Invariant image object recognition using mixture densities

SpinePose was developed in 2024 as a novel artificial intelligence (AI) tool to automatically predict spinopelvic parameters with high accuracy and without the need for manual entry. The authors' published results demonstrated excellent performance comparable to a fellowship-trained spine surgeon with more than 15 years of experience. To date, there have not been any studies that have externally validated the performance of AI-based spinopelvic parameter measurement tools on data acquired from other institutions. To assess the generalizability of SpinePose, the authors report its performance on an external set of heterogeneous whole-spine scoliosis radiographs obtained from an outside institution. SpinePose was trained/validated on a dataset of 761 sagittal whole-spine scoliosis radiographs from a single institution, with expert-level performance on both whole-spine and lumbosacral radiographs. In this study, the existing SpinePose model was used for inference on a new set of 49 whole-spine radiographs acquired at a tertiary academic hospital located out of state. Externally acquired radiographs represented a diverse set of images, incorporating patients who had undergone instrumentation and those who had not, and a wide variety of fusion constructs including complex deformity patients. Predicted measures included sagittal vertical axis (SVA), pelvic tilt (PT), pelvic incidence (PI), lumbar lordosis (LL), and T1-pelvic angle (T1PA). Predicted parameter values relative to ground-truth manual annotations were calculated to determine the model's accuracy. Of the 49 images, 35 (71.4%) had instrumentation compared with 51.0% and 57.5% in the original SpinePose training and testing sets, respectively. All 5 parameters in the external dataset were significantly different at baseline compared with the original test set (p < 0.01). SpinePose accurately predicted all 5 spinopelvic parameters without any statistically significant differences: SVA, 50.7 mm vs 52.3 mm (p = 0.85); PT, 27.6° vs 30.5° (p = 0.24); PI, 58.0° vs 61.8° (p = 0.17); LL, 40.4° vs 42.4° (p = 0.77); and T1PA, 24.8° vs 28.0° (p = 0.21) when comparing ground truth annotations with predicted values. SpinePose was able to accurately predict spinopelvic parameters on an external validation cohort that was generated independently from the images on which the model was trained and validated. This highlights the generalizability of SpinePose to be implemented on novel images from other institutions and geographic settings with high accuracy and minimal preprocessing. The implementation of AI tools more broadly will help standardize our ability to both deliver and provide spine care and assist with surgical treatment and management of spine patients.

There is presently a concern because the US Postal Service now has 8 electron beam accelerators to treat mail in locations along the east coast and that a sufficient dosage to destroy Anthrax is also strong enough to darken and weaken the paper in the postal envelopes. In addition to direct photolytic scission of the cellulose chain, irradiation darkens paper, swells pulp fibres, increases hygroscopicity, and renders the cellulose more soluble in alkaline solution and more susceptible to acid hydrolysis. This report will focus on the effect of electron beam irradiation on degradation of cellulose of fine papers. Three reference papers were selected, sorted into postal envelopes and exposed to electron beam irradiation. The results have shown that irradiation at the dosage used to treat mail by the US Postal Service depolymerizes and oxidizes the cellulose. Depolymerization is responsible for a decrease of paper strength while oxidation induces darkening of the paper. Irradiating at high electron beam energy is less damaging than using lower energy. Moreover, linear relationships have been found between the number of chain scission (CSN) in cellulose and the irradiation dosage as well as between CSN and Zero-Span Breaking Length. These relationships make the strength loss predictable.

Newton Arvin and the Police: Contexts for <i>The Scarlet Professor</i>

(1988). Technical Efficiency within the US Postal Service and the Postal Reorganization Act of 1970. Applied Economics: Vol. 20, No. 9, pp. 1185-1197.

The principle of maximum entropy is a powerful framework that can be used to estimate class posterior probabilities for pattern recognition tasks. In this paper, we show how this principle is related to the discriminative training of Gaussian mixture densities using the maximum mutual information criterion. This leads to a relaxation of the constraints on the covariance matrices to be positive (semi-) definite. Thus, we arrive at a conceptually simple model that allows to estimate a large number of free parameters reliably. We compare the proposed method with other state-of-the-art approaches in experiments with the well known US Postal Service handwritten digits recognition task.KeywordsMaximum EntropyCovariance MatriceGaussian ModelNeural Information Processing SystemRelevance Vector MachineThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The purpose of this study was to report methods currently recommended by commercial laboratories for collecting, shipping, and processing of samples for feline herpesvirus type 1 (FHV-1) testing using polymerase chain reaction (PCR) and to determine the effect of temperature and time on the ability of 1 PCR method to detect FHV-1 DNA in experimental and clinical samples. Eleven laboratories offering FHV-1 PCR were surveyed. There was notable variation in sample types and shipping conditions recommended and PCR protocols used by these laboratories. Subsequently, using a single PCR method, FHV-1 DNA was detected in samples exposed to various temperatures within the laboratory. Finally, FHV-1 PCR was performed on paired clinical samples collected from 25 cats and shipped at ambient temperatures via US Postal Service (USPS) or with an ice pack via a courier. Samples sent by USPS were exposed to significantly longer transit time and arrived at significantly higher temperature than did samples sent by courier. Despite this, all sample pairs yielded concordant results when tested for FHV-1 DNA using this PCR method. Although it may not be necessary for samples collected for detection of FHV-1 DNA using this PCR method to be shipped under the most expedient or temperature-controlled conditions, this should be verified for a variety of PCR assays and sample types.

