Financial openness, democracy, and redistributive policy

The debate about the relationship between democratic forms of government and the free movement of capital across borders dates to the 18th century. It has regained prominence as capital on a massive scale has become increasingly mobile and as free economies experience continuous pressure from rapidly changing technology, market integration, changing consumer preferences, and intensified competition. These changes imply greater uncertainty about citizens'future income positions, which could prompt them to seek insurance through the marketplace or through constitutionally arranged income redistribution. As more countries move toward democracy, the availability of such insurance mechanisms to citizens is key if political pressure for capital controls is to be averted and if public support for an open, liberal international financial order is to be maintained. The author briefly reviews how today's international financial system evolved from one of mostly closed capital accounts immediately after World War II to today's enormous, largely free-flowing market. Drawing on insights from the literature on public choice and constitutional political economy, the author develops an analytical framework for a welfare cost-benefit analysis of financial openness to international capital flows. The main welfare benefits of financial openness derive from greater economic efficiency and increased opportunities for risk diversification. The welfare costs relate to the cost of insurance used as a mechanism for coping with the risks of financial volatility. These insurance costs are the economic losses associated with redistribution, including moral hazard, rent-seeking, and rent-avoidance. A cross-sectional analysis of a large sample of developed and developing countries shows the positive correlation between democracy (as defined by political and civil liberty) and financial openness. More rigorous econometric investigation using logit analysis and controlling for level of income also shows that redistributive social policies are key in determining the likelihood that countries can successfully combine an openness to international capital mobility with democratic forms of government.Economic Theory&Research,Environmental Economics&Policies,Fiscal&Monetary Policy,Banks&Banking Reform,Payment Systems&Infrastructure,Economic Theory&Research,Banks&Banking Reform,Environmental Economics&Policies,Macroeconomic Management,Financial Intermediation

From Patriarchy to Gender Equity: Family Law and Its Impact on Women in Bangladesh.

This thesis constitutes a detailed assessment of the legal position of women in Bangladesh and argues that, despite some recent reforms purporting to improve their status, there is no real change in the situation of patriarchal domination. It is further argued that the dominant patriarchal structures, with the interlinked forces of religion, tradition and seclusion, are sustained not only in family life but also in family law. The thesis illustrates some confusions in the debates about the legal position of women and suggests that the lack of conceptual clarity has handicapped a discussion of the real needs of women in family law within a patriarchally-dominated legal framework. Based on a detailed exposition of family law under the British colonial regime and Pakistani state law, the present study focuses on the more recent Bangladeshi legal developments. These were officially brought about to meet certain social needs and to improve the overall situation of women. However, apart from the reforms concerning the Family Courts Ordinance of 1985, and some glimpses of judicial activism, in reported as well as unreported cases, the existing family law reforms are shown to be mainly procedural. In particular, they appear unable to protect women effectively from violence and economic deprivation. While not arguing for absolute gender equality, although this is apparently provided as a paper right in the Constitution of Bangladesh, the present study proposes that gender equity in family law can be meaningfully developed by better implementation of the existing law, prominently by sensitising the judiciary and society about the particular needs of women

Decoding of human identity by computer vision and neuronal vision

Extracting meaning from a dynamic and variable flow of incoming information is a major goal of both natural and artificial intelligence. Computer vision (CV) guided by deep learning (DL) has made significant strides in recognizing a specific identity despite highly variable attributes. This is the same challenge faced by the nervous system and partially addressed by the concept cells—neurons exhibiting selective firing in response to specific persons/places, described in the human medial temporal lobe (MTL) ⁠. Yet, access to neurons representing a particular concept is limited due to these neurons’ sparse coding. It is conceivable, however, that the information required for such decoding is present in relatively small neuronal populations. To evaluate how well neuronal populations encode identity information in natural settings, we recorded neuronal activity from multiple brain regions of nine neurosurgical epilepsy patients implanted with depth electrodes, while the subjects watched an episode of the TV series “24”. First, we devised a minimally supervised CV algorithm (with comparable performance against manually-labeled data) to detect the most prevalent characters (above 1% overall appearance) in each frame. Next, we implemented DL models that used the time-varying population neural data as inputs and decoded the visual presence of the four main characters throughout the episode. This methodology allowed us to compare “computer vision” with “neuronal vision”—footprints associated with each character present in the activity of a subset of neurons—and identify the brain regions that contributed to this decoding process. We then tested the DL models during a recognition memory task following movie viewing where subjects were asked to recognize clip segments from the presented episode. DL model activations were not only modulated by the presence of the corresponding characters but also by participants’ subjective memory of whether they had seen the clip segment, and by the associative strengths of the characters in the narrative plot. The described approach can offer novel ways to probe the representation of concepts in time-evolving dynamic behavioral tasks. Further, the results suggest that the information required to robustly decode concepts is present in the population activity of only tens of neurons even in brain regions beyond MTL

