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Abstract The combination of a good quality embryo and proper maternal health factors promise higher chances of a successful in vitro fertilization (IVF) procedure leading to clinical pregnancy and live birth. Of these two factors, selection of a good embryo is a controllable aspect. The current gold standard in clinical practice is visual assessment of an embryo based on its morphological appearance by trained embryologists. More recently, machine learning has been incorporated into embryo selection “packages”. Here, we report EVATOM: a machine-learning assisted embryo health assessment tool utilizing an optical quantitative phase imaging technique called artificial confocal microscopy (ACM). We present a label-free nucleus detection method with, to the best of our knowledge, novel quantitative embryo health biomarkers. Two viability assessment models are presented for grading embryos into two classes: healthy/intermediate (H/I) or sick (S) class. The models achieve a weighted F1 score of 1.0 and 0.99 respectively on the in-distribution test set of 72 fixed embryos and a weighted F1 score of 0.9 and 0.95 respectively on the out-of-distribution test dataset of 19 time-instances from 8 live embryos.

The topic of this preliminary study comes from the need to analyze the positioning of the multi-beam bathymetric system on a research vessel on the Danube River. Starting from the variants of bathymetric echo sound systems, they were discussed in advance with the supplying company and the construction shipyard to ensure the most advantageous cases of positioning the bathymetric equipment. The results of this preliminary study provide the researchers and the shipyard with several options for positioning the bathymetric system to choose a constructive solution that will ensure the requested operation conditions for recording the most relevant waterways bathymetric data regarding the safety of navigable channels on the Danube River.

Abstract Treatment of blood smears with Wright’s stain is one of the most helpful tools in detecting white blood cell abnormalities. However, to diagnose leukocyte disorders, a clinical pathologist must perform a tedious, manual process of locating and identifying individual cells. Furthermore, the staining procedure requires considerable preparation time and clinical infrastructure, which is incompatible with point-of-care diagnosis. Thus, rapid and automated evaluations of unlabeled blood smears are highly desirable. In this study, we used color spatial light interference microcopy (cSLIM), a highly sensitive quantitative phase imaging (QPI) technique, coupled with deep learning tools, to localize, classify and segment white blood cells (WBCs) in blood smears. The concept of combining QPI label-free data with AI for the purpose of extracting cellular specificity has recently been introduced in the context of fluorescence imaging as phase imaging with computational specificity (PICS). We employed AI models to first translate SLIM images into brightfield micrographs, then ran parallel tasks of locating and labelling cells using EfficientNet, which is an object detection model. Next, WBC binary masks were created using U-net, a convolutional neural network that performs precise segmentation. After training on digitally stained brightfield images of blood smears with WBCs, we achieved a mean average precision of 75% for localizing and classifying neutrophils, eosinophils, lymphocytes, and monocytes, and an average pixel-wise majority-voting F1 score of 80% for determining the cell class from semantic segmentation maps. Therefore, PICS renders and analyzes synthetically stained blood smears rapidly, at a reduced cost of sample preparation, providing quantitative clinical information.

In order to speed up the microscopy acquisition process, we developed a method, termed GANscan, in which videos are recorded as the stage is moving at high speeds. Using generative adversarial networks (GANs), we achieve 30x the throughput of stop-and-stare systems.

Common methods for characterising cell viability involve cell staining with chemical reagents. Here the authors report a method for cell viability assessment that does not require labelling; this uses quantitative phase imaging combined with deep learning.

Rapid label-free detection of SARS-CoV-2 using phase imaging (spatial light-interference microscopy (SLIM)) with computational specificity. Different virus types captured in SLIM image are detected and classified by 2D U-Net.

The paper outlines the most important shipbuilding strategies in Romania that would allow this industry to increase its performance to be competitive. The research is mainly based on the collection of information from experts in the field from ANCONAV (Romanian Shipbuilders Association), specialists in design and production, and shipyard managers in Romania. The SWOT analysis was based on this information and was the starting point in formulating the strategies. The quantitative determination of the importance of each factor and sub-factor from the SWOT matrix in view of ranking the strategies, but also the verification of the points of view of the experts in the field were performed using the Analytical Hierarchy Process Method (AHP), using Excel applications. The obtained results consisted in identifying the internal and external environmental factors and formulating the main strategies with positive effects on the Romanian naval sector (the development of a Romanian cluster, the consolidation of the position on the global ship market through aggressive marketing, investments regarding the construction of ecological ships, the development of centers of excellence for exploiting the research-development-innovation capacity, the limitation of highly-skilled labor migration, expanding the digitalization, and developing a marketing policy to promote the national shipbuilding companies globally).

