Examining a Hypothesized Causal Chain for the Effects of the 2007 Repeal of the Permit-to-Purchase Licensing Law in Missouri: Homicide Guns Recovered in State and within a Year of Purchase.

Firearm-related deaths are a leading cause of death in the USA. Webster et al. (2014) found an association between Missouri's repeal of a permit-to-purchase handgun licensing law and an increase in firearm-related homicides. The evidence for causality of this association would be strengthened by finding that the increase occurred through the hypothesized mechanism of increasing the ease with which those with violent intent could obtain guns. This study examines two measures: (1) proportion of guns recovered and purchased in-state and (2) time between firearm purchase and recovery by police following criminal use. The repeal was associated from 2008 to 2019 with a 0.05 increase in the proportion own-state gun trace (p < 0.0001, 95% confidence interval: 0.08,0.13) and a 0.10 increase in the proportion of guns recovered prior to 1 year after purchase (p = 0.01, 95% confidence interval: 1.20, 1.90). Our study provides supportive evidence for the repeal increasing firearm-related homicides.

Robust Long-Term Hand Grasp Recognition With Raw Electromyographic Signals Using Multidimensional Uncertainty-Aware Models.

Hand grasp recognition with surface electromyography (sEMG) has been used as a possible natural strategy to control hand prosthetics. However, effectively performing activities of daily living for users relies significantly on the long-term robustness of such recognition, which is still a challenging task due to confused classes and several other variabilities. We hypothesise that this challenge can be addressed by introducing uncertainty-aware models because the rejection of uncertain movements has previously been demonstrated to improve the reliability of sEMG-based hand gesture recognition. With a particular focus on a very challenging benchmark dataset (NinaPro Database 6), we propose a novel end-to-end uncertainty-aware model, an evidential convolutional neural network (ECNN), which can generate multidimensional uncertainties, including vacuity and dissonance, for robust long-term hand grasp recognition. To avoid heuristically determining the optimal rejection threshold, we examine the performance of misclassification detection in the validation set. Extensive comparisons of accuracy under the non-rejection and rejection scheme are conducted when classifying 8 hand grasps (including rest) over 8 subjects across proposed models. The proposed ECNN is shown to improve recognition performance, achieving an accuracy of 51.44% without the rejection option and 83.51% under the rejection scheme with multidimensional uncertainties, significantly improving the current state-of-the-art (SoA) by 3.71% and 13.88%, respectively. Furthermore, its overall rejection-capable recognition accuracy remains stable with only a small accuracy degradation after the last data acquisition over 3 days. These results show the potential design of a reliable classifier that yields accurate and robust recognition performance.

Using Malarial Retinopathy to Improve the Diagnosis of Pediatric Cerebral Malaria.

In malaria endemic areas, a high proportion of children have detectable parasitemia but show no clinical symptoms. When comatose from a cause other than malaria, this group confounds the cerebral malaria (CM) definition, making accurate diagnosis challenging. One important biomarker of CM is malarial retinopathy, a set of specific features visible in the ocular fundus. In this study, we quantified the contribution of malarial retinopathy in discriminating malaria-caused coma from non-malaria-caused coma. We estimated that 10% of our study cohort of N = 1,192 patients who met the WHO clinical definition of CM in Malawi had non-malarial coma based on a Gaussian mixture model using the parasite protein Plasmodium falciparum histidine-rich protein-2. A classification based on platelets, white blood cells, and retinopathy significantly improved the discriminative power of a previously established model including only platelets plus white blood cells (area under the receiver operating characteristic curve: 0.89 versus 0.75, P value < 0.001). We conclude that malarial retinopathy is highly predictive of malaria-caused versus non-malaria-caused coma and recommend that an ocular funduscopic examination to determine malarial retinopathy status be included in the assessment of parasitemic comatose African children.

Spatial Disadvantage and Racial Disparities in Gun Homicides.

A spatially disadvantaged census tract is one that is surrounded by disadvantaged tracts. More spatially disadvantaged neighborhoods may experience more violence, independent of their own level of disadvantage, and majority Black middle-class neighborhoods are more likely to be spatially disadvantaged than majority white neighborhoods. The purpose of this paper is to study how much of the racial difference in gun homicide rates between majority Black and majority white middle-class neighborhoods can be explained by differences in spatial disadvantage. To study this, comparable majority Black and majority white tracts were matched to understand how gun homicide rates differ in neighborhoods with similar levels of disadvantage. Further matching on spatial disadvantage reduced the disparity in gun homicides between majority Black and majority white middle-class neighborhoods, suggesting that spatial disadvantage accounts for some but not all of the disparity.

