The aim of this study was to investigate the production, stability and applicability of colorants produced by filamentous fungi isolated from soil samples from the Amazon. Initially, the isolates were evaluated in a screening for the production of colorants. The influences of cultivation and nutritional conditions on the production of colorants by fungal isolates were investigated. The colorants produced by selected fungal isolates were chemically characterized using the LC-MS technique. The antimicrobial and cytotoxic activities, stability evaluation and applicability of the colorants were investigated. As results, we observed that the isolates Penicillium sclerotiorum P3SO224, Clonostachys rosea P2SO329 and Penicillium gravinicasei P3SO332 stood out since they produced the most intense colorants. Compounds produced by Penicillium sclerotiorum P3SO224 and Clonostachys rosea P2SO329 were identified as sclerotiorin and penicillic acid. The colorant fraction (EtOAc) produced by these species has antimicrobial activity, stability at temperature and at different pHs, stability when exposure to light and UV, and when exposed to different concentrations of salts, as well as being non-toxic and having the ability to dye fabrics and be used as a pigment in creams and soap. Considering the results found in this study, it was concluded that fungi from the soil in the Amazon have the potential to produce colorants with applications in the textile and pharmaceutical industries.
Aim: As the non-vitamin K antagonist oral anticoagulant (NOAC) most recently approved in China, data pertaining to clinical edoxaban use are still scarce. This study investigated the prevalence of and contemporary trends in edoxaban prescription among Chinese patients as well as factors associated with its inappropriate use in a multi-center registry of patients treated in real-world clinical practice. Methods: This real-world, prospective, multicenter, and non-interventional study included 1005 inpatients treated with edoxaban. According to National Medical Products Administration and European Heart Rhythm Association guidelines, edoxaban therapy was determined to be appropriate or inappropriate in each case. Results: The median patient age was 70.0 years (interquartile range, 61.0–78.0 years), and 46.3% were women. Overall, 456 (45.4%) patients received inappropriate edoxaban therapy, and common issues included an inappropriately low (183, 18.2%) or high (73, 7.3%) dosage, wrong drug selection (109, 10.8%), unreasonable off-label use (49, 4.9%), incorrect administration timing (16, 1.6%), and contraindication due to other medications (27, 2.7%). Several factors (e.g., age, weight, kidney function, anemia, and bleeding history) were associated with an increased risk of inappropriate edoxaban therapy, whereas factors associated with cardiovascular specialties (e.g., hospitalized in cardiovascular department and dronedarone or amiodarone use) decreased this risk. Conclusion: In this real-world study, 45.4% of patients received an inappropriate treatment with edoxaban. Multiple clinical characteristics can help identify patients who should receive edoxaban. Further development and implantation of educational activities and management strategies are needed to ensure the correct use of edoxaban.
Introductions of invasive species to new environments often result in rapid rates of trait evolution. While in some cases these evolutionary transitions are adaptive and driven by natural selection, they can also result from patterns of genetic and phenotypic variation associated with the invasion history. Here, we examined the brown anole (Anolis sagrei), a widespread invasive lizard for which genetic data have helped trace the sources of non-native populations. We focused on the dewlap, a complex signaling trait known to be subject to multiple selective pressures. We measured dewlap reflectance, pattern, and size in 30 non-native populations across the southeastern United States. As well, we quantified environmental variables known to influence dewlap signal effectiveness, such as canopy openness. Further, we used genome-wide data to estimate genetic ancestry, perform association mapping, and test for signatures of selection. We found that among-population variation in dewlap characteristics was best explained by genetic ancestry. This result was supported by genome-wide association mapping, which identified several ancestry-specific loci associated with dewlap traits. Despite the strong imprint of this aspect of the invasion history on dewlap variation, we also detected significant relationships between dewlap traits and local environmental conditions. However, we found limited evidence that dewlap-associated genetic variants have been subject to selection. Our study emphasizes the importance of genetic ancestry and admixture in shaping phenotypes during biological invasion, while leaving the role of selection unresolved, likely due to the polygenic genetic architecture of dewlaps and selection acting on many genes of small effect.
