PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network

We present PyCARL, a PyNN-based common Python programming interface for hardware-software co-simulation of spiking neural network (SNN). Through PyCARL, we make the following two key contributions. First, we provide an interface of PyNN to CARLsim, a computationally-efficient, GPU-accelerated and biophysically-detailed SNN simulator. PyCARL facilitates joint development of machine learning models and code sharing between CARLsim and PyNN users, promoting an integrated and larger neuromorphic community. Second, we integrate cycle-accurate models of state-of-the-art neuromorphic hardware such as TrueNorth, Loihi, and DynapSE in PyCARL, to accurately model hardware latencies that delay spikes between communicating neurons and degrade performance. PyCARL allows users to analyze and optimize the performance difference between software-only simulation and hardware-software co-simulation of their machine learning models. We show that system designers can also use PyCARL to perform design-space exploration early in the product development stage, facilitating faster time-to-deployment of neuromorphic products. We evaluate the memory usage and simulation time of PyCARL using functionality tests, synthetic SNNs, and realistic applications. Our results demonstrate that for large SNNs, PyCARL does not lead to any significant overhead compared to CARLsim. We also use PyCARL to analyze these SNNs for a state-of-the-art neuromorphic hardware and demonstrate a significant performance deviation from software-only simulations. PyCARL allows to evaluate and minimize such differences early during model development.Comment: 10 pages, 25 figures. Accepted for publication at International Joint Conference on Neural Networks (IJCNN) 202

Synthetic aerial image generation for miniature aerial system

This paper presents a computer vision based method which generates a synthetic image of the earth as would be viewed by an aerial camera. The method takes geo-referenced, ortho-rectified aerial image database as the source and using a pinhole perspective camera model generates the synthetic image. The method requires the position and attitude of the camera, which act as the extrinsic parameters for the camera model. The intrinsic parameters are chosen to emulate a real camera. Finally, the results of synthetic aerial image generation implemented on MATLAB are presente

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

The bovine QTL viewer: a web accessible database of bovine Quantitative Trait Loci

BACKGROUND: Many important agricultural traits such as weight gain, milk fat content and intramuscular fat (marbling) in cattle are quantitative traits. Most of the information on these traits has not previously been integrated into a genomic context. Without such integration application of these data to agricultural enterprises will remain slow and inefficient. Our goal was to populate a genomic database with data mined from the bovine quantitative trait literature and to make these data available in a genomic context to researchers via a user friendly query interface. DESCRIPTION: The QTL (Quantitative Trait Locus) data and related information for bovine QTL are gathered from published work and from existing databases. An integrated database schema was designed and the database (MySQL) populated with the gathered data. The bovine QTL Viewer was developed for the integration of QTL data available for cattle. The tool consists of an integrated database of bovine QTL and the QTL viewer to display QTL and their chromosomal position. CONCLUSION: We present a web accessible, integrated database of bovine (dairy and beef cattle) QTL for use by animal geneticists. The viewer and database are of general applicability to any livestock species for which there are public QTL data. The viewer can be accessed at

Awareness on antibiotic usage among undergraduate medical students in a teaching hospital, Guntur

Background: Antibiotics are the mainstay treatment of most of the infectious diseases. Inappropriate and irrational use of antimicrobial agents has led to increase in the development of antimicrobial resistance. Medical students are a part of health care system and they must be aware of increasing antibiotic resistance as they are the future prescribers. The objectives of the study were to assess the awareness on antibiotic usage and its resistance among medical students and to study the perception on antibiotic medication among medical students.Methods: This was a cross-sectional, semi-structured questionnaire-based study. Questionnaire consists of 4 parts- socio-demographic details, awareness on antibiotics and its resistance, knowledge on specific treatment of a disease, perception levels. Prior IEC approval was taken. The obtained data was statistically analyzed using Microsoft excel sheet.Results: Among 230 participants, majority of the students have awareness on general antibiotic usage and 97.8% were having knowledge about antibiotic resistance. The main source of information on antibiotic resistance is identified as classroom teaching (67.8%). 46.9% and 25.2% students correctly identified the drug of choice for treating enteric fever and community acquired pneumonia respectively.Conclusions: Degrees of awareness on antibiotics and its resistance is found to be good. Antibiotic usage pattern is not on par with knowledge. Knowledge on specific treatment of infectious disease is found to be less. So, antibiotic teaching should be integrated with clinical subjects

