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Highlights • Cerebral sinus venous thrombosis is a rare cause for a subdural hematoma and should be considered in the differential diagnosis of such cases. • Neuroradiological imaging has a valuable role in diagnosing and follow-up of patients. • Management of subdural hematoma secondary to venous sinus thrombosis could be challenging due to the controversy in using anticoagulation medications., Background Diagnosing cerebral sinus venous thrombosis (CSVT) manifested as a spontaneous subdural hematoma (SDH) is challenging due to variability of its clinical features. The neuroradiological investigation is crucial to confirm the diagnosis. The management of SDH secondary to CVST is controversial and not well established. Case description We present a case of an adult man with Down’s syndrome who underwent a left-sided craniotomy and evacuation of spontaneous subdural hematoma. Post-surgery magnetic resonance venography (MRV) revealed venous sinus thrombosis in the right transverse sinus with loss of flow signal. He was started on Apixaban two weeks post-surgery. Three months later, the patient re-presented with recurrence of the SDH. His anticoagulation was discontinued, and he underwent craniotomy reopening and evacuation of the recurrent hematoma. The patient returned to his baseline following this procedure. The patient was followed up in the out-patient clinic regularly. Interval brain MRI and MRV performed at six months showed further resolution of the thrombosis of the right sigmoid sinus with restoration of the venous flow. Conclusion The management of SDH complicating CVST remains controversial due to the rarity of its presentation and the hazards associated with the use of anticoagulation.

Birth weight is considered a major factor when monitoring any signs of abnormalities in the growth of the fetus and taking timely decisions related to labor management. Existing methods involve specialized equipment and training, which makes them less feasible for underdeveloped areas. Therefore, this study proposed a system for prediction of childbirth weight through kernel extreme reservoir machines and optimized the model parameters by the use of particle swarm optimization. Experimental results showed a significant improvement in the recommended method over existing models. The proposed approach is more economical than the traditional ultrasound making it extremely suited to underprivileged communities.

Background Sepsis is the third leading cause of death in the United States, with 270,000 mortalities annually; therefore, prompt recognition coupled with early treatment can save lives. Better compliance with the Centers for Medicare and Medicaid Services, SEP-1 bundle improves patient safety and save lives. New hospitals are challenged with establishing and promoting a culture of patient safety with prompt recognition of Sepsis. Our hospital opened in June 2017, as a part of a larger health system. Our first SEP-1 scores were 54.17 %. System superior goals for 2017-2018 were greater than 73% and 77.9% respectively. Could our hospital reach the superior goal and promote a culture of patient safety simultaneously? Methods The Plan-Do-Check-Act methodology was used for effectiveness. Interdisciplinary multi-modal methods were implemented such as: case fall out analysis, clinical education, a bundled Sepsis Order Set, real time outcomes communication, committee participation and formation of an Interdisciplinary Work Team. Results Quarter 3 2017 SEP-1 compliance was 54.17%. Promoting the bundled Sepsis Order Set increased compliance to 76.92% by Quarter 1 2018. Case fall out analysis with real time opportunities communicated to physicians coupled with clinical education with badge buddies increased compliance to 83.33% by Quarter 3 2018. Formation of the Sepsis Work Team and committee participation saw compliance increase to 87.10% in Quarter 4 2018. Continued promotion of Sepsis awareness and patient safety upsurged compliance to 89% in Quarter 2 2019 and 100% in Quarter 3 2019. Conclusions Our new hospital met and surpassed the system goal of 73% for 2017-2018, and 77.9% for 2019. Our multi-modal methods incorporating patient safety, sepsis awareness, and education are effective. The use of the Plan-Do-Check-Act methodology guided us to hard wire our culture of patient safety and reduce Sepsis mortalities.

