Epigenetic changes can propagate throughout a tumor via heritable modifications that have long-lasting consequences on ensuing phenotypes. Such heritable epigenetic changes can be evoked profoundly by cancer cell metabolites, which then exercise a broad remit of actions across all stages of carcinogenesis, culminating with a meaningful impact on the tumor's response to therapy. This review outlines some of the cross-talk between heritable epigenetic changes and tumor cell metabolism, and the consequences of such changes on tumor progression.When cancer research advanced into the post-genomic era, it was widely anticipated that the sought-after cure will be delivered promptly. Instead, it became apparent that an understanding of cancer genomics, alone, is unable to translate the wealth of information into successful cures. While gene sequencing has significantly improved our understanding of the natural history of cancer and identified candidates for therapeutic targets, it cannot predict the impact of the biological response to therapies. Hence, patients with a common mutational profile may respond differently to the same therapy, due in part to different microenvironments impacting on gene regulation. This complexity arises from a feedback circuit involving epigenetic modifications made to genes by the metabolic byproducts of cancer cells. New insights into epigenetic mechanisms, activated early in the process of carcinogenesis, have been able to describe phenotypes which cannot be inferred from mutational analyses  Cancer evolution operates through selection, which requires a degree of phenotypic diversity to present a range of possible responses to microenvironmental selection forces, some of which confer selective advantage , 2. TumoHypoxia is one of the main environmental factors a cancer cell must face in order to survive, thrive, and progress. Hypoxia imposes a metabolic stress on the cell, hindering its ability to carry out aerobic respiration. Therefore, the cell must be able to adapt to a hypoxic environment in order to survive. The cellular response to hypoxia is robust, and exerts most of its force via the transcription factors HIF-1α and HIF-2α. Another HIF family protein, HIF-3α, functions to repress the responses directed by HIF-1α and HIF-2α. All three of these proteins carry out their function via dimerization with constitutively expressed HIFβ proteins in the nucleus which allows them to directly modulate transcription of proteins involved in the hypoxia response , 8, or iNutrient deprivation is another major stressor within the tumor microenvironment. When the cell experiences a critical reduction in a particular nutrient, it must swiftly adjust in order to maintain productivity in the terms of metabolism, proliferation, migration, or other processes essential to evolutionary success and survival. Two main nutrient sensing proteins implicated in cancer are AMP-activated protein kinase (AMPK) and the mammalian target of rapamycin (mTOR) , 12. TheThe metabolic pathway directed by AMPK is highly context-specific; depending on the nutrient status of the cell. AMPK is responsible for the cellular response to glucose deprivation and acts as a metabolic switch from a highly glycolytic state to an oxidative state depending on the availability of glucose. This is particularly important in the context of cancer and the highly plastic nature of cancer metabolism. AMPK is activated by 5′-AMP, which indicates that the cell is not regenerating ATP at a fast-enough rate to meet demand. This induces the uptake of glucose and the induction of glycolysis to replenish the cellular ATP . The indmTOR is present in the form of two different complexes, mTORC1 and mTORC2. These two complexes participate in associated, but distinct signaling pathways in nutrient sensing. mTORC1 becomes activated in response to various growth factors and amino acids that promote cell growth and proliferation. When inactive, mTORC1 represses growth and induces an autophagic response. mTORC2 is a sensor of glucose but also plays a role in amino acid signaling. mTORC2 is activated by acetyl-coenzyme A (Ac-CoA), which is produced in the cytoplasm via citrate lyase, when glycolytic flux is abundant. The result of mTORC2 activation is increased cell proliferation, in response to the increased glucose metabolism. mTORC2 has also been implicated in amino acid sensing by having the ability to suppress the function of the glutamine-cysteine transporter, system Xc transporter-related protein .2+ channel that includes transient receptor protein channel vanilloid subfamily 1 and 2 (TRPV1 and TRPV2)  receptors control physiological homeostasis under changing metabolic and dietary conditions  . Lactatenditions .Cancer cells commonly overexpress many of the aforementioned acid sensors, and this can be correlated to tumor progression and poor outcome , 18. TheOne mechanism enabling cancer cells to adapt is through changes to gene expression via epigenetic regulation. Some modalities of epigenetic regulation are transient  and are imposed to help cancer cells survive acute disruptions in their microenvironmental homeostasis. In contrast, other epigenetic mechanisms are more persistent  and have the ability to be passed down through generations to endow further generations with memory on how to survive in the tumoral microenvironment.The term epigenetics was first created by CH Waddington who described it as “the causal interactions between genes and their products, which bring the phenotype into being.” While a commonly agreed upon definition is hard to find today, the term epigenetics in the modern era is commonly described as a permanent change in the way genes are expressed. Types of epigenetic regulation include histone modifications, DNA Methylation, and non-coding RNA , which cIt is commonly known that the epigenetic signatures of cancer cells are different compared to their untransformed counterparts –25. ManyIn many solid tumors, intra-ductal hyperplasia leads to significant alterations in the physical microenvironment, especially in peri-luminal cells that are far >160 microns) from their blood supplies. Importantly, the diffusion distance of oxygen in tissues is 100–200 microns , meaning0 micronsin vitro when exposed to hypoxic conditions, possibly via hypermethylation of tumor suppressor promoters in hypoxic regions of tumor samples , th, th36 on by S6K1 . mTORC2  by S6K1  43). In. In36 on by S6K1 .While the aforementioned mechanisms involve the sensing of nutrients to transduce downstream epigenetic changes, alterations in acetate level can directly influence the epigenetic status of the cell. Free Acetyl-CoA in the cell nucleus is produced by Acetyl-CoA synthetase (ACSS2), which catalyzes the conversion from Acetate, and by ATP-citrate lyase (ACLY) which catalyzes the conversion from citrate. The levels of free Acetyl-CoA directly influence the global acetylation status of histones , and henA recently described mechanism of both environmental sensing and epigenetic modification is that of histone lactylation. In 2019, Zhang et al. described for the first time the modification of histones by lactate . As is cMany epigenetic alterations that are acquired throughout tumor progression alter the metabolism of the tumor's cellular population. While the previous section covered specific epigenetic alterations that occur in response to the metabolic microenvironment, this section will describe the role of epigenetic modifications in altering cancer cell metabolism.+, has been shown to alter cellular metabolism through histone deacetylation leading to increased transcription of HIF-2α. This SIRT1/HIF-2α axis promotes the oxidative metabolism of glutamine, and suppresses the effects of HIF-1α, inhibiting hypoxia mediated induction of glycolysis (Lactate itself has the ability to alter the activity of epigenetic modifier proteins. The inhibition of HDAC's by lactate demonstrated in previous studies , 31 has ycolysis . Corbet ycolysis . From a In response to hypoxia, HIF-1α activation leads to the induction of JMJD2B activity. JMJD2B has been shown to be upregulated in ER-positive breast cancer  and blad36 on Histone 2B is a significant epigenetic mark made by two proteins involved in nutrient sensing: AMPK and S6K1 (36 on Histone 2B is significantly increased upon treatment of cells with 2-Deoxy Glucose (36 on Histone 2B by AMPK is the recruitment of EZH2 (Epigenetic marks induced by nutrient sensing proteins and complexes have the ability to greatly alter cellular metabolism, making a useful feed-forward mechanism for acclimation and adaptation to the current metabolic microenvironment. The phosphorylation of Serand S6K1 , 40. It  Glucose , which m of EZH2 . ConsideThe suppression of SIRT4 by mTORC1 has profound effects on the metabolism of cancer cells, specifically inhibiting glutamine metabolism through inhibition of GDH. In colorectal cancer, decreased SIRT4 expression has been correlated with progression and increased invasive potential of cancer cells , and in mTORC2 has the ability to modulate activity of KMT2D. KMT2D is inhibited downstream during mTORC2 activation, which in response, inhibits the access of the FOXA1-PBX1-ER complex from binding the DNA. Prevention of this complex from binding the DNA has been shown to reduce the expression of key proteins including: GREB1, SERPINA1, cFOS, and MYC . Of thesIn addition to the previously mentioned alterations in gene expression caused by stimulus-induced epigenetic modifications, epigenetic upregulation of MCT4 via hypomethylation of the SLC16A3 promoter has been shown in renal cancers . AlthougAs described previously, a resulting consequence of oxygen deprivation and stabilization of HIF-1α is the induction of TET1/3 and hypomethylation of the genome. Hypomethylation has been shown to upregulate the expression of Hexokinase 2 , 55, whiDespite the differing sensing mechanisms of the mTORC1/mTORC2 complexes and SIRT1, all mechanisms converge on a single metabolic alteration caused by epigenetic modification: the increase of glutamine metabolism. This may pose the opportunity to target glutamine metabolism as a cancer therapeutic, an idea that has drawn enough attention to warrant various reviews on that subject alone , 64. AltGenomic data describing tumors and cancer cell populations is valuable information for studying and classifying a cancers phenotypic characteristics. Although this information is valuable, it does not tell the entire story when it comes to cancer initiation and progression. Recent studies have demonstrated the presence of single and multiple driver mutations in significant proportions of cells comprising healthy tissue , 69. ThiMetabolic cross-talk and feedback is essential for the survival of any cell in a highly variable environment. There are many ways cells can sense perturbations in their microenvironment, including nutrient, and energy demands. These sensing mechanisms can transduce signals that lead to the alteration of the cell's epigenome. The epigenetic alterations driven by the alterations to the cellular environment and metabolic state can go on to influence the metabolism of the cell in a feed-forward mechanism. These changes made can have long lasting and heritable effects and go on to influence the progression of the tumor.Nutrient sensing by cells is essential for the survival and acclimation to an unstable environment . PermaneThe hyper-glycolytic state of cancer cells is a hallmark of their progression and aggressive state . At presMD and RG developed the idea. All authors wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Breast cancer susceptibility gene (BRCA) mutation carriers have an increased risk of breast cancer. Mitigation of this risk can be achieved via surveillance or prophylactic mastectomy with or without breast reconstruction. Those that choose surgery expect to reduce their chance of developing cancer. The purpose of this study was to determine the incidence of patients developing breast cancer prior