CBASS Immunity Uses CARF-Related Effectors to Sense 3'-5'- and 2'-5'-Linked Cyclic Oligonucleotide Signals and Protect Bacteria from Phage Infection.

cGAS/DncV-like nucleotidyltransferase (CD-NTase) enzymes are immune sensors that synthesize nucleotide second messengers and initiate antiviral responses in bacterial and animal cells. Here, we discover Enterobacter cloacae CD-NTase-associated protein 4 (Cap4) as a founding member of a diverse family of >2,000 bacterial receptors that respond to CD-NTase signals. Structures of Cap4 reveal a promiscuous DNA endonuclease domain activated through ligand-induced oligomerization. Oligonucleotide recognition occurs through an appended SAVED domain that is an unexpected fusion of two CRISPR-associated Rossman fold (CARF) subunits co-opted from type III CRISPR immunity. Like a lock and key, SAVED effectors exquisitely discriminate 2'-5'- and 3'-5'-linked bacterial cyclic oligonucleotide signals and enable specific recognition of at least 180 potential nucleotide second messenger species. Our results reveal SAVED CARF family proteins as major nucleotide second messenger receptors in CBASS and CRISPR immune defense and extend the importance of linkage specificity beyond mammalian cGAS-STING signaling.

The DairyPrint Model: A Decision-Support Model to Help Dairy Farmers and Other Stakeholders Towards Improved Sustainability.

Dairy farmers face increasing pressure to reduce greenhouse gas (GHG) emissions [i.e., carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O)], but measuring on-farm GHG emissions directly is costly or impractical. Therefore, the dairy industry has relied upon mathematical models to estimate those emissions. However, current models tend to be not user-friendly, difficult to access or sometimes very research-focused, limiting their practical use. To address this, we introduce the DairyPrint model, a user-friendly tool designed to estimate GHG emissions from dairy farming. The model integrates herd dynamics, manure management, crop, and feed costs considerations, simplifying the estimation process while providing comprehensive insights. The herd module simulates monthly herd dynamics based on inputs as total cows, calving interval, and culling rate, outputting average annual demographics and estimating various animal related variables (i.e., dry matter intake, milk yield, manure excretion, and enteric CH4 emissions). These outputs feed into other modules, such as the manure module, which calculates emissions based on manure, weather data, and facility type. The manure module processes manure according to farm practices, and the crop module accounts for GHG emissions from manure, fertilizers, and limestone application, also estimating nutrient balances. The DairyPrint model was developed using the Shiny framework and the Golem package for robust production-grade shiny applications in the R programming language. We evaluated the model across 32 simulation scenarios by combining various factors and considering a standard free-stall system with 1000 dairy cows averaging 40 kg/day of milk production. These factors included 2 levels of NDF-ADF in the diet (28-22.8% and 24-19.5%), the presence or absence of 3-NOP dietary addition (yes or no) at an average dose of 70 mg/kg DM per cow daily, the type of bedding used (sawdust or sand), the frequency of manure pond emptying [once (only Fall) or twice a year (Fall and Spring)], and the utilization or non-utilization of a biodigester plus solid-liquid separator (Biod + SL). In our results across the 32 scenarios simulated, the average GHG emission was 0.811 kgCO2eq/kg of milk corrected for fat and protein contents (4% and 3.3%, respectively), ranging from 0.644 to 1.082. Notably, the scenario yielding the lowest GHG emission (i.e., 0.644 kgCO2eq/kg) involved a combination of factors, including a lower level of NDF-ADF in the diet in addition to incorporation of 3-NOP, utilization of sand as bedding, application of Biod + SL, and strategic manure pond emptying in both Fall and Spring. Conversely, the scenario that resulted in the highest GHG emission (i.e., 1.082 kgCO2eq/kg) involved a combination of higher level of NDF-ADF in the diet and excluded the incorporation of 3-NOP, utilization of sawdust as bedding, no application of Biod + SL, and manure pond emptying only in Fall. All these scenarios can be easily simulated in the DairyPrint model and results obtained immediately for user evaluation. Therefore, the DairyPrint model can help farmers move toward improved sustainability, providing a user-friendly and intuitive graphical user interface allowing the user to ask what-if questions.

Economic impact of subclinical mastitis treatment in early lactation using intramammary nisin.

