A Degree Bound For The c-Boomerang Uniformity Of Permutation Monomials

Let $\mathbb{F}_q$ be a finite field of characteristic $p$. In this paper we prove that the $c$-Boomerang Uniformity, $c \neq 0$, for all permutation monomials $x^d$, where $d > 1$ and $p \nmid d$, is bounded by $d^2$. Further, we utilize this bound to estimate the $c$-boomerang uniformity of a large class of Generalized Triangular Dynamical Systems, a polynomial-based approach to describe cryptographic permutations, including the well-known Substitution-Permutation Network

Arion: Arithmetization-Oriented Permutation and Hashing from Generalized Triangular Dynamical Systems

In this paper we propose the (keyed) permutation Arion and the hash function ArionHash over $\mathbb{F}_p$ for odd and particularly large primes. The design of Arion is based on the newly introduced Generalized Triangular Dynamical System (GTDS), which provides a new algebraic framework for constructing (keyed) permutation using polynomials over a finite field. At round level Arion is the first design which is instantiated using the new GTDS. We provide extensive security analysis of our construction including algebraic cryptanalysis (e.g. interpolation and Groebner basis attacks) that are particularly decisive in assessing the security of permutations and hash functions over $\mathbb{F}_p$. From a application perspective, ArionHash is aimed for efficient implementation in zkSNARK protocols and Zero-Knowledge proof systems. For this purpose, we exploit that CCZ-equivalence of graphs can lead to a more efficient implementation of Arithmetization-Oriented primitives. We compare the efficiency of ArionHash in R1CS and Plonk settings with other hash functions such as Poseidon, Anemoi and Griffin. For demonstrating the practical efficiency of ArionHash we implemented it with the zkSNARK libraries libsnark and Dusk Network Plonk. Our result shows that ArionHash is significantly faster than Poseidon - a hash function designed for zero-knowledge proof systems. We also found that an aggressive version of ArionHash is considerably faster than Anemoi and Griffin in a practical zkSNARK setting

Mood Disorders Are Glial Disorders: Evidence from In Vivo Studies

It has recently been suggested that mood disorders can be characterized by glial pathology as indicated by histopathological postmortem findings. Here, we review studies investigating the glial marker S100B in serum of patients with mood disorders. This protein might act as a growth and differentiation factor. It is located in, and may actively be released by, astro- and oligodendrocytes. Studies consistently show that S100B is elevated in mood disorders; more strongly in major depressive than bipolar disorder. Successful antidepressive treatment reduces S100B in major depression whereas there is no evidence of treatment effects in mania. In contrast to the glial marker S100B, the neuronal marker protein neuron-specific enolase is unaltered. By indicating glial alterations without neuronal changes, serum S100B studies confirm specific glial pathology in mood disorders in vivo. S100B can be regarded as a potential diagnostic biomarker for mood disorders and as a biomarker for successful antidepressive treatment

S100B Serum Levels in Schizophrenia Are Presumably Related to Visceral Obesity and Insulin Resistance

Elevated blood levels of S100B in schizophrenia have so far been mainly attributed to glial pathology, as S100B is produced by astro- and oligodendroglial cells and is thought to act as a neurotrophic factor with effects on synaptogenesis, dopaminergic and glutamatergic neutrotransmission. However, adipocytes are another important source of S100B since the concentration of S100B in adipose tissue is as high as in nervous tissue. Insulin is downregulating S100B in adipocytes, astrocyte cultures and rat brain. As reviewed in this paper, our recent studies suggest that overweight, visceral obesity, and peripheral/cerebral insulin resistance may be pivotal for at least part of the elevated S100B serum levels in schizophrenia. In the context of this recently identified framework of metabolic disturbances accompanying S100B elevation in schizophrenia, it rather has to be attributed to systemic alterations in glucose metabolism than to be considered a surrogate marker for astrocyte-specific pathologies

Pretreatment levels of the fatty acid handling proteins H-FABP and CD36 predict response to olanzapine in recent-onset schizophrenia patients.