Historically, patients suffering from pathological narcissism, including narcissistic personality disorder (NPD), were considered challenging and hard to treat. Since the second half of the 20th century new treatments have been developing heralding a growing hope that transformative treatment of patients with pathological narcissism is possible. Recent developments of phenomenology, childhood antecedents, longitudinal course, and putative mechanisms inspired a greater hope as well. This invites clinicians and researchers to take an approach that is evidence-based, destigmatizing, and collaborative that considers that at least some of the treatment challenges as co-created by both the therapist and the patient. Further, new treatments add hope by ameliorating such challenges of patients with pathological narcissism as fragile alliance, limitations of reflectiveness and grieving. Novel treatments are evidence- and principles-based and different approaches to effective treatment development are described. Inspired by these developments in the field, this Issue of the Journal of Clinical Psychology: In Session was conceived as an opportunity for clinicians from different treatment approaches to come together and share their experiences in treating patients with pathological narcissism. The hope is to find common language to understand these patients and their treatment, understand what contributes to change, as well as learn from commonalities and differences among these treatments. In doing so, this Issue is hoping to promote destigmatizing, pragmatic approach that prioritizes evidence-based efforts to understand the patient and collaborative approach to promoting change.
Treatment of patients with pathological narcissism presents several challenges and there is paucity of published case reports that document meaningful and durable change in patients suffering from this condition. Using descriptive and atheoretical language, this paper presents a treatment of a young adult in his transition from young adulthood to middle adulthood while he was negotiating complex residues of his experiences of growing up along with developmental challenges related to work and love. Against the backdrop of these transitions, the patient was working through various aspects of functioning related to pathological narcissism. Initially, given academic pressures and past romantic disappointments, he was confronting issues related to perfectionism, self-criticism, and avoidance. While he was able to move past some of these dynamics and function academically, later challenges related to becoming an independent adult led to a retreat into an avoidant state of futility and pessimism. Working through painful family dynamics related to not being seen and controlled, along with a deepening attachment in therapy as well as confrontation with realities of his life, led him to take steps towards greater independence. Thereafter, his treatment focused on learning from life experiences such as a newly developed career and romantic life, accepting the complexity of self and others, and tolerating disillusionments.
Purpose Although monoclonal antibodies specific to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are known, information about the B-cell receptor (BCR) repertoire and its change in patients during COVID-19 disease progression is underreported. Methods We used immunoglobulin heavy chain (IGH) variable region (IGHV) spectratyping and next-generation sequencing of peripheral blood B-cell genomic DNA collected at multiple time points during disease evolution to study B-cell response to SARS-CoV-2 infection in 14 individuals with acute COVID-19. Results We found a broad distribution of responding B-cell clones. The IGH gene usage was not significantly skewed but frequencies of individual IGH genes changed repeatedly. We found predominant usage of unmutated and low mutation-loaded IGHV rearrangements characterizing naïve and extrafollicular B-cells among the majority of expanded peripheral B-cell clonal lineages at most tested time points in most patients. IGH rearrangement usage showed no apparent relation to anti-SARS-CoV-2 antibody titers. Some patients demonstrated mono/oligoclonal populations carrying highly mutated IGHV rearrangements indicating antigen experience at some of the time points tested, including even before anti-SARS-CoV-2 antibodies were detected. Conclusion We present evidence demonstrating that the B-cell response to SARS-CoV-2 is individual and includes different lineages of B-cells at various time points during COVID-19 progression.
Aim: To test the feasibility and accuracy of a new attention-based deep learning (DL) method for right ventricular (RV) quantification using 2D echocardiography (2DE) with cardiac magnetic resonance imaging (CMR) as reference. Methods and results: We retrospectively analyzed images from 50 adult patients (median age 51, interquartile range 32-62 42% women) who had undergone CMR within 1 month of 2DE. RV planimetry of the myocardial border was performed in end-diastole (ED) and end-systole (ES) for 8 standardized 2DE RV views with calculation of areas. The DL model comprised a Feature Tokenizer module and a stack of Transformer layers. Age, gender and calculated areas were used as inputs, and the output was RV volume in ED/ES. The dataset was randomly split into training, validation and testing subsets (35, 5 and 10 patients respectively). Mean RVEDV, RVESV and RV ejection fraction (EF) were 163±70ml, 82±42ml and 51±8% respectively without differences among the subsets. The proposed method achieved good prediction of RV volumes (R 2=0.953, absolute percentage error [APE]=9.75±6.23%) and RVEF (APE=7.24±4.55%). Per CMR, there was 1 patient with RV dilatation and 3 with RV dysfunction in the testing dataset. The DL model detected RV dilatation in 1/1 case and RV dysfunction in 4/3 cases. Conclusions: An attention-based DL method for 2DE RV quantification showed feasibility and promising accuracy. The method requires validation in larger cohorts with wider range of RV size and function. Further research will focus on the reduction of the number of required 2DE to make the method clinically applicable.
