A CIDR-based timed AI protocol can be effectively used for dairy cows with follicular cysts

The present study evaluated whether a controlled internal drug release (CIDR)-based timed AI (TAI) protocol could be used as an efficient tool for the treatment of ovarian follicular cysts in lactating dairy cows. In the first experiment, lactating dairy cows diagnosed with follicular cysts were randomly assigned to two treatments: (1) a single injection of GnRH at diagnosis (Day 0) and AI at estrus (AIE) within 21 days (GnRH group, n=70), or (2) insertion of a CIDR device containing progesterone and an injection of GnRH on Day 0, PGF(2alpha) injection at the time of CIDR removal on Day 7, GnRH injection on Day 9, and TAI 16h after the GnRH injection (CIDR-based TAI group, n=65). Conception rate after the CIDR-based TAI protocol (52.3%) was greater (P<0.05) than that after AIE following a single GnRH injection (26.9%). In the second experiment, lactating dairy cows diagnosed with follicular cysts (Cyst group, n=16) and cows having normal estrous cycles (CYC group, n=15) received the same treatment: a CIDR device containing progesterone and an injection of GnRH on Day 0, PGF(2alpha) injection at the time of CIDR removal on Day 7, and GnRH injection on Day 9. The proportion of cows with follicular wave emergence and the interval from treatment to follicular wave emergence did not differ (P>0.05) between groups. The mean diameters of dominant follicles on Days 4 and 7 as well as preovulatory follicles on Day 9, and the synchrony of ovulation following the second injection of GnRH did not differ (P>0.05) between groups. These data suggest that the CIDR-based TAI protocol results in an acceptable conception rate in dairy cows with follicular cysts.     

Targeted metabolomics: new insights into pathobiology of retained placenta in dairy cows and potential risk biomarkers

A targeted quantitative metabolomics approach was used to study temporal changes of serum metabolites in cows that normally released their fetal membranes and those that retained the placenta. We identified and measured serum concentrations of 128 metabolites including amino acids, acylcarnitines, biogenic amines, glycerophospholipids, sphingolipids and hexose at −8 and −4 weeks before parturition, during the week of retained placenta (RP) diagnosis, and at +4 and +8 weeks after parturition. In addition, we aimed at identifying metabolite signatures of pre-RP in the serum that might be used as predictive biomarkers for risk of developing RP in dairy cows. Results revealed major alterations in the metabolite fingerprints of pre-RP cows starting as early as −8 weeks before parturition and continuing as far as +8 weeks after calving. Biomarker candidates found in this study are mainly biomarkers of inflammation which might not be specific to RP. Therefore, the relevance of serum Lys, Orn, acetylornithine, lysophophatidylcholine LysoPC a C28:0, Asp, Leu and Ile as potential serum biomarkers for prediction of risk of RP in dairy cows will have to be tested in the future. In addition, lower concentrations of LysoPCs, Trp, and higher kynurenine in the serum during prepartum and the week of occurrence of RP suggest involvement of inflammation in the pathobiology of RP.     

DNA fingerprinting of lactococci and streptococci used in dairy fermentations

Summary A DNA fingerprinting procedure was developed for strains of Lactococcus lactis subsps. lactis and cremoris, biovar. diacetylactis, and Streptococcus salivarius subsp. thermophilus, used in dairy fermentations. Total cellular DNA was extracted and digested with restriction endonucleases, HindIII or HaeIII, followed by separation of the fragments using agarose gel electrophoresis. L. lactis C2 was used as a representative strain for examining the effect of growth phase and cell concentration, cell washing conditions prior to lysis, type and concentration of the enzyme used to digest the cell wall, composition of the lysis buffer, and gel electrophoresis conditions. Following optimization of the fingerprinting procedure, electrophoretic migration of fragments from 23 strains produced reproducible gel patterns. L. lactis subsp. lactis strains ML3 and C2 appeared to be identical when restrricted with either Hind III or HaeIII. Similarly, S. salivarius subsp. thermophilus strains 19987 and 19258, and L. lactis subsp. cremoris strains 134 and C3, appeared to have identical DNA fingerprints following digestion with HindIII. To determine the usefulness of this technique for monitoring population changes during fermentation, various ratios of two closely related strains were inoculated into milk and allowed to grow for 16 h at 32° C. The initial inoculum ratios were determined by standard plate counts, and the final ratio was deterimined by DNA fingerprinting. DNA fingerprinting will be useful in the identification, characterization, and comparison of food fermentation microorganisms.Published as paper No. 17,803 of the contribution series of the Minnesota Agricultural Experiment Station Offprint requests to: S. K. Harlander     

