Recent studies on the heritability of methylation patterns in tumor cells, suggest that tumor heterogeneity and progression can be studied through methylation changes. To elucidate methylation-based evolution trajectories in tumors, we introduce a novel computational framework for methylation phylogeny reconstruction, leveraging single cell bisulfite treated whole genome sequencing data (scBS-seq), additionally incorporating copy number information inferred independently from matched single cell RNA sequencing (scRNA-seq) data, when available. Our framework consists of three components: (i) noise-minimizing site selection, (ii) likelihood-based sequencing error correction, and (iii) pairwise expected distance calculation for cells, all designed to mitigate the effect of noise and uncertainty due to data sparsity commonly observed in scBS-seq data. We validate our approach with the scBS-seq data of multi-regionally sampled colorectal cancer cells, and demonstrate that the cell lineages constructed by our method strongly correlate with original sampling regions. Additionally, we show that the constructed phylogeny can be used to impute missing entries, which, in turn, may help reduce sparsity issues in scBS-seq data sets. Contact: cenk.sahinalp@nih.gov

This paper analyses Disturbance Observer- (DOb-) based robust force control systems in the discrete-time domain. The robust force controller is implemented using velocity and acceleration measurements. A DOb is employed in an inner-loop to achieve robustness, and another DOb, viz. Reaction Force Observer (RFOb), is employed in an outer-loop to estimate interaction forces and improve the performance of force control. First, the inner-loop is analysed. It is shown that the DOb works as a phase-lead/lag compensator tuned by the nominal design parameters in the inner-loop. The phase margin of the inner-loop controller and the bandwidth of the velocity-based (i.e., conventional) DOb are constrained not only by noise-sensitivity but also by the waterbed effect. This explains why we observe unstable responses as the bandwidth of the conventional DOb increases in practice. To eliminate the design constraint due to the waterbed effect, this paper proposes an acceleration-based DOb. Then, the robust force controller is analysed. It is shown that the design parameters of the RFOb have a notable effect on the stability of the robust force control system. For example, the robust force controller has a non-minimum phase zero (zeros) when the RFOb is not properly tuned. This may cause severe stability and performance problems when conducting force control applications. By using the stability and robustness analyses, this paper proposes new design tools which enable one to synthesize a high-performance robust force control system. Simulations and experiments are presented to validate the proposed analysis and synthesis methods.

This paper introduces a novel control approach for Doubly-Fed Induction Generator (DFIG) operating in island mode based on the cascaded control structure with disturbance estimation. The control of the DFIG is a challenging task due to its inherent nonlinearity, fast dynamics, and unpredictable disturbances acting on the system. The proposed control structure involves a nominal controller for plant and disturbance observer (DOB) in each of the inner and outer control loop. The first-order disturbance observers are designed to estimate the time-varying and unknown disturbances. With disturbance estimation, the nominal linear dynamics is obtained in both loops. This enables the same approach for designing controllers for the inner and outer loop which significantly simplifies implementation. The controllers are designed based on the demanded error dynamics and ensure stable operation of the system, while proposed DOBs estimate disturbances including external load. Finally, the effectiveness and quality of the proposed control structure were verified through numerical simulations in terms of external disturbances rejection and closed-loop tracking performance.

Coronavirus Disease 2019 (COVID-19), caused by the novel Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), persists as a threat to global health and continues to be a rapidly evolving condition. Although COVID19 is negatively correlated with the existing comorbidities in terms of the clinical outcome, the ability of SARS-CoV-2 to mediate the novel, or to exacerbate the existing autoimmune conditions, has generated considerable interest, due to its potential implications both with regard to patients suffering from autoimmune conditions, as well as to the long-term consequences of the disease. However, although molecular mimicry has been postulated as a potential causative factor in post-COVID19 autoimmunity and multi-organ damage, a substantial body of research needs to emerge in order to achieve a more definitive conclusion. We investigated the possibility of SARS-CoV-2 peptide sequences behaving as molecular mimics with a potential to trigger an autoimmune response. Thus, on the basis of analysis in silico, we were able to develop a plausible case for the molecular mimicry as a potential aetiological mechanism of SARS-CoV-2-mediated autoimmunity, both in a multi-organ damage context or outside of the viral phase of infection. Interestingly, this is the first time that the peptide sequence of MACROD1 has been implicated in the COVID-19 autoimmunity. Additionally, we also confirm that PARP9 and PARP14 may be involved in the process.

The main intention of this paper is to explore the possibility of positioning the discourse on adult identity formation within the context of higher education. To this end, first formational potential of higher education is revisited. Further on, Eriksonian psychosocial theoretical approach and Arnett's concept of emerging adulthood are proposed as the referential framework for conceptualizing adult identity formation processes. It is concluded that by offering instituzionalized moratorium and the possibility for the extended transition from adolescence to adulthood, higher education context provides intensive identity work opportunities. However, in dominant discourses, higher education's humanistic ends have been suppressed by economistic and utilitarian objectives. Therefore, this paper also urges revitalizing higher education's humanistic values by exploring personal growth posibilities.

Bringing deep learning techniques to electromagnetic imaging is of interest considering its great success in various fields. Deep neural nets however are known for being data hungry machines, and in many practical cases, such as electromagnetic medical imaging, there is not enough to feed them. Scarcity of data necessitates reliance on simulations to generate a sufficiently large dataset for deep learning to perform any complicated task. Simulations however, can not perfectly represent real environments and therefore, any neural net trained on simulation data will invariably fail when evaluated on real data. This work customizes a deep domain adaptation technique for matching distributions of complex-valued electromagnetic data. We demonstrate the advantage of using complex-valued models over regular ones. An operational neural network trained on simulation data and adapted to practical data to perform brain injury localization is presented.