The Tumor Proteolytic Landscape: A Challenging Frontier in Cancer Diagnosis and Therapy

In recent decades, dysregulation of proteases and atypical proteolysis have become increasingly recognized as important hallmarks of cancer, driving community-wide efforts to explore the proteolytic landscape of oncologic disease. With more than 100 proteases currently associated with different aspects of cancer development and progression, there is a clear impetus to harness their potential in the context of oncology. Advances in the protease field have yielded technologies enabling sensitive protease detection in various settings, paving the way towards diagnostic profiling of disease-related protease activity patterns. Methods including activity-based probes and substrates, antibodies, and various nanosystems that generate reporter signals, i.e., for PET or MRI, after interaction with the target protease have shown potential for clinical translation. Nevertheless, these technologies are costly, not easily multiplexed, and require advanced imaging technologies. While the current clinical applications of protease-responsive technologies in oncologic settings are still limited, emerging technologies and protease sensors are poised to enable comprehensive exploration of the tumor proteolytic landscape as a diagnostic and therapeutic frontier. This review aims to give an overview of the most relevant classes of proteases as indicators for tumor diagnosis, current approaches to detect and monitor their activity in vivo, and associated therapeutic applications.     

Toward a better understanding of multifunctional cement-based materials: The impact of graphite nanoplatelets (GNPs)

The impact of graphite nanoplatelets (GNPs) on the physical and mechanical properties of cementitious nanocomposites was investigated. A market-available premixed mortar was modified with 0.01% by weight of cement of commercial GNPs characterized by two distinctively different aspect ratios.The rheological behavior of the GNP-modified fresh admixtures was thoroughly evaluated. Hardened cementitious nanocomposites were investigated in terms of density, microstructure (Scanning Electron Microscopy, SEM and micro–Computed Tomography, μ-CT), mechanical properties (three-point bending and compression tests), and physical properties (electrochemical impedance spectroscopy, EIS and thermal conductivity measurements). At 28 days, all GNP-modified mortars showed about 12% increased density. Mortars reinforced with high aspect ratio GNPs exhibited the highest compressive and flexural strength: about 14% and 4% improvements compared to control sample, respectively. Conversely, low aspect ratio GNPs led to cementitious nanocomposites characterized by 36% decreased electrical resistivity combined with 60% increased thermal conductivity with respect to the control sample.     

Passive target detection and tracking from electromagnetic field measurements

This paper presents a novel approach to the localization of moving targets in a complex environment based on the measurement of the perturbations induced by the target presence on an independently‐generated time‐varying electromagnetic field. Field perturbations are measured via a set of sensors deployed over the domain of interest and used to detect and track a possible target by resorting to a particle Bernoulli filter (PBF). To comply with real‐time operation, the PBF works along with an artificial neural network (ANN) model of the environment trained offline via finite elements (FEs). The performance of the proposed algorithm is assessed via simulation experiments.     

Detection,localisation and tracking of pallets using machine learning techniques and 2D range data

The problem of autonomous transportation in industrial scenarios is receiving a renewed interest due to the way it can revolutionise internal logistics, especially in unstructured environments. This paper presents a novel architecture allowing a robot to detect, localise, and track (possibly multiple) pallets using machine learning techniques based on an on-board 2D laser rangefinder only. The architecture is composed of two main components: the first stage is a pallet detector employing a Faster Region-Based Convolutional Neural Network (Faster R-CNN) detector cascaded with a CNN-based classifier; the second stage is a Kalman filter for localising and tracking detected pallets, which we also use to defer commitment to a pallet detected in the first stage until sufficient confidence has been acquired via a sequential data acquisition process. For fine-tuning the CNNs, the architecture has been systematically evaluated using a real-world dataset containing 340 labelled 2D scans, which have been made freely available in an online repository. Detection performance has been assessed on the basis of the average accuracy over k-fold cross-validation, and it scored 99.58% in our tests. Concerning pallet localisation and tracking, experiments have been performed in a scenario where the robot is approaching the pallet to fork. Although data have been originally acquired by considering only one pallet as per specification of the use case we consider, artificial data have been generated as well to mimic the presence of multiple pallets in the robot workspace. Our experimental results confirm that the system is capable of identifying, localising and tracking pallets with a high success rate while being robust to false positives.

     

意大利如何在古丝绸之路上再铸辉煌（英文）

古"丝绸之路"使中国的丝绸取代了地中海地区的丝绸,现如今,同样的"丝绸之路"仍旧以不同的形式存在着,意大利目前仍从中国购买几乎所有的丝绸原材料。外贸对于意大利丝绸业依旧是最重要的,出口以丝织物为主,意大利丝绸产品2016年的出口总值达到8.73亿欧元,较2015年略有增长,但由于价格压力和激烈的竞争,丝绸用量显著减少。不仅仅是意大利,当今世界的丝绸行业正承受着缺乏明确制度性战略的痛苦,针对生态可持续性、公平贸易和质量改善三方面制订丝绸战略迫在眉睫。随着经济走势继续加快,一些精准的政策也需出台,以保护丝绸本身和丝绸经营者。     

