Soils in warmer and less developed countries have less micronutrients globally.

Soil micronutrients are capital for the delivery of ecosystem functioning and food provision worldwide. Yet, despite their importance, the global biogeography and ecological drivers of soil micronutrients remain virtually unknown, limiting our capacity to anticipate abrupt unexpected changes in soil micronutrients in the face of climate change. Here, we analyzed >1300 topsoil samples to examine the global distribution of six metallic micronutrients (Cu, Fe, Mn, Zn, Co and Ni) across all continents, climates and vegetation types. We found that warmer arid and tropical ecosystems, present in the least developed countries, sustain the lowest contents of multiple soil micronutrients. We further provide evidence that temperature increases may potentially result in abrupt and simultaneous reductions in the content of multiple soil micronutrients when a temperature threshold of 12-14°C is crossed, which may be occurring on 3% of the planet over the next century. Altogether, our findings provide fundamental understanding of the global distribution of soil micronutrients, with direct implications for the maintenance of ecosystem functioning, rangeland management and food production in the warmest and poorest regions of the planet.

Primary esophageal diffuse large B-cell lymphoma.

A 76-year-old man presented with dysphagia, epigastric pain and weight loss for the last two months. Heavy sweating was also presented. Past medical conditions included type 2 diabetes. He had no evidence of any immunosupressive disease including HIV infection. Physical examination only revealed low-grade fever. Laboratory data showed leukocytosis. Gastroscopy evidenced a complete esophageal stenosis starting at 30 cm, with a severely friable mucosa of malignant appearance. The results of biopsies were insufficient for diagnosis of malignancy. Computed tomography demonstrated a 10-cm irregular tumor located in the distal and middle thirds of the esophagus, which resulted in narrowing of the lumen. Involving tracheal carina, bronchus, and descending aorta were observed. Perforation signs were also seen. Distant metastases were not found. Empirical treatment with piperacillin/tazobactan was started. A surgical gastrostomy to allows nutritional support was performed. Two other gastroscopies were performed resulting in an inconclusive diagnosis. Finally, flow cytometry performed in samples obtained by endobronchial ultrasound-guided biopsy evidenced prominent clonal B-cell populations consistent with extranodal diffuse large B-cell lymphoma exhibing CD10 expression. A treatment with Rituximab in combination with cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) was started. Primary esophageal diffuse large B-cell lymphoma (DLBCL), a variant of non-Hodgkin's lymphoma, accounts for less than 1% of all cases of gastrointestinal lymphomas.

[Health and social care in an urban nursing home providing healthcare services and primary care's role: A qualitative study with residents, relatives and professionals]. / Atención sociosanitaria en una residencia urbana sanitarizada y el papel de Atención Primaria. Estudio cualitativo con residentes, familiares y profesionales.

AIM: To identify the elements involved in adequate health and social care for old people living in nursing homes, determine their possible barriers and enablers and define primary care's role in it. DESIGN: Qualitative study with phenomenological approach. SETTING: State funded private nursing home and its corresponding primary care center in the southeastern urban area of Madrid. PARTICIPANTS: Elderly residents, their relatives, and professionals from the nursing home and the primary care center. METHOD: Five focus groups were conducted between November 2019 and January 2020, with semi-structured interviews based on the variables of analysis and themes related to the objectives. The sessions were recorded and transcribed. An open and axial coding was performed to identify categories after a triangulation of the data. RESULTS: The elements of adequate care identified are individualized care, promotion of autonomy, adequate information to residents and relatives, quality of services, coordination between professionals, and a continuous end of life care. The main barriers are the deficit of professionals, the differences in expectations between users and workers, and the organizational gap between the healthcare system and nursing homes providing healthcare services. The role identified for primary care is mostly bureaucratic. CONCLUSIONS: It is necessary to continue exploring these elements and to outline the role of primary care in nursing homes with different characteristics.

Designing user interfaces for content simplification aimed at people with cognitive impairments.

