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To stratify the risk of bacteremia at the time of emergency department (ED) admission in patients with hematologic malignancies. To this end, we compared the performance of unsupervised and supervised machine learning algorithms with the classical multivariable logistic regression model. We conducted a multicenter, international, retrospective cohort study including consecutive ED visits of adult patients with hematologic malignancies for whom blood cultures were obtained. The primary outcome was the prediction of bacteremia. The Bacteremia Objective Score System (BOSS)-1 used multivariable logistic regression, BOSS-2 applied K-means clustering, and BOSS-3 employed a support vector machine algorithm. Discriminative performance was assessed using sensitivity and specificity. External validation of the results was performed. The derivation cohort included 679 ED visits. Blood cultures tested positive in 88 cases (13%). BOSS-1 identified low-risk patients (3.1% bacteremia) with high sensitivity (94%; 95% CI, 87-97) but limited specificity (30%; 95% CI, 26-34). BOSS-2 better identified high-risk patients (29% bacteremia; sensitivity 93% [95% CI, 84-97], specificity 30% [95% CI, 26-34]). BOSS-3 classified all patients into low-risk (66.8%) or high-risk (33.2%) categories, without an intermediate group, unlike the other models, showing the best balance between sensitivity (61%; 95% CI, 59-64) and specificity (71%; 95% CI, 70-73) and yielding the most reproducible results in the external validation cohort. The BOSS algorithms enabled effective bacteremia risk stratification in the ED, with supervised machine learning models demonstrating the greatest potential clinical utility. Comparar el rendimiento de algoritmos de aprendizaje automático no supervisado y supervisado con el modelo clásico de regresión logística para estratificar el riesgo de padecer bacteriemia en el momento del ingreso en el servicio de urgencias (SU) en los pacientes con hemopatías malignas. Se realizó un estudio de cohorte retrospectivo multicéntrico internacional que incluyó consultas consecutivas de pacientes adultos con neoplasias hematológicas atendidos en urgencias y con solicitud de hemocultivos; el resultado primario fue la predicción de bacteriemia. El Bacteremia Objective Score System (BOSS)-1 utilizó regresión logística multivariable, BOSS-2 empleó agrupación de medias k y BOSS-3 aplicó un algoritmo de màquina de vectores de soporte. El rendimiento discriminativo se evaluó mediante sensibilidad y especificidad. Se realizó una validación externa de los resultados. La cohorte de derivación incluyó 679 visitas al SU. Los hemocultivos fueron positivos en 88 (13%) casos. BOSS-1 identificó pacientes de bajo riesgo (3,1% de bacteriemia) con alta sensibilidad (94%; IC 95%: 87-97) pero especificidad limitada (30%; IC 95%: 26-34). BOSS-2 identificó mejor los pacientes de alto riesgo [29% de bacteriemia; sensibilidad 93% (IC 95%: 84-97), especificidad 30% (IC 95%: 26-34)]. BOSS-3 permitió clasificar a todos los pacientes en categorías de bajo (66,8%) o alto riesgo (33,2%), sin grupo intermedio, a diferencia de los otros dos modelos, y mostró el mejor equilibrio entre sensibilidad (61%; IC 95%: 59-64) y especificidad (71%; IC 95%: 70-73) y obtuvo los resultados más reproducibles en la cohorte de validación externa. Los algoritmos BOSS permitieron una estratificación eficaz del riesgo de bacteriemia en los SU, destacando como mejor potencial clínico los modelos de aprendizaje automático supervisado.

Rapid and accurate detection of total nitrogen (TN) and total phosphorus (TP) in dairy cow slurry is essential for guiding the scientific application of slurry to fields, preventing soil nutrient imbalance and water pollution, and promoting sustainable agricultural development and resource utilization of slurry. However, the complex composition and high moisture content of slurry, along with the high dimensionality and nonlinear characteristics of its spectral data, pose challenges to accurate and efficient prediction. In this study, near-infrared spectroscopy was combined with partial least squares regression (PLSR), extreme gradient boosting (XGBoost), and deep neural network (DNN) to establish TN and TP prediction models for dairy cow slurry using different spectral preprocessing methods (MSC, SG, and MSC + SG). The optimal preprocessing methods for TN and TP were determined according to model performance. Furthermore, uninformative variable elimination (UVE) and bootstrap soft shrinkage (BOSS) were employed to extract characteristic wavelength variables and construct models, and the performances of different models were compared to identify the optimal one. Results showed that the DNN model achieved the best performance in TN and TP prediction, with Rp2 values of 0.92 and 0.75, and RPD values of 3.49 and 2.01, respectively, significantly outperforming PLSR and XGBoost. When using UVE and BOSS, both methods yielded comparable predictive performance; however, BOSS achieved a higher degree of data compression and modeling efficiency, showing greater potential for practical applications. Overall, the BOSS-DNN approach demonstrated remarkable advantages in predicting complex components of dairy slurry and provides a new technical pathway and theoretical foundation for rapid on-site nutrient detection of livestock and poultry slurry.

