Genetic Parameters and Genetic Trends for Johne's Disease in U.S. Holsteins: An Updated Study.

Johne's disease (JD) is an infectious enteric disease in ruminants, causing substantial economic loss annually worldwide. This work aimed to estimate JD's genetic parameters and the phenotypic and genetic trends by incorporating recent data. It also explores the feasibility of a national genetic evaluation for JD susceptibility in Holstein cattle in the United States (US). The data were extracted from a Johne's disease data repository, maintained at the Council on Dairy Cattle Breeding (CDCB), and initially supplied by 2 dairy records processing centers. The data comprised 365,980 Holstein cows from 1,048 herds participating in a voluntary control program for JD. Two protocol kits, Idexx Paratuberculosis Screening Ab Test (IDX) and Parachek® 2 (PCK), were used to analyze milk samples with the Enzyme-Linked Immunosorbent Assay (ELISA) technique. Test results from the first 5 parities were considered. An animal was considered infected if it had at least one positive outcome. The overall average of JD incidence was 4.72% in these US Holstein cattle. Genotypes of 78,964 SNP markers were used for 25,000 animals randomly selected from the phenotyped population. Variance components and genetic parameters were estimated based on 3 models, namely, a pedigree-only threshold model (THR), a single-step threshold model (ssTHR), and a single-step linear model (ssLR). The posterior heritability estimates of JD susceptibility were low to moderate: 0.11 to 0.16 based on the 2 threshold models and 0.05 to 0.09 based on the linear model. The average reliability of estimated breeding values of JD susceptibility using single-step analysis for animals with or without phenotypes varied from 0.18 (THR) to 0.22 (ssLR) for IDX and from 0.14 (THR) to 0.18 (ssTHR and ssLR) for PCK. Despite no prior direct genetic selection against JD, the estimated genetic trends of JD susceptibility were negative and highly significant (P-value <0.01). The correlations of bulls' predicted transmitting abilities with economically important traits such as milk yield, milk protein, milk fat, somatic cell score, and mastitis were low, indicating a non-overlapping genetic selection process with traits in current genetic evaluations. Our results suggest the feasibility of reducing the JD incidence rate by incorporating it into the national genetic evaluation programs.

Updating test-day milk yield factors for use in genetic evaluations and dairy production systems: a comprehensive review.

Various methods have been proposed to estimate daily yield from partial yields, primarily to deal with unequal milking intervals. This paper offers an exhaustive review of daily milk yields, the foundation of lactation records. Seminal advancements in the late 20th century concentrated on two main adjustment metrics: additive additive correction factors (ACF) and multiplicative correction factors (MCF). An ACF model provides additive adjustments to two times AM or PM milk yield, which then becomes the estimated daily yields, whereas an MCF is a ratio of daily yield to the yield from a single milking. Recent studies highlight the potential of alternative approaches, such as exponential regression and other nonlinear models. Biologically, milk secretion rates are not linear throughout the entire milking interval, influenced by the internal mammary gland pressure. Consequently, nonlinear models are appealing for estimating daily milk yields as well. MCFs and ACFs are typically determined for discrete milking interval classes. Nonetheless, large discrete intervals can introduce systematic biases. A universal solution for deriving continuous correction factors has been proposed, ensuring reduced bias and enhanced daily milk yield estimation accuracy. When leveraging test-day milk yields for genetic evaluations in dairy cattle, two predominant statistical models are employed: lactation and test-day yield models. A lactation model capitalizes on the high heritability of total lactation yields, aligning closely with dairy producers' needs because the total amount of milk production in a lactation directly determines farm revenue. However, a lactation yield model without harnessing all test-day records may ignore vital data about the shapes of lactation curves needed for informed breeding decisions. In contrast, a test-day model emphasizes individual test-day data, accommodating various intervals and recording plans and allowing the estimation of environmental effects on specific test days. In the United States, the patenting of test-day models in 1993 used to restrict the use of test-day models to regional and unofficial evaluations by the patent holders. Estimated test-day milk yields have been used as if they were accurate depictions of actual milk yields, neglecting possible estimation errors. Its potential consequences on subsequent genetic evaluations have not been sufficiently addressed. Moving forward, there are still numerous questions and challenges in this domain.