Hybrid silica membranes are of great interest for molecular separation owing to their outstanding hydrothermal stability. Despite good separation properties in liquid applications, the selectivity for gas separations has yet been too low. Here, we report membranes from 1,2-bis(triethoxysilyl)ethane (BTESE) with H2/N2 permselectivity between 50 and over 400. The membranes are fabricated from a dip-sol with a H+:Si ratio of 0.01 that is applied onto a support system with a controlled low water content (pre-treated at RH

Microporous hybrid silica membranes are known to have superior (hydro)thermal and chemical stability. By incorporating metal ions, such as Ce4+ and Ni2+ into these membranes, their affinity and selectivity towards particular gases may be altered. To promote the dispersion of metal ions within the hybrid silica matrix, the sol–gel precursor N,N,N′,N′-tetrakis-(3-(triethoxysilyl)-propyl)-malonamide (TTPMA) was synthesized. The malonamide ligands clearly coordinated the Ce4+ and Ni2+ metal centers and enhanced their dispersion. During annealing these metal centers redistributed into small nanosized grains of CeO2 (

Arabic handwritten digit recognition is the science of recognition and classification of handwritten Arabic digits. It has been a subject of research for many years with rich literature available on the subject. Handwritten digits written by different people are not of the same size, thickness, style, position or orientation. Hence, many different challenges have to overcome for resolving the problem of handwritten digit recognition. The variation in the digits is due to the writing styles of different people which can differ significantly. Automatic handwritten digit recognition has wide application such as automatic processing of bank cheques, postal addresses, and tax forms. A typical handwritten digit recognition application consists of three main stages namely features extraction, features selection, and classification. One of the most important problems is feature extraction. In this paper, a novel feature extraction approach for off-line handwritten digit recognition is presented. Wavelets-based analysis of image data is carried out for feature extraction, and then classification is performed using various classifiers. To further reduce the size of training data-set, high entropy subbands are selected. To increase the recognition rate, individual subbands providing high classification accuracies are selected from the over-complete tree. The features extracted are also normalized to standardize the range of independent variables before providing them to the classifier. Classification is carried out using k-NN and SVMs. The results show that the quality of extracted features is high as almost equivalently high classification accuracies are acquired for both classifiers, i.e. k-NNs and SVMs.

A facile, versatile and reproducible sol–gel process to make microporous organosilica membranes by using 1,2-bis (triethoxysilyl) ethane (BTESE) as a precursor is reported. The influence of process parameters on sol particle size and rheology of BETSE-derived sols was investigated to produce defect-free composite membranes by a single dipping procedure and subsequent calcination. The microporous structure of a BTESE layer on mesoporous alumina supports enabled selective molecular sieving of gas molecules. Single gas permeation (SGP) experiments, performed at 200 C, showed that a H2/CH4 permselectivity of 24 was reproducibly achieved. NoSF6 gas permeance through these membranes proof the presence of a defect-free microstructure. SEM cross-section analysis showed a hybrid selective layer with a thickness dependent on the sol precursor concentration.

Item does not contain fulltext Bone morphogenic protein-2 (BMP-2) is a well-known growth factor that can improve the biological performance of bone substitute materials. BMP-2 produced via bacterial expression systems are non-glycosylated (ng) whereas native and recombinant equivalents produced in mammalian cell expression systems are glycosylated (g) proteins. ngBMP-2 is less soluble, resulting in lower BMP-2 release from carriers as used as bone substitute materials. This seems promising for reducing the amount of included growth factor in bone substitute materials. Hence, it was hypothesized that ngBMP-2 would induce formation of the same amount of bone at an ectopic site at lower dosage as gBMP-2. To that end, gBMP-2 and ngBMP-2 were firstly in vitro comparatively evaluated for biological activity and release from a calcium phosphate (CaP) based bone substitute material. Thereafter, an ectopic implantation model in rats was used, in which gBMP-2 and ngBMP2 were loaded in various dosages (2-20 mug/implant) on the CaP-based bone substitute material and implanted for 4 and 12 weeks. The results revealed that both the in vitro biological activity of and the in vitro release of ngBMP-2 are lower compared to gBMP2. Upon ectopic implantation, however, ngBMP-2 loaded implants induced more bone formation at lower concentrations from 4-weeks onward compared to gBMP-2 equivalents, indicating the value of ngBMP-2 as a potential alternative for mammalian produced recombinant BMP-2 for bone regenerative therapies.

