Indian Institute of Science Bangalore
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Role of Mint family of Proteins in the nanoscale localization and real-time retention of surface Amyloid Precursor
No full textAmyloid beta, a key determinant in the pathology of Alzheimer's disease (AD), is formed by the sequential proteolysis of Amyloid Precursor Protein (APP) by β-and γ-secretase. Evidence over the last few decades indicates that synaptic dysfunction and loss is an early event in AD that correlates with amyloid production in the whole brain and at the synaptic level. Though not well understood, previous reports have shown that APP retention at the synaptic membrane undergoes a non-amyloidogenic pathway. On the contrary, the internalization of APP at the endocytic zones leads to enhanced amyloid-beta production. Therefore, it is vital to understand the molecular parameters that control the localization and trafficking of APP at these functional zones of synaptic compartments and how this could be regulated. The organization and trafficking of molecules to functional zones of synapses result from the coordination of several regulatory steps and is tightly controlled. APP shows the heterogeneous distribution in the neuronal processes, indicating differential regulation of APP at various sub-compartments. Recent studies have demonstrated differential nanoscale localization of APP and secretases in functional zones of the synapse. Furthermore, the instantaneous distribution of APP on the synaptic membrane can be controlled by passive lateral diffusion of APP between the synaptic membrane and endocytic zone and the active internalization of APP at the endocytic zone.
The dynamics and biophysical properties of APP can be influenced by the proteins which interact with APP. It is known that the scaffolding proteins play a significant role in the trafficking of APP. Munc Interacting family of proteins (Mint) are scaffolding proteins known to regulate the trafficking and processing of APP. However, the mechanism by which it
modulates these processes remains unclear. The Mint family of proteins has three isoforms- Mint1/X11α, Mint2/X11β, Mint3/X11γ, all of which are evolutionary conserved. These Mint isoforms interact with the internalization motif of APP through their phosphotyrosine binding (PTB) domain. To characterize the association with APP, it is essential to understand the differential expression, localization, and exchange kinetics of Mint isoforms. We observed that expression of mRNA transcripts of Mint isoforms is developmentally regulated in the hippocampus and cortex.
Additionally, we studied the differential localization of Mint1 and Mint2 in the functional zones of the synapse with super-resolution microscopy. We observed that Mint1 and Mint2 are differentially distributed in the subsynaptic compartment indicating the differential role of Mint isoforms in APP regulation. They are highly enriched in inhibitory synapses compared to the excitatory synapse. Mint1 is enriched more in the endocytic zone and active zone, while Mint2 is highly enriched in the active zone than the endocytic zone. Further, we investigated if there is a difference in trafficking between Mint isoforms with fluorescence recovery after photobleaching and single particle tracking in heterologous cell lines. The results indicated strong confinement for Mint3 and Mint2 in comparison to Mint1.
Elevation of Mint1 and Mint2 in heterologous cell lines significantly impacted the nanoscale clustering of endogenous APP. Mint1 and Mint2 increased the packing density of APP molecules per cluster, indicating that APP tends to aggregate more in the presence of Mint1 or Mint2. Additionally, we evaluated how co-expression of Mint2 with APP wild type and a detrimental variant of APP (APP Swedish) can affect the lateral mobility of APP on the plasma membrane. In cells with elevated levels of Mint2, confinement of both APP wild type and APP Swedish was higher and consistent with a reduction in lateral mobility. When the interaction of Mint2 with APP was perturbed by the deletion of the PTB domain from Mint2, it resulted in a disparate effect on the lateral mobility of APP wild type and APP Swedish. However, in cells expressing a variant of APP where the internalization motif (YENPTY) of APP was deleted, Mint2 did not affect the lateral mobility of APP. These observations support that, Mint2 is a molecular determinant in controlling lateral mobility and works independently of the mutations near the transmembrane and juxta membrane region resulting in an elevation of Amyloid-beta levels. These results implicate a broader role for C-terminal interactions of APP
for regulating the molecular locus of canonical processing. Additionally, these observations highlight the role of such potential mechanisms that contribute to the sporadic onset of AD
