Pharmacological and Toxicological Properties of the Potent Oral γ-Secretase Modulator BPN-15606.

Alzheimer's disease (AD) is characterized neuropathologically by an abundance of 1) neuritic plaques, which are primarily composed of a fibrillar 42-amino-acid amyloid-β peptide (Aβ), as well as 2) neurofibrillary tangles composed of aggregates of hyperphosporylated tau. Elevations in the concentrations of the Aβ42 peptide in the brain, as a result of either increased production or decreased clearance, are postulated to initiate and drive the AD pathologic process. We initially introduced a novel class of bridged aromatics referred tγ-secretase modulatoro as γ-secretase modulators that inhibited the production of the Aβ42 peptide and to a lesser degree the Aβ40 peptide while concomitantly increasing the production of the carboxyl-truncated Aβ38 and Aβ37 peptides. These modulators potently lower Aβ42 levels without inhibiting the γ-secretase-mediated proteolysis of Notch or causing accumulation of carboxyl-terminal fragments of APP. In this study, we report a large number of pharmacological studies and early assessment of toxicology characterizing a highly potent γ-secretase modulator (GSM), (S)-N-(1-(4-fluorophenyl)ethyl)-6-(6-methoxy-5-(4-methyl-1H-imidazol-1-yl)pyridin-2-yl)-4-methylpyridazin-3-amine (BPN-15606). BPN-15606 displayed the ability to significantly lower Aβ42 levels in the central nervous system of rats and mice at doses as low as 5-10 mg/kg, significantly reduce Aβ neuritic plaque load in an AD transgenic mouse model, and significantly reduce levels of insoluble Aβ42 and pThr181 tau in a three-dimensional human neural cell culture model. Results from repeat-dose toxicity studies in rats and dose escalation/repeat-dose toxicity studies in nonhuman primates have designated this GSM for 28-day Investigational New Drug-enabling good laboratory practice studies and positioned it as a candidate for human clinical trials

Indosinian high-strain deformation for the Yunkaidashan tectonic belt, south China : Kinematics and 40Ar/39Ar geochronological constraints

Structural and 40Ar/39Ar data from the Yunkaidashan Belt document kinematic and tectonothermal characteristics of early Mesozoic Indosinian orogenesis in the southern part of the South China Block. The Yunkaidashan Belt is tectonically divided from east to west into the Wuchuang-Sihui shear zone, Xinyi-Gaozhou block, and the Fengshan-Qinxi shear zone. Indosinian structural elements ascribed to the Indosinian orogeny include D2 and D3 deformation. The early D2 phase is characterized by folding and thrusting with associated foliation and lineation development, related to NW-SE transpression under amphibolite- to greenschist-facies conditions. This event is heterogeneously overprinted by D3 deformation characterized by a gentle-dipping S-3 foliation, subhorizontally to shallowly plunging L3 lineation, some reactived-D2 folds and low-angle normal faults. The D3 fabrics suggest a sinistral transtensional regime under greenschist-facies metamorphism. The timing of the D2 and D3 events have been constrained to the early to middle Triassic (similar to 248-220 Ma) and late Triassic (similar to 220-200 Ma) respectively on the basis of 40Ar/39Ar geochronology and regional geological relations. The change from oblique thrusting (D2) to sinistral transtension (D3) may reflect oblique convergence and crustal thickening followed by relaxation of the overthickened crust. In combination with the regional relations from Xuefengshan to Yunkaidashan and on to Wuyishan, the early phase of the Indosinian orogeny constituted a large-scale positive flower structure and is related to the intracontinental convergence during the assembly of Pangea in which the less competent South China Orogen was squeezed between the more competent North China and Indosinian Blocks.Peer reviewe

Any360D: Towards 360 Depth Anything with Unlabeled 360 Data and M\"obius Spatial Augmentation

Recently, Depth Anything Model (DAM) - a type of depth foundation model - reveals impressive zero-shot capacity for diverse perspective images. Despite its success, it remains an open question regarding DAM's performance on 360 images that enjoy a large field-of-view (180x360) but suffer from spherical distortions. To this end, we establish, to our knowledge, the first benchmark that aims to 1) evaluate the performance of DAM on 360 images and 2) develop a powerful 360 DAM for the benefit of the community. For this, we conduct a large suite of experiments that consider the key properties of 360 images, e.g., different 360 representations, various spatial transformations, and diverse indoor and outdoor scenes. This way, our benchmark unveils some key findings, e.g., DAM is less effective for diverse 360 scenes and sensitive to spatial transformations. To address these challenges, we first collect a large-scale unlabeled dataset including diverse indoor and outdoor scenes. We then propose a semi-supervised learning (SSL) framework to learn a 360 DAM, dubbed Any360D. Under the umbrella of SSL, Any360D first learns a teacher model by fine-tuning DAM via metric depth supervision. Then, we train the student model by uncovering the potential of large-scale unlabeled data with pseudo labels from the teacher model. M\"obius transformation-based spatial augmentation (MTSA) is proposed to impose consistency regularization between the unlabeled data and spatially transformed ones. This subtly improves the student model's robustness to various spatial transformations even under severe distortions. Extensive experiments demonstrate that Any360D outperforms DAM and many prior data-specific models, e.g., PanoFormer, across diverse scenes, showing impressive zero-shot capacity for being a 360 depth foundation model

Unsupervised Visible-Infrared ReID via Pseudo-label Correction and Modality-level Alignment

Unsupervised visible-infrared person re-identification (UVI-ReID) has recently gained great attention due to its potential for enhancing human detection in diverse environments without labeling. Previous methods utilize intra-modality clustering and cross-modality feature matching to achieve UVI-ReID. However, there exist two challenges: 1) noisy pseudo labels might be generated in the clustering process, and 2) the cross-modality feature alignment via matching the marginal distribution of visible and infrared modalities may misalign the different identities from two modalities. In this paper, we first conduct a theoretic analysis where an interpretable generalization upper bound is introduced. Based on the analysis, we then propose a novel unsupervised cross-modality person re-identification framework (PRAISE). Specifically, to address the first challenge, we propose a pseudo-label correction strategy that utilizes a Beta Mixture Model to predict the probability of mis-clustering based network's memory effect and rectifies the correspondence by adding a perceptual term to contrastive learning. Next, we introduce a modality-level alignment strategy that generates paired visible-infrared latent features and reduces the modality gap by aligning the labeling function of visible and infrared features to learn identity discriminative and modality-invariant features. Experimental results on two benchmark datasets demonstrate that our method achieves state-of-the-art performance than the unsupervised visible-ReID methods.Comment: 10 pages, 6 figure

Monitoring and studying rainfall runoff pollution from urban impervious surfaces

Urban impervious surface rainfall runoff pollution is a significant component of non-point source pollution, with pollutants accumulating on these surfaces during dry periods being the primary source of contaminants in rainfall runoff. Using the first ten rainfall events of 2015 as a case study, impervious surfaces such as the roofs of teaching buildings, campus roads, and nearby main traffic roads within the university campus in southeastern Beijing were selected for field sampling and analysis of natural rainfall and rainfall runoff pollution. The findings indicate that the initial rainfall runoff pollution following winter is severe, with water quality falling below Class V. Subsequently, the pollution levels decrease; however, the severity of water pollution varies at different sampling locations. Notably, ammonia nitrogen concentrations are higher near building toilet exhaust outlets, and the presence of pervious surface facilities can mitigate runoff pollution. Based on the analysis and research findings, several recommendations for controlling and managing urban rainfall runoff pollution are proposed