Treatment of subclinical mastitis (SCM) during lactation is rarely recommended due to concerns related to both antimicrobial usage and the costs associated with milk discard. Nisin is a naturally produced antimicrobial peptide with a gram-positive spectrum that, when given to dairy cows, does not require milk discard. We evaluated the economic impact of the treatment of SCM during early lactation using a nisin-based intramammary treatment under different scenarios that included various treatment costs, milk prices, and cure rates. We stochastically simulated the dynamics of SCM detected during the first week of lactation. The net economic impact was expressed in US dollars per case. The probabilities of an event and their related costs were estimated using a model that was based on pathogen-specific assumptions selected from peer-reviewed articles. Nisin cure rates were based on results of pivotal studies included in the US Food and Drug Administration (FDA) approval submission. Based on our model, the average cost of a case of intramammary infection (i.e., only true-positive cases) in early lactation was $170 (90% = $148-$187), whereas the cost of a clinical mastitis case was $521 (90% range = $435-$581). Both estimates varied with etiology, parity, and stage of lactation. When comparing the net cost of SCM cases (i.e., CMT-positive tests) detected during the first week of lactation, nisin treatment generated an average positive economic impact of $19 per CMT-positive case. The use of nisin to treat SCM was beneficial 93% of the time. Based on the sensitivity analysis, treatment would result in an economically beneficial outcome for 95% and 73% of multiparous and primiparous cows, respectively. At the herd level, use of intramammary nisin to treat SCM in cows in early lactation was economically beneficial in most tested scenarios. However, the economic impact was highly influenced by factors such as rate of bacteriological cure, cost of treatment, and parity of the affected animal. These factors should be considered when deciding to use nisin as a treatment for SCM.

Decision-support tool for global allocation of dairy sire semen based on regional demand, supply constraints, and genetic profiles.

An artificial insemination (AI) company seeks to allocate semen units globally by balancing perceived demand with uncertain product supply, in what is an arduous subjective process. This study aimed to objectivize this process by providing a user-friendly linear programming model to allocate bulls' semen units to regions for the next trimester sales period based on maximum revenue, and to describe the features and outcomes of this model when applied to a sample bull herd and global demand scenario reflective of a leading AI company. The objective function of maximizing revenue was calculated by summing the product of units allocated by bull and region with purchase prices assigned by bull and region. Constraints considered were regional demand for overall units, regional preferences for specific genetic traits, bulls' production capacity, and percentage of bulls' units allocated to a single region. A sensitivity analysis was performed to identify the effects of variables and constraints on total revenue. Production, sales, and bull demographic data from 2018 to 2021 from a leading AI company were used to establish base values and build a sample herd of 61 bulls and 5 global regions. The case study provided a maximum revenue of $8,287,197 in semen sales per trimester, with 634,700 units allocated. Of the 61 bulls in the case study, 9 were not allocated to any region. The most limiting constraint was regional demand, which resulted in a surplus of 274,564 units not allocated. A sensitivity analysis confirmed this finding, with the largest shadow prices assigned to regional demands, and indicated that a single unit increase in regional demand would add up to $14.84 in total revenue.

Impact of COVID-19 in pediatric oncology care in Latin America during the first year of the pandemic.

BACKGROUND: The ongoing coronavirus 2019 disease (COVID-19) pandemic strained medical systems worldwide. We report on the impact on pediatric oncology care in Latin American (LATAM) during its first year. METHOD: Four cross-sectional surveys were electronically distributed among pediatric onco-hematologists in April/June/October 2020, and April/2021 through the Latin American Society of Pediatric Oncology (SLAOP) email list and St Jude Global regional partners. RESULTS: Four hundred fifty-three pediatric onco-hematologists from 20 countries responded to the first survey, with subsequent surveys response rates above 85%. More than 95% of participants reported that treatment continued without interruption for new and active ongoing patients, though with disruptions in treatment availability. During the first three surveys, respondents reported suspensions of outpatient procedures (54.2%), a decrease in oncologic surgeries (43.6%), radiotherapy (28.4%), stem cell transplants (SCT) (69.3%), and surveillance consultations (81.2%). Logistic regression analysis showed that at the beginning of the first wave, participants from countries with healthcare expenditure below 7% were more likely to report a decrease in outpatient procedures (odds ratio [OR]: 1.84, 95% CI: 1.19-2.8), surgeries (OR: 3, 95% CI: 1.9-4.6) and radiotherapy (OR: 6, 95% CI: 3.5-10.4). Suspension of surveillance consultations was higher in countries with COVID-19 case fatality rates above 2% (OR: 3, 95% CI: 1.4-6.2) and SCT suspensions in countries with COVID-19 incidence rate above 100 cases per 100,000 (OR: 3.48, 95% CI: 1.6-7.45). Paradoxically, at the beginning of the second wave with COVID-19 cases rising exponentially, most participants reported improvements in cancer services availability. CONCLUSION: Our data show the medium-term collateral effects of the pandemic on pediatric oncology care in LATAM, which might help delineate oncology care delivery amid current and future challenges posed by the pandemic.