to surgery and to identify modifiable contributing factors within the patient journey. This is a historical cohort study of all BRCA mutation carriers identified through the British Columbia Cancer Hereditary Cancer Program between 2000 and 2012. Patients were divided into two groups: surveillance (S) and prophylactic mastectomy with immediate breast reconstruction (PM/IBR). The incidence of cancer, time to PM/IBR and patient journeys were analyzed. A total of 333 women were identified. The time to surgery from mutation disclosure was a median of 31  months. During this period, 6% of patients developed breast cancer compared with a 14% incidence of breast cancer in patients choosing surveillance. The majority of time to surgery was attributed to the period between mutation disclosure and the decision to proceed with surgery. Strategies to facilitate decision-making as well as wait list prioritization and dedicated operative time should be targeted to this population to decrease the number of women developing an interval cancer prior to surgery. The Breast Cancer Susceptibility Gene 1 (BRCA1) and the Breast Cancer Susceptibility Gene 2 (BRCA 2) are tumor suppressor genes that encode proteins involved in the repair of DNA double-strand breaks by way of the homologous recombination repair pathway . The detOnce given a positive BRCA mutation result, individuals can begin surveillance that combines mammography and magnetic resonance imaging (MRI), clinician breast examination and breast self-examination commencing at age 25 to 35 years ,5. WhileThe process leading to a prophylactic mastectomy is complex. Patients require access to health services required to make an informed decision, consultation with specialized health care professionals, time to consider the physical and emotional consequences of this type of surgery and balancing this intervention with life events such as child-bearing. Therefore, once a woman receives a positive mutation result, the time to risk-reducing surgery is affected by a multitude of factors . We examThe HCP offers risk assessment, counseling and genetic testing. Those who test positive for BRCA1/2 are advised of options including surveillance, chemoprevention and prophylactic surgery. Patients can choose to be followed at the High Risk Clinic (HRC) where breast imaging and surgical referrals are arranged. The HRC maintains a database that includes demographics, genetic test results, a history of cancer diagnoses and a history of hereditary cancer-related surgery.A historical cohort study design was employed. The HRC database was interrogated to identify individuals with a positive BRCA1/2 mutation between 1 January 2000 and 31 December 2012. Patient charts were reviewed until 2018. Men, women younger than 25 years and patients who had any cancer diagnosis prior to genetic testing were excluded. Patients were divided into three groups based on a decision at the time of counseling: surveillance (S), prophylactic mastectomy with immediate breast reconstruction (PM/IBR) and prophylactic mastectomy only (PM).Evidence based recommendations during the study period included a yearly breast MRI from age 25 to 65 and a yearly mammography from age 30, alternating every six months. The risk-reduction measures included a bilateral PM, a bilateral salpingo-oophorectomy (BSO) by age 40 and the use of risk-reducing medications . ProphylTime periods were defined as T1: BRCA mutation disclosure to decision for surgery; T2: decision to surgical consultation; T3: consultation to surgery.Approval was obtained from the University of British Columbia Clinical Research Ethics Board (CREB) and the Hereditary Cancer Program (HCP) within the British Columbia Cancer Agency (BCCA). The CREB waived the requirement for informed consent.The baseline patient characteristics for each patient group were described. Discrete variables were summarized by frequencies and percentages. Continuous variables were summarized by mean (SD) and median with percentiles . Wait times were summarized with median with percentiles . For the PM/IBR group time to surgery was summarized with a cumulative frequency curve. The cumulative incidence of breast cancer over time was calculated for each group; death was treated as a competing risk in all groups. A competing risks regression model was used to examine the differences in the development of cancer between the groups  with group (S vs. PM/IBR), BRCA1 vs. BRCA2, years of hormone exposure and age included as covariates. A two-sided P value of less than 0.05 was considered to indicate statistical significance. All analyses were performed with the use of SAS 9.4  and R version 3.4.1.This study was supported by a grant from the Canadian Breast Cancer Research Alliance. This sponsor was not involved in the study design, data collection, analysis, interpretation of data or writing of the manuscript.A total of 1168 mutation positive patients were identified from the HCP database. Of those, 615 had a pre-existing cancer, 128 were men, 45 were younger than age 25 years, 44 had no patient records, one had had a previous PM and two were found to have no genetic mutation after a chart review. After exclusions 340 women were included. Of those, 201 patients chose S, 132 chose PM/IBR and seven chose PM. The PM group was excluded due to the small number of patients. The demographic characteristics and risk factor profile for each patient group are shown in Twenty-eight of the 201 surveillance patients and eight of 132 patients in the PM/IBR group developed invasive breast cancer during the study time period ; Table 3In the PM/IBR group six cases had an invasive ductal carcinoma (IDC) and two had a ductal carcinoma in situ (DCIS). Two of the eight cases were node positive, two were triple negative, three were HER2 positive, all were Grade 2 or higher and two were 2 cm or greater in size. In the S group 24 cases had an IDC and four had a DCIS. Five of 28 were node positive, seven were triple negative, five were HER2 positive and three were 2 cm or greater in size .The median time from mutation diagnosis to surgery in the PM/IBR group was 31.1  months. Patients waited 8.5  months from a BRCA mutation diagnosis to a decision for surgery (T1) with a 3.5  month time from a decision/referral to a surgical consultation (T2) and a 7.7  month time from a consult to surgery (T3) .For patients with available data, we compiled reasons for possible delays for proceeding with prophylactic surgical intervention. During the T1 time period we found 40% of patients made the decision to proceed within three months of disclosure of the BRCA mutation. Of the remaining patients, the majority initially declined a surgical consultation and then changed their mind at a later date for unknown reasons or reasons such as family influence, proceeding with child-bearing or proceeding with other surgery . Other less frequent factors within the T1 time period included the long distance from a tertiary care center and a delay due to health issues. A total of 70% of patients went on to have their surgical consultation within six months. Of the remainder reasons for longer waits to consultation included a change in the requested surgeon, a long wait list for consultation or patient factors such as cancellation or a delay due to health issues. A total of 62% of patients had surgery within one year of their surgical consultation. Of the remainder factors leading to longer times included long surgical wait lists, the patient being undecided after consultation, the patient requesting a second opinion and patient health issues .Of the eight patients that developed cancer while awaiting PM/IBR surgery, six required more than three months to make the decision to proceed with surgery. The majority of these patients initially declined surgical consultation for an unknown reason and then changed their minds. One patient delayed for child-bearing and one patient changed her mind after being provided with new information .The median time from a BRCA diagnosis to cancer detection was 32.0  months in the PM/IBR group and 59.3  months in the S group. In the PM/IBR group five cancers occurred while awaiting surgery and three were found at the time of a mastectomy .The goal of this study was to determine the incidence of BRCA mutation positive patients developing breast cancer prior to prophylactic surgery as well as modifiable patient and/or system factors that may decrease this incidence. We found a 31-month patient journey between BRCA mutation diagnosis disclosure and surgery with 6% of patients developing breast cancer during this time. This can be compared with the 14% incidence of breast cancer in patients choosing no intervention (surveillance), which is comparable with that found in other studies ,18,19,20The protective effect of a prophylactic mastectomy against the subsequent development of cancer in BRCA mutation carriers is well documented with the majority of studies showing zero incidence of cancer in patients after they have proceeded with a prophylactic mastectomy ,16,17,18The time to the decision to proceed with risk-reducing surgery (T1) is a key modifiable factor. This time period is complex as decision-making in the BRCA1/2 patient may be impaired due to psychological distress after the disclosure of the genetic mutation and the time required to make such an important decision . AdditioT2 and T3 are system-related and reflect the prioritization of more “urgent” patients within the health care system. BRCA mutation patients fall into a category somewhere between elective and urgent but are typically wait listed as elective while patients with active cancer are prioritized. Access to specialist consultation and the supply of elective surgery depends upon system capacity and the productivity with which that capacity is used . Many stDespite the strengths of our study including a large sample size from a single institution and a sufficiently long follow-up period for breast cancer risk estimates, a prospective design would more accurately capture the exact reasons for the length of time between BRCA mutation diagnosis and prophylactic surgery. This would also allow the allocation of each cancer diagnosis into a specific time period (T1–T3) to further establish where to target resources. However, we have shown that at the 31-month time point from a BRCA mutation disclosure, 6% of BRCA patients awaiting prophylactic surgery (and still under surveillance) developed cancer. The majority of the patients that chose to proceed with PM/IBR and still developed cancer had a long T1 period, which may be attributed to a difficulty making the decision to proceed with risk-reducing surgery. This can be extrapolated to all healthcare systems that use similar hereditary cancer programs and be used to inform patients of the risk of developing cancer even after deciding to proceed with risk-reducing surgery. This may in turn focus centers on methods to optimize the decision-making process (T1) and, once a patient has decided to proceed, to ensure surgery is expedited in this high-risk group.In conclusion, our data show that our cohort of BRCA mutation carriers that chose PM/IBR had a 31-month journey from mutation diagnosis to surgery and 6% were diagnosed with a DCIS or an IDC prior to or at the time of surgery. In the patients that developed an IDC, 25% had a node positive disease with 25% of tumors being greater than 2 cm in size. Women may therefore develop a high stage disease despite choosing to reduce their risk of cancer by undergoing prophylactic surgery. The implication is that if these patients had access to risk-reducing surgery earlier, the risk to life expectancy may have been altered. Strategies to decrease this wait time and thus decrease the incidence of cancer prior to surgery include the facilitation of decision-making as well as wait list prioritization. Dedicated operative time should also be targeted to this patient population in an effort to decrease the incidence and progression of occult disease.


