Indian Institute of Science Bangalore
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Codes With Combined Locality and Regeneration Having Optimal Minimum Distance and Linear Field Size
No full textThis thesis presents error correcting codes in the setting of distributed storage. The codes presented in this thesis are optimal with respect to the minimum distance bound and have a finite field size which grows linearly with the block length of the code.
Setting of Distributed Storage: In the current digital era, a very large amount of data is generated that needs to be reliably stored. This data is stored in distributed fashion, in a distributed storage system (DSS). To guarantee reliability, these DSSs need to speedily repair any node which is unavailable for use or has failed. In the past, triple replication was the standard used for ensuring reliability in a DSS. More recently, given the excessive overhead associated with triple replication, the use of erasure coding has become increasingly more frequent. In an erasure code, the file is first split into packets and redundant parity packets are then added, to form the erasure code consisting of packets or chunks. These parity packets are computed using a linear combination of the information packets. Erasure codes are excellent when it comes to data recovery but are inefficient when it comes to repairing a failed node. In a DSS the failure of a single node is the most common failure (98\% of the time), thus the repair of a failed node must be handled efficiently both to reduce the amount of traffic over the network associated with node repair as well as the number of helper nodes contacted during node repair. To overcome this hurdle, two new classes of erasure codes namely Regenerating Codes (RGC) and Locally Recoverable Codes (LRC) were introduced. RGCs aim to minimize the repair bandwidth (i.e., the amount of data needed for repairing the failed node) while LRCs aim to minimize the repair degree (i.e., the number of surviving or helper nodes contacted for repairing the failed node).
Regenerating Codes
In an RGC, the file of size over the finite field is encoded into symbols and stored across nodes where each node comprises of symbols. RGCs satisfy two properties, the data-collection property and the node-repair property. The data-collection property requires the DSS to recover the original data corresponding to the file of size , by contacting any out of nodes and downloading symbols from each contacted node. The node-repair property requires the DSS to repair a failed node by contacting any nodes from the surviving nodes and downloading symbols from each contacted node to reconstruct the failed node of size while ensuring the data collection and node repair properties continue to hold. Based on the type of node repair, RGCs can be further sub divided into two categories, functional repair RGC (FR-RGC) and exact repair RGC (ER-RGC). In ER-RGC, the contents of the repaired node are the same as that of the failed node, while in FR-RGC, the contents of the repaired node need not be same as the failed node, the only condition being that the data collection and node repair properties continue to hold. In this thesis, we will restrict attention to the case of ER-RGC. For an RGC there exists a tradeoff between the sub-packetization level of the RGC and the repair bandwidth known as the storage repair bandwidth tradeoff. The two extreme points in the storage bandwidth tradeoff curve are of special importance as they correspond to minimum possible storage overhead and minimum possible repair bandwidth, respectively. The codes which minimize \alp= ha are known as minimum storage regenerating (MSR) codes and the RGCs which minimize are known as minimum bandwidth regenerating (MBR) codes. MSR codes can be shown to be example of maximum distance separable (MDS) codes having a vector code-symbol alpha= bet.
Locally Recoverable Codes
In this thesis we restrict our attention to Locally Recoverable Codes (LRC) over a finite field . Let denote a linear code having block length dimension and minimum distance . In the case of an LRC, there is an additional parameter , termed as the locality parameter and which indicates that an erased code symbol can be reconstructed by contacting a set of un-erased code symbols and is typically much smaller than the dimension of the code. In the context of distributed storage, one associates a data file with a collection of symbols over . To this set, an additional parity symbols are added and the resulting set of code symbols stored over nodes or storage units.
The principle behind an LRC is that the code symbols corresponding to an erased code symbol along with the other code sy= mbols that are used to recover from erasure, form a nontrivial code on their own. Thus one may view each local code as the code that is obtained by restricting the parent code to the subset of code symbols involved in recovery from erasure.
LRCs can be divided into two sub classes, information-symbol locality in which only the information nodes have locality and the more general case of all-symbol locality, in which any failed node has locality .
