Neurobiology of Depression and Irritable Bowel Syndrome Comorbidity

Irritable bowel syndrome is a disabling functional disorder with a frequent comorbidity of depression though underlying mechanisms remain yet little understood. Various signs and symptoms have been determined as diagnostic criteria in recent years and standardized as Rome-III criteria. Irritable bowel syndrome can have constipation-dominant, diarrhea-dominant or mixed clinical presentations. Main features can be summarized as continuous and recurrent abdominal pain or discomfort associated with a change of stool frequency or consistency and usually relief of symptoms with defe-cation in the absence of physical or laboratory abnormalities indicative of an organic etiology. The frequency of major depressive disorder diagnosis reaches up to two thirds of irritable bowel syndrome patients. Moreover, the comorbidity of irritable bowel syndrome among patients with major depression is highly frequent (30%). The mechanism underlying irritable bowel syndrome which have been considered as a kind of a somatization disorder for a long time and now as a functional bowel disease is in the brain-gut axis. Low grade mucosal inflammation and cytokines originating from mucosal inflammation have important functions in the pathophysiology of irritable bowel syndrome and its comorbidity with major depression. Besides the inflammatory factors lumbosacral visceral hyperexcitability which is an individual variation is proposed as the main underlying cause of irritable bowel syndrome. Visceral hyper-excitability is mediated by cytokines and neuro-mediators and stress is known to increase the effect of this mechanism. Furthermore, molecules participating in this mechanism (e.g. cytokines, corticotrophin releasing factor, neurokinins and monoamines) play important roles in the pathophysiology of depression. Increased activation in the pain matrix (thalamus – insula – prefrontal cortex) and insufficiency of endogenous pain inhibitory system are regarded as possible casuses of excessive feeling of irritable bowel syndrome symptoms leading to the dysfunction in the cortical representation of bodily states and negative emotional experiences. Individual variations in the interaction of cytokines, corticotrophin releasing factor, neurokinins (substance P, neurokinin A and neurokinin B) and monoamines (serotonin and norepinephrine), and neuroanatomic functions may answer the question of “why do some irritable bowel syndrome patients experience depression and some do not?”. Moreover, irritable bowel syndrome patients with comorbid depression and anxiety disorders are reported to be complaining more about their irritable bowel syndrome symptoms. Although several treatment strategies are considered by clinicians in the management of irritable bowel syndrome, it is suggested that antidepressant medications to have the priority in the treatment of irritable bowel syndrome with the comorbidity of depression. Selective serotonin re-uptake inhibitors are the drug of choice regarding their safety and side effects profile. Nevertheless, tricyclic antidepressants may also have beneficial effects in lower doses than needed to treat clinical depression. Hypnosis, supportive or cognitive behavioral therapies, dietary and defecation habits management are also suggested as beneficial. The recognition of irritable bowel syndrome by psychiatrists may enhance the success of treatment of depression with the comorbidity of irritable bowel syndrome, which disables the patient and frequently accompanies to major depression. In this review, evidence for depression and irritable bowel syndrome comorbidity, the possible underlying mechanisms of this comorbidity and current treatment approaches regarding proposed mechanisms will be discussed

The Role of Hippocampus in the Pathophysiology of Depression

Hippocampus, as a part of the limbic cortex, has a variety of functions ranging from mating behavior to memory besides its role in the regulation of emotions. The hippocampus has reciprocal interactions of with other brain regions which act in the pathophysiology of major depressive disorder (MDD). Moreover, since the hippocampus is a scene for the neurogenesis, which can be seen as a response to antidepressant treatment, the hippocampus became a focus of attention in neuroimaging studies of MDD. It has been shown that brain derived neurotrophic factor (BDNF), that is responsible from the neurogenesis, is associated with the response to the antidepressants and antidepressant drugs are ineffective if neurogenesis is hindered.Hippocampal atrophy is expected with the decrease of neurogenesis as a result of the lower BDNF levels with the deleterious effects of glucocorticoids in depression. Recurrent and severe depression seems to cause such a volume reduction though first episode MDD subjects do not differ from healthy individuals in respect to their hippocampal volumes (HCVs) measured by magnetic resonance imaging methods. One may argue regarding these findings that the atrophy in the hippocampus may be observed in the long term and the decrease in BDNF levels may predispose the volume reduction. Although it has been postulated that smaller HCV as a result of genetic and environmental factors and prior to the illness, may cause a vulnerability to MDD, sufficient evidence has not been accumulated yet and the view that HCV loss develops as depression progresses is widely accepted. Findings that serum BDNF (sBDNF) is lower in MDD patients though HCVs of patients do not differ from healthy individuals and the positive correlation of sBDNF with HCV seen only in the patient group support this view. It can be assumed that depressed patients have sensitivity for the fluctuations in BDNF levels. Follow-up studies which consider effects of hipotalamo-pituiter-adrenal axis dysregulation and monoamine systems are needed to further elucidate the role of BDNF in the pathogenesis of MDD. Results of these studies may lead the way for the treatment of resistant or recurrent depressive disorder

