Omicron infection increases IgG binding to spike protein of predecessor variants.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in India in 2020-2022 was driven predominantly by Wild (Wuhan-Hu-1 and D614G), Delta, and Omicron variants. The aim of this study was to examine the effect of infections on the humoral immune response and cross-reactivity to spike proteins of Wuhan-Hu-1, Delta, C.1.2., and Omicron. Residual archival sera (N = 81) received between January 2020 and March 2022 were included. Infection status was inferred by a positive SARS-CoV-2 RT-PCR and/or serology (anti-N and anti-S antibodies) and sequencing of contemporaneous samples (N = 18) to infer lineage. We estimated the levels and cross-reactivity of infection-induced sera including Wild, Delta, Omicron as well as vaccine breakthrough infections (Delta and Omicron). We found an approximately two-fold increase in spike-specific IgG antibody binding in post-Omicron infection compared with the pre-Omicron period, whilst the change in pre- and post-Delta infections were similar. Further investigation of Omicron-specific humoral responses revealed primary Omicron infection as an inducer of cross-reactive antibodies against predecessor variants, in spite of the weaker degree of humoral response compared to Wuhan-Hu-1 and Delta infection. Intriguingly, Omicron vaccine-breakthrough infections when compared with primary infections, exhibited increased humoral responses against RBD (7.7-fold) and Trimeric S (Trimeric form of spike protein) (34.6-fold) in addition to increased binding of IgGs towards previously circulating variants (4.2 - 6.5-fold). Despite Delta breakthrough infections showing a higher level of humoral response against RBD (2.9-fold) and Trimeric S (5.7-fold) compared to primary Delta sera, a demonstrably reduced binding (36%-49%) was observed to Omicron spike protein. Omicron vaccine breakthrough infection results in increased intensity of humoral response and wider breadth of IgG binding to spike proteins of antigenically-distinct, predecessor variants.

Development of a highly potent and selective degrader of LRRK2.

The discovery of disease-modifying therapies for Parkinson's Disease (PD) represents a critical need in neurodegenerative medicine. Genetic mutations in leucine-rich repeat kinase 2 (LRRK2) are risk factors for the development of PD, and some of these mutations have been linked to increased LRRK2 kinase activity and neuronal toxicity in cellular and animal models. Furthermore, LRRK2 function as a scaffolding protein in several pathways has been implicated as a plausible mechanism underlying neurodegeneration caused by LRRK2 mutations. Given that both the kinase activity and scaffolding function of LRRK2 have been linked to neurodegeneration, we developed proteolysis-targeting chimeras (PROTACs) targeting LRRK2. The degrader molecule JH-XII-03-02 (6) displayed high potency and remarkable selectivity for LRKK2 when assessed in a of 468 panel kinases and serves the dual purpose of eliminating both the kinase activity as well as the scaffolding function of LRRK2.

Investigation of fullerene cluster growth mechanisms by carbon atom addition using classical molecular dynamics.

The mechanisms of carbon sticking reactions to C36 and C-C80 fullerenes were investigated with molecular dynamics simulations (MD) using the Second-generation Reactive Empirical Bond Order (SREBO) and Adaptive Intermolecular Reactive Empirical Bond Order (AIREBO) potentials that were specifically optimized for carbon-carbon interactions. Results showed the existence of three possible sticking configurations where the projectile atom can stick either to one, two or three atoms of the target fullerene. They also showed that although the two potentials give similar magnitudes for the sticking cross-sections, they yield fairly different results as far as sticking mechanisms and configurations at thermal collision-energies, i.e., in the range 0.05-0.5 eV, are concerned. While AIREBO, that takes into account the long-range Lennard-Jones interaction, essentially results in a surface-sticking configuration with a single atom of the target fullerene, SREBO potential yields both surface- and two neighbors-sticking (2N-sticking) configurations. The fullerene structure is preserved in the last configuration while it can be recovered by a 2000 K annealing in the former configuration. Results obtained with SREBO eventually showed larger sticking probabilities for C36 as compared with C80. In spite of this, the sticking cross-sections obtained for C80 are similar to or even larger than those obtained for C36 due to the larger size of C80 that compensates for its smaller sticking probabilities.

