Miracle-dz/UniFAR-FTUs huggingface dataset:
Range-wide neutral and adaptive genetic structure of an endemic herb from Amazonian Savannas

Abstract Conserving genetic diversity in rare and narrowly distributed endemic species is essential to maintain their evolutionary potential and minimize extinction risk under future environmental change. In this study we assess neutral and adaptive genetic structure and genetic diversity in Brasilianthus carajensis (Melastomataceae), an endemic herb from Amazonian Savannas. Using RAD sequencing we identified a total of 9365 SNPs in 150 individuals collected across the species’ entire distribution range. Relying on assumption-free genetic clustering methods and environmental association tests we then compared neutral with adaptive genetic structure. We found three neutral and six adaptive genetic clusters, which could be considered management units (MU) and adaptive units (AU), respectively. Pairwise genetic differentiation (FST) ranged between 0.024 and 0.048, and even though effective population sizes were below 100, no significant inbreeding was found in any inferred cluster. Nearly 10 % of all analysed sequences contained loci associated with temperature and precipitation, from which only 25 sequences contained annotated proteins, with some of them being very relevant for physiological processes in plants. Our findings provide a detailed insight into genetic diversity, neutral and adaptive genetic structure in a rare endemic herb, which can help guide conservation and management actions to avoid the loss of unique genetic variation.
How to assess for the full economic value of vaccines? From past to present, drawing lessons for the future

ABSTRACT Background:Cost-effectiveness analysis (CEA) is the economic analysis method most commonly applied today in the context of replacing one treatment with a new one in a developed healthcare system to improve efficiency. CEA is often requested by local healthcare decision-makers to grant reimbursement. New preventative interventions, such as new vaccines, may however have much wider benefits inside and outside healthcare, when compared with treatment. These additional benefits include externalities on indirect clinical impact, reallocation of specific healthcare resources, improved quality of care, better productivity, better disease control, better fiscal revenues, and others. But these effects are sometimes difficult to integrate into a meaningful CEA result. They may appear as specific benefits for specific stakeholders, other than the stakeholders in healthcare. Objective: Based on a historical view about the application of economic assessments for vaccines our objective has been to make the inventory of who was/is interested in knowing the economic value of vaccines, in what those different stakeholders are likely to see the benefit from their perspective and how were/are we able to measure those benefits and to report them well. Results: The historical view disclosed a limited interest in the economic assessment of vaccines at start, more than 50 years ago, that was comparable to the assessment of looking for more efficiency in new industries through optimization exercises. Today, we are exposed to a very rich panoply of different stakeholders (n= 16). They have their specific interest in many different facets of the vaccine benefit of which some are well known in the conventional economic analysis (n=9), but most outcomes are hidden and not enough evaluated and reported (n=26). Meanwhile we discovered that many different methods of evaluation have been explored to facilitate the measurement and reporting of the benefits (n=18). Conclusion: Our recommendation for future economic evaluations of new vaccines is therefore to find the right combination among the three entities of stakeholder type selection, outcome measure of interest for each stakeholder, and the right method to apply. We present at the end examples that illustrate how successful this approach can be. Graphical abstract
Sustained IFN-I stimulation impairs MAIT cell responses to bacteria by inducing IL-10 during chronic HIV-1 infection

Sustained IFN-I dampens the antibacterial activity of MAIT cells by inducing IL-10 production during chronic HIV-1 infection. Mucosal-associated invariant T (MAIT) cells in HIV-1–infected individuals are functionally impaired by poorly understood mechanisms. Single-cell transcriptional and surface protein analyses revealed that peripheral MAIT cells from HIV-1–infected subjects were highly activated with the up-regulation of interferon (IFN)–stimulated genes as compared to healthy individuals. Sustained IFN-α treatment suppressed MAIT cell responses to Escherichia coli by triggering high-level interleukin-10 (IL-10) production by monocytes, which subsequently inhibited the secretion of IL-12, a crucial costimulatory cytokine for MAIT cell activation. Blocking IFN-α or IL-10 receptors prevented MAIT cell dysfunction induced by HIV-1 exposure in vitro. Moreover, blocking the IL-10 receptor significantly improved anti–Mycobacterium tuberculosis responses of MAIT cells from HIV-1–infected patients. Our findings demonstrate the central role of the IFN-I/IL-10 axis in MAIT cell dysfunction during HIV-1 infection, which has implications for the development of anti–IFN-I/IL-10 strategies against bacterial coinfections in HIV-1–infected patients.
Influenza vaccines: the potential benefits of cell-culture isolation and manufacturing

