Search

We investigate two research questions using a recent statewide transition from paper to computer-based testing: first, the extent to which test mode effects found in prior studies can be eliminated; and second, the degree to which online and paper assessments offer different information about underlying student ability. We first find very small mode effects for a more recent transition in Massachusetts. Second, we investigate the predictive evidence of validity for paper and online tests for predictions of future test scores and grades. We generally find minimal differences for the extent to which scores on paper tests can differentially predict future online versus paper test scores. Finally, online and paper test scores are similarly predictive of future grade point average. We conclude that the online test penalty can vary substantially by test and that extreme care should be taken when administering online tests to some students and paper tests to others. [ABSTRACT FROM AUTHOR]

Building extraction aims to extract building pixels from remote sensing imagery, which plays a significant role in urban planning, dynamic urban monitoring, and many other applications. UNet3+ is widely applied in building extraction from remote sensing images. However, it still faces issues such as low segmentation accuracy, imprecise boundary delineation, and the complexity of network models. Therefore, based on the UNet3+ model, this paper proposes a 3D Joint Attention (3DJA) module that effectively enhances the correlation between local and global features, obtaining more accurate object semantic information and enhancing feature representation. The 3DJA module models semantic interdependence in the vertical and horizontal dimensions to obtain feature map spatial encoding information, as well as in the channel dimensions to increase the correlation between dependent channel graphs. In addition, a bottleneck module is constructed to reduce the number of network parameters and improve model training efficiency. Many experiments are conducted on publicly accessible WHU,INRIA and Massachusetts building dataset, and the benchmarks, BOMSC-Net, CVNet, SCA-Net, SPCL-Net, ACMFNet, MFCF-Net models are selected for comparison with the 3DJA-UNet3+ model proposed in this paper. The experimental results show that 3DJA-UNet3+ achieves competitive results in three evaluation indicators: overall accuracy, mean intersection over union, and F1-score. The code will be available at https://github.com/EnjiLi/3DJA-UNet3Plus. [ABSTRACT FROM AUTHOR]

Background and aims: A substantial minority of autistic individuals score within typical ranges on standard language tests, suggesting that autism does not necessarily affect language acquisition. This idea is reflected in current diagnostic criteria for autism, wherein language impairment is no longer included. However, some work has suggested that probing autistic speakers' language carefully may reveal subtle differences between autistic and nonautistic people's language that cannot be captured by standardized language testing. The current study aims to test this idea, by determining whether a group of autistic and nonautistic individuals who score similarly on a standardized test show differences in the number of unconventional and erroneous language features they produce in a spontaneous language sample. Methods: Thirty-eight older children and adolescents (19 autistic; 19 nonautistic), between the ages of 10 and 18, were recruited. Both participant groups scored within normal ranges on standardized language and IQ tests. Participants engaged in a "double interview" with an experimenter, during which they were first asked questions by the experimenter about themselves, and then they switched roles, so that it was the participant's turn to ask the experimenter questions. Participants' language during the interview was transcribed and analyzed for linguistic irregularities, including both semantic anomalies and morphosyntactic errors. Results: Group membership accounted for significant variance in irregularity frequency; autistic participants produced more linguistic irregularities than nonautistic participants. Scores on a standardized language test did not improve model fit. Secondary analyses involving irregularity type (semantic vs. morphosyntactic) showed that group differences were primarily driven by relatively high numbers of semantic unconventionalities produced by the autistic group. While the autistic group made more morphosyntactic errors than the nonautistic group, differences in these numbers were only marginally significant. Conclusions and implications: These findings suggest that a commonly used standardized language test does not adequately predict the number and perhaps type of language irregularities produced by some older autistic children and adolescents during spontaneous discourse. Results also suggest that differences in language use, especially semantic differences, may characterize autistic language, even the language produced by people who score within normal ranges on standardized language tests. It is debatable whether differences reflect underlying language impairments and/or a linguistic style adopted/preferred by autistic speakers. In this paper, we discuss both possibilities and offer suggestions to future research for teasing these possibilities apart. [ABSTRACT FROM AUTHOR]

This paper introduces link information value obtained by the traffic sensors and presents a traffic sensor location and flow estimation joint optimization model in an urban road network. In contrast to most previous studies, this paper adds new traffic sensors into the existing sensor network and proposes a data-driven path flow measurement method based on Wasserstein metric, which is utilized to measure the distance between the estimated traffic flow distribution and the actual distribution. Furthermore, this paper develops a customized greedy algorithm by combining a search strategy for the link information value to obtain the optimal sensor location scheme and perform traffic flow estimation under different budget conditions. Numerical experiments are conducted on Sioux-Falls test network and Eastern Massachusetts interstate highway subnetwork to verify the accuracy and effectiveness of the proposed model based on Wasserstein metric and the developed solution method. Computational results show that the sensor location scheme generated by the model based on Wasserstein metric can reduce the estimation error of the traffic flow compared with KL divergence model under the same deployment cost. Additionally, the customized greedy algorithm can achieve the better performance than the Brute force algorithm in terms of computing time and solution quality. • A traffic sensor location and path flow estimation joint optimization model is formulated. • A data-driven path flow measurement method based on Wasserstein metric is proposed. • A customized greedy algorithm by combining a search strategy is developed. • Numerical experiments are conducted based on the real-word transportation network. [ABSTRACT FROM AUTHOR]

Precise road segmentation is an essential part of many applications related to road information extraction from remote sensing data. The effect of color space on road detection has rarely been studied. In this paper, the effects of different color spaces of aerial images and multitask learning methods were experimented on road segmentation using three deep convolutional neural networks, UNet, DenseU-Net, and RoadVecNet. The color spaces included RGB, HSV, LAB, YCbCr, and YUV. The multitask learning methods adopted in this study involved utilizing multiple inputs, and multiple outputs. Multiple inputs were aerial images from the same area with different color spaces, and multiple outputs were road segmentation and road outline segmentation. As remote sensing data, National Land Survey of Finland's true orthophotos (from 2020), Massachusetts road imagery dataset, and Ottawa dataset were applied. Segmentation masks for National Land Survey of Finland's true orthophotos were extracted from Digiroad vectors with road width information. Road outline masks were generated from the segmentation masks. The studied neural networks were trained with the same data, learning rate, loss function, and optimizer for each color space, and pairs of color spaces. Multiple outputs were experimented with RGB color space. The comparative analysis assessed the performance of various neural networks across different color spaces using the F1-score metric. The experimental findings indicate that the choice of color space has little influence on the results of neural networks Deep learning methods can adapt to different color spaces well. In addition, the use of sharpening and edge enhancement augmentations had a slight effect on the results. [ABSTRACT FROM AUTHOR]