Parasitic Strip Loaded Dual Band Notch Circular Monopole Antenna with Defected Ground Structure

In this article a parasitic strip loaded monopole antennas are designed to notch dual and triple bands. The designed models are constructed on one side of the substrate material and on the other end defected ground structures are implemented. The basic antenna comprises a tuning stub and a ground plane with tapered shape slot as DGS. Another model is constructed with circular monopole radiating element on front side and similar kind of ground structure used in the basic rectangular tuning stub antenna. To create notched bands with tuning stubs, two symmetrical parasitic slits are placed inside the slot of the ground plane. The basic model is of the rectangular stub notching triple band and the circular tuning stub antenna notching dual band. Dual band notched circular tuning stub antenna is prototyped on FR4 substrate and measured results from vector network analyzer are compared with simulation results of HFSS for validation

Barriers and enablers in the implementation and sustainability of toothbrushing programs in early childhood settings and primary schools: a systematic review

Background Untreated dental caries negatively impacts a child's quality of life including overall health and wellbeing, growth and development, social interaction ability, and school attendance. School-based toothbrushing programs have been recognised as an effective intervention to reduce the burden of dental caries. However, limited information is available to understand the real-world enablers and challenges in the implementation and sustainability of toothbrushing programs. This review aims to understand the barriers and enablers in the implementation and sustainability of toothbrushing programs in early childhood settings and primary schools. Methods Five electronic databases [i.e., CINAHL (EBSCO), Medline (EBSCO), EMBASE (Ovid), Web of Science, and PsycINFO] and backward citation chasing were performed. The last updated databases searches were conducted in May 2022. Studies reporting on barriers and enablers in the implementation and sustainability of toothbrushing programs in early childhood settings or primary schools were included in the review. The methodological quality of included studies was assessed by using Joanna Briggs Institute [JBI] and mixed methods appraisal tool [MMAT] critical appraisal tools and results were reported in accordance with PRISMA guidelines. Results A total of six studies met the eligibility criteria and were included in the review. Toothbrushing programs in early childhood settings and primary schools were mostly implemented under the supervision of staff and teachers. A positive attitude of the staff, the flexibility of toothbrushing sessions, involvement of community volunteers and parents were a few of the identified enablers. However, the timing of the communication of the program, inadequate transfer of information among staff, frequent staffing turnover, lack of parental support, and staff feeling overburdened while acting as pseudo parents were frequently reported as barriers. Conclusion The results of this systematic review identify key enablers and barriers for toothbrushing programs in early childhood settings and primary schools which need to be considered for developing oral health promotion initiatives

Diagnostic markers of acute encephalitis syndrome and COVID-associated multisystem inflammatory syndrome in children from Southern India.

Acute encephalitis syndrome (AES) in children poses a significant public health challenge in India. This study aims to explore the utility of host inflammatory mediators and neurofilament (NfL) levels in distinguishing etiologies, assessing disease severity, and predicting outcomes in AES. We assessed 12 mediators in serum (n = 58) and 11 in cerebrospinal fluid (CSF) (n = 42) from 62 children with AES due to scrub typhus, viral etiologies, and COVID-associated multisystem inflammatory syndrome (MIS-C) in Southern India. Additionally, NfL levels in serum (n = 20) and CSF (n = 18) were examined. Clinical data, including Glasgow coma scale (GCS) and Liverpool outcome scores, were recorded. Examining serum and CSF markers in the three AES etiology groups revealed notable distinctions, with scrub typhus differing significantly from viral and MIS-C causes. Viral causes had elevated serum CCL11 and CCL2 compared with scrub typhus, while MIS-C cases showed higher HGF levels than scrub typhus. However, CSF analysis showed a distinct pattern with the scrub typhus group exhibiting elevated levels of IL-1RA, IL-1β, and TNF compared with MIS-C, and lower CCL2 levels compared with the viral group. Modeling the characteristic features, we identified that age ≥3 years with serum CCL11 < 180 pg/mL effectively distinguished scrub typhus from other AES causes. Elevated serum CCL11, HGF, and IL-6:IL-10 ratio were associated with poor outcomes (p = 0.038, 0.005, 0.02). Positive CSF and serum NfL correlation, and negative GCS and serum NfL correlation were observed. Median NfL levels were higher in children with abnormal admission GCS and poor outcomes. Measuring immune mediators and brain injury markers in AES provides valuable diagnostic insights, with the potential to facilitate rapid diagnosis and prognosis. The correlation between CSF and serum NfL, along with distinctive serum cytokine profiles across various etiologies, indicates the adequacy of blood samples alone for assessment and monitoring. The association of elevated levels of CCL11, HGF, and an increased IL-6:IL-10 ratio with adverse outcomes suggests promising avenues for therapeutic exploration, warranting further investigation