Neuronal MeCP2 in the dentate gyrus regulates mossy fiber sprouting of mice with temporal lobe epilepsy.

Sprouting of mossy fibers, one of the most consistent findings in tissue from patients with mesial temporal lobe epilepsy, exhibits several uncommon axonal growth features and has been considered a paradigmatic example of circuit plasticity that occurs in the adult brain. Clarifying the mechanisms responsible may provide new insight into epileptogenesis as well as axon misguidance in the central nervous system. Methyl-CpG-binding protein 2 (MeCP2) binds to methylated genomic DNA to regulate a range of physiological functions implicated in neuronal development and adult synaptic plasticity. However, exploring the potential role of MeCP2 in the documented misguidance of axons in the dentate gyrus has not yet been attempted. In this study, a status epilepticus-induced decrease of neuronal MeCP2 was observed in the dentate gyrus (DG). An essential regulatory role of MeCP2 in the development of functional mossy fiber sprouting (MFS) was confirmed through stereotaxic injection of a recombinant adeno-associated virus (AAV) to up- or down-regulate MeCP2 in the dentate neurons. Chromatin immunoprecipitation sequencing (ChIP-seq) was performed to identify the binding profile of native MeCP2 using micro-dissected dentate tissues. In both dentate tissues and HT22 cell lines, we demonstrated that MeCP2 could act as a transcription repressor on miR-682 with the involvement of the DNA methylation mechanism. Further, we found that miR-682 could bind to mRNA of phosphatase and tensin homolog (PTEN) in a sequence specific manner, thus leading to the suppression of PTEN and excessive activation of mTOR. This study therefore presents a novel epigenetic mechanism by identifying MeCP2/miR-682/PTEN/mTOR as an essential signal pathway in regulating the formation of MFS in the temporal lobe epileptic (TLE) mice. SIGNIFICANCE STATEMENT: Understanding the mechanisms that regulate axon guidance is important for a better comprehension of neural disorders. Sprouting of mossy fibers, one of the most consistent findings in patients with mesial temporal lobe epilepsy, has been considered a paradigmatic example of circuit plasticity in the adult brain. Although abnormal regulation of DNA methylation has been observed in both experimental rodents and humans with epilepsy, the potential role of DNA methylation in this well-documented example of sprouting of dentate axon remains elusive. This study demonstrates an essential role of methyl-CpG-binding protein 2 in the formation of mossy fiber sprouting. The underlying signal pathway has been also identified. The data hence provide new insight into epileptogenesis as well as axon misguidance in the central nervous system.

Does modeling causal relationships improve the accuracy of predicting lactation milk yields?

This study compared 3 correlational (best prediction, linear regression, and feed-forward neural networks) and 2 causal models (recursive structural equation model and recurrent neural networks) for estimating lactation milk yields. The correlational models assumed associations between test-day milk yields (health conditions), while the casual models postulated unidirectional recursive effects between these test-day variables. Wood lactation curves were used to simulate the data and served as a benchmark model. Individual Wood lactation curves provided an excellent parametric interpretation of lactation dynamics, with their prediction accuracies depending on the coverage of the lactation curve dynamics. Best prediction outperformed other models in the absence of mastitis but was suboptimal when mastitis was present and unaccounted for. Recurrent neural networks yielded the highest accuracy when mastitis was present. Although causal models facilitated the inference about the causality underlying lactation, precisely capturing the causal relationships was challenging because the underlying biology was complex. Misspecification of recursive effects in the recursive structural equation model resulted in a loss of accuracy. Hence, modeling causal relationships does not necessarily guarantee improved accuracies. In practice, a parsimonious model is preferred, balancing model complexity and accuracy. In addition to the choice of statistical models, the proper accounting for factors and covariates affecting milk yields is equally crucial.

