Determination of recombination length of a non-equilibrium plasma produced by laser ablation

An experimental study of the laser ablation produced plasma evolution is necessary for its deeper understanding, since plasma expansion has both spatially and temporally varying characteristics. We irradiated a Cu target with a KrF laser beam. A small Faraday cup array and an axial Faraday cup were used as diagnostic systems, in order to study the spatial variation in the total charge carried by plasma ions. Charge loss during the plasma expansion was observed, which was attributed to the charged species recombination. This occurred upstream to the critical distance where the plasma density is high enough. Downstream the critical distance the plasma particles collisions were negligible and the ion charge remained frozen. In these experiments it was observed that the critical distance for charge recombination was a function of laser fluence

Lethal Clostridium difficile Colitis Associated with Paclitaxel and Carboplatin Chemotherapy in Ovarian Carcinoma: Case Report and Review of the Literature

Clostridium difficile colitis, although rare, could represent a serious complication following chemotherapy. Prior antibiotic use has been considered the single most important risk factor in the development of C. difficile infection. Recently, the association between antineoplastic therapy and C. difficile-associated diarrhea in the absence of a prior antibiotic therapy has become more apparent. A 75-year-old woman with serous adenocarcinoma of the ovary developed lethal pancolitis caused by C. difficile after five cycles of paclitaxel- and carboplatin-based chemotherapy. She presented with diarrhea, coffee-ground emesis, and oliguria and was hospitalized immediately for aggressive treatment. Despite all the medical efforts, her condition worsened and she died after twenty days. We describe the second case reported of a patient developing a severe C. difficile colitis following chemotherapy without any recent antibiotic use and review the data of the literature, emphasizing the need to a prompt diagnosis and management that can significantly decrease the morbidity and life-threatening complications associated with this infection

Laser offset welding of AZ31B magnesium alloy to 316 stainless steel

In this paper, the feasibility of using a fiber laser to perform a dissimilar metal joining was explored. AZ31B magnesium and 316 stainless steel were autogenously joined in butt configuration. The weldability between different materials is often compromised by a large difference in thermal properties and poor metallurgical compatibility. Thus, the beam was focused onto the top surface of the magnesium plate, at a certain distance from the interfaces (offset), and without using any interlayer or groove preparation. Such a method was called laser offset welding (LOW). Results proved a very good capability. The ultimate tensile strength exceeded the value of 100 MPa, since a resistant and thin layer of hard intermetallic compounds is formed within the fusion zone. The rupture was observed within the magnesium side, far from the centerline. The metallurgy of fusion zone indicated the effectiveness of phases coalescence, without mixing at liquid states. LOW was demonstrated to be a promising technique to join dissimilar metal welds, being capable to produce an effective bonding with good tensile strength

Survival of a sars-cov-2 surrogate on flow-pack polyethylene and polystyrene food trays at refrigeration and room temperature conditions

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent of the current pandemic referred to as coronavirus disease 2019, is spread by direct and indirect transmission between humans, including contact with contaminated surfaces, frozen food, packaging materials, and storage environments. Food contamination may occur in the “farm-to-table” lifecycle through contact with food handlers and environments. In the present study, the survival of a SARS-CoV-2 surrogate (feline coronavirus (FCoV)) at room temperature and refrigeration conditions for different time intervals on two types packaging widely used packaging, namely flow-pack polyethylene and polystyrene food trays, was investigated. FCoV was stable on the flow-pack polyethylene for 48 h and 120 h at room temperature and 4◦C, respectively, while it persisted on polystyrene food trays for 36 h at room temperature and for 120 h at +4◦C. The results of our study highlight the possible implications of food packaging in the spread of SARS-CoV-2 during the current pandemic

Zn-doped titania nanoparticles as building blocks for solid foam filters of water and air via photocatalytic oxidation

