GIS-Automated Delineation of Hospital Service Areas in Florida: From Dartmouth Method to Network Community Detection Methods.

Since the Dartmouth hospital service areas (HSAs) were proposed three decades ago, there has been a large body of work using the unit in examining the geographic variation in health care in the U.S. for evaluating health care system performance and informing health policy. However, many studies question the replicability and reliability of the Dartmouth HSAs in meeting the challenges of ever-changing and a diverse set of health care services. This research develops a reproducible, automated, and efficient GIS tool to implement Dartmouth method for defining HSAs. Moreover, the research adapts two popular network community detection methods to account for spatial constraints for defining HSAs that are scale flexible and optimize an important property such as maximum service flows within HSAs. A case study based on the state inpatient database in Florida from the Healthcare Cost and Utilization Project is used to evaluate the efficiency and effectiveness of the methods. The study represents a major step toward developing HSA delineation methods that are computationally efficient, adaptable for various scales (from a local region to as large as a national market), and automated without a steep learning curve for public health professionals.

Network Optimization Approach to Delineating Health Care Service Areas: Spatially Constrained Louvain and Leiden Algorithms.

Constructing service areas is an important task for evaluating geographic variation of health care markets. This study uses cancer care as an example to illustrate the methodology, with the nine-state Northeast Region of the U.S. as the study area. Two recent algorithms of network community detection are implemented to account for additional constraints such as spatial connectivity and threshold region size. The refined methods are termed "spatially-constrained Louvain (ScLouvain)" and "spatially-constrained Leiden (ScLeiden)" algorithms, corresponding to their predecessors Louvain and Leiden algorithms, respectively. Both are network optimization methods that maximize flows within delineated communities while minimizing inter-community flows. The service areas derived by the methods, termed "Cancer Service Areas (CSAs)", are more favorable than the commonly used comparable unit, Hospital Referral Regions (HRRs) for evaluating cancer-specific variation in care. Between the two, the ScLeiden performs better than ScLouvain in modularity, localization index and computational efficiency, and thus is recommended as an effective and efficient approach for defining functional regions.

Automated delineation of cancer service areas in northeast region of the United States: A network optimization approach.

OBJECTIVE: Derivation of service areas is an important methodology for evaluating healthcare variation, which can be refined to more robust, condition-specific, and empirically-based automated regions, using cancer service areas as an exemplar. DATA SOURCES/STUDY SETTING: Medicare claims (2014-2015) for the nine-state Northeast region were used to develop a ZIP-code-level origin-destination matrix for cancer services (surgery, chemotherapy, and radiation). This population-based study followed a utilization-based approach to delineate cancer service areas (CSAs) to develop and test an improved methodology for small area analyses. DATA COLLECTION/EXTRACTION METHODS: Using the cancer service origin-destination matrix, we estimated travel time between all ZIP-code pairs, and applied a community detection method to delineate CSAs, which were tested for localization, modularity, and compactness, and compared to existing service areas. PRINCIPAL FINDINGS: Delineating 17 CSAs in the Northeast yielded optimal parameters, with a mean localization index (LI) of 0.88 (min: 0.60, max: 0.98), compared to the 43 Hospital Referral Regions (HRR) in the region (mean LI: 0.68; min: 0.18, max: 0.97). Modularity and compactness were similarly improved for CSAs vs. HRRs. CONCLUSIONS: Deriving cancer-specific service areas with an automated algorithm that uses empirical and network methods showed improved performance on geographic measures compared to more general, hospital-based service areas.

The Reactivity of Ambident Nucleophiles: Marcus Theory or Hard and Soft Acids and Bases Principle?

The model reactions CH3 X + (NH-CH=O)M ➔ CH3 -NH-NH═O or NH═CH-O-CH3 + MX (M = none, Li, Na, K, Ag, Cu; X = F, Cl, Br) are investigated to demonstrate the feasibility of Marcus theory and the hard and soft acids and bases (HSAB) principle in predicting the reactivity of ambident nucleophiles. The delocalization indices (DI) are defined in the framework of the quantum theory of atoms in molecules (QT-AIM), and are used as the scale of softness in the HSAB principle. To react with the ambident nucleophile NH═CH-O- , the carbocation H3 C+ from CH3 X (F, Cl, Br) is actually a borderline acid according to the DI values of the forming C…N and C…O bonds in the transition states (between 0.25 and 0.49), while the counter ions are divided into three groups according to the DI values of weak interactions involving M (M…X, M…N, and M…O): group I (M = none, and Me4 N) basically show zero DI values; group II species (M = Li, Na, and K) have noticeable DI values but the magnitudes are usually less than 0.15; and group III species (M = Ag and Cu(I)) have significant DI values (0.30-0.61). On a relative basis, H3 C+ is a soft acid with respect to group I and group II counter ions, and a hard acid with respect to group III counter ions. Therefore, N-regioselectivity is found in the presence of group I and group II counter ions (M = Me4 N, Li, Na, K), while O-regioselectivity is observed in the presence of the group III counter ions (M = Ag, and Cu(I)). The hardness of atoms, groups, and molecules is also calculated with new functions that depend on ionization potential (I) and electron affinity (A) and use the atomic charges obtained from localization indices (LI), so that the regioselectivity is explained by the atomic hardness of reactive nitrogen atoms in the transition states according to the maximum hardness principle (MHP). The exact Marcus equation is derived from the simple harmonic potential energy parabola, so that the concepts of activation free energy, intrinsic activation barrier, and reaction energy are completely connected. The required intrinsic activation barriers can be either estimated from ab initio calculations on reactant, transition state, and product of the model reactions, or calculated from identity reactions. The counter ions stabilize the reactant through bridging N- and O-site of reactant of identity reactions, so that the intrinsic barriers for the salts are higher than those for free ambident anions, which is explained by the increased reorganization parameter Δr. The proper application of Marcus theory should quantitatively consider all three terms of Marcus equation, and reliably represent the results with potential energy parabolas for reactants and all products. For the model reactions, both Marcus theory and HSAB principle/MHP principle predict the N-regioselectivity when M = none, Me4 N, Li, Na, K, and the O-regioselectivity when M = Ag and Cu(I). © 2019 Wiley Periodicals, Inc.

