Structure of Topological Lattice Field Theories in Three Dimensions

We construct and classify topological lattice field theories in three dimensions. After defining a general class of local lattice field theories, we impose invariance under arbitrary topology-preserving deformations of the underlying lattice, which are generated by two new local lattice moves. Invariant solutions are in one--to--one correspondence with Hopf algebras satisfying a certain constraint. As an example, we study in detail the topological lattice field theory corresponding to the Hopf algebra based on the group ring \C[G], and show that it is equivalent to lattice gauge theory at zero coupling, and to the Ponzano--Regge theory for $G=$SU(2).Comment: 63 pages, 46 figure

RAG-1 Mutations Associated with B-Cell-Negative SCID Dissociate the Nicking and Transesterification Steps of V(D)J Recombination

Some patients with B-cell-negative severe combined immune deficiency (SCID) carry mutations in RAG-1 or RAG-2 that impair V(D)J recombination. Two recessive RAG-1 mutations responsible for B-cell-negative SCID, R621H and E719K, impair V(D)J recombination without affecting formation of single-site recombination signal sequence complexes, specific DNA contacts, or perturbation of DNA structure at the heptamer-coding junction. The E719K mutation impairs DNA cleavage by the RAG complex, with a greater effect on nicking than on transesterification; a conservative glutamine substitution exhibits a similar effect. When cysteine is substituted for E719, RAG-1 activity is enhanced in Mn2+ but remains impaired in Mg2+, suggesting an interaction between this residue and an essential metal ion. The R621H mutation partially impairs nicking, with little effect on transesterification. The residual nicking activity of the R621H mutant is reduced at least 10-fold upon a change from pH 7.0 to pH 8.4. Site-specific nicking is severely impaired by an alanine substitution at R621 but is spared by substitution with lysine. These observations are consistent with involvement of a positively charged residue at position 621 in the nicking step of the RAG-mediated cleavage reaction. Our data provide a mechanistic explanation for one form of hereditary SCID. Moreover, while RAG-1 is directly involved in catalysis of both nicking and transesterification, our observations indicate that these two steps have distinct catalytic requirements

Structural Credit Assignment with Coordinated Exploration

A biologically plausible method for training an Artificial Neural Network (ANN) involves treating each unit as a stochastic Reinforcement Learning (RL) agent, thereby considering the network as a team of agents. Consequently, all units can learn via REINFORCE, a local learning rule modulated by a global reward signal, which aligns more closely with biologically observed forms of synaptic plasticity. However, this learning method tends to be slow and does not scale well with the size of the network. This inefficiency arises from two factors impeding effective structural credit assignment: (i) all units independently explore the network, and (ii) a single reward is used to evaluate the actions of all units. Accordingly, methods aimed at improving structural credit assignment can generally be classified into two categories. The first category includes algorithms that enable coordinated exploration among units, such as MAP propagation. The second category encompasses algorithms that compute a more specific reward signal for each unit within the network, like Weight Maximization and its variants. In this research report, our focus is on the first category. We propose the use of Boltzmann machines or a recurrent network for coordinated exploration. We show that the negative phase, which is typically necessary to train Boltzmann machines, can be removed. The resulting learning rules are similar to the reward-modulated Hebbian learning rule. Experimental results demonstrate that coordinated exploration significantly exceeds independent exploration in training speed for multiple stochastic and discrete units based on REINFORCE, even surpassing straight-through estimator (STE) backpropagation.Comment: 17 page

Unbiased Weight Maximization

A biologically plausible method for training an Artificial Neural Network (ANN) involves treating each unit as a stochastic Reinforcement Learning (RL) agent, thereby considering the network as a team of agents. Consequently, all units can learn via REINFORCE, a local learning rule modulated by a global reward signal, which aligns more closely with biologically observed forms of synaptic plasticity. Nevertheless, this learning method is often slow and scales poorly with network size due to inefficient structural credit assignment, since a single reward signal is broadcast to all units without considering individual contributions. Weight Maximization, a proposed solution, replaces a unit's reward signal with the norm of its outgoing weight, thereby allowing each hidden unit to maximize the norm of the outgoing weight instead of the global reward signal. In this research report, we analyze the theoretical properties of Weight Maximization and propose a variant, Unbiased Weight Maximization. This new approach provides an unbiased learning rule that increases learning speed and improves asymptotic performance. Notably, to our knowledge, this is the first learning rule for a network of Bernoulli-logistic units that is unbiased and scales well with the number of network's units in terms of learning speed.Comment: 21 page

A microfluidic oligonucleotide synthesizer

De novo gene and genome synthesis enables the design of any sequence without the requirement of a pre-existing template as in traditional genetic engineering methods. The ability to mass produce synthetic genes holds great potential for biological research, but widespread availability of de novo DNA constructs is currently hampered by their high cost. In this work, we describe a microfluidic platform for parallel solid phase synthesis of oligonucleotides that can greatly reduce the cost of gene synthesis by reducing reagent consumption (by 100-fold) while maintaining a 100 pmol synthesis scale so there is no need for amplification before assembly. Sixteen oligonucleotides were synthesized in parallel on this platform and then successfully used in a ligation-mediated assembly method to generate DNA constructs 200 bp in length

Co-crystal screening of poorly water-soluble active pharmaceutical ingredients. Application of hot stage microscopy on curcumin-nicotinamide system and construction of ternary phase diagram of fenbufen-nicotinamide-water co-crystal system.

Curcumin is the major phenolic diarylheptane derivative in Curcuma longa and has been reported to possess pharmacological activities. Unfortunately this compound suffers from poor bioavailability and rapid neutral-alkaline degradation. Co-crystal of curcumin is one option under exploration, motivated by the fact that a number of active pharmaceutical ingredient (API) co-crystals with improved dissolution have recently been synthesized. Hence, co-crystallization technique highlights an alternative means to improve the performance of curcumin. Within our work evidences for a co-crystal was ascertained from DSC, Kofler hot stage screening and PXRD, and all confirmed a new crystal phase could have been formed between curcumin and a co-crystallizing agent, nicotinamide. We report that re-crystallization step essentially aids the purification of commercial curcumin, a herbal based actives. Otherwise the prevalence of a new crystal phase in solvent-mediated co-crystallization will be significantly reduced. Besides, phase diagram is an effective tool for the study of solubility behaviours in co-crystal system. In order to acquire related techniques, fenbufen, a poorly water soluble drug, was selected. The result showed the huge difference in solubility between fenbufen and nicotinamide lead to difficulty in the construction of phase diagram

U.S. Coast Guard Boat Recovery Simulation at NASA Ames Vertical Motion Simulator

The Boat Recovery Simulation was a collaboration between the U.S. Coast Guard and NASA. The experiment was conducted at the NASA Ames Vertical Motion Simulator (VMS). The goals were to (1) design a VMS experiment that can accurately simulate the motion of high sea conditions and to (2) collect data for the U.S. Coast Guard on human performance related to small boat recovery operations. The experiment setup included a software operation model designed around empirical boat position data; a replica boat section manufactured to incorporate real-world task elements; and the means to collect objective and subjective data from human participants. The VMS provided a viable testbed to assess certified U.S. Coast Guard crewmembers task performance while in motion