological context; whereas the CRF1 gene was found to be 100% conserved in 92% of the isolates studied, Crf proteins were expressed in 98% of the strains. In addition, we highlighted the role of Crf proteins in fungal growth, using a deletion mutant for CRF1 gene, for which a growth decrease of 23.6% was observed after 48 h. It was demonstrated that anti-Crf antibodies neutralized the enzymatic activity of recombinant Crf protein, and delayed fungal growth by 12.3% in vitro when added to spores. In a neutropenic rat model of invasive pulmonary aspergillosis, anti-Crf antibodies elicited a significant recruitment of neutrophils, macrophages and T CD4 lymphocytes but it was not correlated with a decrease of fungal burden in lungs and improvement in survival. Overall, our study highlighted the potential relevance of targeting Crf cell wall protein (CWP) with therapeutic antibodies.

Despite recent advances in immunotherapy against B cell malignancies such as BCMA (B cell maturation antigen) and CD19-targeted treatments using soluble T cell-engaging (TCE) antibodies or chimeric antigen receptor T cells (CAR-T), there is still an important number of patients experiencing refractory/relapsed (R/R) disease. Approaches to avoid tumor-intrinsic mechanisms of resistance such as immune pressure-mediated antigen downmodulation, are being broadly investigated. These strategies include BCMA/CD19 dual-targeting therapies, which may be of particular interest to patients with B cell lymphoma and multiple myeloma, where a specific double-positive immature subpopulation is commonly associated with poor prognosis and poor response to current treatments. In fact, several clinical trials targeting both antigens through different strategies are currently underway. Here, based on the previously validated STAb (in situ secretion of T cell-redirecting bispecific antibodies) concept, we used two different engineering strategies (pool and co-transduction) to generate dual-targeted STAb-T cells simultaneously secreting BCMA TCE and CD19 TCE that outperformed single-targeted STAb-T cells in different in vitro models. These promising results encourage further preclinical clinical testing of dual STAb-T cells in R/R B-cell malignancies.

Fungi are surrounded by an essential structure, the cell wall, which not only confers cell shape but also protects cells from environmental stress. As a consequence, yeast cells growing under cell wall damage conditions elicit rescue mechanisms to provide maintenance of cellular integrity and fungal survival. Through transcriptional reprogramming, yeast modulate the expression of genes important for cell wall biogenesis and remodeling, metabolism and energy generation, morphogenesis, signal transduction and stress. The yeast cell wall integrity (CWI) pathway, which is very well conserved in other fungi, is the key pathway for the regulation of this adaptive response. In this review, we summarize the current knowledge of the yeast transcriptional program elicited to counterbalance cell wall stress situations, the role of the CWI pathway in the regulation of this program and the importance of the transcriptional input received by other pathways. Modulation of this adaptive response through the CWI pathway by positive and negative transcriptional feedbacks is also discussed. Since all these regulatory mechanisms are well conserved in pathogenic fungi, improving our knowledge about them will have an impact in the developing of new antifungal therapies.