Microscopy: phased-in approach puts nanoscale electrical impedance on the map A microscope capable of real-time, label-free analysis of nanoscale structures and their associated electrical properties could find applications in both biology and materials science. High-resolution microscopy methods that detect variations in the phase of a laser as it passes through samples are helping researchers overcome the limits of traditional optical imaging. In a joint effort, the groups led by Eugen Gheorghiu from the International Centre of Biodynamics in Bucharest, Romania, and by Gabriel Popescu from the University of Illinois at Urbana Champaign have now developed an instrument that combines phase imaging with alternating voltage perturbations to measure a target’s refractive index and electrical impedance maps that can reveal properties including tumors in human tissue, or defects on coating surfaces. The team’s microscope proved capable of distinguishing the impedance contrasts and geometry of different nanostructures on a macroelectrode with submicron resolution.

Quantitative phase imaging suffers from a lack of specificity in label-free imaging. Here, the authors introduce Phase Imaging with Computational Specificity (PICS), a method that combines phase imaging with machine learning techniques to provide specificity in unlabeled live cells with automatic training.

Currently cardiovascular disease is the leading cause of mortality worldwide, resulting in approximately one third of all deaths globally. In order to address the role that hypertension plays in the morbidity and mortality of cardiovascular diseases, there is an urgent need for a paradigm shift in global hypertension management and treatment. Obtaining target pressure levels using monotherapy can be a difficult task especially in the case of patients with other associated diseases; consequently modern therapeutic guides recommend combinations of two ore more antihypertensives. Fixed dose combinations associate two or more pharmaceutical substances in a single pharmaceutical formulation; each drug typically working at a separate site, blocking different effector pathways. Compounds that struggle to be efficient in the monotherapy of patients with hypertension often become more effective if combined with another drug; associating drugs with different but complementary mechanism of action often leads to increased therapeutic efficacy. The current review presents the advantages of fixed dose combination and summarizes formulations of common dual, triple combinations used in therapy.

Summary: Human immunodeficiency virus (HIV) can infect cells and take a quiescent and nonexpressive state called latency. In this study, we report insights provided by label-free, gradient light interference microscopy (GLIM) about the changes in dry mass, diameter, and dry mass density associated with infected cells that occur upon reactivation. We discovered that the mean cell dry mass and mean diameter of latently infected cells treated with reactivating drug, TNF-α, are higher for latent cells that reactivate than those of the cells that did not reactivate. Cells with mean dry mass and diameter less than approximately 10 pg and 8 μm, respectively, remain exclusively in the latent state. Also, cells with mean dry mass greater than approximately 28-30 pg and mean diameter greater than 11–12 μm have a higher probability of reactivating. This study is significant as it presents a new label-free approach to quantify latent reactivation of a virus in single cells.

Inadequate myelination in the central nervous system is associated with neurodevelopmental complications. Thus, quantitative, high spatial resolution measurements of myelin levels are highly desirable. We used spatial light interference microcopy (SLIM), a highly sensitive quantitative phase imaging (QPI) technique, to correlate the dry mass content of myelin in piglet brain tissue with dietary changes and gestational size. We combined SLIM micrographs with an artificial intelligence (AI) classifying model that allows us to discern subtle disparities in myelin distributions with high accuracy. This concept of combining QPI label-free data with AI for the purpose of extracting molecular specificity has recently been introduced by our laboratory as phase imaging with computational specificity. Training on 8000 SLIM images of piglet brain tissue with the 71-layer transfer learning model Xception, we created a two-parameter classification to differentiate gestational size and diet type with an accuracy of 82% and 80%, respectively. To our knowledge, this type of evaluation is impossible to perform by an expert pathologist or other techniques.