AAUAAUAA) broadly represented in the cellular RNA pool as well as tightly binding RNA aptamers with affinities comparable and competitive with the Cyp33 MLL1-PHD3 interaction. RNA binding extends beyond the canonical RRM domain, but not to the Cyp domain, suggesting an indirect mechanism of interaction. NMR chemical shift mapping confirms an overlapping, but not identical, interface on Cyp33 for RNA and PHD3 binding. This finding suggests RNA can disrupt the gene repressive Cyp33-MLL1 complex providing another layer of regulation for chromatin remodeling by MLL1.Cyp33 is an essential human cyclophilin prolyl isomerase that plays myriad roles in splicing and chromatin remodeling. In addition to a canonical cyclophilin (Cyp) domain, Cyp33 contains an RNA-recognition motif (RRM) domain, and RNA-binding triggers proline isomerase activity. One prominent role for Cyp33 is through a direct interaction with the mixed lineage leukemia protein 1  complex, which is a histone methyltransferase that serves as a global regulator of human transcription. MLL activity is regulated by Cyp33, which isomerizes a key proline in the linker between the PHD3 and Bromo domains of MLL1, acting as a switch between gene activation and repression. The direct interaction between MLL1 and Cyp33 is critical, as deletion of the MLL1-PHD3 domain responsible for this interaction results in oncogenesis. The Cyp33 RRM is central to these activities, as it binds both the PHD3 domain and RNA. To better understand how RNA binding drives the action of Cyp33, we performed RNA-SELEX against full-length Cyp33 accompanied by deep sequencing. We have identified an enriched Cyp33 binding motif ( Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Partly**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: N/A**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: No**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: Isomerase-dependent gene regulation is an interesting area Cyp33 is one such example. Cyp33 is peptidyl-prolyl isomerase best known for its central role in regulating the activity of the myeloid/lymphoid or mixed lineage leukemia (MLL1) complex. Cyp33 has two domains- a C-terminal the traditional cyclophilin domain that speeds up the isomerisation of a peptidyl prolyl bond, and an N-terminal RRM domain that binds short RNA sequences. Both the cyclophilin and RRM domains are required for Cyp33 regulation of MLL1. Cyp33 isomerises the peptidyl-prolyl bond in the linker region between the PHD3 and Bromo domain of MLL1. The RRM domains appears to have two roles: it interacts with the PHD3 domain as well as an RNA motif. In this paper, RNA-SELEX and deep sequencing was used to identify a strongly binding RNA motif (AAUAAUAA). NMR chemical shift mapping experiments using full-length Cyp33 gave a more complete picture of the RNA and PHD3 binding to Cyp33. The data showed that while the RNA sequence bound to the RRM domain, the residues involved are not wholly identical to those that bind to the PH3 domain of MLL1. Secondly, the RNA binds to residues outside the Cyp33 RRM domain. The results show the tuning of Cyp function through competitive RNA and peptide binding.Line 248: what is SO-1? This was not defined.Line 250: What is the physiological SELEX buffer – define this.Line 252: Assignments for 55/81 non-proline: do you mean 55 out of 81? Clarify.Line 442: it is surprising that there are very few resonances from the flexible 50 amino acid linker region. Is there a reason for this?Figure 4: (a) Panels showing the NMR titration of the most significantly affect residues such as Y28, F68. F70, N99, etc should be shown. (b) The structure of the RRM domain should be annotated to show the numbering system for the �-sheets.Line468: The chemical shifts changes should be displayed as a histogram, with the secondary structures of the RRM domain aligned with the residue numbers. This will an assessment of the significance of the chemical shifts outside the canonical RNA binding site of the RRM domainLine 468: Using chemical shift changes to determine binding sites must be done judiciously. Shift changes caused by direct binding of ligand and indirectly due to relayed effects must be delineated, through analysing titration dependent changes. Small shift changes should be interpreted with caution. The best way to determined if the shift changes represent binding site is to perform site-directed mutagenesis.Line 472: “Notably, the extension of the SO-1 binding surface onto β-sheet 3 and the loop results in large overlap with the binding surface involved in the Cyp33-RRM and MLL1-PHD3 interaction” – are the magnitudes of chemical shifts significant?Line 485 and Supplementary Figure 7: The message here that extra regions of the FL Cyp33 protein is binding to the SO-1 is not discernible from this Figure. Panels showing the most significantly shift changes in the SO-1 titrations should be included. As the spectrum has not yet been fully assigned, concluding drawing form conclusions about expanded binding regions for SO-1 beyond the RRM domain is premature in the absence of addition data such as effects of mutagenesis. The fact that the linewidths of the spectra are similar means that binding is very weak indeed.Line 495: “However, several chemical shifts not attributable to the known domains exhibit slow-exchange peak shifting behaviour. As these cannot be ascribed to either the Cyp33-RRM or Cyp33-Cyp domains, it suggests the involvement of the 46 amino acid linker in the interaction peaks”. How many peaks are in slow exchange? Do the binding site residues in the Cyp33 RRM domain also display slow exchange? If not, why are the linker residues in slow exchange? Slow exchange can be a result of sample conditions – was an excess of SO-1 added the Cyp33Fl sample to try and shift the equilibrium more in favour of the bound form?Line502, Figure 5: The shift changes should be displayed as histograms for the results to be interpretable.Line515: what are the relative binding constants between the Cyp33 RRM domain to SO-1 and to PHD3. Would you not expect the RRM domain to have more preference for the RNA than the PHD3? Was the reverse NMR competition experiment performed using an excess of PHD3 to compete out the RRM domain?**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  21 Jan 2021Response to Editor comments: All data are publicly available through the indicated DOIs.A file named S1_raw_images has been uploaded that includes the requested raw gel images.Response to Reviewer Comments:We thank the reviewer for the careful consideration and review of this manuscript. We have addressed the points raised in review one-by-one as enumerated below. Overall, the review was favorable with some outstanding issues that we have addressed as described. Reviewers comments are italicized.Line 248: what is SO-1? This was not defined. This was defined at a later point in the manuscript, and we have clarified the definition at Line248 as requested. Line 250: What is the physiological SELEX buffer – define this. This buffer is defined in Supplementary Table 2, we now specify this at Line250.Line 252: Assignments for 55/81 non-proline: do you mean 55 out of 81? Clarify. Thank you for pointing out this ambiguity. We have clarified this statement as requested.Line 442: it is surprising that there are very few resonances from the flexible 50 amino acid linker region. Is there a reason for this? This is not so uncommon in NMR, as the observation of flexible regions depends on both the exchange regime and pH of the solution. We have addressed this in the text, Line441.Figure 4: (a) Panels showing the NMR titration of the most significantly affect residues such as Y28, F68. F70, N99, etc should be shown. (b) The structure of the RRM domain should be annotated to show the numbering system for the �-sheets. Regarding (a) we have included a superposition of the full spectrum of all titration points in Supplemental Figure 6. We believe this both clearly illustrates the chemical shift changes for all the significant shifters while also providing with all the data in an unbiased fashion. Regarding (b) we have made the requested changes to Figure 4, which we agree allow for clearer interpretation of the location of shifted sites on the canonical RRM domain. Line468: The chemical shifts changes should be displayed as a histogram, with the secondary structures of the RRM domain aligned with the residue numbers. This will an assessment of the significance of the chemical shifts outside the canonical RNA binding site of the RRM domain. We appreciate this suggestion and include the requested histogram as a new panel C of Figure 4.Line 468: Using chemical shift changes to determine binding sites must be done judiciously. Shift changes caused by direct binding of ligand and indirectly due to relayed effects must be delineated, through analysing titration dependent changes. Small shift changes should be interpreted with caution. The best way to determined if the shift changes represent binding site is to perform site-directed mutagenesis. As pointed out by the reviewer, this is an important caveat, as the observed chemical shift changes could be due to indirect rather than direct interactions. We have clarified this point to include this alternative interpretation through new language at lines 469-472. While mutagenesis of all these residues could provide direct evidence of the binding interface, we encounter the same issue that mutants can have indirect effects. Moreover, we respectfully suggest a new experimental program is beyond the scope of this manuscript.Line 