28 research outputs found
Adrenal gland involvement in 11-ketotestosterone production analyzed using LC-MS/MS
Introduction11-ketotestosterone (11KT), which is derived by the bioconversion of testosterone via 11β-hydroxytestosterone (11OHT), is a potent agonist of the human androgen receptor. The adrenal gland is considered an important organ in 11KT production because CYP11B1, which catalyzes testosterone to 11OHT, is expressed in the adrenal glands. The present study aimed to demonstrate adrenal gland involvement in 11KT production in prepubertal children, a topic which has not yet been addressed by any previous studies.MethodsThree, retrospective, observational studies were performed. Study 1 enrolled patients aged 8 months to 7 years with severe Kawasaki disease (KD) who were treated with mPSL pulse. Studies 2 and 3 included patients who had received a corticotropin-releasing hormone (CRH) stimulation test and adrenocorticotropic hormone (ACTH) stimulation test, respectively. Samples were collected before and after treatment or drug administration, and serum 11KT, 11OHT, and other 11-oxygenated androgens were measured by LC-MS/MS. Steroid hormone values before and after medication were analyzed using the Wilcoxon signed rank test.ResultsStudies 1, 2, and 3 included twenty patients with severe KD, eight patients with a CRH stimulation test, and eight patients with an ACTH stimulation test, respectively. Study 1 demonstrated that the median (IQR) 11KT level was significantly higher before, than after, mPSL pulse (0.39 (0.28-0.47) nmol/L versus 0.064 (0.012-0.075) nmol/L; P < 0.001). Studies 2 and 3 indicated no significant difference in the median 11KT value before and after the CRH or ACTH stimulation test while the 11OHT value was significantly higher after the test.ConclusionIn conclusion, the mediation of 11KT production by ACTH demonstrated the importance of the adrenal glands in the synthesis of this androgen in prepubertal children
EdgePruner: Poisoned Edge Pruning in Graph Contrastive Learning
Graph Contrastive Learning (GCL) is unsupervised graph representation
learning that can obtain useful representation of unknown nodes. The node
representation can be utilized as features of downstream tasks. However, GCL is
vulnerable to poisoning attacks as with existing learning models. A
state-of-the-art defense cannot sufficiently negate adverse effects by poisoned
graphs although such a defense introduces adversarial training in the GCL. To
achieve further improvement, pruning adversarial edges is important. To the
best of our knowledge, the feasibility remains unexplored in the GCL domain. In
this paper, we propose a simple defense for GCL, EdgePruner. We focus on the
fact that the state-of-the-art poisoning attack on GCL tends to mainly add
adversarial edges to create poisoned graphs, which means that pruning edges is
important to sanitize the graphs. Thus, EdgePruner prunes edges that contribute
to minimizing the contrastive loss based on the node representation obtained
after training on poisoned graphs by GCL. Furthermore, we focus on the fact
that nodes with distinct features are connected by adversarial edges in
poisoned graphs. Thus, we introduce feature similarity between neighboring
nodes to help more appropriately determine adversarial edges. This similarity
is helpful in further eliminating adverse effects from poisoned graphs on
various datasets. Finally, EdgePruner outputs a graph that yields the minimum
contrastive loss as the sanitized graph. Our results demonstrate that pruning
adversarial edges is feasible on six datasets. EdgePruner can improve the
accuracy of node classification under the attack by up to 5.55% compared with
that of the state-of-the-art defense. Moreover, we show that EdgePruner is
immune to an adaptive attack
Giant enhancement of cryogenic thermopower by polar structural instability in the pressurized semimetal MoTe2
We found that a high mobility semimetal 1T'-MoTe2 shows a significant
pressure-dependent change in the cryogenic thermopower in the vicinity of the
critical pressure, where the polar structural transition disappears. With the
application of a high pressure of 0.75 GPa, while the resistivity becomes as
low as 10 {\mu}{\Omega}cm, thermopower reached the maximum value of 60
{\mu}VK-1 at 25 K, leading to a giant thermoelectric power factor of 300
{\mu}WK-2cm-1. Based on semiquantitative analyses, the origin of this behavior
is discussed in terms of inelastic electron-phonon scattering enhanced by the
softening of zone center phonon modes associated with the polar structural
instability.Comment: 13 pages, 4 figures Physical review B (accepted
Ultra-low-dose estrogen therapy for female hypogonadism.
In females, endogenous estrogen secretion increases gradually before pubertal development. The benefits of low-dose estrogen therapy in patients with Turner syndrome were originally discussed by Ross et al. and Quigley et al. These seminal studies used ethinyl estradiol (EE2), starting at a dose of 25 ng/kg/d. We hypothesized that the initial dosage of estrogen could be titrated to more closely mimic physiological increments of endogenous estrogen. Therefore, our recent study initiated EE2 treatment at a dosage of 1-2 ng/kg/d, an ultra-low-dose estrogen therapy in pediatric patients with Turner syndrome. The ultra-low-dose estrogen therapy in this syndrome produced a good final height outcome but achieved suboptimal bone mineral density (BMD). In the present review, we have explained our findings to clarify the merits and demerits of this new therapy and to promote further discussion and research. This type of ultra-low-dose estrogen therapy, initiated at an early age, could be ideal for estrogen replacement in female patients with hypogonadism, such as Turner syndrome
Hardware-Trojan Detection Methods Utilizing Machine Learning Based on Hardware-Specific Features
Trojan-Net Feature Extraction and Its Application to Hardware-Trojan Detection for Gate-Level Netlists Using Random Forest
R-HTDetector: Robust Hardware-Trojan Detection Based on Adversarial Training
Hardware Trojans (HTs) have become a serious problem, and extermination of
them is strongly required for enhancing the security and safety of integrated
circuits. An effective solution is to identify HTs at the gate level via
machine learning techniques. However, machine learning has specific
vulnerabilities, such as adversarial examples. In reality, it has been reported
that adversarial modified HTs greatly degrade the performance of a machine
learning-based HT detection method. Therefore, we propose a robust HT detection
method using adversarial training (R-HTDetector). We formally describe the
robustness of R-HTDetector in modifying HTs. Our work gives the world-first
adversarial training for HT detection with theoretical backgrounds. We show
through experiments with Trust-HUB benchmarks that R-HTDetector overcomes
adversarial examples while maintaining its original accuracy