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A key feature of most social relationships is that we like seeing good things happen to others. Research has implicated the anterior cingulate cortex (ACC) in attaching value to social outcomes. For example, single neurons in macaque ACC selectively code reward delivery to the self, a partner, both monkeys, or neither monkey. Here, we assessed whether the ACC's contribution to social cognition is causal by testing rhesus monkeys (Macaca mulatta) on a vicarious reinforcement task before and after they sustained ACC lesions. Prior to surgery, actors learned that 3 different visual cues mapped onto 3 distinct reward outcomes: to self (\"Self\"), to the other monkey (\"Other\"), or to neither monkey (\"Neither\"). On each trial, actors saw a cue that predicted one of the 3 juice offers and could accept the offer by making a saccade to a peripheral target or reject the offer by breaking fixation. Preoperatively, all 6 actors displayed prosocial preferences, indicated by their greater tendency to give reward to Other relative to Neither. Half then received selective, bilateral, excitotoxic lesions of the ACC, and the other half served as unoperated controls. After surgery, all monkeys retained the social preferences they had demonstrated with the preoperatively learned cues, but this preference was reduced in the monkeys with ACC lesions. Critically, none of the monkeys in the ACC lesion group acquired social preferences with a new set of cues introduced after surgery. These data indicate that the primate ACC is necessary for acquisition of prosocial preferences from vicarious reinforcement.

Background Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissue in digital mammograms, as support tool for the radiologists. In particular, we compared the contribution of different methods on the feature selection process in terms of the learning performances and selected features. Results For each ROI, we extracted textural features on Haar wavelet decompositions and also interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg). Then a Random Forest binary classifier is trained on a subset of a sub-set features selected by two different kinds of feature selection techniques, such as filter and embedded methods. We tested the proposed model on 260 ROIs extracted from digital mammograms of the BCDR public database. The best prediction performance for the normal/abnormal and benign/malignant problems reaches a median AUC value of 98.16 % and 92.08 % , and an accuracy of 97.31 % and 88.46 % , respectively. The experimental result was comparable with related work performance. Conclusions The best performing result obtained with embedded method is more parsimonious than the filter one. The SURF and MinEigen algorithms provide a strong informative content useful for the characterization of microcalcification clusters.

The design of modular protein logic for regulating protein function at the posttranscriptional level is a challenge for synthetic biology. Here, we describe the design of two-input AND, OR, NAND, NOR, XNOR, and NOT gates built from de novo–designed proteins. These gates regulate the association of arbitrary protein units ranging from split enzymes to transcriptional machinery in vitro, in yeast and in primary human T cells, where they control the expression of the TIM3 gene related to T cell exhaustion. Designed binding interaction cooperativity, confirmed by native mass spectrometry, makes the gates largely insensitive to stoichiometric imbalances in the inputs, and the modularity of the approach enables ready extension to three-input OR, AND, and disjunctive normal form gates. The modularity and cooperativity of the control elements, coupled with the ability to de novo design an essentially unlimited number of protein components, should enable the design of sophisticated posttranslational control logic over a wide range of biological functions.

We designed a protein biosensor that uses thermodynamic coupling for sensitive and rapid detection of neutralizing antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants in serum. The biosensor is a switchable, caged luciferase–receptor-binding domain (RBD) construct that detects serum-antibody interference with the binding of virus RBD to angiotensin-converting enzyme 2 (ACE-2) as a proxy for neutralization. Our coupling approach does not require target modification and can better distinguish sample-to-sample differences in analyte binding affinity and abundance than traditional competition-based assays. SARS-CoV-2 neutralizing antibodies are readily detected with a biosensor.

The endogenous inhibitor of ATP synthase in mitochondria, called IF₁, conserves cellular energy when the proton-motive force collapses by inhibiting ATP hydrolysis. Around neutrality, the 84-amino-acid bovine IF₁ is thought to self-assemble into active dimers and, under alkaline conditions, into inactive tetramers and higher oligomers. Dimerization is mediated by formation of an antiparallel α-helical coiled-coil involving residues 44–84. The inhibitory region of each monomer from residues 1–46 is largely α-helical in crystals, but disordered in solution. The formation of the inhibited enzyme complex requires the hydrolysis of two ATP molecules, and in the complex the disordered region from residues 8–13 is extended and is followed by an α-helix from residues 14–18 and a longer α-helix from residue 21, which continues unbroken into the coiled-coil region. From residues 21–46, the long α-helix binds to other α-helices in the C-terminal region of predominantly one of the β-subunits in the most closed of the three catalytic interfaces. The definition of the factors that influence the self-association of IF₁ is a key to understanding the regulation of its inhibitory properties. Therefore, we investigated the influence of pH and salt-types on the self-association of bovine IF₁ and the folding of its unfolded region. We identified the equilibrium between dimers and tetramers as a potential central factor in the in vivo modulation of the inhibitory activity and suggest that the intrinsically disordered region makes its inhibitory potency exquisitely sensitive and responsive to physiological changes that influence the capability of mitochondria to make ATP.

In pseudocyclic proteins, such as TIM barrels, β barrels, and some helical transmembrane channels, a single subunit is repeated in a cyclic pattern, giving rise to a central cavity that can serve as a pocket for ligand binding or enzymatic activity. Inspired by these proteins, we devised a deep-learning-based approach to broadly exploring the space of closed repeat proteins starting from only a specification of the repeat number and length. Biophysical data for 38 structurally diverse pseudocyclic designs produced in Escherichia coli are consistent with the design models, and the three crystal structures we were able to obtain are very close to the designed structures. Docking studies suggest the diversity of folds and central pockets provide effective starting points for designing small-molecule binders and enzymes. Here, the authors constructed a deep-learning approach to design closed repeat proteins with central binding pockets—a step towards designing proteins to specifically bind small molecules.

