Complexity Study and Chaos Control in a Prey-Predator System

A prey-predator system has been investigated with the application of random shock. Since the fluctuations of populations are random, the applied shock is also assumed like a random noise. To study complexities during evolution, numerical simulations have been carried out for both cases, without shock and with shock. Stabilities of fixed points have been discussed for both the cases. Also, bifurcation diagrams for both the cases have been drawn by varying a parameter while keeping other parameters fixed. Numerical calculations have been extended to obtain plots of Lyapunov exponents and topological entropies as the measure of complexity in the system. It has been observed that the random shock has little impact to reduce the chaotic motion in the system. Then, certain periodic changes in a parameter have been allowed to some extent,this results in bringing the system from chaos to regularity. Such changes may happen naturally in a prey-predator system and so there exists the possibility of coexistence. The chaos indicator DLI has been used for clarity in detection of regular and chaotic motion. Finally,the correlation dimension for the chaotic set has also been calculated for certain set of parameter values

Constrained neuro fuzzy inference methodology for explainable personalised modelling with applications on gene expression data

Interpretable machine learning models for gene expression datasets are important for understanding the decision-making process of a classifier and gaining insights on the underlying molecular processes of genetic conditions. Interpretable models can potentially support early diagnosis before full disease manifestation. This is particularly important yet, challenging for mental health. We hypothesise this is due to extreme heterogeneity issues which may be overcome and explained by personalised modelling techniques. Thus far, most machine learning methods applied to gene expression datasets, including deep neural networks, lack personalised interpretability. This paper proposes a new methodology named personalised constrained neuro fuzzy inference (PCNFI) for learning personalised rules from high dimensional datasets which are structurally and semantically interpretable. Case studies on two mental health related datasets (schizophrenia and bipolar disorders) have shown that the relatively short and simple personalised fuzzy rules provided enhanced interpretability as well as better classification performance compared to other commonly used machine learning methods. Performance test on a cancer dataset also showed that PCNFI matches previous benchmarks. Insights from our approach also indicated the importance of two genes (ATRX and TSPAN2) as possible biomarkers for early differentiation of ultra-high risk, bipolar and healthy individuals. These genes are linked to cognitive ability and impulsive behaviour. Our findings suggest a significant starting point for further research into the biological role of cognitive and impulsivity-related differences. With potential applications across bio-medical research, the proposed PCNFI method is promising for diagnosis, prognosis, and the design of personalised treatment plans for better outcomes in the future

Investigation of social and cognitive predictors in non-transition ultra-high-risk’ individuals for psychosis using spiking neural networks

Finding predictors of social and cognitive impairment in non-transition Ultra-High-Risk individuals (UHR) is critical in prognosis and planning of potential personalised intervention strategies. Social and cognitive functioning observed in youth at UHR for psychosis may be protective against transition to clinically relevant illness. The current study used a computational method known as Spiking Neural Network (SNN) to identify the cognitive and social predictors of transitioning outcome. Participants (90 UHR, 81 Healthy Control (HC)) completed batteries of neuropsychological tests in the domains of verbal memory, working memory, processing speed, attention, executive function along with social skills-based performance at baseline and 4 × 6-month follow-up intervals. The UHR status was recorded as Remitters, Converters or Maintained. SNN were used to model interactions between variables across groups over time and classify UHR status. The performance of SNN was examined relative to other machine learning methods. Higher interaction between social and cognitive variables was seen for the Maintained, than Remitter subgroup. Findings identified the most important cognitive and social variables (particularly verbal memory, processing speed, attention, affect and interpersonal social functioning) that showed discriminative patterns in the SNN models of HC vs UHR subgroups, with accuracies up to 80%; outperforming other machine learning models (56–64% based on 18 months data). This finding is indicative of a promising direction for early detection of social and cognitive impairment in UHR individuals that may not anticipate transition to psychosis and implicate early initiated interventions to stem the impact of clinical symptoms of psychosis

Involvement in surface antigen expression by a moonlighting FG-repeat nucleoporin in trypanosomes

Components of the nuclear periphery coordinate a multitude of activities, including macromolecular transport, cell-cycle progression, and chromatin organization. Nuclear pore complexes (NPCs) mediate nucleocytoplasmic transport, mRNA processing, and transcriptional regulation, and NPC components can define regions of high transcriptional activity in some organisms at the nuclear periphery and nucleoplasm. Lineage-specific features underpin several core nuclear functions and in trypanosomatids, which branched very early from other eukaryotes, unique protein components constitute the lamina, kinetochores, and parts of the NPCs. Here we describe a phenylalanine-glycine (FG)-repeat nucleoporin, TbNup53b, that has dual localizations within the nucleoplasm and NPC. In addition to association with nucleoporins, TbNup53b interacts with a known trans-splicing component, TSR1, and has a role in controlling expression of surface proteins including the nucleolar periphery-located, procyclin genes. Significantly, while several nucleoporins are implicated in intranuclear transcriptional regulation in metazoa, TbNup53b appears orthologous to components of the yeast/human Nup49/Nup58 complex, for which no transcriptional functions are known. These data suggest that FG-Nups are frequently co-opted to transcriptional functions during evolution and extend the presence of FG-repeat nucleoporin control of gene expression to trypanosomes, suggesting that this is a widespread and ancient eukaryotic feature, as well as underscoring once more flexibility within nucleoporin function

3, 3′-Diindolylmethane Exhibits Antileukemic Activity In Vitro and In Vivo through a Akt-Dependent Process