Development and validation of the interpersonal communication assessment tool for assessing the interpersonal communication skills of public health midwives.

BACKGROUND: Interpersonal Communication Skills (IPCS) are one of the core clinical skills that should be developed by the Public Health Midwives (PHMs), who are grass-root level public healthcare providers in primary healthcare settings in Sri Lanka. This study aimed to develop and validate the Interpersonal Communication Assessment Tool (IPCAT), an observational rating scale, to assess the IPCS of PHMs. METHODS: Item generation, item reduction, instrument drafting, and development of the tool's rating guide were made by an expert panel. A cross-sectional study was conducted in five randomly selected Medical Officer of Health (MOH) areas, the smallest public health administrative division in the district of Colombo, Sri Lanka, to identify the factor structure, which is the correlational relationship between a number of variables in the tool. A sample of 164 PHMs was recruited. The data on IPCS were collected by video-recording the provider-client interaction using simulated clients. All recorded videos were rated by a rater using the drafted IPCAT, which included a Likert scale of 1(poor) to 5 (excellent). Exploratory factor analysis was conducted using the Principal Axis Factoring extraction method and the Varimax rotation technique to explore the factors. Three independent raters were used to rate ten randomly selected videos to assess the tool's internal consistency and inter-rater reliability. RESULTS: The IPCAT obtained a five-factor model with 22 items, and all five factors explained 65% of the total variance. The resulting factors were "Engaging" (six items on making rapport), "Delivering" (four items on paying respect), "Questioning" (four items on asking questions), "Responding" (four items on empathy), and "Ending" (four items to assess the skills of ending a conversation productively). The internal consistency, Cronbach's Alpha value, for all five factors was above 0.8, and the inter-rater reliability (ICC) was excellent (0.95). CONCLUSIONS: The Interpersonal Communication Assessment Tool is a valid and reliable tool for assessing the interpersonal communication skills of Public Health Midwives. TRIAL REGISTRATION: Clinical Trial Registry, Sri Lanka. Ref No, SLCTR/2020/006(February 4th,2020).

Virtual microscopy as a teaching-learning tool for histology in a competency-based medical curriculum.

Background: Histology forms an important component of first-year medical education. Unfortunately, it is limited to the practical laboratory due to the need for a microscope and good quality slides. Virtual microscopy is a recent advancement, which uses computers as an alternative to microscopes. The aim of the study was to compare virtual microscopy (VM)-based practical classes with traditional microscopy (TM)-based practical classes for two cohorts of first-year medical students, by comparing learning achieved using two different test scores as well as a qualitative assessment of student and faculty perspectives regarding the feasibility and usefulness of VM. Methods: Each cohort of students was divided into two equal batches and each batch underwent eight histology modules of which, four utilised traditional microscopes and four utilised virtual microscopes. Quantitative analysis was performed using a theory test (which assessed preparation, theory knowledge and understanding) as well as a spotter test (which assessed identification skills, reasoning, and recall). Qualitative analysis was performed using a structured questionnaire and focus group discussions. Results: Modules using VM were better when compared with those using TM, showing statistically significant and better grades. Qualitative analysis performed, yielded important information as to how this technology can serve as a good adjunct to traditional histology classes in the competency-based curriculum by increasing student interest, enabling self-study, and reducing students dependence on the tutor. Conclusions: VM forms a good adjunct as well as a standalone modality of learning to TM, as it improves accessibility to slides and promotes self-learning.

Functional principal component analysis for identifying the child growth pattern using longitudinal birth cohort data.

