Our data indicate that the efficacy of a flocculant/coagulant in the removal of cyanobacteria is influenced by the uniqueness of individual lakes/reservoirs, implying that mitigation methods should consider the unique characteristic of the lake/reservoir.[This corrects the article DOI 10.1371/journal.ppat.1009232.].[This corrects the article DOI 10.1371/journal.pone.0230615.].
  Even after curative resection, pancreatic ductal adenocarcinoma (PDAC) patients suffer a high rate of recurrence. There is an unmet need to predict which patients will experience early recurrence after resection in order to adjust treatment strategies.
 
  Data of patients with resectable PDAC undergoing surgical resection between January 2005 and September 2018 were reviewed to stratify for early recurrence defined as occurring within 6 months of resection. Preoperative data including demographics, tumor markers, blood immune-inflammatory factors and clinicopathological data were examined. We employed Elastic Net, a sparse modeling method, to construct models predicting early recurrence using these multiple preoperative factors. As a result, seven preoperative factors were selected age, duke pancreatic monoclonal antigen type 2 value, neutrophillymphocyte ratio, systemic immune-inflammation index, tumor size, lymph node metastasis and is peripancreatic invasion. Repeated 10-fold cross-validations were performed, and area under the receiver operating characteristic curve (AUC) and decision curve analysis were used to evaluate the usefulness of the models.
 
  A total of 136 patients was included in the final analysis, of which 35 (34%) experienced early recurrence. Using Elastic Net, we found that 7 of 14 preoperative factors were useful for the predictive model. The mean AUC of all models constructed in the repeated validation was superior to the standard marker CA 19-9 (0.718 vs 0.657), whereas the AUC of the model constructed from the entire patient cohort was 0.767. Decision curve analysis showed that the models had a higher mean net benefit across the majority of the range of reasonable threshold probabilities.
 
  A model using multiple preoperative factors can improve prediction of early resectable PDAC recurrence.
 A model using multiple preoperative factors can improve prediction of early resectable PDAC recurrence.In this article, we present QuASeR, a reference-free DNA sequence reconstruction implementation via de novo assembly on both gate-based and quantum annealing platforms. This is the first time this important application in bioinformatics is modeled using quantum computation. Each one of the four steps of the implementation (TSP, QUBO, Hamiltonians and QAOA) is explained with a proof-of-concept example to target both the genomics research community and quantum application developers in a self-contained manner. The implementation and results on executing the algorithm from a set of DNA reads to a reconstructed sequence, on a gate-based quantum simulator, the D-Wave quantum annealing simulator and hardware are detailed. We also highlight the limitations of current classical simulation and available quantum hardware systems. The implementation is open-source and can be found on https//github.com/QE-Lab/QuASeR.Theoretically-driven models of suicide have long guided suicidology; however, an approach employing machine learning models has recently emerged in the field. Some have suggested that machine learning models yield improved prediction as compared to theoretical approaches, but to date, this has not been investigated in a systematic manner. The present work directly compares widely researched theories of suicide (i.e., BioSocial, Biological, Ideation-to-Action, and Hopelessness Theories) to machine learning models, comparing the accuracy between the two differing approaches.  https://www.selleckchem.com/products/sitagliptin.html  We conducted literature searches using PubMed, PsycINFO, and Google Scholar, gathering effect sizes from theoretically-relevant constructs and machine learning models. Eligible studies were longitudinal research articles that predicted suicide ideation, attempts, or death published prior to May 1, 2020. 124 studies met inclusion criteria, corresponding to 330 effect sizes. Theoretically-driven models demonstrated suboptimal prediction of idand death.[This corrects the article DOI 10.1371/journal.pone.0238442.].Infectious respiratory particles expelled by SARS-CoV-2 positive patients are attributed to be the key driver of COVID-19 transmission. Understanding how and by whom the virus is transmitted can help implement better disease control strategies. Here we have described the use of a noninvasive mask sampling method to detect and quantify SARS-CoV-2 RNA in respiratory particles expelled by COVID-19 patients and discussed its relationship to transmission risk. Respiratory particles of 31 symptomatic SARS-CoV-2 positive patients and 31 asymptomatic healthy volunteers were captured on N-95 masks layered with a gelatin membrane in a 30-minute process that involved talking/reading, coughing, and tidal breathing. SARS-CoV-2 viral RNA was detected and quantified using rRT-PCR in the mask and in concomitantly collected nasopharyngeal swab (NPS) samples. The data were analyzed with respect to patient demographics and clinical presentation. Thirteen of 31(41.9%) patients showed SARS-COV-2 positivity in both the mask and NPS samples, while 16 patients were mask negative but NPS positive. Two patients were both mask and NPS negative. All healthy volunteers except one were mask and NPS negative. The mask positive patients had significantly lower NPS Ct value (26) compared to mask negative patients (30.5) and were more likely to be rapid antigen test positive. The mask positive patients could be further grouped into low emitters (expelling 1000 viral copies). The study presents evidence for variation in emission of SARS-CoV-2 virus particles by COVID-19 patients reflecting differences in infectivity and transmission risk among individuals. The results conform to reported secondary infection rates and transmission and also suggest that mask sampling could be explored as an effective tool to assess individual transmission risks, at different time points and during different activities.