Cognitive accessibility aims to make content more accessible for people with cognitive impairments, such as the elderly and people with intellectual and learning disabilities. In this sense, it is possible to design an accessible user interface from a cognitive point of view. As a contribution, this article presents cognitive accessibility design patterns and their application in designing the Easier web system's user interface. The Easier web system provides a tool that assists in the understanding and readability of text content geared towards people with intellectual disabilities. It detects complex words and offers easier replacements and other resources such as a definition of the complex word. In addition to applying the design patterns, user tests with people with intellectual disabilities and older people have been carried out to evaluate the cognitive accessibility of the Easier system's interface. The results indicate that people with cognitive impairments know how to use the interfaces and have a satisfactory experience. In addition, a design proposal to provide a glossary mechanism to be used in web interfaces with simplified texts is presented and validated.

Analyzing transfer learning impact in biomedical cross-lingual named entity recognition and normalization.

BACKGROUND: The volume of biomedical literature and clinical data is growing at an exponential rate. Therefore, efficient access to data described in unstructured biomedical texts is a crucial task for the biomedical industry and research. Named Entity Recognition (NER) is the first step for information and knowledge acquisition when we deal with unstructured texts. Recent NER approaches use contextualized word representations as input for a downstream classification task. However, distributed word vectors (embeddings) are very limited in Spanish and even more for the biomedical domain. METHODS: In this work, we develop several biomedical Spanish word representations, and we introduce two Deep Learning approaches for pharmaceutical, chemical, and other biomedical entities recognition in Spanish clinical case texts and biomedical texts, one based on a Bi-STM-CRF model and the other on a BERT-based architecture. RESULTS: Several Spanish biomedical embeddigns together with the two deep learning models were evaluated on the PharmaCoNER and CORD-19 datasets. The PharmaCoNER dataset is composed of a set of Spanish clinical cases annotated with drugs, chemical compounds and pharmacological substances; our extended Bi-LSTM-CRF model obtains an F-score of 85.24% on entity identification and classification and the BERT model obtains an F-score of 88.80% . For the entity normalization task, the extended Bi-LSTM-CRF model achieves an F-score of 72.85% and the BERT model achieves 79.97%. The CORD-19 dataset consists of scholarly articles written in English annotated with biomedical concepts such as disorder, species, chemical or drugs, gene and protein, enzyme and anatomy. Bi-LSTM-CRF model and BERT model obtain an F-measure of 78.23% and 78.86% on entity identification and classification, respectively on the CORD-19 dataset. CONCLUSION: These results prove that deep learning models with in-domain knowledge learned from large-scale datasets highly improve named entity recognition performance. Moreover, contextualized representations help to understand complexities and ambiguity inherent to biomedical texts. Embeddings based on word, concepts, senses, etc. other than those for English are required to improve NER tasks in other languages.

Tracheostomy in patients with SARS-CoV-2 reduces time on mechanical ventilation but not intensive care unit stay.

Cross-sectional study to know if tracheostomy influences the time on mechanical ventilation and reduces the ICU stay in patients with SARS-CoV2. From February 14 to May 31, 2020, 29 patients: 23 men and 6 women, with an average age (SD) of 66.4 years (±6,2) required tracheostomy. The average intensive care unit (ICU) stay was 36 days [31-56.5]. The average days on mechanical ventilation was 28,5 days (±9.7). Mean time to tracheostomy was 15.2 days (±9.5) with an average disconnection time after procedure of 11.3 days (±7.4). The average hospital stay was 55 days [39-79]. A directly proportional relation between the number of days of MV and the number of days from ICU admission until tracheostomy showed a significant value of p = 0.008. For each day of delay in tracheostomy, the days of mechanical ventilation were increased by 0.6 days. There was no relation between days to tracheostomy and days to disconnection (p = 0.092). PaO2 / FiO2 (PAFI) before tracheostomy and Simplified Acute Physiology Score III (SAPS III) at admission presented a statistical relation with mortality, with an OR of 1.683 (95%CI; 0.926-2.351; p = 0.078) and an OR of 1.312 (CI95%: 1.011-1.703; p = 0.034) respectively. The length of stay in the ICU until the tracheostomy was not related to the risk of death (p = 0.682). PEEP and PaO2/FiO2 (PAFI) at admission and before tracheostomy and APACHE II, SAPS III and SOFA at admission did not show influence over time on MV. We conclude that the delay in tracheostomy increase the days on mechanical ventilation but does not influence stay or mortality.

