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Background: Activating autoantibodies (AAb) are directed to the gonadotropin releasing hormone receptor second extracellular loop (GnRHR-ECL2) and are pathogenic when induced in rats. We previously reported GnRHR-ECL2-AAb were elevated in sera from patients with PCOS (Rotterdam criteria) compared to ovulatory infertile controls (OIC). Methods:Human studies: ELISA detection of GnRHR Abs used a synthetic h-GnRHR-ECL2 28 mer peptide (LifeTein) as the target antigen. We assayed AAb activity in GnRHR transfected cells using a GeneBLAzer FRET assay (Invitrogen). ELISA AAb epitope locations on the ECL2 were identified on a minipin plate (pins 1-11 containing sequential 2 aa offsetting octapeptides, Mimotope, Inc) using sera from 30 PCOS subjects, 33 OIC and 18 normal controls (NC). Results:Human sera: An ELISA assay for GnRHR-ECL2-AAbs in the PCOS group was markedly higher than the NC group (P 0.2). There was no significant change in activity in the OIC subjects by the addition of peaks 5 + 8 RID peptides. Conclusion: These PCOS GnRHR-AAb data confirm the presence of significant activation of the GnRHR by AAb targeted to specific epitope(s) proximate to the disulfide GnRHR-ECL2 linkage to the nearby ECL1. These data are compatible with a pathophysiological role for GnRHR-AAb in PCOS and may provide both diagnostic and therapeutic opportunities.

The most applications of wireless sensor network have stringent requirements for communication performance. To meet applications requirements, it is crucial to measure the wireless interference between nodes, which is the major factor that reduces the performance of wireless sensor networks (WSNs). However, the key problem of accurately measuring wireless interference is that the node cannot predict the neighbor node information after the network is deployed, and thus cannot establish the correspondence between the wireless interference strength and the neighbor node. To tackle this problem, this paper presents a lightweight interference measurement algorithm for WSNs. The algorithm divides the interference measurement process into three phases. The first two phases are used to gather all two-hop neighbor information by exchanging between nodes. In the third phase, each node performs interference measurements and builds the relationship of wireless interference between nodes. The experimental results show that our proposed approaches can obtain accurate inter-node wireless interference strength with low energy and communication overhead.

A series of isomorphic lanthanide metal-organic frameworks (MOFs) Ln(TATAB)⋅(DMF)4 (H2 O)(MeOH)0.5 (LnTATAB, Ln=Eu, Tb, Sm, Dy, Gd; H3 TATAB=4,4',4''-s-triazine-1,3,5-triyltri-p-aminobenzoic acid) have been solvothermally synthesized and structurally characterized. Among these MOFs, TbTATAB exhibits good water stability and a high fluorescence quantum yield. Because mercury ions (Hg2+ ) have a high affinity to nitrogen atoms, and the space between multiple nitrogen atoms from triazine and imino groups is suitable for interacting with Hg2+ ions, TbTATAB shows highly selective and sensitive detection of Hg2+ in aqueous solution with a detection limit of 4.4 nm. Furthermore, it was successfully applied to detect Hg2+ ions in natural water samples.

3D point cloud semantic segmentation has attracted wide attention with its extensive applications in autonomous driving, AR/VR, and robot sensing fields. However, in existing methods, each point in the segmentation results is predicted independently from each other. This property causes the non-contiguity of label sets in three-dimensional space and produces many noisy label points, which hinders the improvement of segmentation accuracy. To address this problem, we first extend adversarial learning to this task and propose a novel framework Attention Adversarial Networks (AttAN). With high-order correlations in label sets learned from the adversarial learning, segmentation network can predict labels closer to the real ones and correct noisy results. Moreover, we design an additive attention block for the segmentation network, which is used to automatically focus on regions critical to the segmentation task by learning the correlation between multi-scale features. Adversarial learning, which explores the underlying relationship between labels in high-dimensional space, opens up a new way in 3D point cloud semantic segmentation. Experimental results on ScanNet and S3DIS datasets show that this framework effectively improves the segmentation quality and outperforms other state-of-the-art methods.