The Bland-Altman plot was performed to determine the mean bias and limits of agreement between measured and predicted RMRs. Stratification according to sex, the measured RMR was greater in athletes compared to controls. The closest equation to the RMR measured by Fitmate GS was the Harris-Benedict equation in male athletes (mean difference -8.9 (SD 257.5) kcal/day), and Liu’s equation [mean difference -16.7 (SD 195.0) kcal/day] in female athletes. However, the intra-class coefficient (ICC) results indicated that all equations, including Harris-Benedict for male athletes (ICC = 0.524) and Liu’s for female athletes (ICC = 0.575), had a moderate reliability compared to the measured RMR. In sedentary subjects, the closest equation to the measured RMR is the Nelson equation in males, with the lowest RMSE value of 118 kcal/day [mean difference: 10.1 (SD 117.2) kJ/day], whereas, in females, all equations differ significantly from the measured RMR. While Nelson (ICC = 0.790) had good and Owen (ICC = 0.722) and Mifflin  (ICC = 0.700) had moderate reliability in males, all predictive equations showed poor reliability in females. The results indicate that the predictive RMR equations failed to accurately predict RMR levels in the participants. Therefore, it may not suitable to use them in determining total energy expenditure.Predictive resting metabolic rate (RMR) equations are widely used to determine athletes’ resting energy expenditure (REE). However, it remains unclear whether these predictive RMR equations accurately predict REE in the athletic populations. The purpose of the study was to compare 12 prediction equations  with measured RMR in Turkish national team athletes and sedentary controls. A total of 97 participants, 49 athletes , and 48 sedentary , were recruited from Turkey National Olympic Teams at the Ministry of Youth and Sports. RMR was measured using a Fitmate GS . The results of each 12 prediction formulas were compared with the measured RMR using paired  Resting metabolic rate (RMR) is an essential component of daily energy needs and accounts for approximately 60–70% of total energy expenditure in sedentary individuals . The accProviding sufficient energy availability is one of the most critical points for boosting sports performance, physiological function, and maintaining metabolic health . Low eneIt is well-known that indirect calorimetry (IC) is the gold standard to measure RMR by measuring oxygen consumption and carbon dioxide production . In receSeveral factors such as a high equipment cost, the need for trained personnel, and long measurement times limit the use of indirect calorimetry in practice . Thus, vWith the hypothesis that RMR may vary with physical activity level and sex, the present study aimed to estimate RMR using Harris-Benedict, Mifflin, Schofield, Cunningham, Owen, Liu’s, and De Lorenzo equations and to compare the results with the measured RMR (Fitmate GS) in Turkish national team athletes and their sedentary counterparts. Additionally, we aimed to determine whether these equations were suitable for predicting RMR in both groups.n = ln (1 - γ). ln (1 - π) - 1 ), 24 females )], and 48 sedentary , were included in the study. The subjects were recruited from the Turkey National Olympic Teams at the Ministry of Youth and Sports between January 2020 and March 2020. Participants were informed about the study in detail, and verbal and written informed consent of the participants and/or their legal representatives was obtained prior to enrollment. The sample size was calculated by using the following formula developed to calculate the sample size in pilot trials; - π) - 1 . At a poThe inclusion criteria of the study for athletes were: (1) participation in Turkish National team sports for at least 1 year, (2) high physical activity level according to Total Physical Activity Score (TPAS), (3) no history of any metabolic disorders (4) no current injuries or ongoing therapies, and (5) aged between 18 and 25 years. Athletes were recruited from several sporting disciplines including track and field , long-distance swimming , modern pentathlon , fencing , karate , taekwondo (5 men), boxing , and soccer . The inclusion criteria for the sedentary subjects were: (1) low physical activity level according to Total Physical Activity Score (TPAS), (2) no history of any metabolic disorders, and (3) no current injuries or ongoing therapies.The participants were examined by a sports medicine specialist prior to commencing the study. Participants who were determined to have any current health issues or chronic disease history were excluded from the study. The International Physical Activity Questionnaire-Short Form (IPAQ-SF) was applied to the participants to assess their physical activity level . ParticiThe study was planned as a cross-sectional design of Turkish national and Olympic athletes and matched  sedentary controls.All procedures of the study were conducted in accordance with the Declaration of Helsinki. The ethics committee approval was obtained from the University Ethics Committee (2020/37).All participants were required to visit the performance laboratory once between 8:00 a.m. and 9:00 a.m. Both body composition and RMR measurements were performed on the same day. The study was conducted at the Center of Athlete Training and Health Research of the Ministry of Youth and Sports.IPAQ-SF was used to determine the physical activity levels of the participants . The queThe questionnaire aims to determine when a participant was physically active in the past 7 days. More specifically, questions were pertaining to the frequency and duration of each physical activity level  performed in the last 7 days. The physical activity level is determined by the MET (3.5 ml oxygen consumption per kg per minute at rest = 1 MET = 3.5 ml/kg/min) method. The Total Physical Activity Score (TPAS) of the participants were calculated using the following equations:•Walking Score (MET-min/week) = 3.3 × duration (min) × frequency (day)•Moderate-Intensity Physical Activity Score (MET-min/week) = 4.0 × duration (min) × frequency (day)•High-Intensity Physical Activity Score (MET-min/week) = 8.0 × duration (min) × frequency (day)•TPAS (MET- MET-min/week) = Walking Score + Moderate-Intensity Physical Activity Score + High-Intensity Physical Activity ScoreParticipants were then divided into three groups according to their TPAS as low, moderate, and high physical activity levels. According to these groups;•Low: TPAS < 600 MET-min/week•Moderate: 600 MET-min/week < TPAS < 3,000 MET-min/week•High: TPAS > 3,000 MET-min/week.2), body fat percentage (%), and fat-free mass (FFM) (kg/m2) were measured using the Bioelectrical Impedance Analysis (BIA) . Participants were asked to visit the laboratory in a fasted state (at least 4 h), have refrained from caffeine (at least 4 h), alcohol (at least 2 h), and cigarettes (at least 2 h). Further, participants were required to not exercise at a high intensity for at least 24 h prior to the measurements , BMI (kg/murements . Additiourements . We infourements . On the 2 gas) before the measurement period in order to verify an optimal machine functioning. Both calibration gases were run through the metabolic system to check for the drift of the O2 analyzer. The Fitmate GS measured the room air and reference O2 gases at 20.91%  and 16.03% (the O2 percentage of the reference O2 gas: 16.0%), respectively, revealing that the Fitmate GS metabolic cart was valid and accurate to use in the study. We performed a maximum of two RMR measurements per day and run manually a flowmeter calibration once per week according to the manufacturer recommendation. The Fitmate GS metabolic cart also automatically self-calibrated (up to 5 min) before each test.RMR was measured using Fitmate GS . We set the environmental characteristics before each measurement in line with the remarkable review by 2 is <10% during the 30 min measurement [discarding the first 5 min)]. The Fitmate GS metabolic monitor device does not contain a carbon dioxide sensor. Therefore, it calculates the RMR by estimating CO2 production from a fixed RQ of 0.85 based on the abbreviated Weir equation  of the Fitmate GS device was wearied to the participants. The measurement lasted for 30 min to achieve a steady-state (the state that Coefficient of Variation (CV) in VOequation .The measured RMR was compared to the predictive RMR calculated by widely used predictive equations, including weight-based , indicating that 106 RMR measurement tests were completed.Nine RMR measurements were repeated due to violations of RMR measurement requirements [non-compliance with pre-measurement rules; being a dehydrated state . Male and female athletes had significantly higher measured RMR and FFM (kg), and lower body fat percentage in those compared to the sedentary controls’ counterparts (p < 0.001).Descriptive statistics of the participants are presented in r = 0.611), Mifflin (r = 0.711), Schofield (r = 0.703) De Lorenzo (r = 0.748), Johnstone (r = 0.609), and Roza (r = 0.903) equations in male athletes. The Bland–Altman plots for these predictive equations compared with the measured RMR showed proportional bias. The Harris-Benedict equation presented the most accurate RMR prediction in all RMR prediction equations with the lowest RMSE value of 252 kcal/day , and Mifflin (FFM) (r = 0.662) predictions but was not significant for the Liu’s (r = 0.277), Owen (r = 0.182), and Johnstone (r = 0.370) predictions in female athletes.Mifflin , Mifflin (FFM), Owen, Liu’s, and Johnstone predictions did not significantly differ compared to the measured RMR in female athletes . Figure With the highest reliability seen in Liu’s equation (ICC = 0.575), Mifflin  and Liu’s equations had a moderate reliability in female athletes. In addition, Mifflin (FFM), Owen and Johnstone equations showed a low reliability. The Liu’s equation presented an accurate RMR prediction with the RMSE value of 236 kcal/day, predicting RMR of 67% of the subjects . This per = 0.641), and the Owen equations (r = 0.497) but was not significant for the Nelson (r = 0.137) equation in sedentary men (tary men .p < 0.001)  .The main objectives of the study were to estimate the RMR using the Harris-Benedict, Mifflin , Mifflin (FFM), Schofield, Cunningham, Owen, Liu’s, De Lorenzo, Bernstein, Nelson, Johnstone, and Roza equations, and to compare these results with the RMR measured in Turkish Olympic young adult national team athletes and their sedentary counterparts and determine the suitability of these equations for participants. The main findings rejected our hypothesis, revealing that the predictive RMR equations have a low to moderate absolute agreement with the measured RMR at the individual level due to a wide