Background: To address the need for interventions for families with parents with mental illness, the evidence-based intervention Let's Talk about Children (LTC) was adapted in the context of adult mental health services in the United States and reframed as the ParentingWell Practice Approach. This study focuses on the early implementation phase of the adapted practice in Massachusetts. Methods: As part of the adaptation and implementation process, practitioners from provider agencies serving adults with mental illness were invited to participate in the ParentingWell Learning Collaborative (PWLC), which included in-person learning collaborative sessions and follow-up virtual coaching sessions. This paper focuses on data obtained during and in response to the PWLC virtual coaching sessions, from 29 participants. Specific research questions included: (1) What themes emerged in coaching sessions related to practitioners' experiences during the early implementation of the ParentingWell Practice Approach (2) In what ways are coaching sessions helpful to the practitioners as they implement the ParentingWell Practice Approach? Coaching sessions were recorded, and transcribed, and the data were analyzed qualitatively to identify early implementation themes. Practitioners completed feedback surveys online (which included Likert scale items and open-ended questions) following virtual coaching sessions to evaluate the usefulness of coaching sessions. Results: Coaching sessions reflected the following themes related to practitioners' experiences during the early implementation of ParentingWell: (1) practitioners identify and share concrete approaches to supporting parents; (2) practitioners reflect on parents' needs related to support, advocacy, problemsolving, and parenting skills; (3) practitioners reflect on their own personal experiences; and (4) practitioners' recognize the importance of self-care strategies for themselves and for parents served. Practitioners indicated that coaching sessions were useful in supporting the implementation of a new practice. Conclusion: This study provides preliminary evidence for the benefits of coaching in the implementation of ParentingWell. Future research will explore the impact of ParentingWell on outcomes for parents and families served. [ABSTRACT FROM AUTHOR]

The complex environmental impact makes it difficult to predict wind speed with high precision for multiple wind turbines. Most existing research methods model the temporal dependence of wind speeds, ignoring the spatial correlation between wind turbines. In this paper, we propose a multi-wind turbine wind speed prediction model based on Weighted Diffusion Graph Convolution and Gated Attention Network (WDGCGAN). To address the strong nonlinear correlation problem among multiple wind turbines, we use the maximal information coefficient (MIC) method to calculate the correlation weights between wind turbines and construct a weighted graph for multiple wind turbines. Next, by applying Diffusion Graph Convolution (DGC) transformation to the weight matrix of the weighted graph, we obtain the spatial graph diffusion matrix of the wind farm to aggregate the high-order neighborhood information of the graph nodes. Finally, by combining the DGC with the gated attention recurrent unit (GAU), we establish a spatio-temporal model for multi-turbine wind speed prediction. Experiments on the wind farm data in Massachusetts show that the proposed method can effectively aggregate the spatio-temporal information of wind turbine nodes and improve the prediction accuracy of multiple wind speeds. In the 1h prediction task, the average RMSE of the proposed model is 28% and 33.1% lower than that of the Long Short-Term Memory Network (LSTM) and Convolutional Neural Network (CNN), respectively. [ABSTRACT FROM AUTHOR]

High-resolution remote sensing images (HRRSI) have important theoretical and practical value in urban planning. However, current segmentation methods often struggle with issues like blurred edges and loss of detailed information due to the intricate backgrounds and rich semantics in high-resolution remote sensing images. To tackle these challenges, this paper proposes an end-to-end attention-based Convolutional Neural Network (CNN) called Double Hybrid Attention U-Net (DHAU-Net). We designed a new Double Hybrid Attention structure consisting of dual-parallel hybrid attention modules to replace the skip connections in U-Net, which can eliminate redundant information interference and enhances the collection and utilization of important shallow features. Comprehensive experiments on the Massachusetts remote sensing building dataset and the Inria aerial image labeling dataset demonstrate that our proposed method achieves effective pixel-level building segmentation in urban remote sensing images by eliminating redundant information interference and making full use of shallow features, and improves the segmentation performance without significant time costs (approximately 15%). The evaluation metrics reveal significant results, with an accuracy rate of 0.9808, precision reaching 0.9300, an F1 score of 0.9112, a mean intersection over union (mIoU) of 0.9088, and a recall rate of 0.8932. [ABSTRACT FROM AUTHOR]

Automatic extraction of building contours from high-resolution images is of great significance in the fields of urban planning, demographics, and disaster assessment. Network models based on convolutional neural network (CNN) and transformer technology have been widely used for semantic segmentation of buildings from high resolution remote sensing images (HRSI). However, the fixed geometric structure and the local receptive field of the convolutional kernel are not good at global feature extraction, and the transformer technique with self-attention mechanism introduces computational redundancies and extracts local feature details poorly in the process of modeling the global contextual information. In this paper, a dual-branch fused reconstructive transformer network, DFRTNet, is proposed for efficient and accurate building extraction. In the encoder, the traditional transformer is reconfigured by designing the local and global feature extraction module (LGFE); the branch of global feature extraction (GFE) performs dynamic range attention (DRA) based on the idea of top-k attention for extracting global features; furthermore, the branch of local feature extraction (LFE) is used to obtain fine-grained features. The multilayer perceptron (MLP) is employed to efficiently fuse the local and global features. In the decoder, a simple channel attention module (CAM) is used in the up-sampling part to enhance channel dimension features. Our network achieved the best segmentation accuracy on both the WHU and Massachusetts building datasets when compared to other mainstream and state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The aim of the paper is to derive minimization algorithms based on the Nesterov accelerated gradient flow [Y. Nesterov, Gradient methods for minimizing composite objective function. Core discussion paper, (2007). Available at ; Y. Nesterov, A method of solving a convex programming problem with convergence rate $ \mathcal {O}(1/k^2) $ O (1 / k 2). In Doklady Akademii Nauk, Vol. 269, Russian Academy of Sciences, 1983, pp. 543–547; Y. Nesterov, Introductory Lectures on Convex Optimization: A basic course, Kluwer Academic Publishers, Massachusetts, 2004.] to compute the ground state of nonlinear Schrödinger equations, which can potentially include a fractional laplacian term. A comparison is developed with standard gradient flow formulations showing that the Nesterov accelerated gradient flow has some interesting properties but at the same time finds also some limitations due to the nature of the problem. A few simulations are finally reported to understand the behaviour of the algorithms and open the path to further complicate questions that require more advanced studies concerning the application of the Nesterov accelerated gradient flow to nonlinear Schrödinger equations. [ABSTRACT FROM AUTHOR]