Graduate Student Literature Review: Considerations for nutritional grouping in dairy farms.

Each cow in a group has different nutritional requirements even if the group is formed by cows of similar age, number of lactations, and lactation stage. Common dairy farm management setup does not support formulating a diet that accurately matches individual nutritional requirements for each cow; therefore, a proportion of cows in the group will be overfed and another proportion underfed. Overfeeding and underfeeding cows increases the risk of metabolic diseases, decreases milk production, and increases nutrient waste. Consequently, profitability of dairy farms and the environment are negatively affected. Nutritional grouping is a management strategy that aims to allocate lactating cows homogeneously according to their nutritional requirements. Groups of cows with more uniform nutritional requirements facilitates the formulation of more accurate diets for the group. Current availability of large data streams on dairy farms facilitates the design of algorithms to implement nutritional grouping. Our review summarizes important factors to consider when grouping cows, describes nutritional grouping approaches, and summarizes benefits of implementing nutritional grouping in dairy farms.

Improving nutritional accuracy and economics through a multiple ration-grouping strategy.

The objective of this study was to develop a model application to systematize nutritional grouping (NG) management in commercial dairy farms. The model has 4 sub-sections: (1) real-time data stream integration, (2) calculation of nutritional parameters, (3) grouping algorithm, and (4) output reports. A simulation study on a commercial Wisconsin dairy farm was used to evaluate our NG model. On this dairy farm, lactating cows (n = 2,374 ± 185) are regrouped weekly in 14 pens according to their parity and lactation stage, for which 9 diets are provided. Diets are seldom reformulated and nutritional requirements are not factored to allocate cows to pens. The same 14 pens were used to simulate the implementation of NG using our model, closely following the current farm criteria but also including predicted nutritional requirements (net energy for lactation and metabolizable protein; NEL and MP) and milk yield in an attempt to generate more homogeneous groups of cows for improved diet accuracy. The goal of the simulation study was to implement a continuous weekly system for cows' pen allocation and diet formulation. The predicted MP and NEL requirements from the NG were used to formulate the diets using commercial diet formulation software and the same feed ingredients, feed prices, and other criteria as the current farm diets. Diet MP and NEL densities were adjusted to the nutritional group requirements. Results from the simulation study indicated that the NG model facilitates the implementation of an NG strategy and improves diet accuracy. The theoretical diet cost and predicted nitrogen supply with NG decreased for low-nutritional-requirement groups and increased for high-nutritional-requirement groups compared with current farm groups. The overall average N supply in diets for NG management was 15.14 g/cow per day less than the current farm grouping management. The average diet cost was $3,250/cow per year for current farm management and $3,219/cow per year for NG, which resulted in a theoretical $31/cow per year diet cost savings.

Symposium review: Real-time continuous decision making using big data on dairy farms.

We are developing a real-time, data-integrated, data-driven, continuous decision-making engine, The Dairy Brain, by applying precision farming, big data analytics, and the Internet of Things. This is a transdisciplinary research and extension project that engages multidisciplinary scientists, dairy farmers, and industry professionals. Dairy farms have embraced large and diverse technological innovations such as sensors and robotic systems, and procured vast amounts of constant data streams, but they have not been able to integrate all this information effectively to improve whole-farm decision making. Consequently, the effects of all this new smart dairy farming are not being fully realized. It is imperative to develop a system that can collect, integrate, manage, and analyze on- and off-farm data in real time for practical and relevant actions. We are using the state-of-the-art database management system from the University of Wisconsin-Madison Center for High Throughput Computing to develop our Agricultural Data Hub that connects and analyzes cow and herd data on a permanent basis. This involves cleaning and normalizing the data as well as allowing data retrieval on demand. We illustrate our Dairy Brain concept with 3 practical applications: (1) nutritional grouping that provides a more accurate diet to lactating cows by automatically allocating cows to pens according to their nutritional requirements aggregating and analyzing data streams from management, feed, Dairy Herd Improvement (DHI), and milking parlor records; (2) early risk detection of clinical mastitis (CM) that identifies first-lactation cows under risk of developing CM by analyzing integrated data from genetic, management, and DHI records; and (3) predicting CM onset that recognizes cows at higher risk of contracting CM, by continuously integrating and analyzing data from management and the milking parlor. We demonstrate with these applications that it is possible to develop integrated continuous decision-support tools that could potentially reduce diet costs by $99/cow per yr and that it is possible to provide a new dimension for monitoring health events by identifying cows at higher risk of CM and by detecting 90% of CM cases a few milkings before disease onset. We are securely advancing toward our overarching goal of developing our Dairy Brain. This is an ongoing innovative project that is anticipated to transform how dairy farms operate.