New glucosamine-based TLR4 agonists: design, synthesis, mechanism of action, and in vivo activity as vaccine adjuvants

20 p.-15 fig.-1 graph. abst.We disclose here a panel of small-molecule TLR4 agonists (the FP20 series) whose structure is derived from previously developed TLR4 ligands (FP18 series). The new molecules have increased chemical stability and a shorter, more efficient, and scalable synthesis. The FP20 series showed selective activity as TLR4 agonists with a potency similar to FP18. Interestingly, despite the chemical similarity with the FP18 series, FP20 showed a different mechanism of action and immunofluorescence microscopy showed no NF-κB nor p-IRF-3 nuclear translocation but rather MAPK and NLRP3-dependent inflammasome activation. The computational studies related a 3D shape of FP20 series with agonist binding properties inside the MD-2 pocket. FP20 displayed a CMC value lower than 5 μM in water, and small unilamellar vesicle (SUV) formation was observed in the biological activity concentration range. FP20 showed no toxicity in mouse vaccination experiments with OVA antigen and induced IgG production, thus indicating a promising adjuvant activity.The authors acknowledge the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie, project BactiVax (www.bactivax.eu) grant agreement no. 860325; the consortium CINMPIS; the project of excellence CHRONOS, CHRonical multifactorial disorders explored by NOvel integrated Strategies of the Department of Biotechnology and Biosciences; the Agencia Estatal de Investigacion (Spain) for project PID2021-126130OB-I00 (N.G.A.A.), PID2020-113588RB-I00 (S.M.-S.), PRE2018-086249 (A.M.-R), PRE2021-097247 (M.M.-T.); and project FEDER MINECO, the EM-platform at the CIC bioGUNE for support in cryo-EM imaging. J.J.-B. also thanks funding by CIBERES, an initiative of Instituto de Salud Carlos III (ISCIII), Madrid, Spain. Perkin-Elmer Italia is also acknowledged for providing the cell imaging reagents.Peer reviewe