Evaluation of intrauterine antibiotic treatment of clinical metritis and retained fetal membranes in dairy cows

Retained fetal membranes (RFM) and clinical metritis (CM) are frequently diagnosed disease conditions in dairy cows and considered of major economic impact due to negative effect on reproduction and milk production. The objective of this study was to evaluate the efficacy of i.u. tetracycline for the treatment of RFM and CM in dairy cows. Affected cows were randomly assigned to two groups; treatment group animals received i.u. 5 g chlortetracycline twice weekly for 2 wks, and no treatment group.A total of 1416 cows and 804 heifers in 5 herds calved during the study period. CM was diagnosed in 18.6% (inter farm range; 15.2–23.5%) and 30% (19.4–42.3%) of cows and heifers, respectively. RFM was diagnosed in 13.1% (9.4–18.1%) and 9.2% (3.6–13.8%) of cows and heifers, respectively. Conception rates after first insemination were 38.3%, 42.5% and 18% in normal, treated and non-treated CM cows, respectively. Numbers of days open were 140.5, 136.2 and 165.5 in normal, treated and non-treated CM cows, respectively. Based on 305-d corrected milk yield, cows and heifers affected by RFM and CM produced 300–500 kg less milk compared with their normal herd mates. Cows treated for CM produced 654 kg more milk per 305-d corrected lactation compared to non-treated control cows. Treatment of RFM had no effect on reproductive performance or milk production. In conclusion, i.u. chlortetracycline treatment was proven to prevent the detrimental effect of CM on reproductive performance in heifers and cows and on milk production in cows only.     

Evaluation of intrauterine antibiotic treatment of clinical metritis and retained fetal membranes in dairy cows

Retained fetal membranes (RFM) and clinical metritis (CM) are frequently diagnosed disease conditions in dairy cows and considered of major economic impact due to negative effect on reproduction and milk production. The objective of this study was to evaluate the efficacy of i.u. tetracycline for the treatment of RFM and CM in dairy cows. Affected cows were randomly assigned to two groups; treatment group animals received i.u. 5g chlortetracycline twice weekly for 2 wks, and no treatment group. A total of 1416 cows and 804 heifers in 5 herds calved during the study period. CM was diagnosed in 18.6% (inter farm range; 15.2-23.5%) and 30% (19.4-42.3%) of cows and heifers, respectively. RFM was diagnosed in 13.1% (9.4-18.1%) and 9.2% (3.6-13.8%) of cows and heifers, respectively. Conception rates after first insemination were 38.3%, 42.5% and 18% in normal, treated and non-treated CM cows, respectively. Numbers of days open were 140.5, 136.2 and 165.5 in normal, treated and non-treated CM cows, respectively. Based on 305-d corrected milk yield, cows and heifers affected by RFM and CM produced 300-500kg less milk compared with their normal herd mates. Cows treated for CM produced 654kg more milk per 305-d corrected lactation compared to non-treated control cows. Treatment of RFM had no effect on reproductive performance or milk production. In conclusion, i.u. chlortetracycline treatment was proven to prevent the detrimental effect of CM on reproductive performance in heifers and cows and on milk production in cows only.     

