Abstract

Digitization can provide a means of preserving the content of the materials by creating an accessible facsimile of the object in order to put less strain on already fragile originals such as out of print books. The document analysis community formed to address this by digitizing the content thus, making it easily shareable over Internet, making it searchable and, enabling language translation on them. In this thesis, we have tried to see optical character recognition as a mapping problem. We proposed extensions to two method and reduced its limitations. We proposed an application for sequence to sequence learning architecture which removed two limitations of previous state of art method based of connectionist temporal classification output layer. This method also gives representations which can be used for efficient retrieval. We also proposed an extension to profile features which enabled us to use same idea but by learning features from data. In first work, we propose an application of sequence to sequence learning approach for printed text Optical Character Recognition. In contrast to present day existing state-of-art OCR solution which uses Connectionist Temporal Classification (CTC) output layer our approach makes minimalistic assumptions on the structure and length of the sequence. We use a two step encoder-decoder approach – (a) A recurrent encoder reads a variable length printed text word image and encodes it to a fixed dimensional embedding. (b) This fixed dimensional embedding is subsequently comprehended by decoder structure which converts it into a variable length text output. The learnt deep word image embedding from encoder can be used for printed text based retrieval systems. The expressive fixed dimensional embedding for any variable length input expedites the task of retrieval and makes it more efficient which is not possible with other recurrent neural network architectures. Thus single model can do predictions and features learnt with supervision can be used for efficient retrieval. In the second work, we investigate the possibility of learning an appropriate set of features for designing OCR for a specific language. We learn the language specific features from the data with no supervision. In this work, we learn features using a unsupervised feature learning and use it with the RNN based recognition solution. We learn features using a stacked Restricted Boltzman Machines (RBM). These features can be interpreted as deep extension of projection profiles. These features can be used as a plug and play features to get improvements where profile feature are used. We validate these features on five different languages. In addition, these novel features also resulted in better convergence rate of the RNNs.

Abstract

After Bell’s celebrated work in 1964, demonstrating the incompatibility of Quantum Mechanics with the notion of local realism, there have been numerous conceptual and technical developments for studying nonlocality in quantum systems. The Bell inequality and Bell-type inequalities, set an upper bound on the correlations between measurement statistics of many particle systems that cannot be explained by local hidden variable models. Quantum entanglement has proven to be a key resource for various information theoretic protocols such as dense coding, teleportation, etc. Their numerous applications makes it consequential to pinpoint their behaviour. In the first chapter, a cursory delineation of the historical events in the development of Quantum theory, is followed by a concise introduction to the theory and mathematical tools needed for this thesis. The landmark papers of EPR and John Bell are discussed, so as to provide an understanding of the significance of Bell’s theorem and thus the Bell’s inequalities. Quantum Entanglement is discussed, and in particular, measures that are used later to quantify tripartite correlations are described in greater detail. The second chapter discusses Bell-CHSH inequality for two qubit systems and Bell-type inequalities for multi-qubit systems; and the theory independent derivation of Bell-CHSH inequality, known as the Device Independent scenario, where the characteristics of the probability distributions shared between two or more parties are sufficient to define different kinds of nonlocal correlations, without recourse to any underlying theory. The third chapter briefly goes over the disjoint sets of genuinely entangled three qubit pure states that constitute the GHZ and W classes of states. It also introduces a new class of states called the Maximally Bell-Inequality Violating (MBV) class of states. This is followed by the description of the analytical calculations for the maximum bipartite Bell Inequality violations of the three classes. For each of the three classes a complementary relation is then established between the maximum bipartite Bell Inequality Violation and tripartite correlation measures - Generalized Geometric Measure (GGM), Tangle and Discord Monogamy Score (DMS). Numerical evidence for this is also provided. The fourth chapter discusses first the convex roof extension of the complementary relations described above and then it goes on to discuss the results of the obvious next step, comparing tripartite nonlocality with tripartite correlation measures. The analysis is put forth as a future problem which possibly has quite a few fascinating insights in store.

