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ABSTRACT: 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. The data were extracted from a JD data repository, maintained at the Council on Dairy Cattle Breeding, and initially supplied by 2 dairy record 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 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 EBVs 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. The correlations of bulls' PTA with economically important traits such as milk yield, milk protein, milk fat, somatic cell score, and mastitis were low, indicating a nonoverlapping 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.

ABSTRACT: 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.

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.

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.

The microbial conversion of alkanes to methane in hydrocarbon-contaminated environments is an intrinsic bioremediation strategy under anoxic conditions. However, the mechanism of microbial methanogenic alkane degradation is currently unclear. Under ten years of continuous efforts, we obtained a methanogenic n-alkane-degrading (C15–C20) enrichment culture that exhibited sustained improvements in the kinetic properties of methane production. The integrated metagenomic and metatranscriptomic analyses revealed that n-alkanes were mainly attacked by members of Desulfosarcinaceae, Firmicutes, and Synergistetes using the fumarate addition strategy, and were then further degraded in a common effort by Tepidiphilus members. Meanwhile, the abundant members of Anaerolineaceae were mainly responsible for cell debris recycling. However, according to the metatranscriptomic analyses, methane was predicted to be produced mainly via H2-dependent methylotrophic methanogenesis, primarily from necromass-derived trimethylamine mediated by Methanomethyliaceae within the candidate phylum Verstraetearchaeota. These findings reveal that H2-dependent methylotrophic methanogens, as well as methylotrophic methanogens, may play important ecological roles in the carbon cycle of hydrocarbon enriched subsurface ecosystems.

The purpose of this systematic review was to examine the safety and effectiveness of mobile health applications in couples with infertility. Nineteen databases were searched from their inception to August 2022. Only randomized controlled trials (RCTs) in which mobile health was used as an intervention in infertile couples were included. A quantitative analysis of RCTs was employed using RevMan software. Study selection, data extraction and validation were performed by two independent reviewers according to the guidelines. The Cochrane criteria for risk-of-bias were used to evaluate the methodological quality of the trials. Eight RCTs met the inclusion criteria. Among the eight RCTs, two RCTs were from the Netherlands and the remaining six RCTs were from China. Meta-analysis showed that mobile health interventions in infertile couples in China were found to be superior to usual care in terms of clinical pregnancy rate (p = 0.001), psychological status (SAS (Self-Rating Anxiety Scale): p < 0.001; SDS (Self-Rating Depression Scale) p < 0.001;), infertility knowledge levels (p < 0.001), quality of life (p < 0.001), and serum levels of 5-hydroxytryptamine (p < 0.001). The effects on intervention groups using mobile health interventions in the Netherlands were not superior to the control groups that used usual care in terms of improving dietary factors (p > 0.05). In general, the positive effect of mobile health on improving clinical pregnancy rate, psychological status, infertility knowledge levels, quality of life and satisfaction with information was better than that of usual care. Mobile health interventions could be a viable supplement to the usual care for infertile couples. However, more high-quality RCTs need to be included in the future to provide additional evidence of the positive effects of mobile health in infertile couples.

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.

BackgroundThe 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.ObjectiveTo investigate the incidence temporal trend of mPCa stratified by metastatic site, including bone and visceral metastatic involvement, and potential survival improvements.MaterialsBased 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.ResultsFrom 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

Aromatic hydrocarbon molecules with π-conjugated structure have great potential for the search of exotic states such as high temperature superconductivity and novel quantum magnetism. In this work, we found that high temperature weak ferromagnetism can be achieved by intercalating rubidium into the shortest poly(para-phenylene) molecule—biphenyl. Both dc and ac magnetic susceptibility measurements showed that the synthesized material exhibits a typical antiferromagnetic transition at 48.5 K, accompanying a ferromagnetic character in the magnetically ordered region. Combining theoretical calculations and Raman spectroscopic studies, it was found that the mole ratio between rubidium and biphenyl is 1:1, and the molecular magnetic moments are formed by transferring the Rb-5s electrons to the C-2p orbitals. The weak ferromagnetism is produced by intralayer spin-canted antiferromagnetic and interlayer ferromagnetic spin couplings. Our finding enriches the physicochemical functionality of biphenyl and opens a window for the search of π-electron organic magnets in poly(para-phenylene) molecules.