Learning characteristic neuronal dynamics of neurological disorders

The understanding of the origins of neurological disorders,such as Epilepsy, Alzheimer’s disease (AD), represents one of the most urgent and challenging areas of current scientific enquiry. In USA alone, $20\%$ of the general population fall into one of these categories, thus creating an enormous need for medical intervention. Neurological disorders lead to system-level deficits which can cause disruptions in structural connectivity, functional organization, and information processing across various brain regions. Characterizing these system-level deficits from neuronal dynamics perspective is crucial for developing targeted interventions aimed at restoring normal brain function. Fueled by the rapid advancement in neural recording technologies both at the single and population level, we develop a data driven framework to characterize the neuronal dynamics in neurological disorders that can advance our understanding, diagnosis, and treatment of neurological disorders, ultimately improving patient outcomes and quality of life.In this thesis, we study one of the most prevalent and debilitating neurological disorders called Epilepsy: approximately 65 million people suffer from it globally. In patients with epilepsy, the normal signaling mechanism in the brain is disrupted by sudden and synchronized bursts of electrical pulses, leading to recurrent episodes of seizures. Epileptic seizures can be broadly classified into two types: generalized seizures which involve multiple cross-hemisphere epileptic foci and focal seizures where the epileptic focus is localized to a specific brain region. About one-third of patients with focal seizures, cannot be treated with anti-seizure medications and they need to undergo a resective surgery for the removal of Epileptogenic zone (EZ), which is the site of the cortex responsible for generating seizures. In the pre-surgical stage, the patient is placed under intracranial EEG (iEEG) monitoring in the hospital leading to iEEG recordings during actual seizures, referred to as ictal segments. Synchronous electrical signals recorded in the ictal segments have been modeled as network/collective dynamics involving all the channels leading to automated identifications of channels that drive the observed seizures. These channels are referred to as seizure onset zones (SOZs) as they constitute parts of the EZ active during observed seizures. Another correlate of this synchronous activity are short duration oscillatory field potential, known as High Frequency Oscillations (HFO), that are observed at the level of individual electrodes of iEEG. SOZ channels have distinctly higher rates of HFOs during the ictal segment, allowing neurologists to identify SOZs without any explicit network modeling. Once the SOZ has been identified then the surgeons resect the SOZ if possible. However, this approach has led to success in about only $60-70\%$ of the patients because there might be parts of EZ that did not participate in generating seizures during the limited observation window; such unobserved parts of EZ are known as potential seizure onset zones (PSOZs). The inability of SOZ to completely encapsulate EZ in many patients, along with the hardships and risks associated with lengthy hospitalization period -often lasting two weeks or more- has prompted the need to find accurate physiological biomarkers of EZ during the interictal period, i.e, the majority of the time when patients do not have seizures.HFOs observed during ictal periods have also been observed to be present at higher rates in SOZ channels (determined from ictal periods) during interictal periods, leading to the hope that resection of channels with high interictal HFO rates would lead to seizure freedom. However, the presence of HFOs arising from cognitive processes (physiological HFOs) during interictal periods have diminished the predictive power of interictal HFO rate in the context of surgical outcome prediction. In the first part of the thesis, we develop a weakly supervised deep learning model to filter out the physiological HFOs and thus extract the pathological cluster of HFOs: epileptogenic HFOs (eHFO). In retrospective validation on a patient cohort of 15 patients, the eHFO cluster was found to be a better biomarker of EZ compared to Real HFO cluster (HFO cluster after filtering for artifacts) as it was able to correctly predict the post surgical outcomes of patients with an F1 score of $87\%$ in comparison to Real HFO cluster's $72\%$. However, when tested on a much larger patient cohort (159 patients), we found that a significant percentage of patients ($\sim 25\%$) did not have enough HFO detections and as a result the eHFO resection ratio was not able to correctly predict the surgical outcomes of those patients. Therefore, there is a need to look beyond HFOs in the space of potential interictal biomarkers of EZ.Recently, there is a growing interest in determining whether synchronization effects can be observed in the interictal period and their temporal dynamics can be leveraged to delineate EZ. The problem of studying such effects and using them for better surgical outcome prediction is still open and we address this in the second part of the thesis. In particular, we use Power-Phase coupling amongst channels to construct a sequence of directed weighted networks from interictal segments. We leverage the topological dynamics of the network, both local and global, to train a machine learning model to identify SOZ (ground truth obtained from ictal data) from purely interictal segments. The model identifies the SOZ with over $95\%$ accuracy. One of the hallmarks of the constructed networks is that they occasionally transition into a state of hyper-synchrony with SOZ and PSOZ nodes being the hub of these hyper-synchronous states. We hypothesize that these hyper-synchronous states are 'mini seizures' in the interictal phase and our machine learning model is able to identify them and use them for not only accurate SOZ identification but also identify PSOZ. The only way to validate whether our model has truly identified PSOZ and hence EZ is through surgical outcome prediction. In the third part of thesis, we construct a set of features from SOZ model prediction scores along with the constructed network flow dynamics to propose a network based biomarker of EZ. In retrospective validation on a patient cohort of 159 patients, the network based biomarker was able to correctly predict the post surgical outcomes of patients with an F1 score of $89\%$. Finally, we develop an integrated framework to exploit the interplay between the constructed network and pathological HFO cluster to propose a novel biomarker of EZ. At the heart of this framework is a regression model which predicts the pathological HFO rate using a mixture of local and global properties of the constructed network. The summary statistics of the $R^2$ of the regression model along with the previously computed features (SOZ model prediction scores and epileptic network flow dynamics) is proposed as a novel biomarker of EZ. In retrospective validation on a patient cohort of 159 patients, the novel biomarker was able to correctly predict the post surgical outcomes of patients with an F1 score of $98\%$. A closer inspection into the summary statistics of the regression model for non-seizure free patients reveals a cluster of pathological HFO whose rate cannot be explained by the constructed network thus revealing the presence of a brain region capable of generating seizures outside of the one sampled by iEEG