Effect of specific FSH or LH deprivation on testicular function of the adult rat

While the need for FSH in initiating spermatogenesis in the immature rat is well accepted, its requirement for maintenance of spermatogenesis in adulthood is questioned. In the current study, using gonadotropin antisera to neutralize specifically either endogenous FSH or LH, we have investigated the effect of either FSH or LH deprivation for a 10-day period on (i) testicular macromolecular synthesis in vitro, (ii) the activities of testicular germ cell specific LDH-X and hyaluronidase enzymes, and finally (iii) on the concentration of sulphated glycoprotein (SGP-2), one of the Sertoli cell marker proteins. Both immature (35-day-old) and adult (100-day-old) rats have been used in this study. Since LH deprivation leads to a near total blockade of testosterone production, the ability of exogenous testosterone supplementation to override the effects of LH deficiency has also been evaluated. Deprivation of either of the gonadotropins significantly affected in vitro RNA and protein synthesis by both testicular minces as well as single cell preparations. Fractionation of dispersed testicular cells preincubated with labelled precursors of RNA and protein on Percoll density gradient revealed that FSH deprivation affected specifically the rate of RNA and protein synthesis of germ cell and not Leydig cell fraction. LH but not FSH deprivation inhibited [3H]thymidine incorporation into DNA. The inhibitory effect of LH could mostly be overriden by testosterone supplementation. LDH-X and hyaluronidase activities of testicular homogenates of adult rats showed significant reduction (50%; P less than .05) following either FSH or LH deprivation. Again testosterone supplementation was able to reverse the LH inhibitory effect.

Semi-Supervised Classification Using Sparse Gaussian Process Regression

Gaussian Processes (GPs) are promising Bayesian methods for classification and regression problems. They have also been used for semi-supervised learning tasks. In this paper, we propose a new algorithm for solving semi-supervised binary classification problem using sparse GP regression (GPR) models. It is closely related to semi-supervised learning based on support vector regression (SVR) and maximum margin clustering. The proposed algorithm is simple and easy to implement. It gives a sparse solution directly unlike the SVR based algorithm. Also, the hyperparameters are estimated easily without resorting to expensive cross-validation technique. Use of sparse GPR model helps in making the proposed algorithm scalable. Preliminary results on synthetic and real-world data sets demonstrate the efficacy of the new algorithm.

Optimization of an ammonia reactor using regression analysis

This paper presents an optimization algorithm for an ammonia reactor based on a regression model relating the yield to several parameters, control inputs and disturbances. This model is derived from the data generated by hybrid simulation of the steady-state equations describing the reactor behaviour. The simplicity of the optimization program along with its ability to take into account constraints on flow variables make it best suited in supervisory control applications.

Chronic suppression of testicular function by constant infusion of gonadotropin-releasing hormone agonist and testosterone supplementation in the bonnet monkey (Macaca radiata)

Objective: To study the efficacy of long-term buserelin acetate infusion to desensitize pituitary and block testicular function in adult male monkeys (Macaca radiata). Animals: Proven fertile male monkeys exhibiting normal testicular function. Protocol: Each of the control (n = 5) and experimental monkeys (n = 10) received a fresh miniosmotic pump every 21 days, whereas pumps in controls delivered vehicle of experimentals released 50-mu-g buserelin acetate every 24 hours. On day 170 (renewed every 60 days) a silastic capsule containing crystalline testosterone (T) was implanted in the experimental monkeys. At the end of 3 years, treatment was stopped, and recovery of testicular function and fertility monitored. Results: (1) Treatment resulted in marked reduction of nocturnal but not basal serum T; (2) the pituitary remained desensitized to buserelin acetate throughout the 3-year period; (3) animals were largely azoospermic with occasional oligospermia exhibited by two monkeys; and (4) withdrawal of treatment restored testicular function, with 70% of animals regaining fertility. Conclusion: Long-term infertility (but restorable) can be induced in male monkeys by constant infusion of buserelin acetate and T.

Effect of constant infusion of gonadotropin releasing hormone (GnRH) agonist Buserelin and antagonist CDB 2085 A using osmotic minipumps on testicular function in adult male bonnet monkey (Macaca radiata)

The effect of chronic infusion of gonadotropic hormone agonist Buserelin or antagonist CDB 2085 A for 15 weeks via alzet minipumps in adult male bonnet monkeys was studied. Infusion of Buserelin resulted in a decrease in the difference between serum testosterone values at 22.00 hours and 10.00 hours, decrease in responsiveness to injected Buserelin as judged by change in serum testosterone values from pre-injection values and decrease in sperm counts. Infusion of antagonist resulted in a decrease in the difference between serum testosterone values at 22.00 hours and 10.00 hours.