Abstract 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

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.

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.

BackgroundAlthough 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 presentationA 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.ConclusionMultiple 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.

Abstract 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.

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.

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.

Abstract Paraffinic n-alkanes (C22–C30), crucial portions of residual oil, are generally considered to be difficult to be biodegraded owing to their general solidity at ambient temperatures and low water solubility, rendering relatively little known about metabolic processes in different methanogenic hydrocarbon-contaminated environments. Here, we established a methanogenic C22–C30 n-alkane-degrading enrichment culture derived from a high-temperature oil reservoir production water. During two-year incubation (736 days), unexpectedly significant methane production was observed. The measured maximum methane yield rate (164.40 μmol L−1 d−1) occurred during the incubation period from day 351 to 513. The nearly complete consumption (> 97%) of paraffinic n-alkanes and the detection of dicarboxylic acids in n-alkane-amended cultures indicated the biotransformation of paraffin to methane under anoxic condition. 16S rRNA gene analysis suggested that the dominant methanogen in n-alkane-degrading cultures shifted from Methanothermobacter on day 322 to Thermoplasmatales on day 736. Bacterial community analysis based on high-throughput sequencing revealed that members of Proteobacteria and Firmicutes exhibiting predominant in control cultures, while microorganisms affiliated with Actinobacteria turned into the most dominant phylum in n-alkane-dependent cultures. Additionally, the relative abundance of mcrA gene based on genomic DNA significantly increased over the incubation time, suggesting an important role of methanogens in these consortia. This work extends our understanding of methanogenic paraffinic n-alkanes conversion and has biotechnological implications for microbial enhanced recovery of residual hydrocarbons and effective bioremediation of hydrocarbon-containing biospheres.

Abstract As a widely used renewable energy, migrating to wind power can help reduce air pollution and CO2 emissions. However, the construction of wind farms could have certain impacts on the local ecology and meteorology. Based on satellite observations along with the ERA‐Interim reanalysis meteorology, this study examines the difference of vegetation characteristics, local wind speed, and surface temperature between the wind power regions and adjacent comparison areas in XilinGol. It shows that the construction of wind farms results in significant differences (p = 0.01) of normalized difference vegetation index values in the study areas compared to the adjacent areas in summer. The construction of the wind farms in study area has considerable influence on the change of local vegetation growth and related meteorological variables. It reduces the fraction of grassland, disrupts the wind field when speed are less than 4 m/s, and changes the spatial pattern of temperature during daytime and nighttime. However, the influence decreases with time after the installment of wind turbines. This study suggests that it is necessary to assess the impact of renewable energy construction on ecological environment and local climate, particularly for the short‐term period after its installment.

Abstract 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.

In animal models with temporal lobe epilepsy (TLE), the status epilepticus (SE) leads to a dramatic increase in number of newly born neuron in the subgranular zone (SGZ) of dentate gyrus. How the SE confers a modulation in the dentate neurogenesis is mostly unknown. Gadd45b is involved in epigenetic gene activation by DNA demethylation. This study was performed to present a novel mechanism underling SE-induced dentate neurogenesis. A transient induction (12 hrs to 3 days) of Gadd45b was observed in dentate gyrus of mice after pilocarpine-induced SE. Labeling the dividing cells with BrdU, we next found that the induction of Gadd45b was required to increase the rate of cell proliferation in the dentate gyrus at 7 and 14 days after SE. Afterward, the DNA methylation levels for candidate growth factor genes critical for the adult neurogenesis were assayed with Sequenom MassARRAY Analyzer. The results indicated that Gadd45b was necessary for SE-induced DNA demethylation of specific promoters and expression of corresponding genes in the dentate gyrus, including brain-derived neurotrophic factor (BDNF) and fibroblast growth factor-2 (FGF-2). Using Timm staining, we further suggested that SE-induced Gadd45b might contribute to the subsequent mossy fiber sprouting (MFS) in the chronically epileptic hippocampus via epigenetic regulation of dentate neurogenesis at early stage after SE. Together, Gadd45b links pilocarpine-induced SE to epigenetic DNA modification of secreted factors in the dentate gyrus, leading to extrinsic modulation on the neurogenesis.