Hemoglobin Levels Among Male Agricultural Workers: Analyses From the Demographic and Health Surveys to Investigate a Marker for Chronic Kidney Disease of Uncertain Etiology.

OBJECTIVE: Estimate agricultural work's effect on hemoglobin (Hgb) level in men. A negative effect may indicate presence of chronic kidney disease of uncertain etiology. METHODS: We use Demographic and Health Surveys data from seven African and Asian countries and use matching to control for seven confounders. RESULTS: On average, Hgb levels were 0.09 g/dL lower among agricultural workers compared with matched controls. Significant effects were observed in Ethiopia, India, Lesotho, and Senegal, with effects from 0.07 to 0.30 g/dL lower Hgb level among agricultural workers. The findings were robust to multiple control groups and a modest amount of unmeasured confounding. CONCLUSIONS: Men engaged in agricultural work in four of the seven countries studied have modestly lower Hgb levels. Our data support integrating kidney function assessments within Demographic and Health Surveys and other population-based surveys.

Reliability Analysis for Finger Movement Recognition With Raw Electromyographic Signal by Evidential Convolutional Networks.

Hand gesture recognition with surface electromyography (sEMG) is indispensable for Muscle-Gesture-Computer Interface. The usual focus of it is upon performance evaluation involving the accuracy and robustness of hand gesture recognition. However, addressing the reliability of such classifiers has been absent, to our best knowledge. This may be due to the lack of consensus on the definition of model reliability in this field. An uncertainty-aware model has the potential to self-evaluate the quality of its inference, thereby making it more reliable. Moreover, uncertainty-based rejection has been shown to improve the performance of sEMG-based hand gesture recognition. Therefore, we first define model reliability here as the quality of its uncertainty estimation and propose an offline framework to quantify it. To promote reliability analysis, we propose a novel end-to-end uncertainty-aware finger movement classifier, i.e., evidential convolutional neural network (ECNN), and illustrate the advantages of its multidimensional uncertainties such as vacuity and dissonance. Extensive comparisons of accuracy and reliability are conducted on NinaPro Database 5, exercise A, across CNN and three variants of ECNN based on different training strategies. The results of classifying 12 finger movements over 10 subjects show that the best mean accuracy achieved by ECNN is 76.34%, which is slightly higher than the state-of-the-art performance. Furthermore, ECNN variants are more reliable than CNN in general, where the highest improvement of reliability of 19.33% is observed. This work demonstrates the potential of ECNN and recommends using the proposed reliability analysis as a supplementary measure for studying sEMG-based hand gesture recognition.

A normalisation approach improves the performance of inter-subject sEMG-based hand gesture recognition with a ConvNet.

Recently, the subject-specific surface electromyography (sEMG)-based gesture classification with deep learning algorithms has been widely researched. However, it is not practical to obtain the training data by requiring a user to perform hand gestures many times in real life. This problem can be alleviated to a certain extent if sEMG from many other subjects could be used to train the classifier. In this paper, we propose a normalisation approach that allows implementing real-time subject-independent sEMG based hand gesture classification without training the deep learning algorithm subject specifically. We hypothesed that the amplitude ranges of sEMG across channels between forearm muscle contractions for a hand gesture recorded in the same condition do not vary significantly within each individual. Therefore, the min-max normalisation is applied to source domain data but the new maximum and minimum values of each channel used to restrict the amplitude range are calculated from a trial cycle of a new user (target domain) and assigned by the class label. A convolutional neural network (ConvNet) trained with the normalised data achieved an average 87.03% accuracy on our G. dataset (12 gestures) and 94.53% on M. dataset (7 gestures) by using the leave-one-subject-out cross-validation.

Functionalities tuned enantioselectivity of phenylcarbamate cyclodextrin clicked chiral stationary phases in HPLC.