Abstract In this paper we analyzed a batch of 64 clay weights from three archaeological sites located in Romania (Gumelniţa, Măgura-Jilava, and Sultana) that belong to Kodjadermen-Gumelniţa-Karanovo VI cultural complex (4600–3900 cal. BC). Our approach includes an interdisciplinary investigation based on technological analysis, experimental archaeology, and X-ray CT scans coupled with statistical analysis. This investigation has a high potential to reveal relevant information regarding the technological background (e.g., inclusion, voids, temper, etc.), manufacturing stages (e.g., modeling, shaping, kneading, etc.), or transformation processes (e.g., drying and firing vs. weight and size modification) in order to identify, explain and understand the chaîne operatoire for this type of artefacts. Moreover, correlation of the results with the experimental archaeology could offer an integrative interpretation about the material culture of past humans and its multiple meanings, but also critical information about the multiple dimensions of manufacture for these objects (e.g., time, effort, physical–chemical processes, etc.). The multi-analytical approach proposed here also includes a comparative study of technological aspects of these clay weights across the three archaeological sites investigated, as well as the experimental replicas.

Quantitative phase imaging techniques have been limited by multiple scattering of light or its use in transmission mode. Here, the authors show a gradient light interference microscopy method in a reflection geometry which allows for label-free phase imaging of bulk and opaque samples.

Complex brain functions, including learning and memory, arise in part from the modulatory role of astrocytes on neuronal circuits. Functionally, the dentate gyrus (DG) exhibits differences in the acquisition of long-term potentiation (LTP) between day and night. We hypothesize that the dynamic nature of astrocyte morphology plays an important role in the functional circuitry of hippocampal learning and memory, specifically in the DG. Standard microscopy techniques, such as differential interference contrast (DIC), present insufficient contrast for detecting changes in astrocyte structure and function and are unable to inform on the intrinsic structure of the sample in a quantitative manner. Recently, gradient light interference microscopy (GLIM) has been developed to upgrade a DIC microscope with quantitative capabilities such as single-cell dry mass and volume characterization. Here, we present a methodology for combining GLIM and electrophysiology to quantify the astrocyte morphological behavior over the day-night cycle. Colocalized measurements of GLIM and fluorescence allowed us to quantify the dry masses and volumes of hundreds of astrocytes. Our results indicate that, on average, there is a 25% cell volume reduction during the nocturnal cycle. Remarkably, this cell volume change takes place at constant dry mass, which suggests that the volume regulation occurs primarily through aqueous medium exchange with the environment.

Quantitative phase imaging (QPI) has been widely applied in characterizing cells and tissues. Spatial light interference microscopy (SLIM) is a highly sensitive QPI method due to its partially coherent illumination and common path interferometry geometry. However, SLIM’s acquisition rate is limited because of the four-frame phase-shifting scheme. On the other hand, off-axis methods such as diffraction phase microscopy (DPM) allow for single-shot QPI. However, the laser-based DPM system is plagued by spatial noise due to speckles and multiple reflections. In a parallel development, deep learning was proven valuable in the field of bioimaging, especially due to its ability to translate one form of contrast into another. Here, we propose using deep learning to produce synthetic, SLIM-quality, and high-sensitivity phase maps from DPM using single-shot images as the input. We used an inverted microscope with its two ports connected to the DPM and SLIM modules such that we have access to the two types of images on the same field of view. We constructed a deep learning model based on U-net and trained on over 1000 pairs of DPM and SLIM images. The model learned to remove the speckles in laser DPM and overcame the background phase noise in both the test set and new data. The average peak signal-to-noise ratio, Pearson correlation coefficient, and structural similarity index measure were 29.97, 0.79, and 0.82 for the test dataset. Furthermore, we implemented the neural network inference into the live acquisition software, which now allows a DPM user to observe in real-time an extremely low-noise phase image. We demonstrated this principle of computational interference microscopy imaging using blood smears, as they contain both erythrocytes and leukocytes, under static and dynamic conditions.

Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide and are usually multifactorial medical conditions. Usually cardiovascular patients must follow a complicated treatment scheme, containing several drugs. Fixed dose combinations (FDCs) are pharmaceutical formulations containing two or more active ingredients in a one pill. FDCs can add multiple benefits to the treatment of CVDs including increased patient compliance, elimination of some side effects and the simultaneous blockage of multiple pathogenic links. The great prevelance of FDCs in modern therapy brings the necessity of developing new analytical methods for the simultaneous analysis of their components. Capillary electrophoresis (CE) was shown to be as a complementary and attractive alternative to the more frequently used chromatographic methods. CE advantages relate to the high efficiency of separation, rapid method developement, short analysis time and relatively low operational costs. The most frequently used CE techniques in the analysis of FDCs are capilllary zone electrophoresis (CZE) for ionized analytes and micellar electrokinetic chromatography (MEKC) for neutral ones. The current article reviews application of CE in the analysis of FDCs used in the treatment of CVDs.

Current pathology workflow involves staining of thin tissue slices, which otherwise would be transparent, followed by manual investigation under the microscope by a trained pathologist. While the hematoxylin and eosin (H&E) stain is well-established and a cost-effective method for visualizing histology slides, its color variability across preparations and subjectivity across clinicians remain unaddressed challenges. To mitigate these challenges, recently, we have demonstrated that spatial light interference microscopy (SLIM) can provide a path to intrinsic objective markers that are independent of preparation and human bias. Additionally, the sensitivity of SLIM to collagen fibers yields information relevant to patient outcome, which is not available in H&E. Here, we show that deep learning and SLIM can form a powerful combination for screening applications: training on 1660 SLIM images of colon glands and validating on 144 glands, we obtained an accuracy of 98% (validation dataset) and 99% (test dataset), resulting in benign vs cancer classification accuracy of 97%, defined as area under the receiver operating characteristic curve. We envision that the SLIM whole slide scanner presented here paired with artificial intelligence algorithms may prove valuable as a pre-screening method, economizing the clinician’s time and effort.

Deficient myelination of the brain is associated with neurodevelopmental delays, particularly in high-risk infants, such as those born small in relation to their gestational age (SGA). New methods are needed to further study this condition. Here, we employ Color Spatial Light Interference Microscopy (cSLIM), which uses a brightfield objective and RGB camera to generate pathlength-maps with nanoscale sensitivity in conjunction with a regular brightfield image. Using tissue sections stained with Luxol Fast Blue, the myelin structures were segmented from a brightfield image. Using a binary mask, those portions were quantitatively analyzed in the corresponding phase maps. We first used the CLARITY method to remove tissue lipids and validate the sensitivity of cSLIM to lipid content. We then applied cSLIM to brain histology slices. These specimens are from a previous MRI study, which demonstrated that appropriate for gestational age (AGA) piglets have increased internal capsule myelination (ICM) compared to small for gestational age (SGA) piglets and that a hydrolyzed fat diet improved ICM in both. The identity of samples was blinded until after statistical analyses.

The surgical pathology workflow currently adopted by clinics uses staining to reveal tissue architecture within thin sections. A trained pathologist then conducts a visual examination of these slices and, since the investigation is based on an empirical assessment, a certain amount of subjectivity is unavoidable. Furthermore, the reliance on external contrast agents such as hematoxylin and eosin (H&E), albeit being well-established methods, makes it difficult to standardize color balance, staining strength, and imaging conditions, hindering automated computational analysis. In response to these challenges, we applied spatial light interference microscopy (SLIM), a label-free method that generates contrast based on intrinsic tissue refractive index signatures. Thus, we reduce human bias and make imaging data comparable across instruments and clinics. We applied a mask R-CNN deep learning algorithm to the SLIM data to achieve an automated colorectal cancer screening procedure, i.e., classifying normal vs. cancerous specimens. Our results, obtained on a tissue microarray consisting of specimens from 132 patients, resulted in 91% accuracy for gland detection, 99.71% accuracy in gland-level classification, and 97% accuracy in core-level classification. A SLIM tissue scanner accompanied by an application-specific deep learning algorithm may become a valuable clinical tool, enabling faster and more accurate assessments by pathologists.

