Effect of FSH on testicular morphology and spermatogenesis in gonadotrophin-deficient hypogonadal mice lacking androgen receptors

Follicle stimulating hormone (FSH) and androgen act to stimulate and maintain spermatogenesis. FSH acts directly on the Sertoli cells to stimulate germ cell number and acts indirectly to increase androgen production by the Leydig cells. In order to differentiate between the direct effects of FSH on spermatogenesis and those mediated indirectly through androgen action we have crossed hypogonadal (hpg) mice which lack gonadotrophins with mice lacking androgen receptors (AR) either ubiquitously (ARKO) or specifically on the Sertoli cells (SCARKO). These hpg.ARKO and hpg.SCARKO mice were treated with recombinant FSH for 7 days and testicular morphology and cell numbers assessed. In untreated hpg and hpg.SCARKO mice germ cell development was limited and did not progress beyond the pachytene stage. In hpg.ARKO mice testes were smaller with fewer Sertoli cells and germ cells compared to hpg mice. Treatment with FSH had no effect on Sertoli cell number but significantly increased germ cell numbers in all groups. In hpg mice FSH increased numbers of spermatogonia and spermatocytes and induced round spermatid formation. In hpg.SCARKO and hpg.ARKO mice, in contrast, only spermatogonial and spermatocyte numbers were increased with no formation of spermatids. Leydig cell numbers were increased by FSH in hpg and hpg.SCARKO mice but not in hpg.ARKO mice. Results show that in rodents 1) FSH acts to stimulate spermatogenesis through an increase in spermatogonial number and subsequent entry of these cells into meiosis, 2) FSH has no direct effect on the completion of meiosis and 3) FSH effects on Leydig cell number are mediated through interstitial ARs

Androgen-induced rhox homeobox genes modulate the expression of AR-regulated genes

Rhox5, the founding member of the reproductive homeobox on the X chromosome (Rhox) gene cluster, encodes a homeodomain-containing transcription factor that is selectively expressed in Sertoli cells, where it promotes the survival of male germ cells. To identify Rhox5-regulated genes, we generated 15P-1 Sertoli cell clones expressing physiological levels of Rhox5 from a stably transfected expression vector. Microarray analysis identified many genes altered in expression in response to Rhox5, including those encoding proteins controlling cell cycle regulation, apoptosis, metabolism, and cell-cell interactions. Fifteen of these Rhox5-regulated genes were chosen for further analysis. Analysis of Rhox5-null male mice indicated that at least 9 of these are Rhox5-regulated in the testes in vivo. Many of them have distinct postnatal expression patterns and are regulated by Rhox5 at different postnatal time points. Most of them are expressed in Sertoli cells, indicating that they are candidates to be directly regulated by Rhox5. Transfection analysis with expression vectors encoding different mouse and human Rhox family members revealed that the regulatory response of a subset of these Rhox5-regulated genes is both conserved and redundant. Given that Rhox5 depends on AR for expression in Sertoli cells, we examined whether some Rhox5-regulated genes are also regulated by androgen receptor (AR). We provide several lines of evidence that this is the case, leading us to propose that RHOX5 serves as a key intermediate transcription factor that directs some of the actions of AR in the testes

Texture, twinning and metastable "tetragonal" phase in ultrathin films of HfO₂ on a Si substrate

Thin HfO<sub>2</sub> films grown on the lightly oxidised surface of (100) Si wafers have been examined using dark-field transmission electron microscopy and selected area electron diffraction in plan view. The polycrystalline film has a grain size of the order of 100 nm and many of the grains show evidence of twinning on (110) and (001) planes. Diffraction studies showed that the film had a strong [110] out-of-plane texture, and that a tiny volume fraction of a metastable (possibly tetragonal) phase was retained. The reasons for the texture, twinning and the retention of the metastable phase are discussed

Benchmarking Feature Extractors for Reinforcement Learning-Based Semiconductor Defect Localization

As semiconductor patterning dimensions shrink, more advanced Scanning Electron Microscopy (SEM) image-based defect inspection techniques are needed. Recently, many Machine Learning (ML)-based approaches have been proposed for defect localization and have shown impressive results. These methods often rely on feature extraction from a full SEM image and possibly a number of regions of interest. In this study, we propose a deep Reinforcement Learning (RL)-based approach to defect localization which iteratively extracts features from increasingly smaller regions of the input image. We compare the results of 18 agents trained with different feature extractors. We discuss the advantages and disadvantages of different feature extractors as well as the RL-based framework in general for semiconductor defect localization.Comment: 5 pages, 5 figures, 3 table

Contribution of recent transgenic models and transcriptional profiling studies to our understanding of the mechanisms by which androgens control spermatogenesis

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Spermatogenesis and sertoli cell activity in mice lacking Sertoli cell receptors for follicle stimulating hormone and androgen