The fly eye is composed of hundreds of ommatidia, each containing a stereotyped pattern of photoreceptors and assorted support cells. Over the past 50 years, molecular genetic studies have illuminated how the fates of many of these cells are specified. As a result, the field has become a classic paradigm for cell-fate specification during animal development. The initial focus of the studies was the sevenless (sev) mutant, in which the cell normally destined to become the R7 photoreceptor instead develops into a lens-secreting support cell. This phenotype served as the entry point into the patterning mechanisms underlying ommatidial fate specification. sev was found to encode a receptor tyrosine kinase (RTK) expressed on the plasma membrane of the R7 precursor. Its ligand-Bride of Sevenless (Boss)-was then identified as a plasma membrane protein expressed on a neighboring cell, the presumptive R8 photoreceptor. Here, I present a personal perspective on the studies of Sev, from its discovery to the present day, beginning with the characterization of its loss-of-function phenotype and culminating in the highly detailed description of its biological role and the molecular mechanism by which it transduces the R8-to-R7 Boss signal. These studies have revealed surprisingly complex and novel properties of this seemingly simple signaling mechanism, including (i) the unexpected circuitry by which Sev signaling is integrated with that of the Drosophila EGF receptor and Notch to specify 3 distinct cell types (R7 and R1/6 photoreceptors and the lens-secreting cone cells) and (ii) the likely discovery of a new molecular mechanism of RTK activation in which transendocytosis of Boss from the presumptive R8 into the R7 precursor directs Sev to a select endocytic compartment, where it is activated by a conformation state induced by low pH. Further, I address evolutionary studies of Sev and Boss, which indicate that their roles as ligand and receptor are restricted to flies, and pose the intriguing question of whether R7 photoreceptors outside Diptera are specified by similar signaling logic but different molecular machinery.

Rapid assessment of soil pH and nutrient status in tea plantations is essential for precision fertilisation and ecological management, particularly in high-latitude tea-growing regions where related applications remain insufficiently studied. This study developed a rapid, non-destructive framework integrating hyperspectral imaging and machine learning for the detection, retrieval, and spatial visualisation of soil pH, soil organic matter (SOM), alkali-hydrolysable nitrogen (AN), available phosphorus (AP), and available potassium (AK) in the Taishan tea-producing region. A total of 150 soil samples were collected from three profile depths (0-20, 20-40, and 40-60 cm). Hyperspectral images were acquired over 394-1007 nm, and the 481-908 nm range was retained for modelling. Principal component analysis was used to characterise vertical differentiation, while four spectral pre-processing methods, three feature-band selection algorithms-competitive adaptive reweighted sampling (CARS), bootstrapping soft shrinkage (BOSS), and successive projections algorithm (SPA)-and three regression models-partial least squares regression (PLSR), random forest (RF), and support vector regression (SVR)-were systematically compared. The soils were generally acidic, and SOM, AN, AP, and AK exhibited clear surface enrichment and decreasing trends with increasing depth. Among the feature-selection methods, BOSS showed the best overall performance in reducing spectral redundancy and improving prediction accuracy. The optimal SVR models, combined with parameter-specific pre-processing and BOSS-selected bands, achieved strong predictive performance across all indicators, with prediction-set correlation coefficients (Rp) of 0.94-0.99 and relative percent deviation (RPD) values of 2.963-10.425. Furthermore, pixel-wise reconstruction using threshold masking enabled intuitive two-dimensional visualisation of the spatial distributions of the target soil properties. These results demonstrate that hyperspectral imaging coupled with machine learning provides an effective approach for rapid soil nutrient assessment, spatial visualisation, and digital management in high-latitude tea plantations.