Abstract Breast cancer is the most common type of cancer among women worldwide. The standard histopathology of breast tissue, the primary means of disease diagnosis, involves manual microscopic examination of stained tissue by a pathologist. Because this method relies on qualitative information, it can result in inter-observer variation. Furthermore, for difficult cases the pathologist often needs additional markers of malignancy to help in making a diagnosis, a need that can potentially be met by novel microscopy methods. We present a quantitative method for label-free breast tissue evaluation using Spatial Light Interference Microscopy (SLIM). By extracting tissue markers of malignancy based on the nanostructure revealed by the optical path-length, our method provides an objective, label-free and potentially automatable method for breast histopathology. We demonstrated our method by imaging a tissue microarray consisting of 68 different subjects −34 with malignant and 34 with benign tissues. Three-fold cross validation results showed a sensitivity of 94% and specificity of 85% for detecting cancer. Our disease signatures represent intrinsic physical attributes of the sample, independent of staining quality, facilitating classification through machine learning packages since our images do not vary from scan to scan or instrument to instrument.

Challenges in biological imaging include labeling, photobleaching and phototoxicity, as well as light scattering. Here, Nguyen et al. develop a quantitative phase method that uses low-coherence interferometry for label-free 3D imaging in scattering tissue.

For the river shipping the transported cargo values have to be computed based on the DS-draught survey records. Differences for cargo masses are recorded between DS and the on-shore weight. In order to increase the accuracy of the DS - draught survey procedure, we have developed the DRSWIN program based on a digital barge shape model. The quality of the barge shape model has a significant influence on the overall draught processing accuracy. For the barge type B1740T Europe B2, we have developed three hull shapes digital models with different density of the offset-lines. The results point out the most suitable hull shape digital model for the best accuracy on DS - draught survey processing.

We studied collagen fiber organization in tissue affected by pelvic organ prolapse (POP) and compared it to asymptomatic controls. Both the control and POP tissue biopsies were prepared and measured by a highly sensitive quantitative phase imaging (QPI) system, called spatial light interference microscopy (SLIM). Combined with automatic image processing, this modality provides quantitative, high-throughput assessment of fiber morphology. We found the fiber orientation in prolapsed specimens is less homogeneous, indicating an abnormal organization of collagen in the extracellular matrix (ECM).

In broad, deep and dynamic process of knowledge transfer, the cooperative structures can have a decided role in improving the information as a factor of production in agriculture. The process of defining a cooperative structure, as the intermediate link between producers and consumers of information and taking in consideration the principles of economic liberalism requires going through several steps which well-define, in the implementation of projects with European funding philosophy, can become self-standing actions. In this context, it is important to establish criteria through which are defined the target group, tools for promoting the new, and patterns for managing market relationships of agricultural markets.

This paper presents the advantages of using a in the development of integrated marine design CAD-CAM in parallel with realistic 3D visualization in virtual systems. The software programs used are Aveva Marine for Ship Structures 3D designs and Navisworks for their visualization .

Agriculture is the sector that satisfes the demand for food of the population of a state, thus providing key solutions for combating poverty and / or hunger. The need for funding in agriculture is increasingly debated at international level, especially as discrepancies arise in relations between the main actors involved, namely: farmers, credit institutions and national and European public authorities with the aim of the European funds management. The relational problems, and not only, of the three actors mentioned above are manifested in various forms. These are discussed in this paper. Under these conditions, it is proposed to strengthen the relationship between the three actors, frst by assessing the existing problems and, secondly, by solving them.

Tissue refractive index provides important information about morphology at the nanoscale. Since the malignant transformation involves both intra- and inter-cellular changes in the refractive index map, the tissue disorder measurement can be used to extract important diagnosis information. Quantitative phase imaging (QPI) provides a practical means of extracting this information as it maps the optical path-length difference (OPD) across a tissue sample with sub-wavelength sensitivity. In this work, we employ QPI to compare the tissue disorder strength between benign and malignant breast tissue histology samples. Our results show that disease progression is marked by a significant increase in the disorder strength. Since our imaging system can be added as an upgrading module to an existing microscope, we anticipate that it can be integrated easily in the pathology work flow.

Quantitative phase imaging (QPI) is an emerging optical approach that measures the optical path length of a transparent specimen noninvasively. Therefore, it is suitable for studying unstained biological tissues and cells with high sensitivity and resolution. This capability of QPI has fueled itself to grow rapidly as an active field of study for the past two decades. With this trend, QPI has experienced some breakthroughs in methods and applications in the past year. We briefly review some of these breakthroughs in method, including QPI through silicon marker-free phase nanoscopy and white-light diffraction tomography. Furthermore, some of the applications, such as quantitative phase measurement of cell growth and real-time blood testing, are introduced to show the importance and applicability of the field.

