Unknown Target Research Articles

Non-line-of-sight target 2D size detection with few channels at a time

Non-line-of-sight (NLOS) imaging techniques have garnered significant interest in recent years for their ability to reconstruct obscured scenes. This type of imaging typically needs numerous temporal histograms, which require thousands of scans on the relay surface. Reconstructing obscured targets using several channels, i.e., capturing echoes from a few positions of the relay surface, is an attractive prospect but has so far been restricted to localization applications. Herein, we propose a rectangle-based data-driven method and first demonstrate that the 2D size of the hidden target can be detected even with as few as 2 or 3 detectors by exploiting the information inside an echo signal. Specifically, we conceptualize the occluded target as a rectangle representing the target size and train a multi-layer perceptron to learn the correspondence of the hidden scene from the generated data, in which the method can directly respond to unknown targets. Both numerical simulations and experiments are performed to evaluate the feasibility of this method. Furthermore, this work provides a feasible way of detecting hidden scene without scanning.

A biotin targeting chimera (BioTAC) system to map small molecule interactomes in situ

Understanding how small molecules bind to specific protein complexes in living cells is critical to understanding their mechanism-of-action. Unbiased chemical biology strategies for direct readout of protein interactome remodelling by small molecules would provide advantages over target-focused approaches, including the ability to detect previously unknown ligand targets and complexes. However, there are few current methods for unbiased profiling of small molecule interactomes. To address this, we envisioned a technology that would combine the sensitivity and live-cell compatibility of proximity labelling coupled to mass spectrometry, with the specificity and unbiased nature of chemoproteomics. In this manuscript, we describe the BioTAC system, a small-molecule guided proximity labelling platform that can rapidly identify both direct and complexed small molecule binding proteins. We benchmark the system against µMap, photoaffinity labelling, affinity purification coupled to mass spectrometry and proximity labelling coupled to mass spectrometry datasets. We also apply the BioTAC system to provide interactome maps of Trametinib and analogues. The BioTAC system overcomes a limitation of current approaches and supports identification of both inhibitor bound and molecular glue bound complexes.

Overcoming cold start and sensor bias: A deep learning-based framework for IoT-enabled monitoring applications

Target localization is an essential aspect of smart environmental monitoring, which has gained prominence thanks to the Internet of Things (IoT) paradigm. Localization is the process of determining the location of an unknown target within an area of interest (AoI) based on data gathered by IoT sensors. Existing target localization works assume full knowledge about the sensors without considering the cold start problem, which may arise when new sensors join the network. This is exacerbated by the problem of bootstrapping in IoT sensors, where the system needs to identify and register unknown sensors. Additionally, the variability in sensors characteristics is inevitable due to the numerous factors that may alter the sensors behavior, raising the problem of sensor bias. Existing works dealing with the aforementioned problems are inadequate, since they involve a pre-processing step and assume the bias to be static over time, resulting in inaccurate localization systems. Therefore, this work proposes a robust and dynamic, holistic localization system that can find the location of an unknown emitting target regardless of the sensors characteristics. The proposed system comprises of two phases: 1) characterization and localization phase; where a deep learning model is utilized to characterize the sensors based on their locations and readings only, whereas Bayesian algorithm is employed to determine the unknown target location by leveraging the predicted sensors characteristics, and 2) correction phase, where an update metric is devised to refine the predicted sensors characteristics based on the estimated target location. The validation of the proposed approach was conducted using real-life and synthetic datasets and compared against an existing benchmark, where it shows an improvement of ∼79%, in terms of both localization and prediction errors. Thus, showcasing the effectiveness and potential advantages of the proposed approach in localizing unknown target(s).

Collaborative Semantic Aggregation and Calibration for Federated Domain Generalization

Domain generalization (DG) aims to learn from multiple known source domains a model that can generalize well to unknown target domains. The existing DG methods usually exploit the fusion of shared multi-source data to train a generalizable model. However, tremendous data is distributed across lots of places nowadays that can not be shared due to privacy policies. In this paper, we tackle the problem of federated domain generalization where the source datasets can only be accessed and learned locally for privacy protection. We propose a novel framework called Collaborative Semantic Aggregation and Calibration (CSAC) to enable this challenging problem. To fully absorb multi-source semantic information while avoiding unsafe data fusion, we conduct data-free semantic aggregation by fusing the models trained on the separated domains layer-by-layer. To address the semantic dislocation problem caused by domain shift, we further design cross-layer semantic calibration with an attention mechanism to align each semantic level and enhance domain invariance. We unify multi-source semantic learning and alignment in a collaborative way by repeating the semantic aggregation and calibration alternately, keeping each dataset localized, and the data privacy is carefully protected. Extensive experiments show the significant performance of our method in addressing this challenging problem.