We performed a prospective study to assess the repeatability and reproducibility of the novel Brillouin Optical Scanning System (BOSS®) in measuring the biomechanical properties of the cornea and crystalline lens in vivo, to determine device utility in a clinical setting. Adult study participants underwent standard ophthalmic exams to confirm eligibility. At two separate visits to the clinic, monocular lens scans and corneal scans (one point and seven points, respectively) were performed three times on each of three BOSS devices, operated by different technicians. The measured Brillouin parameters per person included the lens stiffness and the stiffnesses (e.g., "Mean", "Minimum") across the seven corneal points. Results were combined across participants to calculate the coefficients of variation (CVs) for repeatability (within each device) and reproducibility (across devices), along with the repeatability and reproducibility limits (2.8 times the respective standard deviations). Eleven of the 33 participants (ages 24-72 years; 15 females) had keratoconus (5 with prior crosslinking, 3 without crosslinking, and 3 post-keratoplasty); the other 22 participants were visually normal. There were no adverse events. The average lens stiffness was 3.33 ± 0.052 GPa (mean ± SD), with an overall repeatability CV of 1.6%, with values ranging from 3.35 ± 0.037 GPa with a CV of 1.1% to 3.30 ± 0.071 with a CV of 2.1% across the separate devices. The mean corneal stiffness was 2.83 ± 0.031 GPa, with an overall repeatability CV of 1.1%, ranging from 2.85 ± 0.015 with a CV of 0.5% to 2.81 ± 0.036 with a CV of 1.3% across devices. The reproducibility CVs were 2.1% and 1.6% for lenses and corneal Means, respectively. The study results demonstrated consistent stiffness values for both lenses and corneas within and between the three BOSS devices tested. The study shows that this device could be a useful new biomechanics-measuring tool for applications in ophthalmology.

Aptamers are single-stranded DNA or RNA molecules that specifically bind to a wide range of target molecules with high affinity, making them powerful tools for synthetic biology. Traditional aptamer selection via Systematic Evolution of Ligands by Exponential Enrichment (SELEX) is labor-intensive and prone to nonspecific binding. To address these issues, we developed the Bead-based One-Step aptamer Selection (BOSS) method. As proof of concept, we used this method to target Arabidopsis thaliana CONSTITUTIVE PHOTOMORPHOGENIC 1 (AtCOP1), a conserved E3 ubiquitin ligase central to photomorphogenesis. We expressed the N-terminal RING (Really Interesting New Gene) domain of AtCOP1, immobilized it on Ni-NTA beads, and incubated the beads with a random ssDNA library. After washing the beads with washing buffer and replacing the buffer five times, followed by high-throughput sequencing, we identified the high-affinity aptamer Lib1-9, with a Kd of 11.14 nM for AtCOP1-RING. Lib1-9 demonstrated species specificity, showing strong binding to AtCOP1, but not to other COP1 homologs. Truncation analysis revealed that the core variable region (△LR) of Lib1-9 retained near-full binding affinity (Kd = 1.679 nM) to AtCOP1, which is comparable to the values observed for thrombin-binding aptamers. When we delivered Cy5-labeled aptamers into the hypocotyl cells of transgenic YFP-NLS-AtCOP1/cop1-4 A. thaliana plants, both Cy5-Lib1-9 and Cy5-Lib2-11 colocalized with YFP-COP1. The BOSS method is an efficient platform for plant aptamer development, enabling the rapid generation of tools for synthetic biology applications such as COP1 biosensing and optogenetic control.

A carbon fiber-wound Type IV hydrogen storage cylinder structure was designed. The winding layers were optimized based on grid theory, with particular attention to the thickness of the dome winding layer. The burst pressure of the hydrogen storage cylinder was predicted using a progressive damage model based on the Hassin damage theory. Additionally, the effects of the length-to-diameter ratio, ellipsoid ratio, polar ratio, and the metal boss support radius on the stress distribution of the hydrogen storage cylinder were investigated. The results show that the finite element analysis predicts a burst pressure of 162.76 MPa, which exceeds the design value of 157.5 MPa, indicating that the adopted layering method satisfies the design requirements. Increasing the ellipsoid ratio reduces the fiber stress in the cylinder. Under working pressure, the length-to-diameter ratio and polar ratio are negatively correlated with the stress in the winding layers. Furthermore, increasing the radius of the metal boss flange reduces the liner stress of the cylinder under working pressure. However, as the flange radius increases, the stress on its upper surface forms an upward-opening parabola, and the central region does not fully utilize its structural potential. These findings provide valuable insights for the practical engineering application of carbon fiber-wound Type IV hydrogen storage cylinders.

