Corridor Roof Causing Almost Decapitation: a Rare Case Report

Background: Cut throat injury can be homicidal, suicidal or accidental in manner. However, in the absence of tentative cuts, deep cut throat injury usually raised suspicion towards homicide, even against the circumstantial history provided by close relatives or eye witness. Decapitation or almost decapitation is usually noticed in homicidal assault using any heavy cutting weapon like sword and axe or in suicidal railway injury.Case Report: A 91-years old, otherwise healthy lady (empress of one high profile Company) died due to almost decapitation as a result of accidental fall of wheel chair and trapping of neck underneath roof of corridor while moving up in an elevator (lift). With suspicion of property dispute, the investigating police agency was not ready to accept it as a case of accident till the neck injury was clarified demonstrating associated injuries during autopsy and later during visit to scene of death.Conclusion: In high profile case, video recording of autopsy findings, even if not mandatory as per the law, should be made to remain in safer side. Although police should not be allowed during conduction of autopsy, it will be healthy practice to clarify any doubts then and there over autopsy table, if the case warrants so

Hybrid brain-computer interface for biomedical cyber-physical system application using wireless embedded EEG systems

© 2017 The Author(s). Background: One of the key challenges of the biomedical cyber-physical system is to combine cognitive neuroscience with the integration of physical systems to assist people with disabilities. Electroencephalography (EEG) has been explored as a non-invasive method of providing assistive technology by using brain electrical signals. Methods: This paper presents a unique prototype of a hybrid brain computer interface (BCI) which senses a combination classification of mental task, steady state visual evoked potential (SSVEP) and eyes closed detection using only two EEG channels. In addition, a microcontroller based head-mounted battery-operated wireless EEG sensor combined with a separate embedded system is used to enhance portability, convenience and cost effectiveness. This experiment has been conducted with five healthy participants and five patients with tetraplegia. Results: Generally, the results show comparable classification accuracies between healthy subjects and tetraplegia patients. For the offline artificial neural network classification for the target group of patients with tetraplegia, the hybrid BCI system combines three mental tasks, three SSVEP frequencies and eyes closed, with average classification accuracy at 74% and average information transfer rate (ITR) of the system of 27 bits/min. For the real-time testing of the intentional signal on patients with tetraplegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4 s

Fatal Dog Bite Injury – A Case Report

Background: Dog bite is one of the most common childhood accidents causing significant morbidity and mortality in pediatrics age group (1). The exposed position compounded by the short stature of children makes the face very vulnerable to dog bite or animal bite injuries. Unlike wounds inflicted by assaults and accidents, animal bite wounds are distinctive as they are puncture type deep wounds which are injected by the bite force, with inoculums of pathogenic bacteria from the saliva of the attacking dog.Case Report: A case of a 2 month-old child who had succumbed to multiple facial and head bite injuries is presented. At autopsy, multiple bite wounds were noted on the upper part of body like face, head, chest and abdomen. Distinctive bite marks diagnostic of canine dentition were present, most prominently on the head, face and chest. Death was due to cranio-cerebral damage.Conclusion: Public health notification should occur for all dog bites. This would facilitate the development of regional dog bite registries with information on incidence and dogs at risk, which in turn could guide policies such as leash laws and licensing

Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module

© 2015 IEEE. An electroencephalography (EEG)-based counter measure device could be used for fatigue detection during driving. This paper explores the classification of fatigue and alert states using power spectral density (PSD) as a feature extractor and fuzzy swarm based-artificial neural network (ANN) as a classifier. An independent component analysis of entropy rate bound minimization (ICA-ERBM) is investigated as a novel source separation technique for fatigue classification using EEG analysis. A comparison of the classification accuracy of source separator versus no source separator is presented. Classification performance based on 43 participants without the inclusion of the source separator resulted in an overall sensitivity of 71.67%, a specificity of 75.63% and an accuracy of 73.65%. However, these results were improved after the inclusion of a source separator module, resulting in an overall sensitivity of 78.16%, a specificity of 79.60% and an accuracy of 78.88% (p < 0.05)

Channels selection using independent component analysis and scalp map projection for EEG-based driver fatigue classification