Locally Regenerating Code: Given that RGCs minimize repair bandwidth and an LRC minimizes the repair degree, a natural question that arises is whether it is possible to construct an error correcting code that minimizes both repair bandwidth and repair degree. Locally Regenerating Codes (LRGCs) are precisely such a class of codes. In an LRGC, the local codes are themselves RGCs.
Contributions: The contributions of the thesis are two constructions of an LRGC, both of which are optimal in the sense of having largest possible minimum distance:
• an explicit construction of an LRGC in which the local codes are MBR codes derived using the well-known product-matrix construction. Interestingly, it turns out that the construction also involves an LRC, one that is obtained by employing the Tamo-Barg (TB) construction of an all-symbol LRC.
• an explicit construction of an LRGC for a restricted range of parameters in which the local codes are MSR codes corresponding to the MSR Clay code. Here again, the construction makes use of the TB construction of an all-symbol locality LRC. The resulting LRGC is optimal in two respects as it achieves with equality, upper bounds on both minimum distance and code rate.
We note that while existing optimal LRGC constructions in the literature require field size that is exponential in the block length , the code families presented in this thesis, require only linear field size
Machine Learning for Decoding Imagined words and Altered State of Consciousness from EEG
No full textIn the first part of the thesis, the results of our studies on the classification of phonological categories in imagined words are presented. We have investigated whether there are any statistically significant differences in the mean phase coherence values in six cortical regions when the participants imagined uttering nasal and bilabial consonants. The cortical regions considered are prefrontal, premotor, motor, somatosensory, sensorimotor and auditory cortices, which are normally involved in speech production. We have observed statistically significant differences in the MPC values in all the six cortical regions in the beta band and in the motor cortex alone in the gamma band. The results obtained support the dual stream prediction model for imagined speech. Further, we have tried to classify the speech imagery EEG epochs based on the phonological category of the prompt using a shallow neural network. We have obtained an accuracy of 84.9% in the classification task when beta band MPC values are used. This is around 12% higher than the benchmark result on this dataset. The accuracies of our model dropped to 59.0% and 69.1%, respectively when only alpha or gamma band MPC values are used. The fact that EEG carries correlates of the phonological categories of the imagined phonemes can help in designing better prompts for a speech imagery-based BCI system.
In part two of the thesis, we have investigated as to whether we can classify the imagined prompt from the EEG recorded during speech imagery. For this, we have developed three different architectures. All these architectures address the problem of lack of sufficient training data. In the first one, which we call the CSP-based architecture, the issue of limited training data is addressed by treating the features extracted from selected EEG channels as distinct inputs to the classifier. Common spatial pattern (CSP) is used for channel selection. The primary classifier is a deep neural network (DNN) with four hidden layers whereas the secondary classifier is a majority voting classifier. The second model, which we call the TL-based architecture handles the problem of limited training data by using data augmentation and transfer learning (TL). In this architecture, MPC and MSC values from the alpha, beta and gamma bands are arranged as a 3D data, and are input to a ResNet50-based classifier, where ResNet50 is used as a fixed feature extractor. The third one, which we call the LSTM-based architecture employs overlapping window-based data augmentation to increase the amount of data, which is possible because the ASU dataset involves repeated imagination by the subjects. The LSTM-based architecture uses CSP for feature extraction, linear discriminant analysis (LDA) for dimensionality reduction and long short-term memory (LSTM) network as the primary classifier and a majority voting classifier as the secondary classifier.
All the three architectures have achieved above chance-level accuracies on the publicly available ASU speech imagery EEG dataset. The TL-based and the LSTM-based architectures have achieved average accuracies of 92.8% and 85.2%, respectively, for the classification of "short-long" words, which are better than the state-of-the-art. Although the LSTM-based architecture has lower accuracy than the TL-based architecture, the former can classify a 5 s EEG epoch in less than 110 ms, making it a suitable algorithm for online BCI systems. We have also performed ablation studies to identify the optimal number of EEG channels in the case of the CSP-based architecture and the optimal EEG frequency band in the case of the TL-based and the LSTM-based architectures. The optimal number of EEG channels in the case of CSP-based architecture is nine whereas the optimal EEG band for both the TL-based and the LSTM-based architectures is the gamma band.
