Explainable Information Retrieval using Deep Learning for Medical images

Image segmentation is useful to extract valuable information for an efficient analysis on the region of interest. Mostly, the number of images generated from a real life situation such as streaming video, is large and not ideal for traditional segmentation with machine learning algorithms. This is due to the following factors (a) numerous image features (b) complex distribution of shapes, colors and textures (c) imbalance data ratio of underlying classes (d) movements of the camera, objects and (e) variations in luminance for site capture. So, we have proposed an efficient deep learning model for image classification and the proof-of-concept has been the case studied on gastrointestinal images for bleeding detection. The Explainable Artificial Intelligence (XAI) module has been utilised to reverse engineer the test results for the impact of features on a given test dataset. The architecture is generally applicable in other areas of image classification. The proposed method has been compared with state-of-the-art including Logistic Regression, Support Vector Machine, Artificial Neural Network and Random Forest. It has reported F1 score of 0.76 on the real world streaming dataset which is comparatively better than traditional methods

Glucosylated cholesterol in mammalian cells and tissues: formation and degradation by multiple cellular β-glucosidases

The membrane lipid glucosylceramide (GlcCer) is continuously formed and degraded. Cells express two GlcCer-degrading β-glucosidases, GBA and GBA2, located in and outside the lysosome, respectively. Here we demonstrate that through transglucosylation both GBA and GBA2 are able to catalyze in vitro the transfer of glucosyl-moieties from GlcCer to cholesterol, and vice versa. Furthermore, the natural occurrence of 1-O-cholesteryl-β-D-glucopyranoside (GlcChol) in mouse tissues and human plasma is demonstrated using LC-MS/MS and 13C6-labelled GlcChol as internal standard. In cells the inhibition of GBA increases GlcChol, whereas inhibition of GBA2 decreases glucosylated sterol. Similarly, in GBA2-deficient mice GlcChol is reduced. Depletion of GlcCer by inhibition of GlcCer synthase decreases GlcChol in cells and likewise in plasma of inhibitor-treated Gaucher disease patients. In tissues of mice with Niemann-Pick type C, a condition characterized by intralysosomal accumulation of cholesterol, marked elevations in GlcChol occur as well. When lysosomal accumulation of cholesterol is induced in cultured cells, GlcChol is formed via lysosomal GBA. This illustrates that reversible transglucosylation reactions are highly dependent on local availability of suitable acceptors. In conclusion, mammalian tissues contain GlcChol formed by transglucosylation through β-glucosidases using GlcCer as donor. Our findings reveal a novel metabolic function for GlcCer.Bio-organic SynthesisMedical Biochemistr

Hybrid SOM based cross-modal retrieval exploiting Hebbian learning

Lately, cross-modal retrieval has attained plenty of attention due to enormous multi-modal data generation every day in the form of audio, video, image, and text. One vital requirement of cross-modal retrieval is to reduce the heterogeneity gap among various modalities so that one modality's results can be efficiently retrieved from the other. So, a novel unsupervised cross-modal retrieval framework based on associative learning has been proposed in this paper where two traditional SOMs are trained separately for images and collateral text and then they are associated together using the Hebbian learning network to facilitate the cross-modal retrieval process. Experimental outcomes on a popular Wikipedia dataset demonstrate that the presented technique outshines various existing state-of-the-art approaches

Comparative analysis on cross-modal information retrieval: A review

Human beings experience life through a spectrum of modes such as vision, taste, hearing, smell, and touch. These multiple modes are integrated for information processing in our brain using a complex network of neuron connections. Likewise for artificial intelligence to mimic the human way of learning and evolve into the next generation, it should elucidate multi-modal information fusion efficiently. Modality is a channel that conveys information about an object or an event such as image, text, video, and audio. A research problem is said to be multi-modal when it incorporates information from more than a single modality. Multi-modal systems involve one mode of data to be inquired for any (same or varying) modality outcome whereas cross-modal system strictly retrieves the information from a dissimilar modality. As the input–output queries belong to diverse modal families, their coherent comparison is still an open challenge with their primitive forms and subjective definition of content similarity. Numerous techniques have been proposed by researchers to handle this issue and to reduce the semantic gap of information retrieval among different modalities. This paper focuses on a comparative analysis of various research works in the field of cross-modal information retrieval. Comparative analysis of several cross-modal representations and the results of the state-of-the-art methods when applied on benchmark datasets have also been discussed. In the end, open issues are presented to enable the researchers to a better understanding of the present scenario and to identify future research directions

Human glucocerebrosidase mediates formation of xylosyl-cholesterol by β-xylosidase and transxylosidase reactions

Deficiency of glucocerebrosidase (GBA), a lysosomal β-glucosidase, causes Gaucher disease. The enzyme hydrolyzes β-glucosidic substrates and transglucosylates cholesterol to cholesterol-β-glucoside. Here we show that recombinant human GBA also cleaves β-xylosides and transxylosylates cholesterol. The xylosyl-cholesterol formed acts as acceptor for subsequent formation of di-xylosyl-cholesterol. Common mutant forms of GBA from patients with Gaucher disease with reduced β-glucosidase activity were similarly impaired in β-xylosidase, transglucosidase and transxylosidase activities, except for a slightly reduced xylosidase/glucosidase activity ratio of N370S GBA and a slightly reduced transglucosylation/glucosidase activity ratio of D409H GBA. XylChol was found to be reduced in spleen from Gaucher disease patients. The origin of newly identified XylChol in mouse and human tissues was investigated. Cultured human cells exposed to exogenous β-xylosides generated XylChol in a manner dependent on active lysosomal GBA but not the cytosol-facing β-glucosidase GBA2. We later sought an endogenous β-xyloside acting as donor in transxylosylation reactions, identifying xylosylated ceramide (XylCer) in cells and tissues that serve as donor in the formation of XylChol. UDP-glucosylceramide synthase (GCS) was unable to synthesize XylChol but could catalyse formation of XylCer. Thus, food-derived β-D-xyloside and XylCer are potential donors for the GBA-mediated formation of XylChol in cells. The enzyme GCS produces XylCer at a low rate. Our findings point to further catalytic versatility of GBA and prompt a systematic exploration of the distribution and role of xylosylated lipids.Biophysical Structural ChemistryMedical BiochemistryBio-organic Synthesi

Chile: mitos y realidades de una transición

Medical BiochemistryBio-organic Synthesi