Biases in inferring dark matter profiles from dynamical and lensing measurements

The degeneracy between disc and halo contributions in spiral galaxy rotation curves makes it difficult to obtain a full understanding of the distribution of baryons and dark matter in disc galaxies like our own Milky Way. Using mock data, we study how constraints on dark matter profiles obtained from kinematics, strong lensing, or a combination of the two are affected by assumptions about the halo model. We compare four different models: spherical isothermal and Navarro-Frenk-White halos, along with spherical and elliptical Burkert halos. For both kinematics and lensing we find examples where different models fit the data well but give enclosed masses that are inconsistent with the true (i.e., input) values. This is especially notable when the input and fit models differ in having cored or cuspy profiles (such as fitting an NFW model when the underlying dark matter distribution follows a different profile). We find that mass biases are more pronounced with lensing than with kinematics, and using both methods can help reduce the bias and provide stronger constraints on the dark matter distributions.Comment: 13 pages, 8 figures, submitted for MNRAS publication Nov 15th 201

How major depressive disorder affects the ability to decode multimodal dynamic emotional stimuli

Most studies investigating the processing of emotions in depressed patients reported impairments in the decoding of negative emotions. However, these studies adopted static stimuli (mostly stereotypical facial expressions corresponding to basic emotions) which do not reflect the way people experience emotions in everyday life. For this reason, this work proposes to investigate the decoding of emotional expressions in patients affected by Recurrent Major Depressive Disorder (RMDDs) using dynamic audio/video stimuli. RMDDs’ performance is compared with the performance of patients with Adjustment Disorder with Depressed Mood (ADs) and healthy (HCs) subjects. The experiments involve 27 RMDDs (16 with acute depression - RMDD-A, and 11 in a compensation phase - RMDD-C), 16 ADs and 16 HCs. The ability to decode emotional expressions is assessed through an emotion recognition task based on short audio (without video), video (without audio) and audio/video clips. The results show that AD patients are significantly less accurate than HCs in decoding fear, anger, happiness, surprise and sadness. RMDD-As with acute depression are significantly less accurate than HCs in decoding happiness, sadness and surprise. Finally, no significant differences were found between HCs and RMDD-Cs in a compensation phase. The different communication channels and the types of emotion play a significant role in limiting the decoding accuracy

Expression levels of the focal adhesion-associated proteins paxillin and p130CAS in canine and feline mammary tumors

International audiencePaxillin and p130$^{{\rm CAS}}$ are two adaptor proteins localized at the focal adhesions which play an important role in cell signaling, cell motility and oncogenic transformation. In this study we evaluated the levels of paxillin and p130$^{{\rm CAS}}$ in feline and canine mammary tumor tissues at different stages of malignancy. The results obtained by Western blotting analysis showed no significant differences in the amounts of paxillin and p130$^{{\rm CAS}}$ between normal and non-invasive tumor tissues. By contrast, mammary tumor tissues with the invasive phenotype showed lower levels of paxillin $P < 0.01$ and higher levels of p130$^{{\rm CAS}}$ $P < 0.001$ than normal tissues. The decrease $P < 0.001$ of the amount of paxillin and the increase $P < 0.001$ of p130$^{{\rm CAS}}$ levels were correlated with the progression stage of malignancy. Since paxillin and p130$^{{\rm CAS}}$ are involved in regulating cell migration, our results suggest that low levels of paxillin together with high levels of p130$^{{\rm CAS}}$ expression may cause certain breast cancers to be more motile and possibly more aggressive. Thus, both paxillin and p130$^{{\rm CAS}}$ may represent useful prognosticators of feline and canine breast cancer malignancy

Detection of Verbal and Nonverbal speech features as markers of Depression: results of manual analysis and automatic classification

