Foxp3 transcription factor is proapoptotic and lethal to developing regulatory T cells unless counterbalanced by cytokine survival signals.

Immune tolerance requires regulatory T (Treg) cells to prevent autoimmune disease, with the transcription factor Foxp3 functioning as the critical regulator of Treg cell development and function. We report here that Foxp3 was lethal to developing Treg cells in the thymus because it induced a unique proapoptotic protein signature (Puma⁺⁺⁺p-Bim⁺⁺p-JNK⁺⁺DUSP6⁻) and repressed expression of prosurvival Bcl-2 molecules. However, Foxp3 lethality was prevented by common gamma chain (γc)-dependent cytokine signals that were present in the thymus in limiting amounts sufficient to support only ∼1 million Treg cells. Consequently, most newly arising Treg cells in the thymus were deprived of this signal and underwent Foxp3-induced death, with Foxp3⁺CD25⁻ Treg precursor cells being the most susceptible. Thus, we identify Foxp3 as a proapoptotic protein that requires developing Treg cells to compete with one another for limiting amounts of γc-dependent survival signals in the thymus.

Real-time milk analysis integrated with stacking ensemble learning as a tool for the daily prediction of cheese-making traits in Holstein cattle.

Cheese-making traits in dairy cattle are important to the dairy industry but are difficult to measure at the individual level because there are limitations on collecting phenotypic information. Mid-infrared spectroscopy has its advantages, but it can only be used during monthly milk recordings. Recently, in-line devices for real-time analysis of milk quality have been developed. The AfiLab recording system (Afimilk) offers significant benefits as phenotypes can be collected from each cow at each milking session. The objective of this study was to assess the potential of integrating AfiLab real-time milk analyzer measures with the stacking ensemble learning technique using heterogeneous base learners for the in-line daily monitoring of cheese-making traits in Holstein cattle with a view to developing a precision livestock farming system for monitoring the technological quality of milk. Data and samples for wet-laboratory analyses were collected from 499 Holstein cows belonging to 2 farms where the AfiLab system was installed. The traits of concern were 9 milk coagulation traits [3 milk coagulation properties (MCP), and 6 curd firming traits (CFt)], and 7 cheese-making traits [3 cheese yield (CY) traits, and 4 milk nutrient recovery in the curd (REC) traits]. The near-infrared AfiLab spectral data and on-farm information (days in milk and parity) were used to assess the predictive ability of different statistical methods [elastic net (EN), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), and artificial neural network (ANN)] across different cross-validation scenarios. These statistical methods were considered the base learners, which were then combined in a stacking ensemble learning. Results indicate that including information on the cows (days in milk and parity) in the AfiLab infrared prediction increased its accuracy by 10.3% for traditional MCP, 13.8% for curd firming, 9.8% for CY, and 11.2% for REC traits compared with those obtained from near-infrared AfiLab alone. The statistical approaches exhibited high prediction accuracies (R2) averaged across the cross-validation scenarios for traditional MCP (0.58 for ANN, 0.55 for EN and GBM, 0.52 for XGBoost, and 0.62 for stacking ensemble), CFt (0.55 for ANN, 0.54 for EN and GBM, 0.53 for XGBoost, and 0.61 for stacking ensemble), and similar R2 averages for CY and REC (0.55 for ANN, 0.54 for EN and GBM, 0.53 for XGBoost, and 0.61 for stacking ensemble). The ANN approach was more accurate than the other base learners (EN, GBM, and XGBoost) and improved accuracy across cross-validation scenarios on average by 7% for traditional MCP, 5% for CFt, 8% for CY, and 7% for REC. The stacking ensemble method improved prediction accuracy by 3% to 31% for traditional MCP, 2% to 26% for CFt, 1% to 38% for CY traits, and 2% to 27% for REC traits compared with the base learners. The prediction accuracies of the different approaches evaluated tended to decrease from the 10-fold cross-validation to the independent validation scenario, although there was a smaller reduction in prediction accuracy with the stacking ensemble learning technique across all the cross-validation scenarios. Our results show that combining in-line on-farm information with stacking ensemble machine learning represents an effective alternative for obtaining robust daily predictions of milk cheese-making traits.