A two-stage deep learning approach for extracting entities and relationships from medical texts.

This work presents a two-stage deep learning system for Named Entity Recognition (NER) and Relation Extraction (RE) from medical texts. These tasks are a crucial step to many natural language understanding applications in the biomedical domain. Automatic medical coding of electronic medical records, automated summarizing of patient records, automatic cohort identification for clinical studies, text simplification of health documents for patients, early detection of adverse drug reactions or automatic identification of risk factors are only a few examples of the many possible opportunities that the text analysis can offer in the clinical domain. In this work, our efforts are primarily directed towards the improvement of the pharmacovigilance process by the automatic detection of drug-drug interactions (DDI) from texts. Moreover, we deal with the semantic analysis of texts containing health information for patients. Our two-stage approach is based on Deep Learning architectures. Concretely, NER is performed combining a bidirectional Long Short-Term Memory (Bi-LSTM) and a Conditional Random Field (CRF), while RE applies a Convolutional Neural Network (CNN). Since our approach uses very few language resources, only the pre-trained word embeddings, and does not exploit any domain resources (such as dictionaries or ontologies), this can be easily expandable to support other languages and clinical applications that require the exploitation of semantic information (concepts and relationships) from texts. During the last years, the task of DDI extraction has received great attention by the BioNLP community. However, the problem has been traditionally evaluated as two separate subtasks: drug name recognition and extraction of DDIs. To the best of our knowledge, this is the first work that provides an evaluation of the whole pipeline. Moreover, our system obtains state-of-the-art results on the eHealth-KD challenge, which was part of the Workshop on Semantic Analysis at SEPLN (TASS-2018).

Effectiveness of direct-acting antiviral therapy in patients with a HCV/HIV coinfection. A multicenter cohort study.

INTRODUCTION: The effectiveness of direct-acting antiviral (DAA) agents has been demonstrated in clinical trials both in patients with mono and coinfections. The goal of the study was to analyze the effectiveness and toxicity of this therapy in real-life patients with a HIV/HCV coinfection and to identify variables that are associated with an unfavorable outcome. METHODS: This was a multicenter ambispective study in a cohort of coinfected patients. Data were collected from eight centers in Castilla-La Mancha from 2014 to 2016. An intent-to-treat analysis was performed and any loss to follow-up, treatment withdrawal or toxicity was considered as a failure. RESULTS: A total of 229 patients were included with a median age of 49.6 years and the majority were male (83%). Fewer than 10% had a detectable HIV-related viral load (VL). The most prevalent HCV genotype was 1 (65.1%). Fifty percent had cirrhotic liver disease and 65% had over 800,000 copies/ml of HCV VL. The global sustained viral response (SVR) was reached by 91.7% of cases. The most commonly used DAA regimen was sofosbuvir/ledipasvir. Ribavirin was included in 52% of regimens, 65.9% of cases completed 12-week regimens and 30% completed 24-week schemes. There were 19 therapy failures. No differences were observed between the various DAA strategies used. No independent predictor was found for SVR. CONCLUSIONS: HCV treatment in coinfected patients is highly successful in terms of SVR rate in the real-life setting and toxicity is exceptional. We identified no specific predictors of an unfavorable outcome.

Amino-terminal pro-B-type natriuretic peptide, inferior vena cava ultrasound, and biolectrical impedance analysis for the diagnosis of acute decompensated CHF.