Economic evaluation of sexed semen use in Iranian dairy farms according to field data.

In this study, at first, the reproductive consequences of sexed semen use were quantified and then the cost-benefit of sexed semen use on Iranian commercial dairy farms was evaluated. Retrospective data collected during 2006 to 2013 from four large dairy farms in the Isfahan province of Iran were used for this study. These data included of 13,003 heifers records, from which 11.2% used sexed semen from 33 different bulls. All data were analysed using a multivariable logistical regression model, GENMOD procedure from SAS software. The analyses included economic values (EVs) when sexed semen was used in 1, 2 and 3 consecutive services compared with conventional semen use for all insemination. Results showed that rates of female born from sexed semen (86.3%) were 1.8 times higher than those from conventional semen (48.5%). Conception rates were 43.8% for sexed and 59.2% for conventional semen (p < 0.0001). Abortion (4.4% vs. 5.4%) and stillbirth (8.4% vs. 7.2%) rates were not significantly different between sexed and conventional semen (p = 0.09). Dystocia rates were 15.5% for sexed and 19.6% for conventional semen (p = 0.002). Sexed semen use showed negative EVs through all investigated scenarios. The EVs from the implementation of 1, 2 and 3 sexed semen breedings were estimated to be $-6.69, $-14.01 and $-19.08, respectively. Total insemination cost and increased cost of age at first calving were the most important components associated with negative EV for sexed semen. Sensitivity analysis showed that proportion of conception rates of sexed semen to conventional semen and female calf value were the most important biological and economic factors influencing on the EV of sexed semen, respectively. Breakeven would be obtained with 77.4%-79.3% conception rates or female calf value of $719.5-$754.7 through investigated breeding scenarios when all other factors remained the same.

Robotic milking: Feeding strategies and economic returns.

Cows in herds equipped with conventional milking parlors follow a structured, consistent, and social milking and feeding routine. Furthermore, in most cases cows in conventional herds receive all their nutrients from a total mixed ration, whereas in herds equipped with robotic or automatic milking systems (AMS) a fraction of their nutrients is provided during milking, mainly as a means to attract cows to the milking system. In this regards, AMS present both a challenge and an opportunity for feeding cows. The main challenge resides in maintaining a minimum and relatively constant milking frequency in AMS. However, milking frequency is dependent on many factors, including the social structure of the herd, the farm layout design, the type of traffic imposed to cows, the type of flooring, the health status of the cow (especially lameness, but also mastitis, metritis, among others), the stage of lactation, the parity, and the type of ration fed at the feed bunk and the concentrate offered in the AMS. Uneven milk frequency has been associated with milk losses and increased risk of mastitis, but most importantly it is a lost opportunity for milking the cow and generating profit. On the other hand, the opportunity from AMS resides in the possibility of milking more frequently and feeding cows more precisely or more closely to their nutrient needs on an individual basis, potentially resulting in a more profitable production system. But, feeding cows in the parlor or AMS has many challenges. On one side, feeding starchy, highly palatable ingredients in large amounts may upset rumen fermentation or alter feeding behavior after milking, whereas feeding high-fiber concentrates may compromise total energy intake and limit milking performance. Nevertheless, AMS (and some milking parlors, especially rotary ones) offer the possibility of feeding the cows to their estimated individual nutrient needs by combining different feeds on real time with the aim of maximizing profits rather than milk yield. This approach requires that not only the amount of feed offered to each cow but also the composition of the feed vary according to the different nutrient needs of the cows. This review discusses the opportunities and pitfalls of milking and feeding cows in an AMS and summarizes different feeding strategies to maximize profits by managing the nutrition of the cows individually.

