Analysis of 3D pathology samples using weakly supervised AI.

Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features. Recurrence risk-stratification models were trained on prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D volume-based prognostication achieves superior performance to traditional 2D slice-based approaches, including clinical/histopathological baselines from six certified genitourinary pathologists. Incorporating greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, further emphasizing the value of capturing larger extents of heterogeneous morphology.

A multimodal generative AI copilot for human pathology.

Computational pathology1,2 has witnessed considerable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders3,4. However, despite the explosive growth of generative artificial intelligence (AI), there have been few studies on building general-purpose multimodal AI assistants and copilots5 tailored to pathology. Here we present PathChat, a vision-language generalist AI assistant for human pathology. We built PathChat by adapting a foundational vision encoder for pathology, combining it with a pretrained large language model and fine-tuning the whole system on over 456,000 diverse visual-language instructions consisting of 999,202 question and answer turns. We compare PathChat with several multimodal vision-language AI assistants and GPT-4V, which powers the commercially available multimodal general-purpose AI assistant ChatGPT-4 (ref. 6). PathChat achieved state-of-the-art performance on multiple-choice diagnostic questions from cases with diverse tissue origins and disease models. Furthermore, using open-ended questions and human expert evaluation, we found that overall PathChat produced more accurate and pathologist-preferable responses to diverse queries related to pathology. As an interactive vision-language AI copilot that can flexibly handle both visual and natural language inputs, PathChat may potentially find impactful applications in pathology education, research and human-in-the-loop clinical decision-making.

Tissue-Specific Macrophage Responses to Remote Injury Impact the Outcome of Subsequent Local Immune Challenge.

Myocardial infarction, stroke, and sepsis trigger systemic inflammation and organism-wide complications that are difficult to manage. Here, we examined the contribution of macrophages residing in vital organs to the systemic response after these injuries. We generated a comprehensive catalog of changes in macrophage number, origin, and gene expression in the heart, brain, liver, kidney, and lung of mice with myocardial infarction, stroke, or sepsis. Predominantly fueled by heightened local proliferation, tissue macrophage numbers increased systemically. Macrophages in the same organ responded similarly to different injuries by altering expression of tissue-specific gene sets. Preceding myocardial infarction improved survival of subsequent pneumonia due to enhanced bacterial clearance, which was caused by IFNÉ£ priming of alveolar macrophages. Conversely, EGF receptor signaling in macrophages exacerbated inflammatory lung injury. Our data suggest that local injury activates macrophages in remote organs and that targeting macrophages could improve resilience against systemic complications following myocardial infarction, stroke, and sepsis.

Absence of 3a0 charge density wave order in the infinite-layer nickelate NdNiO2.

A hallmark of many unconventional superconductors is the presence of many-body interactions that give rise to broken-symmetry states intertwined with superconductivity. Recent resonant soft X-ray scattering experiments report commensurate 3a0 charge density wave order in infinite-layer nickelates, which has important implications regarding the universal interplay between charge order and superconductivity in both cuprates and nickelates. Here we present X-ray scattering and spectroscopy measurements on a series of NdNiO2+x samples, which reveal that the signatures of charge density wave order are absent in fully reduced, single-phase NdNiO2. The 3a0 superlattice peak instead originates from a partially reduced impurity phase where excess apical oxygens form ordered rows with three-unit-cell periodicity. The absence of any observable charge density wave order in NdNiO2 highlights a crucial difference between the phase diagrams of cuprate and nickelate superconductors.

Artificial intelligence-assisted digital pathology for non-alcoholic steatohepatitis: current status and future directions.

