Single session, intrauser repeatability of anterior chamber biometric and corneal pachy-volumetric parameters using a new Scheimpflug+Placido device

AbstractPurposeTo analyze single session, intrauser reliability of a Scheimpflug device for anterior chamber (AC) and corneal parameters.MethodsIn this observational study, 100 normal candidates underwent Scheimpflug analysis with Sirius 3D Rotating Scheimpflug Camera and Topography System (Costruzione Strumenti Oftalmici, Italy). Two scans in dark room conditions were performed by the same experienced user. The candidates were asked to keep both eyes closed for 5min before the scans. Exclusion criteria were previous ocular surgery, corneal scarring and anterior segment/posterior segment anomalies. Only the right eyes were used for the analysis. Both corneal (central, minimum, and apical thickness, volume, horizontal visible iris diameter, and apical curvature) and anterior chamber (volume, depth, angle, horizontal diameter) measurements were evaluated.ResultsThere was no difference in the means of repeated measurements (p>0.05, ANOVA). Intraclass correlations between the measures were high and ranged from 0.995–0.997 for corneal to 0.964–0.997 for anterior chamber (AC) parameters. The precision of repeatability measures (1.96×Sw) was approximately 5μ for the central and minimum corneal thickness, 8μ for the apical corneal thickness, 0.06mm for AC (anterior chamber) depth and less than 2° for the AC angle.ConclusionsSirius Scheimpflug system has high repeatability for both corneal and AC parameters in normal eyes

OmniVec: Learning robust representations with cross modal sharing

Majority of research in learning based methods has been towards designing and training networks for specific tasks. However, many of the learning based tasks, across modalities, share commonalities and could be potentially tackled in a joint framework. We present an approach in such direction, to learn multiple tasks, in multiple modalities, with a unified architecture. The proposed network is composed of task specific encoders, a common trunk in the middle, followed by task specific prediction heads. We first pre-train it by self-supervised masked training, followed by sequential training for the different tasks. We train the network on all major modalities, e.g.\ visual, audio, text and 3D, and report results on $22$ diverse and challenging public benchmarks. We demonstrate empirically that, using a joint network to train across modalities leads to meaningful information sharing and this allows us to achieve state-of-the-art results on most of the benchmarks. We also show generalization of the trained network on cross-modal tasks as well as unseen datasets and tasks.Comment: Accepted to WACV 202

Post-Synthesis Growth of Lanthanide-doped Nanoparticles by Surface Modification

The growth mechanisms occurring during the synthesis of nanoparticles has been previously investigated by varying reaction conditions, such as reaction time, temperature, ligand and pH. The growth mechanisms of nanoparticles are reported only for high-temperature synthesis methods, by mechanisms such as Ostwald ripening and oriented attachment. Ostwald ripening involves the dissolution of small nanoparticles into monomers, with successive growth by ripening of the surface of bigger particles with these monomers. Oriented attachment occurs by the alignment of more particles following a preferential crystal orientation, and their successive attachment at the interface between particles by so-called necking. Among the previous studies, the mechanisms behind the growth of lanthanide-doped nanoparticles have been often reported, considering the interesting properties of these nanomaterials. Despite the thorough investigations reported in the literature, investigations into the potential for post-synthesis growth mechanisms in lanthanide-doped nanoparticles at room temperature has never been explored. The presented research project aims to verify the possibility for tailoring the size and shape of lanthanide-doped nanoparticles at room temperature after their initial bottom-up synthesis at high temperature, in response to the present lack of knowledge in this field. The study of post-synthesis surface modification and growth at room temperature has to be carried out taking into consideration the same parameters that are usually varied in the reported investigations for synthesis at high temperatures, such as reaction time, ligand, pH, and temperature. For this project, the temperature is fixed at room temperature, and all the other parameters were varied. Oleate-capped nanoparticles were obtained by a co-precipitation reaction method. The potential for post-synthesis growth of two nanoparticle compositions were investigated (NaREF4 with RE = Y and Gd) to unveil the mechanism of the occurring surface phenomena. As a general protocol, the synthesized particles were subjected to ligand exchange from the as-synthesized oleate capping ligand to phosphonate ligands by first protonating the oleate ion by acidic treatment, followed by deprotonation of the phosphonate ligand and substitution on the nanoparticle surface in basic conditions. The pH was controlled by using two different bases (NaOH and KOH), and the occurrence of the ligand exchange was assessed by FT-IR spectroscopy. Modifications of nanoparticle size and shape during the post-synthesis growth were assessed by collecting aliquots at established time intervals and characterizing them via transmission electron microscopy and inductively coupled plasma mass spectrometry. The results demonstrated that the ligand can facilitate changes in morphology and growth at room temperature in presence of different bases. NaREY4 nanoparticles do not show significant growth, but a necking behaviour was observed when NaOH was used as a base in the presence of the ligand, in which the nanoparticles orient themselves and connect within each other by forming “necks” between particles (growth by oriented attachment). On the other hand, NaREF4 nanoparticles showed a significant change in size distribution when KOH was used as a base in the presence of the ligand, by forming smaller particles compared to their initial size, which then condense onto the surface of bigger nanoparticles, proving that growth occurs by Ostwald ripening. These results show that it is quite possible to induce room-temperature growth of lanthanide-doped nanoparticles post synthesis by varying the cation of the base. The use of the ligand is crucial for preventing aggregation and inducing the coalescence of smaller nanoparticles into larger ones. To our knowledge, these observations in post-synthesis growth have been reported for the first time