Effect of altering endogenous gonadotrophin concentrations on the kinetics of testicular germ cell turnover in the bonnet monkey (Macaca radiata)

The role of FSH and diurnal testosterone rhythms in specific germ cell transformations during spermatogenesis were investigated using DNA flow cytometry and morphometry of the seminiferous epithelium of the adult male bonnet monkey (Macaca radiata), the endogenous hormone levels of which were altered by two different protocols. (1) Active immunization of five monkeys for 290 days using ovine FSH adsorbed on Alhydrogel resulted in the neutralization of endogenous FSH, leaving the LH and diurnal testosterone rhythms normal. (2) Desensitization of the pituitary gonadotrophs of ten monkeys by chronically infusing gonadotrophin-releasing hormone analogue, buserelin (50 micrograms/day release rate), via an Alzet pump implant (s.c.) led to a 60-80% reduction in LH and FSH as well as total abolition of testosterone rhythms. The basal testosterone level (3.3 +/- 2.0 micrograms/l), however, was maintained in this group by way of an s.c. testosterone silicone elastomer implant. Both of the treatments caused significant (P < 0.01) nearly identical reduction in testicular biopsy scores, mitotic indices and daily sperm production rates compared with respective controls. The germ cell DNA flow cytometric profiles of the two treatment groups, however, were fundamentally different from each other. The pituitary-desensitized group exhibited a significant (P < 0.001) increase in 2C (spermatogonial) and decrease in 1C (round spermatid) populations while S-phase (preleptotene spermatocytes) and 4C (primary spermatocytes) populations were normal, indicating an arrest in meiosis caused presumably by the lack of increment in nocturnal serum testosterone. In contrast, in the FSH-immunized group, at day 80 when the FSH deprivation was total, the primary block appeared to be at the conversion of spermatogonia (2C) to cells in S-phase and primary spermatocytes (4C reduced by > 90%). In addition, at this time, although the round spermatid (1C) population was reduced by 65% (P < 0.01) the elongate spermatid (HC) population showed an increase of 52% (P < 0.05). This, taken together with the fact that sperm output in the ejaculate is reduced by 80%, suggests a blockade in spermiogenesis and spermiation. Administration of booster injections of oFSH at time-points at which the antibody titre was markedly low (at days 84 and 180) resulted in a transient resurgence in spermatogenesis (at day 180 and 228), and this again was blocked by day 290 when the FSH antibody titre increased.

The effect of progesterone replacement on gene expression in the corpus luteum during induced regression and late luteal phase in the bonnet monkey (Macaca radiata)

Background: In higher primates, although LH/CG play a critical role in the control of corpus luteum (CL) function, the direct effects of progesterone (P4) in the maintenance of CL structure and function are unclear. Several experiments were conducted in the bonnet monkey to examine direct effects of P4 on gene expression changes in the CL, during induced luteolysis and the late luteal phase of natural cycles. Methods: To identify differentially expressed genes encoding PR, PR binding factors, cofactors and PR downstream signaling target genes, the genome-wide analysis data generated in CL of monkeys after LH/P-4 depletion and LH replacement were mined and validated by real-time RT-PCR analysis. Initially, expression of these P4 related genes were determined in CL during different stages of luteal phase. The recently reported model system of induced luteolysis, yet capable of responsive to tropic support, afforded an ideal situation to examine direct effects of P4 on structure and function of CL. For this purpose, P4 was infused via ALZET pumps into monkeys 24 h after LH/P4 depletion to maintain mid luteal phase circulating P4 concentration (P4 replacement). In another experiment, exogenous P4 was supplemented during late luteal phase to mimic early pregnancy. Results: Based on the published microarray data, 45 genes were identified to be commonly regulated by LH and P4. From these 19 genes belonging to PR signaling were selected to determine their expression in LH/P-4 depletion and P4 replacement experiments. These 19 genes when analyzed revealed 8 genes to be directly responsive to P4, whereas the other genes to be regulated by both LH and P4. Progesterone supplementation for 24 h during the late luteal phase also showed changes in expression of 17 out of 19 genes examined. Conclusion: These results taken together suggest that P4 regulates, directly or indirectly, expression of a number of genes involved in the CL structure and function.

Comparative studies on the effects of specific immunoneutralization of endogenous FSH or LH on testicular germ cell transformations in the adult bonnet monkey (Macaca radiata)

PROBLEM: It is yet to be determined clearly whether the two hormones FSH and T act synergistically in the same cell type-the Sertoli cells-to control overall spermatogenesis or influence independently the transformation of specific germ cell types during spermatogenesis in the adult mammal. METHOD: Adult male bonnet monkeys specifically deprived of either FSH or LH using immunoneutralization techniques were monitored for changes in testicular germ cell transformation by DNA flow cytometry. RESULTS: FSH deprivation caused a significant reduction (>40%; P < 0.05) in [H-3] thymidine incorporation into DNA of proliferating 2C (spermatogonial) cells, a marked inhibition (>50%) in the transformation of 2C to primary spermatocytes (4C) and a concomitant, belated reduction (50%) in the formation of round spermatids (1C). In contrast, specific LH/T deprivation led to an immediate arrest in the meiotic transformation of 4C to 1C/HC leading to an effective and significant block (<90%; P < 0.01) in sperm production. CONCLUSION: Thus, LH rather than FSH deprivation has a more pronounced and immediate effect as the former primarily blocks meiosis (4C --> 1C/HC) which controls production of spermatids. These data provide evidence for LH/T and FSH regulating spermatogenic process in the adult primate by primarily acting at specific germ cell transformation steps.