Total Synthesis of Piperidine and Pyrrolidine Alkaloids and Towards the Total Synthesis of Strychnine
No full textTotal synthesis of natural products is an important area of contemporary interest in synthetic
organic chemistry. Total synthesis of natural products serves as a platform for the development of
new synthetic strategies and for improving the existing synthetic methods. Chiral sulfinimines are
excellent substrates for the synthesis of amine-containing compounds. The ability of sulfinimines in
rendering the product amines with consistent high selectivity has made these reagents one of the
most used reagents in organic synthesis. The thesis describes the investigations concerning the
addition of nucleophiles such as the Wittig Ylides, and benzyloxy ketones to sulfinimines. Application of
the formed products in the synthesis of piperidine, pyrrolidine alkaloids, and in the total synthesis of
strychnine is described.SERB, Grant No. CRG/2018/00133
A Tropical Survey of Mid-Tropospheric Cyclones, their Classification and Genesis over the Arabian Sea
No full textMiddle Tropospheric Cyclones (MTCs) are moist synoptic tropical systems with vorticity maxima in the middle troposphere and weak signature in the lower troposphere. We begin with a tropical survey of MTCs; in South Asia, manual tracking reveals that MTCs change character during their life, i.e., their track is composed of MTC and LTC (lower troposphere cyclone) phases. The highest MTC-phase density and least motion is over the Arabian Sea, followed by the Bay of Bengal and the South China Sea. An MTC-phase composite shows an east-west tilted warm above deep cold-core temperature anomaly with maximum vorticity at 600 hPa. In contrast, the LTC phase shows a shallow cold-core below 800 hPa and a warm upright temperature anomaly with a lower tropospheric vorticity maximum. Further, the systems with MTC-like morphology are observed over the west and
central Africa and east and west Pacific in boreal summer. In boreal winter, regions that support MTCs include northern Australia, the southern Indian Ocean, and South Africa. The MTC’s kinematic and thermal structure exhibit remarkable similarity among different basins, suggesting a common underlying maintenance mechanism. Given that the Arabian Sea is a hot spot of devastating MTCs, their classification and genesis mechanisms in this region are explored. Both k-means and cyclone tracking approaches reveal four dominant weather patterns that lead to the genesis of these systems; specifically, re-intensification of westward-moving synoptic systems from
Bay of Bengal (Type 1, 51%), in-situ formation with a coexisting cyclonic system over the Bay of Bengal that precedes (Type 2a, 31%) or follows (Type 2b, 10%) genesis in the Arabian Sea, and finally in-situ genesis within a northwestward propagating cyclonic anomaly from the South Bay of Bengal (Type 2c, 8%). Thus, a significant fraction of rainy middle tropospheric synoptic systems in this region form in association with cyclonic activity in the Bay of Bengal (BOB). While in-situ formation with a BOB cyclonic anomaly (Type 2a and 2b) primarily occurs in June, downstream development is more likely in the core of the monsoon season. Type 2a is associated with the highest rain rate and points towards the dynamical interaction between a low-pressure system over the
BOB and the development of MTCs over western India and the northeast Arabian Sea.
The frequent coexistence BOB lows during the Type 2a formation of MTCs is not merely a coincidence. Rather the BOB system induces an off-equatorial Gill type response which deepens the middle tropospheric trough and zonal shear over the Arabian Sea. In turn, this enhances the cyclonic vorticity and intensifies the middle troposphere anomalous easterlies north of 20 ◦ N. This results in reduction of dry and warm desert air advection, depletion of low-level inversion and destabilization of the lower troposphere. Following which, the eddy-induced moisture flux convergence and advection of climatological moisture increases the saturation fraction. These favorable conditions within the middle troposphere trough region triggers the genesis of MTCs over the Arabian
Sea. The proposed role of BOB lows in MTC formation is validated by numerical experiments using the state-of-the-art Weather Research & Forecast (WRF) model. Twenty one ensemble members were generated through addition of balanced vortices to the climatological flow in the BOB. Consistent with observations, in simulations, the BOB low deepens the monsoon trough over western India, enhances the cyclonic shear, reduces the inversion, and increases the middle troposphere relative humidity; supporting the genesis of an MTC over the Arabian Sea within 2.5 − 4 days of model integration. During the first 24 − 36 hours of intensification, advection of absolute
