Growth rules for the repair of Asynchronous Irregular neuronal networks after peripheral lesions.

Several homeostatic mechanisms enable the brain to maintain desired levels of neuronal activity. One of these, homeostatic structural plasticity, has been reported to restore activity in networks disrupted by peripheral lesions by altering their neuronal connectivity. While multiple lesion experiments have studied the changes in neurite morphology that underlie modifications of synapses in these networks, the underlying mechanisms that drive these changes are yet to be explained. Evidence suggests that neuronal activity modulates neurite morphology and may stimulate neurites to selective sprout or retract to restore network activity levels. We developed a new spiking network model of peripheral lesioning and accurately reproduced the characteristics of network repair after deafferentation that are reported in experiments to study the activity dependent growth regimes of neurites. To ensure that our simulations closely resemble the behaviour of networks in the brain, we model deafferentation in a biologically realistic balanced network model that exhibits low frequency Asynchronous Irregular (AI) activity as observed in cerebral cortex. Our simulation results indicate that the re-establishment of activity in neurons both within and outside the deprived region, the Lesion Projection Zone (LPZ), requires opposite activity dependent growth rules for excitatory and inhibitory post-synaptic elements. Analysis of these growth regimes indicates that they also contribute to the maintenance of activity levels in individual neurons. Furthermore, in our model, the directional formation of synapses that is observed in experiments requires that pre-synaptic excitatory and inhibitory elements also follow opposite growth rules. Lastly, we observe that our proposed structural plasticity growth rules and the inhibitory synaptic plasticity mechanism that also balances our AI network both contribute to the restoration of the network to pre-deafferentation stable activity levels.

Life-Threatening Hemoptysis.

Life-threatening hemoptysis (LTH) is any amount of hemoptysis that causes significant hemodynamic decompensation or respiratory distress which may lead to death if left untreated. While the amount of hemoptysis that qualifies as massive hemoptysis has continued to be debated, any amount between 100 to 1,000 mL/day is considered significant. Up to 15% cases of hemoptysis are LTH and need urgent life-saving intervention. Understanding of pulmonary vascular anatomy is of paramount importance to manage LTH. The goal of treatment lies in airway protection, appropriate oxygenation, and prevention of exsanguination. Once the airway is stabilized, a quick diagnosis and control of bleeding site is targeted. This chapter highlights current practices and approach to LTH including medical management, bronchoscopic approach, and advanced therapies such as bronchial artery embolization and surgical resection. We review situations, such as bronchiectasis, vascular malformation, diffuse alveolar hemorrhage, and tracheostomy bleed and specific approach to management of these conditions in a systematic and evidence-based manner.

An Enhanced Memetic Algorithm for Single-Objective Bilevel Optimization Problems.

Bilevel optimization, as the name reflects, deals with optimization at two interconnected hierarchical levels. The aim is to identify the optimum of an upper-level  leader problem, subject to the optimality of a lower-level follower problem. Several problems from the domain of engineering, logistics, economics, and transportation have an inherent nested structure which requires them to be modeled as bilevel optimization problems. Increasing size and complexity of such problems has prompted active theoretical and practical interest in the design of efficient algorithms for bilevel optimization. Given the nested nature of bilevel problems, the computational effort (number of function evaluations) required to solve them is often quite high. In this article, we explore the use of a Memetic Algorithm (MA) to solve bilevel optimization problems. While MAs have been quite successful in solving single-level optimization problems, there have been relatively few studies exploring their potential for solving bilevel optimization problems. MAs essentially attempt to combine advantages of global and local search strategies to identify optimum solutions with low computational cost (function evaluations). The approach introduced in this article is a nested Bilevel Memetic Algorithm (BLMA). At both upper and lower levels, either a global or a local search method is used during different phases of the search. The performance of BLMA is presented on twenty-five standard test problems and two real-life applications. The results are compared with other established algorithms to demonstrate the efficacy of the proposed approach.

Guided Growth of the Distal Posterior Tibial Physis and Short Term Results: A Potential Treatment Option for Children With Calcaneus Deformity.