This study evaluates the impact-attenuation of two softshell headgear models, GameBreaker-Pro and GameBreaker-Prototype, in controlled drop tests. These models were selected to assess the effect of thickness, given the 12 mm thickness constraint specified under World Rugby Law 4 Trial regulations for which limited justification exists. Drop tests were conducted using a Hybrid III headform and neck, from heights of 15, 30, 45, and 60 cm onto a flat Modular Elastomer Programmer (MEP) surface. Impacts were delivered across five orientations; forehead, front boss, rear boss, rear, and side, to simulate a range of plausible on-field rugby impact scenarios. Peak linear acceleration (PLA), peak rotational acceleration (PRA), and peak rotational velocity (PRV) were measured across four headgear conditions: GameBreaker-Pro, GameBreaker-Prototype, NPro (World Rugby Law 4 Trial approved), and a no-headgear baseline. All headgear models demonstrated reductions in impact kinematics relative to the no-headgear baseline. GameBreaker-Pro yielded the highest composite reductions (peak accelerations across all orientations), lowering PLA by 32.3% and PRA by 43.4%, followed closely by GameBreaker-Prototype with reductions of 30.4% and 42.2%, respectively. Both GameBreaker models showed consistent attenuation across drop heights and orientations, with particularly strong performance in the side (reductions of 43.5% and 37.1% in PLA, 59.1% and 54.3% in PRA). These findings demonstrate that softshell headgear can reduce head impact severity across diverse conditions. This underscores the need for ongoing refinement of headgear design and regulatory standards to better safeguard athletes from repeated intensive head impacts.

This study investigated name agreement for a set of 237 photographs from the Bank of Standardized Stimuli (BOSS; Brodeur et al., 2010, 2014), using modal name agreement as a percentage-based measure of naming consensus and the H-value as an information-based measure of response dispersion. Data were collected through an online questionnaire completed by 105 Quebec French speakers and 106 France French speakers, who reported no history of neurological or psychiatric disorders. We hypothesized that cultural context would yield differences in H-values and modal names between the two groups of speakers representing two varieties of French. For each photograph, we report the dominant (modal) name, modal name agreement, alternative responses, and the corresponding H-value. Across the dataset, mean modal name agreement was 83.81% for Quebec French speakers and 83.34% for France French speakers, while mean H-values were 0.67 and 0.73, respectively. Distributional comparisons did not reveal significant differences in H-values between varieties. However, complementary item-level analyses indicated that H-values were slightly but significantly higher in France French, with a small effect size, and that H-values were positively associated across varieties. Descriptive comparisons also showed substantial overlap in modal names across varieties, while identifying a subset of items with different dominant labels. This study provides the first cross-cultural normative dataset for Quebec and France French, underscoring the importance of culturally adapted naming norms in both cognitive research and clinical practice.

Single-cell transcriptomic datasets annotate cell types with diverse schemes and varying resolution. This poses challenges in building unified hierarchical cell-type structures and hinders integration of large-scale datasets. To address this, several computational methods have been developed to harmonize cell type annotations across datasets and build data-driven hierarchies of cell types. Here, we benchmarked three state-of-the-art methods: scHPL, treeArches and CellHint. We evaluated these methods across 5 simulated scenarios and 5 real-world scenarios across cell types and organs. To assess harmonization results, we designed three metrics, Annotation Harmonization F1-score (AH-F1), Tree Edit Distance Similarity (TEDS) and Parent-Children Branches Similarity (PCBS), comparing the constructed cell-type hierarchies and the knowledge-based ones. Based on the benchmarking results, we found that methods performed well in simulated scenarios but still have room for improvement in complex real-world data. Thus, we developed OTHarmonizer, a tool based on partial optimal transport (OT) for cell-type harmonization and hierarchy construction. OTHarmonizer excels in accurately capturing equivalent and hierarchical relationships between cell types, offering a more effective approach for the cell-type hierarchy construction across datasets. The simulated and real-world datasets in the benchmark are available on https://figshare.com/articles/dataset/OTHarmonizer/28243205. The source codes for the benchmark and OTHarmonizer are available online on GitHub at https://github.com/Duck-Boss/OTHarmonizer. Supplementary data are available at Bioinformatics online.