BACKGROUND: Longitudinal studies are important to understand patterns of growth in children and limited in India. It is important to identify an approach for characterising growth trajectories to distinguish between children who have healthy growth and those growth is poor. Many statistical approaches are available to assess the longitudinal growth data and which are difficult to recognize the pattern. In this research study, we employed functional principal component analysis (FPCA) as a statistical method to find the pattern of growth data. The purpose of this study is to describe the longitudinal child growth trajectory pattern under 3 years of age using functional principal component method. METHODS: Children born between March 2002 and August 2003 (n = 290) were followed until their third birthday in three neighbouring slums in Vellore, South India. Field workers visited homes to collect details of morbidity twice a week. Height and weight were measured monthly from 1 month of age in a study-run clinic. Longitudinal child growth trajectory pattern were extracted using Functional Principal Component analysis using B-spline basis functions with smoothing parameters. Functional linear model was used to assess the factors association with the growth functions. RESULTS: We have obtained four FPCs explained by 86.5, 3.9, 3.1 and 2.2% of the variation respectively for the height functions. For height, 38% of the children's had poor growth trajectories. Similarly, three FPCs explained 76.2, 8.8, and 4.7% respectively for the weight functions and 44% of the children's had poor growth in their weight trajectories. Results show that gender, socio-economic status, parent's education, breast feeding, and gravida are associated and, influence the growth pattern in children. CONCLUSIONS: The FPC approach deals with subjects' dynamics of growth and not with specific values at given times. FPC could be a better alternate approach for both dimension reduction and pattern detection. FPC may be used to offer greater insight for classification.

Under-resourced dialect identification in Ao using source information.

This paper reports the findings of an automatic dialect identification (DID) task conducted on Ao speech data using source features. Considering that Ao is a tone language, in this study for DID, the gammatonegram of the linear prediction residual is proposed as a feature. As Ao is an under-resourced language, data augmentation was carried out to increase the size of the speech corpus. The results showed that data augmentation improved DID by 14%. A perception test conducted on Ao speakers showed better DID by the subjects when utterance duration was 3 s. Accordingly, automatic DID was conducted on utterances of various duration. A baseline DID system with the Slms feature attained an average F1-score of 53.84% in a 3 s long utterance. Inclusion of source features, Silpr and S, improved the F1-score to 60.69%. In a final system, with a combination of Silpr, S, Slms, and Mel frequency cepstral coefficient features, the F1-score increased to 61.46%.

Cost of Illness Due to Severe Enteric Fever in India.

BACKGROUND: Lack of robust data on economic burden due to enteric fever in India has made decision making on typhoid vaccination a challenge. Surveillance for Enteric Fever network was established to address gaps in typhoid disease and economic burden. METHODS: Patients hospitalized with blood culture-confirmed enteric fever and nontraumatic ileal perforation were identified at 14 hospitals. These sites represent urban referral hospitals (tier 3) and smaller hospitals in urban slums, remote rural, and tribal settings (tier 2). Cost of illness and productivity loss data from onset to 28 days after discharge from hospital were collected using a structured questionnaire. The direct and indirect costs of an illness episode were analyzed by type of setting. RESULTS: In total, 274 patients from tier 2 surveillance, 891 patients from tier 3 surveillance, and 110 ileal perforation patients provided the cost of illness data. The mean direct cost of severe enteric fever was US$119.1 (95% confidence interval [CI], US$85.8-152.4) in tier 2 and US$405.7 (95% CI, 366.9-444.4) in tier 3; 16.9% of patients in tier 3 experienced catastrophic expenditure. CONCLUSIONS: The cost of treating enteric fever is considerable and likely to increase with emerging antimicrobial resistance. Equitable preventive strategies are urgently needed.

Analysis and modeling of dialect information in Ao, a low resource language.

Ao is a Tibeto-Burman language spoken in Nagaland, India. It is a low resource, tonal language with three lexical tones, namely, high, mid, and low. However, tone assignment on lexical words may differ among the three dialects of Ao, namely, Chungli, Mongsen, and Changki. In this work, an acoustic study is conducted on the three tones in the three dialects of Ao. It was found that the acoustic characteristics of the tones in the Changki dialect are markedly different from that of the Chungli and the Mongsen dialects. Hence, in the latter part of the work, automatic dialect identification (DID) in the Ao dialects is attempted with Mel Frequency Cepstral Coefficients, Shifted Delta Cepstral coefficients, and F0 features using the Gaussian Mixture models. It is confirmed that in both text-dependent and text-independent DID, the F0 features improve the accuracy of classification.

Overlapped speech detection using phase features.