Oesophageal cancer remains an important public health problem worldwide. This multicentre matched case-control study examined the chewing areca nut alone, betel quid with tobacco, oral snuff (snuff dipping) and cigarette smoking as the risk factors for oesophageal squamous-cell carcinoma. We enrolled 91 cases of oesophageal squamous-cell carcinoma and 364 matched controls from three tertiary-care hospitals in Karachi, Pakistan. A structured questionnaire was used to collect the data through face-to-face interview of the participants. Multivariable conditional logistic regression model showed that after adjusting for the effect of ethnicity, ever chewed areca nut alone (adjusted matched odds ratio (mOR(adj))=3.7; 95% confidence interval (CI): 1.6-8.5), ever chewed betel quid with tobacco (mOR(adj)=12.8; 95% CI: 6.3-26.2), ever practiced snuff dipping (mOR(adj)=4.3; 95% CI: 1.6-11.7) and ever smoked cigarettes (mOR(adj)=2.9; 95% CI: 1.4-5.9) were significantly and independently associated with oesophageal squamous-cell carcinoma status. The adjusted summary population attributable risk (PAR) percent for all four substances together was 67.0. Furthermore, despite incomplete synergy, there was manifold increase in the risk of oesophageal squamous-cell carcinoma, if the respondents ever smoked cigarettes and ever chewed betel quid with tobacco (mOR(adj)=21.4; 95% CI: 6.3-72.4) or if they ever smoked cigarettes and ever practiced snuff dipping (mOR(adj)=14.4; 95% CI: 2.3-91.1). The adjusted PAR (%) was higher for the dual practice of smoking cigarettes and chewing betel quid with tobacco (64.3) than the dual practice of smoking cigarettes and snuff dipping (32.2). Public awareness to curtail the addiction to these substances may result in a substantial reduction in the incidence of oesophageal squamous-cell carcinoma and related mortality in this and similar settings.

e research proposes a "conceptual- ERP-framework" for a smart factory during technology diffusion "mega-projects". Application of knowledge discovery and classification algorithms is applied to draw probabilistic inferences based on FP-Growth-algorithm without ignoring the "balanced scorecard (BSC)-model-driven architecture" which is embedded with business intelligence. The prime objective being to illuminate research and scholarly understanding for strategic-alignment of design of vital artifacts of Enterprise resource planning systems (ERP) through collaborative- multidiscipline initiatives amongst the disciplines of "Software- Engineering, Engineering-Management, and Business Science" to overcome the challenges of designing an ERP-system.

Hybrid silica (i.e., bis-triethoxysilylethane: BTESE) membranes doped with B, Ta or Nb were made through a sol–gel process. Triethyl borate, tantalum (V) ethoxide (TPE) and niobium (V) ethoxide (NPE) were selected as doping precursors. The doping concentration was optimized to produce sols, suitable for membrane fabrication. Thermal stability, structural analysis, cross-sectional micrographs and single gas permeation experiments were performed on these membranes, and results are compared with an undoped BTESE membrane. It was observed that the synthesized doped BTESE materials and membranes resulted into a more open (and, in one occurrence, SF6 permeable) pore microstructure, showing high permeances of larger gas molecules, while having a cross-sectional thickness comparable to undoped BTESE membranes. Graphical Abstract: [Figure not available: see fulltext.][Figure not available: see fulltext.][Figure not available: see fulltext.]