The aim of this paper is to study the influence of shape design on flow simulation. A preliminary CFD analysis is done for choosing the environment conditions. Both in water and in air calculus are made. The second part of this research is focused on behavior in air environment for models with different geometry. The numerical analysis and conclusions are presented at the end of the paper.

In terms of the EU enlargement from 15 to 27 states, the need to reform the CAP mechanisms was felt more than ever. Reorientation towards rural development measures and not towards supporting agricultural production raised a whole issue in which the efficiency criteria of the agricultural policies are concerned. If until now the Union entire attention was directed towards industrial farms, the option to promote family farms, with lower returns but with a high social impact by mobilizing the human resources from the rural area towards this field and preventing migration towards the city and the industrial areas, raised fierce debates. This paper presents a brief analysis of the impact of re-focusing the CAP towards promoting family-farms, mostly of the subsistence type, in the Romanian agrarian economy, in the context of an increased globalization of the agricultural relations.

In the philosophical meaning, the cooperative is a result of the knowing experimentallydevelopment and includes the interaction between: persons of consequence – through ideas and attitudes,state – through laws and institutes, experiences – through structures and effects.In Romania, in the first half of the XIXth century, and to the threshold of the Second World War, areremarked numerous persons of consequence who promote and support the cooperative movement, suchas P.S. Aurelian, Spiru Haret, Ion Raducanu, Virgil Madgearu, Mitita Constantinescu and NicolaeCornateanu.The state has accepted the cooperative as an instrument of the democratization of the capital andprofit. The cooperative movement had fight continuously towards promotion of the collaboration principlebetween cooperative companies, principle by virtue of which the organizations can manifest independencein confrontation with the state.The experiences had been substantiated mostly on the ideology of modern cooperative systems:Rochdale, Raiffaisen and Schulttze.The Romanian cooperative movement appeared, just like in the majority of European states, on abackground of some restrictions in the agricultural field, generated by a complex of factors among whichthe main position in a constant way had been hold by the contest between the big and small agriculturalfarms.In Romania, during the period before and after-war, cooperatives’ organization worked successfullyas credit cooperatives or economical cooperatives (consumption cooperatives, supply and sale cooperatives,forestry cooperatives, purchase community, leasing community, etc.).The various shapes of the cooperative movements shows the potential which those have had in thepurpose of their economical development and social situation improvement of the farmers. The potentialwas narrowed not only by the legislative and institutional instability, but more by the agriculturalmarket size and intensity. The cooperatives activities efficiency was depending, before all, on the economicand social environment within which they were acting. But the economic environment was imposingas market partner, the farm as an economic-social entity with autarchic behaviour. In consequencethe cooperative sector’s performances were conditioned by the farms’ performances.

The selling of farm lands is the opposite of the agrarian reforms for livery or reconstitution and constitution of the land ownership right. The sale-purchase has the role to facilitate the concentration of the areas into bigger size farms, which creates real chances for performance and efficiency in the farm activity. Upon the dispute between the agrarian reform and the sale of the farm lands, as major fields of action of the agrarian policy, the ideas are not at all new, they emerged right after the first reform in 1864 and certainly they will continue until a new re-configuration of the agrarian structures in Romania, a result of the lands’ free circulation. The time interval between Law no 18/1991 and Law no 54/ 1998 could be considered as a restrictive factor in the direction of the agricultural performant farm’s formation. The price of the agricultural land area, on the country’s territory is very different, its variation being influenced in an essential manner, by the ratio supply and demand, the consolidation degree, the presence of the irrigation systems and, in a very reduced share, by its quality (fertility). In conclusion, Romania’s integration into EU will give impulse, certainly to the growth of the demand for lands, in general and of those agricultural especially, fact which will impose an increasing trend of the selling prices and thus the increase of the interest of those eager to estrange their land properties.