vorticity and tilting account for the entire vorticity tendency, while during the rapid intensification phase, vortex stretching is the dominant source of vorticity enhancement. Mechanism Denial Runs with cooling and drying of BOB were then performed to show that this hinders the intensification of the low over the Bay and consequently, the MTC did not form over the Arabian Sea. This global survey, classification, identification of precursors, connection with cyclonic activity over the Bay of Bengal, and dependence on the large-scale environment provide an avenue for better understanding and predicting rain-bearing MTCs
Detection of charge-neutral modes to spin-wave excitations in graphene quantum Hall, and periodic magnetic field effect on Dirac electrons
No full textIn the quantum Hall (QH) regime, the electrical transport happens along the downstream chiral edge modes (dictated by the external magnetic field) while the bulk remains a vanilla insulator. However, it has been theoretically predicted that a certain class of fractional QH phases may also contain upstream modes together with the downstream ones (counter-propagating modes). Further, these upstream modes may not carry any electrical current but can carry energy. Detecting the charge-neutral upstream modes is challenging and remains critical for the emergence of renormalized modes with exotic quantum statistics for quantum computing. In this context, QH of graphene is an ideal platform with more degrees of freedom like spin, valley, and orbital together with a unique half-filled zeroth Landau level (ν=0 state), where the bulk is not vanilla type but rather can host canted antiferromagnetic (CAF) phase with charge-neutral Goldstone modes or isospin-ferromagnetic (FM) phase (ν≠0) with spin-wave excitations like magnon, etc. This thesis attempts to detect these charge-neutral upstream modes and spin-wave excitations in graphene QH using “Noise Thermometry”, based on heat transport.
First, we present our study on detection of charge neutral upstream modes at hole-conjugate ν=2/3 and 3/5 fractional QH states of bilayer graphene. We observed excess noise along the edge in the upstream direction at ν=2/3 and 3/5 states, providing smoking gun evidence of upstream modes at these fillings, while no noise is detected at integer and particle-like FQH states. The channel length and temperature dependence of the noise in upstream direction at ν=2/3, together with remarkable agreement of theoretically calculated noise, suggest ballistic nature of upstream modes, quite distinct from the diffusive nature reported in GaAs/AlGaAs based system. Next, using similar technique to detect heat flow in the upstream direction, we detected charge neutral spin-wave excitations at the ν=2 QH ferromagnet in bilayer graphene. We generate spin excitation by creating an imbalance in the chemical potential (> Zeeman energy gap) between the edge states of opposite spin, and detect it in the upstream direction due to heat transport by spin-wave. The observed threshold of bias energy (where spin excitation occurs) at different magnetic fields agrees with the expected Zeeman gap. In a slightly different measurement scheme, we tried to detect heat transport signature of charge neutral Goldstone modes present at ν=0 CAF state. Our findings shed light on the competition between the heat transport via goldstone modes and phonon.
Next, we study the effect of periodic magnetic field on Hall conctivity of graphene. The periodic magnetic field over graphene was created by Abrikosov vortices of a type-II superconductor (NbSe2 in this work). We found a density-dependent reduction of the Hall conductivity of graphene as the temperature is lowered from above the superconducting critical temperature of NbSe2, where the magnetic field is uniform, to below, where the magnetic field bunches into an Abrikosov flux lattice.INSPIRE FELLOWSHI
Scheduling Algorithms for Wireless Networks and Cloud Computing Platforms
No full textThis thesis focuses on designing scheduling algorithms for wireless communication networks, FMCW radar networks, and cloud computing platforms. In each case, the algorithms aim at efficient utilization of resources, e.g., frequency, power, storage etc.
In the first work, we propose a class of binary queue length information based max-weight scheduling algorithms for wireless networks. In these algorithms, the scheduler, in addition to channel states, only needs to know when a link’s queue length crosses a prescribed threshold. We show that these algorithms are throughput optimal. Further, we incorporate time-since-last service (TSLS) information to improve delay and service regularity of the scheduling algorithms while ensuring throughput optimality. Finally, we suggest amendments to the proposed algorithms to facilitate distributed, CSMA-like, implementation in a restricted class of wireless networks.
