Fish transplantation as an ecological tool in boosting fish production: Tiga case study

The paper discusses the status of the Tiga Reservoir Fishery pre-and Clupeid transplantation. This was achieved by examining the species diversity, abundance and distribution with mitigating factors. It concludes with a verdict on the achievement of the transplantation exercise

Synthetic molecular machines for active self-assembly : prototype algorithms, designs, and experimental study

Computer science and electrical engineering have been the great success story of the twentieth century. The neat modularity and mapping of a language onto circuits has led to robots on Mars, desktop computers and smartphones. But these devices are not yet able to do some of the things that life takes for granted: repair a scratch, reproduce, regenerate, or grow exponentially fast–all while remaining functional.

This thesis explores and develops algorithms, molecular implementations, and theoretical proofs in the context of “active self-assembly” of molecular systems. The long-term vision of active self-assembly is the theoretical and physical implementation of materials that are composed of reconfigurable units with the programmability and adaptability of biology’s numerous molecular machines. En route to this goal, we must first find a way to overcome the memory limitations of molecular systems, and to discover the limits of complexity that can be achieved with individual molecules.

One of the main thrusts in molecular programming is to use computer science as a tool for figuring out what can be achieved. While molecular systems that are Turing-complete have been demonstrated [Winfree, 1996], these systems still cannot achieve some of the feats biology has achieved.

One might think that because a system is Turing-complete, capable of computing “anything,” that it can do any arbitrary task. But while it can simulate any digital computational problem, there are many behaviors that are not “computations” in a classical sense, and cannot be directly implemented. Examples include exponential growth and molecular motion relative to a surface.

Passive self-assembly systems cannot implement these behaviors because (a) molecular motion relative to a surface requires a source of fuel that is external to the system, and (b) passive systems are too slow to assemble exponentially-fast-growing structures. We call these behaviors “energetically incomplete” programmable behaviors. This class of behaviors includes any behavior where a passive physical system simply does not have enough physical energy to perform the specified tasks in the requisite amount of time.

As we will demonstrate and prove, a sufficiently expressive implementation of an “active” molecular self-assembly approach can achieve these behaviors. Using an external source of fuel solves part of the the problem, so the system is not “energetically incomplete.” But the programmable system also needs to have sufficient expressive power to achieve the specified behaviors. Perhaps surprisingly, some of these systems do not even require Turing completeness to be sufficiently expressive.

Building on a large variety of work by other scientists in the fields of DNA nanotechnology, chemistry and reconfigurable robotics, this thesis introduces several research contributions in the context of active self-assembly.

We show that simple primitives such as insertion and deletion are able to generate complex and interesting results such as the growth of a linear polymer in logarithmic time and the ability of a linear polymer to treadmill. To this end we developed a formal model for active-self assembly that is directly implementable with DNA molecules. We show that this model is computationally equivalent to a machine capable of producing strings that are stronger than regular languages and, at most, as strong as context-free grammars. This is a great advance in the theory of active self- assembly as prior models were either entirely theoretical or only implementable in the context of macro-scale robotics.

We developed a chain reaction method for the autonomous exponential growth of a linear DNA polymer. Our method is based on the insertion of molecules into the assembly, which generates two new insertion sites for every initial one employed. The building of a line in logarithmic time is a first step toward building a shape in logarithmic time. We demonstrate the first construction of a synthetic linear polymer that grows exponentially fast via insertion. We show that monomer molecules are converted into the polymer in logarithmic time via spectrofluorimetry and gel electrophoresis experiments. We also demonstrate the division of these polymers via the addition of a single DNA complex that competes with the insertion mechanism. This shows the growth of a population of polymers in logarithmic time. We characterize the DNA insertion mechanism that we utilize in Chapter 4. We experimentally demonstrate that we can control the kinetics of this re- action over at least seven orders of magnitude, by programming the sequences of DNA that initiate the reaction.

In addition, we review co-authored work on programming molecular robots using prescriptive landscapes of DNA origami; this was the first microscopic demonstration of programming a molec- ular robot to walk on a 2-dimensional surface. We developed a snapshot method for imaging these random walking molecular robots and a CAPTCHA-like analysis method for difficult-to-interpret imaging data.