472: “Notably, the extension of the SO-1 binding surface onto β-sheet 3 and the loop results in large overlap with the binding surface involved in the Cyp33-RRM and MLL1-PHD3 interaction” – are the magnitudes of chemical shifts significant? The reviewer is asking about the magnitude of the chemical shift changes upon SO-1 binding, and as can now be seen in Figure 4c, indeed the magnitude of chemical shift change is similar in the canonical and non-cannonical regions. However, the magnitude of chemical shift change does not directly reflect the extent of structural engagement at that site. We believe it is difficult to reliably interpret magnitudes of chemical shift changes. As seen from the histogram and examination of the titration data, it is clear the changes are significant  and represent a change in the chemical environment at that site.Line 485 and Supplementary Figure 7: The message here that extra regions of the FL Cyp33 protein is binding to the SO-1 is not discernible from this Figure. As the spectrum has not yet been fully assigned, concluding drawing form conclusions about expanded binding regions for SO-1 beyond the RRM domain is premature in the absence of addition data such as effects of mutagenesis. The fact that the linewidths of the spectra are similar means that binding is very weak indeed. Two points here to address – the first is that we observe  that the free full-length Cyp33 spectrum is well-represented by the sum of the Cyp33-RRM and Cyp33-Cyp spectra. Consistent with this, in Supp Figure 7, we observe the Cyp33-RRM shifts upon SO-1 binding recapitulated in the full-length protein (now labeled to make the point more clearly). The assigned Cyp33-RRM peaks that do not shift upon binding are likewise unpertubed in this spectrum. We observe additional shifts outside of the ones ascribed to the Cyp33-RRM , which we attribute to elements outside of the RRM domain. In light of the reviewer’s concern, we now present a more conservative interpretation and have eliminated the speculation that these lie in the linker, just note that they lie outside of the RRM. We note that this NMR data provides structural support for the biochemical observation that full-length Cyp33 binds SO-1 30-fold more tightly than the isolated RRM domain, suggesting an additional surface is present in the full-length protein and its activity is not fully recapitulated by the RRM domain. The primary binding event of SO-1 to the RRM is in the fast exchange regime, consistent with the binding affinity measured. As noted below, upon re-examination of our data in light of the reviewers comments we are no longer commenting on the exchange regime as it is difficult to see unambiguously in the data. This does not change any of our conclusions.Line 495: “However, several chemical shifts not attributable to the known domains exhibit slow-exchange peak shifting behaviour. As these cannot be ascribed to either the Cyp33-RRM or Cyp33-Cyp domains, it suggests the involvement of the 46 amino acid linker in the interaction peaks”. How many peaks are in slow exchange? We have highlighted the 7 clearest examples of peaks shifting outside of the assigned RRM resonances, circled now in pink in Supp Figure 7. Do the binding site residues in the Cyp33 RRM domain also display slow exchange? If not, why are the linker residues in slow exchange? Slow exchange can be a result of sample conditions – was an excess of SO-1 added the Cyp33Fl sample to try and shift the equilibrium more in favour of the bound form? We appreciate the reviewer’s concerns with the interpretation of these data and have qualified some of our conclusions to reflect a more conservative interpretation of the chemical shift data, as described in the response above and in the text at lines 503-511. Specifically, we have removed discussion of the “slow exchange” regime and tempered our conclusion of the involvement of the linker. As the reviewer correctly points out, it is an overreach to make conclusions in the absence of independent assignments.Line502, Figure 5: The shift changes should be displayed as histograms for the results to be interpretable. The data mapped on this figure were obtained from Hom et al. (reference #7) and they unfortunately did not provide more quantitative data for interpretation. We did not repeat the published experiment, we instead started with an RNA-bound sample and titrated in the peptide.Line515: what are the relative binding constants between the Cyp33 RRM domain to SO-1 and to PHD3. Would you not expect the RRM domain to have more preference for the RNA than the PHD3? Was the reverse NMR competition experiment performed using an excess of PHD3 to compete out the RRM domain? Thank you for this question, as we believe addressing it more careful strengthens our paper. We measured the Kd for the Cyp33 RRM to SO-1 using gel shift, and report that Kd along with the representative gel in Supplementary Figure 3. We find the Kd for RNA is weak, at 180 uM. This is fully consistent with published data, where the RRM bound a short RNA (AAUAAA) with 200 uM affinity, and in comparison to the Kd for the peptide, reported by Hom to be 1.9 uM. We allude to this binding preference now in the text (Lines 528-530). We did not have the material to perform the titration in reverse, but we do not believe that is necessary to show that the interaction is competitive.AttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 26 Jan 2021Cyp33 Binds AU-Rich RNA Motifs via an Extended Interface that Competitively Disrupts the Gene Repressive Cyp33-MLL1 Interaction in vitroPONE-D-20-24207R1Dear Dr. Wuttke,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. 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For more information, please contact Kind regards,Oscar MilletAcademic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments: 29 Jan 2021PONE-D-20-24207R1 in vitroCyp33 Binds AU-Rich RNA Motifs via an Extended Interface that Competitively Disrupts the Gene Repressive Cyp33-MLL1 Interaction Dear Dr. Wuttke:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. 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We use an individual based model and national level epidemic simulations to estimate the medical costs of keeping the US economy open during COVID-19 pandemic under different counterfactual scenarios. We model an unmitigated scenario and 12 mitigation scenarios which differ in compliance behavior to social distancing strategies and in the duration of the stay-home order. Under each scenario we estimate the number of people who are likely to get infected and require medical attention, hospitalization, and ventilators. Given the per capita medical cost for each of these health states, we compute the total medical costs for each scenario and show the tradeoffs between deaths, costs, infections, compliance and the duration of stay-home order. We also consider the hospital bed capacity of each Hospital Referral Region (HRR) in the US to estimate the deficit in beds each HRR will likely encounter given the demand for hospital beds. We consider a case where HRRs share hospital beds among the neighboring HRRs during a surge in demand beyond the available beds and the impact it has in controlling additional deaths. As states push to end social distancing and reopen businesses, it is important to understand the cost of opening in terms of lives lost and medical costs incurred. A premature opening will likely cause more deaths and infections as the healthcare system will likely get overwhelmed, and may wipe out all the gains made in the initial shutdown. We use an agent-based model and simulation framework to estimate the immediate medical cost of COVID-19 under different mitigation scenarios. The scenarios consider social distancing with different durations and varying compliance levels. The simulation framework uses a detailed representation of the US population and their social interactions to study the spread of COVID-19. An SEIR (susceptible-exposed-infected-recovered) model captures the time varying health states of the individuals. The infected individuals arrive at one of the three health states i.e. medically attended, hospitalized, or ventilated before getting to the final health state i.e. recovered or dead, as shown in Fig. Medical costs are applied based on the three health states i.e. medically-attended, hospitalized and ventilated. In addition, if an infected individual dies, then the “value of statistical life” is used to estimate the cost of death. We also estimate the shortage of hospital beds that is likely to occur in each Hospital Referral Region (HRR) given the demand for hospital beds and the number of available beds in each HRR in the US. Data on the number of beds in each HRR is obtained from the American Hospital Association (AHA) and a fraction of them are assumed to be available for COVID-19 patients. We consider cases where neighboring HRRs share or do not share hospital beds during a surge in demand.This information is then used to calculate additional deaths and medical costs for each of the mitigation scenarios. Policy makers can apply this kind of analysis to decide where the temporary hospitals may need to be built to offset the deficit in demand for beds. Our goal is to use this knowledge to provide guidance to public health officials and policy makers on the trade-offs between the length of lockdown, compliance to social distancing, infections, deaths and the medical costs. Our scenario-based analysis estimates the burden of the disease in monetary terms, and helps rank-order mitigation strategies.1 consider