Diversity Forests: Using Split Sampling to Enable Innovative Complex Split Procedures in Random Forests

The diversity forest algorithm is an alternative candidate node split sampling scheme that makes innovative complex split procedures in random forests possible. While conventional univariable, binary splitting suffices for obtaining strong predictive performance, new complex split procedures can help tackling practically important issues. For example, interactions between features can be exploited effectively by bivariable splitting. With diversity forests, each split is selected from a candidate split set that is sampled in the following way: for $$l = 1, \dots , {nsplits}$$ l = 1 , ⋯ , nsplits : (1) sample one split problem; (2) sample a single or few splits from the split problem sampled in (1) and add this or these splits to the candidate split set. The split problems are specifically structured collections of splits that depend on the respective split procedure considered. This sampling scheme makes innovative complex split procedures computationally tangible while avoiding overfitting. Important general properties of the diversity forest algorithm are evaluated empirically using univariable, binary splitting. Based on 220 data sets with binary outcomes, diversity forests are compared with conventional random forests and random forests using extremely randomized trees. It is seen that the split sampling scheme of diversity forests does not impair the predictive performance of random forests and that the performance is quite robust with regard to the specified nsplits value. The recently developed interaction forests are the first diversity forest method that uses a complex split procedure. Interaction forests allow modeling and detecting interactions between features effectively. Further potential complex split procedures are discussed as an outlook.

PubMed-Scale Chemical Concept Embeddings Reconstruct Physical Protein Interaction Networks

PubMed is the largest resource of curated biomedical knowledge to date, entailing more than 25 million documents. Large quantities of novel literature prevent a single expert from keeping track of all potentially relevant papers, resulting in knowledge gaps. In this article, we present CHEMMESHNET, a newly developed PubMed-based network comprising more than 10,000,000 associations, constructed from expert-curated MeSH annotations of chemicals based on all currently available PubMed articles. By learning latent representations of concepts in the obtained network, we demonstrate in a proof of concept study that purely literature-based representations are sufficient for the reconstruction of a large part of the currently known network of physical, empirically determined protein–protein interactions. We demonstrate that simple linear embeddings of node pairs, when coupled with a neural network–based classifier, reliably reconstruct the existing collection of empirically confirmed protein–protein interactions. Furthermore, we demonstrate how pairs of learned representations can be used to prioritize potentially interesting novel interactions based on the common chemical context. Highly ranked interactions are qualitatively inspected in terms of potential complex formation at the structural level and represent potentially interesting new knowledge. We demonstrate that two protein–protein interactions, prioritized by structure-based approaches, also emerge as probable with regard to the trained machine-learning model.

Rare Morphology of The Third Maxillary Molar and Potential Complications in its Extraction

There is a wide variation in the morphology of third maxillary molar which can be difficult to be identified radiographically. We present a case of a 26 year-old Yemeni female patient presented with difficult extraction of her left maxillary third molar. The extracted tooth showed a rare variation of root morphology, having four roots with three roots curving palatally at the apices. This report emphasized the potential complex morphological variation of maxillary third molar which may lead to the difficulty of a routine straight forward procedure thus needing careful extraction maneuvering to prevent any complications.

IMPROVING THE EFFICIENCY OF SYSTEMS OF LOW-POTENTIAL COMPLEX POWER STATIONS BY OPTIMAL CONTROL OF CONSUMPTION CIRCULATION WATER

Purpose. In article is organized analysis of the influence state of working circulation pump on economy of the systems low-potential complex and on the general economy of the work heat and atomic power stations. The organized analysis of the field-performance data of the systems low-potential complex Zaporozhskoy NPS in cut of the year, which has shown that as criterion to economy of the work low-potential complex can be accepted importance of the increase the production to powers of the compartment of steam turbine to power stations. Methodology. On the grounds of mathematical modeling main dependencies, which characterize efficiency of the functioning the systems low-potential complex. Results. The certain reserve energysave at increasing of efficiency of the functioning the systems low-potential complex. So total value of the losses to disposable energy in low-potential complex for energyblock HPS power 300–1200 MWt forms 7–8 % for systems of the water-supply with water-chiller and evaporation and 8–10 % for systems with radiator are determined. The organized analysis of the experimental features energyblock 300 MWt Zmievskoy HPS and is determined dependency of the pressure pair is in capacitor from the temperature circulation water and consumption pair under fixed consumption circulation water. Analytical dependencies, which allow coming from the known importance of the consumption pair, the temperature circulation water and desired pressure in capacitor to define the necessary consumption cooling water, under which will are provided given parameters are brought. Practical value. Comparative feature to energy efficiency of the functioning the system turbine-capacitor coming from features gain powers of the turbine for concrete consumption pair and dependencies of the consumption circulation water from consumed powers circulation pump will built. The broughted model of management system low-potential complex on criterion of the optimum of the consumption circulation water, under which total losses to energy will be minimum. On the grounds of afore-cited theoretical position

TAMERON (SODIUM AMINODIGYROPHTHALAZINEDIONE) AS A POTENT IAL COMPLEX DRUG FOR THE TREATMENT OF CORONAVIRUS INFECTION COVID-19

Coronavirus infection has caused a global pandemic with high mortality rates among the population of different countries. Drugs for non-specific immunotherapy of this infection based on sodium aminodihydrophthalazinedione (in particular, Tameron) were not considered for this purpose. It has immunomodulatory properties, and its use is not accompanied by side effects. Tameron can become a potential complex drug for the treatment of coronavirus infection COVID-19 and its use is especially important in confined spaces on ships and vessels of the military and civilian fleets.