3,3′-diindolylmethane (DIM), one of the active products derived from Brassica plants, is a promising antitumor agent. The present study indicated that DIM significantly induced apoptosis in U937 human leukemia cells in dose- and time-dependent manners. These events were also noted in other human leukemia cells (Jurkat and HL-60) and primary human leukemia cells (AML) but not in normal bone marrow mononuclear cells. We also found that DIM-induced lethality is associated with caspases activation, myeloid cell leukemia-1 (Mcl-1) down-regulation, p21cip1/waf1 up-regulation, and Akt inactivation accompanied by c-jun NH2-terminal kinase (JNK) activation. Enforced activation of Akt by a constitutively active Akt construct prevented DIM-mediated caspase activation, Mcl-1 down-regulation, JNK activation, and apoptosis. Conversely, DIM lethality was potentiated by the PI3K inhibitor LY294002. Interruption of the JNK pathway by pharmacologic or genetic approaches attenuated DIM-induced caspases activation, Mcl-1 down-regulation, and apoptosis. Lastly, DIM inhibits tumor growth of mouse U937 xenograft, which was related to induction of apoptosis and inactivation of Akt, as well as activation of JNK. Collectively, these findings suggest that DIM induces apoptosis in human leukemia cell lines and primary human leukemia cells, and exhibits antileukemic activity in vivo through Akt inactivation and JNK activation

Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial

Background: Glucagon-like peptide 1 receptor agonists differ in chemical structure, duration of action, and in their effects on clinical outcomes. The cardiovascular effects of once-weekly albiglutide in type 2 diabetes are unknown. We aimed to determine the safety and efficacy of albiglutide in preventing cardiovascular death, myocardial infarction, or stroke. Methods: We did a double-blind, randomised, placebo-controlled trial in 610 sites across 28 countries. We randomly assigned patients aged 40 years and older with type 2 diabetes and cardiovascular disease (at a 1:1 ratio) to groups that either received a subcutaneous injection of albiglutide (30–50 mg, based on glycaemic response and tolerability) or of a matched volume of placebo once a week, in addition to their standard care. Investigators used an interactive voice or web response system to obtain treatment assignment, and patients and all study investigators were masked to their treatment allocation. We hypothesised that albiglutide would be non-inferior to placebo for the primary outcome of the first occurrence of cardiovascular death, myocardial infarction, or stroke, which was assessed in the intention-to-treat population. If non-inferiority was confirmed by an upper limit of the 95% CI for a hazard ratio of less than 1·30, closed testing for superiority was prespecified. This study is registered with ClinicalTrials.gov, number NCT02465515. Findings: Patients were screened between July 1, 2015, and Nov 24, 2016. 10 793 patients were screened and 9463 participants were enrolled and randomly assigned to groups: 4731 patients were assigned to receive albiglutide and 4732 patients to receive placebo. On Nov 8, 2017, it was determined that 611 primary endpoints and a median follow-up of at least 1·5 years had accrued, and participants returned for a final visit and discontinuation from study treatment; the last patient visit was on March 12, 2018. These 9463 patients, the intention-to-treat population, were evaluated for a median duration of 1·6 years and were assessed for the primary outcome. The primary composite outcome occurred in 338 (7%) of 4731 patients at an incidence rate of 4·6 events per 100 person-years in the albiglutide group and in 428 (9%) of 4732 patients at an incidence rate of 5·9 events per 100 person-years in the placebo group (hazard ratio 0·78, 95% CI 0·68–0·90), which indicated that albiglutide was superior to placebo (p<0·0001 for non-inferiority; p=0·0006 for superiority). The incidence of acute pancreatitis (ten patients in the albiglutide group and seven patients in the placebo group), pancreatic cancer (six patients in the albiglutide group and five patients in the placebo group), medullary thyroid carcinoma (zero patients in both groups), and other serious adverse events did not differ between the two groups. There were three (<1%) deaths in the placebo group that were assessed by investigators, who were masked to study drug assignment, to be treatment-related and two (<1%) deaths in the albiglutide group. Interpretation: In patients with type 2 diabetes and cardiovascular disease, albiglutide was superior to placebo with respect to major adverse cardiovascular events. Evidence-based glucagon-like peptide 1 receptor agonists should therefore be considered as part of a comprehensive strategy to reduce the risk of cardiovascular events in patients with type 2 diabetes. Funding: GlaxoSmithKline

On construction of some new symmetric and asymmetric orthogonal arrays

The paper presents the construction of new symmetric orthogonal arrays with 3 and 4 levels by using the relationship between linear codes and linear orthogonal arrays. Furthermore, using these symmetric orthogonal arrays some new asymmetric orthogonal arrays wíth hígher strength have been constructed. © 2002 Taylor & Francis Group, LLC

The heads hypothesis: A unifying statistical approach towards understanding multi-headed attention in BERT

Multi-headed attention heads are a mainstay in transformer-based models. Different methods have been proposed to classify the role of each attention head based on the relations between tokens which have high pair-wise attention. These roles include syntactic (tokens with some syntactic relation), local (nearby tokens), block (tokens in the same sentence) and delimiter (the special [CLS], [SEP] tokens). There are two main challenges with existing methods for classification: (a) there are no standard scores across studies or across functional roles, and (b) these scores are often average quantities measured across sentences without capturing statistical significance. In this work, we formalize a simple yet effective score that generalizes to all the roles of attention heads and employs hypothesis testing on this score for robust inference. This provides us the right lens to systematically analyze attention heads and confidently comment on many commonly posed questions on analyzing the BERT model. In particular, we comment on the co-location of multiple functional roles in the same attention head, the distribution of attention heads across layers, and effect of fine-tuning for specific NLP tasks on these functional roles.Comment: accepted at AAAI 2021 (Main conference