Molecular endotypes of type 1 and type 2 SLE

Objective To character the molecular landscape of patients with type 1 and type 2 SLE by analysing gene expression profiles from peripheral blood.Methods Full transcriptomic RNA sequencing was carried out on whole blood samples from 18 subjects with SLE selected by the presence of manifestations typical of type 1 and type 2 SLE. The top 5000 row variance genes were analysed by Multiscale Embedded Gene Co-expression Network Analysis to generate gene co-expression modules that were functionally annotated and correlated with various demographic traits, clinical features and laboratory measures.Results Expression of specific gene co-expression modules correlated with individual features of type 1 and type 2 SLE and also effectively segregated samples from patients with type 1 SLE from those with type 2 SLE. Unique type 1 SLE enrichment included interferon, monocytes, T cells, cell cycle and neurotransmitter pathways, whereas unique type 2 SLE enrichment included B cells and metabolic and neuromuscular pathways. Gene co-expression modules of patients with type 2 SLE were identified in subsets of previously reported patients with inactive SLE and idiopathic fibromyalgia (FM) and also identified subsets of patients with active SLE with a greater frequency of severe fatigue.Conclusion Gene co-expression analysis successfully identified unique transcriptional patterns that segregate type 1 SLE from type 2 SLE and further identified type 2 molecular features in patients with inactive SLE or FM and with active SLE with severe fatigue

Using small peptide segments of amyloid-β and humanin to examine their physical interactions

Background: Amyloid fibrils in Alzheimer’s disease are composed of amyloid-β (Aβ) peptides of variant lengths. Humanin (HN), a 24 amino acid residue neuroprotective peptide, is known to interact with the predominant Aβ isoform in the brain, Aβ (1-40). Methods: Here, we constructed smaller segments of Aβ and HN and identified residues in HN important for both HN-HN and HN-Aβ interactions. Peptides corresponding to amino acid residues 5-15 of HN, HN (5-15), HN (5-15, L11S), where Leu11 was replaced with Ser, and residues 17-28 of Aβ, Aβ (17-28), were synthesized and tested for their ability to block formation of the complex between HN and Aβ (1-40). Results: Co-immunoprecipitation and binding kinetics showed that HN (5-15) was more efficient at blocking the complex between HN and Aβ (1-40) than either HN (5-15, L11S) or Aβ (17-28). Binding kinetics of these smaller peptides with either full-length HN or Aβ (1-40) showed that HN (5-15) was able to bind either Aβ (1-40) or HN more efficiently than HN (5-15, L11S) or Aβ (17-28). Compared to full-length HN, however, HN (5-15) bound Aβ (1-40) with a weaker affinity suggesting that while HN (5-15) binds Aβ, other residues in the full length HN peptide are necessary for maximum interactions. Conclusion: L11 was more important for interactions with Aβ (1-40) than with HN. Aβ (17-28) was relatively ineffective at binding to either Aβ (1-40) or HN. Moreover, HN, and the smaller HN (5-15), HN (5-15 L11S), and Aβ (17-28) peptides, had different effects on regulating Aβ (1-40) aggregation kinetics