Modularity in revision total knee arthroplasty (TKA) is prevalent; however, corrosion mechanisms at modular junctions in these devices are not well investigated compared to total hip arthroplasty (THA). This retrieval study analyzed corrosion damage on modular junctions of cobalt chromium molybdenum (CoCrMo) alloy femoral components with a boss and titanium aluminum vanadium (Ti-6Al-4V) stem components. It is hypothesized that similar corrosion modes would be found in TKA as in THA, including mechanically assisted and non-mechanically driven corrosion. The collected implants were examined using the Goldberg corrosion score, digital optical microscopy, scanning electron microscopy, and energy-dispersive x-ray spectroscopy. Corrosion modes were analyzed, including etching, pitting, intergranular (interphase) corrosion adjacent to carbides, and fretting scars with titanium transfer on CoCrMo. Ti-6Al-4V exhibited plastic deformation, mechanically assisted crevice corrosion (MACC), and titanium-chromium-molybdenum-rich oxides. These findings suggest that TKA junctions may experience corrosion processes similar to THA, warranting further clinical investigation.

Augmenting training on the social and structural determinants of health in medical education is essential for addressing health disparities and fulfilling medical schools' accreditation-mandated social accountability obligations. Despite these mandates, significant implementation challenges impede curricular efforts, including faculty capacity and resistance to teaching equity-focused content. To navigate this resistance, we must first understand it. An institutional ethnography was conducted over 18 months at a Canadian medical school to trace how faculty resistance is socially and institutionally organised. Data included field observations, interviews with 14 faculty and curriculum leaders and analysis of 124 institutional and regulatory texts. Analysis traced how ruling relations and text-mediated practices shape faculty engagement with social justice education. Faculty resistance-manifesting as scepticism, discomfort and disengagement-was shaped by professional identities rooted in biomedicine and authoritative expertise, hierarchical structures and sociopolitical discourses that framed equity as ideological. Licensing examination objectives operated as boss texts that perpetuated these dynamics, producing tensions that sidelined health equity and social justice content in favour of biomedical priorities. A recursive ideological circle and institutional circuit reinforced the marginalisation of social justice in medical education. Faculty resistance to teaching health equity and social justice-oriented content is not simply individual or attitudinal, but an institutional product shaped by professional, structural and sociopolitical relations. Addressing this requires structural re-alignment across governance, regulatory frameworks and professional norms to position social justice as integral, rather than peripheral, to contemporary medical education and practice.

Assessment of surgical skills for educational research purposes is challenging. Behavioral observation systems (BOSs) seek to quantify specific behaviors by observation. The purpose of this study was to develop and provide evidence supporting the validity of a BOS for evaluating surgical skills during gonadectomy. A BOS was developed with input from four American College of Veterinary Surgery board-certified surgeons through a Delphi process. Twelve gonadectomy procedures were filmed, six each from veterinary students and board-certified surgeons. The BOS was designed to count the frequency of skills observed during key steps of a gonadectomy procedure. Four evaluators performed scoring twice, a minimum of 3 months apart. The BOS scores were compared between the surgeons and students using a multivariate linear regression with BOS as the dependent variable and the evaluation time point, evaluator, and surgeon type as independent variables. Intra-and interrater reliability were calculated using the intraclass correlation coefficient (ICC). Significance was set at &#x3b1; = 0.05. An initial list of 33 surgical skills was reduced to a BOS with 11 skills. There was a significant difference in the BOS between student surgeons and board-certified surgeons (-13; 95% CI = -15.5 to -10.6; p < .001). The interrater ICC was 0.814 (0.690-0.904), and the intrarater ICC was 0.890 (0.812-0.937). There was a significant relationship between BOS and duration of surgery time (p = .03, R2 = 0.38). A behavioral observation system was developed and evidence provided for content validity, response processes, reliability, and relationships with other variables. Using the BOS described here in future research pursuits will continue to accrue evidence for validity and produce data that are valid and reliable. This will enhance the quality of the data used to make decisions about student surgical education.