© 2017 IEEE. This paper presents a classification of driver fatigue with electroencephalography (EEG) channels selection analysis. The system employs independent component analysis (ICA) with scalp map back projection to select the dominant of EEG channels. After channel selection, the features of the selected EEG channels were extracted based on power spectral density (PSD), and then classified using a Bayesian neural network. The results of the ICA decomposition with the back-projected scalp map and a threshold showed that the EEG channels can be reduced from 32 channels into 16 dominants channels involved in fatigue assessment as chosen channels, which included AF3, F3, FC1, FC5, T7, CP5, P3, O1, P4, P8, CP6, T8, FC2, F8, AF4, FP2. The result of fatigue vs. alert classification of the selected 16 channels yielded a sensitivity of 76.8%, specificity of 74.3% and an accuracy of 75.5%. Also, the classification results of the selected 16 channels are comparable to those using the original 32 channels. So, the selected 16 channels is preferable for ergonomics improvement of EEG-based fatigue classification system

Improving EEG-based driver fatigue classification using sparse-deep belief networks

© 2017 Chai, Ling, San, Naik, Nguyen, Tran, Craig and Nguyen. This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively

Computational Method for Phase Space Transport with Applications to Lobe Dynamics and Rate of Escape

Lobe dynamics and escape from a potential well are general frameworks introduced to study phase space transport in chaotic dynamical systems. While the former approach studies how regions of phase space are transported by reducing the flow to a two-dimensional map, the latter approach studies the phase space structures that lead to critical events by crossing periodic orbit around saddles. Both of these frameworks require computation with curves represented by millions of points-computing intersection points between these curves and area bounded by the segments of these curves-for quantifying the transport and escape rate. We present a theory for computing these intersection points and the area bounded between the segments of these curves based on a classification of the intersection points using equivalence class. We also present an alternate theory for curves with nontransverse intersections and a method to increase the density of points on the curves for locating the intersection points accurately.The numerical implementation of the theory presented herein is available as an open source software called Lober. We used this package to demonstrate the application of the theory to lobe dynamics that arises in fluid mechanics, and rate of escape from a potential well that arises in ship dynamics.Comment: 33 pages, 17 figure

Increased S-nitrosylation and proteasomal degradation of caspase-3 during infection contribute to the persistence of adherent invasive escherichia coli (AIEC) in immune cells

Adherent invasive Escherichia coli (AIEC) have been implicated as a causative agent of Crohn's disease (CD) due to their isolation from the intestines of CD sufferers and their ability to persist in macrophages inducing granulomas. The rapid intracellular multiplication of AIEC sets it apart from other enteric pathogens such as Salmonella Typhimurium which after limited replication induce programmed cell death (PCD). Understanding the response of infected cells to the increased AIEC bacterial load and associated metabolic stress may offer insights into AIEC pathogenesis and its association with CD. Here we show that AIEC persistence within macrophages and dendritic cells is facilitated by increased proteasomal degradation of caspase-3. In addition S-nitrosylation of pro- and active forms of caspase-3, which can inhibit the enzymes activity, is increased in AIEC infected macrophages. This S-nitrosylated caspase-3 was seen to accumulate upon inhibition of the proteasome indicating an additional role for S-nitrosylation in inducing caspase-3 degradation in a manner independent of ubiquitination. In addition to the autophagic genetic defects that are linked to CD, this delay in apoptosis mediated in AIEC infected cells through increased degradation of caspase-3, may be an essential factor in its prolonged persistence in CD patients

Inner-sphere oxidation of ternary iminodiacetatochromium(III) complexes involving DL-valine and L-arginine as secondary ligands. Isokinetic relationship for the oxidation of ternary iminodiacetato-chromium(III) complexes by periodate