BACKGROUND: Both Framingham criteria and natriuretic peptides (NPs) may worsen their diagnostic validity for acute decompensated heart failure (ADHF) in elderly patients with comorbidities, mainly renal failure. Ultrasound of inferior vena cava (IVCu) and bioelectrical impedance analysis (BIA) are useful tools for detecting ADHF, although their utility compared with NP is not fully established. METHODS AND RESULTS: We conducted a prospective study with 96 patients who presented at the emergency department with dyspnea and were classified as ADHF and non-ADHF groups. Inferior vena cava ultrasonography measured maximum and minimum inferior vena cava diameters and collapsibility index (CIx), whereas BIA calculated resistance (Rz) and reactance (Xc). The primary goal was to compare amino-terminal pro-B-type NP (NT-proBNP), IVCu, and BIA for identifying ADHF. The ADHF group showed significantly (P<.001) higher NT-proBNP values (5801 vs 599 pg/mL), higher maximum IVC diameter (2.26 vs 1.58 cm), higher minimum IVC diameter (1.67 vs 0,7 cm), and lower CIx (27% vs 59%), as well as lower Rz (458.8 vs 627.1 Ohm) and lower Xc (23.5 vs 38.4 Ohm) compared with the non-ADHF group. The estimated area under the curve for ADHF diagnosis was 0.84 for NT-proBNP, 0.90 for maximum IVC diameter, 0.93 for minimum IVC diameter, and 0.90 for CIx, as well as 0.83 and 0.80 for Rz and Xc respectively, without finding significant difference. Cutoff values for diagnosis of ADHF with IVCu and BIA are proposed. Amino-terminal pro-B-type NP values significantly varied in patients with renal impairment, independently of ADHF status, whereas neither IVCu nor BIA did. CONCLUSIONS: Inferior vena cava ultrasonography and BIA analysis are as useful as NT-proBNP to ADHF diagnosis, validated in an elderly population with kidney disease.

DINTO: Using OWL Ontologies and SWRL Rules to Infer Drug-Drug Interactions and Their Mechanisms.

The early detection of drug-drug interactions (DDIs) is limited by the diffuse spread of DDI information in heterogeneous sources. Computational methods promise to play a key role in the identification and explanation of DDIs on a large scale. However, such methods rely on the availability of computable representations describing the relevant domain knowledge. Current modeling efforts have focused on partial and shallow representations of the DDI domain, failing to adequately support computational inference and discovery applications. In this paper, we describe a comprehensive ontology for DDI knowledge (DINTO), which is the first formal representation of different types of DDIs and their mechanisms and its application in the prediction of DDIs. This project has been developed using currently available semantic web technologies, standards, and tools, and we have demonstrated that the combination of drug-related facts in DINTO and Semantic Web Rule Language (SWRL) rules can be used to infer DDIs and their different mechanisms on a large scale. The ontology is available from https://code.google.com/p/dinto/.

Exploring Spanish health social media for detecting drug effects.

BACKGROUND: Adverse Drug reactions (ADR) cause a high number of deaths among hospitalized patients in developed countries. Major drug agencies have devoted a great interest in the early detection of ADRs due to their high incidence and increasing health care costs. Reporting systems are available in order for both healthcare professionals and patients to alert about possible ADRs. However, several studies have shown that these adverse events are underestimated. Our hypothesis is that health social networks could be a significant information source for the early detection of ADRs as well as of new drug indications. METHODS: In this work we present a system for detecting drug effects (which include both adverse drug reactions as well as drug indications) from user posts extracted from a Spanish health forum. Texts were processed using MeaningCloud, a multilingual text analysis engine, to identify drugs and effects. In addition, we developed the first Spanish database storing drugs as well as their effects automatically built from drug package inserts gathered from online websites. We then applied a distant-supervision method using the database on a collection of 84,000 messages in order to extract the relations between drugs and their effects. To classify the relation instances, we used a kernel method based only on shallow linguistic information of the sentences. RESULTS: Regarding Relation Extraction of drugs and their effects, the distant supervision approach achieved a recall of 0.59 and a precision of 0.48. CONCLUSIONS: The task of extracting relations between drugs and their effects from social media is a complex challenge due to the characteristics of social media texts. These texts, typically posts or tweets, usually contain many grammatical errors and spelling mistakes. Moreover, patients use lay terminology to refer to diseases, symptoms and indications that is not usually included in lexical resources in languages other than English.

