Efficient Open World Reasoning for Planning

We consider the problem of reasoning and planning with incomplete knowledge and deterministic actions. We introduce a knowledge representation scheme called PSIPLAN that can effectively represent incompleteness of an agent's knowledge while allowing for sound, complete and tractable entailment in domains where the set of all objects is either unknown or infinite. We present a procedure for state update resulting from taking an action in PSIPLAN that is correct, complete and has only polynomial complexity. State update is performed without considering the set of all possible worlds corresponding to the knowledge state. As a result, planning with PSIPLAN is done without direct manipulation of possible worlds. PSIPLAN representation underlies the PSIPOP planning algorithm that handles quantified goals with or without exceptions that no other domain independent planner has been shown to achieve. PSIPLAN has been implemented in Common Lisp and used in an application on planning in a collaborative interface.Comment: 39 pages, 13 figures. to appear in Logical Methods in Computer Scienc

Uniting Student Musicians and Patients: A Quality Improvement Project

Background: The benefits of exposing hospitalized patients to live music have been well established, both from a quality improvement perspective and as a means of therapy. Hospitals across the country are increasingly seeking to incorporate music into models of patient-centered care. Music offers many benefits to the performer as well, and is well suited for promoting empathic communication, stress relief and a sense of well-being. Medical students in the pre-clinical years who might otherwise experience little patient interaction are able to uniquely engage with patients through musical performance. Purpose: The purpose of this study was to examine the quality improvement impact of a program that facilitated musical performances given by student volunteers to hospitalized patients. Methods: Student musicians were recruited via email from the UMass Medical School and the Graduate School of Nursing, with an initial pool of 47 students expressing interest in performing. Over a period of several months, 21 performances were held on the Oncology floor and in the Bone Marrow Transplant unit. Qualitative data was collected via an online survey from the student volunteers and from the nursing staff. Informal feedback was obtained from patients. Results: The qualitative data collected was almost uniformly positive. Student musicians generally reported positive experiences, and felt that their efforts were appreciated by patients and by the nursing staff. Staff also enjoyed the performances, with most feeling that their workplace environment was positively impacted. Indeed, the major complaint from nursing staff was that the performances were not frequent enough. Although data was not collected from patients, informal questioning indicated that almost all patients enjoyed the experience. Conclusions: Taken as a whole, student musical volunteering in the hospital setting appears to be of great benefit as both a quality improvement tool and as a means of engaging students with patients. Patients appreciate the personal attention and a break from the monotony of hospitalization, while hospital staff reports a more pleasant working environment. Students are able to connect directly with patients in a non-medical role, which can be deeper and more meaningful than a brief encounter during work rounds. Additionally, pre-clinical students are exposed to the hospital setting and to patients

Redundancy in Logic I: CNF Propositional Formulae

A knowledge base is redundant if it contains parts that can be inferred from the rest of it. We study the problem of checking whether a CNF formula (a set of clauses) is redundant, that is, it contains clauses that can be derived from the other ones. Any CNF formula can be made irredundant by deleting some of its clauses: what results is an irredundant equivalent subset (I.E.S.) We study the complexity of some related problems: verification, checking existence of a I.E.S. with a given size, checking necessary and possible presence of clauses in I.E.S.'s, and uniqueness. We also consider the problem of redundancy with different definitions of equivalence.Comment: Extended and revised version of a paper that has been presented at ECAI 200

Direct comparison between confocal and multiphoton microscopy for rapid histopathological evaluation of unfixed human breast tissue

Rapid histopathological examination of surgical specimen margins using fluorescence microscopy during breast conservation therapy has the potential to reduce the rate of positive margins on postoperative histopathology and the need for repeat surgeries. To assess the suitability of imaging modalities, we perform a direct comparison between confocal fluorescence microscopy and multiphoton microscopy for imaging unfixed tissue and compare to paraffin-embedded histology. An imaging protocol including dual channel detection of two contrast agents to implement virtual hematoxylin and eosin images is introduced that provides high quality imaging under both one and two photon excitation. Corresponding images of unfixed human breast tissue show that both confocal and multiphoton microscopy can reproduce the appearance of conventional histology without the need for physical sectioning. We further compare normal breast tissue and invasive cancer specimens imaged at multiple magnifications, and assess the effects of photobleaching for both modalities using the staining protocol. The results demonstrate that confocal fluorescence microscopy is a promising and cost-effective alternative to multiphoton microscopy for rapid histopathological evaluation of ex vivo breast tissue.National Institutes of Health (U.S.) (Grant R01-CA178636-02)National Institutes of Health (U.S.) (Grant R01-CA075289-18)National Institutes of Health (U.S.) (Grant F32-CA183400-03)United States. Air Force. Office of Scientific Research (Grant FA9550-12-1-0551)United States. Air Force. Office of Scientific Research (Grant FA9550-15-1-0473