potential health care costs and resource usage under different attack rates which vary from 20 to 80%. However, it does not consider any interventions or mitigation strategies. Our research focuses on counterfactual mitigation scenarios and their respective costs. We use recent cost estimates for COVID-19 available from the Kaiser Family Foundation (KFF)2 which uses cost of pneumonia cases as a proxy. Our detailed network based model, that captures heterogeneous social interactions and contact times among the individuals in the population, is one of the unique features of the analysis. Additionally, no other research has provided an estimation of medical costs for such detailed mitigation scenarios for the entire US.In related work, authors inOur results show that (1) Without mitigation, the total medical costs would be a significant fraction (5%) of the US GDP; (2) a lockdown of just 2 months, if done early in the epidemic, and with sufficient compliance, could have reduced the medical costs by more than 90%; (3) if 90% compliance could be achieved, then even a 45 day lockdown period would have been enough to contain the epidemic and the medical costs; (4) if HRRs do not share hospital beds with other HRRs, a significant deficit of beds will cause medical costs to skyrocket, through increase in deaths. However, if HRRs shared beds with their neighboring HRRs, the bed-deficits and additional deaths could be reduced to almost zero; and (5) a sensitivity analysis of the parameters shows the costs are most sensitive to the duration of the stay-home order.9 using an individual level synthetic social contact network10—which represents each individual in the population along with their demographic attributes , and their social interactions. The main steps in the first-principles based construction of synthetic populations and social contact networks are: (1) construct a synthetic population by using US Census and other commercial databases; (2) assign daily activities to individuals within each household using activity and time-use surveys ; (3) assign a geo-location to each activity of each person based on data from Dun and BradStreet, land-use, Open Street Maps etc.; and (4) construct a dynamic social co-location based social contact network that is induced when people simultaneously visit locations. These networks have been validated and used for numerous public health analyses before such as16. For details on the construction of social networks, see17.We build on our modeling and simulation framework for epidemic spreadbest guess 2020-04-14 version of “COVID-19 Pandemic Planning Scenarios” document prepared by the Centers for Disease Control and Prevention (CDC) SARS-CoV-2 Modeling Team18. The sequence of health-state transitions and possible paths are shown in Fig. 19.The SEIR disease model and parameters used here for COVID-19 have been defined in the 2. The costs for each health state are shown in Table To estimate the medical cost of treating COVID-19 patients, we use the average cost of treatment for pneumonia, paid among “large employer health insurance” plans, as a proxy22. We use an estimate of $2 million dollar as the value of life to convert deaths into costs23.The total medical costs can be measured through multiple criteria in terms of number of deaths and treatment costs, or a single criterion in dollar terms by converting deaths into dollars using “value of a statistical life”. There are various ways in which the value of a statistical life has been measured. US federal government uses $10 million dollar per life lost regardless of a person’s age while others have used estimates in the range of $160k to $2.4 million based on metrics like average lifetime earnings of a college graduate, 9/11 victim compensation, wrongful death claims, insurance policy value etc.Voluntary home isolation (VHI): Symptomatic people choose to stay at home (non-home type contacts are disabled) for 14 days.School closure (SC): Schools and colleges are closed (school type contacts are disabled).Stay home (SH): People follow public health “stay-home” directive (non-home type contacts are disabled).25. Once closed, schools are assumed to remain closed until end of August after which they reopen. Other social distancing interventions stop at 30, 45 or 60 days from the start date of the intervention, depending on the SH duration. Note that this implies all mitigation efforts end by the end of summer 2020.School closure and stay-home interventions start on different days in different states as stated inWe consider a number of mitigation scenarios that comprise of various social distancing strategies, compliance levels and durations of stay-home order. The following social distancing strategies are used: Durations of stay-home order vary from 0, 30, 45 to 60 days. Compliance to stay-home and voluntary home isolation vary from 60%, 70%, 80% and 90%. Table We use hospital bed capacity data available for each HRR in the US from AHA, to calculate the deficits that are likely to be encountered by each region. We assume three scenarios regarding the use of hospital beds: (1) All COVID-19 patients who need a bed, will have one available i.e. there is no shortage of hospital beds, (2) bed capacity in each HRR is limited and beds cannot be shared among hospitals in other HRRs to accomodate the surge in demand for beds and (3) bed capacity in each HRR is limited but beds can be shared among hospitals in the neighboring HRR regions to accomodate the surge in demand.26.Other factors considered in these scenarios are the average length of hospital stay of patients who are hospitalized, and the percentage of beds dedicated to COVID-19 patients in each HRR. Length of stay is 7 days or 14 days, and the percentage of beds available to COVID-19 patients is 70%, 80%, 90% or 120%. More than 100% bed capacity (120%) has been considered since many hospitals are able to temporarily increase their capacity beyond normal levelsEach day the demand for the number of beds in each HRR is determined by the simulations. The simulation results provide the counts of individuals who are in “hospitalized” state and depending upon the assumed duration of the hospital stay (7 days or 14 days), the demand for beds per day is calculated. Note that this includes individuals who are in “ventilated” state as well since everyone in “ventilated” state has to be in the “hospitalized” state first according to our disease model, see Fig. We assume that patients who need a hospital bed and cannot get it, will die. This is a strong assumption and hence four different values of dedicated bed capacities have been considered to show its sensitivity. Note that only the death counts and overall costs will change in scenarios (2) and (3) when limitations to bed capacity are considered. This assumption of “all bed deficits result in deaths” provides an upper bound on the medical costs resulting from the shortage of beds. If only a fraction of them die then the additional deaths and the costs can be appropriately scaled down.This section reports the simulation results and the medical costs that are likely to incur under various mitigation scenarios. For each of these scenarios we also consider four different possibilities for the availability of dedicated hospital beds for COVID-19 patients. Note that the hospital beds are considered as a post-processing step, after the simulations have been run and infected individuals have arrived at “hospitalized” state.Figure Figure 27. If the demand for beds exceeds the supply, patients who do not find a bed, will die. Each additional death results in extra costs at the rate of $2 millon per person. Figure 27 and 70% of the bed capacity being available to COVID-19 hospitalizations. However, sensitivity analysis has been done by varying length of hospital stay to 7 days and dedicated beds availability to 80%, 90% and 120%.Figure An analysis of costs under the assumption that hospital beds can be shared with neighboring HRRs shows that the shortage of beds can be completely alleviated by sharing. In almost all mitigation scenarios there are no additional deaths due to shortage of beds and hence no additional medical costs, as shown in Figs. 29, to do the sensitivity analysis. We randomly select 30 cells with different combinations of We analyze the sensitivity of our results to multiple factors i.e. the reproductive number  and other private organizations such as insurance companies ($160k). We use an estimate of $2 million per person based onThis study estimates the medical costs of COVID-19 in the US under different mitigation scenarios and helps understand the tradeoffs between deaths, costs, infections, compliance to social-distancing and the duration of stay-home order. Our scenario-based analysis estimates the medical burden of the disease in monetary terms, and helps rank-order mitigation strategies.It shows that costs are most sensitive to the length of the stay home order and then to the level of compliance. A stay home order of 60 days with 80% compliance, can drop the medical costs from $1 trillion to a meager $35 billion. Stay home duration also mitigates the effect of a high We also show that sharing of hospital beds among neighboring “Hospital referral region” during a demand surge can reduce the additional number of deaths to almost zero. Cooperation in sharing medical resources between neighboring regions can save lives and money. This kind of analysis can also help decide where additional bed capacity and temporary hospitals may need to be built to offset the surge in demand.Supplementary Information.


