Mogera robusta, Siberian chipmunks Tamias sibiricus, leopard cats Prionailurus bengalensis, Korean hares Lepus coreanus, water deer Hydropotes inermis, and wild boars Sus scrofa were positively correlated with vegetation area. Moreover, that of large moles, leopard cats, Korean hares, and water deer were negatively correlated with edge length. Remnant vegetation area and edge length are the primary determinants of mammal species richness and field sign frequency in urban woods, highlighting the importance of vegetation patch size for mammal conservation in fragmented urban landscapes.We investigated the relationships between vegetation area, edge length, and mammals in the urban woods of Daejeon Metropolitan City, South Korea. The vegetation patches included in this study varied from 2.1 to 501.0 ha in size. Surveys were conducted monthly between February and October 2015, with a survey route measuring 1 km in length and 10 m width established in each forest patch. Field signs of 14 species of mammals were recorded in the 33 chosen forest patches over the course of the study period, and the number of species in each patch varied from 2 to 11. Mammal species richness was positively correlated with vegetation area, and field sign frequency was positively correlated with vegetation area and negatively correlated with edge length. The field sign frequencies of large moles  There are 603 vegetated patches in the urban woods of Daejeon, ranging from 1 to 1934 ha , South Korea. Daejeon is one of the largest cities in South Korea. In Daejeon, urbanization has progressed rapidly since the 1970s. The transformation and deforestation of natural habitats into urbanized areas has resulted in a mosaic of patch types, including heavily built downtown and semi-natural habitats. Most of the remaining vegetation is composed of small isolated patches. The annual mean temperature is 14.2°C (range − 10.6–36.8°C) and annual precipitation is 883 mm. The metropolitan area of Daejeon encompasses 540, of which 286 kmThirty-three vegetated patches were selected for inclusion in the survey . The patMogera robusta, Asiatic chipmunk Tamias sibiricus, Eurasian red squirrel Sciurus vulgaris, Siberian flying squirrel Pteromys volans, raccoon dog Nyctereutes procyonoides, domestic dog Canis lupus familiaris, leopard cat Prionailurus bengalensis, domestic cat Felis catus, Eurasian badger Meles meles, Siberian weasel Mustela sibirica, yellow-throated marten Martes flavigula, Korean hare Lepus coreanus, water deer Hydropotes inermis, and wild boar Sus scrofa. Field signs included tracks, roosts, pellets, droppings, and skulls in this study.We counted the field signs of the following mammal species along the survey routes in the established vegetation transects in the urban woods: large mole We performed 9 days (1 day/month × 9 months) of tracking on each of 33 survey routes from February to October in 2015. We also noted incidental sightings of mammal species on the survey routes during the survey. The number of field signs recorded per sampling day on each survey route was established as field sign frequency of mammals. We did not count the field signs that were difficult to distinguish by species. We determined the mammal species richness based on the total number of species detected using all of the field signs (Rhim and Lee ω) were determined for each of the variables that were present in at least one selected model resulting from the generalized linear model with Poisson distribution. Values were considered statistically significant at p < .05.Statistical analyses were performed using the SPSS statistical package for Windows. Multiple regression (Poisson regression) analysis based on a generalized linear model (GLM) was used to examine the relationships among vegetation area, edge length and the variables of interest . The vegetation area (ha) and edge length (km) were log-transformed to perform multiple regressions. Multiple regression analysis was also employed to examine the relationships between vegetation area, edge length, and the field sign frequency of each mammal species. Akaike Information Criterion (AIC) model weights  of 0.40–0.59. The top-ranked model for species richness was 0.92 + 0.53 log (vegetation area) with a coefficient of determination (R2) of 0.53. The second-ranked model contained the log (vegetation area) and log (edge length). The top-ranked model for field sign frequency was 1.72 + 1.32 log (vegetation area) – 0.77 log (edge length) with R2 = 1.00. It had an Akaike weight (ω) of 0.00–0.99. The log (vegetation area) was dominated as a predictor variable in the second-ranked model  analysis based on a GLM was used to examine the relationships between vegetation area or edge length and field sign frequency of all 14 mammal species. We determined that the field sign frequencies of large moles, Asiatic chipmunks, leopard cats, Korean hares, water deer, and wild boars were positively correlated with vegetation area. Moreover, the field sign frequencies of large moles, leopard cats, Korean hares, and water deer were negatively correlated with edge length.ω) of 0.00–0.61. The top-ranked model field sign frequency of large moles was −2.41 + 2.41 log (vegetation area) – 1.42 log (edge length) with a coefficient of determination (R2) of 0.93. That of leopard cats was −8.11 + 6.09 log (vegetation area) – 6.01 log (edge length) with R2 = 0.70 (ω = 0.98). Korean hares had − 6.49 + 4.66 log (vegetation area) – 3.86 log (edge length) as the top-ranked model with R2 = 0.75 (ω = 0.87). Moreover, the top-ranked model of field sign frequency of water deer was 1.05 + 1.29 log (vegetation area) – 0.86 log (edge length) with R2 = 0.98 (ω = 0.90). The top-ranked model of Asiatic chipmunks  and wild boars  contained the log (vegetation area). The log (vegetation area) was dominated as a predictor variable in the second-ranked models of large moles, leopard cats, Korean hares, and water deer with R2 of 0.59–0.98. Moreover, the second-ranked model of Asiatic chipmunk and wild boar contained the log (vegetation area) and log (edge length) with R2 of 0.87–0.89 (The best model of field sign frequency of large moles had an Akaike weight (.87–0.89 .Table 44.Effective conservation of mammals requires a deeper understanding of the anthropogenic threats they face, which include expanding agriculture, urbanization, and road construction, among many others (McAlpine et al. In this study, both species richness and field sign frequency of mammals were associated with vegetation area. Our results demonstrate the role that the vegetation patch size and edge length play in determining the species richness and abundance of mammals in urban woods, and highlight the importance of vegetation area for the conservation of mammals in fragmented urban landscapes, given that fragmented areas cannot support the same number of species as did the original larger unfragmented habitat (Fleury and Galetti There were different responses to vegetation area and edge length among mammal species with different habitat requirements, such as forest specialists and habitat generalists. Because leopard cats, Korean hares, and wild boars significantly prefer, and show increasing field sign frequencies in, the larger areas, these species have more affinity for large vegetation areas. Moreover, large moles and water deer might be habitat generalists, because field signs of these mammals were observed in most of our vegetation patches. They were positively correlated with vegetation area and negatively correlated with edge length in these urban woods.Many mammals have highly specific ecological requirements, and thus may be particularly sensitive to habitat loss and fragmentation in urban landscapes. It is thus clear that conservation of mammals affected by loss and fragmentation of native forest should take into consideration both the habitat requirement of the species and the response of the species to the man-made new habitats (Rocha et al. Maintenance of large vegetation patches is an effective means of conserving mammals and their habitat (Galetti et al. Since urban vegetation patches are often patchy and isolated, the spatial context of the patches can influence on the biodiversity within them (Watling and Donnelly


This study tested the relationship between preoperative serum C-reactive protein (CRP) levels and cancer-specific prognosis in patients with esophageal squamous cell carcinoma who have undergone curative resection.We conducted a retrospective study on 961 patients with esophageal squamous cell cancer who underwent curative esophagectomy from 2006 to 2012 at the Sun Yat-sen University Cancer Center. Preoperative serum CRP levels were determined, and a cutoff value of 5.0 mg/mL was established. Propensity score matching (PSM) was performed to reduce the selection bias between patients with low CRP (≤ 5.0 mg/mL) and those with high CRP (> 5.0 mg/mL) levels based on age, tumor-lymph node-metastasis (TNM) stage, and tumor grade. The prognostic value of preoperative CRP levels was determined using life table, Kaplan–Meier, and Cox proportional hazards analyzes.p = 0.004). Univariate survival analysis revealed that the preoperative CRP levels, TNM stage, tumor grade, drinking history, and anastomosis method were prognostic factors for overall survival (OS). Before conducting PSM, the low-CRP group had a lower age (p = 0.001), lower histological grade (p = 0.086), and lower TNM stage (p = 0.254).In the unmatched cohort, the 3-year and 5-year survival rates were 57 and 53%, respectively, in patients with high preoperative CRP levels (> 5.0 mg/mL) and 68 and 56%, respectively, in those with low preoperative CRP levels (≤ 5.0 mg/mL). The difference in the survival rates of the 2 groups was significant (p = 0.044). Multivariate analysis of the matched patients revealed that the TNM stage and preoperative CRP level were independent prognostic factors for OS.After PSM, 176 patients with low CRP levels and 176 of those with high CRP levels were enrolled in the analysis. In the matched cohort, the 3-year and 5-year survival rates were 56 and 50%, respectively, in patients with high preoperative CRP levels (> 5.0 mg/mL) and 68 and 56%, respectively, in those with low preoperative CRP levels (≤ 5.0 mg/mL). The difference in the survival rates between the low- and high-CRP groups was significant (A high preoperative CRP level (> 5.0 mg/mL) predicts worse survival prognosis in patients who have undergone curative resection for esophageal squamous cell cancer. Esophageal cancer (EC) is a common cause of cancer-related deaths worldwide , and almIt is being increasingly acknowledged that the intrinsic properties of tumor cells and the host inflammatory response determine the spread of tumors. C-reactive protein (CRP) is one of the acute-phase nonspecific proteins synthesized by the hepatocytes and regulated by interleukin-1, interleukin-6, and tumor necrosis factor (TNF) , 12. Sevn = 961) in our study. The large sample size enabled us to perform propensity score analysis that allows matching of multiple factors to create similar groups for analysis. In this study, we performed a propensity-matched analysis of patients with high CRP levels and those with low CRP levels to identify the prognostic value of preoperative CRP levels.The present study aimed to test the relationship between preoperative serum CRP levels and EC-specific survival in patients who had undergone curative resection for esophageal squamous cell cancer. In order to arrive at a reliable conclusion, we ensured a good sample size , and tumor grade. The tumors were staged as per the American Joint Committee on Cancer (AJCC) Staging Manual (7th edition) . For patWe enrolled 961 patients with esophageal squamous cell carcinoma who had undergone curative esophagectomy between 2006 and 2012 at the Department of Thoracic Surgery, Sun Yat-sen University Cancer Center. Patients were followed up until death or until study completion. Only patients who survived for at least 60 days postoperatively were enrolled. The median follow-up duration was 24 months, and the maximum follow-up duration was 67 months. Routine preoperative laboratory examinations were performed, including immunoturbidimetry, to determine the serum