A Machine Learning Approach to Simulate Gene Expression and Infer Gene Regulatory Networks.

The ability to simulate gene expression and infer gene regulatory networks has vast potential applications in various fields, including medicine, agriculture, and environmental science. In recent years, machine learning approaches to simulate gene expression and infer gene regulatory networks have gained significant attention as a promising area of research. By simulating gene expression, we can gain insights into the complex mechanisms that control gene expression and how they are affected by various environmental factors. This knowledge can be used to develop new treatments for genetic diseases, improve crop yields, and better understand the evolution of species. In this article, we address this issue by focusing on a novel method capable of simulating the gene expression regulation of a group of genes and their mutual interactions. Our framework enables us to simulate the regulation of gene expression in response to alterations or perturbations that can affect the expression of a gene. We use both artificial and real benchmarks to empirically evaluate the effectiveness of our methodology. Furthermore, we compare our method with existing ones to understand its advantages and disadvantages. We also present future ideas for improvement to enhance the effectiveness of our method. Overall, our approach has the potential to greatly improve the field of gene expression simulation and gene regulatory network inference, possibly leading to significant advancements in genetics.

Foot Loading and Gait Analysis Evaluation of Nonarticular Tibial Pilon Fracture: A Comparison of Three Surgical Techniques.

The aim of our study was to investigate which technique among hybrid external fixation, plate and screws, and intramedullary nailing produces better outcomes in foot loading when treating type 43.A1, 43.A2, and 43.A3 fractures, according to the AO classification. From November 2011 to December 2014, 34 patients, including 25 (73.5%) males and 9 (26.5%) females with an average age of 32.3 (range 16 to 67) years, with a type A tibia fracture were treated with intramedullary nailing, plate and screws, or hybrid external fixation. The patients were divided into 3 groups: 16 (47%) received hybrid external fixation, 10 (29.4%) received plate and screw fixation, and 8 (23.5%) received intramedullary nailing fixation. The follow-up protocol included clinical and radiologic evaluations performed at 15 days, 1 month, 3 months, 6 months, and 12 months after surgery. The selected outcome parameters for the 3 groups were as follows: visual analog scale for pain of the traumatized tibia, interval from surgery to weightbearing, average time required for fracture recovery, subjective and objective Ovadia-Beals scores, baropodometric examination at 12 months, walking recovery at 12 months, outcomes, and surgical complications. The endpoint assessment was set at 12 months. The results showed that incorrect reduction of a type A tibia fracture can lead to changes in the sagittal balance line for foot loading and pace training. In conclusion, these findings have shown that the experience of the surgeon in the reduction of the fracture and knowledge of the method of synthesis is essential.

Protein multiple sequence alignment by hybrid bio-inspired algorithms.

This article presents an immune inspired algorithm to tackle the Multiple Sequence Alignment (MSA) problem. MSA is one of the most important tasks in biological sequence analysis. Although this paper focuses on protein alignments, most of the discussion and methodology may also be applied to DNA alignments. The problem of finding the multiple alignment was investigated in the study by Bonizzoni and Vedova and Wang and Jiang, and proved to be a NP-hard (non-deterministic polynomial-time hard) problem. The presented algorithm, called Immunological Multiple Sequence Alignment Algorithm (IMSA), incorporates two new strategies to create the initial population and specific ad hoc mutation operators. It is based on the 'weighted sum of pairs' as objective function, to evaluate a given candidate alignment. IMSA was tested using both classical benchmarks of BAliBASE (versions 1.0, 2.0 and 3.0), and experimental results indicate that it is comparable with state-of-the-art multiple alignment algorithms, in terms of quality of alignments, weighted Sums-of-Pairs (SP) and Column Score (CS) values. The main novelty of IMSA is its ability to generate more than a single suboptimal alignment, for every MSA instance; this behaviour is due to the stochastic nature of the algorithm and of the populations evolved during the convergence process. This feature will help the decision maker to assess and select a biologically relevant multiple sequence alignment. Finally, the designed algorithm can be used as a local search procedure to properly explore promising alignments of the search space.

Discovering anomalies in big data: a review focused on the application of metaheuristics and machine learning techniques.

