Association between ophthalmoscopic changes and obstetric outcomes in pre-eclampsia and eclampsia

Background: Hypertensive disorder is a major cause of maternal and perinatal mortality and morbidity. The widespread endothelial dysfunction associated with preeclampsia can affect the choroid and the retina leading to characteristic ophthalmoscopic changes. So, we tried to find out the association between ophthalmoscopic changes and obstetric outcomes in women with preeclampsia-eclampsia.Methods: In a comparative, prospective study carried out from July 2011 to July 2015 in Medical College, Kolkata and KPC Medical College, Kolkata we included antenatal women with pre-eclampsia and eclampsia. Based on the ophthalmoscopic findings they were divided into two groups; group A (no ophthalmoscopic abnormalities) and group B (having ophthalmoscopic abnormalities). The results were analyzed by standard statistical methods.Results: Out of the total 200 women included, 102 women belonged to group A and 98 to group B. Majority of the patients were ophthalmologicaly asymptomatic. Most common fundoscopic abnormality in group B was focal arteriolar narrowing. Women in group B had significantly higher level of blood pressure, proteinuria and thrombocytopenia. The need of multiple drugs, incidence of HELLP syndrome and oliguria, rate of Caesarean section and CCU admission were significantly higher in group B compared to group A. There was significantly higher incidence of perinatal complications in group B, viz: IUGR, low birth weight, low Apgar score and NICU admission.Conclusions: Ophthalmoscopic changes correlate positively with adverse feto-maternal outcomes in preeclampsia. Fundoscopy should be carried out in all preeclamptic mother irrespective of the visual symptoms

Female cancer awareness and screening: evaluation from tertiary hospital in Eastern India

Background: Cancer awareness and emphasis on preventive oncology is an essential weapon in our war against cancer. Globally the approach towards cancer awareness is non-targeted with mass media and social media coverage being unselective. This study aimed to demonstrate the disparities prevalent in society especially in a developing country with regard to cancer awareness for two of the commonest female cancers and suggested measures to increase the efficacy of awareness programs in the urban and suburban population of a typical third world Indian metropolitan city.Methods: In a cross-sectional study conducted between January 2021 to June 2021, 119 suitable female patients attending the gynaecology OPD of a medical college and hospital were randomly selected. Informed consent was taken and their knowledge/awareness about preventive oncology in carcinoma breast and carcinoma cervix (two most common cancers in Indian women) was documented using pre tested and pre structured questionnaire. The results were analysed by suitable statistical tests.Results: Higher education and socio-economic condition was associated with increased levels of awareness for preventive measures of carcinoma breast and carcinoma cervix. Within each educational strata, higher socio-economic status predicted for increased awareness for risk factors, screening tests and vaccination.Conclusions: Subtle differences in the patterns and degree of awareness in common female cancers were noticed with regard to educational and socio-economic standards in the studies population. These have widespread implications in planning awareness programs in resource constrained setting to ensure most efficacious utilization of Cancer awareness and Prevention programmes.

Transfer: Cross Modality Knowledge Transfer using Adversarial Networks -- A Study on Gesture Recognition

Knowledge transfer across sensing technology is a novel concept that has been recently explored in many application domains, including gesture-based human computer interaction. The main aim is to gather semantic or data driven information from a source technology to classify / recognize instances of unseen classes in the target technology. The primary challenge is the significant difference in dimensionality and distribution of feature sets between the source and the target technologies. In this paper, we propose TRANSFER, a generic framework for knowledge transfer between a source and a target technology. TRANSFER uses a language-based representation of a hand gesture, which captures a temporal combination of concepts such as handshape, location, and movement that are semantically related to the meaning of a word. By utilizing a pre-specified syntactic structure and tokenizer, TRANSFER segments a hand gesture into tokens and identifies individual components using a token recognizer. The tokenizer in this language-based recognition system abstracts the low-level technology-specific characteristics to the machine interface, enabling the design of a discriminator that learns technology-invariant features essential for recognition of gestures in both source and target technologies. We demonstrate the usage of TRANSFER for three different scenarios: a) transferring knowledge across technology by learning gesture models from video and recognizing gestures using WiFi, b) transferring knowledge from video to accelerometer, and d) transferring knowledge from accelerometer to WiFi signals

Using Writing to Teach

This text represents a year of research, dialogue, collaboration, and difference between teachers and scholars from at least nine disciplines at Syracuse University. The conversations and resources gathered here are intended to function as a practical and pedagogical tool for using writing in the university classroom. It is written in such a way that readers can use it as a linear text or as a sourcebook for specific questions, concerns, and teaching needs.https://surface.syr.edu/books/1010/thumbnail.jp

Merging Deep Learning with Expert Knowledge for Seizure Onset Zone localization from rs-fMRI in Pediatric Pharmaco Resistant Epilepsy

Surgical disconnection of Seizure Onset Zones (SOZs) at an early age is an effective treatment for Pharmaco-Resistant Epilepsy (PRE). Pre-surgical localization of SOZs with intra-cranial EEG (iEEG) requires safe and effective depth electrode placement. Resting-state functional Magnetic Resonance Imaging (rs-fMRI) combined with signal decoupling using independent component (IC) analysis has shown promising SOZ localization capability that guides iEEG lead placement. However, SOZ ICs identification requires manual expert sorting of 100s of ICs per patient by the surgical team which limits the reproducibility and availability of this pre-surgical screening. Automated approaches for SOZ IC identification using rs-fMRI may use deep learning (DL) that encodes intricacies of brain networks from scarcely available pediatric data but has low precision, or shallow learning (SL) expert rule-based inference approaches that are incapable of encoding the full spectrum of spatial features. This paper proposes DeepXSOZ that exploits the synergy between DL based spatial feature and SL based expert knowledge encoding to overcome performance drawbacks of these strategies applied in isolation. DeepXSOZ is an expert-in-the-loop IC sorting technique that a) can be configured to either significantly reduce expert sorting workload or operate with high sensitivity based on expertise of the surgical team and b) can potentially enable the usage of rs-fMRI as a low cost outpatient pre-surgical screening tool. Comparison with state-of-art on 52 children with PRE shows that DeepXSOZ achieves sensitivity of 89.79%, precision of 93.6% and accuracy of 84.6%, and reduces sorting effort by 6.7-fold. Knowledge level ablation studies show a pathway towards maximizing patient outcomes while optimizing the machine-expert collaboration for various scenarios.Comment: This paper is currently under review in IEEE Journa