Simultaneous speech of multiple speakers is known as overlapped speech, which causes problems for speech recognition and speaker diarization systems. The present work uses previously less utilized signal phase information in the task of overlapped speech detection. In this context, Instantaneous Frequency Cosine Coefficient (IFCC) and Modified Group Delay Cepstral Coefficient (MGDCC) features are explored. IFCC captures the time-varying phase characteristics, while MGDCC represents the frequency-varying information of the phase spectrum. A Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM)-based classifier is used for the classification. The present work uses synthetically generated overlapped speech from the GRID corpus. The proposed method is benchmarked against three baseline approaches that use magnitude spectrum features. It is observed that the combination of IFCC and MGDCC features with CNN-LSTM classifier provides better performance than the baselines. The combination of phase features with magnitude-based MFCC feature provides the best performance, indicating the importance of complementary information. The present study also investigates the effect of segment duration, genders, and number of simultaneous speakers on the overlapped speech detection system. Finally, the proposed method is also evaluated on real overlapped data from the AMI corpus.

First Confirmed Report of Leaf Blight on Browntop Millet Caused by Bipolaris setariae in Southern Peninsular India.

Browntop millet (Brachiaria ramosa (L.) Stapf), which is native to the United States, was recently introduced into India as one of the small millet crop. In September 2018, leaf blight symptoms were observed on cv. Dundu Korale on the adaxial side of the leaves in a field at Bangalore, India (13.0784oN, 77.5793oE). Initial lesions were brown with small yellow halo that ranged from 1 to 5 mm and eventually enlarged exhibiting light brown centers. Afterwards, spots coalesced and leaves were blighted. About 75% of the plants were infected in the field of 0.5 ha. Samples of symptomatic and asymptomatic leaves were collected, and nine isolates were recovered from culture on potato dextrose agar (PDA). Single conidial isolation was performed. Colonies were grey to olive green with regular margins at 7 days when cultured on PDA at 27 ± 1oC and 16 h light and 8 h dark cycles. Conidiophores were single or in clusters from 4.92 to 6.04 µm in width. Conidia were fusoid, cylindrical to slightly curved ranging from 38.50 to 130 µm in length and from 8.30 to 17 µm in width, with 4 to 10 distosepta (n =100). Hilum was flat to inconspicuous or slightly protruded. Based on the morphology, the pathogen was identified as a Bipolaris species according to the genus standard descriptions of Helminthosporium (Alcorn 1988). Misra and Prakash (1972) reported that Helminthosporium setariae causing leaf spot on browntop millet in India, but they did not provide any morphological or molecular evidence of identification. For molecular identification, the genomic DNA of isolate BTMH3 was extracted and the internal transcribed spacer region (ITS) and glyceraldehyde-3-phosphate dehydrogenase gene (GPD) were amplified using the pairs of primers ITS1/ITS4 (White et al. 1990) and GPD1/GPD2 (Berbee et al. 1999), respectively. BLAST analysis of ITS (MT750301; 562 matching base pairs) and GPD (MT896702; 562 matching base pairs) sequences showed 99.82% of identity with sequences of Bipolaris setariae (Sawada) Shoemaker type strain CBS 141.31 (ITS: EF452444 and GPD: EF513206). Pathogenicity was proved by spraying 10 healthy 20-day-old browntop millet plants with conidial suspension (106 conidia/ml). Control plants were sprayed with distilled water. Plants were covered with transparent polythene bags in a greenhouse at 28 ± 2oC and high relative humidity of 90%. Symptoms were observed at five days post inoculation. The pathogen was re-isolated from infected areas using the same protocol as before, whereas the control plants were symptomless. The re-isolated pathogen was confirmed as B. setariae based on morphological characters and PCR assay. To the best of our knowledge, this is the first report of leaf blight on browntop millet caused by B. setariae in the southern peninsular region of India. Disease specimen was deposited in Herbarium Cryptogamae Indiae Orientalis (HCIO), New Delhi, India with accession number 52209. Grain yield losses caused by leaf blight on browntop millet remain to be determined, however our findings indicate that cultivar cv. Dundu Korale is susceptible to B. setariae.

Exploration of excitation source information for shouted and normal speech classification.

