A multi-systems approach to human movement after ACL reconstruction: the cardiopulmonary system

The cardiopulmonary system plays a pivotal role in athletic and rehabilitative activities following anterior cruciate ligament reconstruction, along with serving as an important support for the functioning of other physiologic systems including the integumentary, musculoskeletal, and nervous systems. Many competitive sports impose high demands upon the cardiorespiratory system, which requires careful attention and planning from rehabilitation specialists to ensure athletes are adequately prepared to return to sport. Cardiopulmonary function following anterior cruciate ligament reconstruction (ACLR) can be assessed using a variety of methods, depending on stage of healing, training of the clinician, and equipment availability. Reductions in cardiovascular function may influence the selection and dosage of interventions that are not only aimed to address cardiopulmonary impairments, but also deficits experienced in other systems that ultimately work together to achieve goal-directed movement. The purpose of this clinical commentary is to present cardiopulmonary system considerations within a multi-physiologic systems approach to human movement after ACLR, including a clinically relevant review of the cardiopulmonary system, assessment strategies, and modes of cardiopulmonary training to promote effective, efficient movement. Level of evidence: 5

A multi-systems approach to human movement after ACL reconstruction: the cardiopulmonary system

The cardiopulmonary system plays a pivotal role in athletic and rehabilitative activities following anterior cruciate ligament reconstruction, along with serving as an important support for the functioning of other physiologic systems including the integumentary, musculoskeletal, and nervous systems. Many competitive sports impose high demands upon the cardiorespiratory system, which requires careful attention and planning from rehabilitation specialists to ensure athletes are adequately prepared to return to sport. Cardiopulmonary function following anterior cruciate ligament reconstruction (ACLR) can be assessed using a variety of methods, depending on stage of healing, training of the clinician, and equipment availability. Reductions in cardiovascular function may influence the selection and dosage of interventions that are not only aimed to address cardiopulmonary impairments, but also deficits experienced in other systems that ultimately work together to achieve goal-directed movement. The purpose of this clinical commentary is to present cardiopulmonary system considerations within a multi-physiologic systems approach to human movement after ACLR, including a clinically relevant review of the cardiopulmonary system, assessment strategies, and modes of cardiopulmonary training to promote effective, efficient movement. Level of evidence: 5

Chimpanzee reservoirs of pandemic and nonpandemic HIV-1

Human immunodeficiency virus type 1 (HIV-1), the cause of human acquired immunodeficiency syndrome ( AIDS), is a zoonotic infection of staggering proportions and social impact. Yet uncertainty persists regarding its natural reservoir. The virus most closely related to HIV-1 is a simian immunodeficiency virus ( SIV) thus far identified only in captive members of the chimpanzee subspecies Pan troglodytes troglodytes. Here we report the detection of SIVcpz antibodies and nucleic acids in fecal samples from wild-living P.t. troglodytes apes in southern Cameroon, where prevalence rates in some communities reached 29 to 35%. By sequence analysis of endemic SIVcpz strains, we could trace the origins of pandemic ( group M) and nonpandemic ( group N) HIV-1 to distinct, geographically isolated chimpanzee communities. These findings establish P. t. troglodytes as a natural reservoir of HIV-1

High-fidelity operation and algorithmic initialisation of spin qubits above one kelvin

The encoding of qubits in semiconductor spin carriers has been recognised as a promising approach to a commercial quantum computer that can be lithographically produced and integrated at scale. However, the operation of the large number of qubits required for advantageous quantum applications will produce a thermal load exceeding the available cooling power of cryostats at millikelvin temperatures. As the scale-up accelerates, it becomes imperative to establish fault-tolerant operation above 1 kelvin, where the cooling power is orders of magnitude higher. Here, we tune up and operate spin qubits in silicon above 1 kelvin, with fidelities in the range required for fault-tolerant operation at such temperatures. We design an algorithmic initialisation protocol to prepare a pure two-qubit state even when the thermal energy is substantially above the qubit energies, and incorporate high-fidelity radio-frequency readout to achieve an initialisation fidelity of 99.34 per cent. Importantly, we demonstrate a single-qubit Clifford gate fidelity of 99.85 per cent, and a two-qubit gate fidelity of 98.92 per cent. These advances overcome the fundamental limitation that the thermal energy must be well below the qubit energies for high-fidelity operation to be possible, surmounting a major obstacle in the pathway to scalable and fault-tolerant quantum computation

AI for quantum computing in silicon

Spin qubits in silicon-based quantum devices are a candidate quantum computing architecture because of their high fidelities, long coherence times and pathway to scalability. However, their potential for scaling is tainted by device variability. Each device must be tuned to operation conditions. Automated artificial intelligence-based tuning methods are necessary as individual devices scale and the dimensions of the tuning parameter space increase. This thesis presents algorithms for the automatic tuning of silicon-based quantum device architectures. I demonstrate a machine learning-based algorithm that is capable of tuning a 4-gate Si FinFET, a 5-gate GeSi nanowire and a 7-gate Ge/SiGe heterostructure double quantum dot device without human intervention. I achieve double quantum dot tuning times of 30, 10, and 92 minutes, respectively. I construct a new classifier of quantum transport features using machine learning and obtain novel insights into the double quantum dot parameter space across the different device architectures. I demonstrate the first algorithm for the automatic tuning of an ion-implanted donor in silicon device up to the point of readout calibration within 10 minutes. Modules relying on computer vision perform signal processing of quantum transport measurements synonymous with donor spin in silicon devices and enable tuning and characterisation faster than human experts. Finally, using machine learning I infer true qubit states from imperfect measurements and cross-examine our method on simulated data. I estimate initialisation fidelities of 99.34% for a Si-MOS qubit at 1 kelvin, further validating silicon-based architectures as a platform for quantum computing. These results show that AI-enabled automation is integral to the wave which carries silicon-based quantum devices towards the shores of universal fault-tolerant quantum computation