Salmonella on farms is a concern to the swine industry. Much of the focus of on-farm surveillance has been directed to the finishing stage because of food safety issues, but it is important to study Salmonella transmission during the nursery stage in order to develop control strategies. In this study, 50 cohorts of weaned pigs were monitored for Salmonella using blood samples taken at weaning and again near the end of the nursery stage and tested for antibodies. At the time of the second blood sampling, rectal swabs were obtained from the same pigs and cultured for Salmonella. A questionnaire regarding housing and management was also completed and used to evaluate risk factors for herds with active infection. If one pig out of the 20 tested in a cohort was found to be positive either based on the growth of Salmonella on culture or a rising antibody titre, then it was assumed that Salmonella was spreading among the pigs in that cohort. Active spread of Salmonella occurred in 80% of the nursery cohorts. Unfortunately, no risk factors were identified to explain the difference between positive and negative nurseries, including whether or not the farm used antibiotics.The presence of Salmonella infection using combined serological and bacteriological methods, and to try to identify risk factors associated with swine nurseries with active Salmonella spread. Twenty pigs from each of 50 cohorts of weaned pigs from 44 different nursery barns were sampled about the time of weaning and near the end of the nursery stage. Information regarding farm management and biosecurity practices were collected using a questionnaire. Blood samples were obtained at both visits, while rectal swabs were collected at the second visit. An enzyme-linked immunosorbent assay (ELISA) was used to test sera for Salmonella antibodies and rectal samples were cultured for Salmonella. A nursery cohort was identified as having an active Salmonella infection if Salmonella was cultured from one or more of the 20 pigs or if serological evidence suggested exposure to Salmonella. The association between farm-level management covariates and active Salmonella infection was assessed in 46 cohorts using a logistic regression model. Nine of 46 (20%) cohorts produced Salmonella-free pigs. The remaining 37 (80%) cohorts were classified as having an active infection. Examination of risk factors failed to identify how negative and positive nurseries differed.The objectives of this study were: to identify nursery cohorts with an active  Salmonella infection is an important concern for the pig industry, primarily from a public health standpoint but also as a cause of economically important clinical disease in pigs [Salmonella in pork production has been widely studied [Salmonella in the nursery might be an important strategy in reducing the prevalence of Salmonella in later stages of production. Identifying the spread of Salmonella in nursery pigs and assessing associated risk factors would be beneficial in developing control measures to prevent the circulation and maintenance of Salmonella in the later phases of production. in pigs ,2,3. The studied ,12,13,14 studied ,10,11,14Newly weaned pigs are susceptible to disease because at this stage pigs are subjected to multiple stresses ,16 and tSalmonella in the nursery to determine if weaned pigs are becoming actively infected with Salmonella. For example, most infected pigs become asymptomatic carriers, so the absence of clinical signs is not a good indication that Salmonella is not present [Salmonella continuously or shed large numbers of bacteria and therefore negative results from bacterial culture may be misleading [Salmonella from an asymptomatic carrier pig is to use at least 25 g of fecal material [Salmonella infection have been shown to be more effective in identifying the population of intermittent shedders than traditional bacteriological methods [Salmonella and not the presence of colostrum-derived immunity.There are several challenges regarding the monitoring of  present . In addisleading . In addimaterial ,19 which methods  but antiSalmonella infections using serological and bacteriological testing methods and (ii) to determine risk factors associated with nursery cohorts with an active Salmonella infection.The objectives of this study were: (i) to identify Ontario swine nurseries with active The research was approved by the Animal Care Committee of the University of Guelph, in accordance with the guidelines set forward by the Canadian Council of Animal Care . Forty-four Ontario swine farms with nursery barns were included in this study which took place between 2014 and 2019. Farm selection, based on the producer’s willingness to participate in the study, was both purposive and convenient. Along with conventional farms, antibiotic-free systems (including both certified organic and “raised without antibiotics” farms) were purposively selected to help capture a wide spectrum of farming practices in Ontario. A second cohort of nursery pigs was studied on six of these 44 swine farms resulting in a total of 50 study cohorts. These six farms were selected for a second cohort because they had been the first farms included in the study and thus over one year had passed since the first cohort was investigated and the producers were willing to continue to participate. The study involved following a weaned cohort of pigs through the nursery stage of production and involved two farm visits per cohort with the first visit (V1) at weaning or soon after weaning and the second visit (V2) near the end of the nursery stage.g and the sera were separated and stored at −20 °C. Individual rectal swabs were also collected from pigs at V2 and stored at −20 °C.For each cohort, the nursery was visited (V1) within a few days after the pigs were moved into the facility. On six of the nursery farms, where a second cohort of pigs was studied, the initial visit (V1) was conducted at weaning, immediately prior to the pigs being transferred to the nursery barn. In each of the 50 cohorts of weaned pigs, 20 animals were selected, with an attempt to choose pigs representative of the rooms and/or pens in the nursery barn . The choice of an average-sized pig was based on subjective observation. The 20 pigs in each cohort were ear-tagged to provide individual identification and blood samples were collected from either the jugular vein or suborbital sinus. The cohort was visited a second time (V2) within a few days of the pigs being moved from the nursery to a grower barn. At V2, blood samples were collected from the same 20 pigs. Blood samples were centrifuged for 20 min at 1500× During the first farm visit, a questionnaire was administered to the farm owner or manager and information was recorded by the investigator. The cohort-level questionnaire focused on management and biosecurity practices. Farm management questions included; the type of farm , production flow , and antibiotic use . Basic production data and farm details such as herd size, source of nursery pigs, weaning age, length of stay in the nursery barn, and stocking density were also collected. Biosecurity practice questions included; whether there was a hospital pen, controlled entry for human traffic, downtime for visitors, the disposal method of deadstock, and cleaning methods between batches.Salmonella serogroups B, C, D, and E  using an indirect ELISA . The ELISA was performed as described by the manufacturer. A sample-to-positive ratio (S/P) value was determined using the optical density (OD) values using the following equation:Serum samples were assessed for presence of antibodies to S/P ratio at V1 and V2 was identified as the antibody titre level at V1 and V2 for each pig. Based on the manufacturing guidelines, samples with a S/P ratio of ≥0.3 were identified as Salmonella seropositive and <0.3 were classified as Salmonella seronegative.The Salmonella colonies were identified as Salmonella positive.Rectal swabs were added to 9 mL of tetrathionate broth (TTB)  and incubated at 37 °C for 18 to 24 h. Then, 100 μL of TTB culture was transferred to 9.9 mL of Rappaport–Vassiliadis broth  and incubated at 41 °C for 18 to 24 h. Lastly, a loopful (~20 µL) of the RVB was plated onto xylose-lysine-tergoitol 4  agar plates and incubated at 37 °C for 18 to 24 h. Plates with one or more Data were entered into Microsoft Excel for Mac 2019 Version 16.25 . After cleaning, the data were imported into Stata  for further data management and descriptive analysis.S/P ratio) in nursery pigs and to account for the possible presence of passive antibody titres at weaning, 8 possible patterns were established based on the pig’s Salmonella seropositivity status at V1 and V2, along with the change in direction of antibody response and whether the S/P ratio was ≥0.3 or not.To explore the antibody response  resulting in a seropositive status at V2. All other patterns were considered serologically negative including a pattern with an initial high titre and a second lower S/P ratio even if it was ≥0.3. The assumption was that the decrease in antibody response reflects lingering passive immunity and not evidence of A Python  script was developed and performed to identify pigs into patterns (1–8). These results were presented and analyzed graphically. These pig-level data were also aggregated to the cohort-level.Salmonella infection if one of the 20 pigs sampled tested positive by direct (culture) or by indirect  methods.A cohort of nursery pigs was considered to have active The extent of agreement between the bacteriological and serological detection methods was determined using Cohen’s kappa (k) statistic. The kappa statistic was interpreted as follows: <0.2 slight agreement; 0.2–0.4 fair agreement; 0.4–0.6 moderate agreement; 0.6–0.8; substantial agreement; and >0.8 almost perfect agreement.2 allowed assessment of the difference between the positive proportion of the bacteriology and serology testing methods. If the McNemar’s χ2 test was non-significant (p > 0.05), this would mean the proportions do not differ. Meanwhile, a significant McNemar’s χ2 test (p < 0.05) would mean there is a disagreement between the two testing methods and assessment of kappa would not be beneficial.McNemar’s χSalmonella infection, a logistic regression model was used. The active Salmonella infection (yes/no) of nursery barns was used as the dependent variable, while the second cohort conducted on six farms (farm cohort visit) was used as a fixed effect. Farm management and biosecurity practices, as explanatory variables, were initially screened using descriptive statistics and evaluated for collinearity using Spearman correlation coefficients. Continuous variables were assessed for linear relationships with the outcome graphically (lowess) and categorized if the relationship was not linear or quadratic. Explanatory variables were then independently assessed with the dependent variable using univariable analysis. The initial inclusion of variables in the model was based on a liberal p < 0.1. Using stepwise elimination, the full model was manually built excluding variables lacking statistical significance. The likelihood ratio test was used to assess the statistical significance (p < 0.05) of variables before removing them from the full model. Additionally, prior to exclusions, variables were tested for confounding to ensure there was less than 20% change in coefficients in main effects in the model. Since all explanatory variables were removed from the model, no further testing for interactions or fit of the model was required.To assess risk factors associated with nursery barns with an active The 50 cohorts in this study included 19 (38%) farms that did not use antibiotics  and the remaining cohorts were raised on conventional farms that used antibiotics . Of 50 cWith