In free-viewing experiments, primates orient preferentially toward faces and face-like stimuli. To investigate the neural basis of this behavior, we measured the spontaneous viewing preferences of monkeys with selective bilateral amygdala lesions. The results revealed that when faces and nonface objects were presented simultaneously, monkeys with amygdala lesions had no viewing preference for either conspecific faces or illusory facial features in everyday objects. Instead of directing eye movements toward socially relevant features in natural images, we found that, after amygdala loss, monkeys are biased toward features with increased low-level salience. We conclude that the amygdala has a role in our earliest specialized response to faces, a behavior thought to be a precursor for efficient social communication and essential for the development of face-selective cortex.

Pseudosymmetric hetero-oligomers with three or more unique subunits with overall structural (but not sequence) symmetry play key roles in biology, and systematic approaches for generating such proteins de novo would provide new routes to controlling cell signaling and designing complex protein materials. However, the de novo design of protein hetero-oligomers with three or more distinct chains with nearly identical structures is a challenging unsolved problem because it requires the accurate design of multiple protein-protein interfaces simultaneously. Here, we describe a divide-and-conquer approach that breaks the multiple-interface design challenge into a set of more tractable symmetric single-interface redesign tasks, followed by structural recombination of the validated homo-oligomers into pseudosymmetric hetero-oligomers. Starting from de novo designed circular homo-oligomers composed of 9 or 24 tandemly repeated units, we redesigned the inter-subunit interfaces to generate 19 new homo-oligomers and structurally recombined them to make 24 new hetero-oligomers, including ABC heterotrimers, A2B2 heterotetramers, and A3B3 and A2B2C2 heterohexamers which assemble with high structural specificity. The symmetric homo-oligomers and pseudosymmetric hetero-oligomers generated for each system have identical or nearly identical backbones, and hence are ideal building blocks for generating and functionalizing larger symmetric and pseudosymmetric assemblies. Hetero-oligomeric proteins offer many advantages for bioengineering efforts but are difficult to make from scratch. Here, authors present a simple method for creating pseudosymmetric hetero-oligomers from input symmetrical proteins.

Human Theory of Mind (ToM) is so automatic and pervasive that we spontaneously attribute mental states to animated abstract shapes, as evidenced by the classic Heider–Simmel findings. The extent to which this represents a fundamental characteristic of primate social cognition is debated. Prior research suggests that monkeys spontaneously predict behavior and attribute basic goals to conspecifics, but it remains unclear whether, like humans, they spontaneously ascribe mental states to animated shapes. Here, we address this question by analyzing rhesus monkeys’ viewing patterns of the classic Heider–Simmel animations. We hypothesized that if rhesus monkeys also spontaneously attribute mental states to animated shapes, then, like humans, they would have the longest fixation durations for theory of mind animations, medium duration fixation for goal-directed animations, and shortest fixations for animations with random motion. In contrast, if attributing mental states to animations is specific to humans and perhaps other apes, then we predict no differences in looking time across animation categories. Unlike humans, monkeys did not fixate longer on ToM videos. Critically, monkeys’ viewing patterns did not correlate with humans’ viewing patterns or intentionality ratings from previously published research. The only major difference in viewing patterns between animation categories tracked differences in low-level visual motion. Thus, monkeys do not view the classic Heider–Simmel animations like humans do and we found no evidence that they spontaneously attribute mental states to animated shapes.

The macaque orbitofrontal cortex (OFC) is essential for selecting goals based on current, updated values of expected reward outcomes. As monkeys consume a given type of reward to satiety, its value diminishes, and OFC damage impairs the ability to shift goal choices away from devalued outcomes. To examine the contributions of OFC’s components to goal selection, we reversibly inactivated either its anterior (area 11) or posterior (area 13) parts. We found that neurons in area 13 must be active during the selective satiation procedure to enable the updating of outcome valuations. After this updating has occurred, however, area 13 is not needed to select goals based on this knowledge. In contrast, neurons in area 11 do not need to be active during the value-updating process. Instead, inactivation of this area during choices causes an impairment. These findings demonstrate selective and complementary specializations within the OFC. Everyone knows that somehow, somewhere, the brain translates knowledge into action. In some people, however, knowledge and action become disconnected. These people behave in a way that either ignores or contradicts the knowledge that they have. They know what to do and can explain it to others, but – when the time comes to act – they do something else, something wrong. Murray et al. have now investigated how a brain region called the orbitofrontal cortex helps to link knowledge and action in macaque monkeys, which, unlike rodents, have all of the main brain areas that make up the orbitofrontal cortex of humans. The monkeys learned to associate images with different types of food, and then performed a task where they chose between two images in order to get the food they wanted. On some days, one of the foods was less ‘valuable’ because the monkeys had already eaten a lot of it. In these circumstances, monkeys chose fewer of the images associated with that food. By temporarily inactivating either the front or back region of the monkey’s orbitofrontal cortex at different times, Murray et al. showed that these regions make different contributions to decision making. Inactivating the back region of the orbitofrontal cortex disrupted the ability of monkeys to update their knowledge about the value of a particular foodstuff. However, inactivating the front part of the orbitofrontal cortex disrupted their ability to use this knowledge to select the images that led to the most valuable food. This contradicts the widely held belief that the orbitofrontal cortex acts as a single entity to update values and translate this knowledge into action. Future work will need to investigate how, having translated knowledge into a chosen action, the orbitofrontal cortex stimulates the motor areas of the brain to generate the movements needed to perform that action.