CRP levels. All the patients were terminally diagnosed at the pathological department at Sun Yat-sen University Cancer Center. The tumors were pathologically staged as per the AJCC Staging Manual (7th edition). However, because the data were collected over several years, some patients were staged according to the 6th edition before the 7th edition was published in 2009. We recorded the exact locations, invasion depths, positive lymph nodes, metastases, and tumor grades in detail after the surgery. These data were used to compute the location as well as the T, N, and M stages of the tumor and subsequently stage the tumor according to the 7th edition.The institutional review board of the Sun Yat-sen University Cancer Center approved the present study, and the study was approved by the ethics committee of Sun Yat-sen University Cancer Center .p value of < 0.05 was considered statistically significant.A preoperative CRP level of > 5.0 mg/mL was considered high and was established as the cutoff (5.0 mg/mL) level using the professional X-tile program . Differences between the two groups of patients were compared using the chi-square test. Three- and 5-year survival rates were calculated using life table analysis. We compared the survival rates of patients with high and low preoperative CRP levels using Kaplan–Meier analysis and generated survival curves. The log-rank test was used to test the significance of the differences between the high- and low-CRP level groups. Multivariate survival analysis was performed to derive a final model of the variables exhibiting independent significant relationships, including all the covariates that were significant in the univariate analysis, with survival rate. All the analyzes were performed using SPSS . A two-sided Propensity score matching (PSM) was performed to reduce the bias due to age, TNM stage, and tumor grade. One-to-one matching without replacement was performed using a caliper-match algorithm, with the caliper width set to 0.05 times the standard deviation of the logit of the propensity score. The quality of matching was assessed by comparing the standardized differences between the treatment groups, with a threshold of 0.1 indicating good balance between the groups. This procedure was performed using STATA 12.0 .Total 961 patients  were enrolled. The preoperative serum CRP levels were > 5.0 mg/mL  in 250 patients and ≤ 5 mg/mL  in 711 patients.p = 0.044, Fig. The clinicopathological characteristics of the patients are shown in Table p < 0.001), TNM stage (p = 0.001), history of alcohol consumption (p = 0.024), tumor grade (p = 0.039), and preoperative serum CRP levels (p = 0.008) were significant risk factors for postoperative survival. Survival curves were used to illustrate the differences in the OS duration between patients with high and low preoperative CRP levels , TNM stage (p < 0.001), tumor site (p < 0.001), and preoperative serum CRP levels (p = 0.044) were significant risk factors for postoperative survival. Moreover, surgical incision was not a significant risk factor (p = 0.140). The factors mentioned above were included in the multivariate Cox regression analysis. The result showed that the TNM stage and the preoperative CRP level were independent prognostic factors for OS  and cisplatin in combination with 45–54 Gy of radiotherapy . Owing tp = 0.004). In the matched cohort, the survival in the low-CRP group was better than that in the high-CRP group (p = 0.044). Additionally, multivariate Cox regression analysis of the matched patients showed that the CRP level was an independent prognostic factor for OS. As per the result, we concluded that the preoperative serum CRP level was an independent prognostic factor for patients with esophageal squamous cell carcinoma, a finding that is consistent with some previous reports.In addition, the sample size in previous studies was generally low. For example, Shimada et al. included 150 patients in the study, and only 35 patients were grouped into the high-CRP group. Similarly, Crumley et al. analyzed 120 patients; of these, only 15 exhibited high preoperative serum CRP levels. Therefore, the statistical power of these studies was weak because of the small number of patients. Owing to the limitation presented by the relatively smaller sample size, no study has performed PSM analysis to study the prognostic effect of CRP level. In our study, we included more patients (961) than those in previous studies. Moreover, PSM was performed to reduce the potential confounders. Before matching, the patients who exhibited high preoperative serum CRP levels had worse OS than those with low preoperative serum CRP levels  predicts worse survival prognosis in patients who have undergone curative resection for ESCC.


N. gonorrhoeae isolates obtained between 2014 and 2017. Strains with MIC ≥2 μg/mL were characterized according to azithromycin resistance mechanisms and strain typing. Results indicate that azithromycin resistance has emerged in all these years in unrelated MLST-STs, but after 2016 a clonal complex connected with ST1901 has been more frequently detected, grouping isolates with MIC varying from 2 to 64 μg/mL, with DelA mutations at the mtrR promoter region associated or not with mutations at rrl alleles. High rates of azithromycin resistance may compromise the use of this drug in the combined therapy with ceftriaxone. Inclusion of Rio de Janeiro in the Brazilian gonococcal surveillance program is important to evaluate if this data indicates an epidemiological phenomenon in the country.Azithromycin is one of the drugs used in the combined therapy for syndromic treatment of gonorrhoea in many countries, including Brazil. Our research group, which receives isolates from clinical laboratories since 2006, has detected, after 2016, a tendency of rising rates of azithromycin resistance, with isolates showing higher minimal inhibitory concentrations (MICs) than those previously reported in this country. In this study, we report the susceptibility to azithromycin of 93 Neisseria gonorrhoeae, the agent of gonorrhoea, has come to world attention due to a high capacity to become resistant to antimicrobial agents [l agents , 2. Currl agents –5. Combil agents . In manyl agents , 7. N. gonorrhoeae is not under consensus. Whereas EUCAST classifies isolates with MIC >0.5 μg/mL as resistant to azithromycin, CLSI indicates the epidemiological cutoff value (ECV) ≥2 μg/mL as an alert value to recognize isolates carrying resistance mechanisms to this drug [ N. gonorrhoeae, including Erm methylases, mutations in genes encoding ribosomal proteins L4 and L22, and mutations that affect the expression or identity of genes encoding the efflux pump MtrCDE [ rrl, which encodes the target of azithromycin 23S rRNA, usually exert greater impact in the azithromycin MIC, in some cases reaching 256 μg/mL [The minimal inhibitory concentration (MIC) breakpoint that defines azithromycin resistance inhis drug , 9. Diffp MtrCDE , 10. How56 μg/mL .μg/mL [ N. gonorrhoeae isolates received by our research laboratory from 2014 to 2017. Strain typing indicates the emergence of a lineage belonging to a new multilocus sequence type (ST) with MIC higher than those previously reported in Brazil [Bazzo et al.  have recμg/mL . Rio de μg/mL . In thatμg/mL . In the n Brazil , 14. N. gonorrhoeae isolates were sent by public healthcare facilities and private clinical laboratories to our Laboratory of Investigation in Medical Microbiology (LIMM) at the Universidade Federal do Rio de Janeiro. No criteria have been applied for the selection of isolates. At LIMM, isolates were identified by MALDI-TOF MS  and those confirmed as N. gonorrhoeae were preserved in BHI broth supplemented with 20% glycerol at -170°C. Specimen types were urethral (n= 70), urine (n =14), vaginal (n = 7), eye discharge (n=1), and rectal (n = 1) types. Eighty-five patients were males and eight were females. Patient ages varied between 16 and 70 years (median 29). Patient sexual orientation is unknown.Between March 2014 and October 2017, 93μg), ciprofloxacin (5 μg), or ceftriaxone (30 μg). Azithromycin (Sigma) MIC was determined by agar dilution. N. gonorrhoeae ATCC 49226 was used as quality control strain [Antimicrobial susceptibility testing was performed with discs containing penicillin (10 U), tetracycline (30 l strain .http://pubmlst.org/neisseria). Relatedness of allelic profiles was visualized by Bionumerics 7.6 to produce minimum spanning trees. Isolates presenting a single-locus-variation in allelic profiles from a founding ST were considered as belonging to the same MLST clonal complex (CC).MLST analyses followed the guidelines on the Neisseria Multi Locus Sequence Typing website ( SpeI (Promega) as restriction enzyme. The CHEF DR III equipment (Bio-Rad Laboratories) was used to separate fragments, with switch times of 2 s and 17 s, and running time of 17 h at 6 V/cm. PFGE banding pattern similarities were determined with the Dice coefficient and the UPGMA (unweighted pair group method using arithmetic averages) clustering method with Biomerics 7.6. A tolerance in the band positions of 1.5% was used.PFGE was performed according to the protocol described by Ribot and coworkers  using 15 mtrR, the repressor of mtrCDE, and mutations in the four alleles of the gene rrl, which encode 23S rRNA, were investigated in the isolates presenting MIC ≥2 μg/mL. For DNA extraction, N. gonorrhoeae colonies were suspended in 1 mL lysis buffer  and boiled for 10 min. After centrifugation for 5 min at 10,000 rpm to remove cell debris, 1 μl of the supernatant was used as the template DNA for PCR.Mutations at the promoter region of the gene encoding mtrR gene was amplified by PCR as previously described [ rrl alleles was investigated by nested PCR using previously described primers [Promoter region of theescribed . Occurre primers  with modPCR products were purified with ExoSAP IT kit . Both DNA strands of PCR products were Sanger sequenced (Macrogen Korea).Nucleotide sequence mutation search was determined by alignment with strain FA1090 (accession numbers: NC_002946.2). N. gonorrhoeae isolates resulted in nonsusceptibility rates for penicillin, tetracycline ciprofloxacin, and ceftriaxone of 98%, 78%, 72%, and 0%, respectively. Regarding azithromycin, 42 isolates (45%) presented MIC between ≤0.062 and <0.5 μg/mL, 21 (23%) MIC 0.5 μg/mL, and 30 (32%) MIC ≥1 μg/mL, which would be, respectively, classified as susceptible, intermediate, or resistant to azithromycin according to EUCAST [μg/mL, which would be recognized as non-wild-type according to CLSI [μg/mL for one isolate. Throughout the study, antimicrobial susceptibility testing of all 93μg/mL, which would be, according to the current literature, unanimously classified as resistant or moderately resistant [Further characterization regarding strain typing and azithromycin resistance mechanisms were performed for isolates presenting MIC ≥2 esistant , 18, 19.μg/mL belonged to 13 different ST, five of them new . As shown in  N. gonorrhoeae isolates with this MIC range obtained in 2015 and 2016 were mostly nonclonally related. In contrast, most isolates with the same phenotype collected in 2017 belonged to ST1901 and its one allele variant ST13772, both part of clonal complex 1 (CC1). These two STs also concentrate isolates with higher azithromycin MIC  grouped together with ≥ 84% similarity, whereas other isolates were more diverse. μg/mL characterized in this study had mutations at the promoter region of mtrR and/or at rrl alleles. DelA mutation at the promoter region of mtrR was identified in all isolates belonging to CC1. Among these isolates, those with wild type rrl presented azithromycin MIC 2 μg/mL except for one strain with MIC 8 μg/mL. Isolates with additional mutations at one to four rrl alleles had azithromycin MIC varying from 8 to 64 μg/mL.All 23 isolates with MIC ≥2  mtrR and mutations C2611T at four rrl alleles, which correlated to MIC varying from 4 to 32 μg/mL. One singleton isolate was an exception. In this strain with azithromycin MIC 2 μg/mL we detected substitutions, but not deletions, at the promoter region of mtrR, and A2059G mutations in two rrl alleles  was higher than the one reported by the epidemiological study performed in seven regions of Brazil during 2015-2016 (7%) . A studyreported .