Exclusion and Inclusion -- A model agnostic approach to feature importance in DNNs

Deep Neural Networks in NLP have enabled systems to learn complex non-linear relationships. One of the major bottlenecks towards being able to use DNNs for real world applications is their characterization as black boxes. To solve this problem, we introduce a model agnostic algorithm which calculates phrase-wise importance of input features. We contend that our method is generalizable to a diverse set of tasks, by carrying out experiments for both Regression and Classification. We also observe that our approach is robust to outliers, implying that it only captures the essential aspects of the input.Comment: 8 pages, 4 figure

Antigen-based nano-immunotherapy controls parasite persistence, inflammatory and oxidative stress, and cardiac fibrosis, the hallmarks of chronic chagas cardiomyopathy, in a mouse model of trypanosoma cruzi infection

Chagas cardiomyopathy is caused by Trypanosoma cruzi (Tc). We identified two candidate antigens (TcG2 and TcG4) that elicit antibodies and T cell responses in naturally infected diverse hosts. In this study, we cloned TcG2 and TcG4 in a nanovector and evaluated whether nano-immunotherapy (referred as nano2/4) offers resistance to chronic Chagas disease. For this, C57BL/6 mice were infected with Tc and given nano2/4 at 21 and 42 days post-infection (pi). Non-infected, infected, and infected mice treated with pcDNA3.1 expression plasmid encoding TcG2/TcG4 (referred as p2/4) were used as controls. All mice responded to Tc infection with expansion and functional activation of splenic lymphocytes. Flow cytometry showed that frequency of splenic, poly-functional CD4+ and CD8+ T cells expressing interferon-γ, perforin, and granzyme B were increased by immunotherapy (Tc.nano2/4 > Tc.p2/4) and associated with 88%–99.7% decline in cardiac and skeletal (SK) tissue levels of parasite burden (Tc.nano2/4 > Tc.p2/4) in Chagas mice. Subsequently, Tc.nano2/4 mice exhibited a significant decline in peripheral and tissues levels of oxidative stress (e.g., 4-hydroxynonenal, protein carbonyls) and inflammatory infiltrate that otherwise were pronounced in Chagas mice. Further, nano2/4 therapy was effective in controlling the tissue infiltration of pro-fibrotic macrophages and established a balanced environment controlling the expression of collagens, metalloproteinases, and other markers of cardiomyopathy and improving the expression of Myh7 (encodes β myosin heavy chain) and Gsk3b (encodes glycogen synthase kinase 3) required for maintaining cardiac contractility in Chagas heart. We conclude that nano2/4 enhances the systemic T cell immunity that improves the host’s ability to control chronic parasite persistence and Chagas cardiomyopathy.Fil: Lokugamage, Nandadeva. University of Texas Medical Branch; Estados UnidosFil: Choudhuri, Subhadip. University of Texas Medical Branch; Estados UnidosFil: Davies, Carolina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Patología Experimental. Universidad Nacional de Salta. Facultad de Ciencias de la Salud. Instituto de Patología Experimental; ArgentinaFil: Chowdhury, Imran Hussain. University of Texas Medical Branch; Estados UnidosFil: Garg, Nisha Jain. University of Texas Medical Branch; Estados Unido

A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using Hierarchical Temporal Memory and Statistical Tests

Data Drift refers to the phenomenon where the generating model behind the data changes over time. Due to data drift, any model built on the past training data becomes less relevant and inaccurate over time. Thus, detecting and controlling for data drift is critical in machine learning models. Hierarchical Temporal Memory (HTM) is a machine learning model developed by Jeff Hawkins, inspired by how the human brain processes information. It is a biologically inspired model of memory similar in structure to the neocortex and whose performance is claimed to be comparable to state of the art models in detecting anomalies in time series data. Another unique benefit of HTMs is their independence from training and testing cycles; all the learning takes place online with streaming data, and no separate training and testing cycle is required. In the sequential learning paradigm, the Sequential Probability Ratio Test (SPRT) offers unique benefits for online learning and inference. This paper proposes a novel hybrid framework combining HTM and SPRT for real-time data drift detection and anomaly identification. Unlike existing data drift methods, our approach eliminates frequent retraining and ensures low false positive rates. HTMs currently work with one dimensional or univariate data. In a second study, we also propose an application of HTM in a multidimensional supervised scenario for anomaly detection by combining the outputs of multiple HTM columns, one for each data dimension, through a neural network. Experimental evaluations demonstrate that the proposed method outperforms conventional drift detection techniques like the Kolmogorov-Smirnov (KS) test, Wasserstein distance, and Population Stability Index (PSI) in terms of accuracy, adaptability, and computational efficiency. Our experiments also provide insights into optimizing hyperparameters for real-time deployment in domains such as Telecom

Chronic inflammation in polycystic ovary syndrome: A case–control study using multiple markers