Aromatic interactions with membrane modulate human BK channel activation

Large-conductance potassium (BK) channels are transmembrane (TM) proteins that can be synergistically and independently activated by membrane voltage and intracellular Ca2+. The only covalent connection between the cytosolic Ca2+ sensing domain and the TM pore and voltage sensing domains is a 15-residue ‘C-linker’. To determine the linker’s role in human BK activation, we designed a series of linker sequence scrambling mutants to suppress potential complex interplay of specific interactions with the rest of the protein. The results revealed a surprising sensitivity of BK activation to the linker sequence. Combining atomistic simulations and further mutagenesis experiments, we demonstrated that nonspecific interactions of the linker with membrane alone could directly modulate BK activation. The C-linker thus plays more direct roles in mediating allosteric coupling between BK domains than previously assumed. Our results suggest that covalent linkers could directly modulate TM protein function and should be considered an integral component of the sensing apparatus.

Nonspecific membrane interactions can modulate BK channel activation

Large-conductance potassium (BK) channels are transmembrane (TM) proteins that can be synergistically and independently activated by membrane voltage and intracellular Ca2+. The only covalent connection between the cytosolic Ca2+ sensing domain and the TM pore and voltage sensing domains is a 15-residue "C-linker". To determine the linker's role in BK activation, we designed a series of linker sequence scrambling mutants to suppress potential complex interplay of specific interactions with the rest of the protein. The results revealed a surprising sensitivity of BK activation to the linker sequence. Combing atomistic simulations and further mutagenesis experiments, we demonstrated that nonspecific interactions of the linker with membrane alone could directly modulate BK activation. The C-linker thus plays more direct roles in mediating allosteric coupling between BK domains than previously assumed. Our results also suggest that covalent linkers could directly modulate TM protein function and should be considered an integral component of the sensing apparatus.

One Tap at a Time: Correlating Sensorimotor Synchronization with Brain Signatures of Temporal Processing

The ability to integrate our perceptions across sensory modalities and across time, to execute and coordinate movements, and to adapt to a changing environment rests on temporal processing. Timing is essential for basic daily tasks, such as walking, social interaction, speech and language comprehension, and attention. Impaired temporal processing may contribute to various disorders, from attention-deficit hyperactivity disorder and schizophrenia to Parkinson’s disease and dementia. The foundational importance of timing ability has yet to be fully understood; and popular tasks used to investigate behavioral timing ability, such as sensorimotor synchronization (SMS), engage a variety of processes in addition to the neural processing of time. The present study utilizes SMS in conjunction with a separate passive listening task that manipulates temporal expectancy while recording electroencephalographic data. Participants display a larger N1-P2 evoked potential complex to unexpected beats relative to temporally predictable beats, a differential we call the timing response index (TRI). The TRI correlates with performance on the SMS task: better synchronizers show a larger brain response to unexpected beats. The TRI, derived from the perceptually driven N1-P2 complex, disentangles the perceptual and motor components inherent in SMS and thus may serve as a neural marker of a more general temporal processing.

Opportunities for Agricultural Producers to Participate in Compliance and Voluntary Carbon Markets

The agricultural sector’s potential for carbon offset generation is widely recognized, but few offset protocols in North American compliance or voluntary markets have successfully generated large volumes of offset credits. Here we use the Rice Cultivation Projects Compliance Offset Protocol—which has generated no offsets since its adoption by the California Air Resources Board in 2015—as a case study to examine barriers to agricultural offset generation. These barriers, which include small projects; low emissions reduction potential; complex emissions quantification; complex, non-standardized data management; and high verification costs, apply to many unproductive agricultural offset protocols and present an opportunity for additional policy action. By examining other protocols in North America’s compliance and voluntary offset markets, we identify design elements that can overcome these barriers and facilitate offset generation. These elements include standardized, technology-aided data management; streamlined emissions quantification methods such as emissions factors or N-balance; and project bundling.

A Promising Copper(II) Complex as Antifungal and Antibiofilm Drug against Yeast Infection

The high mortality rate of candidemia and the limited option for the treatment of Candida spp. infection have been driving the search for new molecules with antifungal property. In this context, coordination complexes of metal ions and ligands appear to be important. Therefore, this study aimed to synthesize two new copper(II) complexes with 2-thiouracil and 6-methyl-2-thiouracil ligands and to evaluate their mutagenic potential and antifungal activity against Candida. The complexes were synthesized and characterized by infrared vibrational spectroscopy, CHN elemental analysis, UV-Vis experiments and ESI-HRMS spectrometry studies. The antifungal activity was evaluated by broth microdilution against 21 clinical isolates of Candida species. The mutagenic potential was evaluated by the Ames test. The complexes were Cu(Bipy)Cl2(thiouracil) (Complex 1) and Cu(Bipy)Cl2(6-methylthiouracil) (Complex 2). Complex 1 showed fungicidal and fungistatic activities against all isolates. Furthermore, the Minimum Inhibitory Concentration (MIC) from 31 to 125 µg/mL and inhibition percentage of 9.9% against the biofilms of C. krusei and C. glabrata were demonstrated. At the concentrations tested, complex 1 exhibited no mutagenic potential. Complex 2 and the free ligands exhibited no antifungal activity at the concentrations evaluated. Since complex 1 presented antifungal activity against all the tested isolates and no mutagenic potential, it could be proposed as a potential new drug for anti-Candida therapy.