THz emission from Fe/Pt spintronic emitters with L10-FePt alloyed interface.

Recent developments in nanomagnetism and spintronics have enabled the use of ultrafast spin physics for terahertz (THz) emission. Spintronic THz emitters, consisting of ferromagnetic (FM)/non-magnetic (NM) thin film heterostructures, have demonstrated impressive properties for the use in THz spectroscopy and have great potential in scientific and industrial applications. In this work, we focus on the impact of the FM/NM interface on the THz emission by investigating Fe/Pt bilayers with engineered interfaces. In particular, we intentionally modify the Fe/Pt interface by inserting an ordered L10-FePt alloy interlayer. Subsequently, we establish that a Fe/L10-FePt (2 nm)/Pt configuration is significantly superior to a Fe/Pt bilayer structure, regarding THz emission amplitude. The latter depends on the extent of alloying on either side of the interface. The unique trilayer structure opens new perspectives in terms of material choices for the next generation of spintronic THz emitters.

A Hybrid Teaching Factory Model for Supporting the Educational Process in COVID-19 era.

The lockdown due to SARS-CoV-2 (COVID-19) pandemic forced both educators and students to swift and rely on digital technologies in order to ensure the successful completion of modules based on the official curriculum. Universities are still facing the challenging issue to offer high quality learning opportunities without risking participants' health. Towards that end, the current study presents a Hybrid Model under the Teaching Factory framework concept that has been successfully implemented and validated. In a hybrid laboratory case study, Engineering students guided remotely laboratory personnel towards a successful manufacture and assembly of a customized remote-control car. Benefits and boundaries of the current approach are discussed along with future perspectives.

Incorporating patient concerns into design requirements for IoMT-based systems: The fall detection case study.

Internet of Medical Things (IoMT) systems are envisioned to provide high-quality healthcare services to patients in the comfort of their home, utilizing cutting-edge Internet of Things (IoT) technologies and medical sensors. Patient comfort and willingness to participate in such efforts is a prominent factor for their adoption. As IoT technology has provided solutions for all technical issues, patient concerns are those that seem to restrict their wider adoption. To enhance patient awareness of the system properties and enhance their willingness to adopt IoMT solutions, this paper presents a novel methodology to integrate patient concerns in the design requirements of such systems. It comprises a number of straightforward steps that an IoMT designer can follow, starting from identifying patient concerns, incorporating them in system design requirements as criticalities, proceeding to system implementation and testing, and finally, verifying that it fulfills the concerns of the patients. To showcase the effectiveness of the proposed methodology, the paper applies it in the design and implementation of a fall detection system for elderly patients remotely monitored in their homes.

The significance of neutrophil/lymphocyte ratio as a possible marker of underlying papillary microcarcinomas in thyroidal goiters: a pilot study.

BACKGROUND: Preoperatively elevated neutrophil/lymphocyte ratio (NLR) is a negative prognostic factor of survival in various types of cancers. A retrospective study was conducted to examine if preoperative elevation of NLR is associated with higher risk for incidental papillary thyroid microcarcinoma (PTMC). METHODS: The study sample consisted of 26 patients with benign goiters, 31 patients with incidental PTMC, 26 patients preoperatively diagnosed with thyroid cancer, and 26 healthy controls. NLRs were compared regarding thyroidal pathology. RESULTS: The mean preoperative NLR was significantly elevated in patients with PTMC and thyroid cancer. In addition, the third and fourth quartiles of NLR included only patients with either PTMC or thyroid cancer. No significant differences in NLR occurred between patients with multifocal and unifocal PTMC. CONCLUSIONS: NLRs were significantly elevated in patients with incidental PTMC and thyroid cancer. The findings of this pilot study indicate that NLR should be considered an easily accessible biomarker for detecting incidental PTCM; nevertheless, further studies are required to confirm these preliminary results.

Multi-scale modeling of gene regulatory networks via integration of temporal and topological biological data.

Regulome is the dynamic network representation of the regulatory interplay among genes, proteins and other cellular components that control cellular processes. Reconstruction of gene regulatory networks (GRN) delineates one of the main objectives of Systems Biology towards understanding the organization of regulome. Significant progress has been reported the last years regarding GRN reconstruction methods, but the majority of them either consider information originating solely from gene expression data or/and are applied on a small fraction of the experimental dataset. In this paper, we will describe an integrative method, utilizing both temporal information arriving from time-series gene expression profiles, as well as topological properties of protein networks. The proposed methodology detects relations among either groups of genes or specific genes depending on the level of abstraction or resolution requested. Application on real data proved the ability of the method to extract relations in accordance with current biological knowledge as well as discriminate between different experimental conditions.

Revealing the dynamic modularity of composite biological networks in breast cancer treatment.

A major challenge in modern breast cancer treatment is to unravel the effect of drug activity through the systematic rewiring of cellular networks over time. Here, we illustrate the efficacy and discriminative power of our integrative approach in detecting modules that represent the regulatory effect of tamoxifen, widely used in anti-estrogen treatment, on transcriptome and proteome and serve as dynamic sub-network signatures. Initially, composite networks, after integrating protein interaction and time series gene expression data between two conditions (estradiol and estradiol plus tamoxifen), were constructed. Further, the Detect Module from Seed Protein (DMSP) algorithm elaborated on the graphs and constructed modules, with specific 'seed' proteins used as starting points. Our findings provide evidence about the way drugs perturb and rewire the high-order organization of interactome in time.