range of limits of agreement. Therefore, these equations did not accurately predict RMR in young adult Olympic national team athletes and their counterparts.This is the first study to evaluate the RMR of Olympic young adult national team athletes and their counterparts by comparing estimated RMR equations with those measured by Fitmate GS. According to the gender of the participants, the measured RMR was higher in athletes than in their sedentary counterparts. Additionally, the FFM results showed that the athletes had a higher FFM compared to the sedentary controls. It is well-known that FFM is one of the major determinants of RMR . SeveralA well-designed diet combined with an effective training program is the core component of athletic preparation and, if done correctly, can determine their success in Olympic sports . In a sip < 0.001) proportional bias in Harris-Benedict (r = 0.611), and also in Johnstone (r = 0.609), Mifflin (r = 0.711), Schofield (r = 0.703), De Lorenzo (r = 0.748), and Roza (r = 0.903) equations. These findings suggest that the predictive RMR equations underestimate the high RMR values, and overestimate the low RMR values. For female athletes, although Liu’s equation was found to be the most accurate equation predicting accurately in 67% of the subjects  and no proportional bias, ICC results showed moderate relative agreements between the measured RMR and Liu’s equation (ICC = 0.575). It appears that 236 kcal/day may be a small overprediction, but it affects the total energy expenditure by 401.2–566.4 kcal/day as physical activity level calculated by RMR∗physical activity level (PAL). The PAL coefficient ranges from 1.7 to 2.4 depending on moderately active to severely active lifestyle . Further, the Bland-Altman plots of male athletes indicated that there was a significant  and magnetic resonance imaging to estimate body composition. As stated by Additionally, sleep loss could be another factor to alter RMR results . Since wDue to the small number of participants and the inclusion of athletes from different sports disciplines, we could not perform a regression analysis. Therefore, we could not develop group-specific predictive RMR equations. However, our current findings highlight the urgent need for future studies on a new predictive RMR equation to accurately measure the RMR of Olympic young adult athletes.Our study’s findings demonstrate a low to moderate relative agreement between the measured RMR and the predictive RMR equations. We know that RMR is one of the major components of total energy needs. Although some other studies applied in athlete populations , accordiThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.All procedures of the study were conducted in accordance with the Declaration of Helsinki. The ethics committee approval was obtained from the University Ethics Committee (05.02.2020/37). The patients/participants provided their written informed consent to participate in this study.AB, EB, AY, BA, TK, and AH contributed to the study design and conception. EB, BA, TK, and AH completed the data acquisition. AY completed the data analysis. AB, AD-L, EB, and BK completed the writing-original draft preparation. AB, EB, AY, AD-L, BA, TK, AH, LH, TR, and BK completed the review of the final manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


There are many studies on the relationship between serum levels of hyperbilirubinemia and hearing impairment. However, the mechanism of hyperbilirubinemia on auditory impairment is not clear.A total of 1000 children with hyperbilirubinemia who are mainly indirectly elevated bilirubin in the full-term neonatal ward of Xiamen Children's Hospital from March 2020 to September 2020 will be enrolled. Using second-generation high-throughput sequencing technology, 127 deaf-related genes were sequenced from the collected samples. At the same time, physical audiometry was performed on the selected persons and audiometry data were recorded.In this study, we will combine gene sequencing with clinical indications of hyperbilirubinemia to find the loci suitable for high-frequency pathogenic deafness related to hyperbilirubinemia, so as to provide early guidance for deafness gene screening in children with hyperbilirubinemia.Chinese Clinical trial registry: ChiCTR2000030075. Children with hyperbilirubinemia whose serum total bilirubin level reached the blood exchange indicator are one of the common risk factors for hearing loss in children with Neonatal intensive care unit (NICU). The mechanism of hyperbilirubinemia on auditory impairment is not clear. Some scholars believe that hearing loss is a prominent manifestation of bilirubin neurotoxicity. The brain stem auditory pathway is particularly sensitive to the toxic effect of bilirubin.,5 High bilirubin levels can even cause permanent damage to nerve development, mainly including deafness and infant cerebral palsy.,7Congenital hearing impairment is the most common congenital disease, with an incidence of about 0.1% to 0.3% in healthy newborns and as high as 2% to 4% in high-risk newborns.This study protocol will explore the occurrence characteristics of hearing impairment in neonatal children with hyperbilirubinemia, the relationship between serum bilirubin level, unbound bilirubin and the occurrence of hearing impairment, the mutation characteristics of deafness gene in children with hearing impairment, and the difference between deafness gene and normal population.22.1We aim to clarify the correlation analysis between neonatal hyperbilirubinemia and deafness gene. Flow chart of study protocol was shown in Figure 2.2http://www.chictr.org.cn/index.aspx) with an ID of ChiCTR2000030075.The protocol scheme matches PRISMA's reporting standards. This study protocol has been registered on Chinese Clinical trial registry  and automatic auditory brainstem response (AABR) were applicable for hearing screening.2.3.2The exclusion criteria are as follows:1.Family history of deafness;2.History of birth anoxia, neonatal asphyxia;3.Plane malformation, abnormal middle ear function;4.Neonatal intracranial infection, intracranial hemorrhage, neonatal hypoxic-ischemic brain injury;5.History of cytomegalovirus infection and fever during pregnancy, history of ototoxic drugs, and mother's congenital nervous system history of disease;6.Further exclude those with incomplete clinical data.2.3.3According to the 2004 American academy of pediatrics guidelines for neonatal jaundice intervention, neonatal hourly bilirubinemia is defined as hyperbilirubinemia when bilirubin levels exceed the 95th percentile2.4Neonatal patients (1000 cases) diagnosed with hyperbilirubinemia were selected, and 2 ml of blood was collected from the selected persons. Using second-generation high-throughput sequencing technology, 127 deaf-related genes were sequenced from the collected samples. At the same time, physical audiometry was performed on the selected persons and audiometry data were recorded. Genetic test data and physical audiometry data were correlated and analyzed to find the pathogenic loci of hyperbilirubinemia susceptibility to deafness. For those patients with definite symptoms of hyperbilirubinemia and deafness, and no pathogenic loci were detected in 127 genes, blood was collected from these patients, and total exon sequencing was performed to analyze about 22,000 genes and find new pathogenic loci.2.5Excel was used to establish the database, and SPSS 22.0 statistical software was used for statistical analysis in this study.1.Continuous variable data: for continuous variable data conforming to normal distribution or approximate normal distribution, mean ± standard deviation was adopted to describe the comparison of inter-group differences. When data of continuous variables do not follow normal distribution or variance is uneven, it is expressed as median (quartile spacing) M . Wilcoxon rank sum test in non-parametric test is performed, and multi-independent sample test is performed when there is statistical significance.2.P < .05 was considered statistically significant.Categorical variables data were described as adoption rate or composition ratio of. Chi-square test was used for comparison of differences between groups, and 3.P < .05 was considered as a statistically significant difference.Logistic regression analysis: physical audiometry results were taken as the dependent variable, and indicators with statistical significance of univariate analysis results were taken as independent variables for binary logistic regression analysis. 4.ROC curve analysis.2.6This study has been approved by the ethics committee of Xiamen children's hospital. All participants’ immediate family members will sign the informed consent after being informed about the goals and methods of the study. The present study will be conducted in accordance with the tenets of the 1975 Declaration of Helsinki, as revised in 2000. The result of the study will be disseminated by publication as journal articles.3 Most children have no long-term adverse effects, but hyperbilirubinemia is one of the most important risk factors for hearing loss. To identify and intervene the hearing loss caused by neonatal hyperbilirubinemia timely, effectively, safely, and economically in the early stage, and to reduce unnecessary treatment and waste of medical resources are the efforts of the medical community at home and abroad for many years.Neonatal hyperbilirubinemia is one of the common diseases in neonatal period, especially in the first week after birth, accounting for about 50% to 75%.–12 What are the mechanisms by which these risk factors contribute to hearing loss? Could it be that something is causing a mutation in the deafness gene that causes deafness? There are few related researches at home and abroad, which is the problem that this study aims to solve. This study protocol will explore the occurrence characteristics of hearing impairment in neonatal children with hyperbilirubinemia, the relationship between serum bilirubin level, unbound bilirubin and the occurrence of hearing impairment, the mutation characteristics of deafness gene in children with hearing impairment, and the difference between deafness gene and normal population.Central infection, neonatal sepsis, hyperbilirubinemia, perinatal asphyxia and hypoxic-ischemic encephalopathy, preterm delivery, low birth weight, and other risk factors for neonatal hearing loss have a much higher incidence of hearing loss than common children.Xiaohui Wu conceived the idea for this study; Qiuxue Cao, Yufeng Guo and Haiyan Deng provided statistical plan; Gang Li and Yun Zheng drafted the protocol. Xingqiang Gao reviewed the protocol and provided critical feedback. All authors approved the article in its final form.