Incidence trends and survival of metastatic prostate cancer with bone and visceral involvement: 2010-2019 surveillance, epidemiology, and end results.

Background: The incidence of prostate cancer (PCa) has continued to increase since the US Preventive Services Task Force (USPSTF) recommendations against prostate-specific antigen (PSA)-based screening for all men in 2012, approximately half of additional diagnosed cases are advanced-stage, including regional PCa and metastatic PCa (mPCa). It is very important to investigate the shift in mPCa incidence and mPCa-related mortality risk, as the survival of mPCa remains poor. Objective: To investigate the incidence temporal trend of mPCa stratified by metastatic site, including bone and visceral metastatic involvement, and potential survival improvements. Materials: Based on the recently released Surveillance, Epidemiology, and End Results (SEER) data (2010-2019), the age-adjusted incidence rates of mPCa with bone and visceral involvement with annual percentage changes (APCs) were assessed by a joinpoint regression model in men aged 45 years and older by age and race groups, and potential recent improvements in overall survival (OS) and cancer-specific survival (CSS) were estimated by the Kaplan-Meier method and Cox regression model. Results: From 2010 to 2019, a total of 19081 (84.8%) and 3413 (15.2%) mPCa patients with bone and visceral involvement, respectively, were recorded in the SEER database. Considering all races and age groups, the incidence rate of mPCa with bone metastasis remained stable during 2017-2019 (APC, 0.9%; p=0.421) after increasing during 2010-2017 (APC, 5.8%; p<0.001). For visceral metastasis, the incidence rate increased by 12.3% (p<0.001) per year from 2010-2019. Non-Hispanic Black men have higher incidence rates than other populations, and the Non-Hispanic Black to Non-Hispanic White incidence rates ratios of mPCa declined with the greater increasing pace of incidence of Non-Hispanic White men. There was a slight improvement in both OS and CSS among men with bone and visceral metastasis involvement when comparing the 2013-2016 period to the pre-2013 period. Conclusion: Our findings show that the incidence of mPCa with bone and visceral involvement has increased in recent years and that there has been a potential improvement in survival. Future efforts are still needed to watch closely if the rising incidence trends continue.

Isoflurane-induced reduction in neurogenesis derived from the tertiary dentate matrix.

Anesthetics-induced disruption of dentate neurogenesis in the young brain is strongly suggested to contribute to delayed neurocognitive deficit. In postnatal rodents, the neurogenesis of the dentate gyrus (DG) is sequentially derived from the secondary dentate matrix, tertiary dentate matrix and subgranular zone (SGZ). However, the effects of anesthetics on the dentate neurogenesis derived from specific sites are poorly understood. To trace the new cells generated from the postnatal secondary dentate matrix, peak stage of the tertiary dentate matrix and early stage of the SGZ after isoflurane exposure, mice at postnatal day 1 (P1), P7 and P31 were injected with BrdU at 12 h before the exposure. We found that isoflurane exposure significantly reduced the numbers of proliferating cells (1 day old), immature granule cells (21 days old) or mature granule cells (42 days old) derived from the peak stage of the tertiary dentate matrix and postnatal secondary dentate matrix, but not from the SGZ. Quantitative assessment of BrdU-/BrdU+NeuN-positive cells and cleaved caspase-3 level in the DG indicated that the reduction was correlated with cell loss rather than neuronal differentiation. Mechanistically, we demonstrated that the PI3K/Akt/GSK-3ß pathway enriched by mRNA-sequencing is a requirement for the isoflurane-induced loss of 1-day-old proliferating cells generated from the tertiary dentate matrix. In addition, this study demonstrated that P1 and P7 mice, but not P31 mice exposure to isoflurane resulted in subsequent deficits in performance of the tasks of the Morris Water Maze.