FOXP3 renders activated human regulatory T cells resistant to restimulation-induced cell death by suppressing SAP expression.

Restimulation-induced cell death (RICD) is an apoptotic program that regulates effector T cell expansion, triggered by repeated stimulation through the T cell receptor (TCR) in the presence of interleukin-2 (IL-2). Although CD4+ regulatory T cells (Tregs) consume IL-2 and experience frequent TCR stimulation, they are highly resistant to RICD. Resistance in Tregs is dependent on the forkhead box P3 (FOXP3) transcription factor, although the mechanism remains unclear. T cells from patients with X-linked lymphoproliferative disease (XLP-1), that lack the adaptor molecule SLAM-associated protein (SAP), are also resistant to RICD. Here we demonstrate that normal Tregs express very low levels of SAP compared to conventional T cells. FOXP3 reduces SAP expression by directly binding to and repressing the SH2D1A (SAP) promoter. Indeed, ectopic SAP expression restores RICD sensitivity in human FOXP3+ Tregs. Our findings illuminate the mechanism behind FOXP3-mediated RICD resistance in Tregs, providing new insight into their long-term persistence.

SAP facilitates recruitment and activation of LCK at NTB-A receptors during restimulation-induced cell death.

Upon TCR restimulation, activated, cycling T cells can undergo a self-regulatory form of apoptosis known as restimulation-induced cell death (RICD). We previously demonstrated that RICD is impaired in T cells from patients with X-linked lymphoproliferative disease, which lack SLAM-associated protein (SAP) expression. Both SAP and the specific SLAM receptor NK, T, and B cell Ag (NTB-A) are required for RICD, but the mechanism by which these molecules promote a strong, proapoptotic signal through the TCR remains unclear. In this article, we show that the Src-family kinase LCK, but not FYN, associates with NTB-A in activated human T cells. This association increased after TCR restimulation in a SAP-dependent manner, requiring both immunoreceptor tyrosine-based switch motifs in the NTB-A cytoplasmic tail. Both NTB-A-associated LCK phosphorylation and kinase activity were enhanced in restimulated T cells, amplifying proximal TCR signaling. In contrast, TCR-induced LCK association with NTB-A, as well as phosphorylation and kinase activity, was reduced in T cells from patients with X-linked lymphoproliferative disease or normal T cells transfected with SAP-specific small interfering RNA, consistent with RICD resistance. Collectively, our data reveal how SAP nucleates a previously unknown signaling complex involving NTB-A and LCK to potentiate RICD of activated human T cells.

Influence of intramammary infection of a single gland in dairy cows on the cow's milk quality.

Intramammary infection (IMI), comprises a group of costly diseases affecting dairy animals worldwide. Many dairy parlours are equipped with on-line computerised data acquisition systems designed to detect IMI. However, the data collected is related to the cow level, therefore the contribution of infected glands to the recorded parameters may be over estimated. The present study aimed at evaluating the influence of single gland IMI by different bacteria species on the cow's overall milk quality. A total of 130 cows were tested 239 times; 79 cows were tested once and the others were examined 2-8 times. All of the analysed data refer to the number of tests performed, taking into account the repeated testing of the same cows. Of the cows tested ~50% were free of infection in all 4 glands and the others were infected in one gland with different coagulase negative staphylococci (CNS), Streptococcus dysgalactiae, or were post infected with Escherichia coli (PIEc), i.e., free of bacterial infection at the time of sampling but 1-2 months after clinical infection by E. coli. Overall, infection with bacteria had significant effects on somatic cell count (SCC) and lactose concentration. Examining each bacterium reveals that the major influence on those parameters was the sharp decrease in lactose in the PIEc and curd firmness in PIEc and Strep. Individual gland milk production decreased ~20% in Strep. dysgalactiae- and ~50% in PIEc-infected glands with respect to glands with no bacterial findings. Significant differences were found in lactose, SCC, rennet clotting time and curd firmness in the milk of infected glands and among those, these parameters were significantly higher in Strep. dysgalactiae and PIEc than in CNS infected cows. The current results using quarter-milking reinforces the importance of accurate IMI detection in relation to economic and welfare factors, and moreover, emphasises the need for technical sensing and constant reporting to the farmer about changes in the milk quality of every animal.

