Implementation of a School-Based Bullying Program

A bully is defined as anyone who participates in any form of repetitive negative and hurtful behavior, with the intent of inflicting harm (Highmark & Center for Safe Schools, 2013). Purpose: The purpose of this project was to evaluate the feasibility of implementing a bullying awareness, prevention, and screening program for teachers and school nurses. Methods: The sample included 174 fifth-graders from a public-school district in rural, southwestern Pennsylvania. Teachers received an educational program and students were screened for bullying using the PIPSQ. Results: Although not a significant finding, there was an increase in teacher’s knowledge post-education (p = 0.515). Although findings were not significant, the results of the PIPSQ revealed greater victimization in this school (M = 6.93), with bullying behaviors greater among boys (p = 0.000). Conclusions: The educational program and the PIPSQ tool appear to be a promising method to identify victimization and bullying within an elementary school setting; further research can determine significance of screening and faculty education.     

Percutaneous nephrolithotripsy under assisted local anaesthesia for high risk patients: Is it effective?

ObjectivesThe aim of the present study is to evaluate the feasibility and safety of performing PNL under local anesthesia in a selected group of patients who are at high risk for general anesthesia.Patients and methodsForty seven patients underwent PNL under local anesthesia. There were 38 males and 9 females with a mean age of 62 years. All patients were at medical high-risk for general anesthesia, with an American Society of Anesthesiologists (ASA) score of 3. The indications for local anesthesia in this study were obstructed single functioning kidney with azotemia in 29 patients, hepatic insufficiency in 8 patients, cardiac problems in 7 patients and 3 patients had hepatocellular carcinoma. The mean stone size was 2.7 cm (range 2–3.1 cm). Local infiltration with 10–20 cc of 2% lidocaine at the site of puncture was used in all cases. Narcotics were given 30 min prior to the procedure and medazolam was given intraoperatively upon demand. Utrasound guided puncture was performed in all cases and tract dilatation was then done under fluoroscopy using high pressure balloon catheter in 35 and Alken's metal dilators in 12 cases. Stones were then retrieved after disintegration in the same cession in 33 patients, while the other 14 patients underwent staged PNL, where a 12 Fr. nephrostomy tube was placed in the first stage, followed by tract dilatation and stone retrieval one week later.ResultsOut of 47 patients included, 44 had successful PNL either one stage (30 patients) or two stages (14 patients). Only 3 patients could not tolerate pain and the procedure was terminated after placement of nephrostomy tube and stone retrieval was completed later under general anesthesia.ConclusionOur results demonstrated that PNL under local anesthesia with narcotics and sedatives seems to be a satisfying solution for the treatment of a selected group of patients with renal pelvic stones and who have high anesthetic risk. However, additional studies with different groups of patients are required to validate our results.     

Secreted products of Fasciola hepatica inhibit the induction of T cell responses that mediate allergy

There is evidence from epidemiology studies of a negative association between infection with helminth parasites and the development of allergy and asthma. Here, we demonstrate that the excretory/secretory products of the helminth Fasciola hepatica (FHES) protected mice against ovalbumin (OVA)‐induced allergic asthma when administered at time of allergen sensitization. FHES reduced the accumulation of mucus, eosinophils and lymphocytes into the airways of allergen‐challenged mice. Furthermore, FHES treatment suppressed Th2 responses in the airways. Interestingly, systemic administration of FHES at allergen challenge had no effect on airway inflammation, demonstrating that alum‐induced Th2 response is set following initial allergen sensitization. Our findings highlight the immunomodulatory potential of molecules secreted by F. hepatica.     

Friedrich Trendelenburg: Historical background and significant medical contributions

Friedrich Trendelenburg's name is widely known today because it is associated with the Trendelenburg position. However, Trendelenburg made many other valuable contributions to the field of medicine, including a test, a gait, and a sign. A historical review of his life helps to elucidate the factors that contributed to his innovative approaches and techniques. Both Trendelenburg's mentors in his early years and the influences upon him throughout his professional career contributed to his development as a pioneer of surgery, anesthesia, and clinical diagnostics. Clin. Anat. 27:815–820, 2014. © 2014 Wiley Periodicals, Inc.     

Resilient liner vs. clip attachment effect on peri‐implant tissues of bar‐implant‐retained mandibular overdenture: a 1‐year clinical and radiographical study

Purpose: The aim of this study was to compare between the effects of resilient liner and clip attachments of bar‐implant‐retained mandibular overdenture on peri‐implant tissues. Materials and methods: In a randomized‐controlled clinical trial, 30 edentulous male patients (mean age 62.5 years) were equally assigned to two groups. In each patient, two implants were inserted in the canine area of the mandible using a two‐stage surgical protocol. After 3 months, the implants were connected with resilient bars. Mandibular overdentures were retained to the bars with either clips (group I) or silicone‐resilient liners (group II). Peri‐implant tissues were evaluated clinically (with regard to plaque scores, gingival scores and probing depths) and radiographically (with regard to peri‐implant vertical and horizontal alveolar bone changes). Evaluations were performed at the time of overdenture insertion (T0), 6 months (T6) and 12 months (T12) after overdenture insertion. Results: After 12 months of using bar‐implant‐retained mandibular overdenture, the resilient liner attachment had significantly decreased peri‐implant plaque score, gingival score, probing depth, vertical and horizontal bone loss when compared with the clip attachment. Conclusion: Within the limitations of this study, and in terms of peri‐implant tissue health of bar‐implant‐retained mandibular overdenture, we recommend resilient liner rather than clip attachment. To cite this article:
Elsyad MA, EL Shoukouki AH. Resilient liner vs. clip attachment effect on peri‐implant tissues of bar‐implant‐retained mandibular overdenture: a 1‐year clinical and radiographical study.
Clin. Oral Impl. Res. 21 , 2010; 473–480
doi: 10.1111/j.1600‐0501.2009.01879.x     

