Yang-Mills-Higgs connections on Calabi-Yau manifolds

Let $X$ be a compact connected K"ahler--Einstein manifold with $c_1(TX), geq, 0$. If there is a semistable Higgs vector bundle $(E, , heta)$ on $X$ with $heta, ot=, 0$, then we show that $c_1(TX)=0$; any $X$ satisfying this condition is called a Calabi--Yau manifold, and it admits a Ricci--flat K"ahler form cite{Ya}. Let $(E, , heta)$ be a polystable Higgs vector bundle on a compact Ricci--flat K"ahler manifold $X$. Let $h$ be an Hermitian structure on $E$ satisfying the Yang--Mills--Higgs equation for $(E, , heta)$. We prove that $h$ also satisfies the Yang--Mills--Higgs equation for $(E, ,0)$. A similar result is proved for Hermitian structures on principal Higgs bundles on $X$ satisfying the Yang--Mills--Higgs equation. \ua9 2016 International Press

Strategies to identify muscle fatigue from SEMG during cycling

Detection, quantification and analysis of muscle fatigue are crucial in occupational/rehabilitation and sporting settings. Sports organizations, such as the Australian Institute of Sports (AIS), currently monitor fatigue by a battery of tests including invasive techniques that require taking blood samples and/or muscle biopsies, the latter of which is highly invasive, painful, time consuming and expensive. SEMG (surface electromyography) is non-invasive monitoring of muscle activation and is an indication of localized muscle fatigue based on the observed shift of the power spectral density of the SEMG. The success of SEMG based techniques is currently limited to isometric contraction and is not acceptable to the human movement community. The paper proposes and tests a simple signal processing technique to identify the onset of muscle fatigue during cyclic activities of muscles, such as VL and VM, during cycling. Based on experiments conducted with 7 participants, using power output as a measure of fatigue, the technique is able to identify muscle fatigue with 98% significance

Prokaryotic Diversity of the Composting Thermophilic Phase: The Case of Ground Coffee Compost

Waste biomass coming from a local coffee company, which supplied burnt ground coffee after an incorrect roasting process, was employed as a starting material in the composting plant of the Experimental Station of the University of Naples Federico II at Castel Volturno (CE). The direct molecular characterization of compost using 13C-NMR spectra, which was acquired through cross-polarization magic-angle spinning, showed a hydrophobicity index of 2.7% and an alkyl/hydroxyalkyl index of 0.7%. Compost samples that were collected during the early “active thermophilic phase” (when the composting temperature was 63 C) were analyzed for the prokaryotic community composition and activities. Two complementary approaches, i.e., genomic and predictive metabolic analysis of the 16S rRNA V3–V4 amplicon and culture-dependent analysis, were combined to identify the main microbial factors that characterized the composting process. The whole microbial community was dominated by Firmicutes. The predictive analysis of the metabolic functionality of the community highlighted the potential degradation of peptidoglycan and the ability of metal chelation, with both functions being extremely useful for the revitalization and fertilization of agricultural soils. Finally, three biotechnologically relevant Firmicutes members, i.e., Geobacillus thermodenitrificans subsp. calidus, Aeribacillus pallidus, and Ureibacillus terrenus (strains CAF1, CAF2, and CAF5, respectively) were isolated from the “active thermophilic phase” of the coffee composting. All strains were thermophiles growing at the optimal temperature of 60 C. Our findings contribute to the current knowledge on thermophilic composting microbiology and valorize burnt ground coffee as waste material with biotechnological potentialities

A primer on machine learning techniques for genomic applications

High throughput sequencing technologies have enabled the study of complex biological aspects at single nucleotide resolution, opening the big data era. The analysis of large volumes of heterogeneous “omic” data, however, requires novel and efficient computational algorithms based on the paradigm of Artificial Intelligence. In the present review, we introduce and describe the most common machine learning methodologies, and lately deep learning, applied to a variety of genomics tasks, trying to emphasize capabilities, strengths and limitations through a simple and intuitive language. We highlight the power of the machine learning approach in handling big data by means of a real life example, and underline how described methods could be relevant in all cases in which large amounts of multimodal genomic data are available