[Gastrointestinal bleeding due to arteriovenous malformation of the jejunum]. / Hemorragia gastrointestinal baja por malformación arteriovenosa yeyunal.

A 23-year old male patient with no history of importance was admitted to Emergency because of dark red stools, tendency to lethargy and hypotension, with significant anemia proceeding to perform on blood transfusion, upper endoscopy, abdominal angiotomography and arteriography with clinical diagnosis of Dieulafoy lesion motivated exploratory laparotomy finding injury vascular, the jejunum with active bleeding. The management of severe gastrointestinal bleeding with hemodynamic compromise and uncommon cause of arteriovenous malformation in the jejunum is discussed.

Optimization of reproductive management programs using lift chart analysis and cost-sensitive evaluation of classification errors.

The common practice on most commercial dairy farms is to inseminate all cows that are eligible for breeding, while ignoring (or absorbing) the costs associated with semen and labor directed toward low-fertility cows that are unlikely to conceive. Modern analytical methods, such as machine learning algorithms, can be applied to cow-specific explanatory variables for the purpose of computing probabilities of success or failure associated with upcoming insemination events. Lift chart analysis can identify subsets of high fertility cows that are likely to conceive and are therefore appropriate targets for insemination (e.g., with conventional artificial insemination semen or expensive sex-enhanced semen), as well as subsets of low-fertility cows that are unlikely to conceive and should therefore be passed over at that point in time. Although such a strategy might be economically viable, the management, environmental, and financial conditions on one farm might differ widely from conditions on the next, and hence the reproductive management recommendations derived from such a tool may be suboptimal for specific farms. When coupled with cost-sensitive evaluation of misclassified and correctly classified insemination events, the strategy can be a potentially powerful tool for optimizing the reproductive management of individual farms. In the present study, lift chart analysis and cost-sensitive evaluation were applied to a data set consisting of 54,806 insemination events of primiparous Holstein cows on 26 Wisconsin farms, as well as a data set with 17,197 insemination events of primiparous Holstein cows on 3 Wisconsin farms, where the latter had more detailed information regarding health events of individual cows. In the first data set, the gains in profit associated with limiting inseminations to subsets of 79 to 97% of the most fertile eligible cows ranged from $0.44 to $2.18 per eligible cow in a monthly breeding period, depending on days in milk at breeding and milk yield relative to contemporaries. In the second data set, the strategy of inseminating only a subset consisting of 59% of the most fertile cows conferred a gain in profit of $5.21 per eligible cow in a monthly breeding period. These results suggest that, when used with a powerful classification algorithm, lift chart analysis and cost-sensitive evaluation of correctly classified and misclassified insemination events can enhance the performance and profitability of reproductive management programs on commercial dairy farms.

Short communication: Prediction of retention pay-off using a machine learning algorithm.

Replacement decisions have a major effect on dairy farm profitability. Dynamic programming (DP) has been widely studied to find the optimal replacement policies in dairy cattle. However, DP models are computationally intensive and might not be practical for daily decision making. Hence, the ability of applying machine learning on a prerun DP model to provide fast and accurate predictions of nonlinear and intercorrelated variables makes it an ideal methodology. Milk class (1 to 5), lactation number (1 to 9), month in milk (1 to 20), and month of pregnancy (0 to 9) were used to describe all cows in a herd in a DP model. Twenty-seven scenarios based on all combinations of 3 levels (base, 20% above, and 20% below) of milk production, milk price, and replacement cost were solved with the DP model, resulting in a data set of 122,716 records, each with a calculated retention pay-off (RPO). Then, a machine learning model tree algorithm was used to mimic the evaluated RPO with DP. The correlation coefficient factor was used to observe the concordance of RPO evaluated by DP and RPO predicted by the model tree. The obtained correlation coefficient was 0.991, with a corresponding value of 0.11 for relative absolute error. At least 100 instances were required per model constraint, resulting in 204 total equations (models). When these models were used for binary classification of positive and negative RPO, error rates were 1% false negatives and 9% false positives. Applying this trained model from simulated data for prediction of RPO for 102 actual replacement records from the University of Wisconsin-Madison dairy herd resulted in a 0.994 correlation with 0.10 relative absolute error rate. Overall results showed that model tree has a potential to be used in conjunction with DP to assist farmers in their replacement decisions.

