Quantum Field Theories on Algebraic Curves. I. Additive bosons

Using Serre's adelic interpretation of cohomology, we develop a `differential and integral calculus' on an algebraic curve X over an algebraically closed filed k of constants of characteristic zero, define algebraic analogs of additive multi-valued functions on X and prove corresponding generalized residue theorem. Using the representation theory of the global Heisenberg and lattice Lie algebras, we formulate quantum field theories of additive and charged bosons on an algebraic curve X. These theories are naturally connected with the algebraic de Rham theorem. We prove that an extension of global symmetries (Witten's additive Ward identities) from the k-vector space of rational functions on X to the vector space of additive multi-valued functions uniquely determines these quantum theories of additive and charged bosons.Comment: 31 pages, published version. Invariant formulation added, multiplicative section remove

Can HIV self-testing reach first-time testers? A telephone survey among self-test end users in Côte d'Ivoire, Mali, and Senegal

BACKGROUND: Coverage of HIV testing remains sub-optimal in West Africa. Between 2019 and 2022, the ATLAS program distributed ~400 000 oral HIV self-tests (HIVST) in Côte d'Ivoire, Mali, and Senegal, prioritising female sex workers (FSW) and men having sex with men (MSM), and relying on secondary redistribution of HIVST to partners, peers and clients to reach individuals not tested through conventional testing. This study assesses the proportion of first-time testers among HIVST users and the associated factors. METHODS: A phone-based survey was implemented among HIVST users recruited using dedicated leaflets inviting them to anonymously call a free phone number. We collected socio-demographics, sexual behaviours, HIV testing history, HIVST use, and satisfaction with HIVST. We reported the proportion of first-time testers and computed associated factors using logistic regression. RESULTS: Between March and June 2021, 2 615 participants were recruited for 50 940 distributed HIVST (participation rate: 5.1%). Among participants, 30% received their HIVST kit through secondary distribution (from a friend, sexual partner, family member, or colleague). The proportion who had never tested for HIV before HIVST (first-time testers) was 41%. The main factors associated with being a first-time tester were sex, age group, education level, condom use, and secondary distribution. A higher proportion was observed among those aged 24 years or less (55% vs 32% for 25-34, aOR: 0.37 [95%CI: 0.30-0.44], and 26% for 35 years or more, aOR: 0.28 [0.21-0.37]); those less educated (48% for none/primary education vs 45% for secondary education, aOR: 0.60 [0.47-0.77], and 29% for higher education, aOR: 0.33 [0.25-0.44]). A lower proportion was observed among women (37% vs 43%, aOR: 0.49 [0.40-0.60]); those reporting always using a condom over the last year (36% vs 51% for those reporting never using them, aOR: 2.02 [1.59-2.56]); and those who received their HISVST kit through primary distribution (39% vs 46% for secondary distribution, aOR: 1.32 [1.08-1.60]). CONCLUSION: ATLAS HIVST strategy, including secondary distribution, successfully reached a significant proportion of first-time testers. HIVST has the potential to reach underserved populations and contribute to the expansion of HIV testing services in West Africa

ATTITUDES AND PRACTICES OF NURSING STUDENTS CONFRONTED WITH BLOOD EXPOSURE ACCIDENTS IN ABIDJAN

Background: Blood Exposure Accidents (BEAs) are frequent in healthcare settings and may cause such severe consequences as HIV and Hepatitis B and C infections. Objective: to determine the attitudes and practices of nursing students facing BEAs. Materials and Methods: this was a cross-sectional prospective study conducted from August 16 to 23, 2011 at the “Institut National de Formation des Agents de la Santé d’Abidjan” (the National Institute in charge of training Health Workers). Data were collected by means of self-administered questionnaires. Results: out of 266 student nurses included in the study, 73.3% were females while 26.7% were males. Their mean age was 29 years [20 years - 37 years]. A previous training was conducted on BEAs for 67.3% of nursing students. Those students who were immunized against hepatitis B were 75.2%. Needle recapping was practiced by 43.6% of nursing students. Unclean needles were eliminated in containers by 96.2% of the students and waste containers were within close reach of only 65.4%. Glove wearing was systematic in 77.1% of the students. Before the survey 38% of nursing students had been victim of BEAs at least once. Those BEAs were not reported in 68.3% of the victims. Conclusion: the attitudes and practices of nursing students are inadequate with regards to BEAs. A module on Hospital Hygiene is necessary in view of improving the training of nursing students

Pattern Classification of Large-Scale Functional Brain Networks: Identification of Informative Neuroimaging Markers for Epilepsy