Cubic silicon carbide (3C-SiC) has superior mobility and thermal conduction than that of widely applied hexagonal 4H-SiC. Moreover, much lower concentration of interfacial traps between insulating oxide gate and 3C-SiC helps fabricate reliable and long-life devices like metal-oxide-semiconductor field effect transistors (MOSFETs). However, the growth of high quality and wafer-scale 3C-SiC crystals has remained a big challenge up to now despite of decades-long efforts by researchers because of its easy transformation into other polytypes during growth, limiting the development of 3C-SiC based devices. Herein, we report that 3C-SiC can be made thermodynamically favored from nucleation to growth on a 4H-SiC substrate by top-seeded solution growth technique (TSSG), beyond what’s expected by classic nucleation theory. This enables the steady growth of high-quality and large-size 3C-SiC crystals (2~4-inch in diameter and 4.0~10.0 mm in thickness) sustainable. The as-grown 3C-SiC crystals are free of other polytypes and have high crystalline quality. Our findings broaden the mechanism of hetero-seed crystal growth and provide a feasible route to mass production of 3C-SiC crystals, offering new opportunities to develop power electronic devices potentially with better performances than those based on 4H-SiC.
Recently, Large-scale Language Models (LLMs) such as Chat Generative Pre-trained Transformer (ChatGPT) and Generative Pre-trained Transformer 4 (GPT-4) have demonstrated remarkable performance in the general domain. However, Inadaptability in a particular domain have led to hallucination for these LLMs when responding in specific domain contexts. The issue has attracted widespread attention, existing domain-centered fine-tuning efforts have predominantly focused on sectors like medical, financial, and legal, leaving critical areas such as power energy relatively unexplored. To bridge this gap, this paper introduces a novel power energy chat model called PowerPulse. Built upon the open and efficient foundation language models (LLaMA) architecture, PowerPulse is fine-tuned specifically on Chinese Power Sector Domain Knowledge. This work marks the inaugural application of the LLaMA model in the field of power energy. By leveraging pertinent pre-training data and instruction fine-tuning datasets tailored for the power energy domain, the PowerPulse model showcases exceptional performance in tasks such as text generation, summary extraction, and topic classification. Experimental results validate the efficacy of the PowerPulse model, making significant contributions to the advancement of specialized language models in specific domains.
The development of winter-tolerant safflower genotypes is crucial for the improvement of global safflower agriculture. The aim of the present study was to determine the cold tolerance abilities and some agricultural characteristics of advanced safflower genotypes. For this purpose, ten advanced safflower genotypes were used in four different locations. The experimental design was a randomized complete block design with three replications. Winter survival and agricultural characters were significantly affected by growing season, location and genotype. Winter survival varies between 86.43% and 93.91% among the genotypes, and it was promising for winter sowing. As the average of two years, the highest oil content (36.25%) was observed in genotype EC21 and it was followed by genotypes EC11 (35.51%) and EC20 (35.49%). As with the seed yield, the high winter survival of genotypes with high oil content is highly promising in terms of winter sowing. Safflower should be grown in winter with mild temperature regions for high seed yield and sustainable safflower production. Therefore, this study focused on winter-tolerant genotypes that are superior one in terms of seed yield and oil content.
In proteomics, fast, efficient and highly reproducible sample preparation is of utmost importance, particularly in view of fast scanning mass spectrometers enabling analyses of large sample series. To address this need, we have developed the web application MassSpecPreppy that operates on the open science OT-2 liquid handling robot from Opentrons. This platform can prepare up to 96 samples at once, performing tasks like BCA protein concentration determination, sample digestion with normalization, reduction/alkylation and peptide elution into vials or loading specified peptide amounts onto Evotips in an automated and flexible manner. The performance of the developed workflows using MassSpecPreppy was compared with standard manual sample preparation workflows. The BCA assay experiments revealed an average recovery of 101.3% (SD: ±7.82%) for the MassSpecPreppy workflow, while the manual workflow had a recovery of 96.3% (SD: ±9.73%). The species mix used in the evaluation experiments showed that 94.5% of protein groups for OT-2 digestion and 95% for manual digestion passed the significance thresholds with comparable peptide level coefficient of variations. These results demonstrate that MassSpecPreppy is a versatile and scalable platform for automated sample preparation, producing injection-ready samples for proteomics research.