Traditional schizophrenia pharmacotherapy remains a subjective trial and error process involving administration, titration and switching of drugs multiple times until an adequate response is achieved. Despite this time-consuming and costly process, not all patients show an adequate response to treatment. As a consequence, relapse is a common occurrence and early intervention is hampered. Here, we have attempted to identify candidate blood biomarkers associated with drug response in 121 initially antipsychotic-free recent-onset schizophrenia patients treated with widely-used antipsychotics, namely olanzapine (n=40), quetiapine (n=23), risperidone (n=30) and a mixture of these drugs (n=28). Patients were recruited and investigated as two separate cohorts to allow biomarker validation. Data analysis showed the most significant relationship between pre-treatment levels of heart-type fatty acid binding protein (H-FABP) and response to olanzapine (p=0.008, F=8.6, β=70.4 in the discovery cohort and p=0.003, F=15.2, β=24.4 in the validation cohort, adjusted for relevant confounding variables). In a functional follow-up analysis of this finding, we tested an independent cohort of 10 patients treated with olanzapine and found that baseline levels of plasma H-FABP and expression of the binding partner for H-FABP, fatty acid translocase (CD36), on monocytes predicted the reduction of psychotic symptoms (p=0.040, F=6.0, β=116.3 and p=0.012, F=11.9, β=-0.0054, respectively). We also identified a set of serum molecules changed after treatment with antipsychotic medication, in particular olanzapine. These molecules are predominantly involved in cellular development and metabolism. Taken together, our findings suggest an association between biomarkers involved in fatty acid metabolism and response to olanzapine, while other proteins may serve as surrogate markers associated with drug efficacy and side effects.This work was supported by the Stanley Medical Research Institute (SMRI); the European Union FP7 SchizDX research programme (grant reference 223427); the European Union FP7 funding scheme: Marie Curie Actions Industry Academia Partnerships and Pathways (nr. 286334, PSYCH-AID project); by the Virgo consortium, funded by the Dutch Government (project number FES0908); by the Netherlands Genomics Initiative (project number 050-060-452); by the Dutch Fund for Economic Structure Reinforcement, the NeuroBasic PharmaPhenomics project (no. 0908) and by the Engineering and Physical Sciences Research Council UK (EPSRC CASE studentship and Impact Acceleration Award).This is the final version of the article. It was first available from Elsevier via http://dx.doi.org/10.1016/j.bbi.2015.10.01

Molecular Sex Differences in Human Serum

Background: Sex is an important factor in the prevalence, incidence, progression, and response to treatment of many medical conditions, including autoimmune and cardiovascular diseases and psychiatric conditions. Identification of molecular differences between typical males and females can provide a valuable basis for exploring conditions differentially affected by sex. Methodology/Principal Findings: Using multiplexed immunoassays, we analyzed 174 serum molecules in 9 independent cohorts of typical individuals, comprising 196 males and 196 females. Sex differences in analyte levels were quantified using a meta-analysis approach and put into biological context using k-means to generate clusters of analytes with distinct biological functions. Natural sex differences were established in these analyte groups and these were applied to illustrate sexually dimorphic analyte expression in a cohort of 22 males and 22 females with Asperger syndrome. Reproducible sex differences were found in the levels of 77 analytes in serum of typical controls, and these comprised clusters of molecules enriched with distinct biological functions. Analytes involved in fatty acid oxidation/hormone regulation, immune cell growth and activation, and cell death were found at higher levels in females, and analytes involved in immune cell chemotaxis and other indistinct functions were higher in males. Comparison of these naturally occurring sex differences against a cohort of people with Asperger syndrome indicated that a cluster of analytes that had functions related to fatty acid oxidation/hormone regulation was associated with sex and the occurren

Validation of a Blood-Based Laboratory Test to Aid in the Confirmation of a Diagnosis of Schizophrenia