In the second work, we consider a cellular downlink in which the base stations (BSs) can be switched on and off dynamically. We also consider two costs (i) BS running cost (ii) BS switching (ON to OFF or OFF to ON) cost and propose cost-optimal BS switching and rate allocation policies that are also throughput optimal. The proposed policies use the drift-plus-penalty framework. In these, the scheduler first takes BS switching decision based on channel state statistics and queue-lengths and then selects a rate vector depending on channel realization of the BSs that are ON. We subsequently combine these policies with an algorithm that learns the channel statistics using explore-exploit technique.
In the third work, we study medium access in FMCW radar networks. In particular, we propose and analyze slotted ALOHA and CSMA protocols to mitigate narrowband interference. We define a notion of throughput to quantify the performance of the proposed protocols. In the case of ALOHA, we analyse interference probability and throughput as functions of the system parameters. We define a medium sensing procedure, referred to as clear channel assessment (CCA), as a part of the proposed CSMA, and also define CCA success and failure events. We study, CCA success probability, interference probability, and throughput as functions of the system parameters.
Finally, we propose job scheduling algorithms to minimize job migration and server running costs in cloud computing platforms offering Infrastructure as a Service. We first consider algorithms that assume knowledge of job-size on arrival of jobs. We characterize the optimal cost subject to system stability. We develop a drift-plus-penalty framework based algorithm that can achieve optimal cost arbitrarily closely. We then relax the job-size knowledge assumption and give an algorithm that only uses readily offered service to the jobs and still gives order-wise identical cost as the job size based algorithm
Design and Development of an Intubation Catheter Integrated with MEMS-based Sensors for Central Airway Obstruction
No full textAirway pathology leads to alteration in fluid flow, tissue biomechanics, and loss of patency. To address this clinical challenge, we developed an intraoperative tool to locate the site of obstruction, characterize tracheal tissue stiffness, and quantify the lumen diameter. To quantify the three parameters, flows sensors, force sensors, and unfurling compliant actuator evolved. The small size, fast response, and low power consumption of Microelectromechanical system (MEMS) sensors for medical diagnostics and biointerface engineering make them an optimal choice for integrating with healthcare monitoring tools. MEMS sensors allow miniaturization, batch fabrication, conformal mounting on catheters, guidewires, and endoscopes. This work comprehends all phases involved in the tool development, from the fabrication of microengineered sensors to integrating on flexible printed circuit board (FPCB) and further validating its utility in a pseudo-physiological test bench using excised sheep tracheal tissues
Data-efficient Deep Learning Algorithms for Computer Vision Applications
No full textThe performance of any deep learning model depends heavily on the quantity and quality of the available training data. The generalization of the trained deep models improves with the availability of a large number of training samples and hence these models are often referred to as ‘data-hungry’. However, large scale datasets may not always be available in practice due to proprietary/privacy reasons or because of the high cost of generation, annotation, transmission and storage of data. Hence, efficient utilization of available data is of utmost importance, and this gives rise to a class of ML problems, which is often referred to as “data-efficient deep learning”. In this thesis we study the various types of such problems for diverse applications in computer vision, where the aim is to design deep neural network-based solutions that do not rely on the availability of large quantities of training data to attain the desired performance goals. Under the aforementioned thematic area, this thesis focuses on three different scenarios, namely - (1) learning in the absence of training data, (2) learning with limited training data and (3) learning using selected subset of training data.
Absence of training data: Pre-trained deep models hold their learnt knowledge in the form of model parameters that act as ‘memory’ for the trained models and help them generalize well on unseen data. In the first part of this thesis, we present solutions to a diverse set of ‘zero-shot’ tasks, where in absence of any training data (or even their statistics) the trained models are leveraged to synthesize data-representative samples. We dub them Data Impressions (DIs), which act as proxy to the training data. As the DIs are not tied to any specific application, we show their utility in solving several CV/ML tasks under the challenging data-free setup, such as unsupervised domain adaptation, continual learning as well as knowledge distillation (KD). We also study the adversarial robustness of lightweight models trained via knowledge distillation using DIs. Further, we demonstrate the efficacy of DIs in generating data-free Universal Adversarial Perturbations (UAPs) with better fooling rates. However, one limiting factor of this solution is the relatively high computation (i.e., several rounds of backpropagation) to synthesize each sample. In fact, the other natural alternatives such as GAN based solutions also suffer from similar computational overhead and complicated training procedures. This motivated us to explore the utility of target class-balanced ‘arbitrary’ data as transfer set, which achieves competitive distillation performance and can yield strong baselines for data-free KD. We have also proposed data-free solutions beyond classification by extending zero-shot distillation to the object detection task, where we compose the pseudo transfer set by synthesizing multi-object impressions from a pretrained faster RCNN model.