Combining genetic markers, on-farm information and infrared data for the in-line prediction of blood biomarkers of metabolic disorders in Holstein cattle.

BACKGROUND: Various blood metabolites are known to be useful indicators of health status in dairy cattle, but their routine assessment is time-consuming, expensive, and stressful for the cows at the herd level. Thus, we evaluated the effectiveness of combining in-line near infrared (NIR) milk spectra with on-farm (days in milk [DIM] and parity) and genetic markers for predicting blood metabolites in Holstein cattle. Data were obtained from 388 Holstein cows from a farm with an AfiLab system. NIR spectra, on-farm information, and single nucleotide polymorphisms (SNP) markers were blended to develop calibration equations for blood metabolites using the elastic net (ENet) approach, considering 3 models: (1) Model 1 (M1) including only NIR information, (2) Model 2 (M2) with both NIR and on-farm information, and (3) Model 3 (M3) combining NIR, on-farm and genomic information. Dimension reduction was considered for M3 by preselecting SNP markers from genome-wide association study (GWAS) results. RESULTS: Results indicate that M2 improved the predictive ability by an average of 19% for energy-related metabolites (glucose, cholesterol, NEFA, BHB, urea, and creatinine), 20% for liver function/hepatic damage, 7% for inflammation/innate immunity, 24% for oxidative stress metabolites, and 23% for minerals compared to M1. Meanwhile, M3 further enhanced the predictive ability by 34% for energy-related metabolites, 32% for liver function/hepatic damage, 22% for inflammation/innate immunity, 42.1% for oxidative stress metabolites, and 41% for minerals, compared to M1. We found improved predictive ability of M3 using selected SNP markers from GWAS results using a threshold of > 2.0 by 5% for energy-related metabolites, 9% for liver function/hepatic damage, 8% for inflammation/innate immunity, 22% for oxidative stress metabolites, and 9% for minerals. Slight reductions were observed for phosphorus (2%), ferric-reducing antioxidant power (1%), and glucose (3%). Furthermore, it was found that prediction accuracies are influenced by using more restrictive thresholds (-log10(P-value) > 2.5 and 3.0), with a lower increase in the predictive ability. CONCLUSION: Our results highlighted the potential of combining several sources of information, such as genetic markers, on-farm information, and in-line NIR infrared data improves the predictive ability of blood metabolites in dairy cattle, representing an effective strategy for large-scale in-line health monitoring in commercial herds.

In-line near-infrared analysis of milk coupled with machine learning methods for the daily prediction of blood metabolic profile in dairy cattle.

Precision livestock farming technologies are used to monitor animal health and welfare parameters continuously and in real time in order to optimize nutrition and productivity and to detect health issues at an early stage. The possibility of predicting blood metabolites from milk samples obtained during routine milking by means of infrared spectroscopy has become increasingly attractive. We developed, for the first time, prediction equations for a set of blood metabolites using diverse machine learning methods and milk near-infrared spectra collected by the AfiLab instrument. Our dataset was obtained from 385 Holstein Friesian dairy cows. Stacking ensemble and multi-layer feedforward artificial neural network outperformed the other machine learning methods tested, with a reduction in the root mean square error of between 3 and 6% in most blood parameters. We obtained moderate correlations (r) between the observed and predicted phenotypes for γ-glutamyl transferase (r = 0.58), alkaline phosphatase (0.54), haptoglobin (0.66), globulins (0.61), total reactive oxygen metabolites (0.60) and thiol groups (0.57). The AfiLab instrument has strong potential but may not yet be ready to predict the metabolic stress of dairy cows in practice. Further research is needed to find out methods that allow an improvement in accuracy of prediction equations.

Hot injection dynamics: design mechanisms and ideas.