Genomic breed composition (GBC) of an individual animal refers to the partition of its genome according to the inheritance from its ancestors or ancestral breeds. For crossbred or composite animals, knowing their GBC is useful to estimate heterosis, to characterize their actual inheritance from foundation breeds, and to make management decisions for crossbreeding programs. Various statistical approaches have been proposed to estimate GBC in animals, but the interpretations of estimates have varied with these methods. In the present study, we proposed a causality interpretation of GBC based on path analysis. We applied this method to estimating GBC in two composite breeds of beef cattle, namely Brangus and Beefmaster. Three SNP panels were used to estimate GBC: a 10K SNP panel consisting of 10,226 common SNPs across three GeneSeek Genomic Profiler (GGP) bovine SNP arrays (GGP 30K, GGP 40K, and GGP 50K), and two subsets (1K and 5K) of uniformly distributed SNPs. The path analysis decomposed the relationships between the ancestors and the composite animals into direct and indirect path effects, and GBC was measured by the relative ratio of the coefficients of direct (D-GBC) and combined (C-GBC) effects from each ancestral breed to the progeny, respectively. Estimated GBC varied only slightly between different genotyping platforms and between the three SNP panels. In the Brangus cattle, because the two ancestral breeds had a very distant relationship, the estimated D-GBC and C-GBC were comparable to each other in the path analysis, and they corresponded roughly to the estimated GBC from the linear regression and the admixture model. In the Beefmaster, however, the strong relationship in allelic frequencies between Hereford and Shorthorn imposed a challenge for the linear regression and the admixture model to estimated GBC reliably. Instead, D-GBC by the path analysis included only direct ancestral effects, and it was robust to bias due to high genomic correlations between reference (ancestral) breeds.

A variety of statistical methods, such as admixture models, have been used to estimate genomic breed composition (GBC). These methods, however, tend to produce non-zero components to reference breeds that shared some genomic similarity with a test animal. These non-essential GBC components, in turn, offset the estimated GBC for the breed to which it belongs. As a result, not all purebred animals have 100% GBC of their respective breeds, which statistically indicates an elevated false-negative rate in the identification of purebred animals with 100% GBC as the cutoff. Otherwise, a lower cutoff of estimated GBC will have to be used, which is arbitrary, and the results are less interpretable. In the present study, three admixture models with regularization were proposed, which produced sparse solutions through suppressing the noise in the estimated GBC due to genomic similarities. The regularization or penalty forms included the L1 norm penalty, minimax concave penalty (MCP), and smooth clipped absolute deviation (SCAD). The performances of these regularized admixture models on the estimation of GBC were examined in purebred and composite animals, respectively, and compared to that of the non-regularized admixture model as the baseline model. The results showed that, given optimal values for λ, the three sparsely regularized admixture models had higher power and thus reduced the false-negative rate for the breed identification of purebred animals than the non-regularized admixture model. Of the three regularized admixture models, the two with a non-convex penalty outperformed the one with L1 norm penalty. In the Brangus, a composite cattle breed, estimated GBC were roughly comparable among the four admixture models, but all the four models underestimated the GBC for these composite animals when non-ancestral breeds were included as the reference. In conclusion, the admixture models with sparse regularization gave more parsimonious, consistent and interpretable results of estimated GBC for purebred animals than the non-regularized admixture model. Nevertheless, the utility of regularized admixture models for estimating GBC in crossbred or composite animals needs to be taken with caution.