The worldwide prevalence of non-alcoholic steatohepatitis (NASH) is increasing, causing a significant medical burden, but no approved therapeutics are currently available. NASH drug development requires histological analysis of liver biopsies by expert pathologists for trial enrolment and efficacy assessment, which can be hindered by multiple issues including sample heterogeneity, inter-reader and intra-reader variability, and ordinal scoring systems. Consequently, there is a high unmet need for accurate, reproducible, quantitative, and automated methods to assist pathologists with histological analysis to improve the precision around treatment and efficacy assessment. Digital pathology (DP) workflows in combination with artificial intelligence (AI) have been established in other areas of medicine and are being actively investigated in NASH to assist pathologists in the evaluation and scoring of NASH histology. DP/AI models can be used to automatically detect, localise, quantify, and score histological parameters and have the potential to reduce the impact of scoring variability in NASH clinical trials. This narrative review provides an overview of DP/AI tools in development for NASH, highlights key regulatory considerations, and discusses how these advances may impact the future of NASH clinical management and drug development. This should be a high priority in the NASH field, particularly to improve the development of safe and effective therapeutics.

Artificial Intelligence-Enabled Prostate Cancer Diagnosis and Prognosis: Current State and Future Implications.

In this modern era of digital pathology, artificial intelligence (AI)-based diagnostics for prostate cancer has become a hot topic. Multiple retrospective studies have demonstrated the benefits of AI-based diagnostic solutions for prostate cancer that includes improved prostate cancer detection, quantification, grading, interobserver concordance, cost and time savings, and a potential to reduce pathologists' workload and enhance pathology laboratory workflow. One of the major milestones is the Food and Drug Administration approval of Paige prostate AI for a second review of prostate cancer diagnosed using core needle biopsies. However, implementation of these AI tools for routine prostate cancer diagnostics is still lacking. Some of the limiting factors include costly digital pathology workflow, lack of regulatory guidelines for deployment of AI, and lack of prospective studies demonstrating the actual benefits of AI algorithms. Apart from diagnosis, AI algorithms have the potential to uncover novel insights into understanding the biology of prostate cancer and enable better risk stratification, and prognostication. This article includes an in-depth review of the current state of AI for prostate cancer diagnosis and highlights the future prospects of AI in prostate pathology for improved patient care.

Switching the on-surface orientation of oxygen-functionalized helicene.

Helicenes represent an important class of chiral organic material with promising optoelectronic properties. Hence, functionalization of surfaces with helicenes is a key step toward new organic materials devices. The deposition of a heterohelicene containing two furano groups and two hydroxyl groups onto copper(111) surface in ultrahigh vacuum leads to different adsorbate modifications. At low coverage and low temperature, the molecules tend to lie on the surface in order to maximize van der Waals contact with the substrate. Thermal treatment leads to deprotonation of the hydroxyl groups and in part into a reorientation from lying into a standing adsorbate mode.

Artificial intelligence algorithm accurately assesses oestrogen receptor immunohistochemistry in metastatic breast cancer cytology specimens: A pilot study.

OBJECTIVE: The Visiopharm artificial intelligence (AI) algorithm for oestrogen receptor (ER) immunohistochemistry (IHC) in whole slide images (WSIs) has been successfully validated in surgical pathology. This study aimed to assess its efficacy in cytology specimens. METHODS: The study cohort comprised 105 consecutive cytology specimens with metastatic breast carcinoma. ER IHC WSIs were seamlessly integrated into the Visiopharm platform from the Image Management System (IMS) during our routine digital workflow, and an AI algorithm was employed for analysis. ER AI scores were compared with pathologists' manual consensus scores. Optimization steps were implemented and evaluated to reduce discordance. RESULTS: The overall concordance between pathologists' scores and AI scores was excellent (99/105, 94.3%). Six cases exhibited discordant results, including two false-negative (FN) cases due to abundant histiocytes incorrectly counted as negatively stained tumour cells by AI, two FN cases owing to weak staining, and two false-positive (FP) cases where pigmented macrophages were erroneously counted as positively stained tumour cells by AI. The Pearson correlation coefficient of ER-positive percentages between pathologists' and AI scores was 0.8483. Optimization steps, such as lowering the cut-off threshold and additional training using higher input magnification, significantly improved accuracy. CONCLUSIONS: The automated ER AI algorithm demonstrated excellent concordance with pathologists' assessments and accurately differentiated ER-positive from ER-negative metastatic breast carcinoma cytology cases. However, precision in identifying tumour cells in cytology specimens requires further enhancement.