Physics-based prognostic modelling of filter clogging phenomena

In industry, contaminant filtration is a common process to achieve a desired level of purification, since contaminants in liquids such as fuel may lead to performance drop and rapid wear propagation. Generally, clogging of filter phenomena is the primary failure mode leading to the replacement or cleansing of filter. Cascading failures and weak performance of the system are the unfortunate outcomes due to a clogged filter. Even though filtration and clogging phenomena and their effects of several observable parameters have been studied for quite some time in the literature, progression of clogging and its use for prognostics purposes have not been addressed yet. In this work, a physics based clogging progression model is presented. The proposed model that bases on a well-known pressure drop equation is able to model three phases of the clogging phenomena, last of which has not been modelled in the literature yet. In addition, the presented model is integrated with particle filters to predict the future clogging levels and to estimate the remaining useful life of fuel filters. The presented model has been implemented on the data collected from an experimental rig in the lab environment. In the rig, pressure drop across the filter, flow rate, and filter mesh images are recorded throughout the accelerated degradation experiments. The presented physics based model has been applied to the data obtained from the rig. The remaining useful lives of the filters used in the experimental rig have been reported in the paper. The results show that the presented methodology provides significantly accurate and precise prognostic results

A Similarity-Based Prognostics Approach for Remaining Useful Life Prediction

Physics-based and data-driven models are the two major prognostic approaches in the literature with their own advantages and disadvantages. This paper presents a similarity-based data-driven prognostic methodology and efficiency analysis study on remaining useful life estimation results. A similarity-based prognostic model is modified to employ the most similar training samples for RUL estimations on each time instance. The presented model is tested on; Virkler’s fatigue crack growth dataset, a drilling process degradation dataset, and a sliding chair degradation of a turnout system dataset. Prediction performances are compared utilizing an evaluation metric. Efficiency analysis of optimization results show that the modified similarity-based model performs better than the original definition

Spatial distribution and galactic model parameters of cataclysmic variables

The spatial distribution, galactic model parameters and luminosity function of cataclysmic variables (CVs) in the solar neighbourhood have been determined from a carefully established sample of 459 CVs. The sample contains all of the CVs with distances computed from the Period-Luminosity-Colours (PLCs) relation of CVs which has been recently derived and calibrated with {\em 2MASS} photometric data. It has been found that an exponential function fits best to the observational z-distributions of all of the CVs in the sample, non-magnetic CVs and dwarf novae, while the sech^{2} function is more appropriate for nova-like stars and polars. The vertical scaleheight of CVs is 158$\pm$14 pc for the {\em 2MASS} J-band limiting apparent magnitude of 15.8. On the other hand, the vertical scaleheights are 128$\pm$20 and 160$\pm$5 pc for dwarf novae and nova-like stars, respectively. The local space density of CVs is found to be $\sim3\times10^{-5}$ pc^{-3} which is in agreement with the lower limit of the theoretical predictions. The luminosity function of CVs shows an increasing trend toward higher space densities at low luminosities, implying that the number of short-period systems should be high. The discrepancies between the theoretical and observational population studies of CVs will almost disappear if for the z-dependence of the space density the sech^{2} density function is used.Comment: 29 pages, 9 figures and 5 tables, accepted for publication in New Astronom

Major challenges in prognostics: study on benchmarking prognostic datasets

Even though prognostics has been defined to be one of the most difficult tasks in Condition Based Maintenance (CBM), many studies have reported promising results in recent years. The nature of the prognostics problem is different from diagnostics with its own challenges. There exist two major approaches to prognostics: data-driven and physics-based models. This paper aims to present the major challenges in both of these approaches by examining a number of published datasets for their suitability for analysis. Data-driven methods require sufficient samples that were run until failure whereas physics-based methods need physics of failure progression

