Patterns of Elbow Fractures and Complications of Those Seen Late in Children Treated at KCMC in Northern Tanzania

No Abstract

Loss Guided Activation for Action Recognition in Still Images

One significant problem of deep-learning based human action recognition is that it can be easily misled by the presence of irrelevant objects or backgrounds. Existing methods commonly address this problem by employing bounding boxes on the target humans as part of the input, in both training and testing stages. This requirement of bounding boxes as part of the input is needed to enable the methods to ignore irrelevant contexts and extract only human features. However, we consider this solution is inefficient, since the bounding boxes might not be available. Hence, instead of using a person bounding box as an input, we introduce a human-mask loss to automatically guide the activations of the feature maps to the target human who is performing the action, and hence suppress the activations of misleading contexts. We propose a multi-task deep learning method that jointly predicts the human action class and human location heatmap. Extensive experiments demonstrate our approach is more robust compared to the baseline methods under the presence of irrelevant misleading contexts. Our method achieves 94.06\% and 40.65\% (in terms of mAP) on Stanford40 and MPII dataset respectively, which are 3.14\% and 12.6\% relative improvements over the best results reported in the literature, and thus set new state-of-the-art results. Additionally, unlike some existing methods, we eliminate the requirement of using a person bounding box as an input during testing.Comment: Accepted to appear in ACCV 201

Specific Targeting and Labeling of Colonic Polyps in CPC-APC Mice with Mucin 5AC Fluorescent Antibodies: A Model for Detection of Early Colon Cancer

Poor visualization of polyps can limit colorectal cancer screening. Fluorescent antibodies to mucin5AC (MUC5AC), a glycoprotein upregulated in adenomas and colorectal cancer, could improve screening colonoscopy polyp detection rate. Adenomatous polyposis coli flox mice with a Cdx2-Cre transgene (CPC-APC) develop colonic polyps that contain both dysplastic and malignant tissue. Mice received MUC5AC-IR800 or IRdye800 as a control IV and were sacrificed after 48 h for near-infrared imaging of their colons. A polyp-to-background ratio (PBR) was calculated for each polyp by dividing the mean fluorescence intensity of the polyp by the mean fluorescence intensity of the background tissue. The mean 25 μg PBR was 1.70 (±0.56); the mean 50 μg PBR was 2.64 (±0.97); the mean 100 μg PBR was 3.32 (±1.33); and the mean 150 μg PBR was 3.38 (±0.87). The mean PBR of the dye-only control was 2.22 (±1.02), significantly less than the 150 μg arm (p-value 0.008). The present study demonstrates the ability of fluorescent anti-MUC5AC antibodies to specifically target and label colonic polyps containing high-grade dysplasia and intramucosal adenocarcinoma in CPC-APC mice. This technology can potentially improve the detection rate and decrease the miss rate of advanced colonic neoplasia and early cancer at colonoscopy

In silico assessment of potential druggable pockets on the surface of α1-Antitrypsin conformers

The search for druggable pockets on the surface of a protein is often performed on a single conformer, treated as a rigid body. Transient druggable pockets may be missed in this approach. Here, we describe a methodology for systematic in silico analysis of surface clefts across multiple conformers of the metastable protein α1-antitrypsin (A1AT). Pathological mutations disturb the conformational landscape of A1AT, triggering polymerisation that leads to emphysema and hepatic cirrhosis. Computational screens for small molecule inhibitors of polymerisation have generally focused on one major druggable site visible in all crystal structures of native A1AT. In an alternative approach, we scan all surface clefts observed in crystal structures of A1AT and in 100 computationally produced conformers, mimicking the native solution ensemble. We assess the persistence, variability and druggability of these pockets. Finally, we employ molecular docking using publicly available libraries of small molecules to explore scaffold preferences for each site. Our approach identifies a number of novel target sites for drug design. In particular one transient site shows favourable characteristics for druggability due to high enclosure and hydrophobicity. Hits against this and other druggable sites achieve docking scores corresponding to a Kd in the µM–nM range, comparing favourably with a recently identified promising lead. Preliminary ThermoFluor studies support the docking predictions. In conclusion, our strategy shows considerable promise compared with the conventional single pocket/single conformer approach to in silico screening. Our best-scoring ligands warrant further experimental investigation

Mutations in FRMD7, a newly identified member of the FERM family, cause X-linked idiopathic congenital nystagmus.