Comparison of the effectiveness of Morus alba and chlorhexidine gels as an adjunct to scaling and root planing on stage II periodontitis - A randomized controlled clinical trial.

OBJECTIVES: The present study aimed to assess and compare the effect of Morus alba and chlorhexidine gel as an adjunct to scaling and root planing (SRP) in treating stage II periodontitis. METHODS: A single-blind, randomized controlled trial was conducted on 180 patients with stage II periodontitis who received full-mouth SRP. They were randomly assigned to receive chlorhexidine digluconate (CHX) gel, Morus alba (MA) and placebo gel for Groups A, B and C, respectively, at the baseline, 15 days and 30 days. Plaque index (PI), Gingival index (GI), periodontal pocket depth (PPD) and quantitative analysis (culture) of Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis and Tannerella forsythia were assessed at baseline and 45 days. Analysis of variance was used to compare the significant difference in PI, GI, PPD and microbiological parameters between the three groups after the intervention, followed by post hoc Mann-Whitney U and Tukey's HSD test for clinical and microbiological parameters, respectively. RESULTS: Intergroup comparison of the PI, GI and microbiological parameters between the MA and CHX groups at the end of 45 days did not show a statistically significant difference (p > 0.05), whereas a statistically significant difference was observed for PPD between MA and CHX groups with the mean difference of 0.18 mm (p = 0.002). CONCLUSION: Morus alba gel was found to be effective in decreasing PPD. However, there was no difference between Morus alba and chlorhexidine gel as an adjunct to SRP in treating stage II periodontitis.

Intracellular sites of AQP2 S256 phosphorylation identified using inhibitors of the AQP2 recycling itinerary.

Vasopressin (VP)-regulated aquaporin-2 (AQP2) trafficking between cytoplasmic vesicles and the plasma membrane of kidney principal cells is essential for water homeostasis. VP affects AQP2 phosphorylation at several serine residues in the COOH-terminus; among them, serine 256 (S256) appears to be a major regulator of AQP2 trafficking. Mutation of this serine to aspartic acid, which mimics phosphorylation, induces constitutive membrane expression of AQP2. However, the intracellular location(s) at which S256 phosphorylation occurs remains elusive. Here, we used strategies to block AQP2 trafficking at different cellular locations in LLC-PK1 cells and monitored VP-stimulated phosphorylation of S256 at these sites by immunofluorescence and Western blot analysis with phospho-specific antibodies. Using methyl-ß-cyclodextrin, cold block or bafilomycin, and taxol, we blocked AQP2 at the plasma membrane, in the perinuclear trans-Golgi network, and in scattered cytoplasmic vesicles, respectively. Regardless of its cellular location, VP induced a significant increase in S256 phosphorylation, and this effect was not dependent on a functional microtubule cytoskeleton. To further investigate whether protein kinase A (PKA) was responsible for S256 phosphorylation in these cellular compartments, we created PKA-null cells and blocked AQP2 trafficking using the same procedures. We found that S256 phosphorylation was no longer increased compared with baseline, regardless of AQP2 localization. Taken together, our data indicate that AQP2 S256 phosphorylation can occur at the plasma membrane, in the trans-Golgi network, or in cytoplasmic vesicles and that this event is dependent on the expression of PKA in these cells.NEW & NOTEWORTHY Phosphorylation of aquaporin-2 by PKA at serine 256 (S256) occurs in various subcellular locations during its recycling itinerary, suggesting that the protein complex necessary for AQP2 S256 phosphorylation is present in these different recycling stations. Furthermore, we showed, using PKA-null cells, that PKA activity is required for vasopressin-induced AQP2 phosphorylation. Our data reveal a complex spatial pattern of intracellular AQP2 phosphorylation at S256, shedding new light on the role of phosphorylation in AQP2 membrane accumulation.

