Bi-Static Sensing for Near-Field RIS Localization

We address the localization of a reconfigurable intelligent surface (RIS) for a single-input single-output multicarrier system using bi-static sensing between a fixed transmitter and a fixed receiver. Due to the deployment of RISs with a large dimension, near-field (NF) scenarios are likely to occur, especially for indoor applications, and are the focus of this work. We first derive the Cram\ub4er-Rao bounds (CRBs) on the estimation error of the RIS position and orientation and the time of arrival (TOA) for the path transmitter-RIS-receiver. We propose a multi-stage low-complexity estimator for RIS localization purposes. In this proposed estimator, we first perform a line search to estimate the TOA. Then, we use the far-field approximation of the NF signal model to implicitly estimate the angle of arrival and the angle of departure at the RIS center. Finally, the RIS position and orientation estimate are refined via a quasi-Newton method. Simulation results reveal that the proposed estimator can attain the CRBs. We also investigate the effects of several influential factors on the accuracy of the proposed estimator like the RIS size, transmitted power, system bandwidth, and RIS position and orientation

Channel Model Mismatch Analysis for XL-MIMO Systems from a Localization Perspective

Radio localization is applied in high-frequency (e.g., mmWave and THz) systems to support communication and to provide location-based services without extra infrastructure. For solving localization problems, a simplified, stationary, narrowband far-field channel model is widely used due to its compact formulation. However, with increased array size in extra-large MIMO systems and increased bandwidth at upper mmWave bands, the effect of channel spatial non-stationarity (SNS), spherical wave model (SWM), and beam squint effect (BSE) cannot be ignored. In this case, localization performance will be affected when an inaccurate channel model deviating from the true model is adopted. In this work, we employ the MCRB (misspecified Cram\ub4er-Rao lower bound) to lower bound the localization error using a simplified mismatched model while the observed data is governed by a more complex true model. The simulation results show that among all the model impairments, the SNS has the least contribution, the SWM dominates when the distance is small compared to the array size, and the BSE has a more significant effect when the distance is much larger than the array size

Germline mutations in young non-smoking women with lung adenocarcinoma

Objectives: Although the primary cause of lung cancer is smoking, a considerable proportion of all lung cancers occur in never smokers. Gender influences the risk and characteristics of lung cancer and women are over-represented among never smokers with the disease. Young age at onset and lack of established environmental risk factors suggest genetic predisposition. In this study, we used population-based sampling of young patients to discover candidate predisposition variants for lung adenocarcinoma in never-smoking women. Materials and methods: We employed archival normal tissue material from 21 never-smoker women who had been diagnosed with lung adenocarcinoma before the age of 45, and exome sequenced their germline DNA. Results and conclusion: Potentially pathogenic variants were found in eight Cancer Gene Census germline genes: BRCAI, BRCA2, ERCC4, EXT1, HNF1 A, PTCH1, SMARCB1 and TP53. The variants in TP53, BRCAI, and BRCA2 are likely to have contributed to the early onset lung cancer in the respective patients (3/21 or 14%). This supports the notion that lung adenocarcinoma can be a component of certain cancer predisposition syndromes. Fifteen genes displayed potentially pathogenic mutations in at least two patients: ABCC10, ATP7B, CACNA1S, CFTR, CLIP4, COL6A1, COL6A6, GCN1, GJB6, RYR1, SCN7A, SEC24A, SP100, TEN and USH2A. Four patients showed a mutation in COL6A1, three in CLIP4 and two in the rest of the genes. Some of these candidate genes may explain a subset of female lung adenocarcinoma.Peer reviewe

Osteoblastic Wnt1 regulates periosteal bone formation in adult mice

Compelling clinical data together with genetically modified mouse models have demonstrated that Wnt1 is a key Wnt ligand in bone metabolism, regulating both osteoblast activity and osteoclast differentiation. We have previously shown that deletion of Wnt1 in limb mesenchymal cells leads to severe ostepenic bone phenotype and spontaneous fractures very early after birth. However, the function of Wnt1 in mature skeleton remained unknown. To investigate the role of Wnt1 specifically in adult bone metabolism, we generated an osteoblast lineage-targeted inducible Wnt1 knockout mouse model using tetracycline-controlled Osterix-Cre mouse line (Osx-Cre). In this model, the Cre recombinase expression is suppressed by administering doxycycline (Dox) in drinking water. As expected, Wnt1Osx−/− mice without Dox developed spontaneous fractures early by 3 weeks of age due to severe trabecular and cortical osteopenia. Administration of Dox to Wnt1Osx-Dox−/− and control mice until 4 weeks of age suppressed Wnt1 deletion and completely prevented the fractures. Withdrawal of Dox led to deletion in Wnt1 allele but the fracture incidence progressively decreased in Wnt1Osx-Dox−/− mice at 8 or 12 weeks of age (4 and 8 weeks after Dox withdrawal). Interestingly, deletion of Wnt1 at 4 weeks of age resulted only in a modest and transient trabecular osteopenia that was more pronounced in females and was normalized by 12 weeks of age. However, diaphyseal cortical bone mass and cortical thickness in the femurs were significantly decreased in Wnt1Osx-Dox−/− mice of both genders. Mechanisticly, this was due to impaired periosteal bone formation. Based on our data, in addition to its essential role in early skeletal growth, Wnt1 is an important regulator of modeling-based bone formation and cortical thickness in adult mice.</p

PASI: A novel pathway method to identify delicate group effects

Pathway analysis is a common approach in diverse biomedical studies, yet the currently-available pathway tools do not typically support the increasingly popular personalized analyses. Another weakness of the currently-available pathway methods is their inability to handle challenging data with only modest group-based effects compared to natural individual variation. In an effort to address these issues, this study presents a novel pathway method PASI (Pathway Analysis for Sample-level Information) and demonstrates its performance on complex diseases with different levels of group-based differences in gene expression. PASI is freely available as an R package

Next-generation sequencing in a large pedigree segregating visceral artery aneurysms suggests potential role of COL4A1/COL4A2 in disease etiology

Background Visceral artery aneurysms (VAAs) can be fatal if ruptured. Although a relatively rare incident, it holds a contemporary mortality rate of approximately 12%. VAAs have multiple possible causes, one of which is genetic predisposition. Here, we present a striking family with seven individuals affected by VAAs, and one individual affected by a visceral artery pseudoaneurysm. Methods We exome sequenced the affected family members and the parents of the proband to find a possible underlying genetic defect. As exome sequencing did not reveal any feasible protein-coding variants, we combined whole-genome sequencing of two individuals with linkage analysis to find a plausible non-coding culprit variant. Variants were ranked by the deep learning framework DeepSEA. Results Two of seven top-ranking variants, NC_000013.11:g.108154659C>T and NC_000013.11:g.110409638C>T, were found in all VAA-affected individuals, but not in the individual affected by the pseudoaneurysm. The second variant is in a candidate cis-regulatory element in the fourth intron of COL4A2, proximal to COL4A1. Conclusions As type IV collagens are essential for the stability and integrity of the vascular basement membrane and involved in vascular disease, we conclude that COL4A1 and COL4A2 are strong candidates for VAA susceptibility genes.Peer reviewe