Lessons learnt from the DDIExtraction-2013 Shared Task.

The DDIExtraction Shared Task 2013 is the second edition of the DDIExtraction Shared Task series, a community-wide effort to promote the implementation and comparative assessment of natural language processing (NLP) techniques in the field of the pharmacovigilance domain, in particular, to address the extraction of drug-drug interactions (DDI) from biomedical texts. This edition has been the first attempt to compare the performance of Information Extraction (IE) techniques specific for each of the basic steps of the DDI extraction pipeline. To attain this aim, two main tasks were proposed: the recognition and classification of pharmacological substances and the detection and classification of drug-drug interactions. DDIExtraction 2013 was held from January to June 2013 and attracted wide attention with a total of 14 teams (6 of the teams participated in the drug name recognition task, while 8 participated in the DDI extraction task) from 7 different countries. For the task of the recognition and classification of pharmacological names, the best system achieved an F1 of 71.5%, while, for the detection and classification of DDIs, the best result was an F1 of 65.1%. The results show advances in the state of the art and demonstrate that significant challenges remain to be resolved. This paper focuses on the second task (extraction of DDIs) and examines its main challenges, which have yet to be resolved.

Cellular Immunity of SARS-CoV-2 in the Borriana COVID-19 Cohort: A Nested Case-Control Study.

Our goal was to determine the cellular immune response (CIR) in a sample of the Borriana COVID-19 cohort (Spain) to identify associated factors and their relationship with infection, reinfection and sequelae. We conducted a nested case-control study using a randomly selected sample of 225 individuals aged 18 and older, including 36 individuals naïve to the SARS-CoV-2 infection and 189 infected patients. We employed flow-cytometry-based immunoassays for intracellular cytokine staining, using Wuhan and BA.2 antigens, and chemiluminescence microparticle immunoassay to detect SARS-CoV-2 antibodies. Logistic regression models were applied. A total of 215 (95.6%) participants exhibited T-cell response (TCR) to at least one antigen. Positive responses of CD4+ and CD8+ T cells were 89.8% and 85.3%, respectively. No difference in CIR was found between naïve and infected patients. Patients who experienced sequelae exhibited a higher CIR than those without. A positive correlation was observed between TCR and anti-spike IgG levels. Factors positively associated with the TCR included blood group A, number of SARS-CoV-2 vaccine doses received, and anti-N IgM; factors inversely related were the time elapsed since the last vaccine dose or infection, and blood group B. These findings contribute valuable insights into the nuanced immune landscape shaped by SARS-CoV-2 infection and vaccination.

Hotspots of biogeochemical activity linked to aridity and plant traits across global drylands.

Perennial plants create productive and biodiverse hotspots, known as fertile islands, beneath their canopies. These hotspots largely determine the structure and functioning of drylands worldwide. Despite their ubiquity, the factors controlling fertile islands under conditions of contrasting grazing by livestock, the most prevalent land use in drylands, remain virtually unknown. Here we evaluated the relative importance of grazing pressure and herbivore type, climate and plant functional traits on 24 soil physical and chemical attributes that represent proxies of key ecosystem services related to decomposition, soil fertility, and soil and water conservation. To do this, we conducted a standardized global survey of 288 plots at 88 sites in 25 countries worldwide. We show that aridity and plant traits are the major factors associated with the magnitude of plant effects on fertile islands in grazed drylands worldwide. Grazing pressure had little influence on the capacity of plants to support fertile islands. Taller and wider shrubs and grasses supported stronger island effects. Stable and functional soils tended to be linked to species-rich sites with taller plants. Together, our findings dispel the notion that grazing pressure or herbivore type are linked to the formation or intensification of fertile islands in drylands. Rather, our study suggests that changes in aridity, and processes that alter island identity and therefore plant traits, will have marked effects on how perennial plants support and maintain the functioning of drylands in a more arid and grazed world.

