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Accurate prediction of infectious disease outbreaks are vital for effective public health planning and intervention. This paper proposes a novel spatio-temporal prediction framework that integrates Graph Convolutional Networks (GCNs) with Long Short-Term Memory (LSTM) networks to model the complex spatial and temporal dynamics of disease spread. By leveraging multi-source data—including epidemiological time-series, climatic variables, demographic distributions, and human mobility patterns—the model learns region-wise interdependencies and sequential trends in disease progression. The spatial component, implemented via GCN, captures inter-regional transmission patterns, while the temporal component, powered by LSTM, models evolving outbreak trends within each region. Experimental evaluation on real-world datasets demonstrates that the proposed GCN+LSTM model significantly outperforms traditional models such as ARIMA, Random Forest, and standalone LSTM in terms of MAE, RMSE, and F1-score. It also meets high classification performance at low false negative rate and therefore very suitable for real-time outbreak surveillance and early warning system.

A 10-year-old, neutered male, indoor/outdoor domestic shorthair cat was presented for a mass on the right front paw. The mass was treated as an abscess, and despite initial resolution, the mass recurred and ruptured approximately 1month later. This mass successfully resolved with intense management as an open wound. Several days later, the cat developed vomiting and inappetence, and a new mass was noted on the lateral aspect of the right rear limb. Aspirates from the new mass were submitted for cytologic evaluation and bacterial cultures. Anaerobic and aerobic bacterial cultures were negative. Cytologic evaluation revealed septic neutrophilic inflammation with small rod, cocci, curved, and ring forms of bacteria seen, and Mycoplasma spp. infection was suspected based on the morphology of the bacteria. Polymerase chain reaction followed by gene sequencing revealed 86% similarity for Mycoplasma elephantis. The cat was treated with a fluoroquinolone antibiotic and clinically improved, with resolution of the abscess. This case highlights the importance of recognizing the morphologic appearance of Mycoplasma spp. on cytologic examination to help guide additional testing choices and therapeutic planning.

Medical and other health science students are provided a unique theatre in the anatomy lab, to dissect, reflect upon and discover normal anatomy, anomalous variations, disease or trauma‐induced disruptions, and often times, pre‐mortem clinical interventions as exist in modern humankind. This highly effective learning activity is becoming temporally less practical. With diminished time available for cadaveric dissection, alternative approaches have become more common: i.e., prosection‐only approaches, digital/electronic anatomy, discovery using plastinates, and others. Accepting the reality of more limited time, but still appreciating the beneficial attributes of dissection, on many levels, we launched a new approach in our anatomy curriculum. In its inaugural year, we have proceeded to hemi‐dissect each of the student group cadavers such that when the students come into the lab to dissect, the alternate side of their cadaver is already prosected. As such, students have a ready consult at their station that shows them what to strive for as they dissect the side contralateral to the prosection. This gives the students more time to be more careful on their side and gives them an unparalleled and timely opportunity to “learn” from the prosection while dissecting the alternate side (rather than “standing around” while some dissect and others don't). This has proven to be wildly popular for this year's class. Moreover, this has proven to shave dissection time from the calendar, make more efficient the dissection experience for the students, and to actually ease the burden on faculty with regards to “teaching”. For example, “teaching” can commence nearly immediately in any given lab session and while the “student dissectors” are working their hemi‐dissection. This approach markedly increases the temporal efficiency of dissecting while preserving the tangible and more intangible attributes of cadaveric dissection.Support or Funding InformationSupported by SIU School of MedicineThis abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Cloud computing is emerging as a promising paradigm for large-scale data-intensive queries modeled as complex Directed Acyclic Graph (DAG)-structured dataflows with arbitrary data operators as nodes and producer-consumer interactions as directed edges. The optimization problem of scheduling dataflows on the Cloud is a very complex and challenging task which is similar to query optimization. Optimization must satisfy a variety of objectives and constraints, while taking into account the particular characteristics of the underlying Cloud environment. In addition to achieving minimum completion time, the commercialization of Clouds requires policies to take users' economic concerns as well. In this paper, we formulate scheduling of dataflows onto Cloud resources under the objective of minimizing the completion time under certain budget constraint. A heuristic scheduling algorithm, Layer-oriented Resource Allocation within Budget constraint (LRA-B) is proposed and evaluated. Experiments are conducted on numerous dataflows and Cloud environment configurations, and the overall results are quite promising and indicate the effectiveness of our algorithm.

Soil Survey HorizonsVolume 52, Issue 3 p. 89-93 Article The Art and Science of Soil-Landscape Block Diagrams: Examples of One Picture Being Worth More Than 1000 Words Samuel J. Indorante, Corresponding Author Samuel J. Indorante Soil Scientist/Project Leader sam.indorante@il.usda.gov Carbondale MLRA Soil Survey Office, USDA-NRCS, 148 E. Pleasant Hill Road, Suite 105, Carbondale, IL, 62903Search for more papers by this author Samuel J. Indorante, Corresponding Author Samuel J. Indorante Soil Scientist/Project Leader sam.indorante@il.usda.gov Carbondale MLRA Soil Survey Office, USDA-NRCS, 148 E. Pleasant Hill Road, Suite 105, Carbondale, IL, 62903Search for more papers by this author First published: 04 August 2015 https://doi.org/10.2136/sh2011.3.0089Citations: 4Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume52, Issue3Fall 2011Pages 89-93 RelatedInformation

Introduction: The physician assistant (PA) profession is projected to be one of the fastest growing in the nation through 2010, yet physician receptivity in terms of role, responsibilities, and competencies remains a concern of PA educators. Method: This study surveyed primarily rural Illinois physicians to identify incentives and constraints that influence their receptivity to utilization of PAs. Results: Receptive physicians reported 6 incentives related to appointments, workload, productivity, education/counseling, complex cases, and patient satisfaction. Unreceptive physicians identified 4 constraints related to perceived patient opposition, malpractice risk, overstepping authority, and continuity of care. Receptive physicians also identified perceived patient opposition as a constraint. Conclusions: PA educators need to inform PA students about incentives and constraints influencing physician and patient receptivity, especially in rural and underserved communities.

Six normally capable adults first learned three conditional relations in each of two prospective equivalence classes via match-to-sample training with figures as conditional (sample) and discriminative (comparison) stimuli. Then one trained conditional relation in each prospective class was brought under the control of contextual stimuli, two dictated nonsense syllables. Test performances indicated the emergence of untrained conditional relations, and therefore two equivalence classes, that were conditional on the contextual stimuli. These tests involved untrained combinations of contextual stimuli and stimuli in conditional relations, suggesting that the contextual stimuli functioned independently to control conditional relations rather than forming compound stimuli with samples and comparisons in training. Next, two novel figures were made equivalent to each of the original dictated contextual stimuli by match-to-sample training and testing. On subsequent tests, all subjects demonstrated transfer of conditional control of untrained conditional relations from the original auditory contextual stimuli to equivalent visual stimuli. These outcomes further supported the conclusion that the contextual stimuli exerted true conditional control over conditional relations in the equivalence classes and were not merely elements of compound stimuli.