Halomonas bluephagenesis shows the potential as a chassis for competitive bioproduction of various chemicals due to its ability to grow under an open, unsterile and continuous process. Here, we report the strategy for producing 3HP and its copolymer poly(3-hydroxybutyrate-co-3-hydroxypropionate) (P3HB3HP) by the development of H. bluephagenesis. The transcriptome analysis reveals its 3HP degradation and synthesis pathways involving endogenous synthetic enzymes from 1,3-propanediol. Combing the optimized expression of aldehyde dehydrogenase (AldDHb), an engineered H. bluephagenesis strain of whose 3HP degradation pathway is deleted and that overexpresses alcohol dehydrogenases (AdhP) on its genome under a balanced redox state, is constructed with an enhanced 1.3-propanediol-dependent 3HP biosynthetic pathway to produce 154 g L−1 of 3HP with a yield and productivity of 0.93 g g−1 1,3-propanediol and 2.4 g L−1 h−1, respectively. Moreover, the strain could also accumulate 60% poly(3-hydroxybutyrate-co-32–45% 3-hydroxypropionate) in the dry cell mass, demonstrating to be a suitable chassis for hyperproduction of 3HP and P3HB3HP.3-Hydroxypropionic acid (3HP), an important three carbon (C3) chemical, is designated as one of the top platform chemicals with an urgent need for improved industrial production.  Halomonas bluephagenesis by deleting newly identified degradation pathway and balancing redox state to achieve high level production of 3HP and its copolymer under open and unsterile conditions.3-Hydroxypropionic acid (3HP) is an important platform chemical. Here, the authors engineer  The chemical synthesis of 3HP is not suitable for industrial production owing to the high cost of the precursors and processes, and environmental incompatibility5. Since biosynthesis is sustainable and environmental friendly, it is considered a better choice for improved 3HP industrial production. Synthetic pathways were designed for producing 3HP from bio-renewable sources such as glucose, glycerol, or CO29. However, its biosynthesis has been reported to have poor productivity6. Glucose utilizing pathways based on malonyl-CoA or β-alanine has been constructed for 3HP production12, but the complexity of manipulating and regulating multiple genes has proven to be a challenge for enhanced 3HP production13. Instead, glycerol, a byproduct of biodiesel, is more favorable. The glycerol to 3HP pathway involves glycerol dehydratase (GDHt) and aldehyde dehydrogenase (ALDH), converting glycerol to 3-hydroxypropionaldehyde and then 3HP, respectively15.3-Hydroxypropionic acid (3HP) is a platform chemical for the synthesis of C3-based chemicals, including acrylic acid, malonic acid, acrylamide, acrylonitrile (ACN), and poly(3-hydroxypropionate) (P3HP) based polymersEscherichia coli or Corynebacterium glutamicum were reported to have the final 3HP concentration of approximately 70 g L−1 17. Klebsiella pneumoniae, which naturally produces vitamin B12 as a cofactor of GDHt, showed the highest production of 82 g L−1 3HP20. However, a larger-scale production using K. pneumoniae was impossible because it is been known as an opportunity pathogen13. Therefore, to enhance the production of 3HP, we need to develop a more suitable producer as the chassis. Another issue occurs during the production of 3HP: under an aerobic condition, GDHt is deactivated combined with a downregulated expression of vitamin B1221, while under anaerobic condition, cells were grown poorly, and NAD+, a necessary cofactor of ALDH, has difficulty to regenerate24. The trade-off between vitamin B12 synthesis and NAD+ regeneration controls 3HP production and the key is the balance of dissolved oxygen in the cultures23. Pure 1,3-propanediol (PDO), obtainable from microbial process , is a monomer of a high-performed textile material, poly (PTT)25. Using 1.3-propanediol as the substrate may be a possible solution28, as 1,3-propanediol can also be converted into 3HP using 1,3-propanediol oxidoreductase (PDOR) and ALDH27. The two NAD+-dependent enzymes PDOR and ALDH function well under aerobic conditions without vitamin B1221. As an example, Gluconobacter oxydans was reported to produce 45 g L−1 3HP using these two enzymes27. These results paved the way by using 1,3-propanediol as the carbon source to produce 3HP.Traditional microbial chassis such as Halomonas spp. have attracted attentions from industrial biotechnology researchers29. They are capable of growing at high concentrations of NaCl and alkaline pH, thus, avoiding possible contamination by other microorganisms even under open unsterile conditions30. As a result, Halomonas spp.-based fermentations have reduced cost and complexity32. Pilot-scale fermentations using seawater-based artificial media under open and continuous conditions were conducted without contamination over a long period of two months32. Moreover, several synthetic biology and chromosome engineering methods such as CRISPR/Cas9 have been successfully applied in Halomonas spp., turning them into a more suitable chassis for future metabolic engineering and genome editing34. Next generation of industrial biotechnology (NGIB) has been developed based on extremophile Halomonas spp., allowing the production to be conducted under open, unsterile, and continuous process conditions with reduced fresh water and energy consumption, aiming for increasing economic competitiveness of current industrial biotechnology36.Halophilic Halomonas bluephagenesis TD  is a halophile isolated from Aydingol Lake in Xinjiang Province/China, has been used as a chassis for the production of several bioplastic polyhydroxyalkanoates (PHAs), including poly-3-hydroxybutyrate (PHB), poly(3-hydroxybutyrate-co-4-hydroxybutyrate) (P3HB4HB) and poly (PHBV) using various engineering approaches40. H. bluephagenesis is capable of growing at NaCl concentrations of 20–150 g L−1 with an optimal concentration of 40–60 g L−1. The strain can be grown at a pH ranging 5.0–11.0, the optimum of which is 8.5–9.0. However, as a promising biosynthetic chassis, H. bluephagenesis should further expand its product diversity to create more industrial value41. The unique growth condition of high osmotic pressure for H. bluephagenesis is beneficial for producing organic acid at a high concentration35. The organic acids produced are in the form of sodium salt under alkaline culture conditions, preventing the formation of toxic undissociated organic acids during production43. It is therefore reasonable to expect that H. bluephagenesis can be a suitable organic acid producer such as 3-hydroxypropionic acid (3HP).H. bluephagenesis for production of 3HP and its copolymer poly(3-hydroxybutyrate-co-3-hydroxypropionate) (P3HB3HP) by identifying and deleting 3HP degradation pathway, screening and optimizing endogenous biosynthetic enzymes, engineering genomic expression of 3HP synthesis pathway and balancing redox states. The engineered H. bluephagenesis strain produces a higher titer of 3HP with 1,3-propanediol as a carbon source under open and unsterile fed-batch fermentation.Here, we show that a coherent bioprocess based on halophilic H. bluephagenesis is an ideal chassis for the extracellular production of small molecules with a high concentration in the cultures. The extracellular 3HP tolerance has been demonstrated by H. bluephagenesis, further proving its potential as a microbial industrial chassis for 3HP  produced 1.5 g L−1 and H. bluephagenesis (p30) produced 0.2 g L−1 3HP when grown in conical flasks. The former strain was cultured in LB medium supplemented with 30 g L−1 glucose and 10 g L−1 1,3-propanediol, and the latter was grown in the defined minimal medium supplemented with 30 g L−1 glucose and 10 g L−1 1,3-propanediol  accumulated 0.5 g L−1 PHB when 5 g L−1 3HP was added as the sole carbon source   and GME_RS01255 encoding CoA-acylating methylmalonate-semialdehyde dehydrogenase (RefSeq. Accession WP_009721522), were upregulated by 119-folds and 116-folds, respectively  could increase the production with 2.7 g L−1 3HP and 0.7 g L−1 PHB when grown in conical flasks  Fig. . OverallGs) Fig. . Possiblely Fig. . Choline144 Fig. . Deletiosks Fig. .aldDPp, and shifting the order of aldDPp and dhaTPp operon , an intermediate for 3HP biosynthesis, negatively affects cell growth and 3HP production. Expression modules were designed to prevent the possible intracellular accumulation of 3HPA using optimized promoter, ribosome binding site (RBS) sequence of aldDPp gene under the PPorin promoter48. Recombinant strains were cultured in conical flasks and grown for 6, 12, 24, 36, and 48 h, respectively. The results showed that the expression level of aldDPp in H. bluephagenesis TDΔdddA harboring p34 was higher compared to that of H. bluephagenesis TDΔdddA (p32) during the early growth phase, whereas an opposite trend was revealed at the late growth period  sequence of aldDPp, even though the yield of 3HP did not increase significantly  in Klebsiella pneumoniae DSM 2026 as reported49  and the AldDPp from P. putida KT2440 after BLAST analysis.A trace amount of 3HP was detected in the sis Fig. . To analH. bluephagenesis was performed using cells grown with or without 1,3-propanediol in culture media containing 30 g L−1 glucose to investigate alcohol dehydrogenases in H. bluephagenesis. The volcano plot illustrates the distribution of differentially expressed genes (DEGs) -dependent alcohol dehydrogenase (RefSeq. Accession: WP_009721539.1), zinc-binding alcohol dehydrogenase (GenBank Accession: EGP20677.1), zinc-binding dehydrogenase (RefSeq. Accession: WP_009721587.1), and alcohol dehydrogenase AdhP (RefSeq. Accession: WP_039868491.1), respectively  Fig. . Overallely Fig. .Fig. 2SealdDHb encoding aldehyde dehydrogenase, and H. bluephagenesis TDΔdddA was