Multi-source partial domain adaptation method based on pseudo-balanced target domain for fault diagnosis

Multisource domain adaptation (MSDA) for fault diagnosis enables knowledge transfer from multisource domains to unknown target domains, which is crucial for enhancing equipment reliability and safety. However, current MSDA methods face two major challenges: (1) they face serious domain-shift problems not only between the target and source domains, but also between different source domains, and (2) in practical applications, class labels in the target domain are likely to be subsets of class labels in the source domain. Therefore, this study considers a setting called multisource partial domain adaptation (MSPDA). A two-stage intelligent fault diagnosis method for MSPDA based on pseudo-balanced target domains (PBTD) is proposed. In the first stage, we propose a weighted adversarial partial domain adaptation method based on a dual progressive strategy that aligns each source domain with the target domain to construct a series of PBTD. In the second stage, an alternating learning scheme is utilized to align the remaining source domains with the PBTDs, which makes full use of multisource information to bridge the discrepancies between different domains. Additionally, this study proposes a multiscale convolutional neural network based on a three-branch attention mechanism that captures the cross-dimensional interactions of scale, channel, and space to enhance the algorithmic characterization capability. Finally, extensive experiments with gear and bearing datasets are conducted to validate the effectiveness of the proposed method.

An unbiased approach of individual tissue-specific autoantibody identification using Immunoprecipitation coupled to Mass-Spectrometry (IP-MS)

Abstract Background and aim Autoreactive antibodies have gained increasing recognition in autoimmune diseases of the central nervous system (CNS) serving as disease drivers and / or biomarkers. Identification of novel autoantibodies therefore is crucial to optimize diagnostic work and therapy of these diseases as well as understanding their pathogenesis. Hence, we aimed to develop an unbiased strategy to identify patients’ individual main autoantibodies directed against brain epitopes. Methods We used murine tissue-based assays to screen patients’ sera and cerebrospinal fluids (CSFs) for autoreactive IgGs with known and unknown antibody targets. IgGs from autoreactive patients’ samples were coupled to magnetic beads and used as a bait to pull down potential targets from homogenized brain tissue of wildtype mice. After precipitation and several washes to reduce unspecific binding, beads were subjected to on-bead tryptic digestion followed by mass spectrometry based proteomic analyses using liquid chromatography tandem mass-spectrometry (LC-MS/MS). For antibody targets identification the fold change enrichment compared to a negative control or to all other pulldown samples, as well as the protein abundance was used. Antibody target validation was done via recombinant expression of the target protein in HEK-293 cells and neutralization with the respective antigen in tissue-based immunofluorescence assays. Results As proof of principle, we were able to identify Grin1 Caspr2, Trim46 and Nf-h [MOU1] as main targets of patients’ sera or CSF with autoimmune encephalopathy. All four antigens showed highest enrichment and/or highest protein abundance in the respective samples and have been confirmed in cell-based assays. In addition, we identified one novel anti-neuronal antibody target in serum and CSF of a patient showing a speckled staining of the granular cerebellar layer. Preabsorbance of the serum and CSF with the recombinant protein fully neutralized the signal of the tissue-based immunofluorescence. Conclusions Our IP-MS approach is suitable to identify the main autoantibody targets in patients’ serum and CSF within murine CNS tissue thereby providing an unbiased and scalable technique allowing for tissue-specific autoantibody determination.