Influenza continues to cause severe illness in millions and deaths in hundreds of thousands annually. Vaccines are used to prevent influenza outbreaks, however, the influenza virus mutates and annual vaccination is required for optimal protection. Vaccine effectiveness is also affected by other potential factors such as the human immune system, a mismatch with the chosen candidate virus, and egg adaptation associated with egg-based vaccine production. This article reviews the influenza vaccine development process and describes the implications of the changes to the cell-culture process and vaccine strain recommendations by the World Health Organization since the 2017 season. The traditional manufacturing process for influenza vaccines relies on fertilized chicken eggs that are used for vaccine production. Vaccines must be produced in large volumes and the complete process requires approximately 6 months for the egg-based process. In addition, egg adaptation of seed viruses occurs when viruses adapt to avian receptors found within eggs to allow for growth in eggs. These changes to key viral antigens may result in antigenic mismatch and thereby reduce vaccine effectiveness. By contrast, cell-derived seed viruses do not require fertilized eggs and eliminate the potential for egg-adapted changes. As a result, cell-culture technology improves the match between the vaccine virus strain and the vaccine selected strain, and has been associated with increased vaccine effectiveness during a predominantly H3N2 season. During the 2017–2018 influenza season, a small number of studies conducted in the United States compared the effectiveness of egg-based and cell-culture vaccines and are described here. These observational and retrospective studies demonstrate that inactivated cell-culture vaccines were more effective than egg-based vaccines. Adoption of cell-culture technology for influenza vaccine manufacturing has been reported to improve manufacturing efficiency and the additional benefit of improving vaccine effectiveness is a key factor for future policy making considerations.
Paternity tests support a diallelic self‐incompatibility system in a wild olive (Olea europaea subsp. laperrinei, Oleaceae)

Abstract Self‐incompatibility (SI) is the main mechanism that favors outcrossing in plants. By limiting compatible matings, SI interferes in fruit production and breeding of new cultivars. In the Oleeae tribe (Oleaceae), an unusual diallelic SI system (DSI) has been proposed for three distantly related species including the olive (Olea europaea), but empirical evidence has remained controversial for this latter. The olive domestication is a complex process with multiple origins. As a consequence, the mixing of S‐alleles from two distinct taxa, the possible artificial selection of self‐compatible mutants and the large phenological variation of blooming may constitute obstacles for deciphering SI in olive. Here, we investigate cross‐genotype compatibilities in the Saharan wild olive (O. e. subsp. laperrinei). As this taxon was geographically isolated for thousands of years, SI should not be affected by human selection. A population of 37 mature individuals maintained in a collection was investigated. Several embryos per mother were genotyped with microsatellites in order to identify compatible fathers that contributed to fertilization. While the pollination was limited by distance inside the collection, our results strongly support the DSI hypothesis, and all individuals were assigned to two incompatibility groups (G1 and G2). No self‐fertilization was observed in our conditions. In contrast, crosses between full or half siblings were frequent (ca. 45%), which is likely due to a nonrandom assortment of related trees in the collection. Finally, implications of our results for orchard management and the conservation of olive genetic resources are discussed.
Automated assessment of psychiatric disorders using speech: A systematic review

There are many barriers to accessing mental health assessments including cost and stigma. Even when individuals receive professional care, assessments are intermittent and may be limited partly due to the episodic nature of psychiatric symptoms. Therefore, machine‐learning technology using speech samples obtained in the clinic or remotely could one day be a biomarker to improve diagnosis and treatment. To date, reviews have only focused on using acoustic features from speech to detect depression and schizophrenia. Here, we present the first systematic review of studies using speech for automated assessments across a broader range of psychiatric disorders.
Genetic diversity of the rain tree (Albizia saman) in Colombian seasonally dry tropical forest for informing conservation and restoration interventions

Abstract Albizia saman is a multipurpose tree species of seasonally dry tropical forests (SDTFs) of Mesoamerica and northern South America typically cultivated in silvopastoral and other agroforestry systems around the world, a trend that is bound to increase in light of multimillion hectare commitments for forest and landscape restoration. The effective conservation and sustainable use of A. saman requires detailed knowledge of its genetic diversity across its native distribution range of which surprisingly little is known to date. We assessed the genetic diversity and structure of A.saman across twelve representative locations of SDTF in Colombia, and how they may have been shaped by past climatic changes and human influence. We found four different genetic groups which may be the result of differentiation due to isolation of populations in preglacial times. The current distribution and mixture of genetic groups across STDF fragments we observed might be the result of range expansion of SDTFs during the last glacial period followed by range contraction during the Holocene and human‐influenced movement of germplasm associated with cattle ranching. Despite the fragmented state of the presumed natural A. saman stands we sampled, we did not find any signs of inbreeding, suggesting that gene flow is not jeopardized in humanized landscapes. However, further research is needed to assess potential deleterious effects of fragmentation on progeny. Climate change is not expected to seriously threaten the in situ persistence of A. saman populations and might present opportunities for future range expansion. However, the sourcing of germplasm for tree planting activities needs to be aligned with the genetic affinity of reference populations across the distribution of Colombian SDTFs. We identify priority source populations for in situ conservation based on their high genetic diversity, lack or limited signs of admixture, and/or genetic uniqueness.
Weighted gene co-expression network analysis identified MYL9 and CNN1 are associated with recurrence in colorectal cancer