The lease reactivation, within the agrarian relations, at year 1994, was considered as a "normal economic phenomenon". The rent, as a price of the land lease, should equal gratify the interest of those two marketer partners, land owner and leaseholder. The estimations prove the restrictive character of the lease for owner of the land, not through the hectare's quantum, but through the total income size gained by the family, as a result of the farm low size, or of the plot of land gave to lease. The lease as a landed market's action has demonstrated its role within the agriculture farm size increase, merely through the restrictive manufactures factors character at the renters, which has substituted to the households.

Building on previous insights that have decentered the understanding of borders in a simple, linear manner, there have been recent calls for a paradigmatic change in understanding the role borders play in organizing and regulating contemporary social interaction in space. The main argument focuses on the shift of the role of borders from remote limits controlling access to state territories to central ordering devices at the core of societies both in symbolic and material terms. In this view, borders play vital roles in individuals’ lives, reaching deep into the fabric of societies to structure and regulate daily routines as well as long-term strategies. Several developments such as advances in digital technology, globalization-driven mobility imperatives, flow-related security demands, and a topological imagination of space, are converging to shape a new border paradigm that unsettles long established political and economic practices and rises essential questions about power relationships in society in the twenty-first century.

Studying the dynamics of fibrin clot formation and its morphology is an important problem in biology and has significant impact for several scientific and clinical applications. We present a label-free technique based on quantitative phase imaging to address this problem. Using quantitative phase information, we characterized fibrin polymerization in real-time and present a mathematical model describing the transition from liquid to gel state. By exploiting the inherent optical sectioning capability of our instrument, we measured the three-dimensional structure of the fibrin clot. From this data, we evaluated the fractal nature of the fibrin network and extracted the fractal dimension. Our non-invasive and speckle-free approach analyzes the clotting process without the need for external contrast agents.

The detection and treatment of cancer has advanced significantly in the past several decades, with important improvements in our understanding of the fundamental molecular and genetic basis of the disease. Despite these advancements, drug-screening methodologies have remained essentially unchanged since the introduction of the in vitro human cell line screen in 1990. Although the existing methods provide information on the overall effects of compounds on cell viability, they are restricted by bulk measurements, large sample sizes, and lack capability to measure proliferation kinetics at the individual cell level. To truly understand the nature of cancer cell proliferation and to develop personalized adjuvant therapies, there is a need for new methodologies that provide quantitative information to monitor the effect of drugs on cell growth as well as morphological and phenotypic changes at the single cell level. Here we show that a quantitative phase imaging modality known as spatial light interference microscopy (SLIM) addresses these needs and provides additional advantages over existing proliferation assays. We demonstrate these capabilities through measurements on the effects of the hormone estradiol and the antiestrogen ICI182,780 (Faslodex) on the growth of MCF-7 breast cancer cells. Along with providing information on changes in the overall growth, SLIM provides additional biologically relevant information. For example, we find that exposure to estradiol results in rapidly growing cells with lower dry mass than the control population. Subsequently blocking the estrogen receptor with ICI results in slower growing cells, with lower dry masses than the control. This ability to measure changes in growth kinetics in response to environmental conditions provides new insight on growth regulation mechanisms. Our results establish the capabilities of SLIM as an advanced drug screening technology that provides information on changes in proliferation kinetics at the cellular level with greater sensitivity than any existing method.

Spatial light interference microscopy (SLIM) is a highly sensitive quantitative phase imaging method, which is capable of unprecedented structure studies in biology and beyond. In addition to the π/2 shift introduced in phase contrast between the scattered and unscattered light from the sample, 4 phase shifts are generated in SLIM, by increments of π/2 using a reflective liquid crystal phase modulator (LCPM). As 4 phase shifted images are required to produce a quantitative phase image, the switching speed of the LCPM and the acquisition rate of the camera limit the acquisition rate and, thus, SLIM's applicability to highly dynamic samples. In this paper we present a fast SLIM setup which can image at a maximum rate of 50 frames per second and provide in real-time quantitative phase images at 50/4 = 12.5 frames per second. We use a fast LCPM for phase shifting and a fast scientific-grade complementary metal oxide semiconductor (sCMOS) camera (Andor) for imaging. We present the dispersion relation, i.e. decay rate vs. spatial mode, associated with dynamic beating cardiomyocyte cells from the quantitative phase images obtained with the real-time SLIM system.