The accurate prediction of general neuropsychiatric disorders, on an individual basis, using resting-state functional magnetic resonance imaging (fMRI) is a challenging task of great clinical significance. Despite the progress to chart the differences between the healthy controls and patients at the group level, the pattern classification of functional brain networks across individuals is still less developed. In this paper we identify two novel neuroimaging measures that prove to be strongly predictive neuroimaging markers in pattern classification between healthy controls and general epileptic patients. These measures characterize two important aspects of the functional brain network in a quantitative manner: (i) coordinated operation among spatially distributed brain regions, and (ii) the asymmetry of bilaterally homologous brain regions, in terms of their global patterns of functional connectivity. This second measure offers a unique understanding of brain asymmetry at the network level, and, to the best of our knowledge, has not been previously used in pattern classification of functional brain networks. Using modern pattern-recognition approaches like sparse regression and support vector machine, we have achieved a cross-validated classification accuracy of 83.9% (specificity: 82.5%; sensitivity: 85%) across individuals from a large dataset consisting of 180 healthy controls and epileptic patients. We identified significantly changed functional pathways and subnetworks in epileptic patients that underlie the pathophysiological mechanism of the impaired cognitive functions. Specifically, we find that the asymmetry of brain operation for epileptic patients is markedly enhanced in temporal lobe and limbic system, in comparison with healthy individuals. The present study indicates that with specifically designed informative neuroimaging markers, resting-state fMRI can serve as a most promising tool for clinical diagnosis, and also shed light onto the physiology behind complex neuropsychiatric disorders. The systematic approaches we present here are expected to have wider applications in general neuropsychiatric disorders

Effects of Different Correlation Metrics and Preprocessing Factors on Small-World Brain Functional Networks: A Resting-State Functional MRI Study

Graph theoretical analysis of brain networks based on resting-state functional MRI (R-fMRI) has attracted a great deal of attention in recent years. These analyses often involve the selection of correlation metrics and specific preprocessing steps. However, the influence of these factors on the topological properties of functional brain networks has not been systematically examined. Here, we investigated the influences of correlation metric choice (Pearson's correlation versus partial correlation), global signal presence (regressed or not) and frequency band selection [slow-5 (0.01–0.027 Hz) versus slow-4 (0.027–0.073 Hz)] on the topological properties of both binary and weighted brain networks derived from them, and we employed test-retest (TRT) analyses for further guidance on how to choose the “best” network modeling strategy from the reliability perspective. Our results show significant differences in global network metrics associated with both correlation metrics and global signals. Analysis of nodal degree revealed differing hub distributions for brain networks derived from Pearson's correlation versus partial correlation. TRT analysis revealed that the reliability of both global and local topological properties are modulated by correlation metrics and the global signal, with the highest reliability observed for Pearson's-correlation-based brain networks without global signal removal (WOGR-PEAR). The nodal reliability exhibited a spatially heterogeneous distribution wherein regions in association and limbic/paralimbic cortices showed moderate TRT reliability in Pearson's-correlation-based brain networks. Moreover, we found that there were significant frequency-related differences in topological properties of WOGR-PEAR networks, and brain networks derived in the 0.027–0.073 Hz band exhibited greater reliability than those in the 0.01–0.027 Hz band. Taken together, our results provide direct evidence regarding the influences of correlation metrics and specific preprocessing choices on both the global and nodal topological properties of functional brain networks. This study also has important implications for how to choose reliable analytical schemes in brain network studies

Resting-State Functional Connectivity between Fronto-Parietal and Default Mode Networks in Obsessive-Compulsive Disorder

Background: Obsessive-compulsive disorder (OCD) is characterized by an excessive focus on upsetting or disturbing thoughts, feelings, and images that are internally-generated. Internally-focused thought processes are subserved by the ‘‘default mode network’ ’ (DMN), which has been found to be hyperactive in OCD during cognitive tasks. In healthy individuals, disengagement from internally-focused thought processes may rely on interactions between DMN and a frontoparietal network (FPN) associated with external attention and task execution. Altered connectivity between FPN and DMN may contribute to the dysfunctional behavior and brain activity found in OCD. Methods: The current study examined interactions between FPN and DMN during rest in 30 patients with OCD (17 unmedicated) and 32 control subjects (17 unmedicated). Timecourses from seven fronto-parietal seeds were correlated across the whole brain and compared between groups. Results: OCD patients exhibited altered connectivity between FPN seeds (primarily anterior insula) and several regions of DMN including posterior cingulate cortex, medial frontal cortex, posterior inferior parietal lobule, and parahippocampus. These differences were driven largely by a reduction of negative correlations among patients compared to controls. Patients also showed greater positive connectivity between FPN and regions outside DMN, including thalamus, lateral frontal cortex, and somatosensory/motor regions

Resting-State Multi-Spectrum Functional Connectivity Networks for Identification of MCI Patients

