Abnormal behavioral responses to fenfluramine in patients with affective and personality disorders. Correlation with increased serotonergic responsivity

Serotonergic responsivity was assessed in 20 psychiatric patients by the prolactin response to a fenfluramine challenge test. During the fenfluramine challenge 6 of 20 patients (30%) spontaneously reported psychopathologic reactions that included: increased anxiety/agitation, psychotic symptoms, illusions, mood elevation, and anergia. The time of peak behavioral symptoms (2.5 +/- 0.8 hrs) corresponded closely to the time of peak increase in prolactin levels (3.0 +/- 1.1 hr). Abnormal behavioral responders had statistically significant greater increases in prolactin 1 to 4 hr after fenfluramine when compared to normal responders. Patients who developed an abnormal psychopathologic response to fenfluramine were characterized by higher levels of anxiety and agitation at the time of admission to the hospital but otherwise were not distinguishable on the basis of severity of other psychiatric symptoms. This study suggests that increased serotonergic transmission may trigger anxiety, psychosis, and mood elevation in specific vulnerable individuals, whereas other patients with similar psychiatric illnesses are not affected.     

High growth rate process in a SiC horizontal CVD reactor using HCl

The results of a new epitaxial process using an industrial 6 × 2″ wafer reactor with the introduction of HCl during the growth have been reported. A complete reduction of silicon nucleation in the gas phase has been observed even for high silicon dilution parameters (Si/H₂ > 0.05%) and an increase of the growth rate until about 20 μm/h has been measured. Photoluminescence at room temperature and at 50 K was used for defects quantification and distribution. On these wafers grown using HCl high voltage Schottky diodes have been realized. The diodes were analyzed by current-voltage (I-V) characteristics.     

When a Tritrophic Interaction Goes Wrong to the Third Level: Xanthoxylin From Trees Causes the Honeybee Larval Mortality in Colonies Affected by the River Disease

The “River Disease” (RD), a disorder impacting honeybee colonies located close to waterways with abundant riparian vegetation (including Sebastiania schottiana, Euphorbiaceae), kills newly hatched larvae. Forager bees from RD-affected colonies collect honeydew excretions from Epormenis cestri (Hemiptera: Flatidae), a planthopper feeding on trees of S. schottiana. First-instar honeybee larvae fed with this honeydew died. Thus, we postulated that the nectars of RD-affected colonies had a natural toxin coming from either E. cestri or S. schottiana. An untargeted metabolomics characterization of fresh nectars extracts from colonies with and without RD allowed to pinpoint xanthoxylin as one of the chemicals present in higher amounts in nectar from RD-affected colonies than in nectars from healthy colonies. Besides, xanthoxylin was also found in the aerial parts of S. schottiana and the honeydew excreted by E. cestri feeding on this tree. A larva feeding assay where xanthoxylin-enriched diets were offered to 1^st instar larvae showed that larvae died in the same proportion as larvae did when offered enriched diets with nectars from RD-colonies. These findings demonstrate that a xenobiotic can mimic the RD syndrome in honeybee larvae and provide evidence of an interspecific flow of xanthoxylin among three trophic levels. Further, our results give information that can be considered when implementing measures to control this honeybee disease.

     

Design of a decision support system for machine tool selection based on machine characteristics and performance tests

Economic globalization, together with heightened market competition and increasingly short product life cycles are motivating companies to use advanced manufacturing technologies. Use of high speed machining is increasingly widespread; however, as the technology is relatively new, it lacks a deep-rooted knowledge base which would facilitate implementation. One of the most frequent problems facing companies wishing to adopt this technology is selecting the most appropriate machine tool for the product in question and own enterprise characteristics. This paper presents a decision support system for high speed milling machine tool selection based on machine characteristics and performance tests. Profile machining tests are designed and conducted in participating machining centers. The decision support system is based on product dimension accuracy, process parameters such as feed rate and interpolation scheme used by CNC and machine characteristics such as machine accuracy and cost. Experimental data for process error and cycle operation time are obtained from profile machining tests with different geometrical feature zones that are often used in manufacturing of discrete parts or die/moulds. All those input parameters have direct impact on productivity and manufacturing cost. Artificial neural network models are utilized for decision support system with reasonable prediction capability.     