ABSTRACT Euryarchaeal lineages have been believed to have a methanogenic last common ancestor. However, members of euryarchaeal Archaeoglobi have long been considered nonmethanogenic and their evolutionary history remains elusive. Here, three high-quality metagenomic-assembled genomes (MAGs) retrieved from high-temperature oil reservoir and hot springs, together with three newly assembled Archaeoglobi MAGs from previously reported hot spring metagenomes, are demonstrated to represent a novel genus of Archaeoglobaceae, “Candidatus Methanomixophus.” All “Ca. Methanomixophus” MAGs encode an M methyltransferase (MTR) complex and a traditional type of methyl-coenzyme M reductase (MCR) complex, which is different from the divergent MCR complexes found in “Ca. Polytropus marinifundus.” In addition, “Ca. Methanomixophus dualitatem” MAGs preserve the genomic capacity for dissimilatory sulfate reduction. Comparative phylogenetic analysis supports a laterally transferred origin for an MCR complex and vertical heritage of the MTR complex in this lineage. Metatranscriptomic analysis revealed concomitant in situ activity of hydrogen-dependent methylotrophic methanogenesis and heterotrophic fermentation within populations of “Ca. Methanomixophus hydrogenotrophicum” in a high-temperature oil reservoir. IMPORTANCE Current understanding of the diversity, biology, and ecology of Archaea is very limited, especially considering how few of the known phyla have been cultured or genomically explored. The reconstruction of “Ca. Methanomixophus” MAGs not only expands the known range of metabolic versatility of the members of Archaeoglobi but also suggests that the phylogenetic distribution of MCR and MTR complexes is even wider than previously anticipated.

An important economic reason for the loss of local breeds is that they tend to be less productive, and hence having less market value than commercial breeds. Nevertheless, local breeds often have irreplaceable values, genetically and sociologically. In the breeding programs with local breeds, it is crucial to balance the selection for genetic gain and the maintaining of genetic diversity. These two objectives are often conflicting, and finding the optimal point of the trade-off has been a challenge for breeders. Genomic selection (GS) provides a revolutionary tool for the genetic improvement of farm animals. At the same time, it can increase inbreeding and produce a more rapid depletion of genetic variability of the selected traits in future generations. Optimum-contribution selection (OCS) represents an approach to maximize genetic gain while constraining inbreeding within a targeted range. In the present study, 515 Ningxiang pigs were genotyped with the Illumina Porcine SNP60 array or the GeneSeek Genomic Profiler Porcine 50K array. The Ningxiang pigs were found to be highly inbred at the genomic level. Average locus-wise inbreeding coefficients were 0.41 and 0.37 for the two SNP arrays used, whereas genomic inbreeding coefficients based on runs of homozygosity were 0.24 and 0.25, respectively. Simulated phenotypic data were used to assess the utility of genomic OCS (GOCS) in comparison with GS without inbreeding control. GOCS was conducted under two scenarios, selecting sires only (GOCS_S) or selecting sires and dams (GOCS_SD), while kinships were constrained on selected parents. The genetic gain for average daily body weight gain (ADG) per generation was between 18.99 and 20.55 g with GOCS_S, and between 23.20 and 28.92 with GOCS_SD, and it varied from 25.38 to 48.38 g under GS without controlling inbreeding. While the rate of genetic gain per generation obtained using GS was substantially larger than that obtained by the two scenarios of genomic OCS in the beginning generations of selection, the difference in the genetic gain of ADG between GS and GOCS reduced quickly in latter generations. At generation ten, the difference in the realized rates of genetic gain between GS and GOCS_SD diminished and ended up with even a slightly higher genetic gain with GOCS_SD, due to the rapid loss of genetic variance with GS and fixation of causative genes. The rate of inbreeding was mostly maintained below 5% per generation with genomic OCS, whereas it increased to between 10.5% and 15.3% per generation with GS. Therefore, genomic OCS appears to be a sustainable strategy for the genetic improvement of local breeds such as Ningxiang pigs, but keeping mind that a variety of GOCS methods exist and the optimal forms remain to be exploited further.