Online character recognition using regression techniques

This paper introduces a scheme for classification of online handwritten characters based on polynomial regression of the sampled points of the sub-strokes in a character. The segmentation is done based on the velocity profile of the written character and this requires a smoothening of the velocity profile. We propose a novel scheme for smoothening the velocity profile curve and identification of the critical points to segment the character. We also porpose another method for segmentation based on the human eye perception. We then extract two sets of features for recognition of handwritten characters. Each sub-stroke is a simple curve, a part of the character, and is represented by the distance measure of each point from the first point. This forms the first set of feature vector for each character. The second feature vector are the coeficients obtained from the B-splines fitted to the control knots obtained from the segmentation algorithm. The feature vector is fed to the SVM classifier and it indicates an efficiency of 68% using the polynomial regression technique and 74% using the spline fitting method.

Optimal local polynomial regression of noisy time varying signals

We address the problem of local-polynomial modeling of smooth time-varying signals with unknown functional form, in the presence of additive noise. The problem formulation is in the time domain and the polynomial coefficients are estimated in the pointwise minimum mean square error (PMMSE) sense. The choice of the window length for local modeling introduces a bias-variance tradeoff, which we solve optimally by using the intersection-of-confidence-intervals (ICI) technique. The combination of the local polynomial model and the ICI technique gives rise to an adaptive signal model equipped with a time-varying PMMSE-optimal window length whose performance is superior to that obtained by using a fixed window length. We also evaluate the sensitivity of the ICI technique with respect to the confidence interval width. Simulation results on electrocardiogram (ECG) signals show that at 0dB signal-to-noise ratio (SNR), one can achieve about 12dB improvement in SNR. Monte-Carlo performance analysis shows that the performance is comparable to the basic wavelet techniques. For 0 dB SNR, the adaptive window technique yields about 2-3dB higher SNR than wavelet regression techniques and for SNRs greater than 12dB, the wavelet techniques yield about 2dB higher SNR.

Focussed Crawling with large scale Ordinal Regression Solvers

In this paper we propose a novel, scalable, clustering based Ordinal Regression formulation, which is an instance of a Second Order Cone Program (SOCP) with one Second Order Cone (SOC) constraint. The main contribution of the paper is a fast algorithm, CB-OR, which solves the proposed formulation more eficiently than general purpose solvers. Another main contribution of the paper is to pose the problem of focused crawling as a large scale Ordinal Regression problem and solve using the proposed CB-OR. Focused crawling is an efficient mechanism for discovering resources of interest on the web. Posing the problem of focused crawling as an Ordinal Regression problem avoids the need for a negative class and topic hierarchy, which are the main drawbacks of the existing focused crawling methods. Experiments on large synthetic and benchmark datasets show the scalability of CB-OR. Experiments also show that the proposed focused crawler outperforms the state-of-the-art.

Specification Based Regression Testing Using Explicit State Space Enumeration

This paper presents a method of partial automation of specification based regression testing, which we call ESSE (Explicit State Space Enumeration). The first step in ESSE method is the extraction of a finite state model of the system making use of an already tested version of the system under test (SUT). Thereafter, the finite state model thus obtained is used to compute good test sequences that can be used to regression test subsequent versions of the system. We present two new algorithms for test sequence computation - both based on our finite state model generated by the above method. We also provide the details and results of the experimental evaluation of ESSE method. Comparison with a practically used random-testing algorithm has shown substantial improvements.

Probabilistic least squares approach to ordinal regression

This paper proposes a novel approach to solve the ordinal regression problem using Gaussian processes. The proposed approach, probabilistic least squares ordinal regression (PLSOR), obtains the probability distribution over ordinal labels using a particular likelihood function. It performs model selection (hyperparameter optimization) using the leave-one-out cross-validation (LOO-CV) technique. PLSOR has conceptual simplicity and ease of implementation of least squares approach. Unlike the existing Gaussian process ordinal regression (GPOR) approaches, PLSOR does not use any approximation techniques for inference. We compare the proposed approach with the state-of-the-art GPOR approaches on some synthetic and benchmark data sets. Experimental results show the competitiveness of the proposed approach.

Validation based sparse Gaussian processes for ordinal regression

This paper proposes a sparse modeling approach to solve ordinal regression problems using Gaussian processes (GP). Designing a sparse GP model is important from training time and inference time viewpoints. We first propose a variant of the Gaussian process ordinal regression (GPOR) approach, leave-one-out GPOR (LOO-GPOR). It performs model selection using the leave-one-out cross-validation (LOO-CV) technique. We then provide an approach to design a sparse model for GPOR. The sparse GPOR model reduces computational time and storage requirements. Further, it provides faster inference. We compare the proposed approaches with the state-of-the-art GPOR approach on some benchmark data sets. Experimental results show that the proposed approaches are competitive.