μg/mL as a breakpoint to define azithromycin resistance in N. gonorrhoeae, CLSI does not adopt this criterion, characterizing isolates as non-wild-type if they have MIC ≥2 μg/mL. Such difference prompted us to focus on isolates with higher MICs as those with a critical phenotype. In this study, which was performed with isolates sent to our research groups by clinical laboratories without any selection criteria, analysis of strains with MIC ≥2 μg/mL suggested a rising of azithromycin MIC among N. gonorrhoeae obtained in Rio de Janeiro over the last three years, in a phenomenon at least partly associated with the evolution of the clonal complex connected with ST1901. This ST is internationally recognized as a well succeeded clone, related to antimicrobial resistance [Even though EUCAST determines MIC >0.5 sistance , 22. Int mtrR had lower MICs than those presenting mutated rrl alleles. Mutations A2059G in two rrl alleles usually result in azithromycin MIC higher than the one observed in this work, so we confirmed all data regarding this strain. A similar unexpected association has been reported by Cole et al. [Generally, as expected, isolates presenting mutations only at the promoter region ofe et al. . Differee et al. , 10.A limitation of this work is that most isolates evaluated were not obtained directly from patients but were included in the collection based on a collaboration with clinical laboratories. This may represent a bias, possibly selecting isolates from persisting or recidivist infections, since sexually transmitted infections (STI) are usually empirically treated, without microbiological analysis. In this case, it is important to note that although the use of ceftriaxone is recommended for treatment of gonorrhoea in Rio de Janeiro since 2015 , until 2 N. gonorrhoeae strains.The rising tendency of azithromycin resistance here reported, however, probably reflects an epidemiological event, since azithromycin resistant isolates have been obtained from patients from various age groups in different months throughout the study. As previously reported, our research group receives isolates from clinical laboratories since 2006, and our findings are product of comparison with this collection . The pro N. gonorrhoeae strains obtained by clinical laboratories and sent to our research group, we identified, after 2015, a rising tendency of azithromycin resistance rates, especially considering isolates showing MIC ≥2 μg/mL. These resistant isolates belonged to multiple MLST-ST; however, in 2017, a clonal complex evolutionarily related to ST1901 (CC1) drew attention, suggesting a possible expansion of this clone. Azithromycin resistant N. gonorrhoeae belonging to CC1 present DelA mutation at the mtrR promoter region associated or not with mutations in one to four rrl alleles, and MIC varying from 2 to 64 μg/mL.In the present work, based on 93 N. gonorrhoea in Rio de Janeiro may be higher than those reported in other Brazilian sentinel cities involved in the national surveillance program. It is very important to include Rio de Janeiro at the Brazilian GASP, in order to evaluate if these findings would be confirmed in an epidemiologically validated sampling strategy.Azithromycin resistance may pose a risk for the long-term use of this drug in the combined therapy with ceftriaxone. Our data suggest that rates of azithromycin resistance in


Pseudocercospora species and severity of Sigatoka leaf diseases. Pseudocercospora species were identified using species-specific primers. Sigatoka-like leaf diseases were observed in all farms and on all cultivars, but disease severity varied significantly (P < 0.001) between countries, districts/regions within countries, altitudinal ranges and banana cultivars. In all regions except Kilimanjaro, P. fijiensis, the causal agent of black Sigatoka, was the only pathogen associated with Sigatoka disease. Mycosphaerella musae was associated with Sigatoka-like symptoms in Kilimanjaro region. Black Sigatoka disease was more severe in Uganda, with a mean disease severity index (DSI) of 37.5%, than in Tanzania (DSI = 19.9%). In Uganda, black Sigatoka disease was equally severe in Luwero district (mean DSI = 40.4%) and Mbarara district (mean DSI = 37.9%). In Tanzania, black Sigatoka was most severe in Kagera region (mean DSI = 29.2%) and least in Mbeya region (mean DSI = 11.5%). Pseudocercospora fijiensis, the most devastating sigatoka pathogen, was detected at altitudes of up to 1877 m a.s.l. This range expansion of P. fijiensis, previously confined to altitudes lower than 1350 m a.s.l. in East Africa, is of concern, especially for smallholder banana farmers growing the susceptible East African Highland bananas (EAHB). Among the banana varieties sampled, the EAHB, FHIA hybrids and Mchare were the most susceptible. Here, the loss of resistance in Yangambi KM5, a banana variety previously resistant to P. fijiensis, is reported for the first time.Sigatoka leaf diseases are a major constraint to banana production. A survey was conducted in Tanzania and Uganda to assess the distribution of  Musa spp.) are an important staple, providing food and income to millions of people in Africa. Most bananas produced and consumed in the African Great Lakes region are the East Africa Highland bananas (EAHB), a genetically related collection of plants that belong to the Mutika/Lujugira subgroup of triploid AAA bananas  that causes black Sigatoka, P. musae (previously M. musicola) causing yellow Sigatoka, and P. eumusae (previously M. eumusae) that causes eumusae leaf spot. The epidemiology of the three diseases is similar. Infection of young banana leaves is initiated by asexual spores that are dispersed over short distances by rain splash, or ascospores  carried by wind for several kilometres  where it is reported to co-occur with P. fijiensis  determine the incidence and severity of Sigatoka leaf diseases in Tanzania and Uganda; and (ii) identify et al., Surveys of Sigatoka leaf diseases were conducted in five geographic locations in East Africa ; Fig. 1.n = number of leaves in each grade, b = grade (0–6), N = number of grades used in the scale (7), T = total number of leaves scored.Surveys were conducted from April to July 2016. Forty-three banana farms were surveyed in Uganda, and 81 farms in Tanzania. The selected farms were at least 5 km apart. Geographical coordinates for each farm were recorded using a hand-held eTrex 10 global positioning system (GPS) device (Garmin). Farmers were interviewed to obtain information on source of planting materials, banana variety and production system and management practices. On each farm, 15 plants at the preflowering stage were randomly selected and evaluated for Sigatoka leaf disease. The number of standing leaves was counted, and each leaf was visually rated for the stage of symptom development Fouré, , and disLeaves with typical Sigatoka-like leaf disease symptoms at stages 3–6 Fouré,  were colM Tris-HCl, pH 8; 10 mM EDTA, pH 8; 0.5 M NaCl; 1% w/v SDS; 1% w/v sodium sulphite) containing proteinase K (50 μgmL−1). The rest of the extraction was performed as per the protocol except that precipitation in isopropanol was done overnight. The DNA pellet was then resuspended in 100 μL TE buffer, and the DNA integrity determined using a NanoDrop 2000C spectrophotometer (Thermo Fisher Scientific Inc.). DNA concentrations for each sample were adjusted to 50 ng μL−1 for PCR analysis.Fungal DNA was extracted directly from banana leaves with Sigatoka-like disease lesions at stages 3–6 using the protocol described by Mahuku , with soPseudocercospora species were identified using species-specific primers developed by Johanson & Jeger  and consisted of an initial denaturation step at 95 °C for 4 min; followed by 35 cycles of 1 min at 94 °C, 1 min at 68 °C and 1 min at 72 °C; with a final 3 min extension step at 72 °C. The annealing temperature for primers MMactF2/MMactRb and ActF/MEactR was 60 °C. The amplification products were kept at 4 °C until further analysis. Controls included DNA from Pseudocercospora species obtained from the CBS culture collection in Utrecht, Netherlands . The PCR products were resolved on a 1.5% w/v agarose gel in 1 × TAE buffer at 150 V for 45 min. Gel images were captured using the GBOX gel documentation system (SYNGENE). & Jeger  and Arza  regions was amplified using general actin primers ACTF/ACTR  to determine statistically significant association between Sigatoka disease severity and banana cultivar (genome group) at different altitude ranges . Means were separated using Fisher’s least significant difference (LSD) test at P < 0.05. Maps to show the distribution and severity of Sigatoka were generated using ARCGIS software (ESRI). Meteorological data from each region was used to analyse effect of climatic trends on Sigatoka leaf disease incidence and severity. The Mann–Kendall trend nonparametric (MK) test was used to detect temperature (minimum and maximum) trends. The Sen’s slope estimator was used to determine the magnitude of observed (decreasing or increasing) trends. XLSTAT software was employed to analyse the trends with P-values derived at 95% confidence levels.Molecular data was scored as presence (1) and absence (0) of the expected fragment size. The percentage of positive samples was computed from the total number of samples per region. Pearson’s chi-square test was used to determine the existence of significant association between cultivar distribution and altitudes. Analysis of variance (ANOVA) was computed using GMost banana farms (63%) were situated in mid-altitude areas at 1200–1500 m a.s.l., while 26% of the farms were in high altitudes (>1501 m a.s.l.). The rest (11%) of the farms were in low altitude areas (<1200 m a.s.l.). Bananas were produced on farms ranging from 0.2 to 4 ha, mainly for home consumption, and excess sold in local markets. All plantations were established using suckers from the owner’s old farm or from neighbours. None of the farmers used inorganic fertilizers; instead, they relied on organic fertilizers, mostly livestock and chicken manure. None of the farmers interviewed used fungicides to control Sigatoka. Most of the farmers (85%) relied on good sanitation practices  for managing Sigatoka. The leaves and extra suckers were used as animal feed.P < 0.001) associated with altitude  higher in Uganda with a mean DSI of 39.3%, while in Tanzania mean DSI was 20.2%. No significant differences in mean DSI were observed between Luwero and Mbarara districts . These primers were then used to amplify DNA extracted directly from banana leaves with Sigatoka symptoms. The presence of P. fijiensis was confirmed in all regions and altitudes except Kilimanjaro . Amplification of a selected set of isolates from Kilimanjaro, Mbeya and Morogoro regions using the actin gene primers gave the expected 820 bp fragment. A nucleotide BLAST of sequences obtained from the Kilimanjaro region samples revealed that 65% of the samples had 90–99% homology to M. musae strain CBS121386, accession LC121215.1, confirming the visual diagnosis of leaf speckle. The rest of the samples shared identities with other fungi such as Phoma medicaginis, Phoma nigrificans and Amycosphaerella africana (Table S1). Known P. fijiensis isolates collected from Mbeya and Morogoro regions were included as reference and these confirmed PCR identification of P. fijiensis (Table S1).Amplification of DNA from pure imanjaro . The samP < 0.001) between cultivars or genome groups. The EAHB and FHIA hybrids were the most susceptible type of bananas in both Uganda and Tanzania. Disease severity between genome groups was significantly (P = 0.007) associated with altitude range , ABB (Pisang Awak and Bluggoe), AAB (Sukari Ndizi), AA (Mchare), AAAB , AAA , plantain and Yangambi KM5). All cultivars inspected had Sigatoka symptoms but disease severity was significantly different (de range . In Tanzde range . Yangambmin and Tmax) in 1980–2016 for Uganda, and 1980–2010 for Tanzania were 14.8–17.9 °C and 27.6–29.1 °C, respectively. Luwero district was the warmest while Mbarara district had the lowest Tmax (min was recorded in Mbeya region (14.8 ± 0.4 °C) while Luwero district had the highest mean (P < 