Accuracy of right atrial pressure estimation using a multi-parameter approach derived from inferior vena cava semi-automated edge-tracking echocardiography: a pilot study in patients with cardiovascular disorders

The echocardiographic estimation of right atrial pressure (RAP) is based on the size and inspiratory collapse of the inferior vena cava (IVC). However, this method has proven to have limits of reliability. The aim of this study is to assess feasibility and accuracy of a new semi-automated approach to estimate RAP. Standard acquired echocardiographic images were processed with a semi-automated technique. Indexes related to the collapsibility of the vessel during inspiration (Caval Index, CI) and new indexes of pulsatility, obtained considering only the stimulation due to either respiration (Respiratory Caval Index, RCI) or heartbeats (Cardiac Caval Index, CCI) were derived. Binary Tree Models (BTM) were then developed to estimate either 3 or 5 RAP classes (BTM3 and BTM5) using indexes estimated by the semi-automated technique. These BTMs were compared with two standard estimation (SE) echocardiographic methods, indicated as A and B, distinguishing among 3 and 5 RAP classes, respectively. Direct RAP measurements obtained during a right heart catheterization (RHC) were used as reference. 62 consecutive \u2018all-comers\u2019 patients that had a RHC were enrolled; 13 patients were excluded for technical reasons. Therefore 49 patients were included in this study (mean age 62.2\ua0\ub1\ua015.2\ua0years, 75.5% pulmonary hypertension, 34.7% severe left ventricular dysfunction and 51% right ventricular dysfunction). The SE methods showed poor accuracy for RAP estimation (method A: misclassification error, ME\ua0=\ua051%, R2\ua0=\ua00.22; method B: ME\ua0=\ua069%, R2\ua0=\ua00.26). Instead, the new semi-automated methods BTM3 and BTM5 have higher accuracy (ME\ua0=\ua014%, R2\ua0=\ua00.47 and ME\ua0=\ua022%, R2\ua0=\ua00.61, respectively). In conclusion, a multi-parametric approach using IVC indexes extracted by the semi-automated approach is a promising tool for a more accurate estimation of RAP

The puzzling issue of silica toxicity: are silanols bridging the gaps between surface states and pathogenicity?

Background: Silica continues to represent an intriguing topic of fundamental and applied research across various scientific fields, from geology to physics, chemistry, cell biology, and particle toxicology. The pathogenic activity of silica is variable, depending on the physico-chemical features of the particles. In the last 50 years, crystallinity and capacity to generate free radicals have been recognized as relevant features for silica toxicity. The ‘surface’ also plays an important role in silica toxicity, but this term has often been used in a very general way, without defining which properties of the surface are actually driving toxicity. How the chemical features (e.g., silanols and siloxanes) and configuration of the silica surface can trigger toxic responses remains incompletely understood. Main body: Recent developments in surface chemistry, cell biology and toxicology provide new avenues to improve our understanding of the molecular mechanisms of the adverse responses to silica particles. New physicochemical methods can finely characterize and quantify silanols at the surface of silica particles. Advanced computational modelling and atomic force microscopy offer unique opportunities to explore the intimate interactions between silica surface and membrane models or cells. In recent years, interdisciplinary research, using these tools, has built increasing evidence that surface silanols are critical determinants of the interaction between silica particles and biomolecules, membranes, cell systems, or animal models. It also has become clear that silanol configuration, and eventually biological responses, can be affected by impurities within the crystal structure, or coatings covering the particle surface. The discovery of new molecular targets of crystalline as well as amorphous silica particles in the immune system and in epithelial lung cells represents new possible toxicity pathways. Cellular recognition systems that detect specific features of the surface of silica particles have been identified. Conclusions: Interdisciplinary research bridging surface chemistry to toxicology is progressively solving the puzzling issue of the variable toxicity of silica. Further interdisciplinary research is ongoing to elucidate the intimate mechanisms of silica pathogenicity, to possibly mitigate or reduce surface reactivity. Keywords: Silica, Silicosis, Lung cancer, Auto-immune diseases, Surface reactivity, Silanol, Coating, Modelling, Spectroscopy, Atomic force microscop

The puzzling issue of silica toxicity: Are silanols bridging the gaps between surface states and pathogenicity?