Goal: Recent efforts to push hospitals to provide high-value care have relied on payment incentives. However, evidence indicates that 70% to 90% of performance improvement projects do not achieve their desired goals. Therefore, in addition to managing external industry pressures, hospitals need to develop performance improvement (PI) capabilities that enable them to capitalize on improvement opportunities, effectively develop and adopt solutions, and ensure the sustainability of improvements over time. While operational capabilities enable hospitals to produce and deliver services, more is needed to attain and sustain superior performance. Dynamic capabilities drive changes in operational capabilities to meet environmental demands. Dynamic capabilities also enable hospitals to renew and reconfigure their resources to optimize performance. This paper proposes the dynamic-capabilities framework as an appropriate way to develop and manage PI capabilities in hospitals, and it discusses the implications of shifting to a strategy that is driven by dynamic-capabilities PI. Methods: The research team designed a semi-structured interview based on a review of the literature to understand whether hospitals were engaging in the activities outlined in the dynamic-capabilities framework. Nine study participants were recruited from a convenience sample of hospital PI staff at hospitals in Massachusetts and New Hampshire. De-identified transcripts were entered into NVivo12 qualitative data analysis software, and data were thematically indexed and coded following the principles of content analysis. Principal Findings: PI structures, improvement methodologies, and weaknesses did not vary significantly among hospitals. Most hospitals had a PI department and were more likely to adopt PI projects initiated by top management. While PI staff were trained in improvement methodologies, no programs were in place that required the rest of the hospital staff to become familiar with PI methods. Common areas of weakness were PI project selection, communication, coordination, learning from current and former PI projects, and systematic approaches to sustain improvements. Practical Applications: Dynamic PI capabilities provide an opportunity to systematically identify improvement opportunities, seize on and learn from those opportunities, and renew and reconfigure resources to optimize performance. Ad hoc PI projects are insufficient to enable a hospital to sustain superior performance. Internal and external pressures to deliver high-value patient care and services require hospitals to exceed their current PI efforts. By developing dynamic PI capabilities, hospitals will adopt a more systematic and effective approach to PI, which will likely result in superior performance. [ABSTRACT FROM AUTHOR]

Building footprint information is crucial for comprehending global urban development processes. Deep learning algorithms have shown significant potential in building extraction from high spatial resolution imagery. However, the requirement for large-scale annotated data has been a limitation for applying deep learning methods to city-level or national-level building mapping. The dynamic change and distinct landscape variation of cities in different geographic locations further emphasizes the need for automatic building footprint extraction. In this paper, we propose a Self-supervised Knowledge Transfer (SKTrans) framework for extracting building footprints from unlabeled remote sensing images over large areas. To address building tone differences cross regions and time periods, a tone shift mechanism is introduced to reduce the contrast, brightness, and saturation differences between buildings. Furthermore, considering building style diversity across different cities, a knowledge integration module is proposed to develop a comprehensive depiction including tonal differences, multi-scale variance, structural attributes and semantic confusion between roads and buildings. The effectiveness of SKTrans is demonstrated on three public datasets of WHU, Massachusetts, and Inria. To further evaluate the feasibility of large-scale mapping for automatic building footprint, global generalization experiments cross regions and time periods, which is based on imagery from five countries of different continents were implemented. The comprehensive results demonstrate that SKTrans surpasses existing state-of-the-art methods, supporting the time-series city-level building mapping without labeled dataset. Additionally, the architectural landscape pattern changes can be quantitatively analyzed based on the generalization results to facilitate sustainable urban development. Overall, SKTrans provides a new insight to meet the needs of high-precision label-free building mapping and update from large-scale multi-style remote sensing images. [ABSTRACT FROM AUTHOR]

Background: Use of the National Healthcare Safety Network (NHSN) has been essential to the success of the Massachusetts Hemovigilance Program and has allowed for the timely identification of signals and trends over a defined population that correlate with national and international hemovigilance (HV) data. Here, we outline how the NHSN system is used for monitoring HV data in Massachusetts and encourage adoption of NHSN for nationwide HV surveillance. Study Design and Methods: A collaboration that grew over time between local HV stakeholders and the Massachusetts Department of Public Health (MDPH) resulted in the change from a paper‐based method of reporting adverse reactions and monthly transfusion activity for compliance with state requirements to replacement with statewide adoption of reporting via NHSN. Results: Over 1.5 million blood products were transfused in Massachusetts between 2017 and 2021, with 3000 adverse reactions among 10 defined types reported. Using NHSN, MDPH has been able to produce numerous reports, publications, and presentations that have made previously non‐obtainable HV and blood utilization data available. Discussion: Although limitations to these self‐reported data exist, such as lack of external validation, successful statewide implementation of NHSN for hospital blood bank reporting is possible and has benefits beyond those for regulatory oversight. It results in standardized, actionable data at both the hospital and state level, enabling inter‐facility comparisons, benchmarking, and opportunities for practice improvement. [ABSTRACT FROM AUTHOR]

The authors reflect on the past achievements, disappointments, failures, and successes of the journal after assuming the position of Editors-in-Chief in 2023. They talk about the intense process of learning and analysis they have gone through over the past 12 months, their commitment to strengthening the journal's reputation and expanding its reach, and article types they are planning to include to broaden the scope, and impact of the journal.