In the last part of the thesis, we have proposed three architectures for classifying the altered state of consciousness during Rajayoga meditation from the resting state. Classification of Rajayoga meditation is challenging since it is probably unique in that the practitioners meditate with their eyes open. The first model, which we call the CSP-LDA architecture, uses CSP for feature extraction and LDA as the classifier. The second one, which we call the CSP-LDA-LSTM architecture is similar to the LSTM-based architecture used for decoding imagined speech. The third model, which we call the SVD-DNN architecture uses singular value decomposition (SVD) for choosing the relevant subspace of the signal and DNN for classification. The best intra-subject classification performance of 98.2% is obtained using the CSP-LDA-LSTM architecture whereas the best inter-subject performance of 96.4% is obtained using the SVD-DNN architecture. Both these architectures are able to capture subject-invariant features and can be deployed for grading the depth of meditation and for classifying other altered states of consciousness such as disorders of consciousness and hypnosis
Transcription termination in Mycobacteria
No full textTranscription termination is one of the key events of gene expression that decides the boundary of the gene. Termination either before the stop codon or transcriptional read-through into the downstream gene both have unfavourable consequences. The absence of efficient and accurate transcription termination can be deleterious for cell survival and can cause genome instability. In bacteria, transcription can be terminated by either of the two modes – intrinsic or Rho-dependent termination (RDT). Our understanding of bacterial transcription and the associated molecular machinery comes majorly from Escherichia coli (E. coli). Hence the presence of species-specific features with respect to transcription and its termination in distinct phyla across bacterial domain needs to be explored. The success of the pathogen Mycobacterium tuberculosis (Mtb) is credited to its efficient gene expression. However, very little is known about their transcription termination which is an essential part of gene expression. The work presented in the thesis attempts to uncover the unique features associated with both intrinsic termination and RDT in mycobacteria.
Chapter 1 of the thesis provides a general introduction on transcription, associated molecular machinery especially RNA polymerase (RNAP), and the process of transcription termination in bacteria. The introduction emphasis more on the distinct features associated with intrinsic terminators and Rho with a brief compilation of what is already known about them. It also discusses the recently elucidated mechanism of their action and the impact of elongation factors like NusA and NusG on transcription termination. The introduction ends with summarizing briefly about transcription termination in mycobacteria and how this study contributes to the understanding of transcription termination in mycobacteria.
In Chapter 2 the study is directed to understand the importance of U-tract downstream of an intrinsic terminator in transcription termination. A canonical intrinsic terminator has a G/C-rich hairpin structure followed by a 7-9 Us located at the 3’ region of the nascent RNA and are crucial determinants for intrinsic termination. In mycobacteria, there is a scarcity of such intrinsic terminators. However, secondary structures having G/C-rich stem devoid of any U’s or with suboptimal U-tracts were identified earlier as terminators and found to be functional both in vitro and in vivo. Two different observations - that a mycobacterial RNAP does not function at intrinsic terminators devoid of U-tracts and the identification of an altogether new motif for termination in mycobacteria necessitated re-examining a number of putative terminators for their function as terminators. In this chapter, in silico identified non-canonical terminators were subjected to experimental validation, they were found to dissociate RNA from the elongating RNAP. Termination was observed when the U-tracts were reduced, or totally absent both in vitro and in vivo. Our results, thus indicate that the presence of U-tract following the G/C-rich stem in an intrinsic terminator may not be an essential determinant for transcription termination in mycobacteria.
