Transient Disruption of the Inferior Parietal Lobule Impairs the Ability to Attribute Intention to Action

Although it is well established that fronto-parietal regions are active during action observation, whether they play a causal role in the ability to infer others\u2019 intentions from visual kinematics remains undetermined. In the experiments reported here, we combined offline continuous theta burst stimulation (cTBS) with computational modeling to reveal and causally probe single-trial computations in the inferior parietal lobule (IPL) and inferior frontal gyrus (IFG). Participants received cTBS over the left anterior IPL and the left IFG pars orbitalis in separate sessions before completing an intention discrimination task (discriminate intention of observed reach-to-grasp acts) or a kinematic discrimination task unrelated to intention (discriminate peak wrist height of the same acts). We targeted intention-sensitive regions whose fMRI activity, recorded when observing the same reach-to-grasp acts, could accurately discriminate intention. We found that transient disruption of activity of the left IPL, but not the IFG, impaired the observer\u2019s ability to attribute intention to action. Kinematic discrimination unrelated to intention, in contrast, was largely unaffected. Computational analyses of how encoding (mapping of intention to movement kinematics) and readout (mapping of kinematics to intention choices) intersect at the single-trial level revealed that IPL cTBS did not diminish the overall sensitivity of intention readout to movement kinematics. Rather, it selectively misaligned intention readout with respect to encoding, deteriorating mapping from informative kinematic features to intention choices. These results provide causal evidence of how the left anterior IPL computes mapping from kinematics to intention

Biogenic and Synthetic Polyamines Bind Cationic Dendrimers

Biogenic polyamines are essential for cell growth and differentiation, while polyamine analogues exert antitumor activity in multiple experimental model systems, including breast and lung cancer. Dendrimers are widely used for drug delivery in vitro and in vivo. We report the bindings of biogenic polyamines, spermine (spm), and spermidine (spmd), and their synthetic analogues, 3,7,11,15-tetrazaheptadecane.4HCl (BE-333) and 3,7,11,15,19-pentazahenicosane.5HCl (BE-3333) to dendrimers of different compositions, mPEG-PAMAM (G3), mPEG-PAMAM (G4) and PAMAM (G4). FTIR and UV-visible spectroscopic methods as well as molecular modeling were used to analyze polyamine binding mode, the binding constant and the effects of polyamine complexation on dendrimer stability and conformation. Structural analysis showed that polyamines bound dendrimers through both hydrophobic and hydrophilic contacts with overall binding constants of Kspm-mPEG-G3 = 7.6×104 M−1, Kspm-mPEG-PAMAM-G4 = 4.6×104 M−1, Kspm-PAMAM-G4 = 6.6×104 M−1, Kspmd-mPEG-G3 = 1.0×105 M−1, Kspmd-mPEG-PAMAM-G4 = 5.5×104 M−1, Kspmd-PAMAM-G4 = 9.2×104 M−1, KBE-333-mPEG-G3 = 4.2×104 M−1, KBe-333-mPEG-PAMAM-G4 = 3.2×104 M−1, KBE-333-PAMAM-G4 = 3.6×104 M−1, KBE-3333-mPEG-G3 = 2.2×104 M−1, KBe-3333-mPEG-PAMAM-G4 = 2.4×104 M−1, KBE-3333-PAMAM-G4 = 2.3×104 M−1. Biogenic polyamines showed stronger affinity toward dendrimers than those of synthetic polyamines, while weaker interaction was observed as polyamine cationic charges increased. The free binding energies calculated from docking studies were: −3.2 (spermine), −3.5 (spermidine) and −3.03 (BE-3333) kcal/mol, with the following order of binding affinity: spermidine-PAMAM-G-4>spermine-PAMMAM-G4>BE-3333-PAMAM-G4 consistent with spectroscopic data. Our results suggest that dendrimers can act as carrier vehicles for delivering antitumor polyamine analogues to target tissues

In quest of a systematic framework for unifying and defining nanoscience

This article proposes a systematic framework for unifying and defining nanoscience based on historic first principles and step logic that led to a “central paradigm” (i.e., unifying framework) for traditional elemental/small-molecule chemistry. As such, a Nanomaterials classification roadmap is proposed, which divides all nanomatter into Category I: discrete, well-defined and Category II: statistical, undefined nanoparticles. We consider only Category I, well-defined nanoparticles which are >90% monodisperse as a function of Critical Nanoscale Design Parameters (CNDPs) defined according to: (a) size, (b) shape, (c) surface chemistry, (d) flexibility, and (e) elemental composition. Classified as either hard (H) (i.e., inorganic-based) or soft (S) (i.e., organic-based) categories, these nanoparticles were found to manifest pervasive atom mimicry features that included: (1) a dominance of zero-dimensional (0D) core–shell nanoarchitectures, (2) the ability to self-assemble or chemically bond as discrete, quantized nanounits, and (3) exhibited well-defined nanoscale valencies and stoichiometries reminiscent of atom-based elements. These discrete nanoparticle categories are referred to as hard or soft particle nanoelements. Many examples describing chemical bonding/assembly of these nanoelements have been reported in the literature. We refer to these hard:hard (H-n:H-n), soft:soft (S-n:S-n), or hard:soft (H-n:S-n) nanoelement combinations as nanocompounds. Due to their quantized features, many nanoelement and nanocompound categories are reported to exhibit well-defined nanoperiodic property patterns. These periodic property patterns are dependent on their quantized nanofeatures (CNDPs) and dramatically influence intrinsic physicochemical properties (i.e., melting points, reactivity/self-assembly, sterics, and nanoencapsulation), as well as important functional/performance properties (i.e., magnetic, photonic, electronic, and toxicologic properties). We propose this perspective as a modest first step toward more clearly defining synthetic nanochemistry as well as providing a systematic framework for unifying nanoscience. With further progress, one should anticipate the evolution of future nanoperiodic table(s) suitable for predicting important risk/benefit boundaries in the field of nanoscience