<p>Abstract</p> <p>Background</p> <p>In this paper, the kinetics of oxidation of [Cr^III(HIDA)(Val)(H₂O)₂]⁺and [Cr^III(HIDA)(Arg)(H₂O)₂]⁺(HIDA = iminodiacetic acid, Val = DL-valine and Arg = L-arginine) were studied. The choice of ternary complexes was attributed to two considerations. Firstly, in order to study the effect of the secondary ligands DL-valine and L-arginine on the stability of binary complex [Cr^III(HIDA)(IDA)(H₂O)] towards oxidation. Secondly, transition metal ternary complexes have received particular focus and have been employed in mapping protein surfaces as probes for biological redox centers and in protein capture for both purification and study.</p> <p>Results</p> <p>The results have shown that the reaction is first order with respect to both [IO₄^-] and the complex concentration, and the rate increases over the pH range 2.62 – 3.68 in both cases. The experimental rate law is consistent with a mechanism in which both the deprotonated forms of the complexes [Cr^III(IDA)(Val)(H₂O)₂] and [Cr^III(IDA)(Arg)(H₂O)₂] are significantly more reactive than the conjugate acids. The value of the intramolecular electron transfer rate constant for the oxidation of [Cr^III(HIDA)(Arg)(H₂O)₂]⁺, <it>k</it>₃(1.82 × 10^-3s^-1), is greater than the value of <it>k</it>₁(1.22 × 10^-3s^-1) for the oxidation of [Cr^III(HIDA)(Val)(H₂O)₂]⁺at 45.0°C and <it>I </it>= 0.20 mol dm^-3. It is proposed that electron transfer proceeds through an inner-sphere mechanism <it>via </it>coordination of IO₄^-to chromium(III).</p> <p>Conclusion</p> <p>The oxidation of [Cr^III(HIDA)(Val)(H₂O)₂]⁺and [Cr^III(HIDA)(Arg)(H₂O)₂]⁺by periodate may proceed through an inner-sphere mechanism via two electron transfer giving chromium(VI). The value of the intramolecular electron transfer rate constant for the oxidation of [Cr^III(HIDA)(Arg)(H₂O)₂]⁺, <it>k</it>₃, is greater than the value of <it>k</it>₁for the oxidation of [Cr^III(HIDA)(Val)(H₂O)₂]⁺. A common mechanism for the oxidation of ternary iminodiacetatochromium(III) complexes by periodate is proposed, and this is supported by an excellent isokinetic relationship between ΔH* and ΔS* values for these reactions.</p

Antimetastatic Effects of Phyllanthus on Human Lung (A549) and Breast (MCF-7) Cancer Cell Lines

BACKGROUND: Current chemotherapeutic drugs kill cancer cells mainly by inducing apoptosis. However, they become ineffective once cancer cell has the ability to metastasize, hence the poor prognosis and high mortality rate. Therefore, the purpose of this study was to evaluate the antimetastatic potential of Phyllanthus (P. niruri, P. urinaria, P. watsonii, and P. amarus) on lung and breast carcinoma cells. METHODOLOGY/PRINCIPAL FINDINGS: Cytotoxicity of Phyllanthus plant extracts were first screened using the MTS reduction assay. They were shown to inhibit MCF-7 (breast carcinoma) and A549 (lung carcinoma) cells growth with IC(50) values ranging from 50-180 µg/ml and 65-470 µg/ml for methanolic and aqueous extracts respectively. In comparison, they have lower toxicity on normal cells with the cell viability percentage remaining above 50% when treated up to 1000 µg/ml for both extracts. After determining the non-toxic effective dose, several antimetastasis assays were carried out and Phyllanthus extracts were shown to effectively reduce invasion, migration, and adhesion of both MCF-7 and A549 cells in a dose-dependent manner, at concentrations ranging from 20-200 µg/ml for methanolic extracts and 50-500 µg/ml for aqueous extracts. This was followed by an evaluation of the possible modes of cell death that occurred along with the antimetastatic activity. Phyllanthus was shown to be capable of inducing apoptosis in conjunction with its antimetastastic action, with more than three fold increase of caspases-3 and -7, the presence of DNA-fragmentation and TUNEL-positive cells. The ability of Phyllanthus to exert antimetastatic activities is mostly associated to the presence of polyphenol compounds in its extracts. CONCLUSIONS/SIGNIFICANCE: The presence of polyphenol compounds in the Phyllanthus plant is critically important in the inhibition of the invasion, migration, and adhesion of cancer cells, along with the involvement of apoptosis induction. Hence, Phyllanthus could be a valuable candidate in the treatment of metastatic cancers