Detection of superparamagnetic nanoparticles for immunoassays

International audienceWe develop a completely integrated Lab-on-Chip (LoC) for easy, rapid and cost-effective immunoassays. The pathogen sensing system is composed of a microfluidic channel surrounded by planar microcoils which are responsible for the emission and the detection of magnetic fields. The system allows the detection and quantification of superparamagnetic beads used for immunoassays in a “sandwich” antigen-antibody configuration. We successfully tested this device with different concentrations of nanoparticles and determine the limit of detection of the prototype. These results are promising and are a step toward the creation of a portable pathogen sensing device

Klinisches Mismatch zwischen Infarktkern in der CTPerfusion und neurologischer Ausfallssymptomatik bei akuten Mediaverschlüssen: Indikator für das Outcome nach mechanischer Thrombektomie?

Ein zerebrales Blutvolumen (CBV) unter 2 ml/100 g Hirngewebe in der CT-Perfusion gilt mit einer statistischen Wahrscheinlichkeit von 93% als bereits infarziert (Wintermark et al., 2006). Schlaganfallpatienten mit einem Diffusions-Perfusions-Mismatch in der Bildgebung profitieren besonders deutlich von einer Thrombektomie. Allerdings gab es bisher noch keine Studie zu Schlaganfallpatienten mit einem Mismatch zwischen Infarktkern und den akuten neurologischen Defiziten. Neurologische Ausfälle nach akutem ischämischen Schlaganfall, die nicht durch das CBV-markierte Infarktareal in der CT-Perfusion erklärt sind, haben wir als klinisches Mismatch bezeichnet. Wir postulierten, dass Patienten mit einem klinischen Mismatch signifikant häufiger ein im Vergleich zum Ausgangsstatus verbessertes Outcome nach einer mechanischen Rekanalisation erfahren als Patienten ohne klinisches Mismatch. Es wurden retrospektiv 139 Schlaganfallpatienten mit einem Verschluss einer großen hirnversorgenden Arterie im vorderen Kreislauf, die von 2012 bis 2015 mechanisch rekanalisiert wurden, analysiert. Es wurde anhand der nach dem CBV-Infarktkern erwartenden neurologischen Ausfallsymptomatik und der tatsächlichen neurologischen Ausfallsymptomatik bei Krankenhausankunft untersucht, ob ein klinisches Mismatch vorlag. Zudem wurden die neurologischen Ausfälle zum Entlassungszeitpunkt des Patienten erfasst, um eine Verbesserung oder Verschlechterung des neurologischen Ausgangsstatus festhalten zu können. Von 139 Patienten waren 119 Patienten in der Mismatch-Gruppe und 20 Patienten in der Match-Gruppe. Die Ergebnisse zeigten, dass beide Gruppen, das heißt Schlaganfallpatienten mit und ohne ein klinisches Mismatch, von einer mechanischen Thrombektomie profitieren. Unsere Arbeitshypothese, dass Patienten mit einem klinischen Mismatch statistisch häufiger ein gutes Outcome erzielen, konnte nicht signifikant belegt werden (p=0,07). Es konnte allerdings gezeigt werden, dass die Chance für ein verbessertes Outcome für einen Mismatch-Patienten um den Faktor 2,8 größer war als für einen Match-Patienten. Darüber hinaus belegt die vorliegende Studie, dass ein gutes Outcome vor allem vom Rekanalisationsgrad abhängig ist. Weitere Studien sind notwendig, um das Konzept des klinischen Mismatches für den klinischen Alltag zu evaluieren

Damage assessment in beam-like structures by correlation of spectrum using machine learning

Damage assessment in the actual operating process of the structure is a modern and exciting problem of construction engineering due to several practical knowledge about the current condition of the inspected structures. However, the problem faced is the difficulty in controlling the excitation in structures. Therefore, the output-based structural damage identification method is becoming attractive because of its potential to be applied to an actual application without being constrained by the collection of the information excitation source. An approach of damage assessment based on supervised Machine Learning is introduced in this study by using the correlation of spectral signal as an input feature for artificial neural network (ANN) and decision tree. The output of machine learning algorithms consists of the appearance of new cuts, the level of cutting and the cutting position. A supported beam model was constructed as an experiment to determine if the method is reasonable for engineering structures. Two machine learning algorithms have been applied to check the relevance of the proposed feature from vibration data. This study contributes a standard in the damage identification problem based on spectral correlation

Identifying undamaged-beam status based on singular spectrum analysis and wavelet neural networks