transformed using the resulting plasmids, respectively , H. bluephagenesis TDΔdddA (p59) and H. bluephagenesis TDΔdddA (p60) were similar  sequence of aldDPp  and aldehyde dehydrogenase PuuC (RefSeq. Accession: WP_004224052.1) of K. pneumoniae were selected . With the co-overexpression of adhP, the resultant strain H. bluephagenesis TDΔdddA (p90) was shown to have the highest 3HP production . The aldDHb still outperformed its counterparts, producing 3HP at a titer of 7.8 g L−1 (H. bluephagenesis TDΔdddA (p91), Fig. H. bluephagenesis TDΔdddA (p105) containing the K. pneumoniae PuuC combination with K. pneumoniae DhaTKp only generated 3HP at a titer of 2 g L−1  and four different aldehyde dehydrogenases  were analyzed using their corresponding purified enzymes, respectively  of AldDHb was low at 0.05 mM, indicating that the AldDHb has a very high affinity for the substrate 3HPA, and the catalytic efficiency (kcat/Km) of the AldDHb was 100 times higher than that of PuuC at the same condition . Therefore, H. bluephagenesis TD27 was taken as the platform for further enhanced 3HP production from 1,3-propanediol  were co-fed to the culture medium of H. bluephagenesis TD27 with 20 g L−1 glucose and 20 g L−1 1,3-propanediol, respectively  and PHB content (PHB/DCM) increased 108 and 455%, respectively. The addition of 6 g L−1 acetic acid resulted in the highest 3HP, dry cell weight (DCM), and PHB content, reaching approximately 9.3 g L−1 3HP, 8 g L−1 DCM, and 40% PHB/DCM   Fig. , along w59) Fig. .−1) were co-fed with 20 or 30 g L−1 1,3-propanediol, respectively, to cultures of H. bluephagenesis TD27  and aldehyde dehydrogenase (AldDHb) cloned from H. bluephagenesis produced only 0.7 g L−1 3HP. In contrast, the H. bluephagenesis TDΔdddA (p59) generated 11 g L−1 3HP compared to only 5 g L−1 3HP secreted by H. bluephagenesis TDΔdddA (p30) harboring the DhaTPp and AldDPp from P. putida KT2440  and 3HP, given the PHB is natively synthesized by H. bluephagenesis  from E. coli, acetyl-CoA synthase (AcoE) from Ralstonia entropha, and propionaldehyde dehydrogenase (PduP) from Salmonella typhimurium, were selected to generate 3HP-CoA from 3-hydroxypropionic acid and from 3-hydroxypropionaldehyde, respectively. PhaC polymerase from R. entropha was chosen for its strong 3HP polymerization ability when expressed in other bacteria including E. coli , is very brittle due to its high crystallinity that limits its application. In contrast, poly-3-hydroxypropionate (P3HP) shows good combined mechanical properties including an elongation at break of more than 600% and Young′s modulus of 300 MPa–3 GPasis Fig. . To prodoli Fig. 58.Fig. 5H. bluephagenesis TD27 was then transformed with the four constructs, and P3HB3HP was successfully produced in 7 g L−1 DCM containing approximately 60% copolymer  were added, respectively. The DCM and P3HB3HP content increased with the increase of glucose concentration  were identified, together with three peaks of P3HB at δ = 1.24–1.33, δ = 2.47–2.56 and δ = 5.23–5.35 ppm . The molar ratio of P3HP in this copolymer has been shown can achieve approximately 40% when calculated by integrating the areas of specific related signals.The composition of a P3HB3HP was studied using NMR Fig. . Two chaH. bluephagenesis and its recombinants have been demonstrated to be economic chassis for reducing cost for bulk chemicals37. A systematic metabolic engineering approach was taken to achieve efficient production by deleting the 3HP degradation pathway combined with overexpression of DhaTPp and AldDPp proteins from P. putida KT2440 for 3HP production in H. bluephagenesis grown in the presence of 1,3-propanediol  polyesters provide improved mechanical strengths for PHAnes Fig. . The res12 synthesis and NAD+ regeneration. Under the aerobic catalysis of 1,3-propanediol oxidoreductase and aldehyde dehydrogenase, 3HP was formed via the two-step bio-catalysis  can reduce 7.32 mM NADH formation. The 3HP yield was improved by the regeneration of NAD+ (via consuming NADH). The excess NADH resulted from 3HP formation via the 1,3-propanediol oxidation feedback inhibits the glycolysis and 3HP production. On the other hand, PHB production from glucose diverts acetyl-CoA away from the TCA cycle. The decrease in anabolism resulting from a weakened TCA leads to the accumulation of NADPH. The increased NADPH inhibits glucose 6-phosphate dehydrogenase activity, resulting in the accumulation of glucose 6-phosphate, thus causing a lower glucose uptake rate. Therefore, the total amount of glucose used reduced 3 g L−1 (16.67 mM) compared with no PHB production  can consume 33.33 mM NADH and produce 33.33 mM 3HB-CoA (~2.9 g L−1 PHB). Compared with PHB production from glucose, the yield of 3HP should be increased by 1.5 g L−1. In fact, 3HP yield increased by 2.9 g L−1. This may be attributed to the utilization of ATP that can also promote the consumption of NADH by the respiratory chain. When acetate is added, the consumption of NADH improves 3HP synthesis and glycolysis by accelerating PHB synthesis from a bigger acetyl-CoA pool, the true cell mass increased from 3.5 to 4.8 g L−1. As a result, 3HP be produced at 9.3 g L−1 with the yield of 1.8 mol mol−1 glucose from flask scale studies  (P3HB5HV) and Poly(3-hydroxybutyrate-co-6-hydroxyhexanoate) (P3HB6HHX) could also be produced by supplementing corresponding diols to cultures of H. bluephagenesis based on the polymer biosynthetic methods mentioned above as some broad specificity CoA transferase and PhaC have been reported67. Moreover, it can be assumed that pure P3HP can be produced by deleting PHB synthesis pathway. Since PhaB of H. bluephagenesis is dependent on NADH as a cofactor for PHB synthesis, the PHB accumulation can further consume NADH, thus facilitating further synthesis of 3HP. The 3HP monomer synthesis decreased once the deletion of PHB synthesis pathway is completed. In order to produce pure P3HP, the introduction of other metabolic pathways to regenerate NAD+ can promote 3HP synthesis after the loss of PHB synthesis pathways. Further investigations could generate more unusual PHA from this chassis.Since H. bluephagenesis, demonstrating this strain to be an excellent chassis. The 3HP has also been found to be able to become a monomer of the PHA copolymer P3HP3HB. H. bluephagenesis, as a platform for the next generation industrial biotechnology, holds a bright prospect for low-cost productions of both extracellular and intracellular products.In conclusion, this study established an effective 3HP production strategy using 1,3-propanediol as a substrate by halophilic chassis Halomonas bluephagenesis strain TD01, the wild-type isolated from Aydingol Lake of Xinjiang province in China, has been deposited in the China General Microbiological Culture Collection Center (CGMCC) under the collection number 4353. Escherichia coli S17-1 was used as the host for plasmid construction and the conjugation donor. The plasmids were constructed using Gibson Assembly. Plasmid extraction kits were purchased from Tiangen Biotech Co., Ltd. . Plasmids for expression were electroporated into E. coli S17-1. DNA fragments were amplified using Q5® High-Fidelity DNA polymerase .Strains and plasmids used in this study are listed in Supplementary Tables E. coli was cultured at 37 °C in an LB medium containing (g L−1) 10 NaCl , 10 tryptone , and 5 yeast extract . H. bluephagenesis was cultured at 37 °C in a 60-LB medium, namely, the LB medium supplemented with 60 g L−1 NaCl. A 20-LB medium indicates the LB medium containing 20 g L−1 NaCl. Moreover, 15 g L−1 agar  was added before autoclaving for preparing solid media in Petric plates. Ampicillin (100 μg mL−1), chloramphenicol (25 μg mL−1), kanamycin (50 μg mL−1), or spectinomycin (100 μg mL−1), all purchased from BioDee Biotechnology Co., Ltd. , were added to the above media whenever necessary. Absolute ethanol  and chloroform  were used for PHA extraction and analysis. 3HPA, NADH, and NAD+ were purchased from Shanghai Kaiwei Biochemical Co., Ltd. and Abmole . Unless indicated otherwise, 1,3-propanediol and all other chemicals were obtained from Shanghai Sangon Biotech Co., Ltd.For molecular biological studies, H. bluephagenesis were conducted using CRISPR/Cas9 based on homologous recombination68. After PCR verification and DNA sequencing, the engineered H. bluephagenesis TD with dddA gene deleted was designated as H. bluephagenesis TDΔdddA. H. bluephagenesis TD01 deleted with its native PHA synthesis genes phaA, phaB and phaC is named as H. bluephagenesis TDΔphaCAB. H. bluephagenesis TDΔphaCAB deleted with dddA gene is further named as H. bluephagenesis TDΔphaCABΔdddA. H. bluephagenesis TDΔphaCABΔdddA and H. bluephagenesis TDΔdddA inserted with dhaTPp and aldDPp gene from Pseudomonas putida KT2440 under the control of PRe promoter in G4 site on its genome are renamed as H. bluephagenesis TD17 and H. bluephagenesis TD22, respectively. H. bluephagenesis TDΔphaCABΔdddA and H. bluephagenesis TDΔdddA inserted with adhP and aldDHb gene from H. bluephagenesis under the control of PRe promoter on the G4 site are renamed as H. bluephagenesis TD25 and H. bluephagenesis TD27, respectively.Gene insertions and deletions on the chromosome of H. bluephagenesis37. E. coli S17-1, with plasmids electro-transformed in, were used as donors. The donors and H. bluephagenesis recipient cells were cultured at 37 °C for 4 h till the optical density (OD600) reached 0.3–0.5. After collecting 1 mL of cells of E. coli S17-1 and H. bluephagenesis by centrifugation respectively, the pellet cells were washed twice using a 20-LB medium. Then the cells were re-suspended and mixed at a ratio 1:1, followed by incubation on 20-LB plates at 37 °C for 12 h. The mixed bacteria were spread on plates containing corresponding