0.05) increase in Tmin and Tmax in all sites over the same period. Mbarara district experienced the highest rate of Tmin increase at 0.06 °C per year while Mbeya region had the lowest rate at 0.03 °C per year  (Mouliom-Pefoura et al., et al., The change in temperature over the past 20 years might have contributed to the displacement of a et al.  reportedP. fijiensis proliferation or emergence of more virulent patho-types better adapted to higher altitudes (Legler et al., P. fijiensis (Conde-Ferráez et al., P. fijiensis populations from these regions should be conducted to shed more light on possible reasons for habitat expansion and increased disease severity.High black Sigatoka severities were observed at altitudes of 1401–1600 m a.s.l. for most of the banana types surveyed in Uganda. This observation suggests either an environment that is more conducive to et al., P. fijiensis proliferation. A gradual increase in minimum temperature was observed in Mbarara district from 14.2 °C in 1980 to 18.5 °C in 2016, creating more suitable conditions for P. fijiensis to infect bananas. The 15 °C minimum temperature threshold for P. fijiensis proliferation was attained in 1997; thus, it is possible that the pathogen may have gradually established in the high-altitude areas with increasing minimum temperatures, leading to the currently observed dominance. The absence of black Sigatoka at high elevations in Mbarara during the earlier studies was attributed to either temperature-based altitudinal threshold for P. fijiensis proliferation or absence of the pathogen in this part of Uganda (Tushemereirwe et al., et al., Changes in temperature and rainfall patterns may alter a pathogen’s growth, development rate, survival, dispersal and virulence or aggressiveness (Jones, P. fijiensis calls for the deployment of Sigatoka management strategies that involve resistant germplasm and good agronomic practices such as soil fertility regimes, proper drainage, mat management and general field sanitation. Fungicides are not an option for resource-limited smallholder banana farmers in Africa. They rely on a range of cultural practices to reduce inoculum load and minimize pathogen proliferation. Such practices include weeding, pruning of old diseased leaves and removal of excess suckers, which are often fed to animals (Sivirihauma et al., P. fijiensis.The emergence of environments suitable to M. musae, resulting in mycosphaerella speckle. This pathogen has been observed and reported on banana (Jones, P. fijiensis was not detected in Kilimanjaro region, the pathogen is present in other areas close to this region. Kilimanjaro region has experienced significant decline in precipitation over the last 37 years (Muthoni et al., P. fijiensis proliferation. Furthermore, soils in Kilimanjaro region are volcanic and known to be rich in silicon (Zech et al., P. fijiensis (Kablan et al., P. fijiensis. Further studies are required to establish whether P. fijiensis is indeed absent from this region.Sigatoka-like symptoms observed in the Kilimanjaro region were identified as being caused by a Jones,  but is cet al., et al., et al., P. fijiensis infection (Carlier et al., P. fijiensis pathotypes capable of overcoming resistance in Yangambi KM5. Isolates belonging to mating type 1 and 2 of P. fijiensis have been observed under field conditions and occurring in equal ratios in Mexico and Brazil (Conde Ferráez et al., et al., P. fijiensis pathotypes capable of overcoming resistance in Yangambi KM5 arose through sexual recombination. Although breakdown of Sigatoka resistance in Yangambi KM5 has been reported in Costa Rica, Puerto Rico and Cameroon (Mouliom-Pefoura, et al., et al., All banana cultivars grown in the Great Lakes region were susceptible to black Sigatoka (Tushemereirwe P. fijiensis, the cause of black Sigatoka of banana, is widespread in Uganda and Tanzania. Previously confined to lower altitudes, P. fijiensis has moved and adapted to higher elevations previously considered unsuitable. Analysis of historical weather data confirmed that temperatures have increased. The yellow Sigatoka pathogen was not detected, even at higher altitudes where the pathogen was previously present, signifying a change in pathogen profiles. This study also showed that M. musae was the pathogen associated with leaf symptoms in Kilimanjaro region. Studies are needed to document the significance of M. musae on banana production in this region. An integrated disease management strategy that includes use of resistant varieties and good agronomic practices should be deployed to safeguard the livelihood of smallholder farmers in the Great Lakes region of Africa.This study confirms that Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.


CBF genes in model organisms such as Arabidopsis thaliana enhances abiotic stress tolerance but also reduces growth. In addition to these effects, overexpression of the peach (Prunus persica [L.] Batsch) CBF1 gene in transgenic apple  line T166 also results in early entry into and late exit from dormancy. Although the regulation of dormancy-induction and dormancy-release occur while the CBF regulon is operative in perennial, woody plants, how overexpression of CBF1 affects these dormancy-related changes in gene expression is incompletely understood. The objective of the present study was to characterize global changes in gene expression in peach CBF1-overexpressing and non-transformed apple bark tissues at different states of dormancy via RNA-seq. RNA-seq bioinformatics data was confirmed by RT-qPCR on a number of genes. Results indicate that the greatest number of significantly differentially expressed genes (DEGs) occurred in April when dormancy release and bud break normally occur but are delayed in Line T166. Genes involved in storage and inactivation of auxin, GA, and cytokinin were generally upregulated in T166 in April, while those for biosynthesis, uptake or signal transduction were generally downregulated in T166. Genes for cell division and cambial growth were also downregulated in T166 relative to the non-transformed line. These data suggest that overexpression of the peach CBF1 gene impacts growth hormone homeostasis and as a result the activation of growth in the spring, and most likely growth cessation in the fall as well.The CBF signal pathway is responsible for a significant portion of plant responses to low temperature and freezing. Overexpression of  Malus × domestica Borkh., is a member of the family Rosaceae, tribe Pyreae, and is grown world-wide. Over 100 varieties are grown commercially in the United States, with an annual average of 240 million bushels of apples worth close to $4 billion  (http://usapple.org/). Global production of apples for 2016/2017 exceeded 77 million metric tons .The domesticated apple, https://www.rma.usda.gov/informationtools/). The majority of this damage occurred in the spring, when abnormally warm temperatures were followed by freezing temperatures1. For example, spring frost events in 2007 and 2017 each resulted in $1 billion in losses from all crops2 (https://www.ncdc.noaa.gov/billions/events/US/1980-2017). During these events, unseasonably warm temperatures in early spring (late February–March) resulted in the deacclimation of ecodormant buds and early bud break in many temperate, perennial fruit crops. The occurrence of subsequent freezing temperatures several weeks later (April) when flowers and early vegetative growth with little freezing tolerance were present resulted in high levels of frost injury3.Erratic weather patterns, as a symptom of climate change, pose an increasing threat to industry profitability. Data from the USDA Risk Management Agency for the years 2007–2017 indicates that apple growers claimed $157,177,390 in insured losses from freeze damage  and extrinsic (ecodormancy) factors12. Among several other factors, research on cold acclimation has identified several COld-Responsive (COR) proteins, such as dehydrins, and transcription factors that control the expression of suites of COR genes, often referred to as a cold regulon. CBF/DREB transcription factors are integral to responses to low temperature and drought and are responsible for approximately 12–20% of cold-induced transcriptional changes in Arabidopsis14.Cold acclimation, the ability to tolerate freezing temperatures, is a complex and dynamic process. Low temperature survival depends on a combination of biophysical and biochemical factors, often driven by the upregulation or downregulation of specific sets of genesCBF genes have been identified in all investigated higher angiosperm species, including woody perennials. All CBF amino acid sequences feature common motifs in addition to the characteristic AP2 DNA binding domain13. The number of CBF genes in a genome varies from species to species, with some examples displaying the conserved motifs but not actually participating in low temperature responses. Apple and peach (Prunus persica [L.] Batsch) have five and six CBF genes, respectively11. The importance of CBF genes to cold hardiness has been demonstrated repeatedly in different plant systems1. Regulation of CBF genes is complex in Arabidopsis, with a variety of kinases, transcription factors, light- and photoperiod-related proteins, hormones, and degradation factors such as ubiquitin potentially playing roles15. Similar genes and proteins are found in apple, peach and other woody, perennial species suggesting that their CBF genes are regulated in a similar fashion11.CBFs/DREBs are members of the AP2/ERF family of transcription factors that bind to a promoter motif consisting of A/GCCGAC, commonly referred to as the C-repeat or Drought Response Element. 16 reported that over-expression of a peach CBF gene (PpCBF1) in apple results in enhanced freezing tolerance, growth reduction, early onset of dormancy triggered by short days, and delayed bud break in the spring. The response to short days was especially novel, since growth cessation in apple is typically not impacted by short days17. These phenotypes were confirmed in a field planting maintained for three years18. Wisniewski et al.19 provided a model that suggested that CBF regulated cold acclimation, dormancy, and growth through COR, DAM, and RGL genes, respectively. It was also suggested that overexpression of PpCBF1 regulated the expression of an apple Early Bud Break gene (EBB) that had been shown to regulate bud break in poplar20.Wisniewski et al.19, however, represent only a small number of the potential genes whose expression may be altered directly or indirectly by over-expression of the peach CBF gene in apple. Understanding complex pleiotropic effects requires studies of global gene transcription over time. In the current study, field samples of apple bark tissues were collected over several months from non-transgenic “M.26” apple trees and transgenic “M.26” apple trees overexpressing a peach CBF (PpCBF1) gene to provide a comprehensive overview of the differentially expressed genes (DEGs) associated with the phenotypes of these two genotypes from Ecodormancy  to active growth (July), with an emphasis during bud break (April).The growth-related and dormancy-related genes examined by Wisniewski et al.21, and an average of 31,483 genes or protein coding transcripts were identified between both genotypes and over all timepoints. This represents approximately 54.9% of the total predicted transcripts in the apple reference genome v1.0. Log2-transformation of the counts display similar distributions between the samples  comparisons between the genotypes (transgenic vs. non-transgenic) at each of the sampled months; (2) genes that were differentially expressed over time within a genotype; (3) genes that were differentially expressed in one sampling point (month) to the next sampling point, and; (4) genes that were differentially expressed based on an interaction between genotype and time. Upregulation and downregulation was based on logThe four comparisons between the experimental variables provide different types of information. Comparison 1), “M.26” vs. T166 by month, gives a direct view of the differences in gene expression between the two genotypes at specific timepoints. The number of DEGs in the transgenic genotype (T166) during the latter