Abstract

Scientific method of finding a new law pertains to three sequential steps. First we guess a law, second we compute its consequences and third we compare the consequences with experiments. On top of these lies the pillar of science, if these consequences disagree with observation the guessed law is wrong. Science can never assert that any theory, or law explains nature. It can only tell which ones cannot. The twentieth century saw Humankind asking one of the most fundamental question regarding the nature of reality and determinism. EPR in 1935 questioned the completeness of Quantum Mechanics (a proba- bilistic theory as a fundamental theory). Bell’s framework developed to answer the question, introduced first notions of theory independent tests. While we cannot say whether there exists a deterministic ex- planation to every prediction of QM, but we can reject a subclass of deterministic explanation, local deterministic explanation. This experimentally verified departure of predictions of QM or any theory from any local deterministic explanation is referred to as non-locality. In first chapter origins and mathematical formalism of quantum mechanics is presented. Followed by presentation of EPR argument, Bell’s theorem, implications and applications in the second chapter. Fol- lowing chapters contain the main contributions from the author. In third chapter, we study the consequences of both answers set in a device (theory) independent frame- work, based only on observed statistics. We start with taking up post-selection as an assumption and model the same using independent devices governed by Boolean functions. We establish analogy be- tween the post selection functions and the general probabilistic games in a two party binary input-output scenario. As an observation, we categorize all possible post-selection functions based on the effect on a uniform input probability distribution. We find that post-selection can transform simple no signaling probability distributions to signaling. Similarly, solving NP complete problems is easy independent of classical or quantum computation (in particular we prove that Post RP = NP). Finally, we demonstrate an instance of the violation of the pigeon hole principle independent of underlying theory. As result of our theory independent modeling we conclude that post-selection as an assumption adds power to the underlying theory. In particular, quantum mechanics benefits more with the post-selection assumption, only because it admits a more general set of allowed probabilities as compared to the local hidden vari- able model. Without the assumption we associate a device independent efficiency factor to quantify the cost of post selection. Our study shows that in the real world post-selection is not efficient enough to be of any advantage. But from an adversarial perspective it is still of significance. As an application, we obtain robust bounds on faking the bell violation in terms of minimum efficiency required using post selection. In fourth chapter we study a semi device-independent protocol wherein the knowledge of success prob- ability of the associated dimension witness and the observed efficiency of the detectors (η a vg) are suf- ficient to determine the security. We consider practically possible, individual attacks with and without eavesdropper having access to quantum memory. We find critical detection efficiencies required for security in both cases. In fifth chapter we introduce generalizations to PR and (2 → 1) RAC and study their inter-convertibility. We introduce generalizations based on the number of inputs provided to Alice, B n -BOX and (n → 1) RAC. We show that a B n -BOX is equivalent to a no-signaling (n → 1) RACBOX (RB). Further we introduce a signaling (n → 1) RB which cannot simulate a B n -BOX. Finally to quantify the same we provide a resource inequality between (n → 1) RB and B n -BOX, and show that it is saturated. As an application we prove that one requires atleast (n − 1) PRs supplemented with a bit of communication to win a (n → 1) RAC. We further introduce generalizations based on the dimension of inputs provided to Alice and the mes- sage she sends, B n d (+)-BOX, B n d (−)-BOX and (n → 1, d) RAC (d > 2). We show that no-signaling condition is not enough to enforce strict equivalence in the case of d > 2. We introduce classes of no-signaling (n → 1, d) RB, one which can simulate B n d (+)-BOX, second which can simulate B n d (−)- BOX and third which cannot simulate either. Finally to quantify the same we provide a resource in- equality between (n → 1, d) RB and B n d (+)-BOX, and show that it is saturated. In sixth chapter we introduce the measurement-device-independent randomness certification task where one has trusted quantum state preparation device but the mesurement devices are completely unspec- ified. Interestingly we show that there exist entangled states, w