In this paper, a high-dimensional pattern classification framework, based on functional associations between brain regions during resting-state, is proposed to accurately identify MCI individuals from subjects who experience normal aging. The proposed technique employs multi-spectrum networks to characterize the complex yet subtle blood oxygenation level dependent (BOLD) signal changes caused by pathological attacks. The utilization of multi-spectrum networks in identifying MCI individuals is motivated by the inherent frequency-specific properties of BOLD spectrum. It is believed that frequency specific information extracted from different spectra may delineate the complex yet subtle variations of BOLD signals more effectively. In the proposed technique, regional mean time series of each region-of-interest (ROI) is band-pass filtered ( Hz) before it is decomposed into five frequency sub-bands. Five connectivity networks are constructed, one from each frequency sub-band. Clustering coefficient of each ROI in relation to the other ROIs are extracted as features for classification. Classification accuracy was evaluated via leave-one-out cross-validation to ensure generalization of performance. The classification accuracy obtained by this approach is 86.5%, which is an increase of at least 18.9% from the conventional full-spectrum methods. A cross-validation estimation of the generalization performance shows an area of 0.863 under the receiver operating characteristic (ROC) curve, indicating good diagnostic power. It was also found that, based on the selected features, portions of the prefrontal cortex, orbitofrontal cortex, temporal lobe, and parietal lobe regions provided the most discriminant information for classification, in line with results reported in previous studies. Analysis on individual frequency sub-bands demonstrated that different sub-bands contribute differently to classification, providing extra evidence regarding frequency-specific distribution of BOLD signals. Our MCI classification framework, which allows accurate early detection of functional brain abnormalities, makes an important positive contribution to the treatment management of potential AD patients

Altered Small-World Brain Networks in Schizophrenia Patients during Working Memory Performance

Impairment of working memory (WM) performance in schizophrenia patients (SZ) is well-established. Compared to healthy controls (HC), SZ patients show aberrant blood oxygen level dependent (BOLD) activations and disrupted functional connectivity during WM performance. In this study, we examined the small-world network metrics computed from functional magnetic resonance imaging (fMRI) data collected as 35 HC and 35 SZ performed a Sternberg Item Recognition Paradigm (SIRP) at three WM load levels. Functional connectivity networks were built by calculating the partial correlation on preprocessed time courses of BOLD signal between task-related brain regions of interest (ROIs) defined by group independent component analysis (ICA). The networks were then thresholded within the small-world regime, resulting in undirected binarized small-world networks at different working memory loads. Our results showed: 1) at the medium WM load level, the networks in SZ showed a lower clustering coefficient and less local efficiency compared with HC; 2) in SZ, most network measures altered significantly as the WM load level increased from low to medium and from medium to high, while the network metrics were relatively stable in HC at different WM loads; and 3) the altered structure at medium WM load in SZ was related to their performance during the task, with longer reaction time related to lower clustering coefficient and lower local efficiency. These findings suggest brain connectivity in patients with SZ was more diffuse and less strongly linked locally in functional network at intermediate level of WM when compared to HC. SZ show distinctly inefficient and variable network structures in response to WM load increase, comparing to stable highly clustered network topologies in HC

Selective augmentation of striatal functional connectivity following NMDA receptor antagonism: implications for psychosis

The psychotomimetic effect of the N-methyl-D-aspartate receptor (NMDAR) antagonist ketamine is thought to arise from a functional modulation of the brain's fronto-striato-thalamic (FST) circuits. Animal models suggest a pronounced effect on ventral ‘limbic' FST systems, although recent work in patients with psychosis and high-risk individuals suggests specific alterations of dorsal ‘associative' FST circuits. Here, we used functional magnetic resonance imaging to investigate the effects of a subanesthetic dose of ketamine on measures of functional connectivity as indexed by the temporal coherence of spontaneous neural activity in both dorsal and ventral FST circuits, as well as their symptom correlates. We adopted a placebo-controlled, double-blind, randomized, repeated-measures design in which 19 healthy participants received either an intravenous saline infusion or a racemic mixture of ketamine (100 ng/ml) separated by at least 1 week. Compared with placebo, ketamine increased functional connectivity between the dorsal caudate and both the thalamus and midbrain bilaterally. Ketamine additionally increased functional connectivity of the ventral striatum/nucleus accumbens and ventromedial prefrontal cortex. Both connectivity increases significantly correlated with the psychosis-like and dissociative symptoms under ketamine. Importantly, dorsal caudate connectivity with the ventrolateral thalamus and subthalamic nucleus showed inverse correlation with ketamine-induced symptomatology, pointing to a possible resilience role to disturbances in FST circuits. Although consistent with the role of FST in mediating psychosis, these findings contrast with previous research in clinical samples by suggesting that acute NMDAR antagonism may lead to psychosis-like experiences via a mechanism that is distinct from that implicated in frank psychotic illness

Higher harmonic anisotropic flow measurements of charged particles in Pb-Pb collisions at 2.76 TeV

We report on the first measurement of the triangular $v_3$, quadrangular $v_4$, and pentagonal $v_5$ charged particle flow in Pb-Pb collisions at 2.76 TeV measured with the ALICE detector at the CERN Large Hadron Collider. We show that the triangular flow can be described in terms of the initial spatial anisotropy and its fluctuations, which provides strong constraints on its origin. In the most central events, where the elliptic flow $v_2$ and $v_3$ have similar magnitude, a double peaked structure in the two-particle azimuthal correlations is observed, which is often interpreted as a Mach cone response to fast partons. We show that this structure can be naturally explained from the measured anisotropic flow Fourier coefficients.Comment: 10 pages, 4 figures, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/387