While scholars have found that undergraduate involvement in research is beneficial, the lack of such experiences in the social sciences and humanities is glaring. This paper analyzes how an emphasis on community through cohort models impacts undergraduate student experience in research, taking from the Emerging Scholars Program, an interdisciplinary research program where cohorts of undergraduates are matched with faculty and attend meetings, workshops, and presentations together. We find that the cohort model created a robust community that fosters positive relationships that develop professional skills and emotional support, allowing students to collaborate and aim more toward their professional goals. The results from this study offer valuable insights into how universities can ensure that students have meaningful research experience beyond the classroom.

Stream-observing cameras have recently been deployed in stream systems to monitor water depth dynamics. However, most existing image-based water depth monitoring methods require additional gauging equipment, extensive manual annotations, or complex manual calibration. In this paper, we propose the hierarchical model, a novel multi-modal and multi-scale deep learning framework for monitoring water depth in headwater streams with only a field camera capable of night vision and no additional equipment. In particular, the hierarchical model integrates long-term dynamic patterns extracted from large-scale meteorological data with short-term dynamic patterns extracted from small-scale stream image data to jointly monitor water depth at a fine-level temporal resolution. In order to overcome the issue of limited availability of images, we introduce a transfer learning strategy and incorporate more accurate long-term patterns that enable the hierarchical model to perform competitively even with a small number of images. We evaluate our method on a real-world headwater stream monitoring dataset from the West Brook study area in western Massachusetts, United States. Our extensive experiments demonstrate that the hierarchical model outperforms several state-of-the-art methods for water depth monitoring, and that more accurate long-term patterns can better guide the monitoring of short-term patterns with excellent flexibility and less computational cost. The mean absolute error of our hierarchical model achieves a remarkable level of 4. 9 c m at the study site with 0. 89 m average water depths, and only 12. 5 c m at more drastically varied site with 3. 95 m average depths. • A multi-modal and multi-scale deep learning framework for water depth monitoring. • Integrating long and short-term dynamic patterns from two distinct modal sources. • Transfer learning strategy makes it competitive even with limited available images. • The component replaceability provides excellent flexibility to fit various streams. [ABSTRACT FROM AUTHOR]

Objectives. To describe how an innovative, community-engaged survey illuminated previously unmeasured pandemic inequities and informed health equity investments. Methods. The methodological approach of Massachusetts' COVID-19 Community Impact Survey, a cross-sectional online survey, was driven by key health equity principles: prioritizing community engagement, gathering granular and intersectional data, capturing root causes, elevating community voices, expediting analysis for timeliness, and creating data-to-action pathways. Data collection was deployed statewide in 11 languages from 2020 to 2021. Results. The embedded equity principles resulted in a rich data set and enabled analyses of populations previously undescribed. The final sample included 33 800 respondents including unprecedented numbers of populations underrepresented in traditional data sources. Analyses indicated that pandemic impacts related to basic needs, discrimination, health care access, workplace protections, employment, and mental health disproportionately affected these priority populations, which included Asian American/Pacific Islanders and parents. Conclusions. Equity-centered data approaches allow for analyses of populations previously invisible in surveillance data, enable more equitable public health action, and are both possible and necessary to deploy in state health departments. (Am J Public Health. 2024;114(S7):S599–S609. https://doi.org/10.2105/AJPH.2024.307800) [ABSTRACT FROM AUTHOR]

Background and purpose: Conventional measures of withdrawal in newborns with prenatal opioid exposure (POE) rely on nursing assessments, including the subjective judgment of infant irritability. This study investigated limb movement actigraphy as a tool for providing an objective, quantifiable measure of underlying distress. Methods: Correlational analyses compared continuous physiological-detected movement actigraphy and clinical intervallic-scored symptomology (modified Finnegan system) obtained from a control cohort of 37 term neonates with POE studied in their crib in the newborn unit (1–8 days). Results: Infants spent 15% crib time in high movement activity (>100 movements/minute; index irritability) and 38% crib time in low activity (0–5 movements/minute; index calm). There was a significant positive association between actigraphy and Finnegan composite score (r =.28, p =.001) and between actigraphy and subcomponent scores (i.e., central nervous system, gastrointestinal, and metabolic-vasomotor-respiratory). Conclusion: Movement activity via actigraphy captures underlying distress and calm not measured by conventional assessments. Such objective, quantifiable measures can serve to promote equitable assessment and treatment of hospitalized newborns with POE. [ABSTRACT FROM AUTHOR]

Education is seen as a resource at a global level but is currently considered to be in crisis in many parts of the world. This constitutes a significant drawback in terms of humanity's prosperity and well-being since education is the key not only to an educated workforce but also to humane, collaborative, and caring societies. Even within this dim landscape, there are certain educational systems that defy the odds and perform significantly higher than their otherwise comparable systems. This paper proposes using an unusual lens for educational policy comparative studies, that of positive deviance, to aid us in progressing towards a more stable educational state of affairs. Using a positive deviance methodology, which focuses on learning what is working well in systems that defy and overcome substantial challenges, this study investigates the patterns, attitudes, and actions of three selected cases: Massachusetts as a positive deviant in the US, Estonia as a positive deviant in Europe, and Castile-Leon as a positive deviant in Spain. The purpose is, by analysing educational policies, laws, and other related documents, to find commonalities that explain why these systems outperform others. The results of the comparative analysis pinpoint areas and strategies informative to those leading struggling educational systems, such as a strong commitment to equity and justice, placing teachers at the centre of reforms, using assessment as a tool for process monitoring and summative inquiry, and making preschool education accessible to all.