An exposition on field identification of Clariid catfishes as an important tool in fish breeding and genetics

This paper conducts an exposition on the field identification of Clariid catfishes Heterobranchus, Clarias and their hybrid as an important tool in fish breeding and genetics. The paper explained the classification and aquacultural importance of Clariid catfishes. Parameters necessary for fish identification were highlighted. The identification of Heterobranchus, Clarias, their hybrid and sexual differences were also identified. The paper is of the position that the identification of Heterobranchus, Clarias and their hybrid is important in their genetic conservation and in achieving success in breeding and genetic studies

Beta-Hydroxyimino Phosphorus Derivatives. An Efficient Tool in Organic Synthesis

[EN] The purpose of this review article is to illustrate synthetic aspects of functionalized phosphorus derivatives containing an oximo moiety at the beta-position. First section will be focused on the synthesis of phosphine oxides, phosphonates or phosphonium salts containing an oxime group. The synthesis of these derivatives comprises the carbon–phosphorus single bond construction by reaction of haloximes with phosphorus derivatives, nucleophilic addition of phosphorus reagents to carbonyl compounds, or nucleophilic addition of phosphorus reagents to nitro olefins. This section will also concentrate on the most practical routes for the synthesis of the target compounds, through carbon–nitrogen double bond formation, which are as follows: condensation processes of carbonyl compounds and hydroxylamine derivatives or addition of hydroxylamines to allenes or alkynes. The preparative use of beta-oximo phosphorus derivatives as synthetic intermediates will be discussed in a second section, comprising olefination reaction, oxidation of oximes to nitrile oxides by reaction at the C-N double bond of the oxime moiety, oxidation of these substrates to nitrosoalkenes, reduction to the corresponding hydroxylamines and some reactions at the hydroxyl group of the hydroxyimino moiety.

Strategic partnership of stakeholders: a veritable tool for sustainable fishery resources in Nigeria

Fishery resources are very important resource from the aquatic environment to the Nigerian economy. Stakeholders involvement in its management is highly important therefore, this paper proposes two frameworks against which sustainable fishery should be based, vis-a-vis stakeholders participation. The paper showed that decision-making involving stakeholders would enhance the goals of sustainable fishery development and create unity of purpose among various stakeholders

Water quality objectives as a management tool for sustainability

The aim of this paper is to explore the potential role that quality objectives, particularly when backed by statutory force, may play in the sustainable management of river water quality. Economic valuation techniques are discussed, as well as the theory of "critical natural capital". A brief history of water quality legislation includes the implementation of the National Water Council classification in 1979, and the statutory water quality objectives introduced under the Water Resources Act 1991.

A. An investigation of the relation between the tensile strength and (Brinell) hardness of non-ferrous alloys. B. A design of a fatigue testing machine for a college laboratory

[no abstract]

Convex Relaxation for Low-Dimensional Representation: Phase Transitions and Limitations

There is a growing interest in taking advantage of possible patterns and structures in data so as to extract the desired information and overcome the curse of dimensionality. In a wide range of applications, including computer vision, machine learning, medical imaging, and social networks, the signal that gives rise to the observations can be modeled to be approximately sparse and exploiting this fact can be very beneficial. This has led to an immense interest in the problem of efficiently reconstructing a sparse signal from limited linear observations. More recently, low-rank approximation techniques have become prominent tools to approach problems arising in machine learning, system identification and quantum tomography.

In sparse and low-rank estimation problems, the challenge is the inherent intractability of the objective function, and one needs efficient methods to capture the low-dimensionality of these models. Convex optimization is often a promising tool to attack such problems. An intractable problem with a combinatorial objective can often be "relaxed" to obtain a tractable but almost as powerful convex optimization problem. This dissertation studies convex optimization techniques that can take advantage of low-dimensional representations of the underlying high-dimensional data. We provide provable guarantees that ensure that the proposed algorithms will succeed under reasonable conditions, and answer questions of the following flavor:

More specifically, i) Focusing on linear inverse problems, we generalize the classical error bounds known for the least-squares technique to the lasso formulation, which incorporates the signal model. ii) We show that intuitive convex approaches do not perform as well as expected when it comes to signals that have multiple low-dimensional structures simultaneously. iii) Finally, we propose convex relaxations for the graph clustering problem and give sharp performance guarantees for a family of graphs arising from the so-called stochastic block model. We pay particular attention to the following aspects. For i) and ii), we aim to provide a general geometric framework, in which the results on sparse and low-rank estimation can be obtained as special cases. For i) and iii), we investigate the precise performance characterization, which yields the right constants in our bounds and the true dependence between the problem parameters.