Reference Ranges and Association of Age and Lifestyle Characteristics with Testosterone, Sex Hormone Binding Globulin, and Luteinizing Hormone among 1166 Western Chinese Men

Decreased total testosterone (TT) is the recommended metric to identify age-related hypogonadism. However, average TT and the extent to which it varies by age, can vary substantially among different populations. Population-specific reference ranges are needed to understand normal versus abnormal TT levels. Therefore, the goal for this study was to describe androgen concentrations and their correlates among Western Chinese men. We completed a population-based, cross-sectional study including 227 young adults (YA) (20–39 years) and 939 older adults (OA) (40–89 years). We measured TT, sex-hormone binding globulin (SHBG), luteinizing hormone (LH), testosterone secreting index (TSI), and calculated free testosterone (cFT). Reference ranges for this population were determined using average YA concentrations. Multivariable regression models were used to predict hormone concentrations adjusting for age, waist-to-height ratio (WHR), marital status, education, occupation, smoking, alcohol, blood glucose, and blood pressure. Among OA, 3.8% had low TT, 15.2% had low cFT, 26.3% had low TSI, 21.6% had high SHBG, and 6.1% had high LH. Average cFT was significantly lower in OA (0.30 nmol/L; standard deviation (SD): 0.09) versus YA (0.37; SD: 0.11) but TT was not different in OA (16.82 nmol/L; SD: 4.80) versus YA (16.88; SD: 5.29). In adjusted models increasing age was significantly associated with increased SHBG or LH, and decreased cFT or TSI; however, TT was not significantly associated with age (β = 0.02 nmol/L; 95% confidence interval (CI): -0.01, 0.04). Higher WHR was associated with significantly decreased TT, SHBG, TSI, and LH. The only variable significantly related to cFT was age (β = -0.0033; 95% CI:-0.0037, -0.0028); suggesting that cFT measurements would not be confounded by other lifestyle factors. In conclusion, cFT, but not TT, varies with age in this population, suggesting cFT may be a better potential marker for age-related androgen deficiency than TT among Western Chinese men

The association of bone, fingernail and blood manganese with cognitive and olfactory function in Chinese workers

Occupational manganese (Mn) exposure has been associated with cognitive and olfactory dysfunction; however, few studies have incorporated cumulative biomarkers of Mn exposure such as bone Mn (BnMn). Our goal was to assess the cross-sectional association between BnMn, blood Mn (BMn), and fingernail Mn (FMn) with cognitive and olfactory function among Mn-exposed workers. A transportable in vivo neutron activation analysis (IVNAA) system was designed and utilized to assess BnMn among 60 Chinese workers. BMn and FMn were measured using inductively coupled plasma mass spectrometry. Cognitive and olfactory function was assessed using Animal and Fruit Naming tests, World Health Organization/University of California-Los Angeles Auditory Verbal Learning Test (AVLT) and the University of Pennsylvania Smell Identification Test (UPSIT). Additional data were obtained via questionnaire. Regression models adjusted for age, education, factory of employment, and smoking status (UPSIT only), were used to assess the relationship between Mn biomarkers and test scores. In adjusted models, increasing BnMn was significantly associated with decreased performance on average AVLT scores [β (95% confidence interval (CI)) = -0.65 (-1.21, -0.09)] and Animal Naming scores [β (95% CI) = -1.54 (-3.00, -0.07)]. Increasing FMn was significantly associated with reduced performance measured by the average AVLT [β (95% CI) = -0.35 (-0.70, -0.006)] and the difference in AVLT scores [β (95% CI) = -0.40 (-0.77, -0.03)]. BMn was not significantly associated with any test scores; no significant associations were observed with Fruit Naming or UPSIT tests. BnMn and FMn, but not BMn, are associated with cognitive function in Mn-exposed workers. None of th

Twist Promotes Tumor Metastasis in Basal-Like Breast Cancer by Transcriptionally Upregulating ROR1