months of winter (February and March) were similar, averaging about 1400 genes. Notably, there were a much greater number of downregulated genes in the February samples than in the March samples. The greatest number of DEGs between the two genotypes occurred in the April sampling with over 4000 DEGs. This result indicates that significant differences in gene expression between the transgenic (T166) and non-transgenic (“M.26”) exist at the time bud break was beginning in the “M.26” trees but not in the T166 trees  indicates the effect of time on the expression level of genes within each genotype. Genes being identified as DEGs would be any that have expression levels that significantly changed (up or down) over time. Overall, the number of differentially expressed genes were similar in “M.26”  and T166 . This does not indicate similar expression patterns over time, only that a similar number of genes exhibited significant changes in expression over the sampled period (Feb–July).Comparison (3) provides information on changes in gene expression from one sampling timepoint to the next within each genotype. One of the notable differences in this set of comparisons is in the number of DEGs in the Feb–Mar comparison for each genotype. The non-transgenic “M.26” genotype exhibited a much greater number of differentially expressed genes (2813) in this monthly comparison than the transgenic T166 genotype (530), indicating that “M.26” was undergoing a greater shift in gene expression than T166 in the late winter to early spring.Comparison (4) indicates the number of genes who expression was significantly affected by the interaction between genotype and time. Genes identified as being DEGs in this comparison are those whose expression was significantly different between the two genotypes over the time course of sampling (Feb–Jul). These genes would be the most appropriate for further investigation, as their expression differs between the “M.26” and T166 genotypes over time.The data depicted in comparison 1 Table  can alsoPpCBF1 has many pleiotropic effects during winter and early spring, and these downstream effects are minimized during summer.The correlation matrix Fig.  providesHow “M.26” and T166 compare in terms of upregulation and downregulation of gene expression is important to understanding the differences in dormancy and freezing tolerance in T166 relative to “M.26”. A complex pattern of differential gene expression was evident, with numerous genes being upregulated or downregulated in T166 trees relative to “M.26” trees Fig. . The greThe Venn diagrams Fig.  of month2 fold change is then calculated to assess difference of individual genotype means from the base mean. The Wald test approach, which performs a parametric significance test of the selected factor level uses a negative binomial distribution. Significant p-values result from the factor being determined as significant in the Wald test. A positive log2 fold change indicates highly differential expression of the gene in T166 over that seen in “M.26”, while a negative log2 fold change indicates the converse—highly differential expression of the gene in T166 lower than that seen in “M.26”. The trends seen in Table Eight genes were chosen to validate the model by RT-qPCR Fig. . The relCBF1 gene in T166 would be expected to result in the upregulation of downstream, stress-associated DEGs. In fact, a large number Dehydrin genes were highly upregulated in T166 vs. “M.26” in all months  genes, a cold acclimation WCOR413-like gene, and eleven out of twelve genes encoding a Universal Stress Protein domain were also upregulated in T166 compared to “M.26” in April. Two Defender Against Cell Death genes, involved in programmed cell death (PCD), were slightly upregulated in T166 relative to “M.26” in April. Sixty heat shock proteins (HSPs), HSP cognates, or heat stress transcription factors were identified among the expressed genes. Most of the small HSP genes were slightly to strongly upregulated in T166, while the majority of large HSP genes were downregulated in April. The heat-stress transcription factors were also largely upregulated in April. Numerous genes associated with redox reactions or responses to reactive oxygen species (ROS) were upregulated in T166, except for ascorbate oxidases, which were all downregulated in T166 relative to “M.26”. A majority of genes associated with biotic stress were downregulated in T166 relative to “M.26” in April. Eight of nine wound-induced or wound-responsive genes were upregulated, along with two defense-related genes in T166 compared to “M.26” in April. In like manner, seven probable WRKY transcription factors were also upregulated in T166 relative to “M.26” in April. In contrast, pathogenesis-related (PR) group 5 genes encoding thaumatin-like and osmotin-like proteins were largely downregulated. Thirteen Senescence Associated Genes (SAGs) were found to be highly upregulated in T166 relative to “M.26” in April. Twelve disease resistance genes were also identified that exhibited patterns of both upregulation and downregulation. Native CBF genes were not significantly differentially expressed in the April samples of T166 compared to “M.26”.Constitutive overexpression of the peach 18. Most of the genes coding for cell division cycle proteins were downregulated in T166 compared to “M.26” in the April samples, including cyclins, CDKs, and all G2/ mitotic-specific cyclins  gene involved in leaf and flower development. Eleven genes encoding Expansins were all found to be downregulated in T166 compared to “M.26”.Even though the greatest disparity in the number significantly upregulated and downregulated DEGs between T166 and M.26 was observed in February, April samples are of particular importance due to the fact that “M.26” begins to exhibit bud break at that time, while buds inT166 trees are still ecodormant  were differentially expressed in April, mainly auxin-related and GA-related  and ethylene, two inhibitory or senescence-related hormones, would be expected to perhaps exhibit some degree of upregulation in the T166 genotype relative to the “M.26” genotype given that bud break is not readily evident in T166 trees in April. In particular, biosynthetic or signal transduction pathways for these hormones might be upregulated in T166 relative to “M.26”. Instead, the pattern of gene expression for these pathways was inconsistent for both hormones  had no consistent upregulated or downregulated expression trends in T166 relative to “M.26”. Similarly, genes identified with the autonomous and vernalization pathways were not uniformly differentially upregulated or downregulated in T166 relative to “M.26”. Partial homologs of the target of these pathways, FLC, exist in apple. Differentially expressed Agamous-like (AGL) MADS-box genes were identified, one of which was identified as FLC-like. The FLC-like gene was differentially downregulated in April in T166 relative to “M.26”. Two of these MADS-box/ AGL DEGs were previously identified as Dormancy Associated MADS-box (DAM) homologs, MdDAM1 and MdDAM219. MdDAM1 was highly upregulated in T166 compared to “M.26” in early spring, but down-regulated in summer. In contrast, MdDAM2 was differentially expressed only during bud break, again in T166 relative to “M.26”.Numerous genes, other than those associated with hormone biosynthetic or signal transduction pathways, are associated with dormancy and growth. Therefore, the expression of genes reported in models for bud break, vernalization, and floral initiation were examined, recognizing that these processes may differ in herbaceous and woody perennials. The upregulation and downregulation of different light signal transduction pathway genes varied in T166 relative to “M.26” —like homologs were upregulated in T166 compared to “M.26”, but all the gene sequences were poor matches to either the Arabidopsis or Populus versions. Nine Squamosa promoter-binding-like (SPL) protein genes were highly differentially expressed, with only one being upregulated in T166 relative to “M.26”. Five CLAVATA1 genes, associated with meristem and floral initiation in Arabidopsis, were downregulated in T166 vs. “M.26”. No evidence of differential expression patterns was observed for FT and TFL. These genes are typically expressed in buds23, so it is not surprising that expression, differential or otherwise, was not detected since bark tissues were examined in the current study. The gene encoding the PXY receptor, associated with cambial cell division24, was strongly downregulated in T166 relative to “M.26” in both Feb and April. In contrast, several knotted-1-like (KNAP) genes, also involved with cambial cell division, were upregulated in T166 relative to “M.26”.Vegetative or vascular growth initiation genes, such as 25 were also examined. This hypothesis suggests that PD sphincters and their degradation regulate the onset and release of dormancy in buds  genes were downregulated in T166 relative to M.26. Five remorin family genes were detected, with a heterogeneous expression pattern.The expression of genes associated with the plasmodesmata (PD) hypothesis of dormancy regulation27. Numerous abiotic-stress-responsive genes possessing the canonical A/GCCAG core motif in their promoters have been shown to be downstream targets of CBF binding29. Studies in plants overexpressing CBF genes have demonstrated increased survivability under artificial and natural conditions of freezing or water limitation27. These studies reported an improvement of −2 to −5 °C in freezing tolerance depending on the plant system. Wisniewski et al.16 reported increases of −3 to −4 °C in both non-cold-acclimated and cold-acclimated greenhouse-grown apple trees overexpressing the peach CBF1 gene . This effect was later confirmed in field studies, where non-acclimated trees in mid-summer had similar increases in freezing tolerance18. Artlip et al.30 reported that the effect on cold hardiness was not graft transmissible through a transgenic T166 rootstock, but juvenility (time to flowering) was affected in non-transgenic scions grafted to transgenic T166 rootstock.The role of CBF transcription factors in low temperature/freezing and water deficit responses has been well documented in a variety of plant families and genera, including woody perennialsCBF overexpression. Constitutive overexpression of CBF genes has been reported to result in diminished growth in both herbaceous and woody plant systems, including apple19. Overexpression of peach CBF1 in apple also affects entry into and exit from dormancy. Wisniewski et al.16, Artlip et al.18, and Wisniewski et al.19 demonstrated that apple gained a novel sensitivity to short day (SD) photoperiods, manifested by early leaf senescence and bud set. Several genes known to participate in dormancy processes were examined by Wisniewski et al.19. They reported a correlation between gene expression patterns in spring and the bud break patterns exhibited by M.26 and T166. While Wisniewski et al.19 provided information on the expression of a few genes related to growth and dormancy, a more complete analysis is provided in the present study. This information provides a more comprehensive picture of how the peach CBF1 gene affects so many physiological processes and pathways, especially those related to dormancy and growth. Samples taken from trees grown in the field provides an excellent measure of the impact of CBF proteins in a natural setting.Plant processes other than stress tolerance have been reported to be improved by CBF in apple impacts gene expression in a complex manner that potentially impacts numerous different processes and pathways. The RNA-seq analysis conducted in the present study detected approximately 46,500 genes constituting ≈73% of the predicted apple genome  depend solely on temperature to induce entry and release from ecodormancy17. The novel sensitivity to short days reported by Wisniewski et al.16, Artlip et al.18, Wisniewski et al.19, and Artlip et al.30 are reflected in the DEGs that were identified in the current study.The Feb–Mar–Apr months, encompassing late winter and early spring, are a time of transition for perennial, woody plants in the northern hemisphere, as during that time they overcome ecodormancy and undergo bud