Automated Heart Syndrome Forecast Model Exploiting Machine Learning Approaches

Heart disease is a frequent condition that appears as a result of a poor diet and an irregular lifestyle. It is one of the most frequent diseases worldwide, with numerous reasons that damage the heart and have claimed countless lives in recent years. Due to the enormous number of risk factors for heart disease, it is critical to adopt a precise and dependable approach to provide an early diagnosis and correct prognosis. As a result, there is a broad potential for implementing various types of machine learning approaches for retrieving such critical data from the database. This study evaluates numerous machine learning algorithms for correctly predicting cardiac sickness and offers analytical findings, with an emphasis on various methodologies

Indian streets outside India : the construction of identity in Southall and Jackson Heights

Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2003.Includes bibliographical references (p. 106-108).This is a study of how street businesses owned by immigrant Indians in London and New York City construct an identity for themselves, and then lend that to the streets on which they operate. The research is conducted at Southall, a neighbourhood in West London, and at Jackson Heights in Queens, New York City. The former served as the original receiving area for rural Sikhs migrating from Punjab in the 1950s. The latter is a twenty-year-old congregation of Indian businesses in Queens. I pose two questions. First, how have street businesses owned by Indian immigrants adapted inherited physical environments? Second, are such adaptations a deliberate attempt at asserting ethnonationalist identities, while simultaneously or independently furthering economic self-interests? My research aims to establish that in the process of earning a livelihood, immigrant Indian businesspeople construct identities and aesthetics that primarily further economic self-interests, and that these are often then mistakenly believed to be their attempts at 'establishing culture'. When the unit of analysis is the individual business, economic self interest predominates all decisions of identity. There are different sets of circumstances in which Indian immigrant businesses advertise, surrender or disguise an Indian identity. I will also establish that the differing profiles of the Indian immigrants to the US and UK explains the contrasting births and growth trajectories of the businesses in Southall and Jackson Heights.by Gaurav Srivastava.M.C.P

A Review on the use of Artificial Intelligence Techniques in Brain MRI Analysis

Over the past 20 years, the global research going on in Artificial Intelligence in applica-tions in medication is a venue internationally, for medical trade and creating an ener-getic research community. The Artificial Intelligence in Medicine magazine has posted a massive amount. This paper gives an overview of the history of AI applications in brain MRI analysis to research its effect at the wider studies discipline and perceive de-manding situations for its destiny. Analysis of numerous articles to create a taxono-my of research subject matters and results was done. The article is classed which might be posted between 2000 and 2018 with this taxonomy. Analyzed articles have excessive citations. Efforts are useful in figuring out popular studies works in AI primarily based on mind MRI analysis throughout specific issues. The biomedical prognosis was ruled by way of knowledge engineering research in its first decade, whilst gadget mastering, and records mining prevailed thereafter. Together these two topics have contributed a lot to the latest medical domain