antibiotics and incubated at 37 °C for 24–48 h for single colonies, which were verified by PCR for future use. H. bluephagenesis wild-type and recombinant strains were stored in autoclaved 25% (v/v) glycerol at −80 °C if needed.Conjugation was performed as an efficient way to transform plasmids into 4, 1.0% Na2HPO4·12H2O, 0.15% KH2PO4, 1.0% trace element solution I, 0.1% trace element solution II and 0.1% yeast extract, was used. Phosphate buffer was changed to 1.4% K2HPO4·3H2O and 0.52% KH2PO4 in a modified minimal medium. The pH of the solution was adjusted to 8.5–9.0 using 5 M NaOH. Trace element solution I comprises 0.5% Fe (III)-NH4-citrate and 0.2% CaCl2 dissolved in 1 M HCl; Trace element solution II contains 0.01% ZnSO4·7H2O, 0.003% MnCl2·4H2O, 0.03% H3BO3, 0.02% mg L−1 CoCl2·6H2O, 0.003% NaMoO4·2H2O, 0.002% NiCl2·6H2O, and 0.001% CuSO4·5H2O dissolved in 1 M HCl. Glucose was added at a final concentration of 3% at the beginning of the experiment. While 1,3-propanediol  was added at 0.5% or other concentrations as indicated.For shake-flask PHA production, a defined minimal medium containing 6% NaCl, 0.05% urea, 0.02% MgSO600 reached 1.0. Afterward, it was inoculated into 500-mL conical flasks containing 50 mL of the defined minimal medium at a volume ratio of 5% and cultivated for 48 h at 200 rpm. Antibiotics were added if needed. The temperature for all cultivations was 37 °C. All data were analyzed and graphed by GraphPad Prism 8.For all shake-flask studies, the microbial glycerol stocks were resuscitated by streaking on fresh 60-LB plates. Single colonies from streaked or newly-conjugated plates were picked and inoculated in the 60-LB medium for 24 h at 200 rpm to acquire the first seed culture, which was further grown on a fresh 60-LB liquid medium at a volume ratio of 1%. The second seed culture was inoculated for 10 h at 200 rpm until its ODg and 4 °C for 10 min followed by quick-frozen in liquid nitrogen. For RNA sequencing and analysis, they were transported immediately to the testing company in dry ice (solid carbon dioxide). RNA extraction, sequencing, and analysis were performed by Hangzhou Lianchuan Bio Technologies Co. Ltd. Briefly, RNA was extracted by TRIzol® Reagent . The RNA library was constructed using Illumina TruseqTM RNA sample prep Kit . Sequencing was performed using the Illumina Hiseq platform after the processing of libraries via Truseq SBS Kit v3-HS . The expression differences were analyzed using edgeR.The bacteria were sampled from shake-flask experiments after 12 h cultivation. Then, the samples were centrifuged at 4000 × 69. Two independent biological samples with three technical repeats for each sample were performed for each qRT-PCR analysis.For quantitative RT-PCR, total RNA was isolated from bacterial culture using TRIzol® Reagent according to the manufacturer’s instructions. RNA concentration was determined via spectrophotometry at 260 nm. Removal of genomic DNA and synthesis of cDNA were carried out using PrimeScript RT reagent Kit with gDNA Eraser (Takara). qRT-PCR was conducted using 2× RealStar Green Power Mixture with ROX II  with the 7500 Fast Real-Time PCR System (Applied Biosystems). Constitutively transcribed 16s rRNA was used as a reference control to normalize the total RNA quantity of different samples. The relative difference of mRNA level was calculated using the ΔΔCt methodadhP, aldDHb, dhaTPp with a His-tag on C-terminal and aldDPp with a His-tag on N-terminal, respectively. The aldH gene, with a His-tag on N-terminal, was amplified by PCR using E. coli MG1655 genomic DNA as a template and cloned into the p59 plasmid by replacing the aldDHb gene. This plasmid was labeled as p99. The dhaTKp gene with a His-tag on C-terminal or puuC gene with a His-tag on N-terminal was amplified by PCR using K. pneumoniae DSMZ 2026 genomic DNA as a template and cloned into the p59 plasmid by replacing the adhP or aldDHb gene. These plasmids were labeled as p95 and p100. All the recombinant plasmids were conjugated into H. bluephagenesis TD∆dddA and cultivated in a defined minimal medium containing 20 g L−1 glucose, 20 g L−1 1,3-propanediol, and 3 g L−1 acetic acid. The recombinant strains were cultured in conical flasks and grown for 24 h. Subsequently, they were harvested by centrifugation under 4000 × g and 4 °C for 20 min. Proteins were purified by a His-tag Protein Purification Kit  and stored at −80 °C for further usage. The enzyme activity was assayed by a VarioskanFlash plate reader .Plasmids p90, p91, p92, and p98 were constructed encoding genes Pp, and DhaTKp were assayed as follows, respectively: First, the reaction mixture containing 50 mM Tris-HCl buffer (pH 7.0) and 10 nM enzyme was incubated for 2 min at 37 °C. The reaction was initiated by the addition of 0.1 mM of NADH and 5 mM 3HPA. The enzyme activity was determined by measuring the NADH oxidation using the molar extinction coefficient (∆ε340) of 6.22 × 103 M−1 cm−1. The oxidative activity (reverse reaction) of AdhP, DhaTPp, and DhaTKp were measured as follows: The reaction mixture containing 100 mM potassium carbonate buffer (pH 9.0), 30 mM ammonium sulfate, and 100 nM enzyme was incubated for 2 min at 37 °C. The reaction was initiated by the addition of 2 mM of NAD+ and 150 mM 1,3-propanediol51. The activity of the aldehyde dehydrogenase was determined by measuring the level of NAD+ reduction to NADH at 340 nm. The reaction mixture containing 100 mM potassium phosphate buffer (pH 8.0), 1 mM DTT, and 100 nM enzyme was incubated at 37 °C for 5 min. The reaction was initiated by adding 2.0 mM 3HPA and 2.0 mM NAD+. The amount of NADH formed was studied using a molar extinction coefficient (∆ε340) of 6.22 × 103 M−1 cm−1. One unit of aldehyde dehydrogenase activity was defined as the amount of enzyme needed to reduce 1 μmol of NAD+ to NADH in 1 min49.The reductive activity (forward reaction) of AdhP, DhaTg at 4 °C for 10 min. 200 μL cells were collected from each sample, followed by washing using ice-cold PBS at pH 7.4. The obtained pellet was disrupted by a chemical lysis method using 400 μL BugBuster® Master Mix . All operations were strictly conducted based on instructions of NAD/NADH Assay Kit S0175 .All cells were harvested after fermentation via centrifugation under 2500 × BioFlo fermenter  with a 3 L working volume without sterilization. The growth temperature was 37 °C, maintained via a cooling circulation pump . The pH was maintained at 8.5 using an automatic pump added 5 M NaOH into the culture system when needed. The fermentation agitation speed was set at 200 rpm at the beginning, coupled with dissolved oxygen (DO) concentration, and gradually increased to 800 rpm to maintain the DO level above 30% compared with the one in a cell-free fermenter (set as 100%). The agitation speed was maintained at 800 rpm to the end of fermentation. The inlet airflow rate was maintained at 3 L min−1 during the entire fermentation process.Fed-batch studies were performed in a 7 L 600 of the seeds was adjusted to 3. For fermentor studies, low-cost substrates were used and the defined minimal medium was modified by utilizing 16 g L−1 corn steep  instead of the expensive yeast extract, the NaCl concentration was reduced to 40 from 60 g L−1.The seed culture has the same medium as that in shake flasks. A 300 mL seed culture  was prepared for inoculation into the fermenter. The OD−1 glucose and 30 g L−1 urea was used to increase cell mass accumulation and PHB formation during the first 16 h. 200 mL feeding Solution II containing 800 g L−1 glucose and 15 g L−1 urea was added after feeding Solution I exhausted. The flow rates of the feeding solutions depended on the residual glucose concentrations that should be controlled between 5 and 10 g L−1 measured using the GA-3 blood glucose meter and GA-3 blood glucose test strip . 60 g L−1 1,3-propanediol and 6 g L−1 acetic acid were added three times separately at 24, 32, and 38 h after the beginning of the fermentations. OD600 of the cultured bacteria was measured by a V-5600 visible light spectrophotometer .For 3HP production, a two-stage and intermittent feeding strategy were adopted using different feeding solutions. 400 mL feeding solution I containing 800 g L−1 glucose, 30 g L−1 urea, and 75 g L−1 1,3-propanediol was used to increase cell mass and P3HB3HP formation during the first 20 h. 200 mL feeding solution II containing 800 g L−1 glucose, 15 g L−1 urea, and 75 g L−1 1,3-propanediol was added after feeding solution I exhausted.For P3HB3HP production, a two-stage feeding strategy was used with different feeding solutions. 400 mL feeding solution I containing 800 g Lg for 10 min, washed twice with distilled water, and placed in −80 °C for 4 h. After cells lyophilizing for 12 h, their intracellular PHA was extracted using a Soxtec 2050 Soxhlet extractor . PHA polymers were purified by re-dissolving the extracted materials in chloroform, then precipitated using absolute ethanol, centrifuged at 12,000 × g for 10 min to harvest the purified PHA materials, followed by lyophilization again to remove residual ethanol.After shake-flask growth, cells were harvested by centrifugation  at 8000 × The dry cell mass was determined by weighing 30 mL lyophilized centrifuged bacterial cells. For the esterification reaction, 40–60 mg lyophilized cells were placed into a 2 mL esterification reagent containing 3% sulfuric acid and 0.1% benzoic acid, both dissolved in absolute methanol. After adding 2 mL chloroform, the whole reaction was performed at 100 °C for 4 h. PHA contents were determined by GC-2014 gas chromatography . P3HB  was used as standards, respectively. The peak of benzoic acid was regarded as the internal standard.2SO4 with a flow rate of 0.5 mL min−1. The column temperature was maintained at 55 °C70. The culture media were centrifuged at 12,000 × g and 4 °C for 2 min to obtain the supernatant, which was then filtered through a 0.22 μm polyethersulfone membrane syringe filter  and used as the samples with a loading volume of 30 μL. Standards of the above chemicals were prepared by five different concentrations to draw the standard curves, from which the concentrations of the sample were calculated.Concentrations of 3HP, glucose, acetic acid, and 1,3-propanediol were determined via high-performance liquid chromatography (HPLC). An LC-20 instrument  with an Aminex HPX-87H column  and a RID-10A refractive index detector (Shimadzu) was employed. The mobile phase was a 5 mM degassed H1H NMR with a JNM-ECA600 nuclear magnetic resonance instrument . Chemical shifts were given in ppm while 0.03% v/v tetramethylsilane (TMS) served as an internal standard to calibrate against the signal of samples.PHA samples (2–5 mg) were dissolved in deuterated chloroform for Further information on research design is available in the Supplementary InformationReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3