regard to cohort size, the population of nursery pigs ranged from 120 to 6500 . Forty-one of 43 (95%) cohorts sourced nursery pigs from within their own production system, with this information missing from 7 participating nurseries. On average, 837 pigs entered the nursery at the same time, based on data from 41 cohorts . Based on the producers’ survey responses, the average weaning age of pigs varied from 18 days to 38.5 days-old . On average, pigs spent approximately 38.7 days in the barn . At V1, individual pigs ranged in age from 17 days to 41.5 days . While at V2, pigs ranged in age from 50 to 85 days old .Salmonella antibody titres (based on the S/P ratio), seropositivity, and bacteriological results for nursery pigs at V1 and V2 are presented in Salmonella antibody titres and number of pigs classified as Salmonella seropositive are observed in nursery pigs from V1 to V2. At the pig-level, 14.6% of pigs at the end of the nursery were found to be Salmonella-positive based on culture of a rectal swab.Salmonella testing results on a cohort basis are presented in Salmonella based on bacterial culture at V2  from V1 to V2 .At the individual level, 930 (69%) pigs had a decreasing V1 to V2 . MeanwhiSalmonella antibody response patterns at the pig-level were aggregated to the cohort-level capturing the proportion of pigs following patterns 1 to 8 by cohort . In addition, the k statistic revealed there was a fair agreement between the testing methods in identifying an active Salmonella infection on nursery cohorts .Although this study collected data from 50 Ontario nursery cohorts, complete bacteriological and serological testing information was only available on 46 nursery cohorts. A two-by-two table that identifies active Salmonella using both testing methods. The remaining 37 of 46 (80%) nurseries were found positive either using serological or bacteriological methods or both. Specifically, 22 (48%) nursery cohorts were identified with an active Salmonella infection using both identification methods, while 8 (17%) and 7 (15%) were only positive based on serology alone and bacteriology alone, respectively.Only 9 of 46 (20%) nursery cohorts were found to be negative for Salmonella infection was evaluated using a logistic regression model. With univariable analysis, an antibiotic-free system, the use of a shower, and the days the pigs spent in the nursery barn were found significant with a liberal p-value and included in the initial full model (p < 0.1). In the final model, no explanatory variables were significant or required in the model. Thus, no risk factors for active Salmonella infection in nursery barns were found.The association between risk factors and nursery cohorts with an active Salmonella in nursery pigs was investigated on 44 farms (50 nursery cohorts) using both direct (culture) and indirect (serology) techniques to establish whether pigs were becoming infected during the nursery stage or not. Many pigs entered the nursery with high antibody titres to Salmonella, presumably reflecting circulating antibodies obtained via colostrum [Salmonella-seropositive pigs was higher at the beginning of the nursery compared to the end of the nursery. The presence of this passive immunity, as pigs enter the nursery stage, provides some immune protection if challenged with Salmonella shortly after weaning [Salmonella infection in the nursery challenging. During the nursery, the passive immunity wanes and pigs begin to develop acquired immunity if pathogens are encountered [Salmonella during the nursery barn after passive immunity disappears, it is likely for pigs to experience an increase in antibody titres [Salmonella in the nursery barn, the decline in antibody titres continues. However, if piglets entering the nursery barn are Salmonella carriers, they are likely to shed Salmonella due to stress associated from weaning, change in environment, diet and comingling with different litters [Salmonella antibody response.In this study, the epidemiology of olostrum ,20. Over weaning , but theountered , and they titres ,21. But  litters ,15,17. TSalmonella seropositivity from weaning to the end of the nursery in pigs, Salmonella is still present and circulating in many of these nursery cohorts. By understanding the Salmonella status at the cohort level, it can help veterinarians and producers better target the control and prevention methods in the nursery barn. Previous studies have used different methods and classification schemes for identifying Salmonella status in pig herds or scoring them as low, moderate and high-risk, using serological  and/or bacteriological testing methods [Salmonella status in pigs and on farms [Salmonella infections on nursery cohorts. Using the specified S/P ratio cut-off provided by the ELISA kit manufacturers, a nursery cohort was identified serological Salmonella positive (active infection) given 1 or more pigs were positive for Salmonella antibody response patterns 1, 2, or 5. The bacteriological identification of a nursery cohort as positive (active infection), based on if 1 or more pigs were shedding Salmonella, was in line with previous studies [Although there is a decline in  methods ,23,24,25on farms ,25,26. T studies ,23.Salmonella status at the farm level. However, by using the Salmonella antibody response patterns approach introduced in the present study, the change in antibody response for each individual pig on a farm is identified into categories. This provides more detailed information on the Salmonella antibody response during the nursery stage . In addition, since this method explores seropositivity status at weaning and at the end of the nursery along with the change in direction of antibody response, it decreases the likelihood of misinterpretation of passive immunity as a Salmonella infection in nursery pigs. Although there are strengths to this method, limitations include its inability to account for the magnitude of change, the initial antibody titre baseline and age at weaning. For example, pigs with an increase in antibody titres from 0.3 to 0.4 and 1.5 to 3.0 were identified as Salmonella antibody response pattern 1. By categorizing these pigs into the same pattern, data is lost. By incorporating the degree of change along with the initial antibody titre baseline into the Salmonella antibody response patterns, there can be better identification of the severity of Salmonella in nursery cohorts. The wide range in age at weaning presents a limitation as animals that were weaned at an older age may have improved Salmonella antibody response compared to animals that were weaned at a younger age.Traditionally, seropositivity in pigs is assessed at one time and is subsequently tested to monitor S/P ratio cut-off to be identified as seropositive. Kranker et al. [Salmonella culture prevalence. Surveillance of nursery cohorts with a large number of pigs following pattern 6 is important as it is possible that pigs following this pattern may become seropositive a few weeks after the second sample was taken, or in other words, they were exposed but were falsely classified as negative because of the delay in immune response. On the other hand, pigs following patterns  where titres decrease during the nursery are likely experiencing a decrease in passive immunity and no indication that they are beginning to produce active immunity. However, because of the delay from exposure to antibody response, there is still a possibility of a false negative classification as well. Nursery pigs following pattern 4 and 8 are seronegative at the end of the nursery and would have been classified as negative based on a single serological sample at the end of the nursery. However, pigs following pattern 3 demonstrate a decrease in antibody titre but remain seropositive at the end of the nursery and based on a single test at the end of the nursery would generally be classified as positive. However, our interpretation of pigs showing this pattern is that the results reflect passive immunity and these pigs are categorized as Salmonella negative.In the present study, pigs following pattern 6 also had an increase in antibody titre but did not surpass the r et al.  found a Salmonella infection. Some nursery cohorts were identified as having an active infection using serology but would have been considered negative based on culture. Similarly, a few cohorts would have been considered negative by serology, but culture showed that Salmonella was indeed present. Although research has drawn correlation between bacteriological and serological classification, both these testing methods for Salmonella have strengths and weaknesses [Salmonella. Serological testing relies on detecting antibodies and a positive response requires a few weeks to develop, whereas Salmonella culture is more likely to provide positive results shortly after exposure and becomes less reliable after a few weeks [Using this technique of two sampling time points and identifying serological patterns coupled with direct culture of rectal swabs, a large portion of nurseries were identified as having an active aknesses . Resultsew weeks ,7. In geew weeks ,28. Howeew weeks  and is aew weeks .Salmonella infection because there were herds deemed positive using serology that were negative using culture. This might have been partly due to the use of rectal swabs instead of using a fecal sample of greater than 25 g, which has been advocated for monitoring purposes in populations of animals not exhibiting clinical signs of disease [Salmonella serogroups B, C, D, and E , commonly found in Ontario. Serotyping Salmonella isolates, found in the present study, would have been beneficial to capture Salmonella serogroups that may have been unidentified by ELISA testing. This presents a limitation, impacting the serology results, because there is a possibility that some Salmonella infections went undetected. In addition, false negatives are a possibility due to laboratory errors during bacteriology and serological testing. This reinforces the argument for using the two testing methods in combination in order to more accurately identify positive herds.In the present study, there is evidence of the shortcomings of relying solely on culturing rectal swabs to identify herds with active  disease . LikewisSalmonella infection identified in 37 of 46 Ontario nursery cohorts (with complete information on both testing methods) strengthens the point that Salmonella is commonly present on Ontario pig farms, but importantly emphasizes that the nursery stage is a time in production when pigs often become infected and can then carry and spread Salmonella to the grower-finisher barns and eventually to the abattoirs. This information is useful for the timing and implementing of control and prevention strategies. For example, if farmers know that pigs are becoming exposed to Salmonella in the early nursery stage, then the vaccination of sows [Salmonella infection, nine cohorts appeared to be Salmonella-free at the end of the nursery period, based on both serological and bacteriological methods and using a sample population of 20 pigs to represent the cohort. These results suggest that it is possible to have a negative population of pigs to send to the grower-finisher barn.The active  of sows  or young of sows  might beSalmonella infection. It is possibly that no associations were found due to the low variability in risk factors amongst nurseries with an active Salmonella infection compared to nurseries without an active Salmonella infection. Other studies attempting to identify risk factors associated with the presence of Salmonella have been inconsistent [Salmonella to survive long periods of time in the environment [Salmonella in the nursery but the farm to farm variation in the implementation of these protocols are hard to capture in a survey and this aspect of Salmonella control requires further study. Future research should explore additional risk factors  and should include a greater number of nursery cohorts within depth reports on clinical sign related to salmonellosis.Examination of risk factors  didn’t identify why the nine negative farms were different from nurseries with an active nsistent ,14,26,31ironment , it is lSalmonella infection can be identified in a high proportion of swine nurseries. Using both serological and bacteriological testing methods in parallel improves the likelihood of identifying a nursery with active transmission of Salmonella. This study identified that both detection methods have strengths and weaknesses and by combining the techniques, researchers can better monitor active Salmonella infections on farms. Although the present study did not identify any risk factors, further work is warranted to investigate how cohorts of pigs in some nurseries remain negative for Salmonella.Active