Abstract

Background Determining gene and transcript expression patterns is crucial for understanding cellular and molecular biology concepts. Currently, a large amount of gene expression data is available, and many tools and databases are developed to predict or extract gene expression patterns. But, these resources have many limitations. So, there is a need to develop a new resource that can help in establishing expression patterns. Gene expression patterns can also assist in the identification of biomarkers, which in turn is an important step for identification of better diagnostics and prognostics. Microarray experiments have been extensively used to identify genes associated with various physiological conditions. But, there is a scope for making a better use of such available data. For example, the gene-level expression data can be used to identify transcript isoforms associated with a disease or a physiological condition. Establishing expression of the transcript isoforms in various physiological and disease conditions is important, as most of the human genes undergo alternative splicing and produce an enormous number of transcript isoforms, and these isoforms are also associated with diseases. In fact, establishing alternatively spliced isoforms in different tissues and conditions is essential for understanding many fundamental molecular events, such as mechanisms of alternative splicing and its regulation. Moreover, the mechanisms of alternative splicing are far from being understood clearly. Non-obstructive azoospermia is a common male infertility disorder that can be considered as a case for reanalyzing the gene expression data corresponding to it. Microarray studies have enabled identification of genes differentially regulated in this disease. But, no meta-analysis attempts have been made to identify genes strongly associated with the disease, which can be further used as potential biomarkers. It should be possible to identify transcript isoforms as potential biomarkers for non-obstructive azoospermia. Objectives The current study aimed at: Compiling existing gene expression data for one tissue, the mammalian testis Comparing the utilities of the existing gene expression databases with the newly developed testis-specific database (developed using the compiled data) Establishing expression profiles at the transcript isoform level via a new software and validating the results Using the new software and RNA-sequencing experiments to establish the transcriptome for non-obstructive azoospermia and identifying potential biomarkers at transcript isoform level and Analyzing the data to explore the factors associated with alternative splicing, in the context of mammalian testis Results The mass scale gene expression data, corresponding to various testicular physiological conditions and normal physiological condition for other tissues, were curated, and gene expression datasets were accumulated/derived. These datasets were later used to derive genes associated with different physiological conditions. The database developed using such data was found to be better and more reliable than most other gene expression databases. This could be due to both wealth of data and the new features used - including a meta-analysis method employed in the development of the new database. A web server, TIPMaP, was developed to predict expression profiles of transcript-isoforms using the original/existing microarray gene expression data. These data were reanalyzed using good quality probes and the transcript expression profiles were determined. The expression pattern predicted by the tool was validated for a few transcripts using RT-PCR and RNA-sequencing experiments. There was a high percentage agreement between the experimental observations and the web server results, and the extent of such agreement was dependent on the consistency in the results across the microarray hybridizations. Thus, the tool aids in establishing expression patterns of specific transcripts and provides a quantitative means of reproducibility of these patterns. Transcripts differentially regulated/expressed in non-obstructive azoospermia were identified by re-analyzing an already published gene expression dataset using TIPMaP and by RNA-sequencing. The results helped to identify the potential biomarkers for non-obstructive azoospermia. An in-depth analysis of alternatively spliced transcript isoforms helped to compile the type of events that occur in the mammalian testis-associated genes. Additionally, a set of factors that might promote alternative splicing in the testis tissue were identified. These factors include a few testis-specific splice factors, over/under-represented splicing regulatory motifs, and RNA secondary structural stabilities at the splice site junctions. Conclusions The majority of the existing gene expression data was compiled for the mammalian testis tissue via a laborious biocuration process. The new gene expressi

Abstract

The past decade has witnessed a significant improvement in neuroimaging technologies and analysis methods in order to observe and measure brain activity. The advances in the understanding of how the brain is organized is the key to progress in fighting neurological disorders. The efforts to find ways to detect and diagnose these impairments depend largely on the data that we are able to obtain about cerebral hemodynamics, fluctuations, structural changes and the associated cortical activities in the brain. The aim of this thesis is to develop innovative methodologies for examining cortical excitability regulation particularly for stroke patients during transcranial direct current stimulation (tDCS) with the help of Near Infrared Spectroscopy (NIRS) and Electroencephalography (EEG) combined brain imaging. EEG is an electrophysiological method that measures the electrical neural activity directly while NIRS is a method that measures the brain activity indirectly through hemodynamic responses associated with neuron behavior. Transcranial direct current stimulation (tDCS) is a non-invasive technique of modulating brain activity that delivers low intensity direct current to cerebral cortex. It is widely used for stimulating or inhibiting spontaneous neuronal activity. The alterations in cerebrovascular hemodynamics caused by tDCS can be studied through functional NIRS. A low cost tDCS device was developed and integrated with the custom built NIRS data acquisition device to study the effects of electrical stimulation on brain in patients for prediction of neurological disorders such as ischemic stroke. The NIRS – tDCS device was taken for clinical validation at Institute of Neurosciences, Kolkata and it proved to be a good predictor of cerebral vascular status. It can be used for possible identification of acute stroke. The tDCS –NIRS protocol was then further extended to tDCS-NIRS-EEG protocol. It was further investigated that the application of tDCS on cerebral site causes systemic interference in the superficial layers of the head. Due to this, there were some fluctuations found in NIRS measurements. The anterior temporal artery tap technique is discussed in the thesis to remove these fluctuations, which may also be able to classify carotid stenosis, external carotid artery stenosis, and internal carotid artery stenosis patients using the laterality in the hemodynamic response evoked by anodal tDCS both at the brain as well as at the superficial layers. These findings may have important implications for both prognosis and rehabilitation of patients with intracranial stenosis. The EEG and NIRS have different spatial and temporal resolutions, the online tracking of the relation between EEG and NIRS data acquired simultaneously during tDCS has not been investigated earlier. This could provide a sensitive means to monitor the tDCS neuro-modulatory effect. A Kalman filter based approach for online parameter estimation of an autoregressive model to track the relation between tDCS induced cortical neural activity leading to changes in EEG power spectrum and oxy Hemoglobin signals. This new online NIRS EEG tracking method may allow quantitative assessment of the existence of a coupling relationship between electrophysiological and hemodynamic response to tDCS, which could be used to monitor tDCS neuro-modulatory effect in health and disease.