Rationale: Twist is a key transcription factor for induction of epithelial-mesenchymal transition (EMT), which promotes cell migration, invasion, and cancer metastasis, confers cancer cells with stem cell-like characteristics, and provides therapeutic resistance. However, the functional roles and targeted genes of Twist in EMT and cancer progression remain elusive. Methods: The potential targeted genes of Twist were identified from the global transcriptomes of T47D/Twist cells by microarray analysis. EMT phenotype was detected by western blotting and immunofluorescence of marker proteins. The dual-luciferase reporter and chromatin immunoprecipitation assays were employed to observe the direct transcriptional induction of ROR1 by Twist. A lung metastasis model was used to study the pro-metastatic role of Twist and ROR1 by injecting MDA-MB-231 cells into tail vein of nude mice. Bio-informatics analysis was utilized to measure the metastasis-free survival of breast cancer patients. Results: Twist protein was proved to directly activate the transcription of ROR1 gene, a receptor of Wnt5a in non-canonical WNT signaling pathway. Silencing of ROR1 inhibited EMT process, cell migration, invasion, and cancer metastasis of basal-like breast cancer (BLBC) cells. Knockdown of ROR1 also ameliorated the pro-metastatic effect of Twist. Furthermore, analyses of clinical specimens indicated that high expression of both ROR1 and Twist tightly correlates with poor metastasis-free survival of breast cancer patients. Conclusion: ROR1 is a targeted gene of Twist. Twist/ROR1 signaling is critical for invasion and metastasis of BLBC cells

BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition

We summarize the results of a host of efforts using giant automatic speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing approximately a million hours of audio. We find that the combination of pre-training, self-training and scaling up model size greatly increases data efficiency, even for extremely large tasks with tens of thousands of hours of labeled data. In particular, on an ASR task with 34k hours of labeled data, by fine-tuning an 8 billion parameter pre-trained Conformer model we can match state-of-the-art (SoTA) performance with only 3% of the training data and significantly improve SoTA with the full training set. We also report on the universal benefits gained from using big pre-trained and self-trained models for a large set of downstream tasks that cover a wide range of speech domains and span multiple orders of magnitudes of dataset sizes, including obtaining SoTA performance on many public benchmarks. In addition, we utilize the learned representation of pre-trained networks to achieve SoTA results on non-ASR tasks.Comment: 14 pages, 7 figures, 13 tables; v2: minor corrections, reference baselines and bibliography updated; v3: corrections based on reviewer feedback, bibliography update

Maternal exposure to ambient air pollution and congenital heart defects in China

Background: Evidence of maternal exposure to ambient air pollution on congenital heart defects (CHD) has been mixed and are still relatively limited in developing countries. We aimed to investigate the association between maternal exposure to air pollution and CHD in China.Method: This longitudinal, population-based, case-control study consecutively recruited fetuses with CHD and healthy volunteers from 21 cities, Southern China, between January 2006 and December 2016. Residential address at delivery was linked to random forests models to estimate maternal exposure to particulate matter with an aerodynamic diameter of ≤1 µm (PM1), ≤2.5 µm, and ≤10 µm as well as nitrogen dioxides, in three trimesters. The CHD cases were evaluated by obstetrician, pediatrician, or cardiologist, and confirmed by cardia ultrasound. The CHD subtypes were coded using the International Classification Diseases. Adjusted logistic regression models were used to assess the associations between air pollutants and CHD and its subtypes.Results: A total of 7055 isolated CHD and 6423 controls were included in the current analysis. Maternal air pollution exposures were consistently higher among cases than those among controls. Logistic regression analyses showed that maternal exposure to all air pollutants during the first trimester was associated with an increased odds of CHD (e.g., an interquartile range [13.3 µg/m3] increase in PM1 was associated with 1.09-fold ([95% confidence interval, 1.01-1.18]) greater odds of CHD). No significant associations were observed for maternal air pollution exposures during the second trimester and the third trimester. The pattern of the associations between air pollutants and different CHD subtypes was mixed.Conclusions: Maternal exposure to greater levels of air pollutants during the pregnancy, especially the first trimester, is associated with higher odds of CHD in offspring. Further longitudinal well-designed studies are warranted to confirm our findings

PaLM 2 Technical Report

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report