Exploring the Mechanisms of Venetoclax Resistance Via Drug Screening and Genome-Wide CRISPR Screening

Introduction: Combining Venetoclax, a selective BCL-2 inhibitor, with hypomethylating agents has revolutionized frontline acute myeloid leukemia (AML) treatment. Despite its success, the occurrence of resistance remains a significant concern, limiting the range of available treatment alternatives. Little is known about the mechanism by which the cells escape the treatment. Recent studies have observed MCL1 upregulation in AML cells to promote resistance, and the rationale to use an MCL1 inhibitor became attractive. Although MCL1 inhibitors work exceptionally well in vitro their clinical trials were unsuccessful due to their toxicity. Here, we aim to identify other genes responsible for acquiring resistance and tackle the problem through screening for clinically approved drugs to rapidly benefit the patients with our study. Methods: First, we generated a model of the venetoclax resistance from 3 AML cell lines: MOLM-13, HL-60 and MV4-11. Resistance was achieved through chronic administration of the compound, inhibiting cell viability to 80-90% in multiple rounds by gradually increasing concentrations until the cells developed resistance. We have then characterized these Venetoclax-resistant (VeR) cells using quantitative RT-PCR and RNA sequencing to reveal the mechanisms behind their resistance. VeR cell lines were subjected to drug screening with a library of 859 FDA and EMA-approved compounds with various clinical indications. Cell viability in response to the drug library was assessed after 72h by Cell-Titer Glo and evaluated against the wild-type counterparts. Top-performing compounds were validated in dose-response curves, and synergy scores were identified from combinational treatments. Simultaneously, we performed genome-wide CRISPR/Cas9 knockout screening using Brunello CRISPR knockout library on both MOLM-13 wild-type (WT) and VeR cell lines to identify genes that maintain the resistance to venetoclax and genes that may potentially synergize with targeting BCL-2. Results: Results from the CRISPR screen pointed out multiple genes whose loss of function may contribute to venetoclax resistance in MOLM-13 cells, among which BAX, NOXA, ELAVL1 and TP53 ranked as top hits. On the contrary, we also identified several genes whose loss of function sensitized the cells to venetoclax (e.g., MCL1, OPA1, CDK2, MCAT), suggesting them to be promising therapeutic targets. Among these hits, CDK2 correlates with our drug screening data indicating Flavopiridol as an effective compound in treating VeR cell lines. Moreover, we confirmed the elevated MCL1 gene expression for the MOLM-13 VeR cell line through RNAseq (and qPCR) and detected an altered expression of genes mainly in metabolic pathways and PI3K-Akt signaling pathway via differential gene expression analysis. Our drug screening results revealed overall high efficacy of DNA-damaging agents, proteasome and HDAC inhibitors for both WT and VeR cell lines. We also included an MCL1 inhibitor as a positive control in our drug library and confirmed a synergistic effect when combined with venetoclax. Conclusion: Integrating a range of screening approaches may lead to discovering previously unknown therapeutic targets for venetoclax-resistant AML. Our screens confirmed previously described involvement of the apoptotic pathway genes in venetoclax resistance as well as identified some novel promising targets. Our ongoing efforts involve thorough validations of the identified hits from CRISPR and drug screening to enhance the reliability and translatability of the results. This project was partly supported by grants MUNI/A/1330/2021, MUNI/A/1419/2021 OPRDE (No.CZ.02.2.69/0.0/0.0/19_073/0016943) and project NICR (EU program EXCELES, No. LX22NPO5102).

CCRANet: A Two-Stage Local Attention Network for Single-Frame Low-Resolution Infrared Small Target Detection

Infrared small target detection technology is widely used in infrared search and tracking, infrared precision guidance, low and slow small aircraft detection, and other projects. Its detection ability is very important in terms of finding unknown targets as early as possible, warning in time, and allowing for enough response time for the security system. This paper combines the target characteristics of low-resolution infrared small target images and studies the infrared small target detection method under a complex background based on the attention mechanism. The main contents of this paper are as follows: (1) by sorting through and expanding the existing datasets, we construct a single-frame low-resolution infrared small target (SLR-IRST) dataset and evaluate the existing datasets on three aspects—target number, target category, and target size; (2) to improve the pixel-level metrics of low-resolution infrared small target detection, we propose a small target detection network with two stages and a corresponding method. Regarding the SLR-IRST dataset, the proposed method is superior to the existing methods in terms of pixel-level metrics and target-level metrics and has certain advantages in model processing speed.