BACKGROUND: We present our experience of using tension band plates to achieve guided growth in children for correction of calcaneus deformity around the ankle. METHODS: Our study included 9 consecutive patients (11 ankles) with calcaneus deformity, over a period of 4 years. Surgical treatment with extra periosteal application of flexible 2 hole plate and screws on posterior aspect of distal tibial physis was carried out.The indications for treatment were residual clubfoot deformity in 9, posttraumatic in 1, and neurologic in 1. The average age of the patients was 10 years (range, 4 to 13 y). There were 7 males and 2 females.Serial preoperative and postoperative radiographs were used to measure deformity correction and anterior distal tibia angle (ADTA), lateral distal tibial angle (LDTA), and Screw Divergence Angle (angle subtended by lines passing through the screws) were measured. A 2-tailed student t test was used to determine statistical significance. RESULTS: The ADTA showed mean correction of 8.41 degrees (range, 3.1 to 16.6 degrees) this was statistically significant with P-value of 0.0003.The change in LDTA was not statistically significant (P-value=0.05) reinforcing the aptness of the procedure and that the procedure did not result in coronal plane deformities. Six ankles required revision of fixation: 4 due to metalwork reaching its maximum limit of divergence at an average of 1 year, 1 ankle had screw pull-out, and another ankle was revised due to technical error in screw selection. In our study there were no cases of infection. CONCLUSIONS: We report satisfactory short-term results of correction of calcaneus deformity using flexible tension band plates and screws. In our opinion this is an effective alternative providing gradual correction with easy and minimally invasive surgical technique. It does not violate the physis and is easy to remove and revise. It is safe and well tolerated and can be grouped with other procedures with ease. LEVEL OF EVIDENCE: Level IV­case series.

Test problem construction for single-objective bilevel optimization.

In this paper, we propose a procedure for designing controlled test problems for single-objective bilevel optimization. The construction procedure is flexible and allows its user to control the different complexities that are to be included in the test problems independently of each other. In addition to properties that control the difficulty in convergence, the procedure also allows the user to introduce difficulties caused by interaction of the two levels. As a companion to the test problem construction framework, the paper presents a standard test suite of 12 problems, which includes eight unconstrained and four constrained problems. Most of the problems are scalable in terms of variables and constraints. To provide baseline results, we have solved the proposed test problems using a nested bilevel evolutionary algorithm. The results can be used for comparison, while evaluating the performance of any other bilevel optimization algorithm. The code related to the paper may be accessed from the website http://bilevel.org .

Solving Bilevel Optimization Problems Using Kriging Approximations.

Bilevel optimization involves two levels of optimization, where one optimization problem is nested within the other. The structure of the problem often requires solving a large number of inner optimization problems that make these kinds of optimization problems expensive to solve. The reaction set mapping and the lower level optimal value function mapping are often used to reduce bilevel optimization problems to a single level; however, the mappings are not known a priori, and the need is to be estimated. Though there exist a few studies that rely on the estimation of these mappings, they are often applied to problems where one of these mappings has a known form, that is, piecewise linear, convex, etc. In this article, we utilize both these mappings together to solve general bilevel optimization problems without any assumptions on the structure of these mappings. Kriging approximations are created during the generations of an evolutionary algorithm, where the population members serve as the samples for creating the approximations. One of the important features of the proposed algorithm is the creation of an auxiliary optimization problem using the Kriging-based metamodel of the lower level optimal value function that solves an approximate relaxation of the bilevel optimization problem. The auxiliary problem when used for local search is able to accelerate the evolutionary algorithm toward the bilevel optimal solution. We perform experiments on two sets of test problems and a problem from the domain of control theory. Our experiments suggest that the approach is quite promising and can lead to substantial savings when solving bilevel optimization problems. The approach is able to outperform state-of-the-art methods that are available for solving bilevel problems, in particular, the savings in function evaluations for the lower level problem are substantial with the proposed approach.

Electron diffraction characterization of nanocrystalline materials using a Rietveld-based approach. Part I. Methodology.

Transmission electron microscopy is a powerful experimental tool, very effective for the complete characterization of nanocrystalline materials by employing a combination of imaging, spectroscopy and diffraction techniques. Electron powder diffraction (EPD) pattern fingerprinting in association with chemical information from spectroscopy can be used to deduce the identity of the crystalline phases. Furthermore, EPD has similar potential to X-ray powder diffraction (XRPD) for extracting additional information regarding material specimens, such as microstructural features and defect structures. The aim of this paper is to extend a full-pattern fitting procedure, broadly used for analysing XRPD patterns, to EPD. The interest of this approach is twofold: in the first place, the relatively short times involved with data acquisition allow one to speed up the characterization procedures. This is a particularly interesting aspect in the case of metastable structures or kinetics studies. Moreover, the reduced sampling volumes involved with electron diffraction analyses can better reveal surface alteration layers in the analysed specimen which might be completely overlooked by conventional bulk techniques. The first step forward to have an effective application of the proposed methodology concerns establishing a reliable calibration protocol to take into correct account the instrumental effects and thus separate them from those determined by the structure, microstructure and texture of the analysed samples. In this paper, the methodology for determining the instrumental broadening of the diffraction lines is demonstrated through a full quantitative analysis based on the Rietveld refinement of the EPD. In this regard, a CeO2 nanopowder reference specimen has been used. The results provide indications also on the specific features that a good calibration standard should have.