Discrimination between shouted and normal speech is an essential prerequisite for many speech processing applications. Existing works have established that excitation source information plays a significant role in shouted speech production. In speech processing literature, various features have been proposed to model different aspects of the excitation source. The principal contribution of this work is to explore three such features, Discrete Cosine Transform of Integrated Linear Prediction Residual (DCT-ILPR), Mel-Power Difference of Spectrum in Sub-bands (MPDSS), and Residual Mel-Frequency Cepstral Coefficient (RMFCC), for shouted and normal speech classification. The DCT-ILPR feature represents the shape of the glottal cycle, MPDSS estimates the periodicity of the excitation source spectrum, and RMFCC characterizes smoothed spectral information of the excitation source. The authors have also contributed a dataset containing shouted and normal speech. This work is evaluated on three datasets and benchmarked against three baseline methods. Deep neural networks are used to study the classification performance of individual features and their combinations. The generalization performance of features (and combinations) is also investigated. Fusion of excitation source features with Mel-Frequency Cepstral Coefficients (MFCC) provides the best performance compared to other combinations. Noise analysis shows that adding excitation features with MFCC+ΔΔ provides a more robust classification system.

Analysis of Articulation Errors in Dysarthric Speech.

Imprecise articulation is the major issue reported in various types of dysarthria. Detection of articulation errors can help in diagnosis. The cues derived from both the burst and the formant transitions contribute to the discrimination of place of articulation of stops. It is believed that any acoustic deviations in stops due to articulation error can be analyzed by deriving features around the burst and the voicing onsets. The derived features can be used to discriminate the normal and dysarthric speech. In this work, a method is proposed to differentiate the voiceless stops produced by the normal speakers from the dysarthric by deriving the spectral moments, two-dimensional discrete cosine transform of linear prediction spectrum and Mel frequency cepstral coefficients features. These features and cosine distance based classifier is used for the classification of normal and dysarthic speech.

Aspiration in fricative and nasal consonants: Properties and detection.

Unlike aspiration in stops, occurrence of aspiration in non-stop consonants is quite rare. Most of the languages that have aspirated non-stop consonants are low-resource languages. Hence, data driven, quantitative, and statistical analysis of their aspiration phenomena is fairly limited. Rabha and Angami are considered in this study, as previous studies have confirmed the existence of aspiration contrast in fricatives and nasals. This study reports the acoustic characteristics of aspiration in stops, fricatives, and nasals. Among them, distinguishing the aspirated fricatives and aspirated nasals from their unaspirated counterparts is a challenging task. A set of acoustic features is proposed to automatically detect the presence of aspiration in fricatives and nasals. Acoustic features, such as vocal tract constriction (VTC), normalized autocorrelation peak strength (NAPS), strength of excitation (SoE), and variance of successive epoch intervals (VSEI) are used to detect aspiration in fricatives and nasals. These features are extracted from zero-frequency filtered signal of the speech sounds, as it preserves the aspiration information. Results show that VTC, NAPS, and SoE can detect aspiration in nasals, whereas SoE and VSEI can detect aspiration in fricatives. The proposed method improves the performance of an automatic phoneme recognizer by reducing the confusion between aspirated and unaspirated counterparts.

Detection and assessment of hypernasality in repaired cleft palate speech using vocal tract and residual features.

The presence of hypernasality in repaired cleft palate (CP) speech is a consequence of velopharyngeal insufficiency. The coupling of the nasal tract with the oral tract adds nasal formant and antiformant pairs in the hypernasal speech spectrum. This addition deviates the spectral and linear prediction (LP) residual characteristics of hypernasal speech compared to normal speech. In this work, the vocal tract constriction feature, peak to side-lobe ratio feature, and spectral moment features augmented by low-order cepstral coefficients are used to capture the spectral and residual deviations for hypernasality detection. The first feature captures the lower-frequencies prominence in speech due to the presence of nasal formants, the second feature captures the undesirable signal components in the residual signal due to the nasal antiformants, and the third feature captures the information about formants and antiformants in the spectrum along with the spectral envelope. The combination of three features gives normal versus hypernasal speech detection accuracies of 87.76%, 91.13%, and 93.70% for /a/, /i/, and /u/ vowels, respectively, and hypernasality severity detection accuracies of 80.13% and 81.25% for /i/ and /u/ vowels, respectively. The speech data are collected from 30 control normal and 30 repaired CP children between the ages of 7 and 12.