Identifying SOX17 as a Sensitive and Specific Marker for Ovarian and Endometrial Carcinomas.

Similar to PAX8, SOX17 was recently identified as a master transcription factor of ovarian cancer based on RNA sequencing data. We explored SOX17 utility in diagnosing ovarian tumors and other gynecologic tumors. We systematically evaluated SOX17 expression on tissue microarrays of 398 ovarian tumors of various types, 93 endometrial carcinomas, 80 cervical carcinomas, and 1371 nongynecologic carcinomas, such as those of kidney, thyroid, breast, colon, bladder, liver, bile duct, adrenal gland, pancreas, brain, and lung and malignant melanoma. In addition, we evaluated SOX17 expression in whole tissue sections from 60 gynecologic carcinomas and 10 angiosarcomas. The results demonstrated that SOX17 was highly expressed in most ovarian and endometrial tumors with strong intensity. However, unlike PAX8, it was predominately negative in other tested tumor types, including kidney and thyroid tumors. In particular, SOX17 was highly expressed in the following pathologic subtypes of ovarian tumors: serous carcinoma, clear cell carcinoma, endometrioid carcinoma, and germ cell tumors. SOX17 was mostly negative in mucinous carcinoma and sex cord stromal tumors. In addition, SOX17 was expressed in vascular endothelial cells and was positive in all tested angiosarcomas. In summary, our results demonstrate that SOX17 is a sensitive and specific marker for ovarian nonmucinous carcinomas and endometrial carcinomas. For ovarian germ cell tumors and angiosarcomas, SOX17 demonstrates higher specificity than PAX8, with comparable sensitivity. Furthermore, SOX17 positivity in endothelial cells serves as an internal positive control, making it an excellent marker.

Artificial Intelligence-Aided Diagnosis of Breast Cancer Lymph Node Metastasis on Histologic Slides in a Digital Workflow.

Identifying lymph node (LN) metastasis in invasive breast carcinoma can be tedious and time-consuming. We investigated an artificial intelligence (AI) algorithm to detect LN metastasis by screening hematoxylin and eosin (H&E) slides in a clinical digital workflow. The study included 2 sentinel LN (SLN) cohorts (a validation cohort with 234 SLNs and a consensus cohort with 102 SLNs) and 1 nonsentinel LN cohort (258 LNs enriched with lobular carcinoma and postneoadjuvant therapy cases). All H&E slides were scanned into whole slide images in a clinical digital workflow, and whole slide images were automatically batch-analyzed using the Visiopharm Integrator System (VIS) metastasis AI algorithm. For the SLN validation cohort, the VIS metastasis AI algorithm detected all 46 metastases, including 19 macrometastases, 26 micrometastases, and 1 with isolated tumor cells with a sensitivity of 100%, specificity of 41.5%, positive predictive value of 29.5%, and negative predictive value (NPV) of 100%. The false positivity was caused by histiocytes (52.7%), crushed lymphocytes (18.2%), and others (29.1%), which were readily recognized during pathologists' reviews. For the SLN consensus cohort, 3 pathologists examined all VIS AI annotated H&E slides and cytokeratin immunohistochemistry slides with similar average concordance rates (99% for both modalities). However, the average time consumed by pathologists using VIS AI annotated slides was significantly less than using immunohistochemistry slides (0.6 vs 1.0 minutes, P = .0377). For the nonsentinel LN cohort, the AI algorithm detected all 81 metastases, including 23 from lobular carcinoma and 31 from postneoadjuvant chemotherapy cases, with a sensitivity of 100%, specificity of 78.5%, positive predictive value of 68.1%, and NPV of 100%. The VIS AI algorithm showed perfect sensitivity and NPV in detecting LN metastasis and less time consumed, suggesting its potential utility as a screening modality in routine clinical digital pathology workflow to improve efficiency.