Spermatogenesis in the adult male depends on the action of FSH and androgen. Ablation of either hormone has deleterious effects on Sertoli cell function and the progression of germ cells through spermatogenesis. In this study we generated mice lacking both FSH receptors (FSHRKO) and androgen receptors on the Sertoli cell (SCARKO) to examine how FSH and androgen combine to regulate Sertoli cell function and spermatogenesis. Sertoli cell number in FSHRKO-SCARKO mice was reduced by about 50% but was not significantly different from FSHRKO mice. In contrast, total germ cell number in FSHRKO-SCARKO mice was reduced to 2% of control mice (and 20% of SCARKO mice) due to a failure to progress beyond early meiosis. Measurement of Sertoli cell-specific transcript levels showed that about a third were independent of hormonal action on the Sertoli cell, whereas others were predominantly androgen dependent or showed redundant control by FSH and androgen. Results show that FSH and androgen act through redundant, additive, and synergistic regulation of spermatogenesis and Sertoli cell activity. In addition, the Sertoli cell retains a significant capacity for activity, which is independent of direct hormonal regulation

Occurrence of testicular microlithiasis in androgen insensitive hypogonadal mice

<b>Background</b>: Testicular microliths are calcifications found within the seminiferous tubules. In humans, testicular microlithiasis (TM) has an unknown etiology but may be significantly associated with testicular germ cell tumors. Factors inducing microlith development may also, therefore, act as susceptibility factors for malignant testicular conditions. Studies to identify the mechanisms of microlith development have been hampered by the lack of suitable animal models for TM.<BR/> <b>Methods</b>: This was an observational study of the testicular phenotype of different mouse models. The mouse models were: cryptorchid mice, mice lacking androgen receptors (ARs) on the Sertoli cells (SCARKO), mice with a ubiquitous loss of androgen ARs (ARKO), hypogonadal (hpg) mice which lack circulating gonadotrophins, and hpg mice crossed with SCARKO (hpg.SCARKO) and ARKO (hpg.ARKO) mice.<BR/> <b>Results</b>: Microscopic TM was seen in 94% of hpg.ARKO mice (n=16) and the mean number of microliths per testis was 81 +/- 54. Occasional small microliths were seen in 36% (n=11) of hpg testes (mean 2 +/- 0.5 per testis) and 30% (n=10) of hpg.SCARKO testes (mean 8 +/- 6 per testis). No microliths were seen in cryptorchid, ARKO or SCARKO mice. There was no significant effect of FSH or androgen on TM in hpg.ARKO mice.<BR/> <b>Conclusions</b>: We have identified a mouse model of TM and show that lack of endocrine stimulation is a cause of TM. Importantly, this model will provide a means with which to identify the mechanisms of TM development and the underlying changes in protein and gene expression

SEMI-CenterNet: A Machine Learning Facilitated Approach for Semiconductor Defect Inspection

Continual shrinking of pattern dimensions in the semiconductor domain is making it increasingly difficult to inspect defects due to factors such as the presence of stochastic noise and the dynamic behavior of defect patterns and types. Conventional rule-based methods and non-parametric supervised machine learning algorithms like KNN mostly fail at the requirements of semiconductor defect inspection at these advanced nodes. Deep Learning (DL)-based methods have gained popularity in the semiconductor defect inspection domain because they have been proven robust towards these challenging scenarios. In this research work, we have presented an automated DL-based approach for efficient localization and classification of defects in SEM images. We have proposed SEMI-CenterNet (SEMI-CN), a customized CN architecture trained on SEM images of semiconductor wafer defects. The use of the proposed CN approach allows improved computational efficiency compared to previously studied DL models. SEMI-CN gets trained to output the center, class, size, and offset of a defect instance. This is different from the approach of most object detection models that use anchors for bounding box prediction. Previous methods predict redundant bounding boxes, most of which are discarded in postprocessing. CN mitigates this by only predicting boxes for likely defect center points. We train SEMI-CN on two datasets and benchmark two ResNet backbones for the framework. Initially, ResNet models pretrained on the COCO dataset undergo training using two datasets separately. Primarily, SEMI-CN shows significant improvement in inference time against previous research works. Finally, transfer learning (using weights of custom SEM dataset) is applied from ADI dataset to AEI dataset and vice-versa, which reduces the required training time for both backbones to reach the best mAP against conventional training method

Automated Semiconductor Defect Inspection in Scanning Electron Microscope Images: a Systematic Review

A growing need exists for efficient and accurate methods for detecting defects in semiconductor materials and devices. These defects can have a detrimental impact on the efficiency of the manufacturing process, because they cause critical failures and wafer-yield limitations. As nodes and patterns get smaller, even high-resolution imaging techniques such as Scanning Electron Microscopy (SEM) produce noisy images due to operating close to sensitivity levels and due to varying physical properties of different underlayers or resist materials. This inherent noise is one of the main challenges for defect inspection. One promising approach is the use of machine learning algorithms, which can be trained to accurately classify and locate defects in semiconductor samples. Recently, convolutional neural networks have proved to be particularly useful in this regard. This systematic review provides a comprehensive overview of the state of automated semiconductor defect inspection on SEM images, including the most recent innovations and developments. 38 publications were selected on this topic, indexed in IEEE Xplore and SPIE databases. For each of these, the application, methodology, dataset, results, limitations and future work were summarized. A comprehensive overview and analysis of their methods is provided. Finally, promising avenues for future work in the field of SEM-based defect inspection are suggested.Comment: 16 pages, 12 figures, 3 table