Another concern with the deployment of given trained models is their vulnerability against adversarial attacks. The popular adversarial training strategies rely on availability of original training data or explicit regularization-based techniques. On the contrary, we propose test-time adversarial defense (detection and correction framework), which can provide robustness in absence of training data and their statistics. We observe significant improvements in adversarial accuracy with minimal drop in clean accuracy against state-of-the-art ‘Auto Attack’ without having to retrain the model. Further, we explore an even more challenging problem setup and make the first attempt to provide adversarial robustness to ‘black box’ models (i.e., model architecture, weights, training details are inaccessible) under a complete data-free set up. Our method minimizes adversarial contamination on perturbed samples via proposed ‘wavelet noise remover’ (WNR) that remove coefficients corresponding to high frequency components which are most likely to be corrupted by adversarial attack, and recovers the lost image content by training a ‘regenerator’ network. This results in a high boost in adversarial accuracy when WNR combined with the trained regenerator network is prepended to black box network.
Limited training data: In the second part, we assume the availability of a few training samples, where access to trained models may or may not be provided. In the few-shot setup, existing works obtain robustness using sophisticated meta-learning techniques which rely on the generation of adversarial samples in every episode of training - thereby making it computationally expensive. We propose the first computationally cheaper non-meta learning approach for robust few-shot learning that does not require any adversarial sample. We perform pretraining using self-distillation to make the feature representation of low-frequency samples close to original samples of base classes. Similarly, we also improve the discriminability of low-frequency query set features that further boost the robustness. Our method obtains massive improvement in adversarial performance while being ≈5x faster compared to state-of-the-art adversarial meta-learning methods. However, empirical robustness methods do not guarantee robustness of the trained models against all the adversarial perturbations possible within a given threat model. Thus, we also propose a novel problem of certified robustness of pretrained models in limited data settings. Our method provides a novel sample-generation strategy that synthesize ‘boundary’ and ‘interpolated’ samples to augment the limited training data and uses them in training the denoiser (prepended to pretrained classifier) via aligning the feature representations at multiple granularities (both instance and distribution levels). We achieve significant improvements across diverse sample budgets and noise levels in the white-box and observe similar performance under challenging black-box setup.
Selected subset of training data: In the third part, we enforce efficient utilization via intelligently doing selective sampling on existing training datasets to obtain representative samples for the target task such as distillation, incremental learning and person-reid. Adversarial attacks recently have shown robustness bias, where certain subgroups in a dataset (e.g. based on class, gender, etc.) are less robust than others. Existing works characterize a subgroup’s robustness bias by only checking individual sample’s proximity to the decision boundary. We propose a holistic approach for quantifying adversarial vulnerability of a sample by combining different perspectives and further develop a trustworthy system to alert the humans about the incoming samples that are highly likely to be misclassified. Moreover, we demonstrate the utility of the proposed metric for data (and time)-efficient knowledge distillation which achieves better performance compared to competing baselines. Other applications such as incremental learning and video based person-reid can also be framed as a subset selection problem where representative samples need to be selected. We leverage DPP (Determinantal Point Process) for choosing the relevant and diverse samples. In Incremental learning, we propose a new variant of k-DPP that uses the RBF kernel (termed as “RBF k-DPP”) for challenging task of animal pose estimation and further tackle class imbalance by using image warping as an augmentation technique to generate varied poses for a given image, leading to further gains in performance. In video based re-id, we propose SLGDPP method which exploits the sequential nature of the frames in video while avoiding noisy and redundant (correlated) frames, resulting in outperforming the baseline sampling methods
Parallel methods to solve large-scale stochastic linear and nonlinear mechanics problems in a domain decomposition framework
No full textParallel methods to solve large-scale stochastic linear and nonlinear mechanics problems in a domain decomposition framework Mechanics problems with inherent uncertainties are mathematically modeled using stochastic partial differential equations (sPDE). Numerical solution of these sPDE-s becomes prohibitive as the dimension of the problem --- characterized by mesh resolution and number of random variables --- grows. In this work, a domain decomposition based methodology is proposed to solve such large scale sPDE-s for both linear and nonlinear mechanics problems. The methods are built around stochastic collocation, thereby allowing reuse of existing codes. They are demonstrated to be accurate, faster than the state-of-the-art, and scalable on a parallel computer.