Prediction of insemination outcomes in Holstein dairy cattle using alternative machine learning algorithms.

When making the decision about whether or not to breed a given cow, knowledge about the expected outcome would have an economic impact on profitability of the breeding program and net income of the farm. The outcome of each breeding can be affected by many management and physiological features that vary between farms and interact with each other. Hence, the ability of machine learning algorithms to accommodate complex relationships in the data and missing values for explanatory variables makes these algorithms well suited for investigation of reproduction performance in dairy cattle. The objective of this study was to develop a user-friendly and intuitive on-farm tool to help farmers make reproduction management decisions. Several different machine learning algorithms were applied to predict the insemination outcomes of individual cows based on phenotypic and genotypic data. Data from 26 dairy farms in the Alta Genetics (Watertown, WI) Advantage Progeny Testing Program were used, representing a 10-yr period from 2000 to 2010. Health, reproduction, and production data were extracted from on-farm dairy management software, and estimated breeding values were downloaded from the US Department of Agriculture Agricultural Research Service Animal Improvement Programs Laboratory (Beltsville, MD) database. The edited data set consisted of 129,245 breeding records from primiparous Holstein cows and 195,128 breeding records from multiparous Holstein cows. Each data point in the final data set included 23 and 25 explanatory variables and 1 binary outcome for of 0.756 ± 0.005 and 0.736 ± 0.005 for primiparous and multiparous cows, respectively. The naïve Bayes algorithm, Bayesian network, and decision tree algorithms showed somewhat poorer classification performance. An information-based variable selection procedure identified herd average conception rate, incidence of ketosis, number of previous (failed) inseminations, days in milk at breeding, and mastitis as the most effective explanatory variables in predicting pregnancy outcome.

A distant homologue of the FlgT protein interacts with MotB and FliL and is essential for flagellar rotation in Rhodobacter sphaeroides.

In this work, we describe a periplasmic protein that is essential for flagellar rotation in Rhodobacter sphaeroides. This protein is encoded upstream of flgA, and its expression is dependent on the flagellar master regulator FleQ and on the class III flagellar activator FleT. Sequence comparisons suggest that this protein is a distant homologue of FlgT. We show evidence that in R. sphaeroides, FlgT interacts with the periplasmic regions of MotB and FliL and with the flagellar protein MotF, which was recently characterized as a membrane component of the flagellum in this bacterium. In addition, the localization of green fluorescent protein (GFP)-MotF is completely dependent on FlgT. The Mot(-) phenotype of flgT cells was weakly suppressed by point mutants of MotB that presumably keep the proton channel open and efficiently suppress the Mot(-) phenotype of motF and fliL cells, indicating that FlgT could play an additional role beyond the opening of the proton channel. The presence of FlgT in purified filament-hook-basal bodies of the wild-type strain was confirmed by Western blotting, and the observation of these structures under an electron microscope showed that the basal bodies from flgT cells had lost the ring that covers the LP ring in the wild-type structure. Moreover, MotF was detected by immunoblotting in the basal bodies obtained from the wild-type strain but not from flgT cells. From these results, we suggest that FlgT forms a ring around the LP ring, which anchors MotF and stabilizes the stator complex of the flagellar motor.

BullVal$: An Integrated Decision-Support Tool for Predicting the Net Present Value of a Dairy Bull Based on Genetic Merit, Semen Production Potential, and Demographic Factors.

Deciding when to replace dairy bulls presents a complex challenge for artificial insemination (AI) companies. These decisions encompass multiple factors, including a bull's age, predicted semen production, and estimated genetic merit. This study's purpose was to provide a practical, objective tool to assist in these decisions. We utilized a Markov Chain model to calculate the economic valuation of dairy bulls, incorporating key factors such as housing costs, collection and marketing expenses, and the bull's probable tenure in the herd. Data from a leading AI company were used to establish baseline values. The model further compared a bull's net present value to that of a potential young replacement, establishing a relative valuation (BullVal$). The range of BullVal$ observed spanned from -USD 316,748 to USD 497,710. Interestingly, the model recommended culling for 49% of the bulls based on negative BullVal$. It was found that a bull's net present value was primarily influenced by market allocation and pricing, coupled with the interaction of semen production and genetic merit. This study offers a robust, data-driven model to guide bull replacement decisions in AI companies. Key determinants of a bull's valuation included market dynamics, semen production rates, and genetic merit.