Target circumnavigation of mobile robots using range-only measurements

This paper presents a control scheme to steer a nonholonomic robot to enclose an unknown target in both obstacle-free and obstacle-presence environments. Unlike most literature, we consider the situation where the robot with unknown position and orientation obtains only the distance to the target and detects collisions with obstacles. We first select a virtual point and construct new states to facilitate the controller design. For the obstacle-free case, a dynamic output feedback controller is derived to ensure that the distance between the robot and the target globally converges to a desired constant. The smooth bounded controller is computationally efficient and can be easily implemented. For the obstacle-presence case, a collision-allowed control strategy is developed to guarantee that the robot escapes from circular obstacles and eventually maintains a given distance from the target. Numerical simulations and practical experiments are performed to verify the effectiveness of the control method.

Adaptive event-triggered actor–critic algorithm for optimal 3D formation circumnavigation with relative measurement and an unknown moving target

This paper studies the optimal three-dimensional (3D) formation circumnavigation problem for multiple unmanned aerial vehicles (UAVs) with relative measurement and an unknown moving target. First, the 3D formation circumnavigation errors under the relative measurement and directed topology, that is, the spherical enclosing along the normal direction, the latitude surrounding along the north direction, and the longitude formation along the east direction, are constructed. Then, radial basis function neural networks (RBFNNs) based adaptive estimation is designed for each UAV to approximate the unknown target velocity using relative information. Further, to achieve the optimal energy of 3D formation circumnavigation along the normal, north, and east directions with lowered data transmission cost, an event-triggered actor–critic algorithm is designed under limited neighbor and relative target information. The stability of the whole system is analyzed under continuous sampled and event sampled cases, respectively, and the Zeno phenomenon is excluded. The effectiveness is validated through simulation under cases of with and without random noise. Meanwhile, comparative simulations are performed to validate the system energy and convergence time of the proposed method in contrast with two relevant works.

PSDC: A Prototype-Based Shared-Dummy Classifier Model for Open-Set Domain Adaptation.

Open-set domain adaptation (OSDA) aims to achieve knowledge transfer in the presence of both domain shift and label shift, which assumes that there exist additional unknown target classes not presented in the source domain. To solve the OSDA problem, most existing methods introduce an additional unknown class to the source classifier and represent the unknown target instances as a whole. However, it is unreasonable to treat all unknown target instances as a group since these unknown instances typically consist of distinct categories and distributions. It is challenging to identify all unknown instances with only one additional class. In addition, most existing methods directly introduce marginal distribution alignment to alleviate distribution shift between the source and target domains, failing to learn discriminative class boundaries in the target domain since they ignore categorical discriminative information in the adaptation. To address these problems, in this article, we propose a novel prototype-based shared-dummy classifier (PSDC) model for the OSDA. Specifically, our PSDC introduces an auxiliary dummy classifier to calibrate the source classifier and simultaneously develops a weighted adaptation procedure to align class-wise prototypes for adaptation. We further design a pseudo-unknown learning algorithm to reduce the open-set risk. Extensive experiments on Office-31, Office-Home, and VisDA datasets show that the proposed PSDC can outperform existing methods and achieve the new state-of-the-art performance. The code will be made public.

Comparing Gaze, Head and Controller Selection of Dynamically Revealed Targets in Head-mounted Displays.

This paper presents a head-mounted virtual reality study that compared gaze, head, and controller pointing for selection of dynamically revealed targets. Existing studies on head-mounted 3D interaction have focused on pointing and selection tasks where all targets are visible to the user. Our study compared the effects of screen width (field of view), target amplitude and width, and prior knowledge of target location on modality performance. Results show that gaze and controller pointing are significantly faster than head pointing and that increased screen width only positively impacts performance up to a certain point. We further investigated the applicability of existing pointing models. Our analysis confirmed the suitability of previously proposed two-component models for all modalities while uncovering differences for gaze at known and unknown target positions. Our findings provide new empirical evidence for understanding input with gaze, head, and controller and are significant for applications that extend around the user.

SARS-CoV-2 infection engenders heterogeneous ribonucleoprotein interactions to impede translation elongation in the lungs.

