The Observability Radius of Networks

This paper studies the observability radius of network systems, which measures the robustness of a network to perturbations of the edges. We consider linear networks, where the dynamics are described by a weighted adjacency matrix, and dedicated sensors are positioned at a subset of nodes. We allow for perturbations of certain edge weights, with the objective of preventing observability of some modes of the network dynamics. To comply with the network setting, our work considers perturbations with a desired sparsity structure, thus extending the classic literature on the observability radius of linear systems. The paper proposes two sets of results. First, we propose an optimization framework to determine a perturbation with smallest Frobenius norm that renders a desired mode unobservable from the existing sensor nodes. Second, we study the expected observability radius of networks with given structure and random edge weights. We provide fundamental robustness bounds dependent on the connectivity properties of the network and we analytically characterize optimal perturbations of line and star networks, showing that line networks are inherently more robust than star networks.Comment: 8 pages, 3 figure

A novel high-content immunofluorescence assay as a tool to identify at the single cell level γ-globin inducing compounds

The identification of drugs capable of reactivating γ-globin to ameliorate β-thalassemia and Sickle Cell anemia is still a challenge, as available γ-globin inducers still have limited clinical indications. High-throughput screenings (HTS) aimed to identify new potentially therapeutic drugs require suitable first-step-screening methods combining the possibility to detect variation in the γ/β globin ratio with the robustness of a cell line. We took advantage of a K562 cell line variant expressing β-globin (β-K562) to set up a new multiplexed high-content immunofluorescence assay for the quantification of γ-and β-globin content at single-cell level. The assay was validated by using the known globin inducers hemin, hydroxyurea and butyric acid and further tested in a pilot screening that confirmed HDACs as targets for γ-globin induction (as proved by siRNA-mediated HDAC3 knockdown and by treatment with HDACs inhibitors entinostat and dacinostat) and identified Heme-oxygenases as novel candidate targets for γ-globin induction. Indeed, Heme-oxygenase2 siRNA knockdown as well as its inhibition by Tin protoporphyrin-IX (TinPPIX) greatly increased γ-globin expression. This result is particularly interesting as several metalloporphyrins have already been developed for clinical uses and could be tested (alone or in combination with other drugs) to improve pharmacological γ-globin reactivation for the treatment of β-hemoglobinopathie

A Machine-Learning Approach to Target Clinical and Biological Features Associated with Sarcopenia: Findings from Northern and Southern Italian Aging Populations

Epidemiological and public health resonance of sarcopenia in late life requires further research to identify better clinical markers useful for seeking proper care strategies in preventive medicine settings. Using a machine-learning approach, a search for clinical and fluid markers most associated with sarcopenia was carried out across older populations from northern and southern Italy. A dataset of adults >65 years of age (n = 1971) made up of clinical records and fluid markers from either a clinical-based subset from northern Italy (Pavia) and a population-based subset from southern Italy (Apulia) was employed (n = 1312 and n = 659, respectively). Body composition data obtained by dual-energy X-ray absorptiometry (DXA) were used for the diagnosis of sarcopenia, given by the presence of either low muscle mass (i.e., an SMI 2 for males or 2 for females) and of low muscle strength (i.e., an HGS < 27 kg for males or <16 kg for females) or low physical performance (i.e., an SPPB ≤ 8), according to the EWGSOP2 panel guidelines. A machine-learning feature-selection approach, the random forest (RF), was used to identify the most predictive features of sarcopenia in the whole dataset, considering every possible interaction among variables and taking into account nonlinear relationships that classical models could not evaluate. Then, a logistic regression was performed for comparative purposes. Leading variables of association to sarcopenia overlapped in the two population subsets and included SMI, HGS, FFM of legs and arms, and sex. Using parametric and nonparametric whole-sample analysis to investigate the clinical variables and biological markers most associated with sarcopenia, we found that albumin, CRP, folate, and age ranked high according to RF selection, while sex, folate, and vitamin D were the most relevant according to logistics. Albumin, CRP, vitamin D, and serum folate should not be neglected in screening for sarcopenia in the aging population. Better preventive medicine settings in geriatrics are urgently needed to lessen the impact of sarcopenia on the general health, quality of life, and medical care delivery of the aging population

Parathyroid Retrospective Analysis of Neoplasms Incidence (pTRANI Study): An Italian Multicenter Study on Parathyroid Carcinoma and Atypical Parathyroid Tumour

Background: Parathyroid cancer (PC) is a rare sporadic or hereditary malignancy whose histologic features were redefined with the 2022 WHO classification. A total of 24 Italian institutions designed this multicenter study to specify PC incidence, describe its clinical, functional, and imaging characteristics and improve its differentiation from the atypical parathyroid tumour (APT). Methods: All relevant information was collected about PC and APT patients treated between 2009 and 2021. Results: Among 8361 parathyroidectomies, 351 patients (mean age 59.0 ± 14.5; F = 210, 59.8%) were divided into the APT (n = 226, 2.8%) and PC group (n = 125, 1.5%). PC showed significantly higher rates (p &lt; 0.05) of bone involvement, abdominal, and neurological symptoms than APT (48.8% vs. 35.0%, 17.6% vs. 7.1%, 13.6% vs. 5.3%, respectively). Ultrasound (US) diameter &gt;3 cm (30.9% vs. 19.3%, p = 0.049) was significantly more common in the PC. A significantly higher frequency of local recurrences was observed in the PC (8.0% vs. 2.7%, p = 0.022). Mortality due to consequences of cancer or uncontrolled hyperparathyroidism was 3.3%. Conclusions: Symptomatic hyperparathyroidism, high PTH and albumin-corrected serum calcium values, and a US diameter &gt;3 cm may be considered features differentiating PC from APT. 2022 WHO criteria did not impact the diagnosis

Monitoring of apoptosis of HL60 cells by Fourier-transform infrared spectroscopy.

Fourier-transform infrared (FTIR) spectroscopy is a vibrational technique that gives information on the chemical composition of a sample, providing a "molecular fingerprint" of it. It is a powerful approach to study intact cells. The aim of the present study was to analyse and quantify apoptotic cells by using a FTIR approach based on attenuated total reflection (ATR). We incubated human HL60 leukaemic cells with camptothecin, a cytotoxic drug, and monitored apoptosis induction over a period of time. Several ATR-FTIR spectral changes occurred during the apoptotic process. In particular, we observed that the apoptotic index was inversely correlated with the spectral area in the region 1200-900 cm(-1), assigned to the absorption of nucleic acids. We therefore propose that ATR-FTIR spectral features may be used as a diagnostic marker of apoptotic cells

Network composition for optimal disturbance rejection

This paper investigates how the topology of a dynamical network affects its robustness against exogenous disturbances. We consider Laplacian-based network dynamics, and we adopt the H2 system norm to measure the robustness of the network against disturbances. For networks arising from the composition of atomic structures, we provide a closed-form expression of the robustness against disturbances, and we identify optimal composition rules. Specifically, we show that networks consisting of multiple atomic structures are less robust than each isolated part, and that robust structures arise by interconnecting nodes of the atomic components with highest degree. Finally, we describe an algorithm for the design of robust composite networks