Background: Polycystic ovary syndrome (PCOS) is associated with insulin resistance and elevated risk of cardiovascular disease and diabetes. Chronic inflammation has been observed in PCOS in several studies but there is also opposing evidence and a dearth of research in Indians. Objective: To estimate chronic inflammation in PCOS and find its relationship with appropriate anthropometric and biochemical parameters. Materials and Methods: Chronic inflammation was assessed in 30 women with PCOS (Group A) and 30 healthy controls (Group B) with highly sensitive C-reactive protein (hsCRP), interleukin-6 (IL-6), tumour necrosis factor alpha (TNFα), and platelet microparticles (PMP). In group A, the relationship of chronic inflammation with insulin resistance, waist hip ratio (WHR) serum testosterone, and serum glutamate pyruvate transaminase (SGPT) were examined. Results: In group A, the hsCRP, TNFα, and PMP were significantly elevated compared to group B. However, IL-6 level was similar between the groups. In group A, PMP showed a significant positive correlation with waist-hip ratio and serum testosterone. IL-6 showed a significant positive correlation with insulin sensitivity and significant negative correlation with insulin resistance and serum glutamate pyruvate transaminase. Conclusion: PCOS is associated with chronic inflammation and PMP correlates positively with central adiposity and biochemical hyperandrogenism in women with PCOS. Key words: Polycystic ovary syndrome, Inflammation, C-reactive protein, Interleukin-6, Tumor necrosis factor, Microparticles

An Interpretable Deep Learning System for Automatically Scoring Request for Proposals

The Managed Care system within Medicaid (US Healthcare) uses Request For Proposals (RFP) to award contracts for various healthcare and related services. RFP responses are very detailed documents (hundreds of pages) submitted by competing organisations to win contracts. Subject matter expertise and domain knowledge play an important role in preparing RFP responses along with analysis of historical submissions. Automated analysis of these responses through Natural Language Processing (NLP) systems can reduce time and effort needed to explore historical responses, and assisting in writing better responses. Our work draws parallels between scoring RFPs and essay scoring models, while highlighting new challenges and the need for interpretability. Typical scoring models focus on word level impacts to grade essays and other short write-ups. We propose a novel Bi-LSTM based regression model, and provide deeper insight into phrases which latently impact scoring of responses. We contend the merits of our proposed methodology using extensive quantitative experiments. We also qualitatively asses the impact of important phrases using human evaluators. Finally, we introduce a novel problem statement that can be used to further improve the state of the art in NLP based automatic scoring systems.Comment: 8 pages, 4 figure

Investigation on the formation of two dimensional perovskite nanostructures at the water surface through self initiated reaction

The emerging class of hybrid organic-inorganic perovskites (HOIPs) has exhibited fascinating properties for a wide range of technological applications. With halide ions, HOIPs have provided novel optoelectronic devices including efficient solar cells and with pseudohalide anions-like formate (HCOO-), enigmatic electromagnetic properties have been obtained in HOIPs. Large-scale synthesis of such 2D HOIP films is of immense importance for the advancement of its application as solar materials. We have shown using in-situ X-ray measurements that the Langmuir monolayer of perovskite can be formed at the air-water interface by spreading stearic acid molecules on the water subphase having (C4H9NH3)2PbBr4 molecules. The 2D lead formate perovskite films are formed at the air-water interface through a self-initiated reaction and the in-situ X-ray scattering and ex-situ Raman spectroscopy measurements revealed this reaction process. The spreading of lipid molecules having positive and negative head-group charges as surfactants over the water surface shows that the formation of perovskite nanofilms at the air-water interface specifically requires the presence of HCOO- head-group of stearic acid. In this room temperature interfacial reaction, formate anions come from the stearic acid monolayer present on the water surface and completely replace bromines in the perovskite present in water subphase to form (BA)2Pb(HCOO)4 at the air-water interface. Our results show an easy route for large-scale synthesis of 2D pseudohalide perovskites

Global disparities in surgeons’ workloads, academic engagement and rest periods: the on-calL shIft fOr geNEral SurgeonS (LIONESS) study

: The workload of general surgeons is multifaceted, encompassing not only surgical procedures but also a myriad of other responsibilities. From April to May 2023, we conducted a CHERRIES-compliant internet-based survey analyzing clinical practice, academic engagement, and post-on-call rest. The questionnaire featured six sections with 35 questions. Statistical analysis used Chi-square tests, ANOVA, and logistic regression (SPSS® v. 28). The survey received a total of 1.046 responses (65.4%). Over 78.0% of responders came from Europe, 65.1% came from a general surgery unit; 92.8% of European and 87.5% of North American respondents were involved in research, compared to 71.7% in Africa. Europe led in publishing research studies (6.6 ± 8.6 yearly). Teaching involvement was high in North America (100%) and Africa (91.7%). Surgeons reported an average of 6.7 ± 4.9 on-call shifts per month, with European and North American surgeons experiencing 6.5 ± 4.9 and 7.8 ± 4.1 on-calls monthly, respectively. African surgeons had the highest on-call frequency (8.7 ± 6.1). Post-on-call, only 35.1% of respondents received a day off. Europeans were most likely (40%) to have a day off, while African surgeons were least likely (6.7%). On the adjusted multivariable analysis HDI (Human Development Index) (aOR 1.993) hospital capacity > 400 beds (aOR 2.423), working in a specialty surgery unit (aOR 2.087), and making the on-call in-house (aOR 5.446), significantly predicted the likelihood of having a day off after an on-call shift. Our study revealed critical insights into the disparities in workload, access to research, and professional opportunities for surgeons across different continents, underscored by the HDI