Background: Bridge-enhanced anterior cruciate ligament restoration (BEAR) combines suture repair of the anterior cruciate ligament (ACL) with an extracellular matrix implant plus autologous blood to facilitate native ACL healing. Purpose/Hypothesis: The purpose of this study was to compare the 6-year follow-up outcomes of patients who underwent the BEAR procedure with those of a nonrandomized concurrent control group receiving autograft ACL reconstruction (ACLR) in the first-in-human safety study of the BEAR implant (BEAR I trial). Based on the 2-year results, it was hypothesized that isometric hamstring strength after the BEAR procedure would be greater than that after ACLR and that there would be no other differences in outcomes at 6 years. Study Design: Cohort study, Level of evidence, 2. Methods: Ten patients underwent BEAR and 10 received ACLR with a 4-stranded hamstring autograft. Outcomes assessed included the record of subsequent surgeries, the International Knee Documentation Committee (IKDC) Subjective Knee Score, IKDC physical examination grade, the Knee injury and Osteoarthritis Outcome Score, instrumented knee laxity, functional outcomes (ie, muscle strength assessments and hop testing), and qualitative magnetic resonance imaging assessment. Comparisons between treatments were based on computations of the mean differences and the associated 95% CIs. Results: One patient in the BEAR group and 3 patients in the ACLR group were lost to follow-up. In the period between 2 and 6 years, 1 patient in each group underwent revision surgery. There were no differences between groups at the 6-year follow-up in any of the outcome measures except for isometric hamstring strength, which was approximately equal to that of the contralateral knee in the BEAR group and <44% of that in the contralateral knee in the ACLR group (P <.01). Conclusion: This preliminary study suggests that the outcomes of BEAR and ACLR with a hamstring tendon graft may be similar at the 6-year follow-up and warrants investigation of the BEAR procedure in a larger cohort of patients. [ABSTRACT FROM AUTHOR]

Background Non-ischemic cerebral enhancing (NICE) lesions following aneurysm endovascular therapy are exceptionally rare, with unknown longitudinal evolution. Objective To evaluate the radiological behavior of individual NICE lesions over time. Methods Patients included in a retrospective national multicentric inception cohort were analyzed. NICE lesions were defined, using MRI, as delayed onset punctate, nodular, or annular foci enhancements with peri-lesion edema, distributed in the vascular territory of the aneurysm treatment, with no other confounding disease. Lesion burden and the longitudinal behavior of individual lesions were assessed. Results Twenty-two patients were included, with a median initial lesion burden of 36 (IQR 17--54) on the first MRI scan. Of the 22 patients with at least one follow-up MRI scan, 16 (73%) had new lesions occurring mainly within the first 200 weeks after the date of the procedure. The median number of new lesions per MRI was 6 (IQR 2--16). Among the same 22 patients, 7 (32%) had recurrent lesions. The median persistent enhancement of a NICE lesion was 13 weeks (IQR 6--30). No factor was predictive of early regression of enhancement activity with lesion regression kinetics mainly being patient-dependent. Conclusions The behavior of individual NICE lesions was found to be highly variable with an overall patientdependent regression velocity. [ABSTRACT FROM AUTHOR]

Background and aims: Nongenerative speech is the rote repetition of words or phrases heard from others or oneself. The most common manifestations of nongenerative speech are immediate and delayed echolalia, which are a well-attested clinical feature and a salient aspect of atypical language use in autism. However, there are no current estimates of the frequency of nongenerative speech, and the individual characteristics associated with nongenerative speech use in individuals across the autistic spectrum are poorly understood. In this study, we aim to measure and characterize spontaneous and nongenerative speech use in minimally verbal and verbally fluent autistic children and adolescents. Methods: Participants were 50 minimally verbal and 50 verbally fluent autistic individuals aged 6 to 21 years. Spontaneous and nongenerative speech samples were derived from SALT transcripts of ADOS-2 assessments. Participants' intelligible speech utterances were categorized as spontaneous or nongenerative. Spontaneous versus nongenerative utterances were compared between language subgroups on frequency of use and linguistic structure. Associations between nongenerative speech use and a series of individual characteristics (ADOS-2 subscale scores, nonverbal IQ, receptive vocabulary, and chronological age) were investigated over the whole sample and for each language subgroup independently. Results: Almost all participants produced some nongenerative speech. Minimally verbal individuals produced significantly more nongenerative than spontaneous utterances, and more nongenerative utterances compared to verbally fluent individuals. Verbally fluent individuals produced limited rates of nongenerative utterances, in comparison to their much higher rates of spontaneous utterances. Across the sample, nongenerative utterance rates were associated with nonverbal IQ and receptive vocabulary, but not separately for the two language subgroups. In verbally fluent individuals, only age was significantly inversely associated with nongenerative speech use such that older individuals produced fewer nongenerative utterances. In minimally verbal individuals, there were no associations between any of the individual characteristics and nongenerative speech use. In terms of linguistic structure, the lexical diversity of nongenerative and spontaneous utterances of both language subgroups was comparable. Morphosyntactic complexity was higher for spontaneous compared to nongenerative utterances in verbally fluent individuals, while no differences emerged between the two utterance types in minimally verbal individuals. Conclusions: Nongenerative speech presents differently in minimally verbal and verbally fluent autistic individuals. Although present in verbally fluent individuals, nongenerative speech appears to be a major feature of spoken language in minimally verbal children and adolescents. Implications: Our results advocate for more research on the expressive language profiles of autistic children and adolescents who remain minimally verbal and for further investigations of nongenerative speech, which is usually excluded from language samples. Given its prevalence in the spoken language of minimally verbal individuals, nongenerative speech could be used as a way to engage in and maintain communication with this subgroup of autistic individuals. [ABSTRACT FROM AUTHOR]