Abstract

Over the past three decades, there has been a continuous demand for an increase in the transmission data rate in the aeronautical electronics and communication technologies. This has resulted in the update or creation of new defense and aerospace standards. MIL-STD-1553B is one such bus standard, extensively used in the aerospace and aircraft industry which currently falls short of the modern-day payload requirements as its links support a bandwidth capacity of 1-Mb/s. Present day solutions resort to using an aggregated use of numerous serial buses along with multiple MIL-STD-1553B buses to accommodate high bandwidth inter-aircraft communication requirement. This often leads to less deterministic and complicated systems which require a high cost to develop, integrate, maintain and test. This thesis focuses on improving the bandwidth performance of legacy MIL-STD-1553 data bus by retrofitting redesigned 1553 controllers and analog transceiver upgrades to drive the existing buses at higher data-rates. Therefore, this work explores the enhancement at both digital and analog levels in order to minimize wiring complexity, cable weight and integration cost/time while retaining the valuable features of legacy 1553 standard. Firstly, this research proposes a hardware realization of a 100-Mb/s enhanced data rate MIL-STD-1553B digital controller chip: MIL-STD-1553+, for this purpose. This controller features an integrated remote terminal, bus monitor and bus controller, thus offering a complete 1553 solution at this increased throughput. This work first carried out a comparative study of the reported power and critical path timing aspects of possible architectural techniques with which the existing 1-Mb/s controller design can be improved. Based on this study, a finalization of a synchronous back-end and host processor interface to a true dual port memory to facilitate faster read-and-write memory accesses during controller run-time. A suitable technique of employing high speed majority-based free-running decoders has been chosen at the protocol controller unit‟s front-end interface to receive 100-Mb/s 1553 data. These dual-redundant decoders incorporate both inverted and non-inverted clocks in order to bring the overall clocking rate down to 800-MHz, which conventionally would need a clock of at least 1.6-GHz, thus reducing the power dissipation of the suggested controller. This thesis also suggested partitioning the existing protocol control architecture into two concurrently running state machines which enable the scaled-up scheduling, storage and retrieval of 1553 messages, at 100-Mb/s bit-rate over channel. Finally, the throughput and area-power performance analysis done for a gate-level synthesis and post-layout simulation on a 65-nm, 1.2 V CMOS technology node respectively. This enhanced integrated controller is also implemented on FPGA and checked for block and system level functionality using software logic analyzer IP‟s. Secondly, low power hardware realization of a legacy 1553 analog transceiver upgrade in a 1.8 V, 0:18 m CMOS technology has been made to be able to receive upto 100-Mb/s. Moreover, this transceiver upgrade will support existing legacy 1553 hardware and devices not dedicated to this boosted data rate as well. For this design, we propose a power-saving technique which equalizes the channel inter-symbol interference (ISI) at 100-Mb/s, based on a reconstructed discretized eye diagram accumu-lated after averaging out a large set of comparator decisions, clocked at only 1-MHz. This work presents the working principle, analog design components and digital histogram processing algorithm associated to this transceiver upgrade in detail. This thesis post-layout simulated each design component in this upgrade to verify functionality and followed this with a throughput performance analysis of this system in a communication environment, to observe an optimum error-rate performance.