Sol-gel derived microporous silica membranes have shown unprecedented hydrogen gas selectivities in dry atmospheres; however their performance is reduced in humid conditions due insufficient material stability. To deal with this issue, one of the strategies is to incorporate hydrocarbons (either as a bridge or at terminals) in the silica matrix. However, the possibility of utilizing bridged-hydrocarbons in silica system i.e. hybrid silica, has not been studied in detail for molecular separations, which was the main focus of this research. The synthesis protocol of sol-gel derived microporous hybrid silica [1,2-bis(triethoxysilyls)ethane (BTESE)] membranes was developed and the effect of dip-sol rheology on the selective layer thickness and membrane performance was studied in detail. The understanding of sol synthesis and membrane fabrication was further investigated by reducing the acid contents (by a factor 15) during sol synthesis in order to enhance the performance of BTESE membranes. The newly developed BTESE membranes were found nitrogen impermeable, i.e. the pore size and structure of these membranes was smaller than nitrogen gas (below 0.36 nm). Doping of elements and metal ions in hybrid silica matrix was performed with the aim to further improve the stability and permselectivity (permeance and selectivity) of these membranes. It was observed that element doping in BTESE matrix resulted in enhanced fluxes of all the observed gases (He, H2, CO2, N2, CH4, and SF6) with no improvement in the hydrogen permselectivities when compared with undoped BTESE membranes. That triggered us to develop another hybrid silica precursor (N,N,N`,N`- (tetrakis-(3-(triethoxysilyl)propyl) malonamide [TTPMA]; in co-operation with IMS-UT), in which metals and ions can be easily and homogenously dispersed, resulting in membranes with enhanced hydrogen separation index. Two ions Ce and Ni were doped in the TTPMA matrix, and like BTESE, molecular sieving phenomena was observed in Ce-TTPMA membranes while Ni-TTPMA membranes were also found nitrogen impermeable confirming a more constricted pore microstructure of these membranes. An attempt to develop a mesoporous hybrid silica structure as an alternative to traditional mesoporous γ-Al2O3, which degrades fast at low pH systems, was made and it was figured out that the hybrid sol was penetrated inside the macroporous α-Al2O3 membrane supports. Either an increase in size of hybrid silica sol particles up to about 40 nm, or the use of a viscosity modifier can be a way to prevent sol infiltration and developing a smooth mesoporous hybrid silica surface on macroporous alumina supports.

The authors describe symptomatic presentation of glioblastoma within six months of resection of an atypical meningioma, at the same frontal parafalcine cerebral location. The patient had neither prior nor adjuvant radiotherapy nor known genetic risk factors. Possible links between invasive meningioma and transformation of adjacent glial cells or precursors to malignant glioma are discussed.

The water and ion transport through a mesoporous γ-alumina membrane and a microporous organosilica membrane was simulated using the extended Nernst Planck equation combined with models for Donnan, steric and dielectric interfacial exclusion mechanisms. Due to the surface charge within the pore, the electroviscous effect was introduced in the model. The modified model fits well the rejection and permeability data for both membranes. The organosilica membrane shows a higher selectivity compared to the γ-alumina membrane, but the permeate flux is lower. At low ionic strength the electroviscous effect lowers the water flux through the γ-alumina membrane. The electroviscous effect is negligible for the organosilica membrane because its absolute surface potential ( 20 mV) is low compared to the γ-alumina membrane ( 60 mV). The simulation shows that the electroviscous effect should be included for the membranes with high surface potential (420 mV) and a pore size below 2–5 times of the electroviscous double layer thickness.

Handwritten digit recognition is a significant and established problem in computer vision and pattern recognition and a lot of research work has already been carried out in this area. In this paper a new technique for handwritten digit recognition is proposed. As the handwritten digits are not of the same size, thickness, style, position and orientation therefore different challenges have to be faced to resolve the problem of handwritten digit recognition. The uniqueness and variety in the writing styles of different people also influence the pattern and appearance of the digits. Handwritten digit recognition is the method of recognizing and classifying handwritten digits. It has wide application such as automatic processing of bank cheques, postal addresses and tax forms etc. In this paper, we present a wavelets analysis based technique for feature extraction. The task of classification is handled using KNN and SVM classifier. An overall high recognition rate of 97.04 is achieved on the test data set. The proposed scheme is tested on the well known MNIST data set.

Wavelets based analysis has become very popular in a wide area of applications. It has found application in areas ranging from image and video compression to texture analysis and image recognition. Using wavelet analysis, multi-resolution analysis is performed which means that an image is decomposed in to multiple time frequency or scale space resolutions. This leads to decomposition of an image in to various representations allowing for effective analysis of the data. In this tutorial, we would should how wavelets-based analysis may be used to acquire interesting features for analysis and segmentation of medical images for computer assisted diagnosis. Medical image analysis for computer assisted diagnosis falls under the domain of biomedical engineering and has become a very important area of research as it can help in reduction of errors made in diagnosis by physicians and doctors. The aims of the tutorial are as under: • Develop detailed understanding of wavelet analysis using wavelet transform and wavelet packets • Understand how multi-resolution analysis may be performed using wavelets • Apply the technique to a wide variety of problems from the domain of medicine for computer assisted diagnosis