Estimating test-day milk yields by modeling proportional daily yields: Going beyond linearity.

In the United States, lactation milk yields are not measured directly but are calculated from the test-day milk yields. Still, test-day milk yields are estimated from partial yields obtained from single milkings. Various methods have been proposed to estimate test-day milk yields, primarily to deal with unequal milking intervals dating back to the 1970s and 1980s. The Wiggans model is a de facto method for estimating test-day milk yields in the United States, which was initially proposed for cows milked 3 times daily, assuming a linear relationship between a proportional test-day milk yield and milking interval. However, the linearity assumption did not hold precisely in Holstein cows milked twice daily because of prolonged and uneven milking intervals. The present study reviewed and evaluated the nonlinear models that extended the Wiggans model for estimating daily or test-day milk yields. These nonlinear models, except step functions, demonstrated smaller errors and greater accuracies for estimated test-day milk yields compared with the conventional methods. The nonlinear models offered additional benefits. For example, the locally weighted regression model (e.g., locally estimated scatterplot smoothing) could utilize data information in scalable neighborhoods and weigh observations according to their distance in milking interval time. General additive models provide a flexible, unified framework to model nonlinear predictor variables additively. Another drawback of the conventional methods is a loss of accuracy caused by discretizing milking interval time into large bins while deriving multiplicative correction factors for estimating test-day milk yields. To overcome this problem, we proposed a general approach that allows milk yield correction factors to be derived for every possible milking interval time, resulting in more accurately estimated test-day milk yields. This approach can be applied to any model, including nonparametric models.

A look under the hood of genomic-estimated breed compositions for brangus cattle: What have we learned?

The Brangus cattle were developed to utilize the superior traits of Angus and Brahman cattle. Their genetic compositions are expected to be stabilized at 3/8 Brahman and 5/8 Angus. Previous studies have shown more than expected Angus lineage with Brangus cattle, and the reasons are yet to be investigated. In this study, we revisited the breed compositions for 3,605 Brangus cattle from three perspectives: genome-wise (GBC), per chromosomes (CBC), and per chromosome segments (SBC). The former (GBC) depicted an overall picture of the "mosaic" genome of the Brangus attributable to their ancestors, whereas the latter two criteria (CBC and SBC) corresponded to local ancestral contributions. The average GBC for the 3,605 Brangus cattle were 70.2% Angus and 29.8% Brahman. The K-means clustering supported the postulation of the mixture of 1/2 Ultrablack (UB) animals in Brangus. For the non-UB Brangus animals, the average GBC were estimated to be 67.4% Angus and 32.6% Brahman. The 95% confidence intervals of their overall GBC were 60.4%-73.5% Angus and 26.5%-39.6% Brahman. Possibly, genetic selection and drifting have resulted in an approximately 5% average deviation toward Angus lineage. The estimated ancestral contributions by chromosomes were heavily distributed toward Angus, with 27 chromosomes having an average Angus CBC greater than 62.5% but only two chromosomes (5 and 20) having Brahman CBC greater than 37.5%. The chromosomal regions with high Angus breed proportions were prevalent, tending to form larger blocks on most chromosomes. In contrast, chromosome segments with high Brahman breed proportion were relatively few and isolated, presenting only on seven chromosomes. Hence, genomic hitchhiking effects were strong where Angus favorable alleles resided but weak where Brahman favorable alleles were present. The functions of genes identified in the chromosomal regions with high ( ≥ 75 % ) Angus compositions were diverse yet may were related to growth and body development. In contrast, the genes identified in the regions with high ( ≥ 37.5 % ) Brahman compositions were primarily responsible for disease resistance. In conclusion, we have addressed the questions concerning the Brangus genetic make-ups. The results can help form a dynamic picture of the Brangus breed formation and the genomic reshaping.

Daily milk yield correction factors: What are they?