I study the impact of extreme heat on the incidence of harassment and discrimination using data on Equal Employment Opportunity (EEO) charges brought forward by US Postal Service (USPS) workers. I analyze more than 800,000 EEO charges filed between FYs 2004 and 2019. I find that heat stress experienced on days when maximum temperatures exceed 90 °F increases EEO incidents by roughly 5% relative to days when temperatures are between 60 °F and 70 °F. The uncovered effect is widespread across the USPS and appears to be driven by changes in the number of incidents rather than in their reporting.

GM FCV to deliver US mail, smashes distance record across Europe

Environmental epidemiologic studies using geospatial data often estimate exposure at a participant's residence upon enrollment, but mobility during the exposure period can lead to misclassification. We aimed to mitigate this issue by constructing residential histories for participants in the California Teachers Study through follow-up (1995-2018). Address records have been collected from the US Postal Service, LexisNexis, Experian, and California Cancer Registry. We identified records of the same address based on geo-coordinate distance (≤250 m) and street name similarity. We consolidated addresses, prioritizing those confirmed by participants during follow-up questionnaires, and estimating the duration lived at each address using dates associated with records (e.g., date-first-seen). During 23 years of follow-up, about half of participants moved (48%, including 14% out-of-state). We observed greater mobility among younger women, Hispanic/Latino women, and those in metropolitan and lower socioeconomic status areas. The cumulative proportion of in-state movers remaining eligible for analysis was 21%, 32%, and 41% at 5, 10, and 20 years post enrollment, respectively. Using self-reported information collected 10 years after enrollment, we correctly identified 94% of movers and 95% of non-movers as having moved or not moved from their enrollment address. This dataset provides a foundation for estimating long-term environmental exposures in diverse epidemiologic studies in this cohort. IMPACT: Our efforts in constructing residential histories for California Teachers Study participants through follow-up (1995-2018) benefit future environmental epidemiologic studies. Address availability during the exposure period can mitigate misclassification due to residential changes, especially when evaluating long-term exposures and chronic health outcomes. This can reduce differential misclassification among more mobile subgroups, including younger women and those from lower socioeconomic and urban areas. Our approach to consolidating addresses from multiple sources showed high accuracy in comparison to self-reported residential information. The residential dataset produced from this analysis provides a valuable tool for future studies, ultimately enhancing our understanding of environmental health impacts.

With the advent of app-based technology on smartphones, we now live in a “Ratings Economy” where people can provide feedback on everything from Uber drivers to the US Postal Service. So why would leaders in organizations want to receive more feedback from others? Research has shown that individuals who seek more feedback from bosses and coworkers are rated as better performers due to increased clarity about what is expected of them (Whitaker et al., Journal of Management 33:570–591, 2007). How might user-driven feedback tools be designed to allow leaders to ask for and receive more regular, high-quality feedback? And how would organizations best design user-driven feedback tools within a leader development context? The intent of this chapter is two-fold. First, we provide preliminary answers to these questions by drawing on the feedback seeking literature (Anseel et al., Journal of Management 41:318–348, 2015), best practices in designing and implementing feedback interventions for leaders (Bracken and Rose, Journal of Business and Psychology 26:183, 2011), insights from a 2018 professional conference panel on the topic (Young et al., User-driven leader feedback tools. Panel at the annual conference of the Society for Industrial-Organizational Psychology. Chicago, IL, 2018), and our own experience implementing these tools. Second, we suggest future directions for research in this emerging area where organizations are racing to implement tools with little evidence-based guidance to draw on.

Parcel delivery companies (e.g. US Postal Service, UPS, and FedEx) are significant parts of modern transportation systems. The availability, responsiveness, reliability, completeness, and professionalism of service were identified from a literature review, as the five critical factors affecting customer satisfaction in parcel delivery industry. A model of customer satisfaction in parcel delivery service was developed. Academic departments from selected universities across the US responded to written questionnaires and these data were used for the study. The data included information on the characteristics of each above-mentioned factor and ranking of importance of the factors was determined. The research tested the hypotheses to determine the correlation coefficients between the five factors and overall customer satisfaction with respect to all customers for both outgoing and incoming parcel delivery services.