Liveweight and body condition score (BCS)) are related, indicating that it may be possible to predict one from the other. The magnitude of this relationship can be altered by animal and environmental factors. The aim of the present study was to determine the nature of the association between liveweight and BCS over time, and the effect of the interaction between the stage of the annual cycle and the age of the ewe, using individual animal records. The association between liveweight and BCS was found to be linear and was affected by the interaction between ewe age, stage of the annual cycle and pregnancy-rank of the ewe. The results highlight the substantial contribution of BCS to the differences in liveweight of the ewe. The findings suggest that when predicting BCS from the liveweight, consideration of these factors is required, and different prediction equations are needed.p <0.05). The average liveweight per unit change in body condition score increased with the age of the ewe and was greatest at weaning and lowest pre-lambing. Among pregnancy-ranks, the average liveweight per unit change was also greater during pregnancy diagnosis than pre-lambing and was greatest among single and lowest in non-pregnant ewes. The results support the hypothesis that the relationship between liveweight and BCS is affected by the interaction between stage of the annual cycle, pregnancy-rank and ewe age. This study determined the nature of the relationship between liveweight and body condition score (BCS) and assessed the influence of the stage of the annual cycle and pregnancy-rank on the relationship between liveweight and BCS in Romney ewes. Data were collected from the same ewes at different ages , stages of the annual cycle  and pregnancy-rank . Linear regression was determined as being sufficient to accurately describe the relationship between liveweight and BCS. Across all data, a one-unit change in BCS was associated with 6.2 ± 0.05 kg liveweight, however, this differed by stage of the cycle, pregnancy-rank and ewe age ( Ovis aries) by Jefferies [Body condition score (BCS) is a subjective measure which provides an estimate of an animal’s soft tissue reserves (predominantly fat) and is used widely by farmers and researchers to determine the physiological state of an animal ,2. Body efferies  and was efferies ,4. Body efferies ,6. Body efferies . Body coDespite the advantages of using BCS over liveweight to better manage flocks, it is uncommon for producers/farmers to regularly and objectively do so. A survey of sheep producers in Australia indicated that although 96% of respondents said they monitored the body condition of their sheep, only 7% conducted hands-on BCS assessment to estimate the energy requirements of their sheep  reported2), likely due to the simplest linear relationship appearing to be as strong as more complex models. However, using the coefficient of determination of a regression alone, as the criterion for goodness-of-fit, is not suitable to validate models because it does not provide information about the degree to which the predicted values diverge from true values [Body condition score is correlated with liveweight and has been reported to have either a positive linear relationship ,6 or a ce values ,16. Moren = 28, [n = 156, [In cattle, the average liveweight change  associated with each BCS one-unit change is well associated with BCS ,17. Simin = 28, ; n = 156n = 156, ) or singn = 156, ,11,12. In = 3469) and 2011-born (n = 4572). Farm (B) was located in the Wairarapa region of New Zealand, with Romney ewes that were born in 2011 (n = 3760). The number of ewes monitored on each farm fluctuated by stage of the annual cycle. This was influenced by each farm owner’s decision to keep or dispose of (cull) ewes or failure to collect data during any period. Farm (A) did not collect liveweight and BCS data during the pre-lambing period on two occasions.The current study utilized datasets from a database collected between 2011 and 2015. Data was collected as part of normal routine farm management from two commercial New Zealand sheep farms in which all ewes were bred as ewe-lambs at approximately eight months of age at breeding. Farm A was located in the Waikato region of New Zealand and consisted of Romney ewes. Two cohorts of ewes from Farm (A) were included in this study: 2010-born  and were body condition scored (BCS) at the same time. Body condition score was assessed by palpating the soft tissue over the lumbar region on a 1–5 scale  assessed to the nearest 0.5 unit ,4. BCS wn = 15) or that had liveweight for the calibration weights (test weights) recorded (n = 9), so were removed from the analysis. The independent variables included: age group, determined by number of months at the time of breeding time within a 12-month period ; stage of annual cycle ; and pregnancy-rank . In both farms, triplets (n = 67) were not considered due to fewer numbers and high variability in both liveweight and BCS compared to their contemporaries. A variable labelled FarmYear was generated to account for the different birth years as well as the farm of origin. When liveweight was considered as the dependent variable, BCS was considered its covariate and vice versa.Liveweight and BCS data were first exported to Microsoft excel version 2010 for pre-processing, including cleaning, merging and validation. Data were then exported to the R statistical program version 3.3.4  for furtAll analyses were conducted in R program version 3.4.4 . Pearson2) and error metric (mean absolute error and percent error) were defined [To determine the true nature of the relationship between liveweight and BCS, linear (LM), second-order polynomial/quadratic (QUAD), Box-Cox and square root (SQRT) transformation regressions were compared. The best lambda (with greatest likelihood) for Box-Cox transformation was 0.67.  defined ,20,21. F defined . Outlierp <0.001). To quantify the relationship between liveweight and BCS and the factors associated with this relationship, the final analysis was based on the minimal LMM model  incorporating all significant effects using the nlme package [The best linear model for final data fitting was selected by comparing two parameter estimation methods . The linear mixed-effects model (LMM) was selected for fitting the model, as it had the smallest likelihood value and Akaike’s information criterion (AIC) values  Data partitioning, (ii) resampling, (iii) model training and (iv) validation. Data partitioning involved dividing the initial dataset (with stratification preserving the class proportions) into training and testing datasets in a ratio of 3:1, with replacement. Resampling involved using bootstrapping and aggregation procedures ,26 to sen) decreased with ewe age. The majority of ewes were diagnosed as pregnant  with more ewes carrying twin foetus  compared to single   and 2.5  formed the bulk of the records while 1.0  and 5.0  were the least frequent  . Body cofrequent . The ovefrequent . 2 = 0.69) and Cook’s distances suggesting relatively similar robustness of these models to outlier effects . As ewes increased in age, their liveweight increased (p <0.05) across all stages of the annual cycle, plateauing after 55–66 months (p <0.01) at pre-lambing in their last year of observation (≥67 months). Within age (except at 8–18 months), ewes were consistently heaviest (p <0.05) at pre-lambing. Among pregnancy-ranks, liveweight of ewes varied differently over time (p <0.05) with no clear pattern observed (p <0.05) in non-pregnant ewes in the first four age groups  compared to their contemporaries. Twin bearing ewes consistently had greater (p <0.05) liveweight than single or non-pregnant ewes across age up to the 43–54 months. Pre-lambing, liveweight was greater in twin than single bearing ewes (p <0.01) up to the 55–66 months. Age group, stage of the annual cycle and pregnancy-rank all affected ewe liveweight (6 months . Ewes weobserved . There wp <0.01) by age, stage of the annual cycle and pregnancy-rank (p >0.05). Body condition score decreased as the ewe increased in age (p <0.05) across all stages of the annual cycle plateauing after 55–66 months (p <0.05) pre-lambing. Within the annual cycle and over time, the BCS of ewes showed no clear pattern of decline. Among pregnancy-ranks, BCS at pregnancy diagnosis was greater (p <0.05) in the first two years  after which, it decreased remaining comparable (p >0.05) among pregnancy-ranks. Pre-lambing, BCS was greater in single than twin bearing ewes across age except for ≥67 months. Body condition score was influenced (6 months . However6 months . With th2 = 0.21) of the variability in liveweight