Anelastic reorganisation of fibre-reinforced biological tissues

In this work, we contribute to the study of the structural reorganisation of biological tissues in response to mechanical stimuli. We specialise our investigation to a class of hydrated soft tissues, whose internal structure features reinforcing fibres. These are oriented statistically within the tissue, and their pattern of orientation is such that, at each material point, the tissue is anisotropic. From its natural, stress-free state, the tissue can be distorted anelastically into a global reference configuration, and then deformed under the action of external mechanical loads. The anelastic distortions are responsible for changing irreversibly the internal structure of the tissue, which, in the present context, occurs through both the rearrangement of the bonds among the tissue cells and the deformation-driven reorientation of the fibres. The anelastic strains, in addition, are assumed to model the onset and evolution of microcracks in the tissue, which may be triggered by the mechanical loads applied to the tissue in the case of traumatic events, or diseases. For our purposes, we formulate an anisotropic model of remodelling and we consider a fully isotropic model of structural reorganisation for comparison, with the aim to study if, how, and to what extent the evolution of anelastic distortions is influenced by the tissue’s anisotropy.

     

Thin‐film nanostructure and polymer architecture in semicrystalline syndiotactic poly(p‐methylstyrene)–(cis‐1,4‐polybutadiene) multiblock copolymers

The thin‐film morphology of stereoregular syndiotactic poly(p‐methylstyrene)–(cis‐1,4‐polybutadiene) (sP(pMS–B)) multiblock copolymers has been investigated using tapping mode atomic force microscopy with variation of the polymer composition and monomer block lengths. The morphology of the thin films ranges from isolated circular domains of sP(pMS) embedded into a matrix of polybutadiene (PB) to isolated domains of PB embedded into a matrix of sP(pMS), passing through bicontinuous (jagged) lamellae when the pMS concentration is in the range 20–67 mol%. Multiple folding of the polymer segments, i.e. where reciprocal inclusions of polymer segments to each other phase are able to generate greater domain, has been postulated and validated by considerations on the polymer architecture and the thermal and crystalline behaviour. © 2019 Society of Chemical Industry     

Mitochondrial Bioenergetic,Photobiomodulation and Trigeminal Branches Nerve Damage,What’s the Connection? A Review

Background: Injury of the trigeminal nerve in oral and maxillofacial surgery can occur. Schwann cell mitochondria are regulators in the development, maintenance and regeneration of peripheral nerve axons. Evidence shows that after the nerve injury, mitochondrial bioenergetic dysfunction occurs and is associated with pain, neuropathy and nerve regeneration deficit. A challenge for research is to individuate new therapies able to normalise mitochondrial and energetic metabolism to aid nerve recovery after damage. Photobiomodulation therapy can be an interesting candidate, because it is a technique involving cell manipulation through the photonic energy of a non-ionising light source (visible and NIR light), which produces a nonthermal therapeutic effect on the stressed tissue. Methods: The review was based on the following questions: (1) Can photo-biomodulation by red and NIR light affect mitochondrial bioenergetics? (2) Can photobiomodulation support damage to the trigeminal nerve branches? (preclinical and clinical studies), and, if yes, (3) What is the best photobiomodulatory therapy for the recovery of the trigeminal nerve branches? The papers were searched using the PubMed, Scopus and Cochrane databases. This review followed the ARRIVE-2.0, PRISMA and Cochrane RoB-2 guidelines. Results and conclusions: The reliability of photobiomodulatory event strongly bases on biological and physical-chemical evidence. Its principal player is the mitochondrion, whether its cytochromes are directly involved as a photoacceptor or indirectly through a vibrational and energetic variation of bound water: water as the photoacceptor. The 808-nm and 100 J/cm² (0.07 W; 2.5 W/cm²; pulsed 50 Hz; 27 J per point; 80 s) on rats and 800-nm and 0.2 W/cm² (0.2 W; 12 J/cm²; 12 J per point; 60 s, CW) on humans resulted as trustworthy therapies, which could be supported by extensive studies.     

Potential to retrofit existing small-scale gasifiers through steam gasification of biomass residues for hydrogen and biofuels production

This work investigates the opportunity of retrofitting existing small-scale gasifiers shifting from combined heat and power (CHP) to hydrogen and biofuels production, using steam and biomass residues (woodchips, vineyard pruning and bark). The experiments were carried out in a batch reactor at 700 °C and 800 °C and at different steam flow (SF) rates (0.04 g/min and 0.20 g/min). The composition of the producer gas is in the range of 46–70 % H₂, 9–29 % CO, 12–27 % CO₂, and 2–6 % CH₄. A producer gas specific production factor of approx. 10 NL_pg/g_char can be achieved when the lower SFs are used, which allows to provide 80 % of the hydrogen concentration required for biomethanation and MeOH synthesis. As for FT synthesis, an optimal H₂/CO ratio of approx. 2 can be achieved. The results of this work provide further evidence towards the feasibility of hydrogen and biofuels generation from residual biomass through steam gasification.