Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Titanium Dioxide Nanomaterials: Synthesis, Properties, Modifications, and Applications

phenix.refine is a program within the PHENIX package that supports crystallographic structure refinement against experimental data with a wide range of upper resolution limits using a large repertoire of model parameterizations. It has several automation features and is also highly flexible. Several hundred parameters enable extensive customizations for complex use cases. Multiple user-defined refinement strategies can be applied to specific parts of the model in a single refinement run. An intuitive graphical user interface is available to guide novice users and to assist advanced users in managing refinement projects. X-ray or neutron diffraction data can be used separately or jointly in refinement. phenix.refine is tightly integrated into the PHENIX suite, where it serves as a critical component in automated model building, final structure refinement, structure validation and deposition to the wwPDB. This paper presents an overview of the major phenix.refine features, with extensive literature references for readers interested in more detailed discussions of the methods.

/pdf/towards-automated-crystallographic-structure-refinement-with-43hriikvv9.pdf

Towards automated crystallographic structure refinement with phenix.refine

Intermolecular And Surface Forces

Poly(ethylene terephthalate) (PET) is used extensively worldwide in plastic products, and its accumulation in the environment has become a global concern. Because the ability to enzymatically degrade PET has been thought to be limited to a few fungal species, biodegradation is not yet a viable remediation or recycling strategy. By screening natural microbial communities exposed to PET in the environment, we isolated a novel bacterium, Ideonella sakaiensis 201-F6, that is able to use PET as its major energy and carbon source. When grown on PET, this strain produces two enzymes capable of hydrolyzing PET and the reaction intermediate, mono(2-hydroxyethyl) terephthalic acid. Both enzymes are required to enzymatically convert PET efficiently into its two environmentally benign monomers, terephthalic acid and ethylene glycol.

http://science.sciencemag.org/content/sci/351/6278/1196.full.pdf

A Bacterium That Degrades and Assimilates Poly(ethylene Terephthalate)

The self-replication process of a giant vesicle encapsulating double-stranded DNA has been observed, which represents a supramolecular approach to the construction of a protocell. Growth and division of the vesicle occurred rapidly on addition of a membrane precursor, and amplified DNA was distributed amongst the resulting daughter giant vesicles.

Self-reproduction of supramolecular giant vesicles combined with the amplification of encapsulated DNA.

In the multicolor photochromism of TiO2 nanoporous films loaded with photocatalytically deposited Ag nanoparticles, visible light-induced electron transfer from Ag to oxygen molecules plays an essential role. Here we examined the effect of TiO2 on the electron transfer. We found that not only photocatalytically deposited Ag, but also electrodeposited Ag and commercially available Ag nanoparticles in a nanoporous TiO2 film exhibit the multicolor photochromism. The electrodeposited Ag exhibits the multicolor photochromism also in a nanoporous ZnO film, but not in nanoporouns indium–tin oxide (ITO) and SiO2 matrices. Photoelectrochemical measurements for the Ag-TiO2 nanocomposite elucidated that some of the photo-excited electrons on Ag are transferred to oxygen molecules via TiO2 and non-excited Ag. Thus, an n-type semiconductor plays an important role in the charge separation between the excited electrons and Ag+. Non-excited Ag on TiO2 also plays an important role in the charge separation and/or catalysis of oxygen reduction. Replacement of the non-excited Ag with Pt accelerated the electron transport from the photo-excited Ag to oxygen molecules and the photochromic behavior.

Electron transport in silver-semiconductor nanocomposite films exhibiting multicolor photochromism

Self-organized lipid structures (protocells) have been proposed as an intermediate between nonliving material and cellular life. Synthetic production of model protocells can demonstrate the potential processes by which living cells first arose. While we have previously described a giant vesicle (GV)-based model protocell in which amplification of DNA was linked to self-reproduction, the ability of a protocell to recursively self-proliferate for multiple generations has not been demonstrated. Here we show that newborn daughter GVs can be restored to the status of their parental GVs by pH-induced vesicular fusion of daughter GVs with conveyer GVs filled with depleted substrates. We describe a primitive model cell cycle comprising four discrete phases (ingestion, replication, maturity and division), each of which is selectively activated by a specific external stimulus. The production of recursive self-proliferating model protocells represents a step towards eventual production of model protocells that are able to mimic evolution.

/pdf/a-recursive-vesicle-based-model-protocell-with-a-primitive-2yqy7en4p2.pdf

A recursive vesicle-based model protocell with a primitive model cell cycle

Tutorial review: to achieve molecule-based spintronic devices, an organic conducting magnet that exhibits both conductivity and magnetism in a cooperative manner must be constructed. As a building block for such new materials, a spin-polarized donor radical, which serves as a molecular “spin-filter” in its singly oxidized state, was designed and synthesized. The resistivity of ion radical salts of selenium-substituted, tetrathiafulvalene-based spin-polarized donor radicals decreased substantially in the presence of a magnetic field, thus indicating cooperative conductivity and magnetism.

Kentaro Suzuki

Papers

Self-reproduction of supramolecular giant vesicles combined with the amplification of encapsulated DNA.

Electron transport in silver-semiconductor nanocomposite films exhibiting multicolor photochromism

A recursive vesicle-based model protocell with a primitive model cell cycle

Interplay between magnetism and conductivity derived from spin-polarized donor radicals

Production of zigzag-type BN nanotubes and BN cones by thermal annealing