Domain adaptation aims to transfer knowledge from a domain with adequate labeled samples to a domain with scarce labeled samples. Prior research has introduced various open set domain adaptation settings in the literature to extend the applications of domain adaptation methods in real-world scenarios. This paper focuses on the type of open set domain adaptation setting where the target domain has both private (‘unknown classes’) label space and the shared (‘known classes’) label space. However, the source domain only has the ‘known classes’ label space. Prevalent distribution-matching domain adaptation methods are inadequate in such a setting that demands adaptation from a smaller source domain to a larger and diverse target domain with more classes. For addressing this specific open set domain adaptation setting, prior research introduces a domain adversarial model that uses a fixed threshold for distinguishing known from unknown target samples and lacks at handling negative transfers. We extend their adversarial model and propose a novel adversarial domain adaptation model with multiple auxiliary classifiers. The proposed multi-classifier structure introduces a weighting module that evaluates distinctive domain characteristics for assigning the target samples with weights which are more representative to whether they are likely to belong to the known and unknown classes to encourage positive transfers during adversarial training and simultaneously reduces the domain gap between the shared classes of the source and target domains. A thorough experimental investigation shows that our proposed method outperforms existing domain adaptation methods on a number of domain adaptation datasets.

Eph receptors orchestrate cell positioning during normal and oncogenic development. Their function is spatially and temporally controlled by protein tyrosine phosphatases (PTPs), but the underlying mechanisms are unclear and the identity of most regulatory PTPs are unknown. We demonstrate here that PTP1B governs signaling and biological activity of EphA3. Changes in PTP1B expression significantly affect duration and amplitude of EphA3 phosphorylation and biological function, whereas confocal fluorescence lifetime imaging microscopy (FLIM) reveals direct interactions between PTP1B and EphA3 before ligand-stimulated receptor internalization and, subsequently, on endosomes. Moreover, overexpression of wild-type (w/t) PTP1B and the [D-A] substrate–trapping mutant decelerate ephrin-induced EphA3 trafficking in a dose-dependent manner, which reveals its role in controlling EphA3 cell surface concentration. Furthermore, we provide evidence that in areas of Eph/ephrin-mediated cell–cell contacts, the EphA3–PTP1B interaction can occur directly at the plasma membrane. Our studies for the first time provide molecular, mechanistic, and functional insights into the role of PTP1B controlling Eph/ephrin-facilitated cellular interactions.

Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they are directly related to plant health and food production. Direct estimation of these soil properties with traditional methods, for example, the oven-drying technique and chemical analysis, is a time and resource-consuming approach and can predict only smaller areas. With the significant development of remote sensing and hyperspectral (HS) imaging technologies, soil moisture, carbon, and nitrogen can be estimated over vast areas. This paper presents a generalized approach to predicting three different essential soil contents using a comprehensive study of various machine learning (ML) models by considering the dimensional reduction in feature spaces. In this study, we have used three popular benchmark HS datasets captured in Germany and Sweden. The efficacy of different ML algorithms is evaluated to predict soil content, and significant improvement is obtained when a specific range of bands is selected. The performance of ML models is further improved by applying principal component analysis (PCA), a dimensional reduction method that works with an unsupervised learning method. The effect of soil temperature on soil moisture prediction is evaluated in this study, and the results show that when the soil temperature is considered with the HS band, the soil moisture prediction accuracy does not improve. However, the combined effect of band selection and feature transformation using PCA significantly enhances the prediction accuracy for soil moisture, carbon, and nitrogen content. This study represents a comprehensive analysis of a wide range of established ML regression models using data preprocessing, effective band selection, and data dimension reduction and attempt to understand which feature combinations provide the best accuracy. The outcomes of several ML models are verified with validation techniques and the best- and worst-case scenarios in terms of soil content are noted. The proposed approach outperforms existing estimation techniques.