Background: Asians and Asian Americans have the lowest rate of mental health service utilization (25%) in the US compared to other racial/ethnic groups (39 − 52%), despite high rates of depression, anxiety, and suicidal ideation. The lack of culturally-responsive mental health trainings hinders access to mental health services for these populations. We assessed the mental health priorities of Asian communities in Greater Boston and evaluated cultural responsiveness of the Mental Health First Aid (MHFA), a first-responder training teaching participants skills to recognize signs of mental health and substance use challenges, and how to appropriately respond. Methods: This is community-based participatory research with the Boston Chinatown Neighborhood Center (BCNC), Asian Women For Health (AWFH), and the Addressing Disparities in Asian Populations through Translational Research (ADAPT) Coalition. We conducted focus groups with community-based organization staff and community members to assess mental health priorities of Asian populations in Boston, MA. We then evaluated the utility and cultural-responsiveness of the English-language MHFA for Asian populations through pre- and post-training questionnaires and focus groups with community participants. Paired t-tests were used to evaluate questionnaire responses. Thematic analysis was used to analyze interviews. Results: In total, ten staff and eight community members participated in focus groups, and 24 community members completed the MHFA and pre- and post-training questionnaires. Common mental health challenges in the Asian communities reported by participants were loneliness, high stigma around mental illnesses, academic pressure, and acculturation stress. Compared to pre-training, MHFA participants demonstrated lower personal mental health stigma (p < 0.001) and higher mental health literacy (p = 0.04) post-training. Participants also noted the lack of data statistics and case studies relevant to Asian populations in the training, and desired the training be offered in languages spoken by Asian ethnic subgroups (e.g., Chinese, Vietnamese). Conclusion: Cultural-responsiveness of the MHFA for Asian populations could be improved with the inclusion of data and case studies that capture common mental health challenges in the Asian communities and with translation of the MHFA to non-English languages predominant in Asian communities. Increasing the cultural relevance and language accessibility of the MHFA could facilitate wider adoption of these trainings across communities and help to reduce mental health stigma and gaps in literacy and service utilization. [ABSTRACT FROM AUTHOR]

Background: Fatal opioid-involved overdose rates increased precipitously from 5.0 per 100,000 population to 33.5 in Massachusetts between 1999 and 2022. Methods: We used spatial rate smoothing techniques to identify persistent opioid overdose-involved fatality clusters at the ZIP Code Tabulation Area (ZCTA) level. Rate smoothing techniques were employed to identify locations of high fatal opioid overdose rates where population counts were low. In Massachusetts, this included areas with both sparse data and low population density. We used Local Indicators of Spatial Association (LISA) cluster analyses with the raw incidence rates, and the Empirical Bayes smoothed rates to identify clusters from 2011 to 2021. We also estimated Empirical Bayes LISA cluster estimates to identify clusters during the same period. We constructed measures of the socio-built environment and potentially inappropriate prescribing using principal components analysis. The resulting measures were used as covariates in Conditional Autoregressive Bayesian models that acknowledge spatial autocorrelation to predict both, if a ZCTA was part of an opioid-involved cluster for fatal overdose rates, as well as the number of times that it was part of a cluster of high incidence rates. Results: LISA clusters for smoothed data were able to identify whether a ZCTA was part of a opioid involved fatality incidence cluster earlier in the study period, when compared to LISA clusters based on raw rates. PCA helped in identifying unique socio-environmental factors, such as minoritized populations and poverty, potentially inappropriate prescribing, access to amenities, and rurality by combining socioeconomic, built environment and prescription variables that were highly correlated with each other. In all models except for those that used raw rates to estimate whether a ZCTA was part of a high fatality cluster, opioid overdose fatality clusters in Massachusetts had high percentages of Black and Hispanic residents, and households experiencing poverty. The models that were fitted on Empirical Bayes LISA identified this phenomenon earlier in the study period than the raw rate LISA. However, all the models identified minoritized populations and poverty as significant factors in predicting the persistence of a ZCTA being part of a high opioid overdose cluster during this time period. Conclusion: Conducting spatially robust analyses may help inform policies to identify community-level risks for opioid-involved overdose deaths sooner than depending on raw incidence rates alone. The results can help inform policy makers and planners about locations of persistent risk. [ABSTRACT FROM AUTHOR]

BACKGROUND: Parametric g-computation is an attractive analytic framework to study the health effects of air pollution. Yet, the ability to explore biologically relevant exposure windows within this framework is underdeveloped. OBJECTIVES: We outline a novel framework for how to incorporate complex lag-responses using distributed lag models (DLMs) into parametric g-computation analyses for survival data. We call this approach “g-survival-DLM” and illustrate its use examining the association between PM2.5 during pregnancy and the risk of preterm birth (PTB). METHODS: We applied the g-survival-DLM approach to estimate the hypothetical static intervention of reducing average PM2.5 in each gestational week by 20% on the risk of PTB among 9,403 deliveries from Beth Israel Deaconess Medical Center, Boston, Massachusetts, 2011–2016. Daily PM2.5 was taken from a 1-km grid model and assigned to address at birth. Models were adjusted for sociodemographics, time trends, nitrogen dioxide, and temperature. To facilitate implementation, we provide a detailed description of the procedure and accompanying R syntax. RESULTS: There were 762 (8.1%) PTBs in this cohort. The gestational week-specific median PM2.5concentration was relatively stable across pregnancy at ~7μg/m³. We found that our hypothetical intervention strategy changed the cumulative risk of PTB at week 36 (i.e., the end of the preterm period) by -0.009 (95% confidence interval: -0.034, 0.007) in comparison with the scenario had we not intervened, which translates to about 86 fewer PTBs in this cohort. We also observed that the critical exposure window appeared to be weeks 5–20. DISCUSSION: We demonstrate that our g-survival-DLM approach produces easier-to-interpret, policy-relevant estimates (due to the g-computation); prevents immortal time bias (due to treating PTB as a time-to-event outcome); and allows for the exploration of critical exposure windows (due to the DLMs). In our illustrative example, we found that reducing fine particulate matter [particulate matter (PM) with aerodynamic diameter ≤2.5 μm (PM2.5)] during gestational weeks 5-20 could potentially lower the risk of PTB. [ABSTRACT FROM AUTHOR]

Coteaching, in which a general education teacher and special education teacher collaboratively provide instruction to students with and without disabilities in the same classroom, is widely endorsed as a strategy to give instructional support to students with disabilities within inclusive environments. We leverage longitudinal administrative data in Massachusetts to provide the first causal estimate for the effect of coteaching across a large public school system. We find evidence that coteaching leads to statistically significant test score improvements for both students with and without disabilities. However, the benefits for students with disabilities are much smaller than reported in prior studies. [ABSTRACT FROM AUTHOR]