Intro &amp; Background: The Area Deprivation Index (ADI) is a validated neighborhood-level measure that utilizes variables such as income, education, and employment to quantify relative socioeconomic disadvantages. Here we explore the impact of disparities on EVT access. Methods &amp; Materials: From our prospectively maintained multi-hospital registry, we identified patients with LVO AIS from January 2019- June 2020. Patient addresses and zip-codes were validated using US Postal Service codes and matched to census-tract level ADI scores that were obtained from Neighborhood Atlas. ADI were categorized into high and low using the median ADI as the cutoff. The primary outcome was utilization of EVT and IV tPA and was determined using multivariable logistic regression and expressed as OR [95% CI]. All p-values are two-sided with p&lt;0.05 defined as statistically significant. All analyses were conducted using RStudio (Version 1·2·5001). Results: Among 637 patients with LVO AIS, median age was 68, 46% were female, 53% were white, 27% were black, and 78% identified as Hispanic. Median state ADI was 5 IQR [5]. NIHSS was similar between low/high ADI (mean(SD): 13.3(7.75) vs 13.6(8.62), p-value 0.69) regions. ADI was significantly associated with race (6.41 vs 4, black vs. white, p-value 0.03). In the univariable analysis, patients treated with EVT had lower mean ADIs (5.2 vs. 4.6, no EVT vs. EVT, p&lt;0.02). In multivariable analysis adjusted for age, sex, race, ethnicity and NIHSS, higher ADI was significantly associated with greater rates of IV tPA usage (OR 1.7 [1.01-2.98]) but not EVT usage (OR 0.63 [0.04-1.0]) Conclusion: Patients residing in disadvantaged neighborhoods may have reduced rates of reperfusion therapy, despite comparable acute stroke presentation symptoms. These findings are consistent with prior studies demonstrating poorer health outcomes in these populations.

IntroductionThe Area Deprivation Index (ADI) is a validated neighborhood‐level measure that utilizes variables such as income, education, and employment to quantify relative socioeconomic disadvantages. Here we explore the impact of disparities on EVT access.MethodsFrom our prospectively maintained multi‐hospital registry, we identified patients with LVO AIS from January 2019‐ June 2020. Patient addresses and zip‐codes were validated using US Postal Service codes and matched to census‐tract level ADI scores that were obtained from Neighborhood Atlas. ADI were categorized into high and low using the median ADI as the cuto!. The primary outcome was utilization of EVT and IV tPA and was determined using multivariable logistic regression and expressed as OR [95% CI]. All p‐values are two‐sided with p < 0.05 defined as statistically significant. All analyses were conducted using RStudio (Version 1.2.5001).ResultsAmong 637 patients with LVO AIS, median age was 68, 46% were female, 53% were white, 27% were black, and 78% identified as Hispanic. Median state ADI was 5 IQR [5]. NIHSS was similar between low/high ADI (mean(SD): 13.3(7.75) vs 13.6(8.62), p‐value 0.69) regions. ADI was significantly associated with race (6.41 vs 4, black vs. white, p‐value 0.03). In the univariable analysis, patients treated with EVT had lower mean ADIs (5.2 vs. 4.6, no EVT vs. EVT, p< 0.02). In multivariable analysis adjusted for age, sex, race, ethnicity and NIHSS, higher ADI was significantly associated with greater rates of IV tPA usage (OR 1.7 [1.01‐ 2.98]) but not EVT usage (OR 0.63 [0.04‐1.0])ConclusionsPatients residing in disadvantaged neighborhoods may have reduced rates of reperfusion therapy, despite comparable acute stroke presentation symptoms. These findings are consistent with prior studies demonstrating poorer health outcomes in these populations.

The Leeds Multiple Sclerosis Quality of Life (LMSQOL) scale was designed as an 8-item, unidimensional disease-targeted measure of quality of life (QOL). This study (1) tested the unidimensionality of the LMSQOL using confirmatory factor analysis (CFA) and (2) examined the construct validity of scores based on a nomological network. The sample (N=292) included persons with multiple sclerosis (MS) who were recruited from the Midwest region of the USA. Participants completed questionnaires that were delivered and returned using US Postal Service. We tested the fit of a single-factor model for the LMSQOL using CFA with the diagonally weighted least squares estimator in R. We examined the construct validity of the LMSQOL scores using Spearman rank-order correlations (r s) in SPSS. The single-factor model had a reasonable fit for the 8-item LMSQOL [χ (2) (20)=83.19, p<.001, CFI=.97, SRMR=.07], but provided an excellent fit for a 7-item version of the LMSQOL [χ (2) (14)=23.63, p>.05, CFI=.99, SRMR=.05]. LMSQOL scores demonstrated strong correlations with measures of psychological well-being (|r s|=.53-.74) and weak-to-moderate correlations with measures of physical functioning and disability (|r s|=.23-.57). The unidimensional model provides a good fit for the LMSQOL, and its scores provide a valid measure of QOL in MS.

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