Mental workload (MWL) is an imprecise construct, with distinct definitions and no predominant measurement technique. It can be intuitively seen as the amount of mental activity devoted to a certain task over time. Several approaches have been proposed in the literature for the modelling and assessment of MWL. In this paper, data related to two sets of tasks performed by participants under different conditions is reported. This data was gathered from different sets of questionnaires answered by these participants. These questionnaires were aimed at assessing the features believed by domain experts to influence overall mental workload. In total, 872 records are reported, each representing the answers given by a user after performing a task. On the one hand, collected data might support machine learning researchers interested in using predictive analytics for the assessment of mental workload. On the other hand, data, if exploited by a set of rules/arguments as in , may ser Specifications table•These datasets provide the answers related to a set of questionnaires proposed in the literature of MWL , 3 and 5•These are important to the field of human mental workload and knowledge representation and reasoning. They may instantiate knowledge-bases created by researches in the field of knowledge-based systems and automated reasoning. It might also support machine learning researchers interested in using predictive analytics for the assessment of mental workload. Lastly, they may serve as a source of information for mental workload designers interested in investigating the features reported here for mental workload modelling.•They may serve as baselines for comparison against newly developed models of similar purpose. Reported values by three state-of-the-art measurement techniques of mental workload are also displayed: the NASA – Task Load Index 11.A set of tasks performed  or 7.2.A set of questionnaires proposed in the literature of MWL aimed at measuring the mental workload imposed by tasks in 1.3..csv format containing one row per answers of questions in 2 given by each participant who performed one task listed in 1.A dataset in In this section three datasets in the field of human mental workload are described. These datasets are built upon subjective measures of mental workload. In other words, they rely on the subjective feedback - in this case questionnaires - provided by humans engaging with an underlying task. These questionnaires , 3 and 5In 1.1The set of performed tasks in this dataset is listed in 1.2Tasks employed for the construction of this dataset are the same ones described in Table 61.3The set of tasks performed by participants in the construction of this dataset is listed in 2Mental workload (MWL) is an imprecise construct, with distinct definitions and no predominant measurement technique. It can be intuitively seen as the amount of mental activity devoted to a certain task over time. Several approaches have been proposed in the literature ,2,4,5 fo2.1Students attended third level classes in the Technological University Dublin and filled either questionnaires in 2.2Nine information seeking web-based tasks of varying difficulty and demand , were pe


Dear Editor,1, to contain the spread of the coronavirus in the country. During the lockdown only essential goods have been allowed on sale2. Tobacco and nicotine products were designated as non-essential goods and their sales prohibited.South Africa (SA) imposed a 21-day lockdown from 26 March to 16 April 2020, through the disaster management Act (57/2002) and amended regulations [Section 27(2)]3. A systematic review of five Chinese studies published in Tobacco Induced Diseases, found that the odds of a COVID-19 case becoming more severe and leading to death are higher among people with a history of smoking4. SA has one of the highest prevalence of tobacco product use in Africa5 and presently has the highest number of COVID-19 cases in the continent, putting the country at risk of being hard hit by the COVID-19 pandemic if nothing is done to curb the spread of the virus and protect vulnerable citizens. As of 8 April 2020, SA had conducted 63776 COVID-19 tests, had 1845 confirmed cases and 18 resulting deaths6.About 22% of South Africans aged ≥15 years use various tobacco products7. The co-use of substances, especially by persons in vulnerable groups, could increase the risk of developing complications from COVID-19 if infected.The COVID-19 pandemic could particularly be problematic for SA given the high prevalence of diabetes, tuberculosis and HIV in the country, compounded by substance use problem even among vulnerable populations8. During the lockdown ban, family members and neighbors in apartment complexes who share the same space with tobacco users will also be protected from exposure to SHS since people are not expected to go outside their building to smoke during the lockdown. Experts have predicted that the South African economy is in for a bumpy ride and may shrink as a result of the COVID-19 pandemic9 and the associated measures that have been taken to protect citizens during this time.Cigarette smoking affects both smokers and those exposed to secondhand smoke (SHS)10, but this move is heroic and should be viewed as taking a double shot at protecting citizens from COVID-19, given the vulnerability of many South Africans to the disease. There are also concerns about the implications on mental health when people are forced to give up their addictions during the lockdown10. It is understandable that tobacco and nicotine product users will be dealing with the mental stress of restricted movements in addition to giving up their addictions, but of more importance is that they become aware that the lockdown ban provides a good opportunity to quit tobacco use. In addition, resources to help tobacco users quit, or assist them to cope with withdrawals, should be made available and accessible. The National Council Against Smoking’s quitline (011 720 3145), online materials (https://www.againstsmoking.co.za/) and the Cancer Association of South Africa’s online quit tobacco program (https://www.ekickbutt.org.za/) are some good resources accessible to tobacco users to help them quit.The government has received criticism from some pro-tobacco advocates and academics for the sales ban on tobacco products and alcohol during the lockdown


The article presents reliability statistics data in relation to the development of emotional instability and behavioral difficulties scale for youths in a Malaysia context. The data were obtained from youths participants in Kuala Lumpur and Klang Valley, Selangor, Malaysia. The data has four different subscales in describing emotional instability and behavioral difficulties. The data were analyzed using Cronbach's alpha, McDonald's ω, and Gutmann's λ6 to examine internal consistency test. The data showed that this new scale can be used to measure three subscales of emotional instability and one subscale of behavioral difficulties among youths in a Malaysia context. Specifications table•The data provides insights into culturally appropriate items in measuring emotional instability and behavioural difficulties among youths in a Malaysia context. The data can be used as a screening tool in developmental, social, clinical, and educational studies.•The data also have value-added in providing a valuable contribution to develop a specific intervention program for youths to improve the youths’ emotional well-being and enhance their prosocial behaviour. The data also can be used "before" and "after" to audit everyday practice  and to evaluate specific interventions .•Due to the nature of the data, further statistical analysis can be conducted including analysis of variance, factor analysis, and structural equation modelling. The data also can be used to conduct comparison in cross-cultural studies.1Participants were a community sample of 429 at-risk youths  recruited from the low-cost housing in Kuala Lumpur and Klang Valley, Selangor, Malaysia. Youths who are approximately ages 13 to 24 years old (mean age 17.97 years old) were invited. In term of religion, the sample consists primarily of Muslims (86.9%) with the remaining participants identifying themselves as Budhha (6.3%), Christian (3.7%), Hindu (2.8%) and others (0.2%). No information is available for those participants who refused to complete some of the socio-demographic questions.The data comprises nominal, interval and ordinal data and contains five demographic variables  and four subscales of Emotional Instability and Behavioural Difficulties Scale (EIBDS): sadness, anger, fear, and hyperactivity-inattention. The Positive and Negative Affect Schedule  was used to measure emotion. This brief scale is comprised of 20 items, with 10 items measuring positive affect  and 10 items measuring negative affect . Each item is rated on a five-point Likert scale, ranging from 1 = very slightly or not at all to 5 = extremely, to measure the extent to which the affect has been experienced in the past week. Only the negative emotions were used to validate EIBDS constructs.22.1The content of the self-report measure was drawn from a number of sources. From Goodman's work with the construct of emotional symptoms and behavioural problems 2.2Community leaders from low-income apartments in the suburbs of Kuala Lumpur and Selangor were approached and a meeting organized where they agreed to help with the data collection. A master of list of household addresses was created which allowed for a sysmatic random sampling technique of household. Only one individual has been chosen to be parted of this data collection. A door-to-door distribution of survey questionnaires was undertaken. A decision of to distribute 600 questionnaire was made based on estimation of response rate of around 67 percent, which would yield a sample of 400 responses. For door-to-door distribution, 12 enumerators were engaged for a 2-month period to help the principle investigator to distribute the questionnaires door-to-door and collect them back. All participants were required to sign a consent form confirming their voluntary agreement to take part in this data collection, and authorizing the use of their anonymous data in future academic work or publications. In fact, there is no possibility for data to be traced back to a specific individual. For those youths below 18 years old, consent was obtained from their parents. The questionnaire was completed individually in the presence of enumerators and competent researchers during the data collection to clarify and answer any question from the youths. Face-to-face semi-structured interviews were applied for those who were illiterate or unable to fluently read or write.[1]The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.