M13 DNA fingerprinting can be used in studies on phenotypic reversions of forest tree mutants

 Solitary revertants which have been observed on single mutant tree individuals have up to now been believed to be grow-through cells belonging to the rootstock on which they are commonly grafted. In this study three different phenotypically visible mutants revealing revertant shoots on the same tree were chosen for genetic analysis. The mutant Quercus robur L. ‘argenteomarginata’ was grafted on a normal rootstock, an individual of Carpinus betulus L. var. quercifolia Desf. as well as an individual of Picea glauca (Moench) Voss. ‘conica’ are supposed to have grown from seeds. By means of a highly specific M13 PCR fingerprinting technique the mutant and revertant tissues were analysed in comparison to different individuals of each of the species. With the grafted mutant, cambium tissue of the rootstock was also investigated. Whereas conspecific individuals could be clearly distinguished from each other, mutant and revertant tissues revealed the same banding patterns for each of the three trees. In case of the grafted mutant, the fingerprint obtained from cambium tissue of the rootstock was clearly different from the pattern of mutant and revertant tissue. Results demonstrate the potential of the tool for genetic differentiation between individuals of three tree species hence in the case of the grafted mutant, the hypothesis that the observed reversion is caused by a grow-through of the rootstock is rejected. Furthermore, identical fingerprints of mutant and revertant tissue support identical genetic background of the tissues excluding the gene(s) responsible of the mutation. Possible causes of mutations and reversions regarding the three mutant trees are discussed. Received: 15 September 1997 / Accepted: 24 November 1997     

Parameterising a public good: how experiments on predation can be used to predict cheat frequencies

Chemical defence is superficially easy to understand as a means for individuals to protect themselves from enemies. The evolution of chemical defence is however potentially complex because such defences may cause the generation of a public good, protecting members of the population as a whole as well as individuals that deploy toxins defensively. If a public good of protection exists, it may be exploited and degraded by “cheats” that do not invest in defence. This can in turn lead to complex frequency (and density) dependent effects in toxin evolution. To investigate this we used ecologically relevant predators (Great tits, Parus major) and examined how individual and public benefits vary depending on the frequency of non-defended “cheating” prey and their spatial distribution. We found that the public benefit, of reduced attack probability, increased with increasing frequency of defended individuals. In contrast the individual benefit of chemical defence, measured as increased chance of rejection during an attack before injury, did not vary with the frequency of defended forms. Hence the selective dynamics of these two levels of benefits responded differently to the frequency of defended forms. Surprisingly, given the strong associations of chemical defences and grouping in animals, large aggregations did not help individuals in the group regardless of their defence status. The explanation for the result, may be that in our experiment birds did not have information about other potential aggregations (i.e. set up was sequential) and thus their giving up density was lower compared to the situations where set ups were simultaneous. We use behavioural data of our predators to construct a simple model of toxin evolution which can make quantitative predictions about the frequencies to which defence cheats evolve. We use this model to discuss how toxin evolution can be investigated in the wild and in laboratory settings.     

Diagnosis of acute puerperal metritis by electronic nose device analysis of vaginal discharge in dairy cows

The objective of this study was to estimate the diagnostic accuracy of an electronic nose device using vaginal discharge samples to diagnose acute puerperal metritis (APM) in dairy cows. Uterine fluid was sampled manually with a gloved hand and under sterile conditions for electronic nose device analysis (day in milk (DIM) 2, 5, and 10) and bacteriologic examination (DIM 5), respectively, and on additional days, if APM was diagnosed during the daily clinical examinations. A dataset containing samples from 70 cows was used to create a model and to validate the APM status predicted by this model, respectively. Half of the dataset (n = 35; 14 healthy and 21 metritic cows) was provided with information regarding the APM diagnosis and contained all three measurements (DIM 2, 5, and 10) for each cow and was used as a training set whereas the second half was blinded (n = 35; 14 healthy and 21 metritic cows) and contained only the samples collected on DIM 5 of each cow and was used to validate the created prediction model. A receiver operating characteristic curve was calculated using the prediction results of the validation test. The best observed sensitivity was 100% with specificity of 91.6% when using a threshold value of 0.3. The calculated P-value for the receiver operating characteristic curve was less than 0.01. Overall, Escherichia coli was isolated in eight of 28 (28.6%) and 22 of 42 (52.4%) samples collected from healthy and metritic cows, respectively. Trueperella pyogenes and Fusobacterium necrophorum were isolated in 14 and six of 28 (50.0% and 21.4%) and 17 and 16 of 42 (40.5% and 38.1%) samples collected from healthy and metritic cows, respectively. The prevalence of Escherichia coli and Trueperella pyogenes was similar in the samples obtained from metritic cows used for the training set and the validation test. The results are promising especially because of the objective nature of the measurements obtained by the electronic nose device.     