The World Health Organization describes menstruation management as "using a clean menstrual management material to absorb or collect menstrual blood that can be changed in privacy as often as necessary." The menstrual equity policy landscape in the United States, specifically pertaining to the needs of students in public schools, has gained considerable traction within the past decade. Since 2017, 16 states in the United States have signed legislation mandating the provision of menstrual products to public school students. This study explored the experiences of students from a public high school in Massachusetts where there is currently no state policy. The research is driven by the following questions: (1) How do students at the high school manage menstruation while at school? (2) How do students describe the impact of menstruation upon their physical and mental health and their participation in school-related activities? To address these questions, this study utilizes qualitative data, including student focus groups and interviews. Both focus groups and interviews were audio recorded with permission and analyzed using AtlasTI software. The researcher noted and incorporated themes and questions in an iterative and emergent manner. In total, three focus groups were conducted; one consisted of a group of five seniors and the following two included 10th-grade students. The researcher also conducted a total of 11 student interviews. This study ultimately finds that a lack of menstrual management support for high school students may be creating and perpetuating a condition in which students experience social, even academic, exclusion. [ABSTRACT FROM AUTHOR]

The deaf population of Martha's Vineyard has fascinated scholars for more than a century since Alexander Graham Bell's research on the frequent occurrence of deafness there and since Groce's book on the island's signing community (Groce, N. E. (1985). Everyone here spoke sign language: Hereditary deafness on Martha's Vineyard. Cambridge, MA: Harvard University Press.). In Groce's work, and in that of subsequent scholars, the Vineyard signing community has often been portrayed as remote and outlying, having developed independently of mainland signing communities for roughly 133 years until 1825. We re-examine that interpretation in light of historical, demographic, and genealogical evidence covering the period 1692–2008. We argue that the Vineyard signing community began in Chilmark in 1785, 93 years later than previously thought, and that it had had a brief period of independent development, roughly 40 years, before becoming well connected, through deaf education, to the nascent New England signing community. We consider the implications of the Vineyard community's history for our understanding of how village signing communities develop. [ABSTRACT FROM AUTHOR]

Climate change brings intense hurricanes and storm surges to the US Atlantic coast. These disruptive meteorological events, combined with sea level rise (SLR), inundate coastal areas and adversely impact infrastructure and environmental assets. Thus, storm surge projection and associated risk quantification are needed in coastal adaptation planning and emergency management. However, the projections can have large uncertainties depending on the planning time horizon. Excessive uncertainties arise from inadequately quantified ocean-climatic processes that control hurricane formation, storm track, and SLR in time of climate change. For this challenge, we propose an objective-based analytical-statistical approach using the National Oceanic and Atmospheric Administration's (NOAA)'s Sea, Lake, and Overland Surge from Hurricanes (SLOSH) model in scenario analysis of the storm surge impacts. In this approach, synthetic hurricanes (wind profile and track direction) are simulated to yield the likely range of the maximum envelope of water (MEOW), the maximum of the maximum (MOM), local wind speed, and directions. The surge height and time progression at a location are analyzed using a validated SLOSH model for a given adaptation or planning objective with a set of uncertainty tolerance. We further illustrate the approach in three case studies at Mattapoisett (MA), Bridgeport (CT), and Lower Chesapeake Bay along the US Atlantic coast. Simulated MOMs as the worst-case surge scenarios defined the long-term climate risk to the shoreside wastewater plants in Bridgeport and environmental assets in the Lower Chesapeake Bay. The wind-surge probability envelopes in simulated MEOWs provide location-specific estimates of the storm surge probability for local adaptation analysis at four locations in Lower Chesapeake Bay and at Mattapoisett of the southeastern Massachusetts coast. Using the constraints of local bathymetry and topography, the wind-surge probability curves and time progression also provide quantitative probability estimates for emergency response planning, as illustrated in the Mattapoisett case study. [ABSTRACT FROM AUTHOR]

In a 2023 sequential explanatory mixed-methods study in four US states, we identified barriers and facilitators experienced by Vaccines for Children (VFC) program providers in reporting vaccination data to state immunization information systems (IISs). We found the following: VFC providers value accurate, robust, and widely used IISs. IIS reporting is easier with but does not require an electronic health record. Negative interactions with IISs and VFC officials and limited practice capacity are barriers to reporting. The COVID-19 pandemic highlighted the need for a nationwide vaccination database. (Am J Public Health. 2024;114(6):569–574. https://doi.org/10.2105/AJPH.2024.307638) [ABSTRACT FROM AUTHOR]

Objective To pilot measurement of hair cortisol concentration (HCC) in pregnant women with opioid use disorder and their infants over time and study the potential utility of hair cortisol as a biomarker of chronic stress in this population. Study Design In this pilot prospective cohort study of mother–infant dyads with and without prenatal opioid exposure, we obtained mother–infant HCCs at delivery and again within 1 to 3 months' postpartum. HCCs were compared between the opioid and control groups and between the two time points. Results There were no significant differences between opioid and control group maternal or infant HCCs at either time point. However, within the opioid-exposed group, there was a significant increase in infant HCCs across the two time points. Conclusion This pilot study describes our experience with the measurement of HCCs in opioid-exposed mother–infant dyads. Key Points Maternal stress impacts fetal and child health. Many stressors in pregnant women with opioid use disorder. Hair cortisol may be a useful stress biomarker. [ABSTRACT FROM AUTHOR]

This article delves into content-based instruction (CBI) as a didactic methodology employed for teaching foreign languages within a North American state school situated in Massachusetts. It provides an account of the challenges confronted by a specific cohort of teachers during the implementation of this methodology. While a substantial body of literature advocates for CBI as an effective teaching method, scant attention has been directed toward analyzing the practical working conditions of teachers and the dynamics within classrooms where this method is applied. We ran a qualitative analysis on the evaluations and suggestions of specific teachers. To achieve this goal, interviews were conducted, and lesson plans and didactic materials were examined. The analysis resulted in a study that pointed out the handicaps and risks associated with CBI, emphasizing that the implementation process in the school demanded considerable effort from teachers without commensurate benefits for students. This study sheds light on aspects such as curriculum innovation, education leadership, and the clarification of the concept of what it means to be a teacher. It holds relevance for any stakeholder concerned with related issues stemming from curriculum program changes, alongside the professional well-being and motivation of the teaching participants in this setting. [ABSTRACT FROM AUTHOR]