Incidence, Clinicopathologic Features, HER2 Fluorescence In Situ Hybridization Profile, and Oncotype DX Results of Human Epidermal Growth Factor Receptor 2-Low Breast Cancers: Experience From a Single Academic Center.

Human epidermal growth factor receptor 2 (HER2)-low breast cancer, defined by an immunohistochemical (IHC) score of 1+ or 2+ with negative in situ hybridization, is emerging as a predictive marker for the use of the antibody-drug conjugate. To understand how this category differs from HER2-zero cases, we investigated clinicopathological characteristics and HER2 fluorescence in situ hybridization results in a large cohort of 1309 continuous HER2-negative invasive breast carcinomas from 2018 to 2021 evaluated by the Food and Drug Administration-approved HER2 IHC test. Additionally, we compared Oncotype DX recurrence scores and HER2 mRNA expression between HER-low and HER2-zero cases in a separate cohort of 438 estrogen receptor-positive (ER+) early-stage breast carcinoma cases from 2014 to 2016. Based on the cohort from 2018 to 2021, the incidence of HER2-low breast cancers was approximately 54%. HER2-low cases had less frequent grade 3 morphology, less frequent triple-negative results, ER and progesterone receptor negativity, and a higher mean HER2 copy number and HER2/CEP17 ratio than HER2-zero cases (P < .0001). Among ER+ cases, HER2-low cases showed significantly less frequent Nottingham grade 3 tumors. In the cohort from 2014 to 2016, HER2-low cases showed significantly higher ER+ percentages, fewer progesterone receptor-negative cases, lower Oncotype DX recurrence scores, and higher HER2 mRNA expression scores than HER2-zero cases. In summary, this is the first study, to our knowledge, using a large cohort of continuous cases evaluated by the Food and Drug Administration-approved HER2 IHC companion diagnostic test for HER2-low expression and HER2 fluorescence in situ hybridization profile in a real-world setting. Although HER2-low cases showed a higher HER2 copy number, ratio, and mRNA level than HER2-zero cases statistically, such small differences are unlikely to be biologically or clinically meaningful. However, our study suggests that HER2-low/ER+ early-stage breast carcinoma may represent a less aggressive group of breast carcinoma, given its association with a lower Nottingham grade and Oncotype DX recurrence score.

Comprehensive analysis of clinicopathologic features and p53 mutation in neuroendocrine neoplasms of the breast: experience from a large academic center.

PURPOSE: The recent WHO classification of breast cancer (2019) categorizes breast carcinoma with neuroendocrine (NE) differentiation into three morphologically distinct subtypes: well-differentiated neuroendocrine tumor (NET), poorly differentiated neuroendocrine carcinoma (NEC), and invasive breast carcinoma, no special type with neuroendocrine differentiation (IBC-NST-NE). Data regarding the prognostic significance of neuroendocrine differentiation are conflicting and an association, if any, between p53 mutation and neuroendocrine differentiation is largely unknown. METHODS: We examined p53 expression and other clinicopathologic characteristics in three types of invasive breast carcinoma with NE differentiation in a cohort of sixty-three patients, including 45 IBC-NST with NE differentiation, 10 NETs, and 8 NECs. RESULTS: No significant difference of clinicopathologic feature was observed between IBC-NST with NE differentiation and NET, but NECs showed significantly lower expressions of hormone receptors, more mutated p53, and higher frequency of distant metastases than IBC-NST with NE differentiation and NETs. CONCLUSION: NECs of the breast are genetically and clinically different from IBC-NST-NEs and NETs of the breast.

Composition, Morphology, and Interface Engineering of 3D Cauliflower-Like Porous Carbon-Wrapped Metal Chalcogenides as Advanced Electrocatalysts for Quantum Dot-Sensitized Solar Cells.