Abstract Background SNPs are informative to estimate genomic breed composition (GBC) of individual animals, but selected SNPs for this purpose were not made available in the commercial bovine SNP chips prior to the present study. The primary objective of the present study was to select five common SNP panels for estimating GBC of individual animals initially involving 10 cattle breeds (two dairy breeds and eight beef breeds). The performance of the five common SNP panels was evaluated based on admixture model and linear regression model, respectively. Finally, the downstream implication of GBC on genomic prediction accuracies was investigated and discussed in a Santa Gertrudis cattle population. Results There were 15,708 common SNPs across five currently-available commercial bovine SNP chips. From this set, four subsets (1,000, 3,000, 5,000, and 10,000 SNPs) were selected by maximizing average Euclidean distance (AED) of SNP allelic frequencies among the ten cattle breeds. For 198 animals presented as Akaushi, estimated GBC of the Akaushi breed (GBCA) based on the admixture model agreed very well among the five SNP panels, identifying 166 animals with GBCA = 1. Using the same SNP panels, the linear regression approach reported fewer animals with GBCA = 1. Nevertheless, estimated GBCA using both models were highly correlated (r = 0.953 to 0.992). In the genomic prediction of a Santa Gertrudis population (and crosses), the results showed that the predictability of molecular breeding values using SNP effects obtained from 1,225 animals with no less than 0.90 GBC of Santa Gertrudis (GBCSG) decreased on crossbred animals with lower GBCSG. Conclusions Of the two statistical models used to compute GBC, the admixture model gave more consistent results among the five selected SNP panels than the linear regression model. The availability of these common SNP panels facilitates identification and estimation of breed compositions using currently-available bovine SNP chips. In view of utility, the 1 K panel is the most cost effective and it is convenient to be included as add-on content in future development of bovine SNP chips, whereas the 10 K and 16 K SNP panels can be more resourceful if used independently for imputation to intermediate or high-density genotypes.

Background: Intervertebral disc degeneration (IDD) is a major cause of disc protrusion, likely to be associated with decrease of water content. This research aimed to evaluate IDD by diffusion-weighted imaging (DWI) with a 7.0 Tesla (T) magnetic resonance imaging (MRI) machine. Methods: A total of 24 healthy Sprague-Dawley rats were randomly selected and divided into four groups (A, B, C, and D), each consisting of 3 male and 3 female rats (28, 42, 56, and 70 days old, respectively). All the rats were imaged with a 7.0T MRI, producing T2WI, T1WI, and functional DWI sequences. Data were collected and apparent diffusion coefficient (ADC) charts were constructed. Nucleus pulposus and annulus fibrosus regions were identified, several regions of interest were chosen, and their ADC values were obtained. After imaging, rats were sacrificed and their intervertebral discs (L1–L6) were dissected, yielding a total of 144 discs. Protein was extracted for the purpose of Western blotting. Comparison among multiple samples used one-way analysis of variance and least significant difference methods. Results: 7.0T MRI revealed evident decrease in signal intensity within intervertebral discs of Sprague-Dawley rats with age. Intervertebral disc ADC values significantly decreased from Group A (0.00154 ± 0.00008) to Group D (0.00107 ± 0.00007; P < 0.01); nucleus pulposus ADC values significantly decreased from Group A (0.00164 ± 0.00005) to Group D (0.00140 ± 0.00007; P < 0.01) and annulus fibrosus ADC values significantly decreased from Group A (0.00129 ± 0.00014) to Group D (0.00082 ± 0.00012; P < 0.01). Meanwhile, it also revealed evident decrease from high spinal level to low spinal level: nucleus pulposus ADC values in Group A significantly decreased from L1/L2 (0.00163 ± 0.00006) to L6/S1 (0.00139 ± 0.00004; P < 0.01). While annulus fibrosus ADC values did not differ significantly between levels in Group A (P > 0.05). Western blotting showed that aggrecan content of intervertebral discs decreased from Group A (1.88 ± 0.16) to Group D (0.17 ± 0.04) with age (P < 0.01); Type II collagen content of intervertebral discs decreased from Group A (2.22 ± 0.04) to Group D (0.20 ± 0.01) with age (P < 0.01). No significant differences in aggrecan and Type II collagen content of L1–L6 intervertebral discs in Group A were noted (P > 0.05). Mean ADC values of different intervertebral regions were positively correlated with aggrecan and Type II collagen content (aggrecan: r = 0.631, P < 0.01; Type II collagen: r = 0.680, P < 0.01). Conclusion: 7.0T MRI-DWI could be applied to effectively diagnose and research early IDD in tiny variations.