A minimal quantum mechanical model for efficient molecular capture of photon energy is presented. The model is constructed from a bright electronic state which is accessed by a photoinduced transition from the ground state and an acceptor excited state which stores the photoenergy. The model permits rational design of the bright and acceptor electronic states to improve the capture of solar energy. The main design factors are analyzed through examples.

Density-dependent liquid nitromethane decomposition: molecular dynamics simulations based on ReaxFF.

The decomposition mechanism of hot liquid nitromethane at various compressions was studied using reactive force field (ReaxFF) molecular dynamics simulations. A competition between two different initial thermal decomposition schemes is observed, depending on compression. At low densities, unimolecular C-N bond cleavage is the dominant route, producing CH(3) and NO(2) fragments. As density and pressure rise approaching the Chapman-Jouget detonation conditions (∼30% compression, >2500 K) the dominant mechanism switches to the formation of the CH(3)NO fragment via H-transfer and/or N-O bond rupture. The change in the decomposition mechanism of hot liquid NM leads to a different kinetic and energetic behavior, as well as products distribution. The calculated density dependence of the enthalpy change correlates with the change in initial decomposition reaction mechanism. It can be used as a convenient and useful global parameter for the detection of reaction dynamics. Atomic averaged local diffusion coefficients are shown to be sensitive to the reactions dynamics, and can be used to distinguish between time periods where chemical reactions occur and diffusion-dominated, nonreactive time periods.

A phenotypic and functional characterization of NK cells in adenoids.

Adenoids are part of the MALT. In the present study, we analyzed cell surface markers and cytolytic activity of adenoidal NK (A-NK) cells and compared them with NK cells derived from blood of the same donors (B-NK). NK cells comprised 0.67% (0.4-1.2%) of the total lymphoid population isolated from adenoids. The majority (median=92%) of the A-NK cells was CD56(bright)CD16(-). A-NK cells were characterized by the increased expression of activation-induced receptors. NKp44 was detected on >60%, CD25 on >40%, and HLA-DR on >50% of freshly isolated A-NK cells. Functional assays indicated that the cytotoxic machinery of A-NK is intact, and sensitive target cells are killed via natural cytotoxicity receptors, such as NKG2D. Carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1; CD66) expression was up-regulated in 23% (median) of the A-NK cells by IL-2 activation but unchanged in B-NK cells. CEACAM1 inhibited the A-NK killing of target cells. CXCR4 was expressed on more than 40% A-NK cells prior to activation. Its ligand, CXCL12, was found in endothelial cells of the capillaries within the adenoid and in cells of the epithelial lining. In addition, A-NK cells migrated in vitro toward a gradient of CXCL12 in a dose-responsive manner, suggesting a role for this chemokine in A-NK cell recruitment and trafficking. We conclude that the A-NK cells are unique in that they display an activated-like phenotype and are different from their CD16(-) B-NK cell counterparts. This phenotype presumably reflects the chronic interaction of A-NK cells with antigens penetrating the body through the nasal route.

Stochastic surrogate Hamiltonian.

The surrogate Hamiltonian is a general scheme to simulate the many body quantum dynamics composed of a primary system coupled to a bath. The method has been based on a representative bath Hamiltonian composed of two-level systems that is able to mimic the true system-bath dynamics up to a prespecified time. The original surrogate Hamiltonian method is limited to short time dynamics since the size of the Hilbert space required to obtain convergence grows exponentially with time. By randomly swapping bath modes with a secondary thermal reservoir, the method can simulate quantum dynamics of the primary system from short times to thermal equilibrium. By averaging a small number of realizations converged values of the system observables are obtained avoiding the exponential increase in resources. The method is demonstrated for the equilibration of a molecular oscillator with a thermal bath.

Microwave dielectric spectroscopy study of water dynamics in normal and contaminated raw bovine milk.

The role of water in bovine milk is more complicated than that of a background solvent. To understand the interaction between water and the constituents of milk, an extensive dielectric study of the γ-dispersion of raw bovine milk was carried out over the frequency range 0.1-50GHz and the interval of temperatures (10°C-42°C). Samples were provided by utilizing an extended donor pool. The results reveal that the temperature dependence of the characteristic relaxation times is described by the Arrhenius law. Furthermore, it conforms to a Meyer-Neldel compensation, whereby the pre-factor of the relaxation times is dependent on the activation energy. This entropy/enthalpy compensation is traced to the interaction between bulk water dynamic clusters and other milk constituents. A statistical correlation between the Somatic Cell Count, a traditional measure of milk quality, and the relaxation times is provided as well, opening new vistas for the industrial classification of milk.

