Altered pituitary hormone secretion in male rats exposed to Bisphenol A

Bisphenol A (BPA) is a xenobiotic estrogenic compound. This compound has been suspected to have estrogenic effects on reproductive system of males and females. In this present study we investigated possible low-dose effects of BPAon Luteinizing Hormone in rats. Male Wistar rats (12-13 weeks old) were administrated a daily intra peritoneal 10 μg/kgbw/day, 50 μg/kgbw/day, 100 μg/kgbw/ day dose of BPA for 6, 6, and 12 days, and one day after last injection, serum level of Luteinizing Hormone was examined by ELISA method. All data were expressed as means ± SE. Two-way ANOVA was performed. Analysis of data showed that in all dose groups, plasma level of Luteinizing Hormone significantly decreased compared to control group. The present study showed that BPA at low doses affects Luteinizing Hormone, one of main hormones in spermatogenesis in the adult Wistar rats, and subsequently alters the steroidgenesis in testicular Leydig cells

Direct measurements of the magnetocaloric effect in pulsed magnetic fields: The example of the Heusler alloy Ni50_{50}Mn35_{35}In15_{15}

We have studied the magnetocaloric effect (MCE) in the shape-memory Heusler alloy Ni$_{50}$Mn$_{35}$In$_{15}$ by direct measurements in pulsed magnetic fields up to 6 and 20 T. The results in 6 T are compared with data obtained from heat-capacity experiments. We find a saturation of the inverse MCE, related to the first-order martensitic transition, with a maximum adiabatic temperature change of $\Delta T_{ad} = -7$ K at 250 K and a conventional field-dependent MCE near the second-order ferromagnetic transition in the austenitic phase. The pulsed magnetic field data allow for an analysis of the temperature response of the sample to the magnetic field on a time scale of $\sim 10$ to 100 ms which is on the order of typical operation frequencies (10 to 100 Hz) of magnetocaloric cooling devices. Our results disclose that in shape-memory alloys the different contributions to the MCE and hysteresis effects around the martensitic transition have to be carefully considered for future cooling applications.Comment: 5 pages, 4 figure

Expressive Body Capture: 3D Hands, Face, and Body from a Single Image

To facilitate the analysis of human actions, interactions and emotions, we compute a 3D model of human body pose, hand pose, and facial expression from a single monocular image. To achieve this, we use thousands of 3D scans to train a new, unified, 3D model of the human body, SMPL-X, that extends SMPL with fully articulated hands and an expressive face. Learning to regress the parameters of SMPL-X directly from images is challenging without paired images and 3D ground truth. Consequently, we follow the approach of SMPLify, which estimates 2D features and then optimizes model parameters to fit the features. We improve on SMPLify in several significant ways: (1) we detect 2D features corresponding to the face, hands, and feet and fit the full SMPL-X model to these; (2) we train a new neural network pose prior using a large MoCap dataset; (3) we define a new interpenetration penalty that is both fast and accurate; (4) we automatically detect gender and the appropriate body models (male, female, or neutral); (5) our PyTorch implementation achieves a speedup of more than 8x over Chumpy. We use the new method, SMPLify-X, to fit SMPL-X to both controlled images and images in the wild. We evaluate 3D accuracy on a new curated dataset comprising 100 images with pseudo ground-truth. This is a step towards automatic expressive human capture from monocular RGB data. The models, code, and data are available for research purposes at https://smpl-x.is.tue.mpg.de.Comment: To appear in CVPR 201

Personalized Anomaly Detection in PPG Data using Representation Learning and Biometric Identification

Photoplethysmography (PPG) signals, typically acquired from wearable devices, hold significant potential for continuous fitness-health monitoring. In particular, heart conditions that manifest in rare and subtle deviating heart patterns may be interesting. However, robust and reliable anomaly detection within these data remains a challenge due to the scarcity of labeled data and high inter-subject variability. This paper introduces a two-stage framework leveraging representation learning and personalization to improve anomaly detection performance in PPG data. The proposed framework first employs representation learning to transform the original PPG signals into a more discriminative and compact representation. We then apply three different unsupervised anomaly detection methods for movement detection and biometric identification. We validate our approach using two different datasets in both generalized and personalized scenarios. The results show that representation learning significantly improves anomaly detection performance while reducing the high inter-subject variability. Personalized models further enhance anomaly detection performance, underscoring the role of personalization in PPG-based fitness-health monitoring systems. The results from biometric identification show that it's easier to distinguish a new user from one intended authorized user than from a group of users. Overall, this study provides evidence of the effectiveness of representation learning and personalization for anomaly detection in PPG data