was explained by differences in BCS. When adjusted for age, stage of the annual cycle and pregnancy-rank, the overall correlation decreased slightly to 0.44 (R2 = 0.18). The correlation between BCS and liveweight was affected by both age of the ewe, stage of the annual cycle and pregnancy-rank (p <0.05). Overall, the correlation between liveweight and BCS varied from 0.02 pre-lambing to 0.69 at pregnancy diagnosis  across both age of ewe and stage of the annual cycle. Within the age group, the correlation between liveweight and BCS was relatively similar except at ≥67 months. Within the stage of the cycle, the correlation between liveweight and BCS was strongest at weaning and weakest at pre-lambing. Within pregnancy-rank, the correlation between liveweight and BCS varied from 0.02 (p >0.05) pre-lambing to 0.69 (p <0.01) at pregnancy diagnosis. There was no clear pattern in the strength of association among age groups and pregnancy-ranks (p >0.05).The strength of the association between BCS and liveweight differed significantly (p <0.05).p <0.001) as the ewes aged. The magnitude of the incremental liveweight change of ewes in the same age group (p <0.001). The increase in the average incremental liveweight change varied from 2.3 kg for younger ewes pre-lambing (8–18 month) to 9.5 kg for the older ewes at weaning (≥67 months).The incremental liveweight change increased  at 8–18 months but the maximum change varied by stage of the annual cycle, for example at ≥67 months for pre-breeding and at pregnancy diagnosis, 43–54 for pre-lambing and 55–66 for weaning. Weaning was associated with the greatest incremental liveweight change (5.6 to 9.5 kg) while pre-lambing was associated with the lowest (2.3 to 5.9 kg) (p <0.05). Within the stage of the annual cycle, the incremental liveweight change was lowest (p <0.01) at pregnancy diagnosis (4.3 to 13.6 kg) compared to pre-lambing and increased with the age of the ewe. Pre-lambing, the increase in incremental liveweight change had no clear pattern. Generally, incremental liveweight change for similar age groups appears to have varied randomly regardless of pregnancy-rank (p <0.05) in single and twin bearing ewes than non-pregnant ewes at all age groups except at 8–18 and ≥67 months. The incremental liveweight change was also comparable (p >0.05) for both single and twin bearing ewes except at 19–30 months. Pre-lambing, the incremental liveweight change was unexpectedly low (0.4 to 3.8 kg) and varied with no clear pattern among pregnancy-ranks as the ewe aged. Among pregnancy-ranks, the increase in incremental liveweight change was greater  and eventually the relationship between liveweight and BCS. The effect of pregnancy-rank on liveweight was not surprising given that ewe liveweight was potentially confounded by fetal weight from mid to late pregnancy. The effect of pregnancy-rank on BCS is in agreement with earlier findings in Romney sheep , merino The finding that the incremental liveweight change was lower in non-pregnant ewes at pregnant diagnosis was not surprising as their energy demand would be expected to be lower than for pregnant ewes. The energy demand is greater for pregnant ewes and increases with the number of fetuses . It is, In conclusion, the current study demonstrated that in a large population of ewes across a full range of BCS, liveweight and BCS were linearly related, and the relationship depended on the age of ewe, stage of the annual cycle and pregnancy-rank, therefore, supporting our hypothesis. The results indicate that large variability exists in BCS, and BCS contributes substantially to the differences in liveweight. The findings suggest that when predicting BCS from liveweight, consideration of these factors is required, and different prediction equations are needed. Adjustments for differences between BCS should consider age group, stage of the annual cycle and pregnancy-rank. The relationships found between liveweight and body condition score support the possibility of using liveweight as a proxy for body condition score.


Introduction. The location of the electrode inside the cochlea is important for speech performance. However, many variables, including array length, insertion depth, and individual anatomy, may affect the intracochlear position of the electrode. Insertion deeper than 20 mm and revision surgery are critical situations in which residual hearing and electrode integrity may be at risk. This case report challenges this hypothesis and raises the following question: is it possible to achieve a better speech understanding with an electrode afterload without compromising residual hearing? Case Report. A 73-year-old female patient showed up for evaluation of hearing loss. The patient was operated four times in an external hospital due to cholesteatoma formation in the right ear. Related to a poor aided speech understanding, a CI-surgery was performed. 5 months after the surgery, the subject returned with poor speech understanding. A revision surgery was performed, where the first white marker of the electrode was seen in the round window (20 mm). The electrode was inserted 4 mm deeper into the cochlea. After six and twelve months, the results of the Freiburger monosyllabic speech test improved till 25% and 45%, respectively. Discussion. Hearing preservation is possible with a revisional deeper insertion from 20 mm to 24 mm. In this case, a partial obliteration of an open cavity made the electrode surgically easily accessible. This allowed the deeper insertion during the revision surgery. In a regular surgical field with a posterior tympanotomy, the revision surgery is more challenging and brings the electrode into the risk of an iatrogenic destruction. Conclusion. This case of an electrode afterload after having inserted the electrode initially to mm, demonstrates that hearing can be preserved and speech perception can improve after performing this maneuver. Electrode technology and its interaction with the human cochlea continue being one of the most studied and researched topics in modern otology. Recently, shorter straight electrodes for combined electric and acoustic stimulation (hybrid) were introduced to the market . The benIn order to facilitate the hybrid stimulation, the location of the electrode in relation to the cochlea is important for speech performance. Current electrode designs attempt to replicate natural cochlear tonotopic organization by delivering low-frequency information to apical electrodes and high-frequency information to basal electrodes. However, many variables may affect the absolute placement of the electrode including array length, electrode spacing, surgical technique, insertion depth, and individual cochlear anatomy . The optAnother situation, in which hearing may be at risk, is revision surgery (electrode reimplantation). The most common indication for revision surgery is device failure . This prA 73-year-old female patient came to our clinic for evaluation of hearing loss. The patient had a previous history of chronic otitis media mesotympanalis with cholesteatoma formation in the right ear. Due to this reason, the subject was operated four times in an external hospital. In the first surgery, the cholesteatoma was removed and a mastoid open cavity was created. The following surgeries were aimed at a second look and hearing improvement. Unfortunately, an improvement of the hearing could not be obtained. The patient has been wearing a hearing aid (Phonak Naida V) in the right ear since then with decreasing satisfaction and did not want any revision surgery but a definitive solution for the hearing loss. In 2010, the patient received a cochlear implant (Nucleus CI-512) in the left ear due to a progressive hearing loss with functional deafness. The patient was very satisfied with the results in the left ear; therefore, the subject showed up at our department with the question whether one device could improve her symptoms in the right ear.The neurotologic physical examination was normal. An audiogram was performed, which demonstrated a combined hearing loss with a diagonal decay from 30 dB and an air-bone gap from approximately 65 dB in the right ear . The FreThe surgery was performed in a standard fashion: a retroauricular skin incision was made on the right side, the planum mastoideum was presented, and the epithelium to the tympanic cavity was lifted. In the tympanic cavity, a PORP with granulations was seen. The round window was located, and the implant bed was then created. The round window was carefully opened and the slim straight electrode was inserted until the first marker was seen in the round window. The electrode was fixed in the mastoid cavity using fibrin glue and bone-meal. Prednisolon (1gr) and Ceftriaxone (2gr) were systemically administered intraoperatively. The final measurements of the impedances and NRT-potentials yielded favorable results. The patient tolerated the procedure well and without complications. A computerized tomography of the temporal bone was made to verify the position of the electrode after surgery. The CT showed an insertion depth of about 290° . Two dayThe subject returned one month after surgery for her device activation. The four-week unaided audiogram revealed good preservation of residual hearing . The eleFor the revision surgery, an endaural approach was performed. A tympanomeatal flap was made, and the cartilage covering the open cavity was lifted. The electrode was visualized, and the scar-tissue around it was removed. The first marker of the slim straight electrode was once again seen in the round window. The electrode was inserted approximately 4 mm deeper into the cochlea until just before the second marker was seen in the round window. The electrode was fixed again, and