Dielectric spectroscopy study of water dynamics in frozen bovine milk.

Bovine milk is a complex colloidal liquid exhibiting a multi-scaled structure. It is of particular importance, both commercially and scientifically, to investigate both its dynamic and structural properties. In the current study we have employed the broadband dielectric spectroscopy (BDS) technique in the frequency range of 10(-1)-10(6)Hz and the temperature range of 176-230 K in order to examine the molecular structure and dynamics of quenched bovine milk. Four dielectric relaxation processes were identified. Three of them are associated with water in its different forms: water-lactose complexes, bulk hexagonal and cubic ices. The fourth process is attributed to domain wall relaxations linked to the presence of micro-cracks in the ice structures. In addition, the first process, attributed to water-lactose complexes, obeys the Meyer-Neldel compensation law and can be taken as evidence of differing interfaces of these complexes with the bulk water of the milk, mediated by the lactose concentration. Furthermore, an intriguing structural-dynamic transition around 200K was observed. Considering the mentioned above, we conclude that our results emphasize the structural and dynamical significance of water in bovine milk.

Role of vibrationally excited NO in promoting electron emission when colliding with a metal surface: a nonadiabatic dynamic model.

A nonadiabatic quantum dynamic model has been developed to study the process of electron emission from a low-work-function metal surface. The process is initiated by scattering a highly vibrationally excited NO molecule from a surface composed of a Cs layer covering a Ru crystal. The model addresses the increasing quantum yield of the electron emission as a function of the molecular vibrational excitation and incident kinetic energy. The reaction mechanism is identified as a long-range harpooning electron transfer to a molecular ion which is then accelerated toward the surface. Upon impact, the molecular ion emits its excess electron.

Repressed ghosts and dissociated vampires in the enacted dimension of psychoanalytic treatment.

One of the most evocative uses of the metaphor of a ghost in psychoanalytic writing was crafted by Hans Loewald in "On the Therapeutic Action of Psycho-Analysis" (1960). In this seminal work, Loewald likened the process of psychoanalytic change to that of transforming psychic ghosts into ancestors. In the present paper, the author supplements the metaphor of ghosts that haunt with the metaphor of vampires that menace, and links these two alien experiences to two psychological processes: repression and dissociation. Descriptions of ghosts and vampires in folklore, and the ways they are experienced in analytic treatment, are followed by an explication of the enacted dimension of analytic process-the arena of treatment in which all demons are inevitably revivified, "recognized," and ultimately laid to rest. The paper includes a clinical illustration of a dissociated vampire: a Holocaust trauma transmitted across three generations of survivors.

Psychoanalytic supervision of the difficult patient.

Control cases from the broad group of non-neurotic but potentially analyzable patients appear with increasing frequency. The intense, complex transferences they develop place great stress on the psychoanalytic relationship and evoke marked countertransference reactions in psychoanalytic candidates, which reverberate within the supervisory relationship. Through application of a case study method, common themes emerge in the candidate-supervisor dyad: idealization of the supervisor and of classical technique, identification with the patient, parallel process enactments, difficulty maintaining the analytic frame, and the importance of concurrent training analysis. Classical supervisory techniques must be adapted to the "difficult" (non-neurotic) control case. Complex countertransference issues must be carefully addressed while maintaining the teach/treat boundary.

T cell receptor stimulation impairs IL-7 receptor signaling by inducing expression of the microRNA miR-17 to target Janus kinase 1.