Recently, domain decomposition (DD) methods have been successful in reducing the computational complexity and achieving parallelization for linear elliptic sPDE-s. This improvement is due to faster convergence of Karhunen-Loeve expansion in smaller domains and inherent parallelizability of DD methods. However, in order to make this approach more suitable for widespread applications in high performance computing framework, and to re-use existing finite element solvers, departure from the stochastic Galerkin method is necessary. To address this issue, in this thesis a stochastic collocation based formulation is proposed in the finite element tearing and interconnecting - dual primal (FETI-DP) framework. Using this formulation a set of methods are proposed for linear and nonlinear sPDE-s. For linear problems, the non-intrusive formulation uses collocation at both subdomain and interface levels. However, for nonlinear problems a deterministic nonlinear problem is solved using the Newton-Raphson method at each collocation point. The FETI-DP method is then invoked at the Jacobian level for the solution of the linearized system.
Finally, at the post processing stage, realizations of subdomain solutions are computed by sampling from the true distribution for both linear and nonlinear problems. From implementation viewpoint, the proposed methods have two advantages: (i) existing mechanics solvers can be re-used with minimal modification, and (ii) it is independent of the probability distribution of the input random variables. Numerical studies suggest a significant improvement in speed-up compared to the current literature and good parallel performance for the linear case. For the nonlinear case, the proposed method is numerically tested for p-Laplace and plain-strain plasticity problems, where it is found to be computationally efficient, accurate, and exhibit good scalability.
It is commonly observed that nonlinear phenomena in large-scale structures often occur in localized zones while the remaining parts remain linear. The traditional approach to solve this problem is to utilize a variant of Newton's method, and update the Jacobian at every Newton iteration.
Therefore, the computational implementation does not utilize the localized spread of nonlinearity resulting in enormous cost. In this thesis, two closely related methods to solve large-scale problems with localized nonlinearity are introduced. In both of these methods, the FETI-DP domain decomposition is employed at the Jacobian level to solve the linearized problem. In the first method, the Jacobian matrix is computed only for certain elements around the nonlinearity whereas in the second method the initial Jacobian is re-used for all Newton iterations. The new methods can significantly reduce the computational time in parallel platforms when compared with the traditional method. Detailed numerical studies are conducted for two elasto-plastic problems under plane strain conditions --- a plate with a hole and a v-notch, respectively.
Finally, the proposed method is extended to solve localized nonlinear problems in the presence of inherent uncertainties. The proposed method is found to be accurate and scalable when implemented on a stochastic elasto-plastic problem.MHR
Analyses of Performance, Risk and Underpricing of Indian IPOs
No full textAcross geographies, unlisted firms raise capital from individuals and institutions by issuing Initial Public Offering (IPO) through finanical markets. IPOs are of great interest to investors, regulators, issuing firms and financial researchers alike. In this thesis we longitudinally analyze the daily performance (in terms of buy and hold abnormal returns) for three years and daily (total, systematic and unsystematic) risk for one year after the launch for Indian IPOs. We also study the globally occurring stylised phenomenon of underpricing in the context of Indian IPOs. We consider a comprehensive set of financial fundamentals of the issuing firms and several IPO specific variables that might be significantly associated with these IPO characteristics, after controlling for the prevailing market and macroeconomic conditions in which the IPOs are launched. While we employ routine multiple and logistic regression, and regression and classification trees to investigate the associative relationships, we enhance and use an existing algorithm for time series factor analysis to measure and quantify the control variables capturing the prevalent macroeconomic conditions.