Anthropometric variables accurately predict dual energy x-ray absorptiometric-derived body composition and can be used to screen for diabetes

The current world-wide epidemic of obesity has stimulated interest in developing simple screening methods to identify individuals with undiagnosed diabetes mellitus type 2 (DM2) or metabolic syndrome (MS). Prior work utilizing body composition obtained by sophisticated technology has shown that the ratio of abdominal fat to total fat is a good predictor for DM2 or MS. The goals of this study were to determine how well simple anthropometric variables predict the fat mass distribution as determined by dual energy x-ray absorptometry (DXA), and whether these are useful to screen for DM2 or MS within a population. To accomplish this, the body composition of 341 females spanning a wide range of body mass indices and with a 23% prevalence of DM2 and MS was determined using DXA. Stepwise linear regression models incorporating age, weight, height, waistline, and hipline predicted DXA body composition (i.e., fat mass, trunk fat, fat free mass, and total mass) with good accuracy. Using body composition as independent variables, nominal logistic regression was then performed to estimate the probability of DM2. The results show good discrimination with the receiver operating characteristic (ROC) having an area under the curve (AUC) of 0.78. The anthropometrically-derived body composition equations derived from the full DXA study group were then applied to a group of 1153 female patients selected from a general endocrinology practice. Similar to the smaller study group, the ROC from logistical regression using body composition had an AUC of 0.81 for the detection of DM2. These results are superior to screening based on questionnaires and compare favorably with published data derived from invasive testing, e.g., hemoglobin A1c. This anthropometric approach offers promise for the development of simple, inexpensive, non-invasive screening to identify individuals with metabolic dysfunction within large populations.     

Use of physiological parameters to predict milk yield and feed intake in heat-stressed dairy cows

Holstein cows were tested to determine which measures of thermal strain are the better predictors of production over 24–96 h at constant air temperatures of 19 and 29 °C. Both rectal temperature and respiration rate increased within 24 h, followed by milk yield and feed intake reductions after 48 h of heat stress. There were significant correlations between milk yield, feed intake, and rectal temperature. Several physiological strain indices were created to determine if combinations of measures are better than single measures in predicting production under these acute conditions. Mean daily rectal temperature was superior to maximum and minimum daily values of rectal temperature, as well as other indicators of thermal status in predicting dairy cow production. Likewise, mean daily rectal temperature was equal if not better than the physiological strain indices used in this study in predicting production. These data suggest that rectal temperature is superior to both single and combined indicators of thermal status in predicting dairy cow production, and should be considered for future development of physiological strain indices over a longer time period in both laboratory and field environments.     

Using animal-mounted sensor technology and machine learning to predict time-to-calving in beef and dairy cows

Worldwide, there is a trend towards increased herd sizes, and the animal-to-stockman ratio is increasing within the beef and dairy sectors; thus, the time available to monitoring individual animals is reducing. The behaviour of cows is known to change in the hours prior to parturition, for example, less time ruminating and eating and increased activity level and tail-raise events. These behaviours can be monitored non-invasively using animal-mounted sensors. Thus, behavioural traits are ideal variables for the prediction of calving. This study explored the potential of two sensor technologies for their capabilities in predicting when calf expulsion should be expected. Two trials were conducted at separate locations: (i) beef cows (n = 144) and (ii) dairy cows (n = 110). Two sensors were deployed on each cow: (1) Afimilk Silent Herdsman (SHM) collars monitoring time spent ruminating (RUM), eating (EAT) and the relative activity level (ACT) of the cow, and (2) tail-mounted Axivity accelerometers to detect tail-raise events (TAIL). The exact time the calf was expelled from the cow was determined by viewing closed-circuit television camera footage. Machine learning random forest algorithms were developed to predict when calf expulsion should be expected using single-sensor variables and by integrating multiple-sensor data-streams. The performance of the models was tested using the Matthew’s correlation coefficient (MCC), the area under the curve, and the sensitivity and specificity of predictions. The TAIL model was slightly better at predicting calving within a 5-h window for beef cows (MCC = 0.31) than for dairy cows (MCC = 0.29). The TAIL + RUM + EAT models were equally as good at predicting calving within a 5-h window for beef and dairy cows (MCC = 0.32 for both models). Combining data-streams from SHM and tail sensors did not substantially improve model performance over tail sensors alone; therefore, hour-by-hour algorithms for the prediction of time of calf expulsion were developed using tail sensor data. Optimal classification occurred at 2 h prior to calving for both beef (MCC = 0.29) and dairy cows (MCC = 0.25). This study showed that tail sensors alone are adequate for the prediction of parturition and that the optimal time for prediction is 2 h before expulsion of the calf.     