Abstract

Egocentric cameras are wearable cameras mounted on a person’s head or shoulder. With their ability to capture what the wearer is seeing, such cameras are spawning new set of exciting applications in computer vision. Recognising activity of the wearer from an egocentric video is an important but challenging problem. This problem is more challenging than third person activity recognition due to unavailability of wearer’s pose. Unstructured movement of the camera due to natural head motion of the wearer causes sharp changes in the visual field of the egocentric camera making problem even more challenging. This causes many standard third person action recognition techniques to perform poorly on such videos. On the other hand, objects present in the scene and hand gestures of the wearer are the most important cues for first person action recognition. However, such cues are difficult to segment and recognize in an egocentric video. Carefully crafted features based on hands and objects cues for the problem have been shown to be successful for limited targeted datasets. In the first part of our work, we propose a novel representation of the first person actions derived from feature trajectories. The features are simple to compute using standard feature tracking and does not assume segmentation of hand/objects or recognizing object or hand pose unlike in many previous approaches. We train a bag of words classifier with the proposed features and report a significant performance improvement on publicly available datasets. In the second part of the thesis, we propose convolutional neural networks ( CNN s) for end to end learning and classification of wearers actions. The proposed network makes use of egocentric cues by capturing hand pose, head motion and saliency map. The proposed network model is compact and therefore can be trained from relatively small number of labeled egocentric videos that are available in egocentric settings. We show that the proposed network can generalize and give state of the art performance on various egocentric action datasets widely different from each other visually as well as dynamically.

Abstract

Software engineering activities such as code reviews, change management, knowledge management, issue tracking etc. tend to be heavily process oriented and are repetitive in nature. Serious games, simulations, augmented reality, gamification are seen as potential options to improve human motivation towards performing process oriented repetitive tasks. However, gamification differs from other techniques as the original intent of the activity is not overly impacted. The idea of making a user lose or win is optional while gamifying an application. All these factors add on building a healthy competitive environment which is suitable for software product development industry as well as academic community. Prior research has shown gamification helped in building interesting e-learning systems which impact the academic community by improving the learning experience of students. Gamification of such activities is done by composing the core activities with game design elements like extit{badges, points} etc. Gamification can increase developers' interest in performing such activities. While there are various frameworks/applications that assist in gamification, extending and configuring the frameworks to add any/all the desired game design elements has not been adequately addressed. Implying configurable rules within a rule engine instead of implementing business logic to award game design elements is still unaddressed. Addressing the aforementioned issues in this thesis, an extensible architectural framework for gamification of software engineering activities is designed and developed. The framework supports building new game design elements and gamifying software engineering processes. Game design elements are implemented as rest-full services. Gamifying an application involves adding features without modifying the original look and feel of the application. The framework consists of three major components : 1) A base framework consisting of software packages required to support an SE activity. 2) Game design elements which are implemented as micro services using python's Flask micro framework and 3) Basic front end components namely table, matrix and list which support the UI of game design elements. %These components are developed as Ember.js UI components. The validation of this work is done in three ways. 1) A Gamified instance of code review activity is created using the framework developed. 2) To evaluate the ease of use and extensibility of the framework, developers are asked to extend the framework and instantiate gamified applications from the framework and our results are encouraging - developers are able to quickly develop new services as well as use the existing framework to instantiate new gamified instances for software engineering activities. 3) The impact of tool developed is quantitatively analyzed by calculating the usefulness of code review comments. The number of comments given using the gamified code review tool are significantly similar to the number of comments given when code review is performed using existing tools. Thus indicating that the tool developed has the required elements of a code review tool. %We performed a study consisting of nearly 180+ participants to measure the usefulness of code review comments given by them during code review process in a gamified versus non-gamified environment using machine learning techniques. Further, we assess the impact of gamification based on Code smells and bugs identified in a gamified and non-gamified environment. % and has five game design elements implemented as services, and exposed using restful APIs. %We also built and used our own production ready framework to instantiate gamified applications for SE activities. To validate our claims of extensibility and productivity improvement in developer while gamified application development, we considered a group of developers consisting of undergraduate students, industry experts working as teams to build gamified applications. We present out results which demonstrate the adaptability of our framework by developers. %we instantiate a gamified version of the code review process from our extensible architectural framework that supports gamification of various software engineering activities. %In industry, inspections, reviews, and refactoring are considered as necessary software engineering activities for enhancing quality of code. In academia, such activities are rarely taught and practiced at Undergraduate level due to various reasons like limited skill set, limited knowledge of the available tools, time constraints, project setting, project client availability, flexibility with Syllabus, etc. However, we argue that such activities are an essential part of introductory software engineering courses and can result in improvement of coding skills, knowledge of coding standard and compliance to the same, and peer communication within teams. We have studied the use of such a