Changes in macrophage phenotype after infection of pigs with Haemophilus parasuis strains with different levels of virulence.

Haemophilus parasuis is a colonizer of healthy piglets and the etiological agent of Glässer's disease. Differences in virulence among strains of H. parasuis have been widely observed. In order to explore the host-pathogen interaction, snatch-farrowed colostrum-deprived piglets were intranasally infected with 4 strains of H. parasuis: reference virulent strain Nagasaki, reference nonvirulent strain SW114, field strain IT29205 (from a systemic lesion and virulent in a previous challenge), and field strain F9 (from the nasal cavity of a healthy piglet). At different times after infection, two animals of each group were euthanized and alveolar macrophages were analyzed for the expression of CD163, CD172a, SLA I (swine histocompatibility leukocyte antigen I), SLA II, sialoadhesin (or CD169), and CD14. At 1 day postinfection (dpi), virulent strains induced reduced expression of CD163, SLA II, and CD172a on the surfaces of the macrophages, while nonvirulent strains induced increased expression of CD163, both compared to noninfected controls. At 2 dpi, the pattern switched into a strong expression of CD172a, CD163, and sialoadhesin by the virulent strains, which was followed by a steep increase in interleukin 8 (IL-8) and soluble CD163 in serum at 3 to 4 dpi. The early increase in surface expression of CD163 induced by nonvirulent strains went along with higher levels of IL-8 in serum than those induced by virulent strains in the first 2 days of infection. Alpha interferon (IFN-α) induction was observed only in animals infected with nonvirulent strains. Overall, these results are compatible with a delay in macrophage activation by virulent strains, which may be critical for disease production.

The DDI corpus: an annotated corpus with pharmacological substances and drug-drug interactions.

The management of drug-drug interactions (DDIs) is a critical issue resulting from the overwhelming amount of information available on them. Natural Language Processing (NLP) techniques can provide an interesting way to reduce the time spent by healthcare professionals on reviewing biomedical literature. However, NLP techniques rely mostly on the availability of the annotated corpora. While there are several annotated corpora with biological entities and their relationships, there is a lack of corpora annotated with pharmacological substances and DDIs. Moreover, other works in this field have focused in pharmacokinetic (PK) DDIs only, but not in pharmacodynamic (PD) DDIs. To address this problem, we have created a manually annotated corpus consisting of 792 texts selected from the DrugBank database and other 233 Medline abstracts. This fined-grained corpus has been annotated with a total of 18,502 pharmacological substances and 5028 DDIs, including both PK as well as PD interactions. The quality and consistency of the annotation process has been ensured through the creation of annotation guidelines and has been evaluated by the measurement of the inter-annotator agreement between two annotators. The agreement was almost perfect (Kappa up to 0.96 and generally over 0.80), except for the DDIs in the MedLine database (0.55-0.72). The DDI corpus has been used in the SemEval 2013 DDIExtraction challenge as a gold standard for the evaluation of information extraction techniques applied to the recognition of pharmacological substances and the detection of DDIs from biomedical texts. DDIExtraction 2013 has attracted wide attention with a total of 14 teams from 7 different countries. For the task of recognition and classification of pharmacological names, the best system achieved an F1 of 71.5%, while, for the detection and classification of DDIs, the best result was F1 of 65.1%. These results show that the corpus has enough quality to be used for training and testing NLP techniques applied to the field of Pharmacovigilance. The DDI corpus and the annotation guidelines are free for use for academic research and are available at http://labda.inf.uc3m.es/ddicorpus.

EASIER corpus: A lexical simplification resource for people with cognitive impairments.