Chapter 3 is focused on MtbRho and RDT in mycobacteria. Although the two transcription termination mechanisms contribute to nearly in equal measures to terminate transcription in E. coli, understanding their role in mycobacterial gene expression is at the early phase. In this chapter, we highlight the dominance of RDT in mycobacteria and pose Rho as a key regulatory factor. The lower abundance of intrinsic terminators, high cellular levels of Rho, and its genome-wide association with a majority of transcriptionally active genes indicate the predominance of RDT in Mtb. Unlike in other well-studied systems, Rho modulates the termination of RNA synthesis for both protein-coding and stable RNA genes in Mtb. Concordantly, the depletion of Rho in mycobacteria enhances the transcription read-through of transcription units. These properties suggest an evolutionary adaptation of Rho as the efficient and predominant mode of transcription termination in mycobacteria.
Chapter 4 attempts to draw a connection between intrinsic termination and RDT. Generally, the two mechanisms have been illustrated as two independent pathways with contrasting requirements to halt RNA synthesis. However, a majority of the intrinsic terminators terminate transcription inefficiently leading to transcriptional read-through. The unwanted transcription in the downstream region beyond the terminator would have undesired consequences. To prevent such transcriptional read-through, bacteria must have evolved ways to terminate transcription more efficiently at or near the termination sites. This chapter describes the collaboration between the two mechanisms, where intrinsic terminator and Rho factor functionally interact to prevent transcriptional read-through. Contribution from both the termination processes is demonstrated at the downstream regions of the genes both in vitro and in vivo in mycobacteria. Distinct patterns of cooperation between the two modes of termination were observed at the 3’ untranslated regions of the genes to ensure efficient termination. A similar interaction between the two termination processes was observed in E. coli as well suggesting a likely prevalence of this functional cooperation across bacteria. The reporter system developed to assess the Rho – intrinsic termination collaboration in vivo for mycobacteria and E. coli can readily be applied to other bacteria.CSIR, IIS
Spectroscopic and Diffraction Signatures of Quantum Spin Liquids, Skyrmion Lattices and Transition Metal Dichalcogenides at Low Temperatures and High Pressures
No full textThis thesis presents experimental as well as theoretical studies on several contemporary
systems like quantum spin liquids (QSLs), skyrmion lattices, and transition metal dichalcogenides
(TMDs) under extreme conditions like low temperature (down to 4K) and ultra high
pressures (up to 26 GPa). Temperature-dependent Raman studies are carried out to investigate
Raman signatures of Kitaev quantum spin liquid (QSL) state of Cu2IrO3 and Ag3LiIr2O6 and
orbital ordering in Heisenberg quantum magnet Ca10Cr7O28. High-pressure studies are performed
on Kitaev QSL candidates -RuCl3, Cu2IrO3, kagomé QSL ZnCu3(OH)6Cl2, skyrmion
lattice system Cu2OSeO3, and TMD candidate VSe2. Structural and vibrational evolutions of
these systems under pressure are probed by X-ray diffraction measurements using synchrotron
source and Raman scattering, respectively
Mechanical Properties of Nucleic Acids in Bulk and Under Confinement and Their Interaction with Peptide
No full textProteins, DNA and RNA are arguably the most studied biological molecules with curiously interconnected relation that appears ubiquitous in all organisms. Almost all nucleoprotein interactions and DNA manipulation events involve mechanical deformations of DNA. The flexibility of RNA and DNA is crucial for their biological functions. Apart from DNA and RNA, peptide nucleic acids (PNAs), although synthetic nucleic acids, are being widely studied due to their potential application in different fields of biotechniques. PNAs hybridize with complementary DNAs or RNAs with extraordinary affinity, exhibiting remarkable thermal stability.
During interaction with different biomolecules like protein, the nucleic acids modulate their conformations by bending, stretching and twisting such that the shape and size modifications optimize the Watson-Crick geometry for effective interaction. The study of the elasticity of the nucleic acid duplexes is therefore critical. In this thesis, using all-atom molecular dynamics (MD) simulations, we have studied the structure, mechanics and thermodynamics of double-stranded (ds) DNA, RNA, PNA and their hybrid duplexes. We have computed bending persistence length, stretch modulus and torsional stiffness of the duplexes along with different structural and helicoidal parameters. We have also computed the effect on these properties for dsDNA with oxidative modification of the bases and with confinement inside the single-walled carbon nanotube (SWCNT). In addition, we have analysed the interaction affinity of the dsDNA with cationic peptide (mimicking protamine) in both native and charge modified (phosphorylated) states of the peptide.