Cows are typically milked 2 or more times on a test-day, but not all these milkings are sampled and weighed. The initial approach estimated a test-day yield with doubled morning (AM) or evening (PM) yield in the AM-PM milking plans, assuming equal AM and PM milking intervals. However, AM and PM milking intervals can vary, and milk secretion rates may be different between day and night. Statistical methods have been proposed to estimate daily yields in dairy cows, focusing on various yield correction factors in 2 broad categories: additive correction factors (ACF) and multiplicative correction factors (MCF). The ACF are evaluated by the average differences between AM and PM milk yield for various milking interval classes, coupled with other categorical variables. We show that an ACF model is equivalent to a regression model of daily yield on categorical regressor variables, and a continuous variable for AM or PM yield with a fixed regression coefficient of 2.0. Similarly, a linear regression model can be implemented as an ACF model with the regression coefficient for AM or PM yield estimated from the data. The linear regression models improved the accuracy of the estimates compared with the ACF models. The MCF are ratios of daily yield to yield from single milkings, but their statistical interpretations vary. Overall, MCF were more accurate for estimating daily milk yield than ACF. The MCF have biological and statistical challenges. Systematic biases occurred when ACF or MCF were computed on discretized milking interval classes, leading to accuracy loss. An exponential regression model was proposed as an alternative model for estimating daily milk yields, which improved the accuracy. Characterization of ACF and MCF showed how they improved the accuracy compared with doubling AM or PM yield as the daily milk yield. All the methods performed similarly with equal AM and PM milkings. The methods were explicitly described to estimate daily milk yield in AM and PM milking plans. Still, the principles generally apply to cows milked more than 2 times a day and apply similarly to the estimation of daily fat and protein yields with some necessary modifications.

Case report and literature review: Treatment of multiple meningiomas combined with multiple unruptured aneurysms in a single operation.

Background: Although the incidence of a single meningioma or a single aneurysm is common, cases of multiple meningiomas combined with multiple aneurysms are rarely reported, and surgical treatment of the coexisting situation is also relatively uncommon. Case presentation: A 38-year-old male patient presented to the neurosurgery department of our center with a headache. Examination revealed only symptoms of headache. Laboratory tests showed only decreased total protein and albumin. Magnetic resonance imaging showed preoccupation with the frontal lobe and the right temple bone. Magnetic resonance angiography and digital subtraction angiography showed two aneurysms in the anterior communicating artery and right anterior cerebral artery. Based on a combination of the patient's history and imaging, we hypothesized that the patient was simultaneously suffering from meningioma and an aneurysm, and both of them are multiple. The patient underwent tumor resection and clipping procedure based on this hypothesis in one surgery. Intraoperative biopsy proved to be a meningioma. The patient was discharged on the 10th postoperative day, and a postoperative follow-up suggested no complications. Conclusion: Multiple meningiomas combined with multiple aneurysms are rare to be reported in the same patient. For those unruptured intracranial aneurysms (UIAs) located in the visual field of craniotomy prepared for brain tumorlike meningioma, it is possible to do the clipping as well. When the meningiomas are multiple, fitted with the surgical indication, and located in a position that cannot be treated in one surgery, this may lead to a two-stage operation, no matter where the UIAs are located.

Statistical Methods Revisited for Estimating Daily Milk Yields: How Well do They Work?