Designing a low-cost, highly efficient, and stable electrocatalyst that can synergistically speed up the reduction of polysulfide electrolytes while operative for long periods in the open air is critical for the practical application of quantum dot-sensitized solar cells (QDSSCs), but it remains a challenging task. Herein, a simple, straightforward, and two-step nanocomposite engineering approach that simultaneously combines metallic copper chalcogenides (MC) either Cu2- x S or Cu2- x Se with S, N dual-doped carbon (SNC) sources for devising high-quality counter electrode (CE) film are reported. First, the hierarchically assembled MC nanostructures are obtained using microwave-assisted synthesis. Second, these MCs are embedded within an ordered macro-meso-microporous carbon matrix to obtain Cu2- x S@C or Cu2- x SeS@C CE. These CEs are demonstrated to have composition dependents crystal structure, surface morphologies, photovoltaic performance, and electrochemical properties. In terms of power conversion efficiency (PCE), the Cu2- x SeS@C (9.89%) and Cu2- x S@C-CE (8.96%) constructed QDSSCs outperform both Cu2- x Se (8.96%) and Cu2- x S-constructed (7.79%) QDSSCs, respectively. The enhanced PCE could be attributed to the synergistic interaction of S and N dopants with MC interfaces that can not only enrich electric conductivity, and a higher surface-to-volume ratio but also offers a 3D network for superior charge transport at the interface.

Investigating the pharmacodynamic durability of GalNAc-siRNA conjugates.

One hallmark of trivalent N-acetylgalactosamine (GalNAc)-conjugated siRNAs is the remarkable durability of silencing that can persist for months in preclinical species and humans. Here, we investigated the underlying biology supporting this extended duration of pharmacological activity. We found that siRNA accumulation and stability in acidic intracellular compartments is critical for long-term activity. We show that functional siRNA can be liberated from these compartments and loaded into newly generated Argonaute 2 protein complexes weeks after dosing, enabling continuous RNAi activity over time. Identical siRNAs delivered in lipid nanoparticles or as GalNAc conjugates were dose-adjusted to achieve similar knockdown, but only GalNAc-siRNAs supported an extended duration of activity, illustrating the importance of receptor-mediated siRNA trafficking in the process. Taken together, we provide several lines of evidence that acidic intracellular compartments serve as a long-term depot for GalNAc-siRNA conjugates and are the major contributor to the extended duration of activity observed in vivo.

Interactive Multimedia Reporting Technical Considerations: HIMSS-SIIM Collaborative White Paper.

Despite technological advances in the analysis of digital images for medical consultations, many health information systems lack the ability to correlate textual descriptions of image findings linked to the actual images. Images and reports often reside in separate silos in the medical record throughout the process of image viewing, report authoring, and report consumption. Forward-thinking centers and early adopters have created interactive reports with multimedia elements and embedded hyperlinks in reports that connect the narrative text with the related source images and measurements. Most of these solutions rely on proprietary single-vendor systems for viewing and reporting in the absence of any encompassing industry standards to facilitate interoperability with the electronic health record (EHR) and other systems. International standards have enabled the digitization of image acquisition, storage, viewing, and structured reporting. These provide the foundation to discuss enhanced reporting. Lessons learned in the digital transformation of radiology and pathology can serve as a basis for interactive multimedia reporting (IMR) across image-centric medical specialties. This paper describes the standard-based infrastructure and communications to fulfill recently defined clinical requirements through a consensus from an international workgroup of multidisciplinary medical specialists, informaticists, and industry participants. These efforts have led toward the development of an Integrating the Healthcare Enterprise (IHE) profile that will serve as a foundation for interoperable interactive multimedia reporting.

Multimodal Imaging and Phototherapy of Cancer and Bacterial Infection by Graphene and Related Nanocomposites.

