Synthesis and characterization of N-t-BOC protected pyrrole-sulfur oligomers and polymers

The synthesis and characterization of a new class of pyrrole-sulfur compounds is described. These compounds are designed to be precursors for an organic analogue of poly(sulfur nitride). Poly(N-t-BOC-2.5-pyrrolyl sulfide) was prepared from N-t-BOC-2,5-dibromopyrrole by first lithiating this compound with n-BuLi, followed by the addition of bis(p-tosyl) sulfide. Similarly, bis(N-t-BOC-2-pyrrolyl) sulfide was prepared starting from N-t-BOC-2-bromopyrrole. Subsequent selective oxidation with one or two equivalents of m-CPBA quantitatively gave bis(N-t-BOC-2-pyrrolyl) sulfoxide and -sulfone, respectively. Thermal deprotection of the t-BOC groups of the oligomers and the polymer resulted in decomposition of these compounds; the lauer is presumably due to a combination of sulfur-extrusion and polymerization

Novel thiophenes and method for polymerization of said thiophenes

The invention relates to novel thiophenes of formula (I) wherein R and R' are independently - hydrogen, alkyl or alkoxy or together a -O-(CH2)n-O bridge wherein n = 1 to 5. Said thiophenes are suitable for the production of polythiophenes which can be used as organic conductors

Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects are considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at Elsevier Neural Network

Convolutional Neural Networks for Apnea Detection from Smartphone Audio Signals: Effect of Window Size

Although sleep apnea is one of the most prevalent sleep disorders, most patients remain undiagnosed and untreated. The gold standard for sleep apnea diagnosis, polysomnography, has important limitations such as its high cost and complexity. This leads to a growing need for novel cost-effective systems. Mobile health tools and deep learning algorithms are nowadays being proposed as innovative solutions for automatic apnea detection. In this work, a convolutional neural network (CNN) is trained for the identification of apnea events from the spectrograms of audio signals recorded with a smartphone. A systematic comparison of the effect of different window sizes on the model performance is provided. According to the results, the best models are obtained with 60 s windows (sensitivity-0.72, specilicity-0.89, AUROC = 0.88), For smaller windows, the model performance can be negatively impacted, because the windows become shorter than most apnea events, by which sound reductions can no longer be appreciated. On the other hand, longer windows tend to include multiple or mixed events, that will confound the model. This careful trade-off demonstrates the importance of selecting a proper window size to obtain models with adequate predictive power. This paper shows that CNNs applied to smartphone audio signals can facilitate sleep apnea detection in a realistic setting and is a first step towards an automated method to assist sleep technicians. Clinical Relevance- The results show the effect of the window size on the predictive power of CNNs for apnea detection. Furthermore, the potential of smartphones, audio signals, and deep neural networks for automatic sleep apnea screening is demonstrated

Coplanarity by hydrogen bonding in well-defined oligoheterocycles

(Hetero)aryl-aryl coupling for covalent bonding and intramol. hydrogen bond formation for establishing the secondary structure have been united to design and construct well-defined, functionalized macromols. The suitability of azaheterocyclic units to realize this concept is exemplified by star shaped discotic liq. cryst. compds. and ladder-type conjugated copolymers. A review with >40 ref

Well-defined oligo(pyrrole-2,5-diyl)s by the Ullmann reaction

The Ullmann coupling reaction has been used to polymerize N-t-BOC-2,5-dibromopyrrole into well-defined oligo(pyrrole-2,5-diyl)s. After optimization of the reaction conditions, i.e. using 1 wt equiv of Cu-bronze in DMF at 100 "C for 1 h, oligomers up to 25 repeating pyrrole units are obtained. Starting from 5,5‘- and 5,5"-dibrominated N-t-BOC protected bi- and terpyrrole as monomers, the polymerization is slower and a lower degree of polymerization is observed, yielding oligomers with an even lower molecular weight than those resulting from N-t-BOC-2,5-dibromopyrrole. The first 20 oligomers of poly(N-t-BOC-pyrrole)h ave been isolated by preparative HPLC. Characterization of the individual oligomers shows that they all are hydrogen terminated and possess a perfect 2,5-linkage: oligo(pyrro1e-2,5-diyl)s. The isolated oligomers have been used to study the optical and electrical properties of the oligomers as a function of chain length

Early motor outcomes in infants with critical congenital heart disease are related to neonatal brain development and brain injury

Aim To assess the relationship between neonatal brain development and injury with early motor outcomes in infants with critical congenital heart disease (CCHD). Method Neonatal brain magnetic resonance imaging was performed after open-heart surgery with cardiopulmonary bypass. Cortical grey matter (CGM), unmyelinated white matter, and cerebellar volumes, as well as white matter motor tract fractional anisotropy and mean diffusivity were assessed. White matter injury (WMI) and arterial ischaemic stroke (AIS) with corticospinal tract (CST) involvement were scored. Associations with motor outcomes at 3, 9, and 18 months were corrected for repeated cardiac surgery. Results Fifty-one infants (31 males, 20 females) were included prospectively. Median age at neonatal surgery and postoperative brain magnetic resonance imaging was 7 days (interquartile range [IQR] 5-11d) and 15 days (IQR 12-21d) respectively. Smaller CGM and cerebellar volumes were associated with lower fine motor scores at 9 months (CGM regression coefficient=0.51, 95% confidence interval [CI]=0.15-0.86; cerebellum regression coefficient=3.08, 95% CI=1.07-5.09) and 18 months (cerebellum regression coefficient=2.08, 95% CI=0.47-5.12). The fractional anisotropy and mean diffusivity of white matter motor tracts were not related with motor scores. WMI was related to lower gross motor scores at 9 months (mean difference -0.8SD, 95% CI=-1.5 to -0.2). AIS with CST involvement increased the risk of gross motor problems and muscle tone abnormalities. Cerebral palsy (n=3) was preceded by severe ischaemic brain injury. Interpretation Neonatal brain development and injury are associated with fewer favourable early motor outcomes in infants with CCHD