A new sample preparation protocol for SEM and TEM particulate matter analysis.

A new methodology has been developed to prepare electron microscopy, both SEM and TEM, specimens starting from particulate matter collected using environmental sampling systems. The approach is based on the extraction of the particles to be analyzed from the harvesting substrates. The extracted particles can be directly observed in an SEM, possibly in low-vacuum mode to prevent electrical charging. In order to prepare electron transparent samples, TEM observations require a further step, consisting in embedding the particles in an electron transparent carbon film deposited before dissolving the acetate extracting substrate. The protocol has been tested by analyzing particles collected during bench tests on brake pads and discs, carried out on a dynamometer equipped with a particulate matter sampling apparatus. The main advantages of the approach are: the complete extraction of the particulate matter specimens from the original substrates, that in this way do not interfere with the analyses; the extracted samples retain the topological information of the collection in the specimens prepared for SEM; possibility to be applied to any kind of particulate matter harvesting substrates.

An efficient and accurate solution methodology for bilevel multi-objective programming problems using a hybrid evolutionary-local-search algorithm.

Bilevel optimization problems involve two optimization tasks (upper and lower level), in which every feasible upper level solution must correspond to an optimal solution to a lower level optimization problem. These problems commonly appear in many practical problem solving tasks including optimal control, process optimization, game-playing strategy developments, transportation problems, and others. However, they are commonly converted into a single level optimization problem by using an approximate solution procedure to replace the lower level optimization task. Although there exist a number of theoretical, numerical, and evolutionary optimization studies involving single-objective bilevel programming problems, not many studies look at the context of multiple conflicting objectives in each level of a bilevel programming problem. In this paper, we address certain intricate issues related to solving multi-objective bilevel programming problems, present challenging test problems, and propose a viable and hybrid evolutionary-cum-local-search based algorithm as a solution methodology. The hybrid approach performs better than a number of existing methodologies and scales well up to 40-variable difficult test problems used in this study. The population sizing and termination criteria are made self-adaptive, so that no additional parameters need to be supplied by the user. The study indicates a clear niche of evolutionary algorithms in solving such difficult problems of practical importance compared to their usual solution by a computationally expensive nested procedure. The study opens up many issues related to multi-objective bilevel programming and hopefully this study will motivate EMO and other researchers to pay more attention to this important and difficult problem solving activity.

Subacute Aspergillosis "Fungal Balls" Complicating COVID-19.

Severe acute respiratory syndrome coronavirus-2 infection (SARS-CoV-2), commonly known as COVID-19 (coronavirus disease-2019), began in the Wuhan District of Hubei Province, China. It is regarded as one of the worst pandemics, which has consumed both human lives and the world economy. COVID-19 infection mainly affects the lungs triggering severe hypoxemic respiratory failure, also providing a nidus for superimposed bacterial and fungal infections. We report the case of a 73-year-old male who presented with progressive dyspnea; diagnosed with SARS-CoV-2-related severe acute respiratory distress syndrome and complicated with lung cavitations growing Aspergillus sp. COVID-19, to our knowledge, has rarely been associated with subacute invasive pulmonary aspergillosis with aspergillomas. Subacute invasive pulmonary aspergillosis as a superimposed infection in patients with SARS-CoV-2 is a rare entity. By reporting this case, we would like to make the readers aware of this association.

Losartan-induced Severe Hepatic Injury: A Case Report and Literature Review.

Medications have been known to cause adverse drug reactions that affect various organs; these are mostly reversible reactions that improve with the cessation of the culprit medication. Losartan is an angiotensin-one receptor blocker which has been approved by the Food and Drug Administration (FDA) for the treatment of arterial hypertension. Fatigue, anemia, weakness, and cough are some of the common adverse effects of losartan. Acute hepatic injury has rarely been reported as an adverse effect of losartan. We report a case of a 61-year-old female with severe hepatic injury secondary to losartan use. None of the cases reported so far had such a high elevation of liver enzymes as seen in our patient.