The advancements in nanotechnology and nanomedicine are projected to solve many glitches in medicine, especially in the fields of cancer and infectious diseases, which are ranked in the top five most dangerous deadly diseases worldwide by the WHO. There is great concern to eradicate these problems with accurate diagnosis and therapies. Among many developed therapeutic models, near infra-red mediated phototherapy is a non-invasive technique used to invade many persistent tumors and bacterial infections with less inflammation compared with traditional therapeutic models such as radiation therapy, chemotherapy, and surgeries. Herein, we firstly summarize the up-to-date research on graphene phototheranostics for a better understanding of this field of research. We discuss the preparation and functionalization of graphene nanomaterials with various biocompatible components, such as metals, metal oxides, polymers, photosensitizers, and drugs, through covalent and noncovalent approaches. The multifunctional nanographene is used to diagnose the disease with confocal laser scanning microscopy, magnetic resonance imaging computed tomography, positron emission tomography, photoacoustic imaging, Raman, and ToF-SMIS to visualize inside the biological system for imaging-guided therapy are discussed. Further, treatment of disease by photothermal and photodynamic therapies against different cancers and bacterial infections are carefully conferred herein along with challenges and future perspectives.

Ice thickness distribution of Himalayan glaciers inferred from DInSAR-based glacier surface velocity.

Retrieval of glacier ice thickness is extremely important for monitoring water resources and predicting glacier dynamics and changes. The inter-annual glacier ice thickness observations (more than 5 years) exploit the glacier mass changes. Ice thickness is one of the important parameters to predict the future sea-level rise. Without adequate knowledge and precise information of glacier ice thickness distribution, future sea-level changes cannot be accurately assessed. In this study, we use an existing flow model to estimate the ice thickness of the High Mountain Asia (HMA) glaciers, using remote sensing techniques. The glacier ice velocity is one of the significant parameters in the Laminar flow model to retrieve the ice thickness. The glacier ice velocity is derived by utilizing the Differential SAR Interferometry (DInSAR) technique. The most optimum DInSAR data (ALOS-2/PALSAR-2) is used for estimating the ice velocity of the HMA glaciers. The ice thickness is mainly estimated for five different states in the HMA region, namely Himachal Pradesh, Uttarakhand, Sikkim, Bhutan, and Arunachal Pradesh. Most of the states are observed with a mean ice thickness of 100 m. Five benchmark glaciers (Samudra Tapu, Bara Shigri, Chhota Shigri, Sakchum, and Gangotri glaciers) are also selected for validating our results with the existing thickness information. The issues related to velocity-based ice thickness inversion are also emphasized in this study. The high-velocity rate due to the influx of melting water from adjacent glaciers causes an increment in the flow rate. This abnormal velocity derives erroneous ice thickness measurements. This is one of the major problems to be considered in the velocity-based thickness-derived procedures. Finally, the investigation suggests the inclusion of the velocity influencing parameters in the physical-based models for an accurate ice thickness inversion.

Glacier mass balance estimation in Garhwal Himalaya using improved accumulation area ratio method.

Water requirements of the mountain communities living in the Himalaya are supported by snow and glacier melt. The availability of water from the source depends on numerous climatic and glacier parameters. One key parameter is mass balance, which helps to assess the glacier health and future water availability. We have used the improved accumulation area ratio (IAAR) method to estimate mass balance in Alaknanda and Bhagirathi basins, constituting 1055 glaciers covering ~1609 km2. The mean Equilibrium Line Altitude (ELA) of the Alaknanda and Bhagirathi basins are estimated as 6147 ± 130 and 5985 ± 130 m.a.s.l, respectively. The mass balance is estimated using the accumulation area ratio (AAR)-mass balance relationship. The mean specific mass balance of the Alaknanda and Bhagirathi for 2001-2013 is estimated as -1.1 ± 0.03 m.w.e.a-1 and -1.01 ± 0.07 m.w.e.a-1, respectively. Total mass loss from the study area is calculated as ~21.4 ± 1.1Gt during this period. The loss of glaciers in the mountain area will increase the vulnerability of communities living in the region. It suggests a need for better adaptation strategies to improve the resilience of high mountain communities.