Nanoporous microneedle arrays from Al"2O"3 were fabricated via a micromolding process using a PDMS mold generated via a double replication process from a SU-8/Si-master as a template. Hg-porosity measurements showed that the porosity obtained was a function of the temperature used for sintering, resulting in an average pore diameter of 80nm between 1300 and 1500^oC. Using egg plants it was shown that these nanoporous needles allowed both the delivery of substances, and the extraction of compounds. Subsequently, the delivery of compounds has been evaluated in an ex vivo human skin model using a microneedle array saturated with a labeled monoclonal against a specific marker, DC-SIGN, which is representative for dendritic cells when activated by an antigen. By the latter, it was demonstrated that ceramic nanoporous microneedle arrays are potentially useful for the delivery of vaccines.

IEEE 802.11 uses Distributed Coordination Function (DCF) as default channel access method which is based on Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) where all the nodes compete for the channel access. Based on this mechanism a node cannot transmit until the medium is free. Collision can occur if two or more stations within each other's transmission range transmit simultaneously. To overcome this problem several solutions have been proposed, for instance Request To Send/Clear To Send (RTS/CTS) mechanism, through which the channel is reserved for a specified time period known as Network Allocation Vector (NAV). Although this mechanism provide all the nodes to share the medium fairly but it is observed that this mechanism can lead to bandwidth underutilization, especially in networks with higher packet delivery failure rates. In this paper, we propose Intimated Network Allocation Vector (INAV) - a modified version of existing NAV mechanism - which considerably reduce bandwidth underutilization in such networks. This modification works by exploiting the backing-off feature of neighbors of the communicating nodes. The proposed technique recovers more than 93% of idle waiting time for neighbors of the nodes that fail to complete the four way handshake of RTS/CTS.

Geographical analysis and tracking of the spread of epidemics and other diseases is an important issue and is of gre¬¬¬at concern to healthcare professionals all over the world. Information technology has played a vital role in building tools for spatial analysis of spread of diseases and hence has made it possible to track epidemics and early adoption of preventive measures. In this paper, we present a model eSystem for spatial plotting of patients for a disease using our Jaroka Tele-Healthcare System (JTHS) and Google Maps. We use the existing Information and Communication Technology (ICT) infrastructure to provide healthcare to people in remote and rural areas of Pakistan. Lady and Community Health Workers (LCHWs) use simple mobile phones to register patients and report symptoms using the Short Messaging Service (SMS) with the JTHS. Further correspondence for prescription and follow-up diagnosis of the registered patients can also be carried out using the system. This is especially important for remote areas where medical facilities are either rare or completely unavailable. JTHS enables doctors, medical experts and health officials to view the geospatial location of more than twenty thousand patients on the click of a button. This model has been successfully implemented in the rural areas of Mardan, KPK Pakistan and is now being replicated in other parts of the country. The system is unique in the sense that it uses mobile phones based crowd-sourcing to register patients and enable health professionals to track spread of diseases. We also show how population migration affected disease incidence in rural Mardan and how JTHS was able to geographically display the spread of disease.

Virtual Reality based surgical simulators have become quite common for training of surgeons for different surgical skills. Simulators have been widely used particularly in minimal invasive surgery. In this paper we find parameters that would be required to create a real time working simulation for exercises given in the Fundamentals of Laparoscopic Surgery curriculum. We use peg transfer exercise as our example in this work and create simulations for parameter analysis using SOFA, an open source surgical framework [1]. The parameters we choose are generic and can be used to create other more complex simulations like cholecystectomy [2] (gall bladder removal) and appendectomy (appendix removal). We show the implementation of these parameters and their behavior in a virtual reality surgical simulation. This work can be used by researchers and developers to choose the right parameters in the context of the simulation they are developing. It also shows the cost and behavior of achieving good visualization (frames per second), physical characteristics and a realistic behavioral model to be used in simulations for training purposes.