break. A combination of photoperiod and temperature typically fosters extensive changes in gene expression in most perennial woody plantsPpCBF1. Dehydrin and Late Embyrogenesis Abundant (LEA) genes were upregulated in T166, relative to the non-transgenic parent genotype “M.26”, as has been previously documented in other species. These genes typically contain a C-repeat/DRE binding site for CBF/DREBs and are recognized as targets of CBF/DREB proteins. The results of the RNA-seq analysis agree with Wisniewski et al.19. DREB2A genes were also upregulated in T166, along with the CBF/ DREB1 Tiny gene. It is possible that the latter genes may respond to multiple cues or have C-repeat/DREB sites in their promoters. Notably, no native CBF genes were differentially expressed, despite the presence of C-repeats in the promoters of MdCBF1, 2, 3, and 519. Other factors, however, also play a role in CBF expression and regulation15, and it is possible that regulation of apple CBF genes may differ in regard to the Arabidopsis model. Indeed, few of the MdCBFx promoters contain regulatory motifs consistent with Shi et al.15 or Wisniewski et al.1. Additional research is clearly required on how native MdCBFx expression is regulated.Several types of stress-related protein genes were examined to determine if they were upregulated by overexpression of Heat Shock Protein (HSP) genes were consistently up-regulated in T166 in Feb and Mar. HSPs are known to be induced by low temperatures33, and so PpCBF1 overexpression may have contributed to the observed upregulation. In April samples of T166 the upregulation of small HSPs was maintained while large HSPs were largely downregulated. Concurrently, several HSP transcription factors were also upregulated in April samples, suggesting that these may primarily regulate small HSP gene expression. Numerous genes encoding proteins with a bacterial Universal Stress Protein motif were also differentially expressed, generally upregulated in T166 compared to “M.26”. As noted by Kerk et al.34, the functionality of these domains in Arabidopsis thaliana is uncertain. Thus, additional research is needed to discern what the functions of the identified proteins are in apple bark. Genes associated with redox or ROS homeostasis were inconsistent in overall trends, perhaps reflecting the relatively dormant state of T166. Transcripts for osmotin-like and thaumatin-like genes were decidedly downregulated in T166 relative to ‘M.26’ in the monthly samples. These ABA-responsive and wound-responsive proteins are members of the pathogenesis-related 5 (PR5) group of pathogen-response proteins33. Notably, many of other wound-induced and pathogen-induced genes were upregulated in T166, suggesting a complex interplay between CBF and signal transduction pathways responsible for the expression of defense-related genes.Many additional stress proteins were identified as being up-regulated in T166. Both small (<30 kDa) and large (>70 kDa) 18, who reported that bud break in “M.26” was evident in about 5% trees at the time (April) the samples were taken for the RNA-seq study. In contrast bud break in T166 trees was not readily apparent until after an additional two weeks  in T166 and “M.26” trees and determined that some temporal differences in the level of expression of these genes were evident in these two genotypes. In the present study, a more comprehensive analysis of gene expression was conducted that could serve as a basis for understanding the effect of CBF overexpression on freezing tolerance and dormancy, as well as several other non-target parameters, such as growth and flowering.Wisniewski et al.37. Genes responsible for the storage or catabolism of auxins were largely upregulated in April samples of T166 compared to “M.26”, suggesting that auxin levels were low and thus may have inhibited the onset of growth in T166. Indeed, cambial transcriptomic and cambial cell dynamic studies in aspen strongly suggest that auxin transport and perception are vital to transitions from growth to dormancy and the converse38.Plant growth regulator (PGR) levels impact every aspect of plant growth and development, including dormancy. Growth-promoting PGRs such as auxins, gibberellins, and cytokinins have been reported to be affected by CBF overexpressionent-kaurene oxidases are noticeably upregulated in T166 relative to “M.26”, however, several GA2 oxidase (catabolic) genes were also upregulated in T166 in Apr. Achard et al.35, Suo et al.37, and Niu et al.36 reported on changes in the expression levels of GA-biosynthetic and deactivating genes in plants over-expressing CBF genes in Arabidopsis thaliana, soybean (Glycine max), and tobacco (Nicotiana tabacum), respectively. Suo et al.37 and Niu et al.36 reported decreased GA levels in CBF-overexpressing plants. Low levels of GA stabilize growth-inhibiting RGL/DELLA proteins39, which can be exacerbated by CBF overexpression35. Transcripts from four RGL/ DELLA genes were detected, primarily in April, but were slightly downregulated in T166 relative to “M.26”. The expression of two of these genes, RGL1a and 1b, was previously characterized by Wisniewski et al.19, who also found that expression level of these genes in April was not substantially different between T166 and “M.26”. Wisniewski et al.19, however, did find differences during other months, suggesting that those months may be critical to the impact of CBF overexpression on growth inhibition. Important GA-responsive genes may also be downregulated in T166, and thus contribute to reduced growth. For example, transcripts encoding numerous putative GID1 GA-receptors were identified in the current transcriptome analysis and found to be significantly downregulated in T166. GID1 proteins bind to GA and foster the degradation of RGL/DELLA proteins, thus relieving RGL/DELLA growth inhibition40. Data from the current study, however, are equivocal in terms of evident expression patterns and the subject needs to be examined further.GA biosynthetic-enzyme encoded genes such as 7 noted that reduced response to cytokinins can lead to reduced cambial cell division rates, and it is well known that cytokinin levels can also impact growth. Therefore, genes encoding cytokinin storage and inactivation enzymes were examined. In general, these genes were upregulated in T166 relative to “M.26”, suggesting another potential mechanism by which PpCBF1 overexpression may negatively affect growth and development. Indeed, genes encoding ANT/AIL (ANTINTEGUMENTA/ AINTEGUMENTA-LIKE) and CYCD (D-type cyclins) are all downregulated in T166 relative to “M.26” in April. ANT/ AIL positively regulate CYCD, increasing cell division7.Bhalerao and FischerPpCBF1 overexpression does not impact ABA signal transduction directly. The expression of ethylene-related genes was similarly inconsistent with no evident pattern within or between the two genotypes. While terminal biosynthetic enzyme ACC oxidase homologs were generally upregulated in April T166 samples, as well as in a few other months, ethylene receptor genes varied in their level of expression.Inhibitory PGR-related genes associated with ABA and ethylene were examined. Genes encoding multiple members of the ABA signal transduction pathway  were found to be differentially expressed over the entire sampling period, particularly in April. There was no consistent trend in expression, however, suggesting that 43. The general pathway includes photoperiod perception by phytochromes A and B which interact with the circadian rhythm proteins . CO/FT modules are then stimulated, leading to AP1 homolog activation, followed by ANT/AIL1, and resulting in the induction of vegetative growth. Rinne et al.25 proposed the plasmodesmata (PD) hypothesis, wherein callose sphincters close off the PD during autumn, preventing stimulatory PGRs from reaching the apical meristem. As daylength increases, GA-inducible 1,3-β-glucanases degrade the callose plug, allowing stimulatory PGRs to enter meristematic cells and induce bud break. Tylewicz et al.44 further refined this this hypothesis by reporting that dormancy onset is ABA-dependent, with photoperiod playing a role. ABA has also been implicated in pear flower bud endodormancy, as reported by Li et al.45. Apple and pear are closely related, so it is possible that they share this mechanism as well. Indeed, apple and pear have closely-related DAM genes and expression patterns in common as assessed in seasonal tissue collections and growth chamber experiments47.Numerous genes and models have been reported that couple PGRs with entry into and exit from dormancy. Models of the regulation of dormancy in woody perennials have been reported that incorporate aspects of herbaceous models5, Ding and Nilsson40, Bhalerao and Fischer7, and Xing et al.48 were observed in the current study. In April, PhyA and B expression was slightly downregulated in T166 compared to “M.26”, as was CRYPTOCHROME1. PhyC genes were upregulated also but have not been implicated in current models of dormancy regulation. Other prominent members of the circadian regulatory module were either not differentially expressed  or exhibit mixed expression . Mixed expression was also observed for GI, CO, the FT homolog MdFT2, and ANT/ AIL1. It is perhaps unsurprising that GI, CO, and ANT/AIL display expression in cambial tissues, as light perception and transmission has been observed in bud and vascular tissues49. However, the presence of MdFT2 in cambial tissues is apparently novel. Previous reports in apple indicate that MdFT2 is highly expressed in developing fruit23. Given the known role of FT in floral induction, Mimida et al.50 suggested that MdFT2 could have roles in floral organs and fruit development. The present study suggests that MdFT2 could have roles in early-season cambial growth dynamics as well. Autonomous and Vernalization pathway genes were inconsistently differentially expressed. These pathways are typically considered part of the dormancy/flowering cycle of herbaceous plants, but also function in perennial, woody flowering plants as well48. The primary target of the autonomous and vernalization pathways is FLC. An FLC paralog, termed MdMAF2 and previously reported in buds51, was slightly down regulated in T166 relative to “M.26”. Kumar et al.52 disagree with the assignment of MdMAF2 as an FLC paralog, however. Such disagreement, coupled with the low transcript number observed, renders the relevance of MdMAF2 expression in bark, differential or not, murky at best.DEGs that are included in data or models presented by Schrader et al.SOC, SPL, and CLAVATA53 were varied in their expression as well. PXY, associated with cambial cell fate decisions7, was negatively DE in T166 relative to “M.26”. The thinner stem diameters reported for T166 compared to “M.26” by Artlip et al.18 may reflect this downregulated expression. General genes related to growth, such as cyclins, cell division control proteins, and expansins7 were generally found to be downregulated in T166 relative to “M.26”, over the course of the study, especially in April. This may reflect the overall downregulation of growth-stimulating PGRs, resulting in the late bud break and reduced growth observed in T166. Expression of the knotted1-like (MdKNAP) genes were generally upregulated in T166 relative to “M.26”. The MdKNAP1 and 2 genes are known to be expressed during growth and development54 in apple stem internode tissue, but only MdKNAP1 was differentially expressed, with upregulation in T166 in the present study. It is also known that Populus knotted1 homologs can have overlapping, but discrete functions in these processes56. In depth examination of the apple KNAP genes in T166 and “M.26” is clearly warranted to clarify what roles they may play in dormancy and growth.Other genes associated with either vegetative or vascular growth such as 25 reported a positive correlation between their expression and dormancy release.Genes related to the PD hypothesis were generally downregulated in T166 . Downregulated glucan endo-1,3-beta-glucosidases could contribute to delayed dormancy in T166, as Rinne et al.59 and implicated in other species. The expression of apple DAM genes has been reported on by several groups, including Wisniewski et al.19 in bark tissues