The rich physical properties of metal-intercalated polycyclic aromatic hydrocarbon materials have recently attracted considerable attention. Crystals of potassium-intercalated 2,7-dimethylnaphthalene were synthesized via solid phase reaction. The combination of XRD measurements and first-principles calculations indicated that each unit cell contains two potassium atoms and four organic molecules. Magnetization measurements revealed that the samples show a Curie paramagnetism. Theoretical calculations showed that the intercalated structure becomes metallic and has local magnetic moment. Raman spectroscopy confirmed the migration of electron from potassium 4s to carbon 2p orbital, which is the source of magnetism. Our research on naphthalene derivatives is helpful for expanding the range of novel organic magnetic materials and organic superconducting materials.

The development of potential magnetic materials in metal-doped polycyclic aromatic hydrocarbons has been a research hotspot in recent years. Here we have successfully synthesized stable potassium-doped 2,3-dimethylnaphthalene samples. The combination of first-principles calculations and XRD results identifies that doping of potassium into 2,3-dimethylnaphthalene forms a monoclinic structure with a molar ratio of 1:2 between potassium and molecule. The red shifts in the Raman spectra indicate that potassium 4s electrons are transferred to the organic molecules. The magnetic measurements show that the doped materials exhibit a temperature-independent magnetization in the temperature region of 1.8–300 K, which is consistent with the Pauli paramagnetic behavior. This is distinct from the diamagnetism of pristine material. Compared to the previous focus on benzene ring structure, our study of aromatic hydrocarbon derivatives of benzene ring opens a new route for the development of this field.

Low-density (LD) single nucleotide polymorphism (SNP) arrays provide a cost-effective solution for genomic prediction and selection, but algorithms and computational tools are needed for the optimal design of LD SNP chips. A multiple-objective, local optimization (MOLO) algorithm was developed for design of optimal LD SNP chips that can be imputed accurately to medium-density (MD) or high-density (HD) SNP genotypes for genomic prediction. The objective function facilitates maximization of non-gap map length and system information for the SNP chip, and the latter is computed either as locus-averaged (LASE) or haplotype-averaged Shannon entropy (HASE) and adjusted for uniformity of the SNP distribution. HASE performed better than LASE with ≤1,000 SNPs, but required considerably more computing time. Nevertheless, the differences diminished when >5,000 SNPs were selected. Optimization was accomplished conditionally on the presence of SNPs that were obligated to each chromosome. The frame location of SNPs on a chip can be either uniform (evenly spaced) or non-uniform. For the latter design, a tunable empirical Beta distribution was used to guide location distribution of frame SNPs such that both ends of each chromosome were enriched with SNPs. The SNP distribution on each chromosome was finalized through the objective function that was locally and empirically maximized. This MOLO algorithm was capable of selecting a set of approximately evenly-spaced and highly-informative SNPs, which in turn led to increased imputation accuracy compared with selection solely of evenly-spaced SNPs. Imputation accuracy increased with LD chip size, and imputation error rate was extremely low for chips with ≥3,000 SNPs. Assuming that genotyping or imputation error occurs at random, imputation error rate can be viewed as the upper limit for genomic prediction error. Our results show that about 25% of imputation error rate was propagated to genomic prediction in an Angus population. The utility of this MOLO algorithm was also demonstrated in a real application, in which a 6K SNP panel was optimized conditional on 5,260 obligatory SNP selected based on SNP-trait association in U.S. Holstein animals. With this MOLO algorithm, both imputation error rate and genomic prediction error rate were minimal.