We found dsPNA to have higher bending, stretching and twisting flexibility than dsDNA and dsRNA, indicating that dsPNA may easily change its structure to make complexes with biomolecules. The PNA-DNA and PNA-RNA hybrid duplexes have elastic properties between dsPNA and dsDNA/dsRNA. We argue that the neutral backbones of the PNA make it less stiff than dsDNA and dsRNA molecules. We found that the oxidative modification of dsDNA does not weaken the Watson-Crick geometry. Instead, it is strengthened with the increase in electrostatic interaction. The oxidative damage changes the mechanical, helical and groove parameters of dsDNA, and these changes are more significant on twisting and stretching flexibilities compared to bending stiffness. With oxidation, the bending flexibility of dsDNA gets suppressed, and the oxidized dsDNAs are more flexible to stretch and twist than the non-oxidized dsDNA. Compared to the global mechanical properties, the changes in helical and groove properties are found to be more prominent, concentrated locally at the oxidation sites, and causing the transition of the backbone conformations from BI to BII at the regions of oxidative damage.
We observed the denaturation of dsDNA when confined inside narrower SWCNT, and the denaturation is diameter dependent, with a critical diameter of 3.25 nm. Below this critical diameter of SWCNT, the dsDNA is denatured, and above it, the dsDNA retains its native structure. The elastic properties of the dsDNA in cylindrical confinement inside SWCNTs are also found to be diameter dependent. Inside moderately wide SWCNTs, the confined dsDNA is highly rigid to bend, stretch and twist, whereas, inside wider SWCNTs, the elastic properties change surprisingly, making the dsDNA highly flexible.
Regarding the interaction of dsDNA with protamine-like cationic peptide, we found that phosphorylation of the residues of cationic peptides decreases their binding affinity with dsDNA. Among the two events, phosphorylation and dephosphorylation of the protamines, phosphorylation minimizes the shapes of protamines (making them more spherical), reducing the interaction with the nucleobases of dsDNA, and results in weak binding. Also, the level of compaction of dsDNAs under the influence of the phosphorylated peptides is found to be weaker than that caused by non-phosphorylated peptides. Because of the high binding affinity of dephosphorylated (non-phosphorylated) cationic peptides, the dephosphorylation of peptides is responsible for making dsDNA duplexes more compacted.
Thus, the works carried out here in this dissertation explain the different mechanical and thermodynamic properties of the various nucleic acids (namely; dsDNA, dsRNA, dsPNA and their hybrids), the structure and thermodynamic stability of dsDNA inside the confined space of SWCNT, and the effect of phosphorylation of peptides in the DNA compaction process
Improving Healing Outcomes in Diabetic Wounds by Understanding and Modulating Immune Responses
No full textDiabetic Foot Ulcers (DFUs) are chronic wounds that show delayed or absence of healing in individuals with diabetes. Currently, diabetic wounds are managed clinically through regular debridement, pressure off-loading, glycemic control, and patient education. Other treatment strategies used in the clinic include topical antibiotics to prevent infections, growth factor ointments to accelerate tissue regrowth, and negative pressure therapy for drainage of wound fluid. However, such strategies have had limited success in a significant number of individuals with DFU, and the exact reasons remain unclear.
Towards identifying factors associated with poor healing, we designed a single-center cohort study involving 52 DFU patients to evaluate the role of clinical, biochemical, and immunological parameters in foot ulcers. Data from these individuals analyzed using multivariable logistic regression analysis revealed that a combination of low-density lipoprotein (LDL) cholesterol and expression level of CD63 protein on monocytes as strong predictors of non-healing in individuals with early-stage DFUs. Both these parameters are associated with obesity and chronic inflammation, which could be drivers of poor healing.