Translational regulation in tissue environments during in vivo viral pathogenesis has rarely been studied due to the lack of translatomes from virus-infected tissues, although a series of translatome studies using in vitro cultured cells with viral infection have been reported. In this study, we exploited tissue-optimized ribosome profiling (Ribo-seq) and severe-COVID-19 model mice to establish the first temporal translation profiles of virus and host genes in the lungs during SARS-CoV-2 pathogenesis. Our datasets revealed not only previously unknown targets of translation regulation in infected tissues but also hitherto unreported molecular signatures that contribute to tissue pathology after SARS-CoV-2 infection. Specifically, we observed gradual increases in pseudoribosomal ribonucleoprotein (RNP) interactions that partially overlapped the trails of ribosomes, being likely involved in impeding translation elongation. Contemporaneously developed ribosome heterogeneity with predominantly dysregulated 5 S rRNP association supported the malfunction of elongating ribosomes. Analyses of canonical Ribo-seq reads (ribosome footprints) highlighted two obstructive characteristics to host gene expression: ribosome stalling on codons within transmembrane domain-coding regions and compromised translation of immunity- and metabolism-related genes with upregulated transcription. Our findings collectively demonstrate that the abrogation of translation integrity may be one of the most critical factors contributing to pathogenesis after SARS-CoV-2 infection of tissues.

Dual-UAV Collaborative High-Precision Passive Localization Method Based on Optoelectronic Platform

Utilizing the optical characteristics of the target for detection and localization does not require actively emitting signals and has the advantage of strong concealment. Once the optoelectronic platform mounted on the unmanned aerial vehicle (UAV) detects the target, the vector pointing to the target in the camera coordinate system can estimate the angle of arrival (AOA) of the target relative to the UAV in the Earth-centered Earth-fixed (ECEF) coordinate system through a series of rotation transformations. By employing two UAVs and the corresponding AOA measurements, passive localization of an unknown target is possible. To achieve high-precision target localization, this paper investigates the following three aspects. Firstly, two error transfer models are established to estimate the noise distributions of the AOA and the UAV position in the ECEF coordinate system. Next, to reduce estimation errors, a weighted least squares (WLS) estimator is designed. Theoretical analysis proves that the mean squared error (MSE) of the target position estimation can reach the Cramér–Rao lower bound (CRLB) under the condition of small noise. Finally, we study the optimal placement problem of two coplanar UAVs relative to the target based on the D-optimality criterion and provide explicit conclusions. Simulation experiments validate the effectiveness of the localization method.

TCX: Texture and channel swappings for domain generalization

Neural networks suffer performance degradation when the source and target data lie in a different distribution hampering direct deployment of the model to diverse target domains. To this end, domain generalization (DG) aims to generalize the model well to an unknown target domain by utilizing multiple source domains. This paper proposes two simple swapping mechanisms, texture and channel swapping (TCX), for DG. Texture swapping augments the source dataset by replacing textures of an image with other textures in source dataset to alleviate the texture bias problem in convolution neural networks (CNNs). Furthermore, channel swapping switches channels of input feature vectors of the classifier along with its labels to encourage the model to utilize more channels when classifying an image. Together, we expect the model to learn less domain-specific features but more generalized class-specific features resulting in better domain generalization performance. We demonstrate the effectiveness of our approach with state-of-the-art results on three domain generalization benchmarks.

Efficient clathrin-mediated entry of enteric adenoviruses in human duodenal cells.

Enteric adenoviruses have historically been difficult to grow in cell culture, which has resulted in lack of knowledge of host factors and pathways required for infection of these medically relevant viruses. Previous studies in non-intestinal cell lines showed slow infection kinetics and generated comparatively low virus yields compared to other adenovirus types. We suggest duodenum-derived HuTu80 cells as a superior cell line for studies to complement efforts using complex intestinal tissue models. We show that viral host cell factors required for virus entry differ between cell lines from distinct origins and demonstrate the importance of clathrin-mediated endocytosis.

Simultaneous search and monitoring by multiple aerial robots

This paper studies simultaneous search and monitoring (SSM) between multiple unmanned aerial vehicles (UAVs) and multiple moving ground targets. Searching for unknown targets and monitoring known ones are two intrinsically related problems, but they have mostly been addressed in isolation. We combine the two tasks and exploit their interconnection as a synergy rather than a trade-off. We construct the single-robot SSM as a partially observable Markov decision process (POMDP) and the multi-robot SSM as a semi-decentralised POMDP (semi-Dec-POMDP). A novel heuristic reactive policy planning is proposed to solve the POMDP. It is then extended for semi-Dec-POMDP with game-theoretical methods. In simulations and experiments, the searchers will successfully locate unknown targets without losing known ones and cooperate by partitioning their tasks. With theoretical proofs, simulations, and experiments, we demonstrate that our method can perform better than conventional approaches and the state-of-the-art.

