Unveiling facial kinship: The BioKinVis dataset for facial kinship verification and genetic association studies.

Facial image-based kinship verification represents a burgeoning frontier within the realms of computer vision and biomedicine. Recent genome-wide association studies have underscored the heritability of human facial morphology, revealing its predictability based on genetic information. These revelations form a robust foundation for advancing facial image-based kinship verification. Despite strides in computer vision, there remains a discernible gap between the biomedical and computer vision domains. Notably, the absence of family photo datasets established through biological paternity testing methods poses a significant challenge. This study addresses this gap by introducing the biological kinship visualization dataset, encompassing 5773 individuals from 2412 families with biologically confirmed kinship. Our analysis delves into the distribution and influencing factors of facial similarity among parent-child pairs, probing the potential association between forensic short tandem repeat polymorphisms and facial similarity. Additionally, we have developed a machine learning model for facial image-based kinship verification, achieving an accuracy of 0.80 in the dataset. To facilitate further exploration, we have established an online tool and database, accessible at http://120.55.161.230:88/.

Transfer performance of gated recurrent unit model for runoff prediction based on the comprehensive spatiotemporal similarity of catchments.

Accurate runoff prediction in data-poor catchments is important for water resource management, flood mitigation, environmental protection, and other tasks. One possible solution is to transfer a runoff prediction model constructed by using a machine learning model for gauged catchments to data-poor catchments. However, the transfer of runoff prediction model must consider the comprehensive spatiotemporal similarities between the catchments; otherwise, the transfer performance can be massively uncertain. Therefore, to improve the accuracy of runoff prediction and eliminate the uncertainty in identifying the differences between catchment environments, this paper proposes a novel measurement approach of comprehensive spatiotemporal similarity. This approach measures the similarities among catchments by fully considering which of the various catchments' spatiotemporal attributes can better represent the geographical similarity. Then, according to the similarities between the catchments, a runoff prediction model trained in gauged catchments is transformed for the most similar data-poor catchments to predict the runoff and the transfer performance is analyzed. To this end, a runoff prediction model is built using a gated recurrent unit (GRU) network based on the CAMELS catchments data set. A framework to extract the comprehensive spatiotemporal features of catchments is designed using three autoencoders. The catchments' similarities can be measured, further, and their spatiotemporal attributes determined once a measurement model of comprehensive spatiotemporal similarity is constructed. Finally, the transfer performance of the GRU runoff prediction model based on comprehensive spatiotemporal and other geographical similarities is evaluated and analyzed. The experimental results demonstrate that the proposed method outperforms comparable approaches.

Molecular Docking and Molecular Dynamics Simulation of New Potential JAK3 Inhibitors.

INTRODUCTION: JAK3 kinase inhibitor has become an effective means to treat tumors and autoimmune diseases. METHOD: In this study, molecular docking and molecular dynamics simulation were used to study the theoretical interaction mechanism between 1-phenylimidazolidine-2-one molecules and JAK3 protein. RESULT: The results of molecular docking showed that the six 1-phenylimidazolidine-2-one derivatives obtained by virtual screening were bound to the ATP pocket of JAK3 kinase, which were competitive inhibitors of ATP, and were mainly bound to the pocket through hydrogen bonding and hydrophobic interaction. Further, MM/GBSA based on molecular dynamics simulation sampling was used to calculate the binding energy between six molecules and the JAK3 kinase protein. Subsequently, the binding energy was decomposed into the contribution of each amino acid residue, of which Leu905, Lys855, Asp967, Leu956, Tyr904, and Val836 were the main energy-contributing residues. Among them, the molecule numbered LCM01415405 can interact with the specific amino acid Arg911 of JAK3 kinase, suggesting that the molecule may be a selective JAK3 kinase inhibitor. The root-mean-square fluctuation (RMSF) of JAK3 kinase pocket residues during molecular dynamics simulation showed that the combination of six new potential small molecule inhibitors with JAK3 kinase could reduce the flexibility of JAK3 kinase pocket residues. CONCLUSION: These findings reveal the mechanism of 1-phenylimidazolidine-2-one derivatives on JAK3 protein and provide a relatively solid theoretical basis for the development and structural optimization of JAK3 protein inhibitors.

Artesunate Suppresses the Proliferation and Development of Estrogen Receptor-α-Positive Endometrial Cancer in HAND2-Dependent Pathway.

Endometrial cancer (EC) is a common leading cause of cancer-related death in women, which is associated with the increased level of estrogen in the body. Artesunate (ART), an active compound derived from Artemisia annua L., exerted antitumor properties in several cancer types. However, the role of artesunate and the molecular basis on EC remains unclear. Here, we aimed to explore the effects and mechanisms of artesunate. Our results identified that estrogen receptor-α (ER-α) was a key factor for the type I EC (ER-α-positive), which might suppress the downstream LKB1/AMPK/mTOR pathway. Besides, we found ART significantly inhibited tumor proliferation in a dose-dependent manner. Mechanistic studies identified that ART led to tumor cell apoptosis and cell cycle arrest by downregulating the ER-α expression and activating the LKB1/AMPK/mTOR pathway. In addition, we found ART could increase the expression of heart and neural crest derivatives expressed 2 (HAND2) in the ER-α-positive EC cells, which could interact with ER-α. Through the gain-and loss-function experiments, we showed that over expression of HAND2 repressed the proliferation and migration of ER-α-positive EC cells via inhibition of ER-α expression. HAND2 knockdown increased ER-α expression and alleviated the antitumor effect of ART in vitro and in vivo. Overall, our study first showed that ART could be an effective antitumor agent through modulating ER-α-mediated LKB1/AMPK/mTOR pathway in the HAND2 dependent manner. Our findings provide an effective therapeutic agent for ER-α-positive EC treatment.