Photocatalytic oxidation (PCO) could provide energy-efficient purification of water and air. Its efficacy is constrained mainly by limited photocatalytic activity and active surface. To address both, solid foams with hierarchic porous structures spanning multiple length-scales, stabilized by photocatalytic Zn-doped titania nanoparticles (NP) were synthesized and tested. The NP were characterized by SEM, EDS, DLS, XRD, Raman and UV–Vis spectroscopies. Solid foams were stabilized by NP complexes with cationic surfactants. The foam morphology was characterized and photocatalytic activity was demonstrated in water. The present work paves the way for the development of efficient systems for air and water purification in demanding technological sectors, such as aerospace

Artificial intelligence weights the importance of factors predicting complete cytoreduction at secondary cytoreductive surgery for recurrent ovarian cancer

Objective: Accumulating evidence support that complete cytoreduction (CC) at the time of secondary cytoreductive surgery (SCS) improves survival in patients affected by recurrent ovarian cancer (ROC). Here, we aimed to determine whether artificial intelligence (AI) might be useful in weighting the importance of clinical variables predicting CC and survival. Methods: This is a retrospective study evaluating 194 patients having SCS for ROC. Using artificial neuronal network (ANN) analysis was estimated the importance of different variables, used in predicting CC and survival. ANN simulates a biological neuronal system. Like neurons, ANN acquires knowledge through a learning-phase process and allows weighting the importance of covariates, thus establishing how much a variable influences a multifactor phenomenon. Results: Overall, 82.9% of patients had CC at the time of SCS. Using ANN, we observed that the 3 main factors driving the ability of achieve CC included: disease-free interval (DFI) (importance: 0.231), retroperitoneal recurrence (importance: 0.178), residual disease at primary surgical treatment (importance: 0.138), and International Federation of Gynecology and Obstetrics (FIGO) stage at presentation (importance: 0.088). Looking at connections between different covariates and overall survival (OS), we observed that DFI is the most important variable influencing OS (importance: 0.306). Other important variables included: CC (importance: 0.217), and FIGO stage at presentation (importance: 0.100). Conclusion: According to our results, DFI should be considered as the most important factor predicting both CC and OS. Further studies are needed to estimate the clinical utility of AI in providing help in decision making process

A proposal of quantum-inspired machine learning for medical purposes: An application case

Learning tasks are implemented via mappings of the sampled data set, including both the classical and the quantum framework. Biomedical data characterizing complex diseases such as cancer typically require an algorithmic support for clinical decisions, especially for early stage tumors that typify breast cancer patients, which are still controllable in a therapeutic and surgical way. Our case study consists of the prediction during the pre-operative stage of lymph node metastasis in breast cancer patients resulting in a negative diagnosis after clinical and radiological exams. The classifier adopted to establish a baseline is characterized by the result invariance for the order permutation of the input features, and it exploits stratifications in the training procedure. The quantum one mimics support vector machine mapping in a high-dimensional feature space, yielded by encoding into qubits, while being characterized by complexity. Feature selection is exploited to study the performances associated with a low number of features, thus implemented in a feasible time. Wide variations in sensitivity and specificity are observed in the selected optimal classifiers during cross-validations for both classification system types, with an easier detection of negative or positive cases depending on the choice between the two training schemes. Clinical practice is still far from being reached, even if the flexible structure of quantum-inspired classifier circuits guarantees further developments to rule interactions among features: this preliminary study is solely intended to provide an overview of the particular tree tensor network scheme in a simplified version adopting just product states, as well as to introduce typical machine learning procedures consisting of feature selection and classifier performance evaluation

Possible human-to-dog transmission of SARS-CoV-2, Italy, 2020

We detected severe acute respiratory syndrome coronavirus 2 in an otherwise healthy poodle living with 4 family members who had coronavirus disease. We observed antibodies in serum samples taken from the dog, indicating seroconversion. Full-length genome sequencing showed that the canine and human viruses were identical, suggesting human-to-animal transmission