Strategic testing approaches for targeted disease monitoring can be used to inform pandemic decision-making

More than 1.6 million Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) tests were administered daily in the United States at the peak of the epidemic, with a significant focus on individual treatment. Here, we show that objective-driven, strategic sampling designs and analyses can maximize information gain at the population level, which is necessary to increase situational awareness and predict, prepare for, and respond to a pandemic, while also continuing to inform individual treatment. By focusing on specific objectives such as individual treatment or disease prediction and control (e.g., via the collection of population-level statistics to inform lockdown measures or vaccine rollout) and drawing from the literature on capture–recapture methods to deal with nonrandom sampling and testing errors, we illustrate how public health objectives can be achieved even with limited test availability when testing programs are designed a priori to meet those objectives.

COVID-19 testing programs are very important to help control the pandemic. In this Essay, the authors show that objective-driven, strategic sampling designs and analytics can be used to maximize the information gained by COVID-19 testing programs and improve population-level decisions, while maintaining the value of these programs for patient-level management.     

In vivo buccal nerve activity that distinguishes ingestion from rejection can be used to predict behavioral transitions in Aplysia

1. We are studying the neural basis of consummatory feeding behavior in Aplysia using intact, freely moving animals.
2. Video records show that the timing of radula closure during the radula protraction-retraction cycle constitutes a major difference between ingestion (biting or swallowing) and rejection. During ingestion, the radula is closed as it retracts. During rejection, the radula is closed as it protracts.
3. We observed two patterns of activity in nerves which are likely to mediate these radula movements. Patterns I and II are associated with ingestion and rejection, respectively, and are distinguished by the timing of radula nerve activity with respect to the onset of buccal nerve 2 activity.
4. The association of ingestion with pattern I is maintained when the animal feeds on a polyethylene tube, the same food substrate used to elicit rejection responses. Under these conditions, pattern I is associated with either swallowing or no net tube movement.
5. Most transitions from swallowing to rejection were preceded by one or more occurrences of pattern I in which there was no net tube movement, suggesting that these transitions can be predicted.
6. Our data suggest that these two patterns can be used to distinguish ingestion from rejection.

     

Defining 5S rRNA structure space: point mutation data can be used to predict the phenotype of multichange variants

A portion of the 5S ribosomal RNA (rRNA) structure space in the vicinity of the Vibrio proteolyticus 5S rRNA sequence is explored in detail with the intention of establishing principles that will allow a priori prediction of which sequences would be valid members of a particular RNA structure space. Four hundred and one sequence variants differing from the V. proteolyticus 5S rRNA wild-type sequence in 1-7 positions were characterized using an in vivo assay system. Most significantly, it was found that in general, the phenotypic effects of single changes were independent of the phenotypic effect of a second change. As a result, it was possible to use the new data in conjunction with results from prior studies of the same RNA to develop "truth tables" to predict which multiple change variants would be functional and which would be nonfunctional. The actual phenotype of 93.8% of the multichange variants studied was consistent with the predictions made using truth tables thereby providing for perhaps the first time an upper limit estimate of how frequent unexpected interactions are. It was also observed that single changes at positions involved in secondary structure were no more likely to be invalid than changes in other regions. In particular, internal changes in long standard stems were in fact almost always tolerated. Changes at positions that were hypervariable in the context of an alignment of related sequences were, as expected, usually found to be valid. However, the potential validity of changes that were idiosyncratic to a single lineage of related sequences when placed in the V. proteolyticus 5S rRNA context was unpredictable.     