Abstract

Computational modeling helps us study the mechanisms underlying different cognitive and neural processes and relate them to the corresponding experimental data. Besides relating to the experimental data, a computational model should be able to propose a testable hypothesis. This thesis attempts to develop a computational model of a group of sub-cortical nuclei in the brain, called the Basal Ganglia (BG), to understand their role in reward based action selection and decision making. One of the exist-ing biologically plausible models of the BG is simulated and the model is able to learn a probabilistic two-armed bandit task using reinforcement learning. This model is able to choose the best option and reach optimal performances after only a few trials. Further, an extended decision making framework involving the BG is developed, accommodating a variety of stimulus representations and realistic scenarios. The framework is used to study the influ- ence of exogenous factors such as stimulus salience and delay in stimulus presentation. Analysis of the results suggests that a reward based action selection model, which performs optimally otherwise, performs sub-optimally when the visual characteristics of the stimuli such as salience are changed and when delays are introduced in the successive presentation of stimuli. The influence of salience changes and delays of presentation of stimuli on decision making form novel testable predictions from the com-putational experiments reported here. The developed framework is quite simplistic with as few as 72 neurons representing all the brain structures involved in decision making. It has been successfully expanded to a large scale both in terms of neuron populations and extending to a variety of tasks that the BG are known to be involved in. Scaling up involved exploring various large scale neural modeling frameworks like DANA 1 and NEF 2 (Nengo). The model is scaled using DANA to as many as 66,000 neurons. With the large scale model, the functional properties and neural activity patterns have been found to be consistent with the simplistic model. Scaling up using the large scale neural modeling platform, Nengo, also yielded the same dynamics of the simplistic model but now the model is expanded to a massive scale of 300K neurons. The power of converting existing models to efficiently weighed neural networks in Nengo is exploited in this work. Challenges involving scaling simple models, the dynamics while connecting largely and differently populated structures and pros and cons involved in using various modeling frameworks are discussed. Crucial aspects in a computational model, like parameter tuning and detailed neural implementations, while moving from a simplistic to large-scale model, are also studied. While the focus of the thesis has been to match the qualitative behavior at small and large scales, such large scale models have intrinsic advantages such as the ability to implement sophisticated stimulus representation, graceful degradation of performance in the face of injury / insult to the neural substrate and implementation of a biologically plausible architecture that matches details of the anatomy.

Abstract

Membrane proteins account for more than half of present day drug targets, but an understanding of structure-function relationships in these proteins continues to be a challenge, owing to the difficulties associated with experimental investigations of these proteins. The work in this thesis makes use of all-atom molecular dynamics simulations to address important problems pertaining to membrane proteins. A major problem in drug design targeting membrane proteins is limited availability of experimental structures. Toward this end, an approach has been proposed here for modeling the structures of α-helical membrane proteins. While the approach is able to provide structural models for a number of channel proteins, possible improvements, that would make the approach more suitable for other membrane proteins, are proposed. The other aspect of membrane proteins that has been difficult to investigate experimentally is an atomistic level understanding of the conduction and selectivity mechanism in channels and transporters. This thesis provides insights into the mechanism of transport in a number of channels and transporters, including two viral ion channels, a mammalian aquaporin, and a bacterial urea transporter. In the first part of the thesis, it is shown that the protein Vpu from human immunodeficiency virus type 1 (HIV-1) exists in a pentameric state, and a structural model for the oligomeric form of the protein is proposed. Free energy calculations performed on the structural model are able to provide a thermodynamic basis for the weak ion channel activity of the protein. In the next part of the thesis, a structure modeling approach is described that attempts to predict the structures of α-helical membrane proteins by minimizing unfavorable contacts. The approach works well for α-helical channels, and can be extended to all α-helical membrane proteins. This is followed by an investigation of ion conduction and selectivity in the p7 channel from hepatitis C virus (HCV). It is shown that a hydrophobic stretch in the channel preferentially allows the singly charged K+ rather than the doubly charged Ca2+ to pass through, and that interionic repulsion is crucial for ion conduction through the pore. Water transport through the human aquaporin AQP2 is then examined, revealing an almost barrierless permeation profile for water transport. The implications of the restriction of water orientation in the middle region of the pore are discussed. Finally, the mechanism of urea transport through the bacterial urea transporter dvUT is studied, and it is shown that permeating urea molecules are able to overcome a hydrophobic barrier arising from the existence of pore-facing phenylalanine residues by forming stacking interactions with these residues. While the work on structure modeling reveals some “rules” that membrane proteins follow at the time of oligomerization, the studies on transport across membrane proteins suggest that transport proteins have their unique mechanisms for determining selectivity, depending on what chemical species they conduct.