In parallel, in a mouse model of diabetes (Leptin receptor knockout mouse) used to test therapeutic strategies, we characterized the phenotype and function of myeloid cells driving chronic inflammation in diabetes that remains to be unknown. These diabetic mice showed extensive changes in the numbers and phenotype of monocytes and neutrophils in circulation, suggestive of underlying inflammation. As such alterations in immune cells can drive aberrant wound healing activity, we hypothesized that targeting myeloid cells for immunomodulation at wound site might accelerate wound closure.
To achieve immunomodulation of myeloid cells in chronic wounds, we developed an immuno-modulatory drug-eluting bandage that could be applied on wounds for improving healing outcomes. A chitosan-based hydrogel scaffold system containing rapamycin or tetracycline was used as a bandage and evaluated for accelerated healing using the leptin receptor knockout mouse model. In vivo studies revealed that treatment with low dose rapamycin loaded scaffold significantly accelerated wound healing compared to tetracycline and blank scaffold in diabetic mouse suggesting that rapamycin can effectively modulate immune cells at wound environment. The developed novel strategy show promise for clinical translation upon further validation in higher animal models.
In summary, this thesis explores the role of innate immune cells in DFU pathology and describes a new treatment approach for healing diabetic wounds. We specifically show that neutrophils and monocytes are phenotypically altered in individuals with early-stage diabetic foot ulcers, identify parameters that clinicians may use to stratify the risk of poor healing, demonstrate that a leptin receptor knockout diabetic mouse model has similar changes in myeloid cells, and develop a possible treatment strategy for healing diabetic wounds.SERB ECF, RGUHS, MHR
Quasi-analytic Functions, Spherical Means, and Uncertainty Principles on Heisenberg Groups and Symmetric Spaces
No full textThis thesis has two parts. The first part revolves around certain theorems related to an uncertainty principle and quasi-analyticity. In contrast, the second part reflects a different mathematical theme, focusing on the classical problem of boundedness of spherical maximal function on the Heisenberg group.
The highlights of the first part are as follows:
An uncertainty principle due to Ingham (proved initially on ) investigates the best possible decay admissible for the Fourier transform of a function that vanishes on a nonempty open set. One way to establish such a result is to use a theorem of Chernoff (proved originally on ), which provides a sufficient condition for a smooth function to be quasi-analytic in terms of a Carleman condition involving powers of the Laplacian. In this part of this thesis, we aim to prove various analogues of theorems of Ingham and Chernoff in different contexts such as the Heisenberg group, Hermite and special Hermite expansions, rank one Riemannian symmetric spaces and Euclidean space with Dunkl setting.
More precisely, we prove various analogues of Chernoff's theorem for the full Laplacian on the Heisenberg group, Hermite and special Hermite operators, Laplace-Beltrami operators on rank one symmetric spaces of both compact and non-compact type, and Dunkl Laplacian. The main idea is to reduce the situation to the radial case by employing appropriate spherical means or spherical harmonics and then to apply Chernoff type theorems to the radial parts of the operators indicated above. Using those Chernoff type theorems, we then show several analogues of Ingham's theorem for the spectral projections associated with those aforementioned operators. Furthermore, we provide examples of compactly supported functions with Ingham type decay in their spectral projections, demonstrating the sharpness of Ingham's theorem in all of the relevant contexts mentioned above.