Idiopathic congenital nystagmus is characterized by involuntary, periodic, predominantly horizontal oscillations of both eyes. We identified 22 mutations in FRMD7 in 26 families with X-linked idiopathic congenital nystagmus. Screening of 42 singleton cases of idiopathic congenital nystagmus (28 male, 14 females) yielded three mutations (7%). We found restricted expression of FRMD7 in human embryonic brain and developing neural retina, suggesting a specific role in the control of eye movement and gaze stability

Accountability for Practical Reasoning Agents

Postprin

A Review of Interventions to Reduce Mechanical Restraint and Seclusion among Adult Psychiatric Inpatients

Objective: This review examines nature and effectiveness of interventions to reduce the use of mechanical restraint and seclusion among adult psychiatric inpatients. Method: Electronic searches were conducted to locate post-1960 empirical studies of restraint and seclusion reduction in English. A total of 36 studies were identified, mostly from the USA. Analysis was conducted using a structured data extraction tool. Results: The majority of studies reported reduced levels or mechanical restraint and/or seclusion, but the standard of evidence was poor. There were no randomised trials. Most were retrospective studies of official records before and after the intervention was introduced, with varying follow-up periods. The interventions were diverse, but tended to include one or more of the following: new restraint and/or seclusion policies, staffing changes, staff training, review procedures and crisis management initiatives. The research was unable to address which of these elements was most effective. There was also evidence that some improved outcomes were achieved by substituting restraint or seclusion for each other or for alternatives forms of containment (medication in particular). Nurses’ attitudes, skills and approach to patient care were absent from the literature. Conclusions: Interventions probably can reduce the use of restraint and seclusion, but better designed research is required to demonstrate their effectiveness conclusively. More attention should also be paid to understanding how interventions work, particularly from the perspective of nursing staff. This is essential to the successful implementation of restraint and seclusion interventions across different psychiatric settings and treatment populations

Can Plan Recommendations Improve the Coverage Decisions of Vulnerable Populations in Health Insurance Marketplaces?

OBJECTIVE: The Affordable Care Act's marketplaces present an important opportunity for expanding coverage but consumers face enormous challenges in navigating through enrollment and re-enrollment. We tested the effectiveness of a behaviorally informed policy tool--plan recommendations--in improving marketplace decisions. STUDY SETTING: Data were gathered from a community sample of 656 lower-income, minority, rural residents of Virginia. STUDY DESIGN: We conducted an incentive-compatible, computer-based experiment using a hypothetical marketplace like the one consumers face in the federally-facilitated marketplaces, and examined their decision quality. Participants were randomly assigned to a control condition or three types of plan recommendations: social normative, physician, and government. For participants randomized to a plan recommendation condition, the plan that maximized expected earnings, and minimized total expected annual health care costs, was recommended. DATA COLLECTION: Primary data were gathered using an online choice experiment and questionnaire. PRINCIPAL FINDINGS: Plan recommendations resulted in a 21 percentage point increase in the probability of choosing the earnings maximizing plan, after controlling for participant characteristics. Two conditions, government or providers recommending the lowest cost plan, resulted in plan choices that lowered annual costs compared to marketplaces where no recommendations were made. CONCLUSIONS: As millions of adults grapple with choosing plans in marketplaces and whether to switch plans during open enrollment, it is time to consider marketplace redesigns and leverage insights from the behavioral sciences to facilitate consumers' decisions