the NRT-potentials showed normal results. The surgery and the hospital stay were without complications. A CT-scan of the temporal bone was performed, and an insertion depth was reported from approximately 380° .Three months after the revision surgery, the subject described a subjective hearing improvement. Once again, the audiogram showed good preservation of residual hearing . The FreIt was once thought that only shorter electrodes with short insertion depth had the ability to preserve residual hearing. On the other hand, the short insertion depth has been correlated with poor speech recognition. Over time, longer electrodes with greater insertion depth have been developed and continued to show favorable results in terms of hearing preservation and speech understanding , 13. TheHearing preservation is possible with a revisional deeper insertion from 20 mm to 24 mm . The CI-Hearing loss is progressive, and the patients implanted with a hybrid device then have to rely on shallower insertion depths for electric stimulation only. Following this concept, an electrode afterload seems attractive and logical when hearing loss progresses and greater insertion depths are required for better outcomes. Also, in the case of postoperative hearing loss, the electrode can be further inserted, so the patients can benefit from greater cochlear coverage using electric stimulation only .During revision surgery, mechanical trauma to the device must be avoided at all time. Normally the electrode array may have integrated soft tissue, and inadvertent drill contact with adherent soft tissue may damage the seal from the device. In a regular surgical field with a posterior tympanotomy, an afterload of the electrode might be more challenging and brings the electrode into the risk of an iatrogenic destruction. In this particular case, a partial obliteration of an open cavity made the electrode surgically easily accessible. This allowed an easy deeper insertion during the revision surgery, without compromising the integrity of the device.Electrode migration or partial insertion during the first surgery could be the causes of no improvement in speech perception . After tThis unique case report of an electrode afterload after having inserted the electrode completely demonstrates that the cochlea might be more robust than once thought, since hearing was preserved and speech perception improved after performing this maneuver.


Background—The clinical characterization of hearing deficits for hearing-aid fitting purposes is typically based on the pure-tone audiogram only. In a previous study, a group of hearing-impaired listeners completed a comprehensive test battery that was designed to tap into different dimensions of hearing abilities. A data-driven analysis of the data yielded four clinically relevant patient sub-populations or “auditory profiles”. The purpose of the current study was to propose and pilot-test profile-based hearing-aid settings in order to explore their potential for providing more targeted hearing-aid treatment. Methods—Four candidate hearing-aid settings were developed and evaluated by a subset of the participants tested previously. The evaluation consisted of multi-comparison preference ratings that were carried out in realistic sound scenarios. Results—Listeners belonging to the different auditory profiles showed different patterns of preference for the tested hearing-aid settings that were largely consistent with the expectations. Conclusions—The results of this pilot evaluation support further investigations into stratified, profile-based hearing-aid fitting with wearable hearing aids. Hearing loss is typically treated with hearing aids (HAs). The primary purpose of HAs is to provide gain to the input signal to compensate for reduced audibility. In addition, modern HAs incorporate advanced signal processing algorithms for signal-to-noise ratio (SNR) improvement . ConsequIn current clinical practice, the assessment of the hearing deficits of a patient mainly relies on pure-tone audiometry. Based on a fitting rule that typically uses the audiogram of the patient as the only information, the HA amplification is then adjusted. For example, the “National Acoustic Laboratories—Nonlinear 2” fitting rule  listeners while using a data-driven approach . The lisIn the medical field, personalized treatments aim at providing tailored solutions to clinically relevant subgroups of patients . Here, aFor profile-based HA fitting, a fitting rule combining the prescription of insertion gain and SNR improvement required for each profile is proposed here. This fitting rule was named Better hEAring Rehabilitation-Level-Frequency-Profile fitting rule (BEAR-LFP), and it can be defined, as follows:mization  and priodings of , where fdings of  and SNR For the pilot experiment, a hearing-aid simulator (HASIM) was used, which consisted of three stages: a beamforming stage, a noise reduction stage, and an amplitude compression stage. The beamformer and noise reduction settings were selected based on the achievable SNR improvement . Four caThe pilot evaluation of the BEAR-LFP rationale was based on four candidate HAS . These four HAS were evaluated together with a standard clinical HA fitting (HAS-O). In HAS-I and HAS-II, fast-acting compression was applied in order to provide non-linear gain according to an audibility-based prescription formula. In HAS-III and HAS-IV, slow-acting compression was applied that was based on the principle of loudness normalization. Furthermore, in HAS-II and HAS-III, advanced HA features were activated in order to provide around 2.5 dB of SNR improvement under noisy conditions.The gain prescription that was used in the current study was derived from an empirical comparison of two existing gain prescriptions: that according to NAL-NL2 . The indpression . BecauseThe recruited participants were stereotypical individuals of their respective auditory profile. Seven listeners participated in the current study . All of the listeners had previously completed a comprehensive auditory test battery , based oNine sound scenarios were tested. In each scenario, a fragment of a realistic conversation taken from a publicly available database  was usedThe multi-comparison procedure was realized while using the SenselabOnline software . On a giThe current study proposed a fitting rationale for more individualized HA fitting and explored patterns of HAS preference in listeners that belonged to four distinct auditory profiles. The results suggest that the Profile-A and Profile-C listeners based their judgements on similar criteria, especially under noisy conditions. In contrast, the Profile-B and Profile-D listeners showed significantly different patterns. While the Profile-B listener disliked the HAS with loudness-based gain prescription and SNR improvement, the Profile-D listeners favored loudness-based gain prescription and showed no preference for SNR improvement. The results that were obtained for the quiet condition support the use of loudness-based gain prescriptions for profiles with a high degree of LP-related deficits. In contrast, SNR improvement was only preferred by one of the two profiles with a high degree of SI-related deficits when tested at positive SNRs (Profile C). The Profile-B listener showed a preference for fast-acting compression, as consistent with previous research ,18.In rehabilitative audiology, the patients are stratified depending on their type and degree of hearing loss. For example, bone-anchored devices are only only prescribed for conductive hearing losses and cochlear implants for severe to profound sensorineural hearing loss. Assuming that listeners belonging to the four auditory profiles are candidates for bilateral hearing aids, further considerations should be made in terms of the gain prescription, the advanced features, and the acoustic coupling of real HAs.The gain prescription could be prescribed by different formulas in different sub-populations based on their hearing deficits. Here, the proposed fitting formula applied different correction factors to an audiogram-based nonlinear gain prescription (Expression ). This sThe results of the pilot evaluation suggested that listeners in Profiles C and D may benefit from a prescription aiming for loudness normalization in opposition to NAL-NL2, which is based on audibility maximization. However, the approach of the NAL-NL2 fitting formula may be revised to allow for a more personalized HA fitting. Regarding the findings from  and the Advanced HA features are useful for providing listening comfort and increasing the satisfaction of the listener in complex situations. Often, the HA features are modified, depending on the sound scene, providing an optimized set of parameters for specific sound environments . It can Advanced HA features are beneficial for noise reduction and listening comfort. However, these algorithms also provide audible distortions. For example, aggressive single-channel noise reduction leads to “musical noise” , whereasIn real HAs, the acoustic coupling can compromise the targeted insertion gain and noise reduction. Ear-moulds with large vents or instant eartips affect the effective amplification at low frequencies, the effective noise reduction , and incThe present study proposed an auditory profile-based fitting rationale for more stratified HA treatment. The results of a pilot evaluation showed small, but significant, differences in terms of preferred HA setting among listeners that belonged to four distinct profiles. Overall, these initial findings provide a useful basis for further investigations into profile-based HA fitting, which will include field trials with wearable devices and objective assessments, such as speech intelligibility tests.