The Economic Evaluation of Mastitis Control Strategies in Holstein-Friesian Dairy Herds.

The economic evaluation of mastitis control is challenging. The objective of this study was to perform the economic evaluation of mastitis control, under different intervention scenarios, quantifying the total cost of mastitis caused by S. aureus in Holstein cows in Argentina. A model was set for a dairy herd of Holstein cows endemically infected with S. aureus. A basic mastitis control plan including proper milking procedures, milking machine test, dry cow therapy, and treatment for clinical mastitis, was compared against other more complex and costly interventions, such as segregation and culling of chronically infected cows. Sensitivity analysis was performed by modifying the intramammary infection transition probabilities, economic parameters, and efficacy of treatment strategies. The basic mastitis control plan showed a median total cost of USD88.6/cow per year, which was close to the infected cows culling scenarios outputs. However, the segregation scenario was the most efficient, in which the total cost was reduced by about 50%. Such cost was more sensitive to probabilities and efficacy than the economic parameters. The model is flexible and can be customized by producers and veterinarians according to different control and herd settings.

SARS-CoV-2 viral RNA detection using the novel CoVradar device associated with the CoVreader smartphone app.

The COVID-19 pandemic has highlighted the need for innovative approaches to its diagnosis. Here we present CoVradar, a novel and simple colorimetric method that combines nucleic acid analysis with dynamic chemical labeling (DCL) technology and the Spin-Tube device to detect SARS-CoV-2 RNA in saliva samples. The assay includes a fragmentation step to increase the number of RNA templates for analysis, using abasic peptide nucleic acid probes (DGL probes) immobilized to nylon membranes in a specific dot pattern to capture RNA fragments. Duplexes are formed by labeling complementary RNA fragments with biotinylated SMART bases, which act as templates for DCL. Signals are generated by recognizing biotin with streptavidin alkaline phosphatase and incubating with a chromogenic substrate to produce a blue precipitate. CoVradar results are analysed by CoVreader, a smartphone-based image processing system that can display and interpret the blotch pattern. CoVradar and CoVreader provide a unique molecular assay capable of detecting SARS-CoV-2 viral RNA without the need for extraction, preamplification, or pre-labeling steps, offering advantages in terms of time (∼3 h/test), cost (∼€1/test manufacturing cost) and simplicity (does not require large equipment). This solution is also promising for developing assays for other infectious diseases.

A novel component of the Rhodobacter sphaeroides Fla1 flagellum is essential for motor rotation.

Here we describe a novel component essential for flagellar rotation in Rhodobacter sphaeroides. This protein is encoded by motF (RSP_0067), the first gene of a predicted transcriptional unit which contains two hypothetical genes. Sequence analysis indicated that MotF is a bitopic membrane-spanning protein. Protease sensitivity assays and green fluorescent protein (GFP) fusions confirmed this prediction and allowed us to conclude that the C terminus of MotF is located in the periplasmic space. Wild-type cells expressing a functional GFP-MotF fusion show a single fluorescent focus per cell. The localization of this protein in different genetic backgrounds allowed us to determine that normal localization of MotF depends on the presence of FliL and MotB. Characterization of a ΔmotF pseudorevertant strain revealed that a single nucleotide change in motB suppresses the Mot(-) phenotype of the motF mutant. Additionally, we show that MotF also becomes dispensable when other mutant alleles of motB previously isolated as second-site suppressors of ΔfliL were expressed in the motF mutant strain. These results show that MotF is a new component of the Fla1 flagellum, which together with FliL is required to promote flagellar rotation, possibly through MotB.

Hepatotoxicity associated with dietary energy supplements: use and abuse by young athletes.

 In recent years there has been a significant increase in the consumption of dietary energy supplements (DES) associated with the parallel advertising against obesity and favoring high physical performance. We present the case and outcome of a young patient who developed acute mixed liver injury (hepatocellular and cholestatic) after ingestion of various "over the counter" products to increase muscle mass and physical performance (NO Xplode®, creatine, L-carnitine, and Growth Factor ATN®). The diagnosis was based on the exclusion of other diseases and liver biopsy findings. The dietary supplement and herbal multivitamins industry is one with the highest growth rates in the market, with annual revenues amounting to billions and constantly lacking scientific or reproducible evidence about the efficacy and/or safety of the offered products. Furthermore, and contrary to popular belief, different forms of injury associated with these natural substances have been documented particularly in the liver, supporting the need of a more strict regulation.