The mixed chloro- and methyl- functionalities can greatly modulate the enantioselectivities of phenylcarbamate cyclodextrin (CD) clicked chiral stationary phases (CSPs). A comparison study is herein reported for per(4-chloro-3-methyl)phenylcarbamate and per(2-chloro-5-methyl)phenylcarbamate ß-CD clicked CSPs (i.e., CCC4M3-CSP and CCC2M5-CSP). The enantioselectivity dependence on column temperature was studied in both normal-phase and reversed-phase mode high performance liquid chromatography (HPLC). The thermodynamic study revealed that the stronger intermolecular interactions can be formed between CCC4M3-CSP and chiral solutes to drive the chiral separation. The higher enantioselectivities of CCC4M3-CSP were further demonstrated with the enantioseparation of 17 model racemates in HPLC.

[Preparation of phenylcarbamoylated ß-cyclodextrin chiral stationary phases and the enantioseparation of flavonoids].

Cyclodextrin (CD) based chiral stationary phases (CSPs) have simulated great attention due to their unique chiral recognition ability. Functionalized cyclodextrins bonded silica gel as chiral stationary phases have been dramatically developed due to their chemical stability and solvent tolerability. To explore the functionalization of phenylcarbamoylated ß-cyclodextrin CSPs on their enantioselectivities, 4-chloro-3-methylaniline and 5-chloro-2-methyl phenyl isocyanate were employed. Two CSPs have been thus developed by clicking the CD derivatives onto alkynylated silica support. They include per-4-chloro-3-methylphenylcarbamoylated ß-cyclodextrin clicked CSP (CSP1) and per-5-chloro-2-methylphenylcarbamoylated ß-cyclodextrin clicked CSP (CSP2), which have both electron-donating (methyl) and withdrawing (chlorine) groups in the phenylcarbamate moieties, but different locations. The CSPs were successfully characterized in terms of structure using nuclear magnetic resonance (NMR), Fourier transform infrared spectroscopy (FT-IR) and elementary analysis. Their enantioselectivities were evaluated in reversed-phase high performance liquid chromatography (HPLC). The comparison study on the enantioseparation of nine flavonoids with the two CSPs demonstrates the higher enantioselectivities of CSP1 over CSP2, because of the different locations of electron-donating (methyl) and withdrawing (chlorine) groups in the phenylcarbamate moieties of CD derivatives. The baseline enantioseparations achieved in water/methanol as mobile phase offered great potential for the CSPs to be used in practical application.

Engineering Cyclodextrin Clicked Chiral Stationary Phase for High-Efficiency Enantiomer Separation.

The separation of racemic molecules is of crucial significance not only for fundamental research but also for technical application. Enantiomers remain challenging to be separated owing to their identical physical and chemical properties in achiral environments. Chromatographic techniques employing chiral stationary phases (CSPs) have been developed as powerful tools for the chiral analysis and preparation of pure enantiomers, most of which are of biological and pharmaceutical interests. Here we report our efforts in developing high-performance phenylcarbamated cyclodextrin (CD) clicked CSPs. Insights on the impact of CD functionalities in structure design are provided. High-efficiency enantioseparation of a range of aryl alcohols and flavanoids with resolution values (Rs) over 10 were demonstrated by per(3-chloro-4-methyl)phenylcarbamated CD clicked CSP. Comparison study and molecular simulations suggest the improved enantioselectivity was attributed to higher interactions energy difference between the complexes of enantiomers and CSPs with phenylcarbamated CD bearing 3-chloro and 4-methyl functionalities.

Cyclodextrin clicked chiral stationary phases with functionalities-tuned enantioseparations in high performance liquid chromatography.

In this work, two cyclodextrin (CD) chiral stationary phases (CSPs) have been developed by clicking per-4-chloro-3-methylphenylcarbamoylated mono-6(A)-azido-ß-CD (CSP1) and per-5-chloro-2-methylphenylcarbamoylated mono-6(A)-azido-ß-CD (CSP2) onto alkynylated silica support. The enantioslectivies of the as-obtained new CSPs have been evaluated using 29 model racemates including aromatic alcohols, flavonoids, ß-blocker and FMOC-amino acids in both reversed-phase (RP) and normal-phase (NP) high performance liquid chromatography (HPLC). The CD functionalities tuned enantioselectivities were elucidated in different HPLC elution modes. Higher chiral resolutions were achieved in RP-elution mode with the aid of the inclusion complexation in comparison to NP-elution mode. The π-π stacking interaction and dipole-dipole interaction provided by phenylcarbamate moieties can also contribute to the enantioseparation.