We describe the validation of a serum-based test developed by Rules-Based Medicine which can be used to help confirm the diagnosis of schizophrenia. In preliminary studies using multiplex immunoassay profiling technology, we identified a disease signature comprised of 51 analytes which could distinguish schizophrenia (n = 250) from control (n = 230) subjects. In the next stage, these analytes were developed as a refined 51-plex immunoassay panel for validation using a large independent cohort of schizophrenia (n = 577) and control (n = 229) subjects. The resulting test yielded an overall sensitivity of 83% and specificity of 83% with a receiver operating characteristic area under the curve (ROC-AUC) of 89%. These 51 immunoassays and the associated decision rule delivered a sensitive and specific prediction for the presence of schizophrenia in patients compared to matched healthy controls

Development of a blood-based molecular biomarker test for identification of schizophrenia before disease onset

Recent research efforts have progressively shifted towards preventative psychiatry and prognostic identification of individuals before disease onset. We describe the development of a serum biomarker test for the identification of individuals at risk of developing schizophrenia based on multiplex immunoassay profiling analysis of 957 serum samples. First, we conducted a meta-analysis of five independent cohorts of 127 first-onset drug-naive schizophrenia patients and 204 controls. Using least absolute shrinkage and selection operator regression, we identified an optimal panel of 26 biomarkers that best discriminated patients and controls. Next, we successfully validated this biomarker panel using two independent validation cohorts of 93 patients and 88 controls, which yielded an area under the curve (AUC) of 0.97 (0.95-1.00) for schizophrenia detection. Finally, we tested its predictive performance for identifying patients before onset of psychosis using two cohorts of 445 pre-onset or at-risk individuals. The predictive performance achieved by the panel was excellent for identifying USA military personnel (AUC: 0.90 (0.86-0.95)) and help-seeking prodromal individuals (AUC: 0.82 (0.71-0.93)) who developed schizophrenia up to 2 years after baseline sampling. The performance increased further using the latter cohort following the incorporation of CAARMS (Comprehensive Assessment of At-Risk Mental State) positive subscale symptom scores into the model (AUC: 0.90 (0.82-0.98)). The current findings may represent the first successful step towards a test that could address the clinical need for early intervention in psychiatry. Further developments of a combined molecular/symptom-based test will aid clinicians in the identification of vulnerable patients early in the disease process, allowing more effective therapeutic intervention before overt disease onset

Towards a blood-based diagnostic panel for bipolar disorder

BACKGROUND: Bipolar disorder (BD) is a costly, devastating and life shortening mental disorder that is often misdiagnosed, especially on initial presentation. Misdiagnosis frequently results in ineffective treatment. We investigated the utility of a biomarker panel as a diagnostic test for BD. METHODS AND FINDINGS: We performed a meta-analysis of eight case-control studies to define a diagnostic biomarker panel for BD. After validating the panel on established BD patients, we applied it to undiagnosed BD patients. We analysed 249 BD, 122 pre-diagnostic BD, 75 pre-diagnostic schizophrenia and 90 first onset major depression disorder (MDD) patients and 371 controls. The biomarker panel was identified using ten-fold cross-validation with lasso regression applied to the 87 analytes available across the meta-analysis studies. We identified 20 protein analytes with excellent predictive performance [area under the curve (AUC)?0.90]. Importantly, the panel had a good predictive performance (AUC 0.84) to differentiate 12 misdiagnosed BD patients from 90 first onset MDD patients, and a fair to good predictive performance (AUC 0.79) to differentiate between 110 pre-diagnostic BD patients and 184 controls. We also demonstrated the disease specificity of the panel. CONCLUSIONS: An early and accurate diagnosis has the potential to delay or even prevent the onset of BD. This study demonstrates the potential utility of a biomarker panel as a diagnostic test for BD.We would like to thank all participants of this study as well as all participating centres for the collaboration and for access to the serum samples. We gratefully acknowledge support by the Stanley Medical Research Institute (no. 07R-1888). The infrastructure for the NESDA study (www.nesda.nl) has been funded through the Geestkracht program of the Netherlands Organisation for Health Research and Development (Zon-Mw, grant number 10-000-1002) and participating universities (VU University Medical Center, Leiden University Medical Center, University Medical Center Groningen). DNC, DWN and NSW efforts were funded by the Stanley Medical Research Institute and the US Department of the Army