Research progress of metabolomics in acute kidney injury

Acute kidney injury (AKI) is caused by a variety of diseases, which leads to acute renal function decline, azotemia, water and electrolyte disorders and acid-base balance disorders. Metabolomics is a research method that can quantitatively analyze all metabolites in an organism and find the relative relationship between metabolites and physiological and pathological changes. In recent years, several metabolites screened based on metabolomics have been proposed as potential biomarkers to assess the early development and prognosis of AKI and for the discovery of unknown potential therapeutic targets. Based on metabolomics, this paper reviews the risk prediction, early diagnosis, disease monitoring, prognosis assessment and the application of corresponding drugs for AKI, so as to provide reference for precision medicine.

Determination of Women’s Level of Knowledge and Attitudes towards AIDS

Objective: This study aims to determine women’s level of knowledge and attitudes towards HIV/AIDS. Method: Cross-sectional design was used for this study. It included women who were aged 18 to 65 years and registered in FHCs (Family Health Centers) a city in eastern Turkey between May and August 2021. Women registered in these centers meeting the research criteria consisted of the target population, and the sample was 384 women selected using the sampling method with an unknown target population. Data collection was performed using the Personal Information Form, the AIDS Knowledge Scale, and the AIDS Attitude Scale. Results: Participating women’s average age was 26.91±9.20, and 74.7% reportedly heard about HIV/AIDS before; 62.5% knew about HIV/AIDS; and 75.8% of these women obtained information from friends, social circle, or media. The HIV/AIDS Knowledge Scale mean score of the group was 7.81±4.92, and the HIV/AIDS Attitude Scale mean score was 53.38±10.23. The HIV/AIDS Knowledge Scale mean was significantly higher in university graduates, in those worked and reportedly knew about HIV/AIDS (p<0.05). University graduates’ HIV/AIDS Attitude Scale mean was significantly higher (p<0.05). Conclusion: In this study, it was determined that higher education level had a positive effect on knowledge and attitude toward HIV/AIDS. Public health nurses could organize education programs about the issue by using appropriate materials and methods for women with low education levels.

Human osteoarthritic articular cartilage stem cells suppress osteoclasts and improve subchondral bone remodeling in experimental knee osteoarthritis partially by releasing TNFAIP3

BackgroundThough articular cartilage stem cell (ACSC)-based therapies have been demonstrated to be a promising option in the treatment of diseased joints, the wide variety of cell isolation, the unknown therapeutic targets, and the incomplete understanding of the interactions of ACSCs with diseased microenvironments have limited the applications of ACSCs.MethodsIn this study, the human ACSCs have been isolated from osteoarthritic articular cartilage by advantage of selection of anatomical location, the migratory property of the cells, and the combination of traumatic injury, mechanical stimuli and enzymatic digestion. The protective effects of ACSC infusion into osteoarthritis (OA) rat knees on osteochondral tissues were evaluated using micro-CT and pathological analyses. Moreover, the regulation of ACSCs on osteoarthritic osteoclasts and the underlying mechanisms in vivo and in vitro were explored by RNA-sequencing, pathological analyses and functional gain and loss experiments. The one-way ANOVA was used in multiple group data analysis.ResultsThe ACSCs showed typical stem cell-like characteristics including colony formation and committed osteo-chondrogenic capacity. In addition, intra-articular injection into knee joints yielded significant improvement on the abnormal subchondral bone remodeling of osteoarthritic rats. Bioinformatic and functional analysis showed that ACSCs suppressed osteoarthritic osteoclasts formation, and inflammatory joint microenvironment augmented the inhibitory effects. Further explorations demonstrated that ACSC-derived tumor necrosis factor alpha-induced protein 3 (TNFAIP3) remarkably contributed to the inhibition on osteoarhtritic osteoclasts and the improvement of abnormal subchondral bone remodeling.ConclusionIn summary, we have reported an easy and reproducible human ACSC isolation strategy and revealed their effects on subchondral bone remodeling in OA rats by releasing TNFAIP3 and suppressing osteoclasts in a diseased microenvironment responsive manner.Graphical abstract