From the Cover: Convolutional networks for fast,energy-efficient neuromorphic computing

Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware’s underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.The human brain is capable of remarkable acts of perception while consuming very little energy. The dream of brain-inspired computing is to build machines that do the same, requiring high-accuracy algorithms and efficient hardware to run those algorithms. On the algorithm front, building on classic work on backpropagation (1), the neocognitron (2), and convolutional networks (3), deep learning has made great strides in achieving human-level performance on a wide range of recognition tasks (4). On the hardware front, building on foundational work on silicon neural systems (5), neuromorphic computing, using novel architectural primitives, has recently demonstrated hardware capable of running 1 million neurons and 256 million synapses for extremely low power (just 70 mW at real-time operation) (6). Bringing these approaches together holds the promise of a new generation of embedded, real-time systems, but first requires reconciling key differences in the structure and operation between contemporary algorithms and hardware. Here, we introduce and demonstrate an approach we call Eedn, energy-efficient deep neuromorphic networks, which creates convolutional networks whose connections, neurons, and weights have been adapted to run inference tasks on neuromorphic hardware.For structure, typical convolutional networks place no constraints on filter sizes, whereas neuromorphic systems can take advantage of blockwise connectivity that limits filter sizes, thereby saving energy because weights can now be stored in local on-chip memory within dedicated neural cores. Here, we present a convolutional network structure that naturally maps to the efficient connection primitives used in contemporary neuromorphic systems. We enforce this connectivity constraint by partitioning filters into multiple groups and yet maintain network integration by interspersing layers whose filter support region is able to cover incoming features from many groups by using a small topographic size (7).For operation, contemporary convolutional networks typically use high precision ( ≥ 32-bit) neurons and synapses to provide continuous derivatives and support small incremental changes to network state, both formally required for backpropagation-based gradient learning. In comparison, neuromorphic designs can use one-bit spikes to provide event-based computation and communication (consuming energy only when necessary) and can use low-precision synapses to colocate memory with computation (keeping data movement local and avoiding off-chip memory bottlenecks). Here, we demonstrate that by introducing two constraints into the learning rule—binary-valued neurons with approximate derivatives and trinary-valued ({−1,0,1}) synapses—it is possible to adapt backpropagation to create networks directly implementable using energy efficient neuromorphic dynamics. This approach draws inspiration from the spiking neurons and low-precision synapses of the brain (8) and builds on work showing that deep learning can create networks with constrained connectivity (9), low-precision synapses (10, 11), low-precision neurons (12–14), or both low-precision synapses and neurons (15, 16). For input data, we use a first layer to transform multivalued, multichannel input into binary channels using convolution filters that are learned via backpropagation (12, 16) and whose output can be sent on chip in the form of spikes. These binary channels, intuitively akin to independent components (17) learned with supervision, provide a parallel distributed representation to carry out high-fidelity computation without the need for high-precision representation.Critically, we demonstrate that bringing the above innovations together allows us to create networks that approach state-of-the-art accuracy performing inference on eight standard datasets, running on a neuromorphic chip at between 1,200 and 2,600 frames/s (FPS), using between 25 and 275 mW. We further explore how our approach scales by simulating multichip configurations. Ease-of-use is achieved using training tools built from existing, optimized deep learning frameworks (18), with learned parameters mapped to hardware using a high-level deployment language (19). Although we choose the IBM TrueNorth chip (6) for our example deployment platform, the essence of our constructions can apply to other emerging neuromorphic approaches (20–23) and may lead to new architectures that incorporate deep learning and efficient hardware primitives from the ground up.     

Further delineation of the KBG syndrome caused by ANKRD11 aberrations

Loss-of-function variants in ANKRD11 were identified as the cause of KBG syndrome, an autosomal dominant syndrome with specific dental, neurobehavioural, craniofacial and skeletal anomalies. We present the largest cohort of KBG syndrome cases confirmed by ANKRD11 variants reported so far, consisting of 20 patients from 13 families. Sixteen patients were molecularly diagnosed by Sanger sequencing of ANKRD11, one familial case and three sporadic patients were diagnosed through whole-exome sequencing and one patient was identified through genomewide array analysis. All patients were evaluated by a clinical geneticist. Detailed orofacial phenotyping, including orthodontic evaluation, intra-oral photographs and orthopantomograms, was performed in 10 patients and revealed besides the hallmark feature of macrodontia of central upper incisors, several additional dental anomalies as oligodontia, talon cusps and macrodontia of other teeth. Three-dimensional (3D) stereophotogrammetry was performed in 14 patients and 3D analysis of patients compared with controls showed consistent facial dysmorphisms comprising a bulbous nasal tip, upturned nose with a broad base and a round or triangular face. Many patients exhibited neurobehavioural problems, such as autism spectrum disorder or hyperactivity. One-third of patients presented with (conductive) hearing loss. Congenital heart defects, velopharyngeal insufficiency and hip anomalies were less frequent. On the basis of our observations, we recommend cardiac assessment in children and regular hearing tests in all individuals with a molecular diagnosis of KBG syndrome. As ANKRD11 is a relatively common gene in which sequence variants have been identified in individuals with neurodevelopmental disorders, it seems an important contributor to the aetiology of both sporadic and familial cases.