Pathogenic variants in the Surfactant Protein C gene ( SFTPC) result in fibrotic childhood interstitial lung disease (chILD). We previously reported three children with SFTPC pathogenic variants with respiratory failure who were supported by chronic invasive ventilation via tracheostomy as an alternative to lung transplantation or comfort care [(1)](#ref-0001). We present two children with SFTPC pathogenic variants treated with non-invasive ventilation (NIV) (Figure 1).
Artificial intelligence (AI) will impact many aspects of clinical pharmacology including drug discovery and development, clinical trials, personalised medicine, pharmacogenomics, pharmacovigilance and clinical toxicology. The rapid progress of AI in healthcare means clinical pharmacologists should have an understanding of AI and its implementation into clinical practice. As with any new therapy or health technology, it is imperative that AI tools are subject to robust and stringent evaluation to ensure that they enhance clinical practice in a safe and equitable manner. This review serves as an introduction to AI for the clinical pharmacologist, highlighting current applications, aspects of model development and issues surrounding evaluation and deployment. The aim of this article is to empower clinical pharmacologists to embrace and lead on the safe and effective use of AI within healthcare.
Polyadenylation occurs at numerous sites within 3’ untranslated regions (3’ UTRs) but rarely within coding regions. How does Pol II travel through long coding regions without generating poly(A) sites, yet then permits promiscuous polyadenylation once it reaches the 3’ UTR? The cleavage/polyadenylation (CpA) machinery preferentially associates with 3’ UTRs, but it is unknown how its recruitment is restricted to 3’ UTRs during Pol II elongation. Unlike coding regions, 3’ UTRs have long AT-rich stretches of DNA that may be important for restricting polyadenylation to 3’ UTRs. Recognition of the 3’ UTR could occur at the DNA (AT-rich), RNA (AU-rich), or RNA:DNA hybrid rU:dA- and/or rA:dT-rich) level. Based on the nucleic acid critical for 3’ UTR recognition, there are three classes of models, not mutually exclusive, for how the CpA machinery is selectively recruited to 3’ UTRs, thereby restricting where polyadenylation occurs: 1) RNA-based models suggest that the CpA complex directly (or indirectly through one or more intermediary proteins) binds long AU-rich stretches that are exposed after Pol II passes through these regions. 2) DNA-based models suggest that the AT-rich sequence affects nucleosome depletion or the elongating Pol II machinery, resulting in dissociation of some elongation factors and subsequent recruitment of the CpA machinery. 3) RNA:DNA hybrid models suggest that preferential destabilization of the Pol II elongation complex at rU:dA- and/or rA:dT-rich duplexes bridging the nucleotide addition and RNA exit sites permits preferential association of the CpA machinery with 3’ UTRs. Experiments to provide evidence for one or more of these models are suggested.
Deep learning (DL) techniques have grown in leaps and bounds in both academia and industry over the past few years. Despite the growth of DL projects, there has been little study on how DL projects evolve, whether maintainers in this domain encounter a dramatic increase in workload, and whether or not existing maintainers can guarantee the sustained development of projects. To address this gap, we perform an empirical study to investigate the sustainability of DL projects, understand maintainers’ workloads and workloads growth in DL projects, and compare them with traditional OSS projects. In this regard, we first investigate how DL projects grow, then, understand maintainers’ workload in DL projects, and explore the workload growth of maintainers as DL projects evolve. After that, we mine the relationships between maintainers’ activities and the sustainability of DL projects. Eventually, we compare it with traditional OSS projects. Our study unveils that although DL projects show increasing trends in most activities, maintainers’ workloads present a decreasing trend. Meanwhile, the proportion of workload maintainers conducted in DL projects is significantly lower than in traditional OSS projects. Moreover, there are positive and moderate correlations between the sustainability of DL projects and the number of maintainers’ releases, pushes, and merged pull requests. Our findings shed lights that help understand maintainers’ workload and growth trends in DL and traditional OSS projects, and also highlight actionable directions for organizations, maintainers, and researchers.