Plant nuclear shape is independently determined by the SUN-WIP-WIT2-myosin XI-i complex and CRWN1.

Nuclei undergo dynamic shape changes during plant development, but the mechanism is unclear. In Arabidopsis, Sad1/UNC-84 (SUN) proteins, WPP domain-interacting proteins (WIPs), WPP domain-interacting tail-anchored proteins (WITs), myosin XI-i, and CROWDED NUCLEI 1 (CRWN1) have been shown to be essential for nuclear elongation in various epidermal cell types. It has been proposed that WITs serve as adaptors linking myosin XI-i to the SUN-WIP complex at the nuclear envelope (NE). Recently, an interaction between Arabidopsis SUN1 and SUN2 proteins and CRWN1, a plant analog of lamins, has been reported. Therefore, the CRWN1-SUN-WIP-WIT-myosin XI-i interaction may form a linker of the nucleoskeleton to the cytoskeleton complex. In this study, we investigate this proposed mechanism in detail for nuclei of Arabidopsis root hairs and trichomes. We show that WIT2, but not WIT1, plays an essential role in nuclear shape determination by recruiting myosin XI-i to the SUN-WIP NE bridges. Compared with SUN2, SUN1 plays a predominant role in nuclear shape. The NE localization of SUN1, SUN2, WIP1, and a truncated WIT2 does not depend on CRWN1. While crwn1 mutant nuclei are smooth, the nuclei of sun or wit mutants are invaginated, similar to the reported myosin XI-i mutant phenotype. Together, this indicates that the roles of the respective WIT and SUN paralogs have diverged in trichomes and root hairs, and that the SUN-WIP-WIT2-myosin XI-i complex and CRWN1 independently determine elongated nuclear shape. This supports a model of nuclei being shaped both by cytoplasmic forces transferred to the NE and by nucleoplasmic filaments formed under the NE.

Characteristics of atrial fibrillation cycle length predict restoration of sinus rhythm by catheter ablation.

BACKGROUND: Successful termination of atrial fibrillation (AF) during catheter ablation (CA) is associated with arrhythmia-free follow-up. Preablation factors such as mean atrial fibrillation cycle length (AFCL) predict the likelihood of AF termination during ablation but recurring patterns and AFCL stability have not been evaluated. OBJECTIVE: To investigate novel predictors of acute and postoperative ablation outcomes from intracardiac electrograms: (1) recurring AFCL patterns and (2) localization index (LI) of the instantaneous fibrillatory rate distribution. METHODS: Sixty-two patients with AF (32 paroxysmal AF; 45 men; age 57 ± 10 years) referred for CA were enrolled. One-minute electrogram was recorded from coronary sinus (CS; 5 bipoles) and right atrial appendage (HRA; 2 bipoles). Atrial activations were detected automatically to derive the AFCL and instantaneous fibrillatory rate (inverse of AFCL) time series. Recurring AFCL patterns were quantified by using recurrence plot indices (RPIs): percentage determinism, entropy of determinism, and maximum diagonal length. AFCL stability was determined by using the LI. The CA outcome predictivity of individual indices was assessed. RESULTS: Patients with terminated atrial fibrillation (T-AF) had higher RPI (P < .05 in CS7-8) and LI than did those with nonterminated atrial fibrillation (P < .005 in CS3-4; P < .05 in CS5-6, CS7-8, and HRA). Patients free of arrhythmia after 3-month follow-up had higher RPI and LI (all P < .05 in CS7-8). All indices except percentage determinism predicted T-AF in CS7-8 (area under the curve [AUC] ≥ 0.71; odds ratio [OR] ≥ 4.50; P < .05). The median AFCL and LI predicted T-AF in HRAD (AUC ≥ 0.75; OR ≥ 7.76; P < .05). The RPI and LI predicted 3-month follow-up in CS7-8 (AUC ≥ 0.68; OR ≥ 4.17; P < .05). CONCLUSIONS: AFCL recurrence and stability indices could be used in selecting patients more likely to benefit from CA.