as MdDAMs1-3, and by Wu et al.47 in apical buds as MdDAMa, MdDAMc, and MdSVPb, respectively. In the present study, MdDAM1/ MdDAMa was detected as being slightly, but statistically significantly, upregulated in T166 in Feb, Mar, and April. MdDAM2/ MdDAMc was upregulated in T166 relative to “M.26” only in April, with no statistically significant differential expression data from Feb, Mar, and July. A lack of statistically significant differential expression data does not imply a lack of expression in either T166 or “M.26”. Rather, the expression levels in both genotypes were so similar that they simply were filtered out of the bioinformatics pipeline used in this study. As such, MdDAM2/ MdDAMc, along with MdDAM3/ MdSVPb, were not differentially expressed during the other months examined in this study. The expression data observed in the present study generally agrees with data presented by Wisniewski et al.19 with little difference in the expression of these genes between T166 and “M.26” bark tissues. Wu et al.47 focused primarily on MdDAM and MdSVP expression in apical buds. Similarly, Falavigna et al.60 compiled data from these studies and others, creating a model of DAM expression in apical buds as a function of dormancy. Their model implies elevated expression of MdDAM2/ MdDAMc coincident with the end of the growth cycle, followed by elevated expression of MdDAM1/ MdDAMa as a component of endodormancy. Elevated expression of a third DAM, MdDAMb (not reported in this study), fosters ecodormancy; its decreased expression then heralds budbreak60. It is likely that MdDAMb is not a DEG in bark tissues, or was not a DEG at the sampling timepoint. Overall, the data and interpretations of Wu et al.47 and Falavigna et al.60 largely agree with those presented by Wisniewski et al.19 and the present study, which both focused on bark  tissues rather than buds. In addition, many of the findings of the current study reflect those presented by Schrader et al.5 in poplar, indicating that conserved cambial growth dynamics exist between two species from different taxonomic families.Dormancy associated MADS-box (DAM) proteins have been reported to regulate dormancy in peach19 and Artlip et al.30 A hypothetical framework of potential direct and indirect effects of PpCBF1 overexpression is presented in Fig. PpCBF1 overexpression in Line T166 is complex as evidenced by the significant differences in gene expression observed in the two genotypes in any given month  trees that were approximately three-years-old were used in this study. The T166 (PpCBF1-OX) line was initially described by Wisniewski et al.Young, lateral branches were collected from “M.26” and T166 trees monthly in February, March, April, and July 2013. Bark tissues  were scraped from current year and one-year-old shoots and immediately placed in liquid nitrogen, lyophilized, and stored at −20 °C until use. Tissues from three trees of each genotype were sampled and maintained as independent biological replicates. Temperature and precipitation data were recorded daily during 2013. Trees had not been pruned during their initial three years of growth.Dates of bud break for each tree were recorded during spring 2013. Percent bud break was determined thusly: three shoots on each of three trees of M.26 and T166 were tagged and bud break from 20 individual lateral buds from the terminal bud were tracked.61, with purification and library construction as previously described by Ballester et al.62. In brief, 1 ml Sarkosyl of 20% (w/v) was added to 10 ml of extraction buffer  K-30 (soluble), 100 mM Tris HCl (pH 8.0), 25 mM EDTA, 2.0 M NaCl, 0.5 g/l spermidine (free acid) (HRS), 2% β-mercaptoethanol (added just before use). The extracted total RNA was dissolved in EB buffer  supplemented by 1× Ambion RNA secure . To activate RNA secure, the samples were incubated at 60 °C (in a water bath) for 10 min and then immediately put on ice. RNA quantity and quality were evaluated using a Nanodrop 1000 . Immediately prior to mRNA isolation, RNA samples were treated with DNase I  at 37 °C for 30 min followed by heat inactivation at 65 °C for 15 min. Each RNA sample was adjusted to contain 5 μg of total RNA. Library construction was performed using the protocol outlined in Zhong et al.63 and run in two lanes using the Illumina HiSeq2000 platform to obtain 51-bp single-end reads. Libraries from three independent biological replicates of each genotype at each timepoint were sequenced and analyzed.RNA was isolated from bark samples as described by Bai et al.Malus x domestica. Each genotype was sampled once during February, March, April, and July and each sampling timepoint contained three independent bioreplicates (trees) at each of the timepoints. After the 24 datasets were sequenced, each was run through an optimized RNA-seq pipeline to determine statistically significant differences in gene expression between specific comparisons. The v1.0 apple reference genome and annotation used for the assembly and annotation was downloaded from Phytozome64.The analyzed data comprised 24 datasets, 12 for the “M.26” wild-type genotype and 12 for the T166 transgenic genotype of 65; (2) data trimming using Btrim66; (3) reference genome indexing using HISAT267; (4) alignment of trimmed reads to indexed reference genome using HISAT267; (5) read count quantification using HTSeq68; and (6) differential expression analysis using DESeq269 in R.A computational pipeline consisting of optimized tools was developed to identify differences in gene expression between the T166 and “M.26” genotypes. The pipeline consisted of: (1) read quality check using FastQCFour distinct comparisons of differential gene expression were considered: (1) pairwise comparisons of “M.26” vs. T166 at each timepoint; (2) separate time main effect for each genotype  pairwise comparisons of each consecutive timepoint for “M.26” and T166; and (4) interaction effect of genotype and time. The four types of comparisons provided a total of 13 comparisons, with comparison 1 being responsible for four, comparison 2 being responsible for two, comparison three being responsible for six, and comparison 4 being responsible for one. The four types of comparisons provided a comprehensive overview of the changes in gene expression corresponding to genotype (comparison 1) and time differences (comparisons 2 and 3) and which genes exhibited expression patterns that differ due to genotype over the course of the entire study (comparison 4).p-values result from the factor being determined as significant in the Wald test. The more complex comparisons (2 and 4) utilize a Likelihood Ratio test, which compares a full linear model considering appropriate additive and interactive effects and compares the fit against a reduced linear model with the selected factor(s) removed. Significant p-values result from a significant fitted improvement in the full model over the reduced model.The specific results for differential gene expression were determined using DESeq2, which implements a Wald test or Likelihood Ratio test to determine which genes exhibit different transcript levels within a respective comparison. The pairwise comparisons (1 and 3) utilize the Wald Test approach, which performs a parametric significance test of the selected factor level using a negative binomial distribution. Significant 2 fold-change and standard error, statistical test value, p-value, and adjusted p-value. DESeq2 adjusts the p-values to account for multiple testing using an FDR method. For this study, genes were considered differentially expressed if their adjusted p-value was below 0.05. Principal Coordinates Analysis (PCoA) was performed using Qlucore v3.2  bioinformatic software, with statistical significance set at 0.05.DESeq2 compiles a results file containing the gene ID, mean expression value, log19. Total RNA was diluted to 12.5 ng/μl. RT-qPCR analysis was performed using the Invitrogen SuperScript III Platinum SYBR Green One-Step RT-qPCR Kit with ROX , with each reaction containing 25 ng of input RNA and 2 pmol of each primer; no-RT control reactions were included to ensure that there was no residual DNA contamination. The Applied Biosystems ViiA 7  was set to cycle as follows: cDNA synthesis at 48.0 °C for 30 min; 95.0 °C denaturation for 5 min; 40 cycles of 95.0 °C for 15 s followed by 55 °C annealing for 1 min; followed by the default ViiA 7 hold and melt curve stages. Gene-specific primers were designed using CLC Genomics Workbench   were assessed as to the stability of their expression within the two genotypes and across timepoints70. LTL1 was deemed the best overall reference gene using NormFinder software71. Expression levels of each of the analyzed genes were calculated using the comparative ΔΔCt (threshold cycle) method. Data from biological replicates were used to calculate mean ± standard error (SE) expression values.RT-qPCR was conducted on selected genes to validate the results obtained by the RNA-Seq analysis as previously describedArtlip et al revised SOM


Despite the widespread existence of guidelines regarding undernutrition monitoring and reporting, there is scarce information whether they are followed. We aimed to evaluate the adherence to guidelines regarding undernutrition monitoring and reporting as well as their determinants in a university hospital.Retrospective analysis of discharged patients with data on Nutritional Risk Screening score (NRS-2002) from the department of internal medicine of the Lausanne University Hospital for years 2013–14. Adherence to the hospital monitoring guidelines, i.e.: 1) discharged patients with NRS-2002 score≥3 should have prealbumin levels measured, and 2) discharged patients with prealbumin levels<0.20 g/l should be rechecked 7 days afterwards, was assessed. Reporting of nutritionally ‘at-risk’ status in the discharge letter was also assessed. Multivariable logistic regression was used to examine potential determinants of adherence to guidelines.Of the 2,539 discharged patients with NRS-2002 data, 1,605 (63.0%) were nutritionally ‘at-risk’. Complete adherence to the monitoring guideline was observed in 238 (14.8%) of ‘at-risk’ patients. After multivariable analysis, adherence to the first step of monitoring guideline was associated with older age (≥ 80 years) [OR (95% CI): 2.03 (1.29–3.18)], high comorbidity index [1.36 (1.05–1.77)], and nutritional management [5.57 (4.38–7.07)]. Nutritional management was also associated with adherence to the second step of monitoring [3.98 (2.33–6.78)]. Adherence to the reporting guideline was observed in 343 (21.4%) of ‘at-risk’ patients. Multivariable analysis showed that adherence to the reporting guideline was associated with NRS-2002 score>4 [1.97 (1.47–2.64)], nutritional management [3.80 (2.85–5.07)], and adherence to the monitoring guideline [3.33 (2.35–4.71)].Our results show a poor adherence to guidelines regarding undernutrition monitoring and reporting, possibly due to lack of training, staff, and time. Undernutrition is a common condition among hospitalized patients, varying between 20 and 60% depending on study population and definition applied ,2. UnderSwitzerland has one of the best health systems in the world , but thewww.chuv.ch) and the Internal medicine unit of the CHUV is the largest in Switzerland, with over 4,000 admissions per year.This is a retrospective study using medical information from of the department of internal medicine of the Lausanne University Hospital (CHUV). The CHUV is one of the Swiss university hospitals  score in 2013. Data collection, merging, and coding was performed by the Lausanne university hospital team; investigators were blinded to patients’ identifiers.For the present analyses, we used hospitalized patients’ data from January 1S1 Fig”. Thus, the final sample consisted of 1,605 hospital discharges.All hospital discharges of adult patients (>18 years old) with NRS-2002 score data were considered as eligible. Of the 8,541 hospital discharges that occurred during the study period, 2,539 (29.7%) were considered as eligible, of whom 934 (36.8%) were excluded due to an NRS-2002 score <3 “S2 Fig”. The CHUV monitoring guidelines have a two-step approach: 1) all nutritionally ‘at risk’ patients should have their prealbumin levels