We demonstrate a real-time blood testing system that can provide remote diagnosis with minimal human intervention in economically challenged areas. Our instrument combines novel advances in label-free optical imaging with parallel computing. Specifically, we use quantitative phase imaging for extracting red blood cell morphology with nanoscale sensitivity and NVIDIA's CUDA programming language to perform real time cellular-level analysis. While the blood smear is translated through focus, our system is able to segment and analyze all the cells in the one megapixel field of view, at a rate of 40 frames/s. The variety of diagnostic parameters measured from each cell (e.g., surface area, sphericity, and minimum cylindrical diameter) are currently not available with current state of the art clinical instruments. In addition, we show that our instrument correctly recovers the red blood cell volume distribution, as evidenced by the excellent agreement with the cell counter results obtained on normal patients and those with microcytic and macrocytic anemia. The final data outputted by our instrument represent arrays of numbers associated with these morphological parameters and not images. Thus, the memory necessary to store these data is of the order of kilobytes, which allows for their remote transmission via, for example, the cellular network. We envision that such a system will dramatically increase access for blood testing and furthermore, may pave the way to digital hematology.

The fundamental problem, and at the same time, the great challenge of Romanian agrarian policy, as well as related sciences, is the chronic poverty of peasants, agricultural land owners. This issue suggests that the land, as an essential element of ownership, paradoxically, no longer creates welfare for the peasants. Hence, is only natural to ask: What does the peasant still wants or expects from the land? or Why is the peasant still attached to the land? Most researchers of Agricultural Economics would respond to these questions quantifying and analyzing the results of household production. Their approach is not wrong, yet not suffcient. After all, they study the visible part of the iceberg. A complete or nearly complete answer requires deeper inquiries, with reference to other areas of knowledge than the economic one, such as sociology, psychology, culture, history, morality, religion.

Studying the 3D sub-cellular structure of living cells is essential to our understanding of biological function. However, tomographic imaging of live cells is challenging mainly because they are transparent, i.e., weakly scattering structures. Therefore, this type of imaging has been implemented largely using fluorescence techniques. While confocal fluorescence imaging is a common approach to achieve sectioning, it requires fluorescence probes that are often harmful to the living specimen. On the other hand, by using the intrinsic contrast of the structures it is possible to study living cells in a non-invasive manner. One method that provides high-resolution quantitative information about nanoscale structures is a broadband interferometric technique known as Spatial Light Interference Microscopy (SLIM). In addition to rendering quantitative phase information, when combined with a high numerical aperture objective, SLIM also provides excellent depth sectioning capabilities. However, like in all linear optical systems, SLIM's resolution is limited by diffraction. Here we present a novel 3D field deconvolution algorithm that exploits the sparsity of phase images and renders images with resolution beyond the diffraction limit. We employ this label-free method, called deconvolution Spatial Light Interference Tomography (dSLIT), to visualize coiled sub-cellular structures in E. coli cells which are most likely the cytoskeletal MreB protein and the division site regulating MinCDE proteins. Previously these structures have only been observed using specialized strains and plasmids and fluorescence techniques. Our results indicate that dSLIT can be employed to study such structures in a practical and non-invasive manner.

The economic theory recognizes the necessity of fusion, as an important action of agrarian policy, through which it should be realized the fusion of the land ownership in the peasant households, but in a legislative plan, there is no own disposition, which could norm it as it was done with the other actions which have in view the movement of the land ownership, as are buying, sale, leasing, association and cooperation. From this reason, all efforts in the last period, made for the parcels fusion, have pertained exclusively to the farmers, and they were done through the actions mentioned above. The land fusion, in a conceptual plan, legislative and practical, is presently approached in a diffuse manner, superficial, and, mostly timidly and distortedly. That is why, through this study, even it is synthetic, we are addressing ourselves to the professional readers, but especially to the deciders in the agrarian policy, in the goal of forming up a favorable opinion current, to energize the actions of coagulating the land ownerships in our agriculture The agrarian policy, in an imperative formulation, must put into law, and at the same time, apply the fusion, which should have as finality the solving up of the land parceling problem in the peasant households, in a reasonable time period.