Reducing infant mortality is one of the primary Millennium Development Goals 2015. A lot of effort has been made to reduce infant mortality but it remains high in most of the developing countries and the underdeveloped world. Perinatal Mortality is a cause of great emotional pain and social unrest. The main cause of pregnancy failure in the developed world is obesity but in the under-developed world the main cause remains malnutrition. However, their are a mix of factors that affect pregnancy failure in the developing countries. Pakistan has a very high infant mortality rate which stands at 78 deaths per 1000 births. The reasons for this are many including lack of proper healthcare. This is because of a severe shortage of healthcare professionals and specialists in Pakistan. The gap in healthcare may be overcome by leveraging IT to provide automated healthcare. In this paper, we show how machine learning may be used to predict perinatal failure. We examine the relationship between pre-pregnancy weight, weight gain during pregnancy and the body mass index (BMI) to investigate how they relate to foetal failure. We employ the K Nearest Neighbor (K-NN) technique to automatically differentiate between successful and failed pregnancies. Our method is able to predict the the outcome of a pregnancy with about 95% accuracy.

Background A considerable body of evidence supporting the use of external drainage after evacuation of primary chronic subdural hematoma (CSDH) exists in the literature. However, no systematic study of the value of postoperative drainage in the treatment of recurrent CSDH has been published. The aim of the study was to investigate external drains and subdural-to-peritoneal conduit in the treatment of recurrent CSDH. Methods A retrospective review of cases of CSDH treated in our institution between October 2002 and October 2006 was conducted. Results During the study period, 408 patients had burr hole evacuation. Sixty-four patients (15.9%) had treatment for recurrence. One patient had craniotomy, and the remaining 63 had another burr hole evacuation: 36 without placement of a drain (BHO), 14 with external drainage (SED), and 13 with placement of subdural-peritoneal catheter (SPC). Fifteen patients (24%) developed a secondary recurrence requiring a third drainage procedure. Postoperative drainage (SED or SPC) was associated with a significantly lower secondary recurrence rate when compared to BHO: 3/27 (11%) versus 12/36 (33%) (χ 2 , P =.040). There was no significant difference in recurrence rates between SED and SPC. Postoperative complications included acute subdural hematoma (2), subdural empyema (2), brain edema (2), pneumonia (3), and in-hospital death (2). None of the complications was associated with the use of a specific technique. Conclusions The results indicate that, as in the treatment of primary CSDHs, the use of drain (SED or SPC) with burr hole evacuation is safe and is associated with lower recurrence rate. Further investigation is needed to clarify the indications of currently available surgical techniques in the treatment of recurrent CSDH.

The inherent complexity and non-homogeneity of texture makes classification in medical image analysis a challenging task. In this paper, we propose a combined approach for meningioma subtype classification using subband texture (macro) features and micro-texture features. These are captured using the Adaptive Wavelet Packet Transform (ADWPT) and Local Binary Patterns (LBPs), respectively. These two different textural features are combined together and used for classification. The effect of various dimensionality reduction techniques on classification performance is also investigated. We show that high classification accuracies can be achieved using ADWPT. Although LBP features do not provide higher overall classification accuracies than ADWPT, it manages to provide higher accuracy for a meningioma subtype that is difficult to classify otherwise.

Diagnosis and cure of colon cancer can be improved by efficiently classifying the colon tissue cells into normal and malignant classes. This paper presents the classification of hyperspectral colon tissue cells using morphological analysis of gland nuclei cells. The application of hyperspectral imaging technique in medical image analysis is a new domain for researchers. The main advantage in using hyperspectral imaging is the increased spectral resolution and detailed subpixel information. Biopsy slides with several microdots, where each microdot is from a distinct patient, are illuminated with a tuned light source and magnification is performed up to 400times. The proposed classification algorithm combines the hyperspectral imaging technique with linear discriminant analysis. Dimensionality reduction and cellular segmentation is achieved by independent component analysis (ICA) and k-means clustering. Morphological features, which describe the shape, orientation and other geometrical attributes, are next to be extracted. For classification, LDA is employed to discriminate tissue cells into normal and malignant classes. Implementation of LDA is simpler than other approaches; it saves the computational cost, while maintaining the performance. The algorithm is tested on a number of samples and its applicability is demonstrated with the help of measures such as classification accuracy rate and the area under the convex hull of ROC curves