measured, and 2) patients with prealbumin levels <0.20 g/l should have a second prealbumin measurement performed 7 days after the first one to monitor their nutritional status. Adherence to the CHUV monitoring guideline was assessed at those two steps; for step 2) the analysis period was extended to 10 days because of possible delays due to weekends or holidays.The procedure regarding undernutrition screening and monitoring in the department of internal medicine is summarized in “th revision (ICD-10). We systematically searched for all ICD-10 codes related to undernutrition and adherence to the reporting guideline was defined by presence of at least one undernutrition related codes “S1 Table”. If both criteria were not met, adherence to the reporting guideline were considered as not implemented.The CHUV reporting guideline states that all nutritionally ‘at-risk’ patients should be reported as such in the discharge letter. This condition is then coded using the International Classification of Diseases 10S2 Table”. The cores are added and scores ≥3 indicate that the patient is nutritionally ‘at-risk’. In this study, NRS-2002 scores were further categorized into medium (NRS-2002 score: 3–4) and high (NRS-2002 score >4) risk [Nutrition risk screening was defined by the presence of NRS-2002 scores in patients’ electronic medical record. The NRS-2002 is one of the most commonly used validated nutrition screening tools ,19. BrieS3 Table”. Severity of disease was assessed using the Swiss version of the Charlson Comorbidity Index (CCI) [We extracted the following explanatory variables from the patients discharge files: gender, age , and main diagnosis “ex (CCI)  and dichex (CCI) . Height Statistical analyses were performed using Stata version 14.1 for Windows . Statistical significance was assessed for a two-sided test with p-value <0.05. Descriptive results were expressed as average ± standard deviation for continuous data and as number of participants (percentage) for categorical data. Bivariate analysis was performed using logistic regression to identify potential determinants of adherence to guidelines. In this model, adherence to the guideline (yes or no) was the outcome and each socio- demographic or clinical characteristic was the predictor. Results were expressed as odds ratio (OR) and 95% confidence interval. Multivariable analysis was then performed using logistic regression to identify the factors significantly and independently associated with adherence to guidelines. Model fit for the multivariable analysis was tested using the Hosmer-Lemeshow goodness-of-fit test for each outcome. The p-value for the tests were not significant (>0.05), suggesting adequate model fit. The linear relationship with age with adherence to guidelines was evaluated categorically using orthogonal polynomial contrasts (command contrast p. of Stata) and continuously by calculating the odds ratio of a 10-year increase.2, which are the criteria suggested by the European Society of Parenteral and Enteral Nutrition (ESPEN) for definite undernutrition status [To investigate the potential effect of in-hospital mortality on adherence to the reporting guideline, analyses were performed after excluding patients who died during hospitalization. Further, in January 2014, the Swiss society of clinical nutrition issued a recommendation regarding undernutrition coding . We thusn status .www.cer-vd.ch, decision 428–14, of Dec. 2, 2014) and the CHUV board of directors  approved this study. Only routinely collected data was used. All information was extracted and anonymized by the hospital clinical research staff before being handled for analysis.The Ethics Committee of the Canton Vaud  had their prealbumin levels measured. In bivariate analysis (Table 1), adherence to the first step of the monitoring guideline was positively associated with older age, having circulatory system disease, mental and behavioral disorder, NRS-2002 score>4, high CCI, and nutritional management. Except for circulatory system disease, the associations remained significant on multivariable analysis, which also showed a positive association with year of admission and a negative association with cancer and digestive system diseases.Of the 1,605 nutritionally ‘at-risk’ cases  “S5 Table”.Among the 530 cases with prealbumin measurement, 439 (82.8%) had levels below 0.20 g/l. No associations were found between low prealbumin levels and their socio-demographic and clinical characteristics; similarly, no association was found between low prealbumin levels and nutritional management “Table 2), adherence to the second step of monitoring guideline was positively associated with having NRS-2002 score>4, high CCI, and nutritional management. In multivariable analysis  had a hospital stay ≥10 days and were thus eligible for a second measurement of prealbumin levels. Of the 346 eligible cases, 189 (54.6%) had a second prealbumin measurement. In bivariate analysis , adherenTable 3), adherence to reporting guideline was positively associated with having infectious disease, NRS-2002 score>4, nutritional management, and adherence to the monitoring guideline. In multivariable analysis  had undernutrition-related ICD-10 codes in the discharge data “2 led to comparable results, although the associations of NRS-2002, cancer, and diseases of the respiratory system with undernutrition reporting were no longer significant “S6 Table”.Exclusion of those who died during hospitalization (n = 119) did not appreciably change the adherence to the reporting guideline (21.6%). Sensitivity analysis restricted to 193 patients with both NRS-2002 ≥3 and BMI <18.5 kg/mLess than one sixth of patients ‘at-risk’ of undernutrition was adequately monitored, and only one fifth was reported as such in the discharge letter. Our results indicate that adherence to guidelines regarding undernutrition monitoring and reporting is low in a Swiss hospital setting.nutrition programs in hospitals’ [Inadequate recognition and proper treatment of hospital undernutrition has prompted the issuing of guidelines for screening, management, monitoring, and documentation . Still, spitals’ , and thespitals’ . Pre-albspitals’ . Prealbuspitals’ .The CHUV guidelines state that all nutritionally ‘at-risk’ patients (NRS-2002 score ≥3) should have their prealbumin levels measured. Still, our results indicate that this measurement is performed in only one third of them. Possible explanations for this low adherence rate are lack of manpower, excessive workload, inadequate nutrition knowledge, and no defined responsibilities , but no Adherence rate to the second step of the guidelines  was higher than to the first one. Nevertheless, it only concerned half of the patients, leading to a full adherence rate (i.e. to the first and the second steps) of only 13%. This value is lower than in Italy (21.6%) among 24 medical and surgical departments  or Cuba,‘nutrition programs in hospital’ [Only one out of five patients nutritionally ‘at-risk’ was reported as such in the discharge data. Our results agree with one study conducted in one university medical center in Amsterdam, where nutritional status was documented in only 15.5% of referral letters by the general practitioner . Conversospital’ ,33,34.Cancer patients who were nutritionally ‘at-risk’ had a lower likelihood of being reported as such. This finding is in agreement with a previous study which reported that undernutrition is underestimated among cancer patients  or to thIn January 2014, the Swiss society of clinical nutrition issued several recommendations regarding undernutrition reporting . Still, Overall, our results highlight that adherence to the reporting guideline is inadequate.Monitoring patient’s nutritional status is imperative to check the effectiveness of the nutritional care plans and medical treatments, especially among patients who are ‘at-risk’ of undernutrition ,37. UndeThe strength of this study is its large sample size of ‘at-risk’ or undernourished patients. The main limitations are that it was restricted to a single department of one university hospital in Switzerland; as there is no consensus regarding undernutrition monitoring guidelines, generalizability is thus limited. Still, our results provide the first evaluation of adherence to guidelines on monitoring and reporting of undernutrition status, and its methodology could be replicated in other health care settings. Second, it is possible that the hospital staff unconsciously selected the most nutritionally ‘at-risk’ patients for screening, thus leading to a higher prevalence of the condition. Still, we were interested in the implementation of the monitoring and the reporting guidelines rather than the screening itself. Hence, even in the presence of a selection bias, our results indicate that adherence to the guidelines regarding nutrition monitoring and reporting is far from optimal.In a Swiss hospital setting, less than one sixth of patients ‘at-risk’ of undernutrition was adequately monitored, and only one fifth was reported as such in the discharge letter. Adherence to guidelines regarding undernutrition monitoring and reporting varies according to the patients’ characteristics and could be due to lack of training, staff, and time. Implementation measures are urgently required to improve management of undernourished patients and to estimate prevalence of undernutrition using hospital discharge data.S1 FigAbbreviations: NRS-2002, nutrition risk screening.(TIF)Click here for additional data file.S2 Figth revision; LOS, length of hospital stay.Abbreviations: NRS-2002, nutrition risk screening 2002; ICD-10, International Classification of Diseases, 10(TIF)Click here for additional data file.S3 Figth revision.Abbreviations: NRS-2002, nutrition risk screening 2002; ICD-10, International Classification of Diseases, 10(TIF)Click here for additional data file.S1 TableAbbreviation: ICD-10, International Classification of Diseases 10th revision.(DOCX)Click here for additional data file.S2 Table10.1177/0884533617692527.Adopted from Kondrup et al., 2002, doi: (DOCX)Click here for additional data file.S3 TableAbbreviation: ICD-10, International Classification of Diseases 10th revision.(DOCX)Click here for additional data file.S4 Tablea where prevalence is expressed as number of patients (row %) or as average ± standard deviation. P-value for comparisons between groups performed using chi-square for categorical variables and student’s t-test for continuous variables.Abbreviation: CCI, Charlson comorbidity index. Analyses were performed among patients with NRS-2002 score in their medical file . Results are expressed as number of participants (column %) except for (DOCX)Click here for additional data file.S5 TableAbbreviations: ref., reference category; CCI, Charlson comorbidity index; NRS-2002, nutrition risk screening 2002. Analyses were performed among patients with NRS-2002≥3 and first prealbumin measurement (n = 530). Results for bivariate analysis are presented as number of patients (column %) and for multivariate analysis as OR (95% CI). Bivariate analysis performed using chi-square or Fisher’s exact test (ⱡ); multivariable analysis performed using logistic regression adjusting for all variables in the table.(DOCX)Click here for additional data file.S6 Table2 (n = 193). Results are presented as OR (95% CI). Bivariate analysis performed using logistic regression analysis in which adherence to the guideline (yes or no) was an outcome and each characteristic was a predictor; multivariable analysis performed using logistic regression adjusting for all variables in the table. * P-values for trend are presented, for which an ordinal variable was included as a continuous term in a logistic regression model.Abbreviations: ref., reference category; CCI, Charlson comorbidity index; NRS-2002, nutrition risk screening 2002. Analyses were performed among patients with NRS-2002≥3 and body mass index<18.5 kg/m(DOCX)Click here for additional data file.S1 Filehttp://www.plosmedicine.org/, Annals of Internal Medicine at http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.* Give information separately for exposed and unexposed groups. Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at (DOC)Click here for additional data file.