Colorectal cancer (CRC) is one of the most common carcinomas and the fourth leading cause of cancer-related death worldwide. One of the obstacles in the successful treatment of CRC is a high rate of recurrence. We aimed to construct weighted gene co-expression network analysis (WGCNA) to identify key modules and hub genes in association with recurrence in CRC patients. We firstly used the microarray data, GSE41258, to construct a co-expression network and identify gene modules. Furthermore, protein and protein interaction (PPI) network was also performed to screen hub genes. To validate the hub genes, an independent dataset GSE17536 was used for survival analyses. Additionally, another two databases were also performed to investigate the survival rates and expression levels of hub genes. Gene set enrichment analyses (GSEA) combined with gene ontology (GO) were performed to further explore function and mechanisms. In our study, the midnightblue module was identified to be significant, 15 hub genes were screened, four of which were identified as hub nodes in the PPI network. In the test dataset, we found higher expression of MYL9 and CNN1 were significantly associated with shorter survival time of CRC patients. GO analyses showed that MYL9 and CNN1 were enriched in “muscle system process” and “cytoskeletal protein binding”. GSEA found the two hub genes were enriched in “pathways in cancer” and “calcium signaling pathway”. In conclusion, our study demonstrated that MYL9 and CNN1 were hub genes associated with the recurrence of CRC, which may contribute to the improvement of recurrence-free survival time of CRC patients.
Fatal heart block from intentional yew tree (Taxus baccata) ingestion: a case report

Abstract Background Taxus baccata, also known as English yew, is a poison that causes cardiac arrhythmias and can result in death from cardiogenic shock. Case summary A 49-year-old gentleman was admitted following yew ingestion with suicidal intent. He was bradycardic at 30 b.p.m. and hypotensive on arrival. Electrocardiography revealed complete heart block with broad complex ventricular escape rate of 30 b.p.m. Bedside echocardiography revealed severe global impairment of right and left ventricular systolic function. Urgent temporary transvenous pacing was instituted, and the patient was considered for veno-arterial extracorporeal membrane oxygenation. Unfortunately, he deteriorated rapidly and cardiorespiratory arrest ensued, and despite prolonged in-hospital resuscitation, the patient died. Post-mortem examination revealed small needle-shaped plant leaves together with seeds found in the stomach. Ante mortem serum sample analysis sent to the Royal Botanical Gardens and revealed the presence of taxine Type B alkaloids in the patient’s blood. Discussion Yew poisoning is a rare occurrence, and there is currently no effective antidote. Treatment involves supportive management, comprising prolonged effective cardiopulmonary resuscitation, pacing, and mechanical cardiac support. This case illustrates the importance of prompt recognition of yew poisoning, alongside early consideration of pacing and mechanical cardiac support. Due to the rarity of this cause of heart block, and since patients may not always volunteer a history of yew ingestion, yew poisoning is something that physicians should be aware of and this should be considered in the differential diagnosis in patients with unexpected heart block. Serum analysis for taxine alkaloids can be used to confirm the diagnosis.
Specialization of plant–pollinator interactions increases with temperature at Mt. Kilimanjaro

Abstract Aim Species differ in their degree of specialization when interacting with other species, with significant consequences for the function and robustness of ecosystems. In order to better estimate such consequences, we need to improve our understanding of the spatial patterns and drivers of specialization in interaction networks. Methods Here, we used the extensive environmental gradient of Mt. Kilimanjaro (Tanzania, East Africa) to study patterns and drivers of specialization, and robustness of plant–pollinator interactions against simulated species extinction with standardized sampling methods. We studied specialization, network robustness and other network indices of 67 quantitative plant–pollinator networks consisting of 268 observational hours and 4,380 plant–pollinator interactions along a 3.4 km elevational gradient. Using path analysis, we tested whether resource availability, pollinator richness, visitation rates, temperature, and/or area explain average specialization in pollinator communities. We further linked pollinator specialization to different pollinator taxa, and species traits, that is, proboscis length, body size, and species elevational ranges. Results We found that specialization decreased with increasing elevation at different levels of biological organization. Among all variables, mean annual temperature was the best predictor of average specialization in pollinator communities. Specialization differed between pollinator taxa, but was not related to pollinator traits. Network robustness against simulated species extinctions of both plants and pollinators was lowest in the most specialized interaction networks, that is, in the lowlands. Conclusions Our study uncovers patterns in plant–pollinator specialization along elevational gradients. Mean annual temperature was closely linked to pollinator specialization. Energetic constraints, caused by short activity timeframes in cold highlands, may force ectothermic species to broaden their dietary spectrum. Alternatively or in addition, accelerated evolutionary rates might facilitate the establishment of specialization under warm climates. Despite the mechanisms behind the patterns have yet to be fully resolved, our data suggest that temperature shifts in the course of climate change may destabilize pollination networks by affecting network architecture.
Normal mode analysis of a relaxation process with Bayesian inference