Background: Silica continues to represent an intriguing topic of fundamental and applied research across various scientific fields, from geology to physics, chemistry, cell biology, and particle toxicology. The pathogenic activity of silica is variable, depending on the physico-chemical features of the particles. In the last 50 years, crystallinity and capacity to generate free radicals have been recognized as relevant features for silica toxicity. The 'surface' also plays an important role in silica toxicity, but this term has often been used in a very general way, without defining which properties of the surface are actually driving toxicity. How the chemical features (e.g., silanols and siloxanes) and configuration of the silica surface can trigger toxic responses remains incompletely understood. Main body: Recent developments in surface chemistry, cell biology and toxicology provide new avenues to improve our understanding of the molecular mechanisms of the adverse responses to silica particles. New physico-chemical methods can finely characterize and quantify silanols at the surface of silica particles. Advanced computational modelling and atomic force microscopy offer unique opportunities to explore the intimate interactions between silica surface and membrane models or cells. In recent years, interdisciplinary research, using these tools, has built increasing evidence that surface silanols are critical determinants of the interaction between silica particles and biomolecules, membranes, cell systems, or animal models. It also has become clear that silanol configuration, and eventually biological responses, can be affected by impurities within the crystal structure, or coatings covering the particle surface. The discovery of new molecular targets of crystalline as well as amorphous silica particles in the immune system and in epithelial lung cells represents new possible toxicity pathways. Cellular recognition systems that detect specific features of the surface of silica particles have been identified. Conclusions: Interdisciplinary research bridging surface chemistry to toxicology is progressively solving the puzzling issue of the variable toxicity of silica. Further interdisciplinary research is ongoing to elucidate the intimate mechanisms of silica pathogenicity, to possibly mitigate or reduce surface reactivity

Transcutaneous immunization as preventative and therapeutic regimens to protect against experimental otitis media due to nontypeable Haemophilus influenzae

We have developed three nontypeable Haemophilus influenzae (NTHI) adhesin-derived immunogens that are significantly efficacious against experimental otitis media (OM) due to NTHI when delivered parenterally. We now expanded our preventative immunization strategies to include transcutaneous immunization (TCI) as a less invasive, but potentially equally efficacious, regimen to prevent OM due to NTHI. Additionally, we examined the potential of TCI as a therapeutic immunization regimen to resolve ongoing experimental OM. Preventative immunization with NTHI outer membrane protein (OMP) P5- and type IV pilus-targeted immunogens, delivered with the adjuvant LT(R192G-L211A), induced significantly earlier clearance of NTHI from the nasopharynges and middle ears of challenged chinchillas compared with receipt of immunogen or adjuvant alone. Moreover, therapeutic immunization resulted in significant resolution of established NTHI biofilms from the middle ear space of animals compared with controls. These data advocate TCI with the adhesin-directed immunogens as an efficacious regimen for prevention and resolution of experimental NTHI-induced OM

Modelling the neuropathology of lysosomal storage disorders through disease-specific human induced pluripotent stem cells

Mucopolysaccharidosis II (MPS II) is a lysosomal storage disorder (LSD), caused by iduronate 2-sulphatase (IDS) enzyme dysfunction. The neuropathology of the disease is not well understood, although the neural symptoms are currently incurable. MPS II-patient derived iPSC lines were established and differentiated to neuronal lineage. The disease phenotype was confirmed by IDS enzyme and glycosaminoglycan assay. MPS II neuronal precursor cells (NPCs) showed significantly decreased self-renewal capacity, while their cortical neuronal differentiation potential was not affected. Major structural alterations in the ER and Golgi complex, accumulation of storage vacuoles, and increased apoptosis were observed both at protein expression and ultrastructural level in the MPS II neuronal cells, which was more pronounced in GFAP + astrocytes, with increased LAMP2 expression but unchanged in their RAB7 compartment. Based on these finding we hypothesize that lysosomal membrane protein (LMP) carrier vesicles have an initiating role in the formation of storage vacuoles leading to impaired lysosomal function. In conclusion, a novel human MPS II disease model was established for the first time which recapitulates the in vitro neuropathology of the disorder, providing novel information on the disease mechanism which allows better understanding of further lysosomal storage disorders and facilitates drug testing and gene therapy approaches