T cell receptor (TCR)-mediated inhibition of interleukin-7 (IL-7) signaling is important for lineage fate determination in the thymus and for T cell survival in the periphery because uninterrupted IL-7 signaling results in T cell death. The initial event in IL-7 signaling is the transactivation of Janus kinases 1 and 3 (Jak1 and Jak3), which are associated with the cytosolic tails of the IL-7 receptor α chain (IL-7Rα) and the γc subunit, the two cell surface proteins that constitute IL-7R. We found that Jak1 is a highly unstable protein with a half-life of only 1.5 hours, so that continuous Jak1 protein synthesis is required to maintain Jak1 protein in sufficient abundance to support IL-7 signaling. However, we also found that Jak1 protein synthesis was acutely reduced by TCR-responsive microRNAs in the miR-17 family, which targeted Jak1 mRNA (messenger RNA) to inhibit its translation. Thus, this study identifies a molecular mechanism by which TCR engagement acutely disrupts IL-7 signaling.

Fluorescence-activated cell sorting-based quantitation of T cell receptor restimulation-induced cell death in activated, primary human T cells.

After initial stimulation with antigen and exposure to the growth cytokine interleukin-2, activated T lymphocytes become sensitized to apoptosis upon antigen restimulation through the T cell receptor. This self-regulatory, restimulation-induced cell death (RICD) program constrains the proliferative capacity of activated T cells to help prevent excessive T cell accumulation and associated immunopathology. Here we describe a simple FACS-based approach for measuring RICD sensitivity in activated human T cells following polyclonal restimulation in vitro. This procedure is a straightforward research and clinical diagnostic tool for assessing RICD sensitivity for T cells derived from normal donors and patients suffering from diseases causing dysregulated T cell homeostasis.

Real-time evaluation of milk quality as reflected by clotting parameters of individual cow's milk during the milking session, between day-to-day and during lactation.

Real-time analysis of milk coagulation properties as performed by the AfiLab™ milk spectrometer introduces new opportunities for the dairy industry. The study evaluated the performance of the AfiLab™ in a milking parlor of a commercial farm to provide real-time analysis of milk-clotting parameters -Afi-CF for cheese manufacture and determine its repeatability in time for individual cows. The AfiLab™ in a parlor, equipped with two parallel milk lines, enables to divert the milk on-line into two bulk milk tanks (A and B). Three commercial dairy herds of 220 to 320 Israeli Holstein cows producing ∼11 500 l during 305 days were selected for the study. The Afi-CF repeatability during time was found significant (P < 0.001) for cows. The statistic model succeeded in explaining 83.5% of the variance between Afi-CF and cows, and no significant variance was found between the mean weekly repeated recordings. Days in milk and log somatic cell count (SCC) had no significant effect. Fat, protein and lactose significantly affected Afi-CF and the empirical van Slyke equation. Real-time simulations were performed for different cutoff levels of coagulation properties where the milk of high Afi-CF cutoff value was channeled to tank A and the lower into tank B. The simulations showed that milk coagulation properties of an individual cow are not uniform, as most cows contributed milk to both tanks. Proportions of the individual cow's milk in each tank depended on the selected Afi-CF cutoff. The assessment of the major causative factors of a cow producing low-quality milk for cheese production was evaluated for the group that produced the low 10% quality milk. The largest number of cows in those groups at the three farms was found to be cows with post-intramammary infection with Escherichia coli and subclinical infections with streptococci or coagulase-negative staphylococci (∼30%), although the SCC of these cows was not significantly different. Early time in lactation together with high milk yield >50 l/day, and late in lactation together with low milk yield<15 l/day and estrous (0 to 5 days) were also important influencing factors for low-quality milk. However, ∼50% of the tested variables did not explain any of the factors responsible for the cow producing milk in the low - 10% Afi-CF.

Decoherence control by tracking a Hamiltonian reference molecule.

A molecular system in contact with a bath undergoes strong decoherence processes. We examine a control scheme to minimize dissipation, while maximally retaining coherent evolution, by relating the evolution of the molecule to that of an identical freely propagating system. We seek a driving field that maximizes the projection of the open molecular system onto the freely propagated one. The evolution in time of a molecular system consisting of two nonadiabatically coupled electronic states interacting with a bath is followed. The driving control field that overcomes the decoherence is calculated. A proposition to implement the scheme in the laboratory using feedback control is suggested.