Raspberry ketone (RK) is a phytochemical compound that enhances male mating performance and accelerates sexual development in immature male Queensland fruit fly (Bactrocera tryoni). While the behavioural effects of RK are well established, the tissue-specific molecular responses remain unclear. We investigated how RK reshapes gene expression in a tissue-specific and time-dependent manner during sexual maturation. We performed tissue-specific transcriptomic profiling across five key tissues, including testes, accessory gland, ejaculatory apodeme, rectal gland, and carcass, over four developmental time points (days 3, 6, 9, and 12) in RK-fed and RK-unfed males. RK ingestion induced distinct, tissue-specific transcriptional changes, with the accessory gland and ejaculatory apodeme showing the strongest responses. Differential gene expression and weighted gene co-expression network analysis revealed that, compared to unfed males, RK-fed males exhibited fewer differentially expressed genes but higher expression intensity, suggesting a focused and efficient transcriptional response. Key genes involved in spermatogenesis (dhd, opa, onecut), muscle development (act88F, flightin, zasp67), cuticle remodelling (kkv, cht7), and neuroendocrine signalling (DopEcR, DIP-delta, BOSS) were upregulated in RK-fed males. Temporal analysis showed early and mid-phase transcriptional surges across reproductive tissues, aligning with accelerated reproductive organ development and reproductive readiness. RK accelerates male sexual maturation by amplifying time-dependent transcriptional responses in reproductive tissues. The most pronounced temporal responses to RK were observed in the accessory and rectal glands. This study provides the first molecular framework to understand the effects of phytochemical ingestion on developmental timing and reproductive readiness in tephritid flies.

Retention of healthcare personnel is a global challenge with severe implications for securing stable, high-quality healthcare services. The common saying that 'people do not leave a bad job, but they leave a bad boss' stresses the influence a formal leader has on employees' turnover intentions (TI) and organizational commitment (OC). Understanding what makes an effective leader is essential for understanding managers' agency in dealing with employee retention; yet, few systematic reviews compare the effectiveness of leaders. This study conducts a systematic review and identifies 54 studies that include 147 correlation effect sizes from public hospitals worldwide. Conducting a multi-level meta-analysis, we find that leadership is significantly associated with employee's TI (r&#x202f;=&#x202f;-0.158, [-0.213; -0.102]) and OC (r&#x202f;=&#x202f;0.266, [0.194; 0.336]); however, the meta-regression reveals that the correlation effect sizes of leadership styles vary significantly across studies where leader-member exchange (LMX) (OC: r&#x202f;=&#x202f;0.32; TI: r&#x202f;=&#x202f;-0.37), servant (OC: r&#x202f;=&#x202f;0.40), supportive (OC: r&#x202f;=&#x202f;0.35; TI: r&#x202f;=&#x202f;-0.36), and ethical (OC: r&#x202f;=&#x202f;0.43) leadership styles are most strongly associated with the employee outcomes. A deeper dive into the leadership styles reveals that social distance moderates the relationship between leadership styles and OC and TI as correlation effect sizes decrease when social distance increase (TI: r&#x202f;=&#x202f;0.161, std error&#x202f;=&#x202f;0.046, p&#x202f;=&#x202f;0.032; OC: r&#x202f;=&#x202f;-0.298, std. error&#x202f;=&#x202f;0.072, p&#x202f;=&#x202f;0.008). With minor exceptions, findings remain relatively stable across cultures, methods, and participant characteristics. These findings nuance previous leadership research in healthcare and public management and have important implications for research and practice.

Grief is a universal human experience that extends well beyond the death of a loved one to encompass a broad range of everyday losses including job loss, relationship dissolution, identity disruption, and the ending of significant friendships. Despite its pervasiveness, grief remains largely absent from mainstream communication discourse, leaving individuals who feel unseen, unsupported, and without the language to express their experience. This article introduces grief-conscious communication as a novel theoretical construct and proposes a multilevel framework designed to address this critical gap. Drawing on foundational grief theories including disenfranchised grief (Doka, 1989, 2002), ambiguous loss (Boss, 1999), and the Dual Process Model (Stroebe & Schut, 1999), the proposed framework is organized across three interconnected tiers: personal consciousness, relational practice, and systemic transformation. The framework offers a comprehensive lens through which individuals, practitioners, and organizations can cultivate more empathic, authentic, and culturally responsive communication about grief. Implications for clinical practice, organizational policy, and future research are discussed.