Thanks to technologies such as the Internet and devices now available to people, we have increasingly greater access to larger quantities of information. However, people with ageing disabilities or intellectual disabilities, non-native speakers, and others have difficulties reading and understanding information. For this reason, it is essential to provide text simplification mechanisms when accessing information. Natural Language Processing methods can be applied to simplify textual content and improve understanding. These methods often use machine learning algorithms and models which require resources, such as corpora, to be trained and tested. This article presents the EASIER corpus, a resource that can be used to build lexical simplification methods to process Spanish domain-independent texts. The EASIER corpus is composed of 260 annotated documents with 8,155 words labelled as complex and 5,130 words with at least one proposed context-aware synonym associated. Expert linguists in easy-to-read and plain language guidelines have annotated the corpus based on their experience adapting texts for people with intellectual disabilities. Sixteen annotation guidelines that discriminate between complex and simple words have been defined to help other groups of experts to generate new annotations. Additionally, an inter-annotator agreement test was performed to validate the corpus, obtaining a Fleiss Kappa coefficient of 0.641. Furthermore, a qualitative evaluation was conducted with 45 users (including people with intellectual disabilities, elderly people, and a control audience). Complex word identification tasks achieved moderate results, but the synonyms proposed to replace complex words achieved almost perfect ratings. This resource has been integrated into the EASIER platform, a tool that helps people with cognitive impairments and intellectual disabilities to read and understand texts more easily.

Biocrusts Modulate Climate Change Effects on Soil Organic Carbon Pools: Insights From a 9-Year Experiment.

Accumulating evidence suggests that warming associated with climate change is decreasing the total amount of soil organic carbon (SOC) in drylands, although scientific research has not given enough emphasis to particulate (POC) and mineral-associated organic carbon (MAOC) pools. Biocrusts are a major biotic feature of drylands and have large impacts on the C cycle, yet it is largely unknown whether they modulate the responses of POC and MAOC to climate change. Here, we assessed the effects of simulated climate change (control, reduced rainfall (RE), warming (WA), and RE + WA) and initial biocrust cover (low (< 20%) versus high (> 50%)) on the mineral protection of soil C and soil organic matter quality in a dryland ecosystem in central Spain for 9 years. At low initial biocrust cover levels, both WA and RE + WA increased SOC, especially POC but also MAOC, and promoted a higher contribution of carbohydrates, relative to aromatic compounds, to the POC fraction. These results suggest that the accumulation of soil C under warming treatments may be transitory in soils with low initial biocrust cover. In soils with high initial biocrust cover, climate change treatments did not affect SOC, neither POC nor MAOC fraction. Overall, our results indicate that biocrust communities modulate the negative effect of climate change on SOC, because no losses of soil C were observed with the climate manipulations under biocrusts. Future work should focus on determining the long-term persistence of the observed buffering effect by biocrust-forming lichens, as they are known to be negatively affected by warming. Supplementary Information: The online version contains supplementary material available at 10.1007/s10021-022-00779-0.

Non-thermal structural transformation of diamond driven by x-rays.

Intense x-ray pulses can cause the non-thermal structural transformation of diamond. At the SACLA XFEL facility, pump x-ray pulses triggered this phase transition, and probe x-ray pulses produced diffraction patterns. Time delays were observed from 0 to 250 fs, and the x-ray dose varied from 0.9 to 8.0 eV/atom. The intensity of the (111), (220), and (311) diffraction peaks decreased with time, indicating a disordering of the crystal lattice. From a Debye-Waller analysis, the rms atomic displacements perpendicular to the (111) planes were observed to be significantly larger than those perpendicular to the (220) or (311) planes. At a long time delay of 33 ms, graphite (002) diffraction indicates that graphitization did occur above a threshold dose of 1.2 eV/atom. These experimental results are in qualitative agreement with XTANT+ simulations using a hybrid model based on density-functional tight-binding molecular dynamics.

Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition.

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.