Cost-effective milking plans have been adapted to supplement the standard supervised twice-daily monthly testing scheme since the 1960s. Various methods have been proposed to estimate daily milk yields (DMY), focusing on yield correction factors. The present study evaluated the performance of existing statistical methods, including a recently proposed exponential regression model, for estimating DMY using 10-fold cross-validation in Holstein and Jersey cows. The initial approach doubled the morning (AM) or evening (PM) yield as estimated DMY in AM-PM plans, assuming equal 12-h AM and PM milking intervals. However, in reality, AM milking intervals tended to be longer than PM milking intervals. Additive correction factors (ACF) provided additive adjustments beyond twice AM or PM yields. Hence, an ACF model equivalently assumed a fixed regression coefficient or a multiplier of "2.0" for AM or PM yields. Similarly, a linear regression model was viewed as an ACF model, yet it estimated the regression coefficient for a single milk yield from the data. Multiplicative correction factors (MCF) represented daily to partial milk yield ratios. Hence, multiplying a yield from single milking by an appropriate MCF gave a DMY estimate. The exponential regression model was analogous to an exponential growth function with the yield from single milking as the initial state and the rate of change tuned by a linear function of milking interval. In the present study, all the methods had high precision in the estimates, but they differed considerably in biases. Overall, the MCF and linear regression models had smaller squared biases and greater accuracies for estimating DMY than the ACF models. The exponential regression model had the greatest accuracies and smallest squared biases. Model parameters were compared. Discretized milking interval categories led to a loss of accuracy of the estimates. Characterization of ACF and MCF revealed their similarities and dissimilarities and biases aroused by unequal milking intervals. The present study focused on estimating DMY in AM-PM milking plans. Yet, the methods and relevant principles are generally applicable to cows milked more than two times a day.

CT psoas calculations on the prognosis prediction of emergency laparotomy: a single-center, retrospective cohort study in eastern Asian population.

BACKGROUND: Emergency laparotomy (EL) has a high mortality rate. Clinically, frail patients have a poor tolerance for EL. In recent years, sarcopenia has been used as an important indicator of frailty and has received much attention. There have been five different calculation methods of psoas for computed tomography (CT) to measure sarcopenia, but lack of assessment of these calculation methods in Eastern Asian EL patients. METHODS: We conducted a 2-year retrospective cohort study of patients over 18 years of age who underwent EL in our institution. Five CT measurement values (PMI: psoas muscle index, PML3: psoas muscle to L3 vertebral body ratio, PMD: psoas muscle density, TPG: total psoas gauge, PBSA: psoas muscle to body face area ratio) were calculated to define sarcopenia. Patients with sarcopenia defined by the sex-specific lowest quartile of each measurement were compared with the rest of the cohort. The primary outcome was "ideal outcome", defined as: (1) No postoperative complications of Clavien-Dindo Grade ≥ 4; (2) No mortality within 30 days; (3) When discharged, no need for fluid resuscitation and assisted ventilation, semi-liquid diet tolerated, and able to mobilize independently. The second outcome was mortality at 30-days. Multivariate logistic regression and receiver operating characteristic (ROC) analysis were used. RESULTS: Two hundred and twenty-eight patients underwent EL met the inclusion criteria, 192 (84.2%) patients had an ideal outcome after surgery; 32 (14%) patients died within 30 days. Multivariate analysis showed that, except PMD, each calculation method of psoas was independently related to clinical outcome (ideal outcome: PML3, P < 0.001; PMI, P = 0.001; PMD, P = 0.157; TPG, P = 0.006; PBSA, P < 0.001; mortality at 30-days: PML3, P < 0.001; PMI, P = 0.002; PMD, P = 0.088; TPG, P = 0.002; PBSA, P = 0.001). In ROC analysis, the prediction model containing PML3 had the largest area under the curve (AUC) value (AUC value = 0.922 and 0.920, respectively). CONCLUSION: The sarcopenia determined by CT psoas measurements is significantly related to the clinical outcome of EL. The calculation of CT psoas measurement is suitable for application in outcome prediction of EL. In the future, it is necessary to develop a scoring tool that includes sarcopenia to evaluate the risk of EL better.

Identification of microRNA/target gene in the dentate gyrus of 7­day­old mice following isoflurane exposure.