Assessment of breast pathologies using nonlinear microscopy

Rapid intraoperative assessment of breast excision specimens is clinically important because up to 40% of patients undergoing breast-conserving cancer surgery require reexcision for positive or close margins. We demonstrate nonlinear microscopy (NLM) for the assessment of benign and malignant breast pathologies in fresh surgical specimens. A total of 179 specimens from 50 patients was imaged with NLM using rapid extrinsic nuclear staining with acridine orange and intrinsic second harmonic contrast generation from collagen. Imaging was performed on fresh, intact specimens without the need for fixation, embedding, and sectioning required for conventional histopathology. A visualization method to aid pathological interpretation is presented that maps NLM contrast from two-photon fluorescence and second harmonic signals to features closely resembling histopathology using hematoxylin and eosin staining. Mosaicking is used to overcome trade-offs between resolution and field of view, enabling imaging of subcellular features over square-centimeter specimens. After NLM examination, specimens were processed for standard paraffin-embedded histology using a protocol that coregistered histological sections to NLM images for paired assessment. Blinded NLM reading by three pathologists achieved 95.4% sensitivity and 93.3% specificity, compared with paraffin-embedded histology, for identifying invasive cancer and ductal carcinoma in situ versus benign breast tissue. Interobserver agreement was κ = 0.88 for NLM and κ = 0.89 for histology. These results show that NLM achieves high diagnostic accuracy, can be rapidly performed on unfixed specimens, and is a promising method for intraoperative margin assessment.National Institutes of Health (U.S.) (Grant R01-CA178636-01)National Institutes of Health (U.S.) (Grant R01-CA75289-16)United States. Air Force Office of Scientific Research (Grant FA9550-10-1-0551)United States. Air Force Office of Scientific Research (Grant FA9550-12-1-0499)National Institutes of Health (U.S.) (National Research Service Award Postdoctoral Fellowship F32-CA165484

Fully transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study

Background: Deep learning (DL) can extract predictive and prognostic biomarkers from routine pathology slides in colorectal cancer. For example, a DL test for the diagnosis of microsatellite instability (MSI) in CRC has been approved in 2022. Current approaches rely on convolutional neural networks (CNNs). Transformer networks are outperforming CNNs and are replacing them in many applications, but have not been used for biomarker prediction in cancer at a large scale. In addition, most DL approaches have been trained on small patient cohorts, which limits their clinical utility. Methods: In this study, we developed a new fully transformer-based pipeline for end-to-end biomarker prediction from pathology slides. We combine a pre-trained transformer encoder and a transformer network for patch aggregation, capable of yielding single and multi-target prediction at patient level. We train our pipeline on over 9,000 patients from 10 colorectal cancer cohorts. Results: A fully transformer-based approach massively improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training on a large multicenter cohort, we achieve a sensitivity of 0.97 with a negative predictive value of 0.99 for MSI prediction on surgical resection specimens. We demonstrate for the first time that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem. Interpretation: A fully transformer-based end-to-end pipeline trained on thousands of pathology slides yields clinical-grade performance for biomarker prediction on surgical resections and biopsies. Our new methods are freely available under an open source license

Spatial distribution of B cells and lymphocyte clusters as a predictor of triple-negative breast cancer outcome.

While tumor infiltration by CD8+ T cells is now widely accepted to predict outcomes, the clinical significance of intratumoral B cells is less clear. We hypothesized that spatial distribution rather than density of B cells within tumors may provide prognostic significance. We developed statistical techniques (fractal dimension differences and a box-counting method 'occupancy') to analyze the spatial distribution of tumor-infiltrating lymphocytes (TILs) in human triple-negative breast cancer (TNBC). Our results indicate that B cells in good outcome tumors (no recurrence within 5 years) are spatially dispersed, while B cells in poor outcome tumors (recurrence within 3 years) are more confined. While most TILs are located within the stroma, increased numbers of spatially dispersed lymphocytes within cancer cell islands are associated with a good prognosis. B cells and T cells often form lymphocyte clusters (LCs) identified via density-based clustering. LCs consist either of T cells only or heterotypic mixtures of B and T cells. Pure B cell LCs were negligible in number. Compared to tertiary lymphoid structures (TLS), LCs have fewer lymphocytes at lower densities. Both types of LCs are more abundant and more spatially dispersed in good outcomes compared to poor outcome tumors. Heterotypic LCs in good outcome tumors are smaller and more numerous compared to poor outcome. Heterotypic LCs are also closer to cancer islands in a good outcome, with LC size decreasing as they get closer to cancer cell islands. These results illuminate the significance of the spatial distribution of B cells and LCs within tumors