Insulin resistance: the link between metabolic disorders and cystic ovarian disease in high yielding dairy cows?

A preliminary investigation was performed to examine whether insulin resistance is a factor in the pathogenesis of cystic ovarian disease (COD) in high-yielding dairy cows. In total 30 cows, of which 15 were diagnosed as suffering from COD based on the anamnesis and clinical examination, and the other 15 served as matched controls, were subjected to an intravenous glucose tolerance test (IVGTT). The aim of the study was to investigate whether insulin activity was altered in COD cows. Differences in glucose clearance between the COD cows and their controls were analyzed comparing the fractional turnover rate (k), the glucose half-time (T1/2), and the area under the curve (AUC) 60 and 120 min after infusion. Differences in insulin response were analyzed comparing the insulin increment, the insulin peak concentration, and the AUC 60 and 120 min after glucose infusion. Although insulin resistance, attended by a secondary hyperinsulinemia, is stated to directly contribute to the ovarian abnormalities that characterize the polycystic ovary syndrome (PCOS) in human medicine, this was not observed in COD cows. On the contrary, COD cows appeared to have a low insulin response following an intravenous glucose load as compared with their matched controls. This was illustrated by significantly lower insulin increments (P = 0.04) and lower insulin peak concentrations (P = 0.04). As COD cows had a significantly lower insulin response to a standard glucose load, it was concluded that insulin could be a factor in the pathogenesis of COD in dairy cows.     

Potential use of milk mid-infrared spectra to predict individual methane emission of dairy cows

This study investigates the feasibility to predict individual methane (CH₄) emissions from dairy cows using milk mid-infrared (MIR) spectra. To have a large variability of milk composition, two experiments were conducted on 11 lactating Holstein cows (two primiparous and nine multiparous). The first experiment aimed to induce a large variation in CH₄ emission by feeding two different diets: the first one was mainly composed of fresh grass and sugar beet pulp and the second one of maize silage and hay. The second experiment consisted of grass and corn silage with cracked corn, soybean meal and dried pulp. For each milking period, the milk yields were recorded twice daily and a milk sample of 50 ml was collected from each cow and analyzed by MIR spectrometry. Individual CH₄ emissions were measured daily using the sulfur hexafluoride method during a 7-day period. CH₄ daily emissions ranged from 10.2 to 47.1 g CH₄/kg of milk. The spectral data were transformed to represent an average daily milk spectrum (AMS), which was related to the recorded daily CH₄ data. By assuming a delay before the production of fermentation products in the rumen and their use to produce milk components, five different calculations were used: AMS at days 0, 0.5, 1, 1.5 and 2 compared with the CH₄ measurement. The equations were built using Partial Least Squares regression. From the calculated R²_cv, it appears that the accuracy of CH₄ prediction by MIR changed in function of the milking days. In our experimental conditions, the AMS at day 1.5 compared with the measure of CH₄ emissions gave the best results. The R² and s.e. of the cross-validation were equal to 0.79 and 5.14 g of CH₄/kg of milk. The multiple correlation analysis performed in this study showed the existence of a close relationship between milk fatty acid (FA) profile and CH₄ emission at day 1.5. The lower R² (R² = 0.76) obtained between FA profile and CH₄ emission compared with the one corresponding to the obtained calibration (R²_c = 0.87) shows the interest to apply directly the developed CH₄ equation instead of the use of correlations between FA and CH₄. In conclusion, our preliminary results suggest the feasibility of direct CH₄ prediction from milk MIR spectra. Additional research has the potential to improve the calibrations even further. This alternative method could be useful to predict the individual CH₄ emissions at farm level or at the regional scale and it also could be used to identify low-CH₄-emitting cows.