The present PhD project was the result of a multidisciplinary work involving psychiatrists, computing scientists, social signal processing experts and psychology students with the aim to analyse verbal and nonverbal behaviour in patients affected by Depression. Collaborations with several Clinical Health Centers were established for the collection of a group of patients suffering from depressive disorders. Moreover, a group of healthy controls was collected as well. A collaboration with the School of Computing Science of Glasgow University was established with the aim to analysed the collected data. Depression was selected for this study because is one of the most common mental disorder in the world (World Health Organization, 2017) associated with half of all suicides (Lecrubier, 2000). It requires prolonged and expensive medical treatments resulting into a significant burden for both patients and society (Olesen et al., 2012). The use of objective and reliable measurements of depressive symptoms can support the clinicians during the diagnosis reducing the risk of subjective biases and disorder misclassification (see discussion in Chapter 1) and doing the diagnosis in a quick and non-invasive way. Given this, the present PhD project proposes the investigation of verbal (i.e. speech content) and nonverbal (i.e. paralingiuistic features) behaviour in depressed patients to find several speech parameters that can be objective markers of depressive symptoms. The verbal and nonverbal behaviour are investigated through two kind of speech tasks: reading and spontaneous speech. Both manual features extraction and automatic classification approaches are used for this purpose. Differences between acute and remitted patients for prosodic and verbal features have been investigated as well. In addition, unlike other literature studies, in this project differences between subjects with and without Early Maladaptive Schema (EMS: Young et al., 2003) independently from the depressive symptoms, have been investigated with respect to both verbal and nonverbal behaviour. The proposed analysis shows that patients differ from healthy subjects for several verbal and nonverbal features. Moreover, using both reading and spontaneous speech, it is possible to automatically detect Depression with a good accuracy level (from 68 to 76%). These results demonstrate that the investigation of speech features can be a useful instrument, in addition to the current self-reports and clinical interviews, for helping the diagnosis of depressive disorders. Contrary to what was expected, patients in acute and remitted phase do not report differences regarding the nonverbal features and only few differences emerges for the verbal behaviour. At the same way, the automatic classification using paralinguistic features does not work well for the discrimination of subjects with and without EMS and only few differences between them have been found for the verbal behaviour. Possible explanations and limitations of these results will be discussed

Droplets I: Pressure-Dominated Sub-0.1 pc Coherent Structures in L1688 and B18

We present the observation and analysis of newly discovered coherent structures in the L1688 region of Ophiuchus and the B18 region of Taurus. Using data from the Green Bank Ammonia Survey (GAS), we identify regions of high density and near-constant, almost-thermal, velocity dispersion. Eighteen coherent structures are revealed, twelve in L1688 and six in B18, each of which shows a sharp "transition to coherence" in velocity dispersion around its periphery. The identification of these structures provides a chance to study the coherent structures in molecular clouds statistically. The identified coherent structures have a typical radius of 0.04 pc and a typical mass of 0.4 Msun, generally smaller than previously known coherent cores identified by Goodman et al. (1998), Caselli et al. (2002), and Pineda et al. (2010). We call these structures "droplets." We find that unlike previously known coherent cores, these structures are not virially bound by self-gravity and are instead predominantly confined by ambient pressure. The droplets have density profiles shallower than a critical Bonnor-Ebert sphere, and they have a velocity (VLSR) distribution consistent with the dense gas motions traced by NH3 emission. These results point to a potential formation mechanism through pressure compression and turbulent processes in the dense gas. We present a comparison with a magnetohydrodynamic simulation of a star-forming region, and we speculate on the relationship of droplets with larger, gravitationally bound coherent cores, as well as on the role that droplets and other coherent structures play in the star formation process.Comment: Accepted by ApJ in April, 201

Species- and strain-specific gene expression of the prostaglandin pathway in <i>Skeletonema marinoi</i> and <i>Thalassiosira rotula</i>

Diatoms are critical to marine ecosystems, contributing to primary production and nutrient cycling. Prostaglandins (PGs), a group of bioactive lipid derivatives, are implicated in various cellular processes such as chemical signaling, stress response, and a plethora of other physiological processes. This study investigates the species- and strain-specific variability in prostaglandin biosynthesis gene expression in Skeletonema marinoi and Thalassiosira rotula, alongside strain-specific differences in oxylipin productivity. The research analyses four distinct strains—two low oxylipin-producing (FE60 and CCMP1018) and two high oxylipin-producing (FE7 and FE80)—across different growth phases and diel cycles. Gene expression was measured using RT-qPCR, targeting enzymes involved in prostaglandin synthesis. Cultures were sampled across exponential, stationary, and senescent phases, as well as under controlled light-dark cycles to capture diel variation. In parallel, a bioinformatic analysis expanded the current understanding of prostaglandin-related enzymes in diatoms by identifying both microsomal and cytosolic PGES non-homologous isofunctional enzymes, along with multiple PGFS candidates, like the putative CBR1-like enzymes, animal-related orthologs, and kinetoplastid-type PGFS. Notably, this work presents the first-ever characterisation of the prostaglandin biosynthetic pathway in freshwater diatoms, broadening the known distribution of these pathways beyond marine taxa. Results revealed significant strain-specific differences in the expression of key prostaglandin-related genes, particularly in response to light-dark transitions and across different phases of growth. These findings suggest that prostaglandin biosynthesis may be finely regulated by both genetic and environmental factors, influencing diatom physiology and ecological interactions. Furthermore, the results allow for speculation about a putative involvement of prostaglandins in programmed cell death (PCD) and cell duplication, drawing parallels to similar mechanisms in other organisms. The insights gained from this research enhance our understanding of the molecular mechanisms governing lipid derivative signalling in marine and freshwater diatoms, offering broader implications for their roles in chemical communication, cellular regulation, and environmental adaptation