Data challenges of time domain astronomy

Astronomy has been at the forefront of the development of the techniques and methodologies of data intensive science for over a decade with large sky surveys and distributed efforts such as the Virtual Observatory. However, it faces a new data deluge with the next generation of synoptic sky surveys which are opening up the time domain for discovery and exploration. This brings both new scientific opportunities and fresh challenges, in terms of data rates from robotic telescopes and exponential complexity in linked data, but also for data mining algorithms used in classification and decision making. In this paper, we describe how an informatics-based approach—part of the so-called “fourth paradigm” of scientific discovery—is emerging to deal with these. We review our experiences with the Palomar-Quest and Catalina Real-Time Transient Sky Surveys; in particular, addressing the issue of the heterogeneity of data associated with transient astronomical events (and other sensor networks) and how to manage and analyze it.     

Initial pH influences microbial communities composition in dark fermentation of scotta permeate

This study addresses for the first time the influence of initial pH on the evolution of microbial consortia in dark fermentation of scotta permeate, using a high-throughput sequencing approach. Three fermentation phases could be detected: 1) a lag phase with no substantial differences in microbial composition at different initial pH values; 2) an exponential H₂ production phase, accompanied by a general increase of Clostridium genus components and higher incidence of Trichococcus genus at neutral and alkaline pH; 3) a final stationary phase, characterized by a general increase of Bifidobacterium and Lactobacillus genera in all reactors. The initial pH value influenced the relative abundance of Trichococcus at 16–48 h of incubation. The metabolic activity of this genus increased the amount of metabolic precursors of H₂ so that, when pH lowered to 5.4, clostridia in the reactors with initial alkaline pH become more active H₂-producers than those in the others.     

Physical,dynamic-mechanical and thermogravimetric analysis of aromatic and cycloaliphatic-based poly(urethaneurea)s

Experimental batches of linear rubber-like polyurethanes characterized by the presence of chemically different hard segments are studied and their thermal and dynamic-mechanical properties are related to the differences in chemical compositions. Interesting correlations are found between the development of crystallinity in such elastomeric poly(urethaneurea)s and the aromatic or alicyclic nature of the diisocyanate. Moreover, for a fixed ratio between hard and soft segments, and for a given composition of the soft segment, the results seem to indicate a better degree of phase separation in cycloaliphatic-based hard segment poly(urethaneurea)s than in aromatic-based hard segment poly(urethaneurea)s.     

Novel Therapeutic Opportunities in Neoadjuvant Setting in Urothelial Cancers: A New Horizon Opened by Molecular Classification and Immune Checkpoint Inhibitors

Muscle invasive bladder cancer (MIBC) is a widespread malignancy with a worse prognosis often related to a late diagnosis. For early-stage MIBC pts, a multidisciplinary approach is mandatory to evaluate the timing of neoadjuvant chemotherapy (NAC) and surgery. The current standard therapy is platinum-based NAC (MVAC-methotrexate, vinblastine, doxorubicin, and cisplatin or Platinum–Gemcitabine regimens) followed by radical cystectomy (RC) with lymphadenectomy. However, preliminary data from Vesper trial highlighted that dose-dense NAC MVAC is endowed with a good pathological response but shows low tolerability. In the last few years, translational-based research approaches have identified several candidate biomarkers of NAC esponsiveness, such as ERCC2, ERBB2, or DNA damage response (DDR) gene alterations. Moreover, the recent consensus MIBC molecular classification identified six molecular subtypes, characterized by different sensitivity to chemo- or targeted or immunotherapy, that could open a novel procedure for patient selection and also for neoadjuvant therapies. The Italian PURE-01 phase II Trial extended data on efficacy and resistance to Immune Checkpoint Inhibitors (ICIs) in this setting. In this review, we summarize the most relevant literature data supporting NAC use in MIBC, focusing on novel therapeutic strategies such as immunotherapy, considering the better patient stratification and selection emerging from novel molecular classification.