Studies on rodents and nonhuman primates suggest that exposure to anesthetics, particularly in the young brain, is associated with neuronal apoptosis as well as hippocampal­dependent cognitive dysfunction. Disruption of the development of dentate gyrus may play an important role in anesthetics­induced neurotoxicity. However, the anesthetics triggered molecular events in the dentate gyrus of the developing brain are poorly understood. By integrating two independent data sets obtained from miRNA­seq and mRNA­seq respectively, this study aims to profile the network of miRNA and potential target genes, as well as relevant events occurring in the dentate gyrus of isoflurane exposed 7­day­old mice. We found that a single four hours exposure to isoflurane yielded 1059 pairs of differently expressed miRNAs/target genes in the dentate gyrus. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis further indicates that dysregulated miRNAs/target genes have far­reaching effects on the cellular pathophysiological events, such as cell apoptosis, axon development, and synaptic transmission. Our results would greatly broaden our functional understanding of the role of miRNA/target gene in the context of anesthetics­induced neurotoxicity.

Genetic Evaluations of Stillbirth for Five United States Dairy Breeds: A Data-Resource Feasibility Study.

Genetic selection has been an effective strategy to improve calving traits including stillbirth in dairy cattle. The primary objectives of the present study were to characterize stillbirth data and determine the feasibility of implementing routine genetic evaluations of stillbirth in five non-Holstein dairy breeds, namely Ayrshire, Guernsey, Milking Shorthorn, Brown Swiss, and Jersey. An updated sire-maternal grandsire threshold model was used to estimate genetic parameters and genetic values for stillbirth. Stillbirth data with the birth years of dams from 1995 to 2018 were extracted from the United States national calving ease database maintained by the Council on Dairy Cattle Breeding. The extracted stillbirth records varied drastically among the five dairy breeds. There were approximately 486K stillbirth records for Jersey and more than 80K stillbirth records for Brown Swiss. The direct and maternal heritability estimates of stillbirth were 6.0% (4.5-7.6%) and 4.7% (3.3-6.1%) in Jersey and 6.8% (3.2-10.5%) and 1.1% (0.6-2.9%) in Brown Swiss. The estimated genetic correlations between direct and maternal genetic effects for stillbirth were -0.15 (-0.38 to -0.08) in Jersey and -0.35 (-0.47 to -0.12) in Brown Swiss. The estimated genetic parameters for stillbirth in these two breeds were within close ranges of previous studies. The reliabilities of predicted transmitting abilities in Jersey and Brown Swiss increased substantially, thanks to the substantial increase in available stillbirth data in the past 10 years. The stillbirth records for Ayrshire, Guernsey, and Milking Shorthorn, which ranged approximately between 3K and 12K, are insufficient to implement reliable routine genetic evaluations of stillbirth in these three dairy breeds. Estimated genetic (co)variances and genetic values deviated considerably from the reported ranges of previous studies, and the reliabilities of predicted transmitting abilities were low in these three breeds. In conclusion, routine genetic evaluations of stillbirth are feasible in Brown Swiss and Jersey. However, reliable genetic evaluations of stillbirth in Ayrshire, Guernsey, and Milking Shorthorn require further data collection on stillbirth.

Neuronal Glypican4 promotes mossy fiber sprouting through the mTOR pathway after pilocarpine-induced status epilepticus in mice.

In temporal lobe epilepsy (TLE), abnormal axon guidance and synapse formation lead to sprouting of mossy fibers in the hippocampus, which is one of the most consistent pathological findings in patients and animal models with TLE. Glypican 4 (Gpc4) belongs to the heparan sulfate proteoglycan family, which play an important role in axon guidance and excitatory synapse formation. However, the role of Gpc4 in the development of mossy fibers sprouting (MFS) and its underlying mechanism remain unknown. Using a pilocarpine-induced mice model of epilepsy, we showed that Gpc4 expression was significantly increased in the stratum granulosum of the dentate gyrus at 1 week after status epilepticus (SE). Using Gpc4 overexpression or Gpc4 shRNA lentivirus to regulate the Gpc4 level in the dentate gyrus, increased or decreased levels of netrin-1, SynI, PSD-95, and Timm score were observed in the dentate gyrus, indicating a crucial role of Gpc4 in modulating the development of functional MFS. The observed effects of Gpc4 on MFS were significantly antagonized when mice were treated with L-leucine or rapamycin, an agonist or antagonist of the mammalian target of rapamycin (mTOR) signal, respectively, demonstrating that mTOR pathway is an essential requirement for Gpc4-regulated MFS. Additionally, the attenuated spontaneous recurrent seizures (SRSs) were observed during chronic stage of the disease by suppressing the Gpc4 expression after SE. Altogether, our findings demonstrate a novel control of neuronal Gpc4 on the development of MFS through the mTOR pathway after pilocarpine-induced SE. Our results also strongly suggest that Gpc4 may serve as a promising target for antiepileptic studies.