In this second part of this thesis, we investigate the boundedness of the lacunary maximal function M_{\Ha}^{lac} associated to the spherical means taken over Koranyi spheres on the Heisenberg group. Closely following an approach used by M. Lacey in the Euclidean case, we obtain sparse bounds for these maximal functions leading to new unweighted and weighted estimates. The key ingredients in the proof are the improving property of the operator and a continuity property of the difference , where is the right translation operator
Two-dimensional materials based artificial synapses for neuromorphic applications
No full textThe need and demand for continuous high-speed, energy-efficient hardware advancement is undisputed. Traditional computing system with von Neumann architecture leads to high energy consumption and latency due to a huge amount of data transfer between the separated memory unit and the logic unit. In response to this discrepancy, extensive research has been conducted to develop brain-inspired electronic devices that can provide alternate computing platforms needed for implementing hardware neural networks. Artificial synapse, which emulates the dynamics of biological synapses, such as “update” and “memorize,” is one approach toward solid-state implementation of bio-inspired devices. Recently, two-dimensional (2D) van der Waals (vdW) materials have been actively explored for such artificial synapses. The distinctive electronic, optoelectronic, and mechanical properties of two-dimensional (2D) materials make these quite attractive for a wide variety of applications. This thesis explores the electronic and optoelectronic properties of 2D materials for mimicking the synaptic performance of the neuron. Materials of interest include MoS2, which is semiconducting, and α-In2Se3, a ferroelectric semiconducting material, investigated as active elements for synaptic applications. In the first part of the dissertation, we try to understand the working mechanism, i.e., charge trapping and de-trapping in synaptic devices using MoS2 as the channel material in a simple back-gated configuration. To this end, we have used a high-k dielectric (Ta2O5) as the gate oxide, which is expected to reduce the voltage swing and hence the power consumption, which is beneficial when used in neuromorphic networks. The hysteresis in the transfer characteristics
of the transistor arising out of the Ta2O5/MoS2 interface and interface trap charges within the oxide are exploited to demonstrate excitatory Post Synaptic current (EPSC) / Inhibitory Post Synaptic current (IPSC), Long Term Potentiation (LTP) / Long Term Depression (LTD), Spike Amplitude Dependent Plasticity (SADP), Spike Timing Dependent Plasticity (STDP) at a relatively lower energy budget. In the second part, we discuss the working mechanism of 2D ferroelectric semiconducting channel material (α-In2Se3) for synaptic applications. Ferroelectric materials have emerged as a promising candidate for enabling synaptic devices as they lead to fast operation, non-destructive readout, low-power, low variations, and high on/off ratios. The partial polarization switching behavior of the ferroelectric material can be exploited to emulate the biological synaptic functions by gradually modulating the channel conductance through an external electrical field. We also explored the continuous weight modulation through partial polarization of the channel displaying an excellent linear weight update trajectory with multiple stable conductance states. In the next part of the dissertation, we discuss artificial neural networks for pattern recognition using the conductance weights obtained from device-level emulation of synaptic dynamics. By updating the synaptic weights with conductance weight values on 18,000 digits, we achieved a successful recognition rate of 93% on the testing data. The introduction of 0.10 variance of noise pixels results in an accuracy of more than 70%, showing the strong fault-tolerant nature of the conductance states. These synaptic functionalities, learning rules, and device-to-subsystem-level simulation results based on α-In2Se3 could facilitate the development of more complex neuromorphic hardware systems based on FeS-FETs.
In the last part of the dissertation, we introduce a light-sensing function merged into the artificial synapses to realize an optoelectronic synapse. The optical input signal (λ = 527 nm) is used as a presynaptic signal with various frequencies and strengths to imitate the synaptic functionalities such as short-term memory (STM) and long-term memory (LTM), paired-pulse facilitation (PPF), spike rate-dependent plasticity (SRDP) spike duration-dependent plasticity (SDDP) and memory functions like learning, forgetting, and relearnin
Insights into the conformational stability & pharmacology of peptides through side chain and backbone modification
No full textThe first part of the work presented in the thesis deals with CH-pi interaction, which is a lesser-known non-covalent interaction in proteins. We identified several CH-pi donor-acceptor pairs involving amino acids side chains and less polarized C-H bonds at a solvent-exposed site between the strands of a model beta-hairpin peptide. Therein, we observe a distance-dependent induction of CH-pi interaction within the aliphatic-aromatic amino acid pair. Our results also suggest an interplay of hydrophobicity and CH-pi interaction in dictating the stability of beta-hairpin. In the second part, we have attempted to establish thioamidation, which is a single atom ‘O’ to ‘S’ substitution, as a chemical strategy for amide bond desolvation. Thioamide substitution on a model and a bioactive cyclic peptide has led to an enhancement in membrane permeability and metabolic stability which are the two major deterrents of peptide bioavailability. We observed improved plasma exposure of the thioamidated peptides upon oral intake. Thioamidation could successfully improve the pharmacokinetic properties of the peptides and subsequently, their oral bioavailability. Additionally, thioamidated somatostatin agonists displayed prolonged inhibition of growth hormone release in vivo.