High Fidelity Fast Simulation of Human in the Loop Human in the Plant (HIL-HIP) Systems

Non-linearities in simulation arise from the time variance in wireless mobile networks when integrated with human in the loop, human in the plant (HIL-HIP) physical systems under dynamic contexts, leading to simulation slowdown. Time variance is handled by deriving a series of piece wise linear time invariant simulations (PLIS) in intervals, which are then concatenated in time domain. In this paper, we conduct a formal analysis of the impact of discretizing time-varying components in wireless network-controlled HIL-HIP systems on simulation accuracy and speedup, and evaluate trade-offs with reliable guarantees. We develop an accurate simulation framework for an artificial pancreas wireless network system that controls blood glucose in Type 1 Diabetes patients with time varying properties such as physiological changes associated with psychological stress and meal patterns. PLIS approach achieves accurate simulation with greater than 2.1 times speedup than a non-linear system simulation for the given dataset.Comment: To appear in ACM MSWIM 202

EdGCon: Auto-assigner of Iconicity Ratings Grounded by Lexical Properties to Aid in Generation of Technical Gestures

Gestures that share similarities in their forms and are related in their meanings, should be easier for learners to recognize and incorporate into their existing lexicon. In that regard, to be more readily accepted as standard by the Deaf and Hard of Hearing community, technical gestures in American Sign Language (ASL) will optimally share similar in forms with their lexical neighbors. We utilize a lexical database of ASL, ASL-LEX, to identify lexical relations within a set of technical gestures. We use automated identification for 3 unique sub-lexical properties in ASL- location, handshape and movement. EdGCon assigned an iconicity rating based on the lexical property similarities of the new gesture with an existing set of technical gestures and the relatedness of the meaning of the new technical word to that of the existing set of technical words. We collected 30 ad hoc crowdsourced technical gestures from different internet websites and tested them against 31 gestures from the DeafTEC technical corpus. We found that EdGCon was able to correctly auto-assign the iconicity ratings 80.76% of the time.Comment: Accepted for publication in ACM SAC 202

Enabling precision medicine in neonatology, an integrated repository for preterm birth research.

Preterm birth, or the delivery of an infant prior to 37 weeks of gestation, is a significant cause of infant morbidity and mortality. In the last decade, the advent and continued development of molecular profiling technologies has enabled researchers to generate vast amount of 'omics' data, which together with integrative computational approaches, can help refine the current knowledge about disease mechanisms, diagnostics, and therapeutics. Here we describe the March of Dimes' Database for Preterm Birth Research (http://www.immport.org/resources/mod), a unique resource that contains a variety of 'omics' datasets related to preterm birth. The database is open publicly, and as of January 2018, links 13 molecular studies with data across tens of thousands of patients from 6 measurement modalities. The data in the repository are highly diverse and include genomic, transcriptomic, immunological, and microbiome data. Relevant datasets are augmented with additional molecular characterizations of almost 25,000 biological samples from public databases. We believe our data-sharing efforts will lead to enhanced research collaborations and coordination accelerating the overall pace of discovery in preterm birth research

Upfront Biology-Guided Therapy in Diffuse Intrinsic Pontine Glioma: Therapeutic, Molecular, and Biomarker Outcomes from PNOC003

PURPOSE PNOC003 is a multicenter precision medicine trial for children and young adults with newly diagnosed diffuse intrinsic pontine glioma (DIPG). PATIENTS AND METHODS Patients (3-25 years) were enrolled on the basis of imaging consistent with DIPG. Biopsy tissue was collected for whole-exome and mRNA sequencing. After radiotherapy (RT), patients were assigned up to four FDA-approved drugs based on molecular tumor board recommendations. H3K27M-mutant circulating tumor DNA (ctDNA) was longitudinally measured. Tumor tissue and matched primary cell lines were characterized using whole-genome sequencing and DNA methylation profiling. When applicable, results were verified in an independent cohort from the Children's Brain Tumor Network (CBTN). RESULTS Of 38 patients enrolled, 28 patients (median 6 years, 10 females) were reviewed by the molecular tumor board. Of those, 19 followed treatment recommendations. Median overall survival (OS) was 13.1 months [95% confidence interval (CI), 11.2-18.4] with no difference between patients who followed recommendations and those who did not. H3K27M-mutant ctDNA was detected at baseline in 60% of cases tested and associated with response to RT and survival. Eleven cell lines were established, showing 100% fidelity of key somatic driver gene alterations in the primary tumor. In H3K27-altered DIPGs, TP53 mutations were associated with worse OS (TP53mut 11.1 mo; 95% CI, 8.7-14; TP53wt 13.3 mo; 95% CI, 11.8-NA; P = 3.4e-2), genome instability (P = 3.1e-3), and RT resistance (P = 6.4e-4). The CBTN cohort confirmed an association between TP53 mutation status, genome instability, and clinical outcome. CONCLUSIONS Upfront treatment-naïve biopsy provides insight into clinically relevant molecular alterations and prognostic biomarkers for H3K27-altered DIPGs