With the increase in available data from computer systems and their security threats, interest in anomaly detection has increased as well in recent years. The need to diagnose faults and cyberattacks has also focused scientific research on the automated classification of outliers in big data, as manual labeling is difficult in practice due to their huge volumes. The results obtained from data analysis can be used to generate alarms that anticipate anomalies and thus prevent system failures and attacks. Therefore, anomaly detection has the purpose of reducing maintenance costs as well as making decisions based on reports. During the last decade, the approaches proposed in the literature to classify unknown anomalies in log analysis, process analysis, and time series have been mainly based on machine learning and deep learning techniques. In this study, we provide an overview of current state-of-the-art methodologies, highlighting their advantages and disadvantages and the new challenges. In particular, we will see that there is no absolute best method, i.e., for any given dataset a different method may achieve the best result. Finally, we describe how the use of metaheuristics within machine learning algorithms makes it possible to have more robust and efficient tools.

Bowel bacterial overgrowth as another cause of malnutrition, inflammation, and atherosclerosis syndrome in peritoneal dialysis patients.

Bowel bacterial overgrowth syndrome (BBOS) is an important cause of gastrointestinal (GI) abnormalities. Proinflammatory cytokines (PICs) are excessively produced and accumulate because of kidney failure in dialysis patients who experience chronic infections such as BBOS. We explored the association between GL function, BBOS, and the malnutrition, inflammation, and atherosclerosis (MIA) syndrome. We studied GI malabsorption and maldigestion by analyzing fecal starch, sugar, fat, and nitrogen; intestinal protein permeability (alpha1-antitrypsin fecal clearance); and fecal chymotrypsin. We evaluated BBOS by breath hydrogen test (BHT) after a 3-day fat-and-carbohydrate-overload diet. Positive BHT was present in 10 patients, showing a high prevalence of GI macronutrient malabsorption and maldigestion, and compared with the other patients, the highest plasma levels of tumor necrosis factor alpha and interleukin 6 and lower levels of albumin and prealbumin. Those 10 patients were treated with a combination of several antibiotics, including neomycin, amoxicillin-clavulanate, and quinolones. Between 2 and 3 months later, the BHT, markers of nutrition, and PIC were re-tested. All treated patients showed an improvement in nutrition status and a lesser inflammatory pattern. The BBOS infectious process is found frequently in dialysis patients in association with GI malabsorption and maldigestion, malnutrition, and systemic inflammation. Hyperproduction of PIC because of BBOS induces MIA through a double pathway: GI disorders and deleterious systemic effects.

Abnormalities in Glucose Metabolism, Appetite-Related Peptide Release, and Pro-inflammatory Cytokines Play a Central Role in Appetite Disorders in Peritoneal Dialysis.

Background: Appetite disorders are frequent and scantly studied in peritoneal dialysis (PD) patients and are associated with malnutrition and cardiovascular complications. Objective: We investigated the relationship between uremic insulin resistance, pro-inflammatory cytokines, and appetite-related peptides release (ARPr) with eating-behavior disorders in PD patients. Methods: We included 42 PD patients (12 suffering anorexia, 12 obese with high food-intake, and 18 asymptomatic) and 10 controls. We measured blood levels of ARPr including orexigens [neuropeptide-Y (NPY), ghrelin, and nitric-oxide], anorexigens [cholecystokinin, insulin, corticotropin-releasing factor, leptin, and adiponectin (Ad)], and cytokines (TNF-α, sTNFα-R2, and IL-6) both at baseline and after administering a standard-food stimulus (SFS). We also measured the expression of TNF-α, leptin and Ad-encoding mRNAs in abdominal adipose tissue. We compared these markers with eating motivation measured by a Visual Analog Scale (VAS). Results: Anorexics showed both little appetite, measured by a VAS, and low levels of orexigens that remained constant after SFS, coupled with high levels of anorexigens at baseline and after SFS. Obeses showed higher appetite, increased baseline levels of orexigens, lower baseline levels of anorexigens and cytokines and two peaks of NPY after SFS. The different patterns of ARPr and cytokines pointed to a close relationship with uremic insulin resistance. In fact, the euglycemic-hyperglycemic clamp reproduced these disorders. In anorexics, TNF-α fat expression was increased. In obese patients, leptin expression in fat tissue was down-regulated and showed correlation with the appetite. Conclusion: In PD, appetite is governed by substances that are altered at baseline and abnormally released. Such modulators are controlled by insulin metabolism and cytokines and, while anorexics display inflammatory predominance, obese patients predominantly display insulin resistance.

Estudio y tratamiento de la pareja en ITU poscoital de la mujer / Study and treatment of the couple in post-coital urinary tract infection in women

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