Objective assessment of cleft lip and palate speech intelligibility using articulation and hypernasality measures.

Assessment of intelligibility is required to characterize the overall speech production capability and to measure the speech outcome of different interventions for individuals with cleft lip and palate (CLP). Researchers have found that articulation error and hypernasality have a significant effect on the degradation of CLP speech intelligibility. Motivated by this finding, the present work proposes an objective measure of sentence-level intelligibility by combining the information of articulation deficits and hypernasality. These two speech disorders represent different aspects of CLP speech. Hence, it is expected that the composite measure based on them may utilize complementary clinical information. The objective scores of articulation and hypernasality are used as features to train a regression model, and the output of the model is considered as the predicted intelligibility score. The Spearman's correlation coefficient based analysis shows a significant correlation between the predicted and perceptual intelligibility scores (ρ = 0.77, p < 0.001).

Intelligibility assessment of cleft lip and palate speech using Gaussian posteriograms based on joint spectro-temporal features.

Intelligibility is considered as one of the primary measures for speech rehabilitation of individuals with a cleft lip and palate (CLP). Currently, speech processing and machine-learning-based objective methods are gaining more research interest as a way to quantify speech intelligibility. In this work, joint spectro-temporal features computed from a time-frequency representation of speech are explored to derive speech representations based on Gaussian posteriograms. A comparative framework using dynamic time warping (DTW) is used to quantify the intelligibility of child CLP speech. The DTW distance is used to score sentence-level intelligibility and tested for correlation with perceptual intelligibility ratings obtained from expert speech-language pathologists. A baseline DTW system using the conventional Mel-frequency cepstral coefficients (MFCCs) is also developed to compare the performance of the proposed system. Spearman's rank correlation coefficient between the objective intelligibility scores and the perceptual intelligibility rating is studied. A Williams significance test is conducted to assess the statistical significance of the correlation difference between the methods. The results show that the system based on joint spectro-temporal features significantly outperforms the MFCC-based system.

Detection of hypernasality based on vowel space area.

This study proposes a method for differentiating hypernasal-speech from normal speech using the vowel space area (VSA). Hypernasality introduces extra formant and anti-formant pairs in vowel spectrum, which results in shifting of formants. This shifting affects the size of the VSA. The results show that VSA is reduced in hypernasal-speech compared to normal speech. The VSA feature plus Mel-frequency cepstral coefficient feature for support vector machine based hypernasality detection leads to an accuracy of 86.89% for sustained vowels and 89.47%, 90.57%, and 91.70% for vowels in contexts of high pressure consonants /k/, /p/, and /t/, respectively.

Importance of glottis landmarks for the assessment of cleft lip and palate speech intelligibility.

The present work explores the acoustic characteristics of articulatory deviations near g(lottis) landmarks to derive the correlates of cleft lip and palate speech intelligibility. The speech region around the g landmark is used to compute two different acoustic features, namely, two-dimensional discrete cosine transform based joint spectro-temporal features, and Mel-frequency cepstral coefficients. Sentence-specific acoustic models are built using these features extracted from the normal speakers' group. The mean log-likelihood score for each test utterance is computed and tested as the acoustic correlates of intelligibility. Derived intelligibility measure shows significant correlation (ρ = 0.78, p < 0.001) with the perceptual ratings.

Acoustic analysis of misarticulated trills in cleft lip and palate children.

In this paper, acoustic analysis of misarticulated trills in cleft lip and palate speakers is carried out using excitation source based features: strength of excitation and fundamental frequency, derived from zero-frequency filtered signal, and vocal tract system features: first formant frequency (F1) and trill frequency, derived from the linear prediction analysis and autocorrelation approach, respectively. These features are found to be statistically significant while discriminating normal from misarticulated trills. Using acoustic features, dynamic time warping based trill misarticulation detection system is demonstrated. The performance of the proposed system in terms of the F1-score is 73.44%, whereas that for conventional Mel-frequency cepstral coefficients is 66.11%.