ABSTRACT Measurements of relaxation processes are essential in many fields, including nonlinear optics. Relaxation processes provide many insights into atomic/molecular structures and the kinetics and mechanisms of chemical reactions. For the analysis of these processes, the extraction of modes that are specific to the phenomenon of interest (normal modes) is unavoidable. In this study we propose a framework to systematically extract normal modes from the viewpoint of model selection with Bayesian inference. Our approach consists of a well-known method called sparsity-promoting dynamic mode decomposition, which decomposes a mixture of damped oscillations, and the Bayesian model selection framework. We numerically verify the performance of our proposed method by using coherent phonon signals of a bismuth polycrystal and virtual data as typical examples of relaxation processes. Our method succeeds in extracting the normal modes even from experimental data with strong backgrounds. Moreover, the selected set of modes is robust to observation noise, and our method can estimate the level of observation noise. From these observations, our method is applicable to normal mode analysis, especially for data with strong backgrounds. GRAPHICAL ABSTRACT
Structural and socio-cultural barriers to accessing mental healthcare among Syrian refugees and asylum seekers in Switzerland

ABSTRACT Background: Due to their experiences of major stressful life events, including post-displacement stressors, refugees and asylum seekers are vulnerable to developing mental health problems. Yet, despite the availability of specialized mental health services in Western European host countries, refugees and asylum seekers display low mental healthcare utilization. Objective: The aim of this study was to explore structural and socio-cultural barriers to accessing mental healthcare among Syrian refugees and asylum seekers in Switzerland. Method: In this qualitative study, key-informant (KI) interviews with Syrian refugees and asylum seekers, Swiss healthcare providers and other stakeholders (e.g. refugee coordinators or leaders) were conducted in the German-speaking part of Switzerland. Participants were recruited using snowball sampling. Interviews were audiotaped and transcribed, and then analysed using thematic analysis, combining deductive and inductive coding. Results: Findings show that Syrian refugees and asylum seekers face multiple structural and socio-cultural barriers, with socio-cultural barriers being perceived as more pronounced. Syrian key informants, healthcare providers, and other stakeholders identified language, gatekeeper-associated problems, lack of resources, lack of awareness, fear of stigma and a mismatch between the local health system and perceived needs of Syrian refugees and asylum seekers as key barriers to accessing care. Conclusions: The results show that for Syrian refugees and asylum seekers in Switzerland several barriers exist. This is in line with previous findings. A possible solution for the current situation might be to increase the agility of the service system in general and to improve the willingness to embrace innovative paths, rather than adapting mental healthcare services regarding single barriers and needs of a new target population.
Precise prediction of the radiation pneumonitis in lung cancer: an explorative preliminary mathematical model using genotype information

Purpose: Radiation pneumonitis (RP) is the most significant dose-limiting toxicity and is one major obstacle for lung cancer radiotherapy. Grade ≥2 RP usually needs clinical interventions and serve RP could be life threatening. Clinically, tissue response could be strikingly different even two similar patients after identical radiotherapy. Previous methods for the RP prediction can hardly distinguish substantial variations among individuals. Reliable predictive factors or methods emphasizing the individual differences are strongly desired by clinical radiation oncologists. The purpose of this study is to develop an approach for the personalized RP risk prediction. Experimental Design: One hundred eighteen lung cancer patients who received radiotherapy were enrolled. Seven hundred thousand single-nucleotide polymorphism (SNP) sites were assessed via Generalized Linear Models via Lasso and Elastic-Net Regularization (GLMNET) to determine their synergistic effects on the RP risk prediction. Non-genetic factors including patient's phenotypes and clinical interventional parameters were separately assessed by statistic test. Based on the results of the aforementioned analysis, a multiple linear regression model named Radiation Pneumonitis Index (RPI) was built, for the assessment of Grade ≥2RP risk. Results: Only previous surgery and fractional dose were discovered statistical significantly associated with grade ≥2RP. Thirty-nine effective SNPs for predicting the Grade ≥2RP risk were discovered and their coefficients of the synergistic effect were determined. The RPI score can successfully distinguish the RP≥2 population with 92.0% sensitivity and 100% specificity. Conclusions: Individual radiation sensitivity can be determined with genotype information and personalized radiotherapy could be achieved based on mathematical model result.