In this paper, the identifying undamaged-beam status  based on singular spectrum analysis (SSA) and wavelet neural networks (WNN)  is presented. First, a database is built from measured sets and SSA which  works as a frequency-based filter. A WNN model is then designed which consists of the wavelet frame building, wavelet structure designing and  establishing a solution for training the WNN. Surveys via an experimental  apparatus for estimating the method are carried out. In this work, a  beam-typed iron frame, Micro-Electro-Mechanical (MEM) sensors and a  vibration-signal processing and measuring system named LAM_BRIDGE are all  used

Miliutine A acid, a new cyclofarnesane sesquiterpene from the stems of <i>Miliusa velutina</i>

Six compounds were isolated from the ethyl acetate extract of the stems of Miliusa velutina, including miliutine A acid (1), a new cyclofarnesane sesquiterpenoid; miliutine B methyl ester (2), a cyclofarnesane sesquiterpenoid which was determined the absolute configuration for the first time and four known phenol derivatives (3–6). NMR spectroscopic and mass spectrometry were used for identifying relative configurations. The assignments of the absolute configurations were determined based on Electronic Circular Dichroism (ECD) and NOESY spectra analysis. All six compounds were screened for their in vitro cytotoxic activities against HepG2 cell line using the SRB assay and they showed weak or none activities.</p

Damage assessment in beam-like structures by correlation of spectrum using machine learning

Damage assessment in the actual operating process of the structure is a modern and exciting problem of construction engineering due to several practical knowledge about the current condition of the inspected structures. However, the problem faced is the difficulty in controlling the excitation in structures. Therefore, the output-based structural damage identification method is becoming attractive because of its potential to be applied to an actual application without being constrained by the collection of the information excitation source. An approach of damage assessment based on supervised Machine Learning is introduced in this study by using the correlation of spectral signal as an input feature for artificial neural network (ANN) and decision tree. The output of machine learning algorithms consists of the appearance of new cuts, the level of cutting and the cutting position. A supported beam model was constructed as an experiment to determine if the method is reasonable for engineering structures. Two machine learning algorithms have been applied to check the relevance of the proposed feature from vibration data. This study contributes a standard in the damage identification problem based on spectral correlation

Experiences of Housing Adapted to Sea Level Rise and Applicability for Houses in the Can Gio District, Ho Chi Minh City, Vietnam

As a coastal district located in the Southeast of Ho Chi Minh City (HCMC), Vietnam, Can Gio is characterized by low average terrains ranging up to only 1.5m above the sea level. Impacted by climate change and sea level rise in recent years, certain neighborhoods in the Can Gio District have been facing the loss of their residential and arable lands, as well as undesired relocations. Together with riverbank and coastal erosion, this phenomenon has several negative impacts on the lives of people in residential areas and on their economic activities. This research uses a literature review and observation as the main methods to explore the experiences of sea level rise adaptive housing and thereby suggests certain solutions for the Can Gio District. The solutions include saving space for water, elevating floors, constructing with floating floors, and creating biological ditches and osmotic lines to help quickly drain flooded water. These solutions aim to protect people’s lives and houses against the rising sea level and ensure the sustainable development of the neighborhoods

Some algorithms to solve a bi-objectives problem for team selection

In real life, many problems are instances of combinatorial optimization. Cross-functional team selection is one of the typical issues. The decision-maker has to select solutions among (kh) solutions in the decision space, where k is the number of all candidates, and h is the number of members in the selected team. This paper is our continuing work since 2018; here, we introduce the completed version of the Min Distance to the Boundary model (MDSB) that allows access to both the "deep" and "wide" aspects of the selected team. The compromise programming approach enables decision-makers to ignore the parameters in the decision-making process. Instead, they point to the one scenario they expect. The aim of model construction focuses on finding the solution that matched the most to the expectation. We develop two algorithms: one is the genetic algorithm and another based on the philosophy of DC programming (DC) and its algorithm (DCA) to find the optimal solution. We also compared the introduced algorithms with the MIQP-CPLEX search algorithm to show their effectiveness

Morphometrical signs of vicia faba l. for breading

Most vegetable bean varieties have signs of xeromorphism, which testifies in favor of their high biological resistance to drought, for example Aquadul, Belarusian, White Large-Fruited, Bobchinsky, Velena, Children’s Delight, Dachnik, Optics, Russian Black, Royal Harvest). For breeding, samples with amphestomatic vertically arranged leaves (varieties Windsor and Gourmet Dish) may be of interest. The most productive in the soil and climatic conditions of the Belgorod region (Russia) were the selections Belgorod-1 and Belgorod-2, as well as the varieties Royal Harvest, Bobchinsky, Batrom and Leade