Porcine reproductive and respiratory syndrome (PRRS) has devastated pig industries worldwide for many years. It is caused by a small RNA virus (PRRSV), which targets almost exclusively pig monocytes or macrophages. In the present study, five SAGE (serial analysis of gene expression) libraries derived from 0 hour mock-infected and 6, 12, 16 and 24 hours PRRSV-infected porcine alveolar macrophages (PAMs) produced a total 643,255 sequenced tags with 91,807 unique tags. Differentially expressed (DE) tags were then detected using the Bayesian framework followed by gene/mRNA assignment, arbitrary selection and manual annotation, which determined 699 DE genes for reactome analysis. The DAVID, KEGG and REACTOME databases assigned 573 of the DE genes into six biological systems, 60 functional categories and 504 pathways. The six systems are: cellular processes, genetic information processing, environmental information processing, metabolism, organismal systems and human diseases as defined by KEGG with modification. Self-organizing map (SOM) analysis further grouped these 699 DE genes into ten clusters, reflecting their expression trends along these five time points. Based on the number one functional category in each system, cell growth and death, transcription processes, signal transductions, energy metabolism, immune system and infectious diseases formed the major reactomes of PAMs responding to PRRSV infection. Our investigation also focused on dominant pathways that had at least 20 DE genes identified, multi-pathway genes that were involved in 10 or more pathways and exclusively-expressed genes that were included in one system. Overall, our present study reported a large set of DE genes, compiled a comprehensive coverage of pathways, and revealed system-based reactomes of PAMs infected with PRRSV. We believe that our reactome data provides new insight into molecular mechanisms involved in host genetic complexity of antiviral activities against PRRSV and lays a strong foundation for vaccine development to control PRRS incidence in pigs.

Adipokine adiponectin (APN) has been recently reported to play a role in regulating bone mineral density (BMD). To explore the mechanism by which APN affects BMD, we investigated BMD and biomechanical strength properties of the femur and vertebra in sham-operated (Sham) and ovariectomized (OVX) APN knockout (KO) mice as compared to their operated wild-type (WT) littermates. The results show that APN deficiency has no effect on BMD but induces increased ALP activity and osteoclast cell number. While OVX indeed leads to significant bone loss in both femora and vertebras of WT mice with comparable osteogenic activity and a significant increase in osteoclast cell number when compared to that of sham control. However, no differences in BMD, ALP activity and osteoclast cell number were found between Sham and OVX mice deficient for APN. Further studies using bone marrow derived mesenchymal stem cells (MSCs) demonstrate an enhanced osteogenic differentiation and extracellular matrix calcification in APN KO mice. The possible mechanism for APN deletion induced acceleration of osteogenesis could involve increased proliferation of MSCs and higher expression of Runx2 and Osterix genes. These findings indicate that APN deficiency can protect against OVX-induced osteoporosis in mice, suggesting a potential role of APN in regulating the balance of bone formation and bone resorption, especially in the development of post-menopausal osteoporosis.

In the present study, thirteen genes involved in the reverse cholesterol transport (RCT) pathway were investigated for their associations with three fat depositions, eight fatty acid compositions and two growth-related phenotypes in a Wagyu x Limousin reference population, including 6 F(1) bulls, 113 F(1) dams, and 246 F(2) progeny. A total of 37 amplicons were used to screen single nucleotide polymorphisms (SNPs) on 6 F(1) bulls. Among 36 SNPs detected in 11 of these 13 genes, 19 were selected for genotyping by the Sequenom assay design on all F(2) progeny. Single-marker analysis revealed seven SNPs in ATP binding cassette A1, apolipoproteins A1, B and E, phospholipid transfer protein and paraoxinase 1 genes significantly associated with nine phenotypes (P

Palladin is an actin cytoskeleton-associated protein which is crucial for cell morphogenesis and motility. Previous studies have shown that palladin is localized to the axonal growth cone in neurons and may play an important role in axonal extension. Previously, we have generated palladin knockout mice which display cranial neural tube closure defect and embryonic lethality before embryonic day 15.5 (E15.5). To further study the role of palladin in the developing nervous system, we examined the innervation of palladin-deficient mouse embryos since the 200 kd, 140 kd, 90-92 kd and 50 kd palladin isoforms were undetectable in the mutant mouse embryo brain. Contrary to the results of previous studies, we found no inhibition of the axonal extension in palladin-deficient mouse embryos. The cortical neurons derived from palladin-deficient mice also showed no significant difference in neurite outgrowth as compared with those from wild-type mice. Moreover, no difference was found in neurite outgrowth of neural stem cell derived-neurons between palladin-deficient mice and wild-type mice. In conclusion, these results suggest that palladin is dispensable for normal neurite outgrowth in mice.