Autoregressive fragment-based diffusion for pocket-aware ligand design

In this work, we introduce AutoFragDiff, a fragment-based autoregressive diffusion model for generating 3D molecular structures conditioned on target protein structures. We employ geometric vector perceptrons to predict atom types and spatial coordinates of new molecular fragments conditioned on molecular scaffolds and protein pockets. Our approach improves the local geometry of the resulting 3D molecules while maintaining high predicted binding affinity to protein targets. The model can also perform scaffold extension from user-provided starting molecular scaffold.Comment: Accepted, NeurIPS 2023 Generative AI and Biology Workshop. OpenReview: https://openreview.net/forum?id=E3HN48zja

Investigation of the effect of mineralogy as rate-limiting factors in large particle leaching

Although heap leaching is by now well established in the mining industry, the process remains limited by low recoveries with different rate-limiting factors that are not clearly understood. In this study, three large particle size classes (+19/-25, +9.5/-16, +4.75/-5 mm) were prepared from a sphalerite ore by two different methods of comminution (HPGR and cone crusher). The particles were then packed into leach reactors that were operated continuously for eleven months with well-mixed internal circulation of the leach solution. Characterization of the residue of the leach reactors indicated that there are areas within the ore particles where although sphalerite grains are accessible to the solution, they remain unreacted. X-ray tomography and QEMSCAN® analysis of the selected samples before, during and after leaching, showed increased leaching of sphalerite grains associated with pyrite due to galvanic interactions. Mineral chemistry (Fe, Mn content of sphalerite) and jarosite precipitation were also investigated as factors influencing sphalerite leaching

On 1-factorizations of Bipartite Kneser Graphs

It is a challenging open problem to construct an explicit 1-factorization of the bipartite Kneser graph $H(v,t)$, which contains as vertices all $t$-element and $(v-t)$-element subsets of $[v]:=\{1,\ldots,v\}$ and an edge between any two vertices when one is a subset of the other. In this paper, we propose a new framework for designing such 1-factorizations, by which we solve a nontrivial case where $t=2$ and $v$ is an odd prime power. We also revisit two classic constructions for the case $v=2t+1$ --- the \emph{lexical factorization} and \emph{modular factorization}. We provide their simplified definitions and study their inner structures. As a result, an optimal algorithm is designed for computing the lexical factorizations. (An analogous algorithm for the modular factorization is trivial.)Comment: We design the first explicit 1-factorization of H(2,q), where q is a odd prime powe

Muslim Work Ethics: Relationships with Religious Orientations and the “Perfect Man” (\u3ci\u3eEnsan-e Kamel\u3c/i\u3e) in Managers and Staff in Iran

Weber’s association of a work ethic with Protestantism has been extended to religions, including Islam, more generally. Managers and staff in a bank and department store in Tehran responded to Muslim religiousness measures along with the multidimensional work ethics profile (MWEP). The MWEP is a 7-factor instrument that records Weber’s interpretation of work ethics. Intrinsic, extrinsic personal, and extrinsic cultural religious orientations predicted a higher work ethic. Two extrinsic cultural religious orientation factors exhibited especially strong connections with MWEP factors. The morality/ethics MWEP factor most consistently predicted Muslim commitments. Integrative self-knowledge and self-control served as empirical markers of an Iranian Muslim spiritual ideal called ensan-e kamel or the “perfect man.” Both correlated positively with morality/ethics and with three of the four extrinsic cultural religious orientations. Managers scored higher than staff on morality/ethics, on the two characteristics of the “perfect man”, and on the three of four extrinsic cultural religious orientation factors. These data supported the existence of a Muslim work ethic