The molecular biology of pancreatic adenocarcinoma: translational challenges and clinical perspectives.

Pancreatic cancer is an increasingly common cause of cancer mortality with a tight correspondence between disease mortality and incidence. Furthermore, it is usually diagnosed at an advanced stage with a very dismal prognosis. Due to the high heterogeneity, metabolic reprogramming, and dense stromal environment associated with pancreatic cancer, patients benefit little from current conventional therapy. Recent insight into the biology and genetics of pancreatic cancer has supported its molecular classification, thus expanding clinical therapeutic options. In this review, we summarize how the biological features of pancreatic cancer and its metabolic reprogramming as well as the tumor microenvironment regulate its development and progression. We further discuss potential biomarkers for pancreatic cancer diagnosis, prediction, and surveillance based on novel liquid biopsies. We also outline recent advances in defining pancreatic cancer subtypes and subtype-specific therapeutic responses and current preclinical therapeutic models. Finally, we discuss prospects and challenges in the clinical development of pancreatic cancer therapeutics.

An alternative interpretation of residual feed intake by phenotypic recursive relationships in dairy cattle.

There has been increasing interest in residual feed intake (RFI) as a measure of net feed efficiency in dairy cattle. Residual feed intake phenotypes are obtained as residuals from linear regression encompassing relevant factors (i.e., energy sinks) to account for body tissue mobilization. By rearranging the single-trait linear regression, we showed a causal RFI interpretation underlying the linear regression for RFI. It postulates recursive effects in energy allocation from energy sinks on dry matter intake, but the feedback or simultaneous effects are nonexistent. A Bayesian recursive structural equation model was proposed for directly predicting RFI and energy sinks and estimating relevant genetic parameters simultaneously. A simplified Markov chain Monte Carlo algorithm was described. The recursive model is asymptotically equivalent to one-step linear regression for RFI, yet extends the analytical capacity to multiple-trait analysis.

The neurotoxic effect of isoflurane on age-defined neurons generated from tertiary dentate matrix in mice.

INTRODUCTION: Recent animal studies showed that isoflurane exposure may lead to the disturbance of hippocampal neurogenesis and later cognitive impairment. However, much less is known about the effect of isoflurane exposure on the neurons generated form tertiary dentate matrix, even though a great increase of granule cell population during the infantile period is principally derived from this area. METHODS: To label the new cells originated from the tertiary dentate matrix, the mice were injected with BrdU on postnatal day 6 (P6). Then, the mice were exposed to isoflurane for 4 hr at 1, 8, 21, and 42 days after BrdU injection, and the brains were collected 24 hr later. The loss of newly generated cells/neurons with different developmental stage was assessed by BrdU, BrdU + DCX, BrdU + NeuN, or BrdU + Prox-1 staining, respectively. RESULTS: We found that the isoflurane exposure significantly decreased the numbers of nascent cells (1 day old) and mature neurons (42 days old), but had no effect on the immature (8 days old) and early mature neurons (8 and 21 days old, respectively). CONCLUSION: The results suggested isoflurane exposure exerts the neurotoxic effects on the tertiary dentate matrix-originated cells with an age-defined pattern in mice, which partly explain the cognitive impairment resulting from isoflurane exposure to the young brain.