We evaluate the predictive validity of the Massachusetts Candidate Assessment of Performance (CAP), a practice-based assessment of teaching skills that is now a requirement for teacher preparation program completion in Massachusetts. We find that candidates' performance on the CAP significantly predicts their in-service summative performance evaluations in their first 2 years in the teaching workforce, but it is not predictive of their value added to student test scores in these years. These findings suggest that the CAP captures aspects of candidates' skills and competencies that are better reflected in their future performance evaluations than by their impacts on student performance. [ABSTRACT FROM AUTHOR]

Purpose: This project addressed the uses of a loudspeaker array for audiometric measurements. It sought to evaluate a prototype compact array in terms of the reliability of test results across sound booths. Method: A seven-loudspeaker array was developed to deliver sounds from -60° to +60° on an arc with a radius of 0.5 m. The system was equipped with a head position sensing system to maintain the listener's head near the optimal test position. Three array systems were distributed to each of the two test sites for within-subject assessments of booth equivalence on tests of sound localization, speech reception in noise, and threshold detection. A total of 36 subjects participated, 18 at each test site. Results: Results showed excellent interbooth consistency on tests of sound localization using speech and noise signals, including conditions in which one or both ears were covered with a muff. Booth consistency was also excellent on sound field threshold measurements for detecting quasi-diffuse noise bands. Nonequivalence was observed in some cases of speech-in-noise tests, particularly with a small one-person booth. Acoustic analyses of in situ loudspeaker responses indicated that some of the nonequivalent comparisons on speech-innoise tests could be traced to the effects of reflections. Conclusions: Overall, the results demonstrate the utility and reliability of a compact array for the assessment of localization ability, speech reception in noise, and sound field thresholds. However, the results indicate that researchers and clinicians should be aware of the reflection effects that can influence the results of sound field tests in which signal and noise levels from separate loudspeakers are critical. [ABSTRACT FROM AUTHOR]

To assess whether a measure of leadership support for worker safety, health, and well-being predicts staff turnover in nursing homes after controlling for other factors. This paper uses administrative payroll data to measure facility-level turnover and uses a survey measure of nursing home leadership commitment to workers. In addition, we use data from Medicare to measure various nursing home characteristics. Nursing homes with at least 30 beds serving adults in California, Ohio, and Massachusetts were invited to participate in the survey. The analysis sample included 495 nursing homes. We used a multivariable ordinary least squares model with turnover rate as the dependent variable. We used an indicator for nursing homes who scored above the median on the measure of leadership that supports worker safety, health, and well-being. Control variables include bed count (deciles), ownership (corporate/noncorporate × for-profit/not-for-profit), percent of residents on Medicaid, state, being in a nonmetropolitan county, and total nurse staffing per patient day in the 2 quarters before the survey. The unadjusted turnover rate was lower for those nursing homes that scored higher on leadership commitment to worker safety, health, and well-being. After controlling for additional variables, greater leadership commitment was still associated with lower turnover but with some attenuation. We find that nursing homes with leadership that communicated and demonstrated commitment to worker safety, health, and well-being had relatively fewer nurses leave during the study period, with turnover rates approximately 10% lower than homes without. These findings suggest that leadership may be a valuable tool for reducing staff turnover. [ABSTRACT FROM AUTHOR]

Automated radio telemetry systems have become a popular and invaluable tool in tracking the activity and movement of wild animals. However, many environmental conditions can hinder accuracy when tracking with this technology. For instance, study sites may contain multiple habitat types, each habitat uniquely affecting the signal strength received from tagged species. To investigate the influence of a structurally diverse study site on an automated radio telemetry system, we conducted this project at a restored and managed pine barren habitat that consisted of a mix of mature pitch pine, treated pitch pine, scrub oak, and hardwood forests. This site, Montague Plains Wildlife Management Area, Montague, Massachusetts, is also a known breeding ground for Eastern whip-poor-will (Antrostomus vociferus). To measure the relationship of radio signal strength with distance across each habitat, we used radio telemetry equipment manufactured by Cellular Tracking Technologies. We produced negative exponential decay functions measuring radio signal strength over distance and tested for differences among habitat types on radio signal strength (RSS). We found that decay function parameters significantly differed by habitat type, prompting us to investigate if accounting for these differences improved location estimate accuracy. To test this, we estimated known locations using trilateration methods with and without habitat calibration. Comparing these tests indicates that habitat-specific adjustments significantly improved location accuracy. Lastly, we visualized estimated RSS-based locations of 1 week of whip-poor-will data and compared them to GPS data generated from the same individual. Previous studies have accounted for types of environmental interference (like elevation) in the field but have avoided incorporating habitat-specific factors by working with node networks covering a relatively small area, but in this study, we examined the potential to scale up for larger areas and in more complex habitats. [ABSTRACT FROM AUTHOR]

Background: Automatic extraction of roads from remote sensing images can facilitate many practical applications. However, thus far, thousands of kilometers or more of roads worldwide have not been recorded, especially low-grade roads in rural areas. Moreover, rural roads have different shapes and are influenced by complex environments and other interference factors, which has led to a scarcity of dedicated low level category road datasets. Methods: To address these issues, based on convolutional neural networks (CNNs) and tranformers, this article proposes the Dual Path Information Fusion Network (DPIF-Net). In addition, given the severe lack of low-grade road datasets, we constructed the GaoFen-2 (GF-2) rural road dataset to address this challenge, which spans three regions in China and covers an area of over 2,300 km, almost entirely composed of low-grade roads. To comprehensively test the low-grade road extraction performance and generalization ability of the model, comparative experiments are carried out on the DeepGlobe, and Massachusetts regular road datasets. Results: The results show that DPIF-Net achieves the highest IoU and F1 score on three datasets compared with methods such as U-Net, SegNet, DeepLabv3+, and D-LinkNet, with notable performance on the GF-2 dataset, reaching 0.6104 and 0.7608, respectively. Furthermore, multiple validation experiments demonstrate that DPIF-Net effectively preserves improved connectivity in low-grade road extraction with a modest parameter count of 63.9 MB. The constructed low-grade road dataset and proposed methods will facilitate further research on rural roads, which holds promise for assisting governmental authorities in making informed decisions and strategies to enhance rural road infrastructure. [ABSTRACT FROM AUTHOR]