Using the above methodology and a sample of 324 IPOs launched in the National Stock Exchange, India from 1999 to 2016, it is found that on an average, the performance of Indian IPOs deteriorates both in the short and long run. Though endogenous firm/IPO-specific variables such as age, percentage of stakes diluted by the promoter and non-promoter group in the IPO, etc. are found to be significantly associated with the IPO performance, the exogeneous market and macro-economic conditions during the launch of an IPO are also found to play critical roles in determining its performance. It is found that the IPO risk is primarily associated with the macro-economic conditions in which the IPOs are launched and no firm/IPO-specific variable is found to have any significant association with the risk of investing in an IPO. The average quantum of underpricing (first day return) for the sample of 324 IPOs is found to be 22.5%, with 66.1% of them being (just categorically) underpriced. Like their performances, while several endogenous firm/IPO-specific variables such as earnings per share, percentage of secured loan, growth rate of pre-tax profit margin etc. are found to be significantly associated with underpricing, the main takeaway of the analysis is that it is the exogeneous market sentiment that is the primary determinant of underpricing. IPOs launched in a bull market are more likely to be underpriced, and it is also found that most IPOs are also launched when the market is ascending. This is proferred as an alternative explanation for the empirically observed global phenomenon of IPO underpricing, at least for the case of the Indian IPOs
Unravelling the functional role of Arf-like GTPases 14 and 15 in mammalian cells
No full textSmall G-proteins of Arf-like (Arl) GTPase subfamily are shown to regulate several cellular processes including intracellular trafficking, cytoskeletal organization, organelle biogenesis, cell adhesion and migration. Around 21 genes belong to this family have been identified in human. However, the critical function of Arl14 and Arl15 in cargo transport was unclear. In this study, we have attempted to characterize the role of Arl14 and Arl15 in multiple cellular processes, including intracellular trafficking using HeLa cells.
Objective I: Elucidating the role of Arl15 in modulating cell adhesion, motility and filopodia biogenesis.
Our study characterized the intracellular localization of Arl15 using epitope tagged Arl15-GFP in multiple mammalian types. We have observed that Arl15-GFP localizes to Golgi, plasma membrane (PM) including filopodia, and a cohort to recycling endosomes in HeLa, A549, neuro 2a, and primary keratinocytes. Additionally, we noticed the localization of Arl15 to long extracellular tube structures (resembling tunneling nanotubes, TNTs) connecting the Neuro 2a cells. The dual localization of Arl15 to Golgi and PM is independent of the actin cytoskeleton, but it is dependent on Golgi integrity. The dissociation of Golgi using small molecular inhibitors or the expression of Arf1 dominant-negative mutant completely mislocalizes Arl15 to the cytosol. We identified a novel V80A mutation in the GTP-binding domain that turns the Arl15 into a dominant-negative form and results in a reduced number of filopodia. Depletion of Arl15 in HeLa cells causes mislocalization of cargo such as caveolin-2, STX6, and ectopically expressed GFP-GPI from Golgi and accumulation of lipid droplets. Further, Arl15 knockdown cells display reduced filopodial number, dispersion of vinculin localization (focal adhesion kinase), and enhanced soluble and receptor-mediated cargo uptake without affecting the recycling kinetics. In addition, Arl15 knockdown decreases cell migration and increases cell adhesion, and displays enhanced cell spreading. Traction force microscopy studies revealed that Arl15 depleted cells exert higher traction force and generate multiple focal adhesion points. These studies demonstrated a function to Arl15 in Golgi, which regulates cargo transport to organize membrane domains at the cell surface to control cell migration, spreading and adhesion, including filopodial biogenesis.
Objective II: Studying the role of Arl14 in vesicular trafficking
Studies have suggested that Arl14 regulates the movement of MHC-II vesicles along the actin cytoskeleton in dendritic cells. We have studied the localization of Arl14 using epitope tagged Arl14-GFP in HeLa cells. Arl14 localizes to REs, late endosomes, and lysosomes. Expression of Arl14S27N-GFP showed no change in its localization, indicating that Arl14S27N-GFP is not acting as a dominant negative mutant while constitutive active mutant of Arl14 (Arl14Q68L-GFP) majorly localized as punctate structures that are clustered and positive for RFP-STX13 (represents endosomal structures). Depletion of Arl14 showed an enhanced number of LAMP-1-positive lysosomes without changing the localization of lysosome biogenesis transcription factors TFEB and TFE3. Immunoblotting analysis showed no change in Rab5, STX13, Rab11, annexinA2, LAMP-1, and LAMP-2 expression in siArl14 compared to siControl. Overall, these studies showed that Arl14 localizes to endo-lysosomal organelles, and its depletion altered the number of LAMP-1 positive compartments