Altogether, the work done in this thesis highlights the roles of both side chains and amide backbone in modulating conformations and other functional properties of peptides and proteins. Our strategies underscore how these local components can be fine-tuned to achieve the desired biophysical properties and activities to design next generation peptide-based therapeutics.Mo
Investigating Mechanical Properties of Suspended Ovarian Cancer Cells and Clusters
No full textThe peritoneal cavity of a patient suffering from advanced epithelial ovarian cancer is filled with disseminated multicellular aggregates, commonly known as spheroids. These spheroids colonize abdominal organs leading to metastasis. Metastasizing spheroidal cancer cells frequently become resistant to chemotherapeutic drugs, thus demanding a rigorous investigation of mechanisms underlying their formation and stability. Spheroids obtained by tapping the malignant ascites of ovarian cancer patients show heterogeneous morphologies: some exhibit a dysmorphic ‘moruloid’ (mulberry-like) phenotype, and others show smooth compacted surfaces and an internal lumen, giving them a ‘blastuloid’ appearance. Additionally, blastuloid spheroids reveal the presence of a basement membrane coat surrounding them which was also raised the interest in understanding its role in their structure, and its contribution to their localized stiffness. These morphologies could represent consequences of phenotypically heterogenous cell types, or indicate progressive stages of metastasis with moruloid phenotypes maturing into blastuloid counterpart.
There is a burgeoning body of literature on biophysical investigations of tumorigenic cellular ensembles. Of these, most studies focus on the migrational dynamics of spheroidal or tumoroidal cells within stromal-like extracellular matrix (ECM) microenvironments. In fluid microenvironments, the assembly of multicellular structures from suspended single cells likely employ distinct mechanisms. Although elegant theoretical models have been constructed recently to explain dynamical structural transitions, technical difficulties of efficiently imaging floating clusters have allowed few biophysical characterizations of spheroids. Notable experimental exceptions include efforts to mechanically analyze spheroids using microtweezers, wherein those constituted from breast cancer cells were found to be softer than from untransformed controls, and investigations using cavitational rheology to determine the cortical tension in spheroids of HEK293 cells. A pertinent study by Panwhar and coworkers recently describes a high throughput approach using virtual liquid-bound channels to show that the stiffness of multicellular spheroids is an order of magnitude lower than that of cells that constitute them. Although these investigations have not studied temporal topological transitions between multicellular morphologies, they lay the foundation for such studies within fluid microenvironments.
In this dissertation, I try to investigate the mechanics and biophysics of ovarian cancer starting at the single cell level, thereby moving to spheroids. I used AFM as an elegant tool to characterize the stiffness of single cells in suspension. The difficulty in imaging suspended cells and 3D cultures was overcome using a novel technique employing noble agar as a substratum to immobilize them. Three different cell lines representing ovarian cancer were studied for this and compared to their respective adhered states. The technique was also used for spheroids of the mentioned morphologies to sense localized stiffness changes as they mature, thereby making it versatile and size independent.
Subsequently, we combine microfluidics with high- speed time-lapse videography and imaging analysis to investigate the consequences of lumen formation and the basement membrane (BM) coat on the architecture and integrity of ovarian cancer spheroids providing architectural robustness to the transitory ovarian cancer metastatic niche within constrained flow spaces. We investigate their time of transit, relaxation dynamics, disintegration statistics, particle image velocimetry (PIV), shape evolution, etc as tools to further understand their structural and temporal integrity.
We further extended these techniques to study the effect of chemoresistant drugs and inter-cellular motion inhibitors, and their effect on the cluster mechanics and biophysics.
In future, some design iterations can be tried to trap the clusters and monitor the long-term effect of shear on them. Keeping them trapped for a longer duration can give insight into the real-time changes in a fluidic environment