A simple state-based prognostic model for filter clogging

In today's maintenance planning, fuel filters are replaced or cleaned on a regular basis. Monitoring and implementation of prognostics on filtration system have the potential to avoid costs and increase safety. Prognostics is a fundamental technology within Integrated Vehicle Health Management (IVHM). Prognostic models can be categorised into three major categories: 1) Physics-based models 2) Data-driven models 3) Experience-based models. One of the challenges in the progression of the clogging filter failure is the inability to observe the natural clogging filter failure due to time constraint. This paper presents a simple solution to collect data for a clogging filter failure. Also, it represents a simple state-based prognostic with duration information (SSPD) method that aims to detect and forecast clogging of filter in a laboratory based fuel rig system. The progression of the clogging filter failure is created unnaturally. The degradation level is divided into several groups. Each group is defined as a state in the failure progression of clogging filter. Then, the data is collected to create the clogging filter progression states unnaturally. The SSPD method consists of three steps: clustering, clustering evaluation, and remaining useful life (RUL) estimation. Prognosis results show that the SSPD method is able to predicate the RUL of the clogging filter accurately

Kinematics of W UMa-type binaries and evidences on the two types of formation

The kinematics of 129 W UMa binaries is studied and its implications on the contact binary evolution is discussed. The sample is found to be heterogeneous in the velocity space that kinematically younger and older contact binaries exist in the sample. Kinematically young (0.5 Gyr) sub-sample (MG) is formed by selecting the systems which are satisfying the kinematical criteria of moving groups. After removing the possible MG members and the systems which are known to be members of open clusters, the rest of the sample is called Field Contact Binaries (FCB). The FCB has further divided into four groups according to The orbital period ranges. Then a correlation has been found in the sense that shorter period less massive systems have larger velocity dispersions than the longer period more massive systems. Dispersions in the velocity space indicates 5.47 Gyr kinematical age for the FCB group. Comparing with the field chromospherically active binaries (CAB), presumably detached binary progenitors of the contact systems, the FCB appears to be 1.61 Gyr older. Assuming an equilibrium in the formation and destruction of CAB and W UMa systems in the Galaxy, this age difference is treated as empirically deduced lifetime of the contact stage. Since the kinematical ages of the four sub groups of FCB are much longer than the 1.61 Gyr lifetime of the contact stage, the pre-contact stages of FCB must dominantly be producing the large dispersions. The kinematically young (0.5 Gyr) MG group covers the same total mass, period and spectral ranges as the FCB. But, the very young age of this group does not leave enough room for pre-contact stages, thus it is most likely that those systems were formed in the beginning of the main-sequence or during the pre-main-sequence contraction phase.Comment: 19 pages, including 11 figures and 5 tables, accepted for publication in MNRA

Lazy AC-Pattern Matching for Rewriting

We define a lazy pattern-matching mechanism modulo associativity and commutativity. The solutions of a pattern-matching problem are stored in a lazy list composed of a first substitution at the head and a non-evaluated object that encodes the remaining computations. We integrate the lazy AC-matching in a strategy language: rewriting rule and strategy application produce a lazy list of terms.Comment: In Proceedings WRS 2011, arXiv:1204.531

On the eclipsing cataclysmic variable star HBHA 4705-03

We present observations and analysis of a new eclipsing binary HBHA 4705-03. Using decomposition of the light curve into accretion disk and hot spot components, we estimated photometrically the mass ratio of the studied system to be q=0.62 +-0.07. Other fundamental parameters was found with modeling. This approach gave: white dwarf mass M_1 = (0.8 +- 0.2) M_sun, secondary mass M_2=(0.497 +- 0.05) M_sun, orbital radius a=1.418 R_sun, orbital inclination i = (81.58 +- 0.5) deg, accretion disk radius r_d/a = 0.366 +- 0.002, and accretion rate dot{M} = (2.5 +- 2) * 10^{18}[g/s], (3*10^{-8} [M_sun/yr]). Power spectrum analysis revealed ambiguous low-period Quasi Periodic Oscillations centered at the frequencies f_{1}=0.00076 Hz, f_2=0.00048 Hz and f_3=0.00036 Hz. The B-V=0.04 [mag] color corresponds to a dwarf novae during an outburst. The examined light curves suggest that HBHA 4705-03 is a nova-like variable star.Comment: 7 figures and 2 tables, accepted for publication in Acta Astronomic